0CAN091401, Probable Maximum Precipitation and Local Intense Precipitation Analysis for Arkansas Nuclear One

Probable Maximum Precipitation and Local Intense Precipitation Analysis for Arkansas Nuclear One

ML14307A913
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Issue date:	02/28/2014
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PO Box 680 Monument, Co 80132 (719) 488-9117 http://www.appliedweatherassociates.com Probable Maximum Precipitation and Local Intense Precipitation Analysis for Arkansas Nuclear One Prepared for GZA GeoEnvironmental, Inc.

249 Vanderbilt Avenue Norwood, MA 02062 Prepared by Applied Weather Associates, LLC Monument, Colorado William D. Kappel, Senior Meteorologist/ Project Manager Geoff A. Muhlestein, GIS/Staff Scientist Douglas M. Hultstrand, Hydrometeorologist Edward M. Tomlinson, PhD, Chief Meteorologist Dana McGlone, Staff Meteorologist Steve Lovisone, Staff Meteorologist Patrice Sutter, Staff Meteorologist February 2014

NOTICE This report was prepared by Applied Weather Associates (AWA). The results and conclusions in this report are based upon our best professional judgment using currently available data. Due to the uncertainty associated with this type of work, neither AWA nor any person acting on behalf of AWA can (a) make any warranty, express or implied, regarding future use of any information or method shown in the report or (b) assume any future liability regarding use of any information or method contained in the report. The results contained in this report are based on the professional judgment of the experts in this subject field at AWA. The included report is conservative and accurate to the best of our knowledge at the time of its preparation based on available information, methodology, and data.

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Table of Contents Table of Contents iii List of Figures v List of Tables vii Executive Summary viii Glossary x

1. Introduction 1 1.1 Background 1 1.2 Objectives 4 1.3 Approach 4 1.4 ANO Location and Description 7

2. Weather and Climate of ANO 10 2.1 ANO PMP Storm Type Climatology 10 2.2 ANO-Area General Weather Patterns 11

3. Extreme Storm types 14 3.1 Synoptic Fronts 14 3.2 Mesoscale Convective Systems 15

4. Extreme Storm Identification 17 4.1 Storm Search Area 17 4.2 Data Sources 19 4.3 Short Storm List Derivation 19 4.3.1 New SPAS Storm Analyses 21

5. Storm Depth-Area-Duration (DAD) Analyses for New Storms 22

6. Updated Data Sets Used in this Study 24 6.1 Development of the Updated Dew Point Climatology 24 6.2 HYSPLIT Trajectory Model 24 6.3 Use of Grid Points to Spatially Distribute PMP Values 27

7. Storm Maximization 28 7.1 Use of Dew Point Temperatures for Storm Maximization 28 7.1.1 Rationale for Using Average Dew Point Climatology 29 7.1.2 Rationale for Adjusting HMR 51 Persisting Dew Point Values 31

8. Storm Transpositioning 33 8.1 Storm Transposition Calculations 33 8.2 Storm Spreadsheet Development Process 34 iii

9. Development of PMP Values for the ANO Basin 37 9.1 Envelopment Procedures and DAD Derivation 37

10. Storm Dimensions 40 10.1 PMP Design Storm Parameters 40 10.2 Storm Orientation 40 10.3 Storm Movement Analysis 41

11. Local Intense Precipitation 44 11.1 Development of LIP Values 44 11.2 Local Intense Precipitation Storm List 44 11.3 LIP Storm Calculation Process 46

12. Results 48 12.1 PMP Values 49 12.2 Comparison of the PMP Values with HMR 51 PMP 50 12.3 Reasons for Reductions of PMP versus HMR 51 53

13. Sensitivity Analysis 56 13.1 Assumptions 56 13.1.1 Saturated Storm Atmospheres 56 13.1.2 Maximum Storm Efficiency 56 13.2 Parameters 57 13.2.1 Storm Representative Dew Point and Maximum Dew Point 57 13.2.2 Sensitivity of the Elevation Adjustment Factor 58

14. Recommendations for Application 59 14.1 PMP and LIP Applications 59 14.2 Discussion on the Spatial Limits of the PMP Values 59 14.3 Climate Change Assumptions 60 References 61 Appendix A ANO Probable Maximum Precipitation Maps A-1 Appendix B 100-year Return Frequency Maximum Average Dew Point Climatology Maps Used in the Storm Maximization and Transposition Calculations B-1 Appendix C Procedure for using Dew Point Temperatures for Storm Maximization and Transposition C-1 Appendix D Procedure for Deriving PMP Values from Storm Depth-Area-Duration (DAD)

Analyses D-1 Appendix E Storm Precipitation Analysis System (SPAS) Description E-1 Appendix F PMP Short Storm List Storm Data See Separate Binding Appendix G LIP Short Storm List Storm Data See Separate Binding iv

LIST OF FIGURES Figure 1.0 Example of a HMR 51 PMP map for 24-hour rainfall over 1,000-square mile (Schreiner and Riedel 1978). 2 Figure 1.1 Locations of AWA PMP studies as of December 2013. 3 Figure 1.2 Flow chart showing the major steps involved in PMP development. 5 Figure 1.3 Grid points used in the study. 7 Figure 1.4 ANO regional setting and basin statistics. 8 Figure 1.5 Elevations contours across the ANO basin at 500 foot intervals. 9 Figure 2.0 Locations of surface features associated with a strong flow of moisture from the Gulf of Mexico into the upper Midwest. Error! Bookmark not defined.

Figure 2.1 Locations of surface features associated with a common synoptic storm pattern across the United States. 11 Figure 2.3 Air mass source regions affecting ANO. 13 Figure 3.0 Color enhanced infrared satellite image of an MCS. Note the nearly circular structure, very cold cloud tops at the center (red, black, and center white colors),

and a size similar to the state of Iowa. 15 Figure 4.0 AWA storm search domain. 17 Figure 4.1 AWA storm search domains through 2013. All storms used to develop the PMP values were identified from the storm search results. 18 Figure 4.2 Storm locations in relation to the ANO basin by AWA storm number. 21 Figure 6.0 HYSPLIT trajectory model results for Council Grove, KS, July 1951 storm (AWA 18). 26 Figure 7.0 Dew point climatology development dates and regions. 29 Figure 7.1 Mass Curve as analyzed by SPAS for David City, NE 1963 storm event 30 Figure 8.0 An example of inflow wind vector transpositioning for Albany, TX, August, 1978 storm (AWA 18). The storm representative dew point location is 260 miles south/southeast of the storm location. 34 Figure 8.1 Example of the storm spreadsheet for the Warner Park, TN, May 2010 storm (AWA

2) transpositioned to grid point 1. 36 Figure 9.0 72-hour DA curves for ANO grid point 1. 38 Figure 9.1 DD curves for ANO grid point 1. 39 Figure 10.0 Example for Warner Park, TN, May, 2010 storm (AWA 2) showing elliptical used to determine orientation of the fourth 12-hour increment. 41 Figure 10.1 Example for Warner Park, TN, May, 2010 storm (AWA 2). Storm movement between each point at each 12-hour increment was calculated based on regression line and storm center points. 42 Figure 10.2 Example for Warner Park, TN, May, 2010 storm (AWA 2), 24-hour longest storm center movement and orientation. 43 Figure 11.0 Storms used in the LIP calculations in relation to the ANO site. 46 Figure C.0 HYSPLIT trajectory model results for C-4 Figure E.0 SPAS flow chart. E-3 Figure E.1 Sample SPAS basemaps: (a) A pre-existing (USGS) isohyetal pattern across flat terrain (SPAS #1209), (b) PRISM mean monthly (October) precipitation (SPAS #1192) and (c) A 100-year 24-hour precipitation grid from NOAA Atlas 14 (SPAS #1138). E-7 v

Figure E.2 U.S. radar locations and their radial extents of coverage below 10,000 feet above ground level (AGL). Each U.S. radar covers an approximate 285 mile radial extent over which the radar can detect precipitation. E-8 Figure E.3 (a) Level-II radar mosaic of CONUS radar with no quality control, (b) WDT quality controlled Level-II radar mosaic. E-9 Figure E.4 Illustration of SPAS-beam blockage infilling where (a) is raw, blocked radar and (b) is filled for a 42-hour storm event. E-10 Figure E.5 Example of disaggregation of daily precipitation into estimated hourly precipitation based on three (3) surrounding hourly recording gauges. E-12 Figure E.6 Sample mass curve plot depicting a precipitation gauge with an erroneous observation time (blue line). X-axis is the SPAS index hour and the y-axis is inches. The statistics in the upper left denote gauge type, distance from target gauge (in km), and gauge ID. In this example, the center gauge (blue line) was found to have an observation error/shift of 1 day. E-13 Figure E.7 Depictions of total storm precipitation based on the three SPAS interpolation methodologies for a storm (SPAS #1177, Vanguard, Canada) across flat terrain:

(a) no basemap, (b) basemap-aided and (3) radar. E-14 Figure E.8 Example SPAS (denoted as Exponential) vs. default Z-R relationship (SPAS

1. 1218, Georgia September 2009) E-15 Figure E.9 Commonly used Z-R algorithms used by the NWS. E-16 Figure E.10 Comparison of the SPAS optimized hourly Z-R relationships (black lines) versus a default Z=75R2.0 Z-R relationship (red line) for a period of 99 hours0.00115 days <br />0.0275 hours <br />1.636905e-4 weeks <br />3.76695e-5 months <br /> for a storm over southern California. E-17 Figure E.11 A series of maps depicting 1-hour of precipitation utilizing (a) inverse distance weighting of gauge precipitation, (b) gauge data together with a climatologically-aided interpolation scheme, (c) default Z-R radar-estimated interpolation (no gauge correction) and (d) SPAS precipitation for a January 2005 storm in southern California, USA. E-18 Figure E.12 Z-R plot (a), where the blue line is the SPAS derived Z-R and the black line is the default Z-R, and the (b) associated observed versus SPAS scatter plot at gauge locations. E-19 Figure E.13 Depiction of radar artifacts. (Source: Wikipedia) E-21 Figure E.14 "Pyramidville Total precipitation. Center = 1.00, Outside edge = 0.10. E-23 Figure E.15 10-hour DA results for Pyramidville; truth vs. output from DAD software. E-23 Figure E.16 Various examples of SPAS output, including (a) total storm map and its associated (b) basin average precipitation time series, (c) total storm precipitation map, (d) depth-area-duration (DAD) table and plot, and (e) precipitation gauge catalog with total storm statistics. E-26 vi

LIST OF TABLES Table 4.0 Storm list used to calculate PMP, sorted in alphabetical order. 20 Table 5.0 SPAS storms used in this study. 23 Table 7.0 Comparison of 6-hour average storm representative dew point vs. 12-hour persisting storm representative dew point for David City, NE 1963 31 Table 10.0 List of SPAS storms used in development of storm orientation and movement parameters 40 Table 11.0 Storms used in the 1-hour 1-square mile Local Intense Precipitation analysis. 45 Table 11.1 Site specific 1-hour 1-square mile LIP values at the ANO site. 47 Table 12.0 Percent difference between PMP values at each grid point at the 6-hour duration vs HMR 51 PMP values. Negative values represent reductions from HMR 51. Only grid points with HMR 51 values are included in the comparisons. 51 Table 12.1 Percent difference between PMP values at each grid point at the 12-hour duration vs HMR 51 PMP values. Negative values represent reductions from HMR 51. Only grid points with HMR 51 values are included in the comparisons. 51 Table 12.2 Percent difference between PMP values at each grid point at the 24-hour duration vs HMR 51 PMP values. Negative values represent reductions from HMR 51. Only grid points with HMR 51 values are included in the comparisons. 52 Table 12.3 Percent difference between PMP values at each grid point at the 48-hour duration vs HMR 51 PMP values. Negative values represent reductions from HMR 51. Only grid points with HMR 51 values are included in the comparisons. 52 Table 12.4 Percent difference between PMP values at each grid point at the 72-hour duration vs HMR 51 PMP values. Negative values represent reductions from HMR 51. Only grid points with HMR 51 values are included in the comparisons. 53 Table E.0 Different precipitation gauge types used by SPAS. E-5 Table E.1 The percent difference [(AWA-NWS)/NWS] between the AWA DA results and those published by the NWS for the 1953 Ritter, Iowa storm. E-24 Table E.2 The percent difference [(AWA-NWS)/NWS] between the AWA DA results and those published by the NWS for the 1955 Westfield, Massachusetts storm. E-25 vii

Executive Summary Applied Weather Associates (AWA) has completed site-specific Probable Maximum Precipitation (PMP) and Local Intense Precipitation (LIP) analyses for Arkansas Nuclear One (ANO) located along the Arkansas River in the state of Arkansas. The purpose of the study was to determine PMP specific to entire drainage basin affecting the site, approximately 153,000 square miles and LIP values at the ANO site. These analyses analyzed storms throughout all twelve months of the year to produce all-season PMP values, and analyzed thunderstorms and Mesoscale Convective Complexes (MCC) for the LIP analysis over the ANO site location. This study took into account topography, climate and storm types that affect this region which could produce the Probable Maximum Flood (PMF). The calculation of the Probable Maximum Flood is not within the scope of this study.

The ANO drainage basin lies within the domains of National Oceanic and Atmospheric Administration's (NOAA) National Weather Service (NWS)

Hydrometeorological Reports No. 51 (HMR 51) and HMR 55A. The methods and procedures used to derive the PMP and LIP values are similar to other site-specific PMP studies conducted by AWA within the HMR 51 and HMR 55A domains (e.g.

Tomlinson 1993, Tomlinson et al 2008, Tomlinson et al 2011, Kappel et al 2012, Tomlinson et al. 2013, Kappel et al. 2013). The approach used in this study is a storm-based approach that utilizes many of the procedures used by the National Weather Service (NWS) in the development of the HMRs. These same procedures are recommended by the World Meteorological Organization (WMO) for PMP determination (WMO 1986, 2009). This approach identifies extreme rainfall events that have occurred in a region that has meteorological and topographical characteristics similar to extreme rain storms that could occur over the ANO basin and over the ANO site location. The largest of these rainfall events are selected for detailed analyses.

The basin affecting the ANO site is large and diverse. Therefore, many different storm types affect the overall basin. A gridded system was set up to capture the spatial variability of storms and extreme rainfall amounts across the large basin. In total, 75 storm events were used in the development of the PMP values and 23 storms were used in the LIP analysis. Each of these storms has characteristics of extreme rainfall production that could potentially occur over some part the ANO basin and could potentially influence PMP values at one or more of the area sizes and/or durations analyzed or influence the LIP values at the ANO site.

HMR procedures for maximization, transposition, and elevation moisture adjustments are used with refinements (e.g. average vs. persisting dew points and 1,000 foot transposition limitations). Updated techniques and databases are used in the study to increase accuracy and reliability, while adhering to the basic procedures in the HMRs and in the WMO Manuals. The updated maximum dew point climatology maps that were developed for previous PMP studies was used in the storm maximization and storm transpositioning processes.

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For newly analyzed storms, maximization factors were determined using the updated climatologies and storm representative dew point data. A parcel trajectory model (HYSPLIT) (Draxler and Rolph 2003, 2010) was used along with the National Center for Environmental Prediction (NCEP) Reanalysis (Mesinger 2006) database to assist in the determination of storm inflow moisture vectors.

Each storm on the short storm list (the final 78 storm centers used to derive the PMP and 23 used to derive LIP) was maximized, transpositioned, and elevation adjusted to the ANO site and to each of the 22 grid points as appropriate and used to distribute PMP across this large basin. Depth-Area (DA) plots were made for 6-, 12-, 24-, 48-,

and 72-hour durations and for area sizes of 10-, 200-, 1,000-, 5,000-, 10,000-, 20,000-,

50,000-, and 100,000-square miles. Enveloping curves were constructed using storm rainfall values at each grid point and the basin centroid. Depth-Duration (DD) curves were plotted for each duration and envelop curves constructed. The final DD envelop curves provide PMP values for each grid point and the basin centroid. The final step was to spatially interpolate the resulting values using a Geographic Information System (GIS) with manual adjustments to ensure continuity in space and time across the entire basin. The results of this final step allow PMP values for standard durations and area sizes to be determined for any location within the basin.

The PMP values were determined using procedures described in HMR 51. In addition, because the size of the basin is well beyond the 20,000 square mile upper limit in HMRs 51 and 52, the PMP values were determined for area sizes to 100,000 square miles. Further, analysis results were provided to allow for movement of the design storm during PMF calculations, unlike the stationary design storm center provided in HMR 52. Design storm movement allows for a more realistic storm scenario to be used for the application of the PMP values for PMF determination.

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GLOSSARY Adiabat: Curve of thermodynamic change taking place without addition or subtraction of heat. On an adiabatic chart or pseudo-adiabatic diagram, a line showing pressure and temperature changes undergone by air rising or condensation of its water vapor; a line, thus, of constant potential temperature.

Adiabatic: Referring to the process described by adiabat.

Advection: The process of transfer (of an air mass property) by virtue of motion. In particular cases, advection may be confined to either the horizontal or vertical components of the motion. However, the term is often used to signify horizontal transfer only.

Air mass: Extensive body of air approximating horizontal homogeneity, identified as to source region and subsequent modifications.

Average Dew Point: The average dew point value calculated using a simple mathematically running mean over a specific duration of consecutive hours (i.e. 6-hours) at a given station or set of stations.

This value is used in the storm maximization calculation as the storm representative dew point.

Barrier: A mountain range that partially blocks the flow of warm humid air from a source of moisture to the basin under study.

Basin centroid: The point at the exact center of the drainage basin as determined through geographical information systems calculations using the basin outline.

Basin shape: The physical outline of the basin as determined from topographic maps, field survey, or GIS.

Cirrus shield: In this study, the area of cirrus cloud that covers a mesoscale convective complex.

Cirrus anvil: The cirrus cloud that is advected downwind from the top of a cumulonimbus cloud.

Cold front: Front where relatively colder air displaces warmer air.

Convective rain: Rainfall caused by the vertical motion of an ascending mass of air that is warmer than the environment and typically forms a cumulonimbus cloud. The horizontal dimension of such a mass of air is generally of the order of 12 miles or less. Convective rain is typically of greater intensity than either of the other two main classes of rainfall (cyclonic and orographic) and is often accompanied by thunder. The term is more particularly used for those cases in which the precipitation covers a large area as a result of the agglomeration of cumulonimbus masses.

Convergence: Horizontal shrinking and vertical stretching of a volume of air, accompanied by net inflow horizontally and internal upward motion.

Cooperative station: A weather observation site where an unpaid observer maintains a climatological station for the National Weather Service.

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Cyclone: A distribution of atmospheric pressure in which there is a low central pressure relative to the surroundings. On large-scale weather charts, cyclones are characterized by a system of closed constant pressure lines (isobars), generally approximately circular or oval in form, enclosing a central low-pressure area. Cyclonic circulation is counterclockwise in the northern hemisphere and clockwise in the southern. (That is, the sense of rotation about the local vertical is the same as that of the earth's rotation.)

Depth-Area curve: Curve showing, for a given duration, the relation of maximum average depth to size of area within a storm or storms.

Depth-Area-Duration: The precipitation values derived from Depth-Area and Depth-Duration curves at each time and area size increment analyzed for a PMP evaluation.

Depth-Area-Duration Curve: A curve showing the relation between an averaged areal rainfall depth and the area over which it occurs, for a specified time interval, during a specific rainfall event.

Depth-Area-Duration values: The combination of depth-area and duration-depth relations. Also called depth-duration-area.

Depth-Duration curve: Curve showing, for a given area size, the relation of maximum average depth of precipitation to duration periods within a storm or storms.

Dew point: The temperature to which a given parcel of air must be cooled at constant pressure and constant water vapor content for saturation to occur.

Effective Barrier Height: The height of a barrier determined from elevation analysis that reflects the effect of the barrier on the precipitation process for a storm event. The actual barrier height may be either higher or lower than the effective barrier height.

Envelopment: A process for selecting the largest value from any set of data. In estimating PMP, the maximum and transposed rainfall data are plotted on graph paper, and a smooth curve is drawn through the largest values.

Explicit Transposition: The movement of the rainfall amounts associated with a storm within boundaries of a region throughout which a storm may be transposed with only relatively minor modifications of the observed storm rainfall amounts. The area within the transposition limits has similar, but not identical, climatic and topographic characteristics throughout.

First-order NWS station: A weather station that is either automated, or staffed by employees of the National Weather Service and records observations on a continuous basis.

Front: The interface or transition zone between two air masses of different parameters. The parameters describing the air masses are temperature and dew point.

General storm: A storm event, that produces precipitation over areas in excess of 500-square miles, has a duration longer than 6 hours6.944444e-5 days <br />0.00167 hours <br />9.920635e-6 weeks <br />2.283e-6 months <br />, and is associated with a major synoptic weather feature.

Gulf Stream Current: A warm, well-defined, swift, relatively narrow, ocean current in the western North Atlantic that originates where the Florida Current and the Antilles Current begin to curve xi

eastward from the continental slope of Cape Hatteras, North Carolina. East of the Grand Banks, the Gulf Stream meets the cold Labrador Current, and the two flow eastward separated by the cold wall.

HYSPLIT: HYbrid Single-Particle Lagrangian Integrated Trajectory. A complete system for computing parcel trajectories to complex dispersion and deposition simulations using either puff or particle approaches. Gridded meteorological data, on one of three conformal (Polar, Lambert, or Mercator latitude-longitude grid) map projections, are required at regular time intervals.

Calculations may be performed sequentially or concurrently on multiple meteorological grids, usually specified from fine to coarse resolution.

Implicit Transpositioning: The process of applying regional, areal, or durational smoothing to eliminate discontinuities resulting from the application of explicit transposition limits for various storms.

Isohyets: Lines of equal value of precipitation for a given time interval.

Isohyetal Pattern: The pattern formed by the isohyets of an individual storm.

Isohyetal orientation: The term used to define the orientation of precipitation patterns of major storms when approximated by elliptical patterns of best fit. It is also the orientation (direction from north) of the major axis through the elliptical PMP storm pattern.

Jet Stream: A strong, narrow current concentrated along a quasi-horizontal axis (with respect to the earths surface) in the upper troposphere or in the lower stratosphere, characterized by strong vertical and lateral wind shears. Along this axis it features at least one velocity maximum (jet streak).

Typical jet streams are thousands of kilometers long, hundreds of kilometers wide, and several kilometers deep. Vertical wind shears are on the order of 10 to 20 mph per kilometer of altitude and lateral winds shears are on the order of 10 mph per 100 kilometer of horizontal distance.

Local storm: A storm event that occurs over a small area in a short time period. Precipitation rarely exceeds 6 hours6.944444e-5 days <br />0.00167 hours <br />9.920635e-6 weeks <br />2.283e-6 months <br /> in duration and the area covered by precipitation is less than 500-square miles.

Frequently, local storms will last only 1 or 2 hours2.314815e-5 days <br />5.555556e-4 hours <br />3.306878e-6 weeks <br />7.61e-7 months <br /> and precipitation will occur over areas of up to 200-square miles. Precipitation from local storms will be isolated from general-storm rainfall. Often these storms are thunderstorms.

Low Level Jet: A band of strong winds at an atmospheric level well below the high troposphere as contrasted with the jet streams of the upper troposphere.

Mass curve: Curve of cumulative values of precipitation through time.

Mesoscale Convective Complex: For the purposes of this study, a heavy rain-producing storm with horizontal scales of 10 to 1000 kilometers (6 to 625 miles) which includes significant, heavy convective precipitation over short periods of time (hours) during some part of its lifetime.

Mesoscale Convective System: A complex of thunderstorms which becomes organized on a scale larger than the individual thunderstorms, and normally persists for several hours or more. MCSs may be round or linear in shape, and include systems such as tropical cyclones, squall lines, and MCCs (among others). MCS often is used to describe a cluster of thunderstorms that does not satisfy the size, shape, or duration criteria of an MCC.

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Mid-latitude frontal system: An assemblage of fronts as they appear on a synoptic chart north of the tropics and south of the polar latitudes. This term is used for a continuous front and its characteristics along its entire extent, its variations of intensity, and any frontal cyclones along it.

Moisture maximization: The process of adjusting observed precipitation amounts upward based upon the hypothesis of increased moisture inflow to the storm.

Observational day: The 24-hour time period between daily observation times for two consecutive days at cooperative stations, e.g., 6:00PM to 6:00PM.

One-hundred year rainfall event: The point rainfall amount that has a one-percent probability of occurrence in any year. Also referred to as the rainfall amount that on the average occurs once in a hundred years or has a 1 percent chance of occurring in any single year.

Polar front: A semi-permanent, semi-continuous front that separates tropical air masses from polar air masses.

Precipitable water: The total atmospheric water vapor contained in a vertical column of unit cross-sectional area extending between any two specified levels in the atmosphere; commonly expressed in terms of the height to which the liquid water would stand if the vapor were completely condensed and collected in a vessel of the same unit cross-section. The total precipitable water in the atmosphere at a location is that contained in a column or unit cross-section extending from the earth's surface all the way to the "top" of the atmosphere. The 30,000 foot level (approximately 300mb) is considered the top of the atmosphere in this study.

Persisting dew point: The dew point value at a station that has been equaled or exceeded throughout a period. Commonly durations of 12 or 24 hours2.777778e-4 days <br />0.00667 hours <br />3.968254e-5 weeks <br />9.132e-6 months <br /> are used, though other durations may be used at times.

Probable maximum precipitation: Theoretically, the greatest depth of precipitation for a given duration that is physically possible over a given size storm area at a particular geographic location at a certain time of the year.

Probable maximum flood: The flood that may be expected from the most severe combination of critical meteorological and hydrologic conditions that are reasonably possible in a particular drainage area.

Pseudo-adiabat: Line on thermodynamic diagram showing the pressure and temperature changes undergone by saturated air rising in the atmosphere, without ice-crystal formation and without exchange of heat with its environment, other than that involved in removal of any liquid water formed by condensation.

Pseudo-adiabatic: Referring to the process described by the pseudo-adiabat.

Rainshadow: The region, on the lee side of a mountain or mountain range, where the precipitation is noticeably less than on the windward side.

PMP storm pattern: The isohyetal pattern that encloses the PMP area, plus the isohyets of residual precipitation outside the PMP portion of the pattern.

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Saturation: Upper limit of water-vapor content in a given space; solely a function of temperature.

Short Storm List: The final list of storms used to derive the PMP values.

Spatial distribution: The geographic distribution of precipitation over a drainage according to an idealized storm pattern of the PMP for the storm area.

Storm transposition: The hypothetical transfer, or relocation of storms, from the location where they occurred to other areas where they could occur. The transfer and the mathematical adjustment of storm rainfall amounts from the storm site to another location is termed "explicit transposition."

The areal, durational, and regional smoothing done to obtain comprehensive individual drainage estimates and generalized PMP studies is termed "implicit transposition" (WMO, 1986).

Synoptic: Showing the distribution of meteorological elements over an area at a given time, e.g., a synoptic chart. Use in this report also means a weather system that is large enough to be a major feature on large-scale maps (e.g., of the continental U.S.).

Temperature Inversion: An increase in temperature with an increase in height.

Temporal distribution: The time order in which incremental PMP amounts are arranged within a PMP storm.

Tropical Storm: A cyclone of tropical origin that derives its energy from the ocean surface.

Total storm area and total storm duration: The largest area size and longest duration for which depth-area-duration data are available in the records of a major storm rainfall.

Transposition limits: The outer boundaries of the region surrounding an actual storm location where similar, but not identical, meteorological and topographic characteristics occur. The storm can be transpositioned within the transposition limits without modification of the expected storm dynamics and adjustments can be applied to the difference in elevation and moisture availability between the two locations. Transpositioning greatly increases the available data for evaluating the rainfall potential for a given drainage location.

Undercutting: The process of placing an envelopment curve somewhat lower than the highest rainfall amounts on depth-area and depth-duration plots.

Warm front: Front where relatively warmer air replaces colder air.

Warm sector: Sector of warm air bounded on two sides by the cold and warm fronts extending from a center of low pressure.

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Acronyms and Abbreviations used in the report ALERT: Automated Local Evaluation in Real Time ANO: Arkansas Nuclear One AWA: Applied Weather Associates, LLC COCORAHS: Community Collaborative Rain, Hail, and Snow Network COOP: Cooperative Observer Program DA: Depth-Area DAD: Depth-Area-Duration DD: Depth-Duration dd: decimal degrees DND: drop number distribution DSD: drop size distribution EPRI: Electric Power Research Institute F: Fahrenheit GIS: Geographical Information System GRASS: Geographic Resource Analysis Support System HMR: Hydrometeorological Report HR: Hour HYSPLIT: Hybrid Single Particle Lagrangian Integrated Trajectory Model IPCC: Intergovernmental Panel on Climate Change km: kilometer MADIS: Meteorological Assimilation Data Ingest System mb: millibar MCC: Mesoscale Convective Complex xv

MCS: Mesoscale Convective System mph: miles per hour NCAR: National Center for Atmospheric Research NCDC: National Climatic Data Center NCEP: National Centers for Environmental Prediction NEXRAD: Next Generation Radar NOAA: National Oceanic and Atmospheric Association NWS: National Weather Service PMF: Probable Maximum Flood PMP: Probable Maximum Precipitation PW: Precipitable water RAWS: Remote Automatic Weather Stations SMC: Spatially based Mass Curve SPAS: Storm Precipitation and Analysis System USACE: US Army Corps of Engineers USGS: United States Geological Survey WMO: World Meteorological Organization xvi

1. Introduction This study provides both Probable Maximum Precipitation (PMP) and the Local Intense Precipitation (LIP) values for use in the computation of the Probable Maximum Flood (PMF) for the Arkansas Nuclear One (ANO) basin and location. The site-specific study builds on the previous PMP studies completed by AWA in the region (e.g.,

Tomlinson 1993, Tomlinson et al 2002-2012, Kappel et al 2011-2013).

1.1 Background Definitions of PMP are found in most Hydrometeorological Reports (HMRs) published by the National Weather Service (NWS). The definition used in the most recently published HMR is "theoretically, the greatest depth of precipitation for a given duration that is physically possible over a given storm area at a particular geographical location at a certain time of the year." (HMR 59, p. 5). Since the mid-1940s, several government agencies have been developing methods to calculate PMP in various regions of the United States. The NWS (formerly the U.S. Weather Bureau) and the Bureau of Reclamation have been the primary agencies involved in this activity. PMP values from their reports are used to calculate the PMF which, in turn, is often used for the design of significant hydraulic structures.

The generalized PMP studies currently in use in the conterminous United States include HMR 49 (1977) for the Colorado River and Great Basin drainage; HMRs 51 (1978), 52 (1982) and 53 (1980) for the U.S. east of the 105th meridian; HMR 55A (1988) for the area between the Continental Divide and the 103rd meridian; HMR 57 (1994) for the Columbia River Drainage; and HMRs 58 (1998) and 59 (1999) for California. Figure 1.0 shows the coverage of the various HMRs. For the ANO basin, the majority of the region is covered by HMR 51, with areas west of 103° longitude covered by HMR 55A. In addition to these HMRs, numerous Technical Papers and Reports deal with specific subjects concerning precipitation. Examples are NOAA Technical Report NWS 25 (1980) and NOAA Technical Memorandum NWS HYDRO 45 (1995). Topics include maximum observed rainfall amounts; return periods for various rainfall amounts, and specific storm studies. Climatological atlases (Technical Paper No. 40, 1961; NOAA Atlas 2, 1973; and NOAA Atlas 14, 2003) are available for use in determining rainfall amounts for specified return periods for selected regions of the U.S.

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Figure 1.0 Regions covered by current Hydrometeorological Reports.

A number of site-specific and regional PMP studies augment generalized HMRs. These studies are for specific regions or drainage basins within the large areas addressed by HMR 51 and HMR 55A as well as areas covered by other HMRs. The meteorological conditions producing extreme rainfall events vary significantly in different regions within large geographic areas such as the large area covered by the ANO basin. In much of the Midwest, extreme events are usually linked to either Mesoscale Convective Systems (MCSs) or synoptic storms with embedded convection. For the ANO basin, the main storm type leading to PMF level flooding is a synoptic event with embedded convection which moves slowly across the region, generally in a west to east direction. This type of storm provides steady rainfall over long durations and large area sizes, with periods of heavy rainfall over smaller areas. Individual thunderstorms would not lead to PMF level flood across the basin and therefore were not a focus of the overall basin-wide PMP development. Instead, individual thunderstorms and MCSs were the storm type analyzed for the LIP analysis at the ANO site location, as a high intensity, short duration, and localized rainfall over the site would potentially produce the LIP.

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The time of year when the storm environment leading to PMP-level rain occurs is during a time of the year when no significant snowpack would be available. In areas of the upper basin where significant snow pack accumulates, significant rainfall that could lead to PMF level flooding at the ANO site does not occur. Therefore, no explicit cool-season PMP values and/or rain-on-snow analyses needed to be completed beyond this quantitative assessment.

Although it provides generalized estimates of PMP values for a large climatologically diverse area, HMR 51 recognizes that studies addressing PMP over specific regions can incorporate more site-specific considerations and provide improved PMP estimates. By periodically reviewing storm data and advances in meteorological concepts, PMP analysts can identify relevant new data and procedures for use in determining PMP values (HMR 51, Section 1.4.1).

As described previously, several site-specific PMP studies have been completed by AWA within the region covered by HMRs 51 and 55A (Figure 1.1). Each of these studies provided PMP values which replaced those from the HMRs. These are examples of PMP studies that explicitly consider the meteorology and topography of the study location along with characteristics of historic extreme storms over climatically similar regions. These regional and site-specific PMP studies have received extensive review and been accepted by the appropriate regulatory agencies. Results have been used in computing the PMF for individual watersheds.

Figure 1.1 Locations of AWA PMP studies as of December 2013.

This report presents details of the ANO PMP study. Section 1 provides an overview of the study. The weather and climate of the upper Midwest and northern Great Plains are discussed in Section 2. Section 3 details the storms types important for PMP development for the basin.

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The steps involved with identifying extreme storms are discussed in Section 4 and procedures used to analyze these storms are discussed in Section 5. Discussion on the development of the maximum dew point climatology is provided in Section 6. Adjustments for storm maximization, storm transpositioning, and elevation adjustments are presented in Sections 7 and 8. The final procedure of developing PMP values from the adjusted storm rainfall amounts is provided in Section 9. Section 10 provides information on PMP storm dimensions and movement. Section 11 provides analysis and results of the LIP analysis for the ANO site. PMP results are discussed in Section 12. Section 13 provides discussions related to the sensitivity analysis of the parameters used in the study. The recommended application of results are given in Section 14.

1.2 Objectives The objective of this study was to perform a PMP analysis to determine reliable estimates of PMP values for the entire ANO basin and LIP analysis to provide the 1-hour 1-square mile PMP value at the ANO site location. The most reliable methods and data currently available have been used, with updated methods, techniques, and data used where appropriate.

1.3 Approach The approach used in this study follows the same general procedures that were used in the development of the HMRs. These procedures were applied considering the meteorological and topographic characteristics of the basin.

The study maintains as much consistency as possible with the general methods used in HMRs 51 and 55A as well as the numerous site-specific, statewide, and regional AWA PMP studies. Deviations are incorporated where justified by developments in meteorological analyses and available data. The basic approach identifies PMP-type storms that occurred within the central and southern Plains of the United States to the Front Range and mountains of Wyoming, Colorado, and New Mexico east of the Continental Divide. This ensured a sufficiently large region was included in the development of the storm list so that any transpositionable storm that could potentially affect the PMP values at any area size or duration was included.

The moisture content of each of these storms is maximized to provide an estimate of the maximum rainfall for each storm at the location where it occurred. This is accomplished by computing the ratio of the maximum amount of atmospheric moisture that could have been entrained into the storm at that time of year to the actual atmospheric moisture entrained into the storm as it occurred. After maximization, the storms are transpositioned to each grid point to the extent supportable by similarity of meteorological conditions and topography. Maximized and transpositioned adjusted rainfall values are enveloped at each grid point and then contoured throughout the entire domain to provide PMP estimates for various area sizes and durations at any point within the entire basin. Figure 1.2 shows the flow chart of the major steps in the PMP development process.

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Figure 1.2 Flow chart showing the major steps involved in PMP development.

For some applications, this study applied standard methods (e.g. WMO Operational Hydrology Report No. 1, 1986), while for other applications, improved techniques were used.

Advanced computer-based technologies, Weather Service Radar WSR-88D NEXt generation RADar (NEXRAD), and HYSPLIT model trajectories were used for storm analyses along with updated meteorological data sources. Improved technology and data were incorporated into the study when they provided improved reliability, while maintaining as much consistency as possible with previous studies. This approach provides the most complete scientific application compatible with the engineering requirements of consistency and reliability for credible PMP estimates.

Moisture analyses in HMRs 51 and 55A used monthly maximum observed 12-hour persisting dew points to quantify atmospheric moisture. Maximum dew point values used in HMR 51 were provided by Climatic Atlas of the United States, published by the Environmental Data Services, Department of Commerce (1968). This study, however, used an updated maximum dew point return frequency analysis developed during several recent and on-going AWA PMP studies. This dew point analysis incorporated data sets with longer periods of record than were available for use in HMRs 51 or 55A. This updated climatology produced 20-, 50-,

and 100-year return frequencies for maximum average dew point values for 6-, 12-, and 24-hour 5

duration periods. GIS was used extensively in the development of the updated maximum dew point climatology maps.

A reanalysis of transposition limits was completed that evaluated the elevation of each storms isohyetal pattern versus the elevation of each grid point used in this study. It was confirmed from this analysis that storms should not be transpositioned more than +/- 1,000 feet in elevation from their original storm elevations. This same conclusion was found in several other AWA PMP studies in the region (e.g. Tomlinson et al. 2008, Kappel et al. 2013) as well as stated in HMR 51 (Section 2.4.2 c.). This procedure provided explicit guidance and constraints on the regions of influence for individual storms. Appendix F details which storms were ultimately transpositioned to various grid point(s).

As mentioned previously, a set of 22 grid points (Figure 1.3) were placed over the region.

The gridded analysis procedure was used with the total adjusted rainfall amounts applied across the grid that not only covers the entire basin, but extended into bordering regions to ensure continuity across the basin boundaries. PMP values were analyzed at each grid point using standard procedures. Envelopment of the largest rainfall totals was applied to ensure spatial and temporal continuity of the final PMP values. Once values were derived for each area size and duration, values were spatially and temporally distributed using GIS technologies and manual adjustments. This process produced the final set of PMP maps for the study. It should be noted that the PMP values over the Front Range and up to the Continental Divide of the Rocky Mountains in Colorado and New Mexico used the least amount of data and analyses. This is because no explicit evaluation and quantification of topography and how it relates to rainfall production was completed as part of this study. However, this has minimal to no effect on the resulting PMF at the ANO site, as any flood resulting from rainfall in these locations would have little to no affect effect on the PMF at the ANO site. Instead, values at the grid points in the mountains (7, 13, 14, 21, and 22) provided spatial and temporal continuity of PMP across the western portion of the basin.

A preferred storm orientation analysis was evaluated using storm isohyetal patterns from storms used in this study and results from previous investigations. In addition, an analysis was completed to determine the potential movement of the PMP storm across the basin over the 72-hour period. This procedure was required because the size of the basin is so large that the stationary PMP design storm in HMR 52 does not appropriately replicate a true PMP-type scenario for the basin. Actual storm events used to provide PMP for this study were used to determine the range of movement that could be expected to occur during extreme rainfall events.

Recommendations for orientation constraints and storm movement are made.

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Figure 1.3 Grid points used in the study.

1.4 ANO Location and Description The drainage basin for the ANO site encompasses the Arkansas River drainage basin, extending from the Continental Divide of Colorado and New Mexico east through northern Texas and the Red River basin to the ANO site location (Figure 1.4). Because this basin extends across a large latitudinal and longitudinal extent, PMP-type storm events can vary across the basin, and any given storm event will not be affecting the entire basin at one time. The large size of the basin and its geographic location have been explicitly evaluated and considered during the study to ensure appropriate PMP development.

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Figure 1.4 ANO regional setting and basin statistics.

Elevations across the basin range from 350 feet along the Arkansas River at the ANO site to over 14,000 feet in Colorado Rockies (Figure 1.6). Elevation changes gradually moving from ANO site west across the basin until reaching eastern Colorado and New Mexico. Elevation gains become dramatic once the Front Range of the Rocky Mountains are reached, generally around 105°W longitude. These elevation changes from east to west within the basin in combination with increased distance from the low-level moisture source (the Gulf of Mexico) create varying storm dynamics and storm types across the basin. Therefore, different storms are used to derive PMP values at various gird points, providing for a significant gradient in PMP values across the basin both in east to west and north to south directions. Therefore, for storm transpositioning, the +/- 1,000 foot limitation was implemented, along with a latitudinal limitation based on distance from the moisture source. This had the most affect on the far western and northern grid points, as many of the central and southern Great Plains storms were not transpositioned to these locations.

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Figure 1.5 Elevations contours across the ANO basin at 500 foot intervals. Grid points used in the study are also shown.

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2. Weather and Climate of ANO 2.1 ANO PMP Storm Type Climatology The region around ANO is influenced by several factors that can potentially contribute to extreme rainfall. First is the proximity of the region to the Gulf of Mexico and the fact that no intervening mountain barriers prevents moisture from moving north (Figure 2.0). This allows high amounts of moisture to move directly into the region. The limiting factor is the duration that these high levels of atmospheric moisture are able to feed into storms in the region. More atmospheric moisture is available over the more southern and eastern regions of the basin compared with the northern and western portions of the basin. Because of the movement and strength of the upper level winds in the region, storm patterns generally do not stay fixed over any location for long periods. Therefore, the synoptic situations which lead to high levels of Gulf of Mexico moisture moving into the region are transient and limit the magnitude of PMP-type rainfall as well as limiting the spatial extent of such storms. This lack of consistent moisture is somewhat compensated for by the stronger storm dynamics associated with synoptic weather systems which move through the region and added lift as the atmospheric moisture is forced to rise over elevated terrain moving south to north and east to west across the basin.

Figure 2.0 Locations of surface features associated with a strong flow of moisture from the Gulf of Mexico into the upper Midwest.

But moisture alone does not create rainfall. Instead a mechanism to lift and condense that moisture is required. The lift required to convert these high levels of atmospheric moisture into rainfall on the ground is provided in several ways in and around the region. Synoptic storm dynamics are very effective in converting atmospheric moisture into rainfall on the ground.

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These are most often associated with fronts which affect the region (Figure 2.1). Numerous large scale weather systems with their associated fronts traverse the region throughout the year, with the fewest and weakest occurring in the summer period. The fronts (boundaries between two different air masses) can be a focusing mechanism providing upward motion in the atmosphere.

These are often locations where heavy rainfall is produced. Normally, a front will move through with enough speed that no one area receives excessive amounts of rainfall. However, in extreme instances the pattern can become blocked and some of these fronts will stall or move very slowly across the region. This allows large amounts of rainfall to continue for several days in the same general area, which can lead to extreme widespread flooding.

Figure 2.1 Locations of surface features associated with a common synoptic storm pattern across the United States.

Another mechanism which creates lift in the region is heating of the surface and lower atmosphere by the solar radiation. This creates warmer air below colder air resulting in atmospheric instability and leads to rising motions. This will often form ordinary afternoon and evening thunderstorms. However, in unique circumstances the instability and moisture levels in the atmosphere can reach very high levels and stay over the same region for an extended period of time. This can lead to intense thunderstorms and very heavy rainfall. If these storms are focused over the same area for a long period, flooding rains can be produced. This type of storm produces some of the largest point rainfall amounts recorded, but often do not affect larger areas with extreme rainfall amounts. Therefore, this scenario is common in the spring and summer and is often responsible for the LIP storm.. However, this storm scenario does not lead to PMF level flood events across the very large ANO basin. More details on the PMP storm types which produce PMP level rainfalls in and around the basin are given in Section 3.

2.2 General Weather Patterns over the ANO Basin The weather patterns in the region are characterized by passages of fronts with differing air masses that lead to large ranges in temperatures and rainfall. Fronts are most prevalent in the fall, winter, and spring, with more stagnant patterns common from late spring through early fall.

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There are several air mass types that affect the weather and climate of the region and produce heavy rainfall (Figure 2.2). The continental polar (cP) air mass, with origins from the arctic regions of Canada, is most common during the winter months. This air mass is often associated with a strong cold front passage and stratiform snowfall events. When this air mass type arrives, it often collides with a more humid air mass from warmer regions to the south. Low pressure (rising air) often results, and when combined with strong winds aloft, can produce extreme rainfall. However, this air mass type is often highly modified by the time it reaches the southern half of the ANO basin, as it is now a great distance from its original source, has moved over non-snow covered land, and is significantly modified by the warmer conditions from the Gulf of Mexico .

The second type of air mass observed in the region is the maritime polar (mP) which originates in the Gulf of Alaska and Pacific Ocean. This air mass often arrives on strong winds from the west and northwest, but is usually devoid of significant amounts of low-level moisture because it has traveled across several mountain ranges. This storm type often produces precipitation (rain and snow) at these upwind locations, losing much of its low-level moisture on its way to the Central and Southern plains. However, in extreme cases, moisture flowing north from the Gulf of Mexico can replenish low-level atmospheric moisture enough to produce heavy rainfall. If the storm system stalls over the region, flood producing rains can result. This storm type can occur anytime of the year, but is most common from fall through late spring.

Another type of air mass which affects the region and produces rainfall originates from the Gulf of Mexico and can contain copious amounts of atmospheric moisture in a conditionally unstable atmosphere. This type of air mass is called maritime tropical (mT). This type of air mass is most directly responsible for producing heavy rainfall in the region when interacting with a front and as well as an air mass of polar origins moving from the north. Often, the front is located over the basin, allowing high amounts of moisture to stream in from the south, where it is lifted, resulting in widespread rainfall. The release of the conditional instability in the atmosphere provides a very efficient mechanism to convert atmospheric moisture to rain on the ground. This can be enhanced by elevation changes in the underlying topography. If this pattern is able to remain in place for an extended period and to continue to draw in Gulf of Mexico moisture, flooding can result. This storm type is most common from late spring to early fall and is therefore the most common storm type for the PMP scenario.

In rare cases, this type of pattern can include moisture from a decaying tropical system that had previous made landfall along the Gulf Coast states. This scenario has led to the most extreme rainfall events in the historical record for durations of 24-hours and less in the southern portions of the ANO basin. Examples include Albany, TX August, 1978 (AWA 18) and Thrall, TX September, 1921 (AWA 77).

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Figure 2.2 Air mass source regions affecting the ANO drainage basin.

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3. Extreme Storm types The ANO basin and the surrounding region have very active and varied weather patterns throughout the year. Consequently heavy rainfall events at both short and long durations are common. By far, the largest amount of moisture available for rainfall over the region comes from the Gulf of Mexico. The major types of extreme rainfall events in the region are produced by Mesoscale Convective Systems (MCS) (short durations and small area sizes), synoptic events/fronts (large areas sizes and longer durations), and remnant moisture from tropical systems which have made landfall along the Gulf of Mexico coastline.

3.1 Synoptic Fronts The polar front and jet stream, which separate cool, dry Canadian air to the north from warm, moist air to the south, is often a cause of heavy rainfall over large areas and long durations. This boundary provides large amounts of energy and strong storm dynamics to the atmosphere as fronts move through the region. These features are strongest and most active over the area during fall, winter, and spring months. A common type of storm occurrence with the polar front is an overrunning event. Frontal overrunning occurs when warm, humid air carried northward around the western edge of the Bermuda High circulation encounters the frontal zone and is forced to rise over the cooler, drier air mass to the north of the front. This forced ascent condenses atmospheric moisture in the air mass, forming clouds and producing precipitation while releasing latent heat. This process most often results in widespread rainfall over longer durations, but can also help enhance convection. Air that arrives at the frontal location is conditionally unstable, where the lower layers are much warmer and more humid than the air above. This conditionally unstable air mass needs a mechanism to initiate lift to begin energy release, leading to more instability and further lift. The forced ascent over the polar front initiates the lifting of the moist air mass, release of its energy, and initiates the conversion of the atmospheric moisture to rainfall.

A stationary or slow moving polar front located within the ANO basin will often provide the mechanism necessary for this warm, humid air mass to release its convective potential. When this occurs, rainfall is produced, sometimes associated with pockets of convection and extremely heavy rainfall. The pockets of heavy rain are usually associated with a minor wave riding along the frontal boundary, called a shortwave. These are not strong enough to move the overall large scale pattern, but instead add to the storm dynamics and energy available for producing rainfall.

This type of storm environment (synoptic frontal) will usually not produce the highest rainfall rates over short durations, but instead leads to flooding situations as moderate to heavy rain falls over the same regions for an extended period of time. In addition, this scenario can occur in succession with only a few dry days in between and therefore enhance runoff on a previously saturated basin. The rainfall and flooding event which occurred during May 1943 from Oklahoma eastward through the ANO site location is a good example of this type of storm.

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3.2 Mesoscale Convective Systems Mesoscale Convective Systems (MCSs) are capable of producing extreme amounts of rainfall for short durations and over small area sizes, generally 12 hours1.388889e-4 days <br />0.00333 hours <br />1.984127e-5 weeks <br />4.566e-6 months <br /> or less over area sizes of 500-square miles or less. The current understanding of MCS type storms has progressed tremendously with the advent of satellite technology starting in the 1970s and early 1980s. The current name of MCS was first applied in the late 1970s to these type of flood producing, strong thunderstorm complexes (Maddox 1980). Mesoscale systems are so named because they are small in areal extent (10s to 100s of square miles), whereas synoptic storm events are 100s to 1,000s of square miles. MCSs also exhibit a distinctive signature on satellite imagery where they show rapidly growing cirrus clouds shields with very high cloud tops. Furthermore, the high level cloud shield associated with MCSs usually take on a nearly circular pattern about the size of the state of Iowa with constantly regenerating thunderstorms fed by a low-level-jet (LLJ) bringing an inflow of atmospheric moisture (Figure 3.0).

Figure 3.0 Color enhanced infrared satellite image of an MCS. Note the nearly circular structure, very cold cloud tops at the center (red, black, and center white colors), and a size similar to the state of Iowa.

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The vast majority of MCSs have distinctive features and evolve in a standard pattern. A typical MCS begins as an area of thunderstorms over the western High Plains or Front Range of the Rocky Mountains. As these storms begin to form early in the day, the predominantly westerly winds aloft move them in a generally eastward direction. As the day progresses, the rain-cooled air below and around the storms begins to form a mesoscale high pressure area. This mesoscale high moves along with the area of thunderstorms. During nighttime hours, the MCS undergoes rapid development as it encounters increasingly warm and humid air from the Gulf of Mexico, usually associated with the LLJ 3,000-5,000 feet above the ground. The area of thunderstorms will often form a ring around the leading edge of the mesoscale high and continue to intensify, producing heavy rain, damaging winds, hail, and/or tornadoes. An MCS will often remain at a constant strength as long as the LLJ continues to provide an adequate supply of moisture. Once the mesoscale environment begins to change, the storms weaken, usually around sunrise, but may persist into the early daylight hours.

MCSs are included in the more general definition of MCCs, which include a wider variety of mesoscale sized storm systems, such as squall lines and tropical cyclones, and MCSs that do not fit the strict definition of size, duration, and/or appearance on satellite imagery. MCSs primarily form during the warm season months (April through October) around the ANO basin region.

Many of the storms previously analyzed by the USACE and NWS Hydrometeorological Branch in support of pre-1979 PMP research have features that indicate they were most likely MCCs or MCSs. However, this nomenclature had not yet been introduced into the scientific literature, nor were the events fully understood. For ANO basin, pure MCS storms do not produce PMF level flood events because of the very large basin size and the relatively small areas of rainfall produced by MCSs. However, intense convection similar to this storm type can occur within an overall synoptic frontal event. This can lead to intense areas of embedded heavy rainfall within the overall lighter rainfall pattern. This combination of synoptic and convective storm types is very important for determining PMP values for the basin.

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4. Extreme Storm Identification 4.1 Storm Search Area A comprehensive storm search covering the region important for the ANO basin has been conducted during previous site-specific and regional PMP studies. This included an analysis of all extreme rainfall storms in meteorological and topographically similar regions, where extreme rainfall storms similar to those that could occur over some part of the ANO basin may have been observed (Figure 4.0). These previous storm search results are current through 2013 and include all 12 months of the year (Figure 4.1). This ensured a large enough area was analyzed to capture all significant storms that could potentially influence the final PMP values for the basin.

Figure 4.0 AWA storm search domain.

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Figure 4.1 AWA storm search domains through 2013. All storms used to develop the PMP values were identified from the storm search results.

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4.2 Data Sources AWA storm searches were conducted by searching the National Climatic Data Center (NCDC) hourly and daily rainfall records for maximum rainfall amounts that occurred during 6-hour, 24-hour/1-day, and 72-hour/3-day periods within the storm search domain. Further searches were conducted from additional sources listed below:

1. Cooperative Summary of the Day / TD3200 through 2013. These data are published by the National Climatic Data Center (NCDC)

2. Hourly Weather Observations published by NCDC, U.S. Environmental Protection Agency, and Forecast Systems Laboratory (now National Severe Storms Laboratory)

3. Hydrometeorological Reports

4. Corps of Engineers Storm Studies

5. Other data published by state climate office

6. American Meteorological Society journals

7. Various weather books

8. Data from supplemental sources, such as Community Collaborative Rain, Snow, and Hail Network (CoCoRaHS), Weather Underground, Forecast Systems Laboratories, RAWS 4.3 Short Storm List Derivation The final short storm list used to determine the PMP values for the ANO basin was derived using the results of previous PMP studies in regions similar to this basin (Tomlinson 1993, Tomlinson et al. 2008, Kappel et al. 2012, Kappel et al. 2012, Tomlinson et al. 2013).

During this process, the storm lists used in each of these studies was combined and evaluated. The first set of parameters used to delineate the storms was whether they were transpositionable to any grid point used to derive the PMP values for the ANO basin. Factors such as elevation differences of more than +/- 1,000 feet and/or distances from moisture source were considered. Next, the storm type was evaluated. Storm types which would not result in a PMP/PMF scenario for the large ANO watershed were not considered. This included storms which were individual thunderstorms.

These analyses resulted in the final short storm lists used to derive both the PMP values for the basin. Table 4.0 provides the storm list. Figures 4.2 displays the locations of the storms in relation to the basin and ANO site. An AWA Storm Number is used to identify each storm used in this study to derive PMP values.

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Table 4.0 Storm list used to calculate PMP, sorted in alphabetical order.

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Figure 4.2 Storm locations in relation to the ANO basin by AWA storm number.

4.3.1 New Storm Precipitation Analysis System (SPAS) Storm Analysis The results of the storm search and short storm list development identified one new storm important for PMP derivation which had not been previously analyzed by either the NWS or AWA. This was the September 2013 rainfall across the Front Range and Eastern Plains of Colorado and Wyoming (SPAS 1302, AWA Storm Number 85/86).

A full storm analysis using the Storm Precipitation Analysis System (SPAS) was completed which produced the required storm DAD values (see Section 5 and Appendix G for a full description of the SPAS storm analysis process). The SPAS analysis produced all the necessary rainfall information required to evaluate and utilize the storm in the PMP derivation process. In addition, the current study included 35 previously analyzed SPAS storms used in other PMP studies1.

1 The precipitation/storm analysis source for each short list storm is listed in Tables 4.0. Each SPAS storm analysis is assigned a unique SPAS number (e.g. SPAS 1242), "EPRI" refers to storms analyzed during the 21

5. Storm Depth-Area-Duration (DAD) Analyses for New Storms For the new extreme rainfall event, a full storm analysis needed to be completed.

SPAS was used to compute the Depth-Area-Duration (DAD) table for this storm.

There are two main steps in a SPAS DAD analysis: 1) Creation of high-resolution hourly precipitation grids and 2) Computation of depth-area rainfall amounts for various durations.

Reliability of results from step 2) depends on the accuracy of step 1). Historically the process has been very labor intensive. SPAS utilizes Geographic Information Systems (GIS) concepts to create more spatially-oriented and accurate results in an efficient manner (step 1). Furthermore, the availability of NEXRAD data allows SPAS to better account for the spatial and temporal variability of storm precipitation for events occurring since the early 1990s. Prior to NEXRAD, the National Weather Service (NWS) developed and used a method based on the research of several scientists (Corps of Engineers, 1936-1973). Because this process has been the standard for many years and holds merit, the DAD analysis process developed within the SPAS program attempts to mimic it as much as possible. By adopting this approach, some level of consistency between the newly analyzed storms and the hundreds of storms already analyzed can be achieved. Comparisons between the NWS DAD results and those computed using the new method for two storms (Westfield, MA, 1955 and Ritter, IA, 1953) indicated very similar results (see Appendix G for complete discussion, comparisons, and results). The SPAS program and process is certified in this calculation.

Table 5.0 lists the SPAS storm used in during the development of PMP and LIP values during this study. The results of each SPAS storm analysis are included in Appendix F.

EPRI Michigan/Wisconsin Regional PMP study, while the remaining identifiers reference nomenclature from the NWS/USACE storm studies files.

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Table 5.0 SPAS storms used in this study.

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6. Updated Data Sets Used in this Study Several new data sets not used in the development of HMRs 51 and 55A were employed as part of this study in the development of the PMP and LIP values. These include the development of updated maximum dew point climatology maps for use in storm maximization and transposition, as well as the use of the HYSPLIT trajectory model to help in identifying the moisture source region for individual storm events. The identification and use of these data sets provide a significant improvement in storm adjustments, especially relating to the determination of each storms moisture source and derivation of appropriate maximization factors.

6.1 Development of the Updated Dew Point Climatology Updated maximum average dew point climatologies provide 20-year, 50-year, and 100-year return frequency values for 6-hour, 12-hour, and 24-hour durations. This process followed the same reasoning and use as described in the other AWA PMP studies. These analyses demonstrated that the maximum 12-hour persisting dew point climatology used in HMRs 51 and 55A were outdated and more importantly did not adequately represent the atmospheric moisture available in extreme rainfall storm environments. The updated climatology more accurately represents the atmospheric moisture fueling storms by using average maximum dew point values observed over durations specific to each storms rainfall duration. The maximum average dew point values replace the maximum 12-hour persisting dew point values which often missed or underestimated the atmospheric moisture available and hence led to inaccurate maximization calculations.

6.2 HYSPLIT Trajectory Model The HYSPLIT trajectory model developed by the NOAA Air Resources Laboratory (Draxler and Rolph 2003, 2010) was used during the analysis of each of the rainfall events included on the short storm list when available (1948-present). Use of a trajectory model provides increased confidence for determining moisture inflow vectors and storm representative dew points. The HYSPLIT model trajectories have been used to analyze the moisture inflow vectors in other PMP studies completed by AWA over the past several years. During these analyses, the model trajectory results were verified and the utility explicitly evaluated (e.g. Tomlinson et al. 2006-2011, Kappel et al. 2012-2013).

Instead of subjectively determining the moisture inflow trajectory, the HYSPLIT model interface was used to determine the trajectory of the atmospheric moisture inflow, both location and altitude, for various levels in the atmosphere associated with the storms rainfall production. The HYSPLIT model was run for trajectories at several levels of the lower atmosphere to capture the moisture source for each storm event.

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These included 700mb (approximately 10,000 feet), 850mb (approximately 5,000 feet),

and storm center location surface elevation. For the majority of the analyses a combination of all three levels was determined to be most appropriate for use in evaluation of the upwind moisture source location. It is important to note that the resulting HYSPLIT model trajectories are only used as a general guide of where to evaluate the moisture source for storms in space and time. The final determination of the storm representative dew point and its location is determined following the standard procedures used by AWA in previous PMP studies and as outlined in the HMRs and WMO manuals. Appendix F of this report contains each of the HYSPLIT trajectories analyzed as part of this study for each storm. As an example, Figure 6.0 shows the HYSPLIT trajectory model results used to analyze the inflow vector for the Council Grove, KS, July 1951 storm (AWA 38).

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Figure 6.0 HYSPLIT trajectory model results for Council Grove, KS, July 1951 storm (AWA 18).

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6.3 Use of Grid Points to Spatially Distribute PMP Values To appropriately distribute rainfall values spatially and temporally across the large ANO basin, a series of grid points were used. The grid consisted of 22 locations, one of which was the ANO site location. In addition, the overall grid was extended outside of the basin boundaries over bordering regions (see Figure 1.3). This grid design ensured that no extrapolation of adjusted rainfall values were required for any location within the basin.

All appropriate storm rainfall values were maximized and transpositioned to each of the 22 grid points as appropriate (Appendix F lists the grid point(s) where each storm was transpositioned). Depth-Area (DA) curves for each duration (6-hours to 72-hours) and for area sizes from 10- through 100,000-square miles were plotted for each grid point and envelop curves constructed. Using results from the DA analyses, Depth-Duration (DD) curves were constructed for each grid point (see Section 9 for details). Results from the DD analysis were input into GIS where the values for each duration and area size at each grid point were spatially analyzed. The final PMP maps derived using the grid point methodologies are provided in Appendix A.

Having the contoured PMP maps to analyze on a regional basis proved to be a very valuable asset compared to having only rainfall values at a single location. The ability to look at the relationships among grid points at various spatial and temporal scales as a whole proved very insightful and was of great importance in deriving the final PMP values across the large ANO basin.

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7. Storm Maximization Storm maximization is the process of increasing rainfall associated with an observed extreme storm under the potential condition that additional atmospheric moisture could have been available to the storm for rainfall production. Maximization is accomplished by increasing surface dew points to some climatological maximum and calculating the enhanced rainfall amounts that could potentially be produced. An additional consideration is usually applied that selects the climatological maximum dew point for a date two weeks towards the warm season from the date that the storm actually occurred. This procedure assumes that the storm could have occurred with the same storm dynamics two weeks earlier or later in the year when maximum dew points (and hence moisture levels) could be higher. A more detailed discussion of this procedure and example calculations are provided in Appendix C.

7.1 Use of Dew Point Temperatures for Storm Maximization HMR and WMO procedures for storm maximization use a representative storm dew point as the parameter to represent available moisture to a storm. Prior to the mid-1980s, maps of maximum dew point values from the Climatic Atlas of the United States (1968) were the source for maximum dew point values. HMR 55A published in 1988 updated maximum dew point values for a portion of United States from the Continental Divide eastward into the central plains. The regional PMP study for Michigan and Wisconsin produced return frequency maps using the L-moments method. The Review Committee for that study included representatives from NWS, FERC, Bureau of Reclamation, and others. They agreed that the 50-year return frequency values were appropriate for use in PMP calculations. HMR 57 was published in 1994 and HMR 59 in 1999. These more recent NWS publications also updated the maximum dew point climatology, but used maximum observed dew points instead of return frequency values.

For the Nebraska statewide study, the Review Committee and FERC Board of Consultants agreed that the 100-year return frequency maximum dew point climatology maps were appropriate because this added a layer of conservatism over the use of 50-year return period values. This has subsequently been employed in all AWA PMP studies.

This study is again using the 100-year return frequency climatology with the data updated through the first half of 2013 (Figure 7.0).

Observed storm rainfall amounts are maximized using the ratio of precipitable water for the maximum dew point to precipitable water for the storm representative dew point, assuming a vertically saturated atmosphere. This procedure was followed in this study using the updated maximum dew point climatology developed and described in Section 6. A more detailed discussion, along with examples of this procedure, is provided in Appendices C and D.

For storm maximization, average dew point values for the appropriate duration which was most representative of the actual rainfall accumulation period for an individual 28

storm (6-, 12-, or 24-hour) was used to determine the storm representative dew point. To determine which time frame was most appropriate, the total rainfall amount was analyzed. The duration (6-, 12- or 24-hour) closest to when approximately 90% of the rainfall had accumulated was used to determine the duration used, i.e. 6-hour, 12-hour, or 24-hour.

Figure 7.0 Dew point climatology development dates and regions.

7.1.1 Rationale for Using Maximum Average Dew Point Climatology In previous storm analyses performed by the NWS and the USACE, a 12-hour persisting dew point was used for both the storm representative and maximum dew points. The 12-hour persisting dew point is the value equaled or exceeded at all observations during the 12-hour period (e.g. WMO 1986). However, as was established in previous and ongoing AWA PMP studies, this dew point methodology tends to underestimate the storm representative dew point value associated with the rainfall event.

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An excellent example of this, from the Nebraska statewide PMP study but relevant for the storm types that affect the ANO basin, is illustrated by the David City, NE 1963 storm. During this extreme storm event, a narrow tongue of moisture was advected into the region by strong southeasterly flow during a short time period. Most of the rain with this event (approximately 15 inches) accumulated in less than 6 hours6.944444e-5 days <br />0.00167 hours <br />9.920635e-6 weeks <br />2.283e-6 months <br /> (Figure 7.1). For this storm, hourly dew point data were collected from several locations near the rainfall event. These included Omaha, NE; Des Moines, IA; Topeka, KS; and Kansas City, MO. Following standard procedures for determining storm representative dew point location, it was determined that Topeka, KS and Kansas City, MO were the two stations that best represented the air mass that produced the extreme rainfall. Using hourly dew point data for these two stations clearly showed that use of 6-hour average dew point values better represented the atmospheric moisture available to the storm event than did use of 12-hour persisting dew point values. The 6-hour average dew point representing the moisture in the air mass associated with the rainfall was 71.5°F at Kansas City, MO and 71°F at Topeka, KS. Using these dew point values, a 1,000 mb 6-hour average dew point of 73.5°F was determined for Kansas City, MO and a dew point of 73°F was determined for Topeka, KS. Using the NWS approach, the 12-hour persisting dew point is 63°F (65°F at 1,000 mb) at Kansas City, MO and 66°F (68°F at 1,000 mb) at Topeka, KS for an average 1,000 mb adjusted value of 66.5°F (Table 7.0).

Figure 7.1 Mass Curve as analyzed by SPAS for David City, NE 1963 storm event 30

Table 7.0 Comparison of 6-hour average storm representative dew point vs. 12-hour persisting storm representative dew point for David City, NE 1963 Observed Dew Point Values for David City, NE 1963 Kansas City, MO Hour 00Z 01Z 02Z 03Z 04Z 05Z 06Z 07Z 08Z 09Z 10Z 11Z 12Z 13Z 14Z 15Z 16Z 17Z 18Z 19Z 20Z 21Z 22Z 23Z Dew Point 58 61 62 62 63 63 63 64 66 68 69 71 72 72 72 71 71 69 68 67 67 67 67 67 Air Mass Supplying Rainfall Event 12-Hour Persisting Td 63 ( 65 reduced to 1000mb) 12 Hour Persisting Td Timeframe 6-Hour Average Td 71.5 (73.5 reduced to 1000mb) 6 Hour Average Td timeframe Topeka, KS Hour 00Z 01Z 02Z 03Z 04Z 05Z 06Z 07Z 08Z 09Z 10Z 11Z 12Z 13Z 14Z 15Z 16Z 17Z 18Z 19Z 20Z 21Z 22Z 23Z Dew Point 61 62 64 65 65 65 66 66 67 68 69 72 71 71 71 70 70 70 69 70 69 68 66 69 Air Mass Supplying Rainfall Event 12-Hour Persisting Td 66 (68 reduced to 1000mb) 12 Hour Persisting Td Timeframe 6-Hour Average Td 71 (73 reduced to 1000mb) 6 Hour Average Td timeframe The 12-hour persisting dew point analysis included dew point values from a six hour period not associated with the rainfall. The hourly dew point value that provides the 12-hour persisting dew point occurred outside of the rainfall period after adjustment for advection time from the dew point observing station(s) to the storm location.

7.1.2 Rationale for Adjusting HMR 51 Persisting Dew Point Values In some cases, storms on the short storm list previously analyzed in the USACE Storm Studies and used in NWS HMRs, an adjustment factor was applied to provide consistency in storm maximization while utilizing the updated dew point climatology.

The adjustment factor was determined using the same procedure used in the EPRI and other AWA PMP studies.

Results from the dew point analyses showed consistent results for MCS type storms for differences between the older method for determining 12-hour persisting storm representative dew points and the approach using average storm representative dew points. The following discussion from the EPRI report addresses these differences:

The average difference between dew points for the synoptic storms was five degrees less than that for the MCS storms. This may be attributed to the greater homogeneity of inflow moisture associated with the synoptic events. With most of the modern MCS storms, limited-area, short-duration pockets of relatively moist air were found within the inflow moisture at one or two locations. The analyses may indicate that for MCS events, bubbles of extremely moist air interact with storm catalysts to create extreme rainfall events of short duration. A warm humid air mass over a broad area with small moisture gradients more aptly describes the synoptic inflow moisture. Several stations within the air mass may have the same or similar dew points. Much smaller variations in dew points along the inflow moisture vector are expected.

Large spatial and temporal variations in moisture associated with MCS-type storms are not represented well with 12-hour persisting dew points, especially when only two observations a day are available. Average dew point values, temporally consistent with the duration of the storm event provide a much improved description of the inflow moisture available for conversion to precipitation. The more homogeneous moist air 31

masses associated with synoptic storms result in smaller differences between average and persisting values.

This analysis has provided correlations between 12-hour persisting storm dew points and average storm dew points for both MCS and synoptic storms. Despite the small sample size, the consistent results tend to support the reliability of the analysis.

However, the small sample size has been considered in making recommendations for adjusting the old storm representative dew points for use in determining PMP estimations. The eight degree difference for MCS-type storms has been decreased to five degrees to provide a conservative adjustment. A similar consideration is made for synoptic-type storms. The three-degree difference is decreased to two degrees to provide a conservative adjustment. The adjusted representative storm dew points are used with the new maximum average dew point climatology to maximize storms.

Similar analyses were completed in the Nebraska statewide PMP study, the Ohio statewide PMP study, and the Wyoming statewide PMP study. These analyses investigated additional modern storms. The results of these analyses of MCS storm data provided an average difference of 7°F between the average and 12-hour persisting dew points. For synoptic storms, the average difference was 2°F. Results of the more recent analyses were very consistent with the EPRI study. This again validated the process of adjusting the maximum 12-hour persisting dew points in order to achieve compliance with using the maximum average dew point climatology.

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8. Storm Transpositioning Extreme rain events that have occurred over geographically and climatically similar regions surrounding a study area are a very important part of the historical evidence on which PMP estimates for a drainage basin are based. Study locations usually have a limited period of record for rainfall data collected within the basin boundaries and hence have a limited number of extreme storms that have been observed over the basin.

Storms observed regionally with a similar meteorology and topography are analyzed and adjusted to provide information describing the storm rainfall as if the storm had occurred over the study basin. Transfer of a storm from where it occurred to a location that is meteorologically and topographically similar is called storm transpositioning. The underlying assumption is that storms transposed to the study area could occur over the basin under similar meteorological conditions. To properly relocate such storms, it is necessary to address issues of similarity as they relate to topography and meteorological conditions and make appropriate adjustments.

The area considered to contain storms which were potentially transpositionable to one or more grid points analyzed as part of this study extended from the Continental Divide of the Rocky Mountains south of 48°N east through the first upslopes on the west side of the Appalachians, south the southern Plains to approximately 50 miles north of the Gulf of Mexico (see Section 4.1). This region was considered meteorologically homogenous to one or more locations within the overall ANO basin. Further analysis of storm patterns on both a temporal and spatial scale within non-orographic regions of the basin revealed that only storms that occurred within a +/- 1,000 feet of elevation of a particular location possessed similar enough storm dynamics to be transpositionable to that location. Further, the limits of transpositionability were refined for specific storms after all adjustments were applied based on meteorological judgment and fit with other similar storms in the region.

8.1 Storm Transposition Calculations The procedure for in-place storm maximization has been discussed (see Section 7.0). The same maps used for deriving maximum dew points were used in the storm transpositioning procedure. The procedure for deriving the climatological maximum dew points for use in the calculating the transposition maximization ratio uses the information derived during the calculation of the in-place maximization factor. The wind inflow vector connecting the storm location with the storm representative dew point location was transpositioned to each grid point. The value of the maximum dew point at that upwind location provided the transpositioned maximum dew point value used to compute the transposition adjustment factor for relocating the storm to the appropriate grid point and basin centroid. Figure 8.0 shows an example inflow vector map and transpositioned vector to grid point 2 for the Albany, TX, August, 1978 (AWA 18) storm. The primary effect of storm transpositioning was to adjust storm rainfall amounts to account for 33

enhanced (or reduced) atmospheric moisture made available to the storm at the transposed location versus the original location. A more detailed discussion of this procedure and example calculations are provided in Appendix D. The inflow vector map and data used to calculate the transposition factor for each storm are included in Appendix F.

Figure 8.0 An example of inflow wind vector transpositioning for Albany, TX, August, 1978 storm (AWA 18). The storm representative dew point location is 260 miles south/southeast of the storm location.

8.2 Storm Spreadsheet Development Process AWA has developed an Excel spreadsheet for each storm on the PMP and LIP short storm lists which incorporates relevant storm information, automatically calculates appropriate adjustment factors, and computes the adjusted rainfall DAD table. These storm spreadsheets used the observed storm DADs, storm representative dew points, maximum dew points (both in-place and transposition), storm elevation, and transposition location elevation information either as published in the USACE Storm Studies reports, HMR 51 tables, or as developed during AWA SPAS storm analyses. This information was entered into individual storm spreadsheets, one for each short list storm for each appropriate grid point. Using the storm center location and inflow vector, the in-place maximum dew point was determined. The same inflow vector was then moved to each appropriate grid point to determine the transpositioned maximum dew point value and 34

total adjustment factor for that storm at each location. This information was entered into the storm spreadsheet to calculate the in-place maximization factor, the transposition factor, and finally the total adjustment factor. This total adjustment factor was applied to the storm DAD table values to provide the final adjusted DAD table for the maximized and transpositioned storm rainfall values at each location.

Once all the storms were adjusted to each appropriate grid point, DA and DD plots were constructed for each location for analysis and envelopment. This ensured spatial and temporal continuity for each grid point location. The resulting analysis results were subsequently plotted and contoured within GIS to produce the final basin-wide PMP maps. Appendix F includes the storm spreadsheets developed for each storm transpositioned to a specific grid point. Figure 8.1 displays an example storm spreadsheet for the Warner Park, TN, May, 2010 storm (AWA 2) at the basin centroid.

The information in Appendix F allows a user the opportunity to explicitly evaluate, verify, and recalculate the values derived in this study, if so desired.

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Figure 8.1 Example of the storm spreadsheet for the Warner Park, TN, May 2010 storm (AWA 2) transpositioned to grid point 1.

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9. Development of PMP Values for the ANO Basin Storm maximization and transposition provide an indication of the maximum amount of rainfall that a particular storm could have produced at any location within the region analyzed for the ANO basin. Use of these values alone does not ensure that PMP values are provided for all area sizes and durations since some of the maximized and transpositioned values could be less than the PMP. By enveloping the rainfall amounts from all the major storms, rainfall values indicative of the PMP magnitude are produced (e.g. WMO, 1986, 2009). The standard process for deriving DAD values for all grid point was used in the project.

9.1 Envelopment Procedures and DAD Derivation Enveloping is a process for selecting the largest value from a set of data. This technique provides continuous smooth curves based on the largest rainfall values from the set of maximized and transpositioned storm rainfall values. The largest rainfall amounts provide guidance for drawing the curves.

During the enveloping process, values which are not consistent (are either high or low) are re-evaluated to insure reliability. High values are enveloped unless an explanation can be provided to justify undercutting the value. No undercutting of rainfall values was done in this study. Low values are also re-evaluated for reliability and then enveloped to maintain consistency with surrounding values. This enveloping procedure addresses the possibility that for certain area sizes and durations, no significantly large storms have been observed that provide large enough values after being maximized and transposed to represent PMP at an area size and/or duration. The result of this procedure is a set of smooth curves that maintain continuity among temporal periods and areal sizes.

The envelopment process was used in PMP determination for this study, following the same procedures used for envelopment in the derivation of PMP in the HMRs, the WMO PMP Manual, and previous AWA PMP studies. Once the total storm adjusted rainfall values for the appropriate storms at each grid point were determined, they were plotted on individual DA charts for each duration for analysis. Envelopment was applied to each DA curve for each duration. The DA envelopment curves were drawn to provide continuity in space. Figure 9.0 is an example of an DA chart with the envelopment curve for the 72-hour duration at the ANO grid point 1.

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Figure 9.0 72-hour DA curves for ANO grid point 1.

The second application of the envelopment process was used with the DD curves at each location. Curves for each of the area sizes were constructed using results from the DA analysis at each grid point. The DD curves were drawn to produce smooth curves that provide continuity in time among all durations. Figure 9.1 gives an example of the DD curves for grid point 1.

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Figure 9.1 DD curves for ANO grid point 1.

The final set of DD curves for all durations at each grid point defines the initial set of PMP values. The envelopment of the adjusted storms together with the curve smoothing process insured that all storm data were included and that the resulting set of PMP values provides rainfall values that are consistent spatially and temporally at each location. These are the values that were then plotted and contoured in GIS to begin the process of manual smoothing. Several smoothing iterations were completed to provide spatial and temporal continuity of the PMP values across all grid points. The final version of this process produced the gridded PMP values.

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10. Storm Dimensions 10.1 PMP Design Storm Parameters Storm isohyetal patterns for 10 storms evaluated with SPAS were evaluated during a previous PMP study and compared to HMR 52 procedures and data over the ANO basin. Each of these storms were representative of the PMP storm type used in the PMP development. The SPAS storm analysis results were used to develop guidance for the hydrologist regarding the PMP design storm's preferred isohyetal orientation and range of movement (direction and speed) following the same approach as the overall development of the PMP, i.e. a storm based, data driven approach. This was required because of the large size of the ANO basin. The stationary PMP design storm as given in HMR 52 may not be as conservative or as meteorologically consistent as would occur in an actual PMP storm environment. Instead, the PMP storm isohyetal pattern would exhibit a preferred orientation based on the storm type(s) that would potentially produce the PMP rainfall over the basin. Further, those storm type(s) would exhibit some amount of movement during the PMF analysis period. This movement would be directly related to the storm dynamics and general meteorological synoptic pattern occurring with the PMP rainfall.

AWA analyzed the hourly gridded rainfall from the 10 SPAS storm events in Table 10.0 to derive these PMP design storm parameters. This procedure allowed for continuity in the overall PMP development by following a storm based, data driven approach to arrive at quantifiable results specific for the ANO basin.

Table 10.0 List of SPAS storms used in development of storm orientation and movement parameters.

10.2 Storm Orientation Storm orientation is an important storm characteristic when considering the placement of an isohyetal pattern over a basin. The orientations of the 10 storm events in Table 10.0 were evaluated to determine a preferred storm orientation for a design PMP storm pattern over the basin. The shape of each storm's total storm isohyetal pattern was 40

examined to determine the orientation of the major axis. Storm orientations are described by an angle of 180° to 359°, where 180° is equivalent to south-to-north and 270° is equivalent to west-to-east orientation. Results of these investigations showed that the orientation parameters as given in HMR 52 Figure 8 are appropriate for use in the ANO basin.

10.3 Storm Movement Analysis Storm movement for the 10 SPAS storm events were analyzed to determine a maximum and minimum speed of movement and range of directions of storm movement that could be expected for PMP storms. SPAS hourly rainfall grids were accumulated in 12-hour increments and 24-hour increments through the total storm duration and used in the analysis. The rainfall storm center for each set of 12-hour and 24-hour grids were used to get the distance and direction of movement. An elliptical with a ratio of 2.5 was centered over each storm center (lat/lon), and the orientation for each 12-hour and 24-hour pattern were determined. For example, the Warner Park, TN, May 2010 storm (AWA 2) had five 12-hour increments. The ellipse used to determine the orientation for the fourth 12-hour increment is shown in Figure 10.0.

This process was repeated for each 12-hour and 24-hour hour increment.

Figure 10.0 Example for Warner Park, TN, May, 2010 storm (AWA 2) showing elliptical used to determine orientation of the fourth 12-hour increment.

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The rainfall storm centers for each 12-hour and 24-hour increment were used to calculate an average storm center movement (in miles) for each increment. This was done using least squares linear regression, the slope of the line is the direction of the storm movement and the length of the line represents the storm movement speed. For example, the Warner Park, TN, May 2010 storm (AWA 2) is shown below. This analysis results in a total storm center movement distance to the east-northeast of 175 miles during the fourth 12-hour increment (Figure 10.1).

Figure 10.1 Example for Warner Park, TN, May, 2010 storm (AWA 2). Storm movement between each point at each 12-hour increment was calculated based on regression line and storm center points.

The 12-hour and 24-hour distances that a storm center moved were calculated and the direction of movement was determined using the storm centers for each 12-hour and 24-hour increment. The largest distance between to storm center locations for the 12-hour and 24-hour increments was measured and the line between these points was used to determine the orientation. For example, the Warner Park, TN, May 2010 storm (AWA 2) had three 24-hour increments, the greatest storm center movement was between the first and second storm center. The distance and direction of this line was 165 miles with an orientation of 255° (Figure 10.2).

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Figure 10.2 Example for Warner Park, TN, May, 2010 storm (AWA 2), 24-hour longest storm center movement and orientation.

The average storm center movement, by ordinary least squares regression, and maximum 12-hour and 24-hour distance and orientation were analyzed to determine the most appropriate range storm movement. The 24-hour movement was used for PMP/PMF determination. This analysis led to the following recommendations for PMP design storm movement of the all-season and cool-season PMP isohyetal patterns across the basin.

PMP Design Storm Isohyetal Movement and Orientation Recommendation The maximum amount a storm can be moved in a 24-hour period is 200 miles and the minimum amount is 20 miles. The orientation of the isohyetal pattern should follow the guidance produced in HMR 52 and will vary from west to east and north to south across the ANO basin.

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11. Local Intense Precipitation (LIP)

AWA completed an analysis to derive the 1-hour 1-square mile Local Intense Precipitation (LIP) for the ANO site location. This analysis followed the storm-based approach as used in the overall PMP development and as given in HMRs 51 and 52.

The storm-based approach utilizes observed rainfall data from rainfall events which have occurred over the site and in regions where storms are considered to be transpositionable to the ANO site location. These rainfall data are maximized in-place following standard maximization procedures, then transpositioned to the ANO location. The transpositioning process accounts for differences in moisture and elevation between the original storm location and the ANO site. The process produces a total adjustment factor that is applied to the original rainfall data for each storm. The result represents the maximum rainfall each storm could have produced at the site had all factors leading to the rainfall been ideal and maximized. Information is included in this section detailing the storms used, how they were analyzed, and how the LIP values were derived. Information on each individual storm event evaluated is included in Appendix G, with the dew point climatologies used to maximize the storms provided in Appendix B.

11.1 Development of LIP Values The PMP values provided in HMR 51 for the ANO site provide values starting with the 6-hour duration and the 10-square mile area size. There are no explicit values provided at the 1-hour duration and/or 1-square mile area size. HMR 52 provides information to derive the 1-hour 1-and 10-square mile values based on HMR 51 6-hour 10-square mile storm analyzed values. Unfortunately, the most recent storm evaluated in HMR 51 occurred in 1972. In addition, because HMR 51 covers a large domain, generalization and conservatism were employed in the development of the respective PMP and LIP values. This resulted in LIP values which were influenced by storms not appropriate for the ANO site location (e.g.

Smethport, PA July 1942) and therefore are not reliable values for the ANO site.

The site-specific LIP analysis performed during this study for the ANO site corrected many of the issues in the HMRs by explicitly evaluating storms which are directly transpositionable to the ANO site. In addition, the understanding of the meteorology of these events has advanced significantly since HMR 51 was published.

These corrections and the updated storm database were employed in this calculation.

In addition, the results and data from numerous SPAS storm analyses used in the PMP development in this study and several others in the region were used extensively in this analysis.

11.2 LIP Storm List 44

The initial step in the development of the LIP values was to identify a set of storms which represent rainfall events that are LIP-type local storm events. This included storms where extreme heavy rainfall accumulated over short durations and small area sizes. These include observed rainfall amounts associated with MCS and individual thunderstorms. This procedure is similar to what is described in HMR 52 Section 6.

AWA evaluated all storms used in previous PMP studies in the region considered transpositionable to the ANO location to develop a list of the storms needed for proper LIP evaluation and determination. This resulted in 23 events being evaluated (Table 11.0 and Figure 11.0). Fourteen of these storms were previously analyzed in HMRs 33 and 51 by the NWS and USACE. The remaining nine were analyzed using SPAS.

Table 11.0 Storms used in the 1-hour 1-square mile Local Intense Precipitation analysis.

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Figure 11.0 Storms used in the LIP calculations in relation to the ANO site location.

11.3 LIP Storm Calculation Process Most of the 14 storms analyzed by the NWS/USACE did not contain explicit 1-hour 1-square mile rainfall data. This is the result of the lack of hourly recording information available during the original analyses. To correct for this, information presented in HMR 52, Section 6 was utilized. This information provided ratios which allowed for the computation of the 1-hour 1-square mile value to be derived from the 6-hour 10-square mile PMP value (HMR 52 Figure 23). Although these ratios were derived to apply to the HMR 51 PMP values, they are implicitly relevant for use in this calculation because both processes are using the same data set and following the storm-based approach, i.e. it is only a scaling variation that is occurring. No inherent change or adjustment to the data is taking place that would result in a different data set or storm type. For the Bonaparte, IA June 1905 and Holt, MO June 1947 storm events analyzed by the NWS/USACE, explicit 1-hour data was available and therefore no ratio application was required.

The nine storms analyzed using SPAS allowed for explicit hourly rainfall to be evaluated with a spatial resolution of 1/3rd square mile. This provided data for the storm rainfall 1-hour 1-square mile area sizes to be explicitly evaluated.

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Once all the storms were identified and their 1-hour 1-square mile values derived, the final step in the process was to maximize each storm specific to the ANO location. This was a two-step process. First, the in-place maximization factor was calculated. This provides a value that is applied to the observed storm values which represents what the storm rainfall would have been had the atmospheric conditions and moisture been at maximum levels when the storm occurred. Next, the resulting in-place maximized values for each storm was adjusted as if the storm had occurred over the ANO site. To accomplish this, the transposition calculation process was followed to adjust the storm from its original location to the ANO site. The transposition calculation adjusts for differences in available moisture both in the horizontal (north/south and east/west directions) and vertical (differences in elevation) at the site versus the original storm location. All the calculations and resulting values for each storm used in the LIP analysis are provided in Appendix G.

After the maximization and transposition factors were calculated for each of the storms, the results were applied to the maximum 1-hour value for each storm to calculate the maximized 1-hour 1-square mile values. The largest of these values results in the site-specific LIP for the ANO site (see Table 11.0 for all resulting values). After adjustments were applied, the Thrall, TX September 1921 storm (AWA 77) had the highest 1-hour rainfall, with four other storms providing slightly smaller values and support for this value. Note that use of the Thrall, TX storm at the ANO site is beyond the transposition limits noted by the NWS. Therefore, AWA"s judgment to use this storm at the site produces LIP values that are higher than would be calculated had Thrall, TX not be transpositioned. However, this transposition limits of the storm, as well as the meteorology which led to the rainfall, were deemed similar enough during this analysis to allow it to be transpositioned.

For final application of the LIP hydrology, this value is then required to be split into sub-hourly increments of 5-, 15-, 30-minutes. Updated evaluations of the appropriate amount of rainfall to assign to each increment for the site based on storm data would have been ideal. However, a lack of sub-hourly PMP-type storm data from the 14 storms analyzed by the NWS/USACE prevented an updated evaluation from being completed. Therefore, it is recommended that the ratios derived in HMR 52 be applied at the ANO site (HMR 52 Figures 36-38). Table 11.1 shows the result of applying these ratios to the maximized Thrall, TX September 1921 (AWA 77) rainfall.

Table 11.1 Site specific 1-hour 1-square mile LIP values at the ANO site.

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12. Results The following are the main conclusions from this study:

HMR 51 and 55A PMP values are outdated. This study provided updated PMP values to replace HMR 51 and 55A PMP values.

HMR 52 PMP design storm parameters were based on a set of storms that were not specifically transpositionable to this basin. This study provided updated PMP design storm movement guidance based on storms transpositionable to this basin and taking into consideration the large size of this basin.

The most recent storm used to derive PMP values in HMR 51 occurred in 1972.

This study updated the storm database to include storms through 2013.

HMRs 51 and 52 did not use computer based technologies in the storm analyses procedures. This study used computer technology and GIS to more accurately analyze storm rainfall patterns and implement the spatially distributed PMP values.

HMRs 51, 52, and 55A did not have weather radar to help spatially distribute rainfall among rain gauge locations. SPAS storm analyses incorporates this information when available to provide the most advanced spatial representation of rainfall storm patterns possible.

Understanding of meteorological processes, interactions, and storm patterns have advanced greatly since the publication of HMRs 51 and 55A. Satellite and radar technology have greatly added to the understanding of storm patterns over the last 40 years. This study incorporated the state-of-the-science understanding and technology associated with analyzing extreme rainfall events.

HMRs 51 and 52 provide generalized and smoothed LIP values over a large geographic domain that covers the United States east of the 105th meridian. This calculation considered characteristics specific to the site, and produced PMP values that explicitly considered the meteorology of the PMP storm type which would result in the 1-hour 1-square mile area size LIP values.

The transposition limits of the Smethport, PA July 1942, which produced the 4-and 6-hour world record rainfall, were not allowed to influence the LIP values at the ANO site. The refined transposition limits used in this calculation result in lower LIP values compared to HMR 52 for locations where the Smethport storm apparently influenced PMP values in HMR 51. Smoothing of the PMP/LIP isolines in HMRs 51 and 52 necessarily had to encompass the Smethport maximized in-place rainfall far beyond its explicit transposition limits. Note, Section 3.2.4 of HMR 51 states that they "slightly undercut" the maximized 6-,

12-, and 24-hour values by up to 7% to avoid "excessive envelopment of all other data in a large region surrounding the Smethport location." This over envelopment effect extended well beyond the intended transposition limits of the Smethport storm because the PMP/LIP isolines required smoothing and fitting over surrounding regions.

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Each storms inflow vector was re-evaluated and combined with an updated set of dew point climatologies and when necessary, updated storm representative dew point values were used for the in-place maximization and transposition factors.

The HYSPLIT trajectory model (Draxler and Rolph 2003, Draxler and Rolph 2010) was used to evaluate moisture inflow vectors for storms on the short storm list. Trajectory models were not available in HMR studies. Use of HYSPLIT allowed for a high degree of confidence when evaluating moisture inflow vectors and storm representative dew points.

Several new storms have been analyzed and included in this LIP analysis that were not included in HMRs 33, 51, and 52. This provided a higher level of confidence in the final PMP values. Further, this allowed for a refined set of values that better represent the LIP estimates at the site. This expanded the data set used to derive LIP includes a large number of recent storms where weather radar data were available.

The calculation provided adjustments for storm elevation to the nearest 100 feet of elevation, whereas HMRs 51 and 52 made no explicit adjustment for elevation.

This adjustment depends on the elevation of the historic storm's maximum rainfall location and therefore varies from storm to storm.

Storms analyzed by the NWS/USACE which occurred prior to 1948 and used 12-hour persisting dew points in the storm maximization process were adjusted so that the updated dew point climatology could be utilized consistently. For thunderstorms and MCC storm events 7°F was added to the NWS/USACE storm representative dew point. This was done to adjust for using average dew point values for varying durations vs. 12-hour persisting dew point values. Recent evaluations of 12-hour persisting storm representative dew points showed those used in HMR 51 underestimated the storm representative dew point values.

12.1 PMP Values This PMP study has produced PMP values for use in computing the PMF using HMR 52 procedures with modifications to account for the large size of the ANO basin.

This includes updated quantification of the PMP design storm movement across the basin. Values for all durations and area sizes provided in HMRs 51 and 55A, as well as for additional area sizes out to 100,000 square miles have been computed using the procedures described in this report.

The study provides PMP values for use in computing the PMF at any location within the basin. Values for all durations up to 72 hours8.333333e-4 days <br />0.02 hours <br />1.190476e-4 weeks <br />2.7396e-5 months <br /> and areal sizes up to 100,000 square miles have been computed in gridded GIS format. Note, HMR 51 standard area sizes extend only to 20,000 square miles and HMR 55A only extends to 5,000 square miles. Appendix A contains the PMP maps produced in this study.

The study was designed to retain as much continuity as possible with the methodology used in HMRs 51 and 55A, as well as previous AWA studies, while incorporating improvements based on changes in technology, meteorological 49

understanding, and availability of updated data. In addition, special consideration was given to the size of the ANO basin, especially relating to the PMP design storm movement.

Full SPAS storm rainfall analyses were completed for one storm not analyzed in the HMRs or the previous AWA studies. The study continued the use of surface dew point data to quantify moisture inflow to storms. However, instead of using the 12-hour persisting value as in HMR 51, an average dew point value for a duration (6-, 12-, or 24-hours) consistent with the storm precipitation was used. This approach provides a more representative parameterization of the moisture available to the storm.

An updated dew point climatology was developed during previous AWA studies and was used in this study. This allows for average dew point values and maximum average dew point climatology maps at the 100-year return frequency level for 6-, 12-,

and 24-hour durations to be used for storm maximization and transposition. Storms were maximized and transpositioned to a set of 22 grid points. This covered the entire basin and provided a margin for boundary conditions (see Figure 1.4).

12.2 Comparison of the PMP Values with HMR 51 PMP A comparison was made at various area sizes and durations to determine the difference between results of PMP values versus HMR 51 values at each grid point where HMR 51 PMP values were available for direct comparison. Tables 12.0-12.4 provide the percent reductions from HMR 51 PMP values throughout the basin at each area size and duration analyzed for all grid points.

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Table 12.0 Percent difference between PMP values at each grid point at the 6-hour duration vs HMR 51 PMP values. Negative values represent reductions from HMR 51.

Only grid points with HMR 51 values are included in the comparisons.

Table 12.1 Percent difference between PMP values at each grid point at the 12-hour duration vs HMR 51 PMP values. Negative values represent reductions from HMR 51.

Only grid points with HMR 51 values are included in the comparisons.

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Table 12.2 Percent difference between PMP values at each grid point at the 24-hour duration vs HMR 51 PMP values. Negative values represent reductions from HMR 51.

Only grid points with HMR 51 values are included in the comparisons.

Table 12.3 Percent difference between PMP values at each grid point at the 48-hour duration vs HMR 51 PMP values. Negative values represent reductions from HMR 51.

Only grid points with HMR 51 values are included in the comparisons.

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Table 12.4 Percent difference between PMP values at each grid point at the 72-hour duration vs HMR 51 PMP values. Negative values represent reductions from HMR 51.

Only grid points with HMR 51 values are included in the comparisons.

12.3 Reasons for Reductions of PMP versus HMR 51 This PMP study provided differences in PMP values from those presented in HMR 51. This study explicitly addressed elevation, whereas detailed terrain effects were not evaluated in HMR 51. All HMR 51 storms on both the PMP and LIP short storm lists were re-evaluated to determine the updated storm representative dew point and maximized using an updated dew point climatology.

Since the study followed the same basic storm rainfall adjustment procedures as HMR 51, it would be useful to understand the cause of the differences in the PMP values.

Detailed working papers are not available for HMR 51, so explicit differences in calculations and procedures cannot be evaluated. However, the following issues were treated differently in the two studies:

1. HMR 51 provides generalized and smoothed PMP values over a large geographic domain covering the United States east of the 105th meridian. Specific characteristics unique to individual basins, such as ANO, were not addressed. This study considered characteristics specific to the basin, and produced PMP values explicitly considered the meteorology of the PMP storm type which would results in the PMF for the basin.

2. Each storms inflow vector was re-evaluated and combined with an updated set of dew point climatology data and when necessary, updated storm representative dew point values were used for the in-place maximization and computation of the total 53

adjustment factors. The HYSPLIT trajectory model was used to evaluate and verify moisture inflow vectors for storms on the short storm list. Trajectory models were not available in previous HMR studies. The use of HYSPLIT allowed for a high degree of confidence when evaluating moisture inflow vectors and storm representative dew points.

3. Several new storms have been analyzed and included in this PMP study that were not included in HMRs 51 and 55A. This provided a higher level of confidence in the final PMP and LIP values. Further, this allowed for a refined set of values that better represent the PMP values, as the data set used to derive PMP has been expanded to include a larger set of more recent storms.

4. The site-specific PMP study provided adjustments for storm elevation to the nearest 100 feet of elevation, whereas HMR 51 made no explicit adjustment for elevation for PMP value over the basin. This adjustment depends on the elevation of the historic storm's maximum rainfall location and therefore varies from storm to storm. Further, the average basin elevation for each grid point was evaluated in this study using GIS, providing a much more accurate representation and calculation to account for loss of available moisture up to that elevation.

5. SPAS was used in conjunction with NEXRAD data (when available) to evaluate the spatial and temporal distribution of rainfall. Use of NEXRAD data generally produced higher point rainfall amounts than were observed using only rain gauge observations and provides objective spatial distributions of storm rainfall for locations among rain gauges. SPAS results provided storm DADs, total storm precipitation patterns, and mass curves for the newly analyzed storms. Using these technologies, significant improvements of the storm rainfall analyses were achieved.

6. Previously analyzed storm events that occurred prior to 1948 that used 12-hour persisting dew points were adjusted using storm representative dew point adjustments of 2°F for synoptic type storm events and 7°F for MCS type storm events. This was done to adjust for using average dew point values for varying durations vs. 12-hour persisting dew point values. Recent evaluations of 12-hour persisting storm representative dew points show those used in HMRs 51 and 55A underestimated the storm representative values. An updated set of maximum dew point climatology maps were produced. These maps have higher maximum dew point values than those used in HMR studies and therefore compensate to some extent for the higher storm representative dew points.

7. HMRs 51 and 52 provide generalized and smoothed LIP values over a large geographic domain that covers the United States east of the 105th meridian.

Specific characteristics unique to the ANO site were not addressed. This calculation considered characteristics specific to the site, and produced PMP 54

values that explicitly considered the meteorology of the PMP storm type which would result in the 1-hour 1- square mile area size LIP values.

8. The transposition limits of the Smethport, PA July 1942, which produced the 4-and 6-hour world record rainfall, were not allowed to influence the LIP values at the ANO site. The refined transposition limits used in this calculation result in lower LIP values compared to HMR 52 for locations where the Smethport storm apparently influenced PMP values in HMR 51. Smoothing of the PMP/LIP isolines in HMRs 51 and 52 necessarily had to encompass the Smethport maximized in-place rainfall far beyond its explicit transposition limits. Note, Section 3.2.4 of HMR 51 states that they "slightly undercut" the maximized 6-,

12-, and 24-hour values by up to 7% to avoid "excessive envelopment of all other data in a large region surrounding the Smethport location." This over envelopment effect extended well beyond the intended transposition limits of the Smethport storm because the PMP/LIP isolines required smoothing and fitting over surrounding regions.

55

13. Sensitivity Analysis In the process of deriving site-specific PMP values, various assumptions were made and explicit procedures were adopted for use. Additionally, various parameters and derived values are used in the calculations. It is of interest to assess the sensitivity of PMP values to assumptions that were made and to the variability of parameter values.

13.1 Assumptions 13.1.1 Saturated Storm Atmospheres The atmospheric air masses that provide moisture to both the historic storm and the PMP storm are assumed to be saturated through the entire depth of the atmosphere and to contain the maximum moisture possible based on the surface dew point. This assumes moist pseudo-adiabatic temperature profiles for both the historic storm and the PMP storm. Limited evaluation of this assumption in the EPRI Michigan/Wisconsin Regional PMP study (Tomlinson, 1993) and the Blenheim Gilboa study (Tomlinson et al.

2008) indicated that historic storm atmospheric profiles are generally not entirely saturated and contain somewhat less precipitable water than is assumed in the PMP procedure. It follows that the PMP storm (if it were to occur) would also have somewhat less precipitable water available than the assumed saturated PMP atmosphere would contain. What is used in the PMP procedure is the ratio of precipitable water associated with each storm. If the precipitable water values for each storm are both slightly overestimated, the ratio of these values will be essentially unchanged. For example, consider the case where instead of a historic storm with a storm representative dew point of 70oF degrees having 2.25 inches of precipitable water assuming a saturated atmosphere, it actually had 90% of that value or about 2.02 inches. The PMP procedure assumes the same type of storm with similar atmospheric characteristics for the maximized storm but with a higher dew point, say 76oF degrees. The maximized storm, having similar atmospheric conditions, would have about 2.69 inches of precipitable water instead of the 2.99 inches associated with a saturated atmosphere with a dew point of 76oF degrees. The maximization factor computed using the assumed saturated atmospheric values would be 2.99/2.25 = 1.33. If both storms were about 90% saturated instead, the maximization factor would be 2.69/2.02 = 1.33. Therefore potential inaccuracy of assuming saturated atmospheres (whereas the atmospheres may be somewhat less than saturated) should have a minimal impact on storm maximization and subsequent PMP calculations.

13.1.2 Maximum Storm Efficiency The assumption is made that if a sufficient period of record is available for rainfall observations, at least a few storms would have been observed that attained or came close to attaining the maximum storm efficiency possible in nature for converting atmospheric moisture to rainfall for regions with similar climates and topography. The 56

further assumption is made that if additional atmospheric moisture had been available, the storm would have maintained the same efficiency for converting atmospheric moisture to rainfall. The ratio of the maximized rainfall amounts to the actual rainfall amounts would be the same as the ratio of the precipitable water in the atmosphere associated with each storm.

There are two issues to be considered. First is the assumption that a storm has occurred that has rainfall efficiency close to the maximum possible. Unfortunately, state-of-the-science in meteorology does not support a theoretical evaluation of storm efficiency for use in PMP evaluation. However, if the period of record is considered (generally over 100 years), along with the extended geographic region with transpositionable storms, it is accepted that there should have been at least one storm with dynamics that approach the maximum efficiency for rainfall production.

The other issue is the assumption that storm efficiency does not change if additional atmospheric moisture is available. Storm dynamics could potentially become more efficient or possibly less efficient depending on the interaction of cloud microphysical processes with the storm dynamics. Offsetting effects could indeed lead to the storm efficiency remaining essentially unchanged. For the present, the assumption of no change in storm efficiency is accepted, mirroring the HMR and WMO assumptions.

13.2 Parameters 13.2.1 Storm Representative Dew Point and Maximum Dew Point The in-place maximization factor depends on the determination of storm representative dew points, along with maximum historical dew point values. The magnitude of the maximization factor varies depending on the values used for the storm representative dew point and the maximum dew point. Holding all other variables constant, the maximization factor is smaller for higher storm representative dew points as well as for lower maximum dew point values. Likewise, larger maximization factors result from the use of lower storm representative dew points and/or higher maximum dew points. The magnitude of the change in the maximization factor varies depending on the dew point values. For the range of dew point values used in most PMP studies, the maximization factor for a particular storm will change about 5% for every 1oF difference between the storm representative and maximum dew point values. The same sensitivity applies to the transposition factor, with ~ 5% change for every 1oF change in either the in-place maximum dew point or the transposition maximum dew point2.

For example, consider the following case:

Storm representative dew point: 75oF Precipitable water: 2.85 "

2 Note that the amount of moisture per degree of dew point temp is not linear, but this 5% formula fits within the range of dew points used in this analysis.

57

Maximum dew point: 79oF Precipitable water: 3.44" Maximization factor = 3.44"/2.85" = 1.21 If the storm representative dew point were 74oF with precipitable water of 2.73",

Maximization Factor = 3.44"/2.73" = 1.26 (an increase of approximately 4%)

If the maximum dew point were 78oF with precipitable water of 3.29",

Maximization Factor = 3.29"/2.85" = 1.15 (a decrease of approximately 5%)

13.2.2 Sensitivity of the Elevation Adjustment Factor Variations in elevation associated with topographic features remove atmospheric moisture from an air mass as it moves over the terrain. When storms are transpositioned, the elevation of the storm center location is used to compute the amount of atmospheric moisture depleted from the storm atmosphere during the in-place moisture maximization process. The absolute amount of moisture depletion is somewhat dependent on the dew point values, but is primarily dependent on the elevation at the original storm location compared to the elevation of the basin centroid and each grid point. The elevation adjustment is slightly less than 1% for every 100 feet of elevation change between the original storm location and the study basin elevation.

For example, consider the following case:

Maximum dew point: 79°F Elevation: 1,000 '

Precipitable water between 1000-mb and the top of the atmosphere: 3.44" Precipitable water between 1000-mb and 1,000': 0.28" Elevation Adjustment Factor = (3.44"-0.28")/3.44" = 0.92 (approximately 1% per 100 feet)

If the elevation were 2,000', the precipitable water between 1000mb and 2,000' is 0.55" Elevation Adjustment Factor = (3.44"-0.55")/3.44" = 0.84 (approximately 1% per 100 feet) 58

14. Recommendations for Application 14.1 PMP and LIP Applications PMP values have been computed that provide maximum rainfall amounts for use in computing the PMF at any location within the ANO basin. The study addressed several issues that could potentially affect the magnitude of the PMP storm over basin as compared with HMRs 51 and 55A and the LIP storm over the ANO site location.

Analysis of moisture availability for previously analyzed storms and analysis of recent extreme storms with up to date state-of-the-science techniques resulted in PMP values which replace HMRs 51 and 55A and LIP values which replace those in HMR 52.

These represent the most current PMP values that should be used together with the procedures in HMR 52 and updated PMP design storm parameters to provide PMP rainfall at any location within the basin.

HMR 52 uses a procedure for locating the largest amounts of rainfall associated with the PMP storm, such that the largest volume of rain falls within the watershed boundaries. Because the ANO basin is much larger than the largest HMR 52 design storm, updated parameters in addition to HMR 52 were evaluated. This included storm movement recommendations for the PMP design storm.

14.2 Discussion on the Spatial Limits of the PMP Values The grid system used in this study was designed such that no regions within the basin required extrapolation of storm data but allowed for interpolation between rainfall values at grid point or the use of the gridded data within GIS. The grid extended beyond the geographic boundaries of the basin. The emphasis was to provide the most reliable and consistent analysis within the geographic region. PMP maps are provided to allow for PMP values to be extracted for any location in basin. As an option, a user who has GIS software can use the gridded data to explicitly determine PMP values at any location within the basin.

For each of the storms analyzed, appropriate transposition grid points were defined (see Appendix F). After all the storms were analyzed, the largest rainfall values were determined for each grid point for each duration and area size. These largest values were enveloped to insure both spatial and temporal continuity.

Once the enveloped values were finalized, lines of constant PMP values were drawn using GIS interpolation software for each duration and area size. These iso-PMP lines were extended beyond the basin boundary such that PMP values could be interpolated at all locations within the basin. Hence, the reason that some iso-PMP lines 59

extend beyond the basin boundary is to allow for gradients to be determined between lines for all locations within the ANO basin.

For regions outside of the basin where extrapolation would be required, the gradient is uncertain. There are probably regions where the extended lines provide reasonable PMP values while for other regions, PMP values are less reliable. This study provides PMP values only for locations within the ANO basin.

14.3 Climate Change Assumptions AWA recognizes that the climate is in a constant state of change. However, the current scientific consensus and understanding cannot agree how climate is changing and more importantly what those changes will be for the region. Therefore, one cannot say whether the region will be wetter or drier, warmer or colder and/or experience more or less extreme rainfall events with any quantitative and statistically significant certainty.

Further, most projects of this type have a projected life between 30 to 50 years before they are redeveloped. In general, most projected changes that may occur within the Earths climate system would be unlikely to significantly affect the projects hydrology beyond the bounds of the PMP and/or LIP values derived as part of this project during its useful life. Based on these discussions, it is apparent that the current practice of PMP determination should not be modified in an attempt to address potential changes associated with climate change. This study has continued the practice of assuming no climate change, as climate trends are not considered when preparing PMP estimates (WMO, Section 1.1.1).

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Stewarts Bridge Drainage Basin, Prepared for Reliant Energy Corporation, Liverpool, New York.

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and B. Rappolt, June 2008: Site-Specific Probable Maximum Precipitation (PMP) Study for the Blenheim Gilboa Drainage Basin, Prepared for New York Power Authority, White Plains, NY.

Tomlinson, E.M., Kappel W.D., and T.W. Parzybok, February 2008: Site-Specific Probable Maximum Precipitation (PMP) Study for the Magma FRS Drainage Basin, Prepared for AMEC, Tucson, Arizona.

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Tomlinson, E.M., Kappel, W.D., Muhlestein, G.A., and Tye W. Parzybok, July 2009:

Site-Specific Probable Maximum Precipitation (PMP) Study for the Scoggins Dam Drainage Basin, Oregon.

Tomlinson, E.M., Kappel, W.D., Muhlestein, G.A., and Tye W. Parzybok, February 2009: Site-Specific Probable Maximum Precipitation (PMP) Study for the Tuxedo Lake Drainage Basin, New York.

Tomlinson, E.M., Kappel, W.D., Parzybok, T.W., Hultstrand, D.M., Muhlestein, G.A.,

and Tye W. Parzybok, June 2009: Site-Specific Probable Maximum Precipitation (PMP) Study for the DeForest Lake Drainage Basin, New York.

64

Tomlinson, E.M., Kappel, W.D., Muhlestein, G.A., and Tye W. Parzybok, February 2010: Site-Specific Probable Maximum Precipitation (PMP) Study for the Magma FRS Drainage Basin, Arizona.

Tomlinson, E.M., Kappel W.D., Parzybok, T.W., Hultstrand, D.M., Muhlestein, G.A.,

March 2011: Site-Specific Probable Maximum Precipitation Study for the Tarrant Regional Water District, Prepared for Tarrant Regional Water District, Fort Worth, Texas.

Tomlinson, E.M., Kappel, W.D., Hultstrand, D.M., Muhlestein, G.A., and T. W.

Parzybok, November 2011: Site-Specific Probable Maximum Precipitation (PMP) Study for the Lewis River basin, Washington State.

Tomlinson, E.M., Kappel, W.D., Hultstrand, D.M., Muhlestein, G.A., and T. W.

Parzybok, December 2011: Site-Specific Probable Maximum Precipitation (PMP) Study for the Brassua Dam basin, Maine.

Tomlinson, E.M., Kappel, W.D., Hultstrand, D.M., Muhlestein, G.A., S. Lovisone, and T.

W. Parzybok, March 2013: Statewide Probable Maximum Precipitation (PMP)

Study for Ohio.

Tomlinson, E.M., and W.D. Kappel, October 2009: Revisiting PMPs, Hydro Review, Vol. 28, No. 7, 10-17.

US Army Corps of Engineers, 1973: Hydrologic Engineering Methods For Water Resources Development, Vol. 4, Hydrograph Analysis, Davis, CA., 160pp.

US Army Corps of Engineers Storm Studies, 1973: Storm Rainfall in the United States Depth-Area-Duration Data.

US Weather Bureau, 1946: Manual for Depth-Area-Duration analysis of storm precipitation. Cooperative Studies Technical Paper No. 1, U.S. Department of Commerce, Weather Bureau, Washington, D.C., 73pp.

US Weather Bureau, 1951: Tables of Precipitable Water and Other Factors for a Saturated Pseudo-adiabatic Atmosphere, Technical Paper Number 14, US.

Department of Commerce, Washington, DC, 66 pp.

US Weather Bureau, 1956: Seasonal Variation of the Probable Maximum Precipitation East of the 105th Meridian For Areas From 10 to 1000 Square Miles and Durations of 6, 12, 24, and 48 Hours, Hydrometeorological Report Number 33, US. Department of Commerce, Washington, DC, 33 pp.

Weather Underground, http://www.wunderground.com/stationmaps/

65

World Meteorological Organization, 1986: Manual for Estimation of Probable Maximum Precipitation.

World Meteorological Organization, 2009: Manual for Estimation of Probable Maximum Precipitation, Operational Hydrology Report No 1045, WMO, Geneva, 259 pp.

66

Appendix A ANO Probable Maximum Precipitation Maps A- 1

A- 2 A- 3 A- 4 A- 5 A- 6 A- 7 A- 8 A- 9 A-10 A-11 A-12 A-13 A-14 A-15 A-16 A-17 A-18 A-19 A-20 A-21 A-22 A-23 A-24 A-25 A-26 A-27 A-28 A-29 A-30 A-31 A-32 A-33 A-34 A-35 A-36 A-37 A-38 A-39 A-40 A-41 Appendix B 100-year Return Frequency Maximum Average Dew Point Climatology Maps Used in the Storm Maximization and Transposition Calculations B- 1

B- 2 B- 3 B- 4 B- 5 B- 6 B- 7 B- 8 B- 9 B-10 B-11 B-12 B-13 B-14 B-15 B-16 B-17 B-18 B-19 B-20 B-21 B-22 B-23 B-24 B-25 B-26 B-27 B-28 B-29 B-30 B-31 B-32 B-33 B-34 B-35 B-36 Appendix C Procedure for using Dew Point Temperatures for Storm Maximization and Transposition C- 1

Maximum dew point temperatures (hereafter referred to as dew points) have historically been used for two primary purposes in the PMP computation process:

1. Increase the observed rainfall amounts to a maximum value based on a potential increase in atmospheric moisture available to the storm.

2. Adjust the available atmospheric moisture to account for any increases or decreases associated with the maximized storm potentially occurring at another location within the transposition limits for that storm.

HMR and WMO procedures for storm maximization use a representative storm dew point as the parameter to represent available moisture to a storm. Prior to the mid-1980s, maps of maximum dew point values from the Climatic Atlas of the United States, Environmental Data Services, Department of Commerce (1968), were the source for maximum dew point values. HMR 55 published in 1984 updated maximum dew point values for a portion of the United States from the Continental Divide eastward into the central plains. A regional PMP study for Michigan and Wisconsin produced return frequency maps using the L-moments method (Tomlinson 1993). The Review Committee for that study included representatives from NWS, FERC, Bureau of Reclamation, and others. They agreed that the 50-year return frequency values were appropriate for use in PMP calculations. HMR 57 was published in 1994 and HMR 59 in 1999. These latest NWS publications also update the maximum dew point climatology but use maximum observed dew points instead of return frequency values. For this study, the 100-year return frequency dew point climatology maps were appropriate because this added a layer of conservatism and the extra 17 years of data available since the EPRI and Nebraska studies allow the 100-year return frequency to be more reliable. Storm precipitation amounts are maximized using the ratio of precipitable water for the maximum observed dew point to precipitable water for the storm representative dew point, assuming a vertically saturated atmosphere. This procedure was followed in this study using the updated maximum dew point climatology developed during recent and ongoing PMP studies. The climatological maximum 100-year return frequency maps for the 6-, 12-, and 24-hour durations are giving in Appendix B.

The procedure for determining a storm representative dew point begins with the determination of the inflow wind vector (direction and magnitude) for the air mass that contains the atmospheric moisture available to the storm. Beginning and ending times of the rainfall event at locations of the most extreme rainfall amounts are determined using rainfall mass curves from those locations.

The storm inflow wind vector is determined using available wind data. The inflow wind vector has historically been determined using winds reported by weather stations, together with upper air winds, when available. Recently, re-analyzed weather model data representing various atmospheric parameters including wind direction and speed in the atmosphere have become available for use from the HYSPLIT trajectory model and the North American Reanalysis Project (Kalnay et al 1996). These analyses C- 2

are available back to 1948. Use of these wind fields in the lower portion of the atmosphere provides much improved reliability in the determination of the storm inflow wind vectors. The program is available through an online interface through the Air Resources Laboratory section of NOAA. Users are able to enter in specific parameters that then produce a trajectory from a starting point going backwards (or forwards) for a specified amount of time. Users can define variables such as the starting point (using latitude and longitude or a map interface), the date and time to start the trajectory, the length of time to run the trajectory, and the pressure level at which to delineate the inflow vector. Figure C.0 shows example inflow vectors generated by HYSPLIT at three levels:

700mb, 850mb, and surface for an example storm event. The data generated from the HYSPLIT runs is then used in conjunction with standard methods to help delineate the source region of the air mass responsible for the storm precipitation. Also, this serves as another tool to determine from which weather stations to derive hourly dew point data for storm representative dew point analysis.

C- 3

Figure C.0 HYSPLIT trajectory model results for Council Grove, KS, July 1951 storm (AWA 18).

The inflow wind vector is followed upwind until a location is reached that is outside of the storm rainfall. The nearest weather stations that report dew point values are identified. At least two stations are desired but a single station with reliable dew C- 4

points observations can be used. The time period used to identify the appropriate dew point values is determined by computing the time required for the air mass to be transported from the location of the weather station(s) to the location of maximum rainfall. The start time of the extreme rainfall is then adjusted back in time to account for transit time from the dew point observing station(s) to the maximum rainfall location.

For example, consider the following case:

1. Rainfall begins at 11:00am and ends at 6:00pm the following day at the location of maximum rainfall,

2. The storm representative dew point location (the location of the weather stations observing the dew points) is 100 miles from the maximum rainfall location in the direction of the inflow wind vector, and

3. The inflow wind speed is 20 mph.

The transit time for the air mass from the weather stations to the maximum rainfall location is five hours (100 miles divided by 20 mph). The time to begin using the dew point observations is five hours before the rainfall began (11:00am minus 5 hours5.787037e-5 days <br />0.00139 hours <br />8.267196e-6 weeks <br />1.9025e-6 months <br /> =

6:00am) and the time to stop using the dew point observations is five hours before the rainfall ended (6:00pm minus 5 hours5.787037e-5 days <br />0.00139 hours <br />8.267196e-6 weeks <br />1.9025e-6 months <br /> = 1:00pm the following day). Dew point observations taken between these times are used to determine the storm representative average 24-hour 1000mb dew point value. The storm representative dew point location can come from a single location if only one station is used or from a location between the reporting weather stations if more than one station is used. The vector connecting this location and the location of maximum rainfall becomes the wind inflow vector used for storm transpositioning.

The storm representative dew point determined from the hourly dew point observations needs to be corrected to the 1000mb level. The elevation of the storm representative dew point location is used in this correction. The correction factor of 2.4oF per 1,000 feet of elevation is used. This is the same correction factor used in the Climatic Atlas of the United States (Environmental Data Services, Department of Commerce, 1968). For example, a storm representative dew point of 72oF at a station location with an elevation of 800 feet above sea level is corrected with a factor of 800 X 2.4 /1000 =

1.9oF. The dew point value corrected to 1000mb (sea level) is 72oF + 1.9oF = 74oF after rounding.

The procedure that computes the in-place maximized rainfall for a storm provides an estimate of the maximum amount of rainfall that could have been produced by the same storm at the same location if the maximum amount of atmospheric moisture had been available. This procedure requires that a maximum value for the storm representative dew point be determined. The maximum dew point value is selected at the same location where the storm dew point was determined using a maximum dew point climatology. The maximum dew point values must be corrected to 1000mb. The precipitable water in the atmosphere is determined using the storm representative and maximum dew point values. Precipitable water is defined in this study as the total C- 5

amount of moisture in a column of the atmosphere from sea level to 30,000 feet assuming a vertically saturated atmosphere. Values of atmospheric precipitable water are determined using the moist pseudo-adiabatic assumption, i.e. assume that for the given 1000mb dew point value, the atmosphere holds the maximum amount of moisture possible. The ratio of the precipitable water associated with the maximum 1000mb dew point to the precipitable water associated with the 1000-mb storm representative dew point is the maximization factor.

For example, consider the following case:

1000mb storm representative dew point: 72oF 1000mb maximum dew point: 76oF Precipitable water associated with a 1000mb dew point of 72oF: 2.47 Precipitable water associated with a 1000mb dew point of 76oF: 2.99 Maximization factor: PW(76oF)/PW(72oF) = 2.99/2.47 = 1.21 For transpositioning, the storm inflow vector (determined by connecting the storm representative dew point location with the location of maximum rainfall) is moved to the basin location being studied. The new location of the upwind end of the vector is determined. The maximum dew point associated with that location is then selected using the same maximum dew point climatology map used for in-place maximization. The transpositioning factor is the ratio of the precipitable water associated with the maximum 1000mb dew point value at the transpositioned location to the precipitable water associated with the maximum 1000mb dew point for the storm representative dew point location.

An example is provided.

1000mb maximum dew point at the storm representative dew point location: 76oF 1000mb maximum dew point at the transpositioned location: 74oF Precipitable water associated with a 1000mb dew point of 76oF: 2.99 Precipitable water associated with a 1000mb dew point of 74oF: 2.73 Transposition factor: PW(74oF)/PW(76oF) = 2.73/2.99 = 0.91 C- 6

Appendix D Procedure for Deriving PMP Values from Storm Depth-Area-Duration (DAD) Analyses D- 1

Although PMP rainfall amounts are theoretical values, there currently is no theoretical method for determining the values. The accepted procedure for determining PMP values begins with the identification of the largest identified historic observed rainfall amounts in the region and applies the following procedures:

1. Increase the rainfall amounts to some maximized value (in-place maximization),

2. Adjust the "maximized" rainfall amounts to the potential situation where the historic storm occurs over the basin being studied (transposition),

3. Adjust the "maximized transpositioned" rainfall amounts for elevation changes or intervening topographic barriers which could potentially affect the storm moisture and subsequently the rainfall amounts for the "maximized transpositioned" storm (barrier adjustment).

The procedure begins with the Depth-Area-Duration (DAD) analysis from the largest of the identified storms that have occurred over regions that are climatologically and topographically similar to the area being studied. Identification of the largest rainfall events is relatively straight forward and is accomplished by identifying the largest station rainfall amounts and correlating the dates among adjacent stations to identify the areal extent of the heavy rainfall and the storm period. The DAD for each storm is computed using isohyetal analyses for each hour during the storm and determining the largest rainfall totals for each duration of interest over each area size of interest. HMR 51 uses temporal periods of 6-, 12-, 24-, 48- and 72- hours. Standard area sizes of 10-, 200-,

1,000-, 5,000-, 10,000- and 20,000-square miles area used. Other durations and area sizes can also be used in the DAD analysis as desired. In this study, area sizes of 50,000-and 100,000-square miles were analyzed in addition to the standard area sizes.

The US Army Corps of Engineers, the Bureau of Reclamation and the National Weather Service have performed storm studies and produced DADs for many storms.

This study reviewed additional weather station data to identify extreme rainfall storms that had not been identified and studied previously. The new storms identified primarily occurred since the publication of HMRs 51 and 55A, but additional storms that occurred prior to HMR publication were also identified. DADs that had been previously developed are used in this report. Newly identified storms are analyzed in this study, and DADs are developed for these storms. These DADs quantify the rainfall associated with each storm event, providing the largest rainfall amounts for each of the durations and area sizes used in this study.

Identification of storms that can be transpositioned to the ANO basin is largely based on subjective judgments. For a storm to be transpositionable, it should have occurred over a region that is climatologically and topographically similar to the basin being studied. Storms generally should not be transpositioned across significant topographic features or into different climate regions. The largest rainfall events identified in the storm search generally occurred over locations closer to the Gulf of Mexico with moisture moving in from the south and north. These storms occurred in D- 2

similar meteorological, climatological, and topographical settings. Therefore, it is assumed that the same moisture sources and dynamics that produced these events could have produced a similar storm over the basin.

Maximization of the storm DADs involves deriving the in-place and transposition factors to adjust the observed rainfall to look like it would have occurred had the storm been located over the basin. This accounts for the three factors which could affect a particular storm as it's moved from its original location to the ANO basin; the storm could have been some amount bigger in-place had more moisture been available, the storm would have had more or less moisture available to it versus where it originally occurred based on it being moved toward or away from its moisture source, and the storm would have occurred at a lower or higher elevation than its original location.

For this study, all computations associated with historic storms are computed at the 1000mb level (approximately sea level). The elevation of the location where the largest rainfall was observed is used as the storm elevation. An adjustment is applied to the storm moisture to account for the elevation of the storm above sea level. For example, if the maximum rainfall occurred at an elevation of 500 feet, the total atmospheric moisture (500 to 30,000 feet) is decreased by the amount of moisture associated with the storm representative dew point between sea level and 500 feet. The adjustment factor uses precipitable water contained in the moisture maximized atmosphere above the storm elevation, i.e., the moisture contained in the entire depth of the moisture maximized atmosphere, minus the moisture contained in the moisture maximized atmosphere below the storm elevation. An adjustment was made to account for the storms elevation (either higher or lower than the particular grid point basin centroid elevation) and the amount of precipitable water that would be available, more if the elevation was lower and less if the elevation was higher. This elevation adjustment factor is determined by computing the ratio of precipitable water in the moisture maximized atmosphere above the elevation to the precipitable water in the entire depth of the moisture maximized atmosphere.

The equations for the computation of the in-place maximization factor, transposition and elevation adjustment factors are as follows:

In-place maximization factor =

(storm representative maximum dew point PW - in-place storm elevation maximum dew point PW) / (storm representative dew point PW - in-place storm elevation representative dew point PW)

Transpositioned/elevation to basin factor =

(transpositioned maximum dew point PW - average basin elevation maximum dew point PW)/(storm representative maximum dew point PW - in-place storm elevation representative dew point PW)

D- 3

Multiplication of these terms leads to a simplified computation where all the required adjustments are combined in a single equation.

Total adjustment factor =

(in-place max factor) * (transpositioned/elevation to basin factor) * (barrier/elevation adjustment factor)

The total adjustment factor modifies the storm DAD by a factor using two computed values:

1) The maximum atmospheric moisture available to a historic storm if it were to occur over the study basin. This air mass is assumed to contain the maximum amount of atmospheric moisture for the basin location and is adjusted for elevation upwind of the basin and within the basin.

2) The atmospheric moisture available for the historic storm at the location and elevation where it occurred.

The total adjustment factor is applied as a linear multiplier for all rainfall amounts in the storm DAD.

As an example, the DAD from the Warner Park, TN SPAS 1208 AWA Storm Number 2W storm center is maximized, transpositioned, and elevation/barrier adjusted to the basin centroid. The following are values for the parameters used in computing the adjustments:

Storm representative Td: 75.0° F In-place maximum Td: 76.5° F Transpositioned maximum Td: 74.0° F Storm elevation: 600' Average basin elevation: 1,150 Total atmospheric precipitable water for 75.0° F: 2.85" Total atmospheric precipitable water for 76.5° F: 3.07" Total atmospheric precipitable water for 74.0o F: 2.73" Adjustment for storm elevation, 1000mb to 600' at 75.0°F: 0.15" Adjustment for storm elevation, 1000mb to 600' at 76.5°F: 0.16" Adjustment for ave basin elevation, 1000mb to 1,150' at 74.0°F: 0.28" Adjustment for inflow barrier elevation, 1000mb to 1150' at 74.0°F: 0.28" Total adjustment factor =

(in-place max factor) * (transpositioned to basin factor) * (elevation/barrier adjustment factor)

= ((3.07" - 0.16") / (2.85" - 0.15")) * ((2.73" - 0.28") / (3.07" - 0.16")) * ((2.73" - 0.28") /

(2.73" - 0.28")) = (1.08) * (0.84) * (1.00) = 0.91 D- 4

To explicitly show how each adjustment factor (in-place maximization, transposition and elevation/barrier adjustment) affects the total adjustment, separate computation are provided.

In-place maximization factor Storm representative dew point: 75.0° F In-place maximum dew point: 76.5° F Storm atmospheric precipitable water for 75.0° F: 2.85" Maximum atmospheric precipitable water for 76.5° F: 3.07" Adjustment for storm elevation, 1000mb to 600' at 75.0°F: 0.15" Adjustment for storm elevation, 1000mb to 600' at 76.5°F: 0.16" In-place maximization factor =

(storm representative maximum dew point PW - in place storm elevation maximum PW)/(storm representative dew point PW - in place storm elevation maximum dew point PW)

= (3.07"- 0.16) / (2.85" - 0.15)

= 2.91 / 2.70

1.08 Transposition factor In-place maximum dew point 76.5° F Transpositioned maximum dew point 74.0° F Maximum atmospheric precipitable water for 82.0° F: 3.07 Maximum atmospheric precipitable water for 80.5° F: 2.73 Adjustment for storm elevation, 1000mb to 600' at 76.0°F: 0.16" Adjustment for storm elevation, 1000mb to 1,150' at 74.0°F: 0.28" Transposition factor

(transpositioned maximum dew point PW - basin elevation maximum dew point PW)/(storm representative maximum dew point PW - in place storm elevation maximum dew point PW)

= (2.73" - 0.28) / (3.07" - 0.16)

= 2.45 / 2.91

= 0.84 Moisture inflow barrier adjustment factor For this study there were no intervening barriers that would deplete moisture before reaching any of the grid points or basin centroid. Therefore, in all cases this factor was equal to 1.00.

Total adjustment factor = (In-Place maximization) X (Transposition) X (Barrier Adjustment/Storm elevation)

D- 5

= 1.08

• 0.84

• 1.00

= 0.91 This is the same total adjustment computed earlier (within round-off error) using the single equation to compute the total adjustment factor.

Since these procedures involve linear multiplication, Excel spreadsheets can be used to incorporate the storm DAD and apply the factors to compute the total adjusted DAD. Each storm spreadsheet and all the data used for the calculations are presented for the PMP storms in Appendix F and the LIP storm list in Appendix G Once the total adjustment factors are applied to all of the storms being considered, rainfall amounts from largest storms are plotted on a log-linear plot with rainfall depth plotted on the linear scale and area size plotted on the log scale. A separate graph is constructed for each duration period, e.g. 6-hour, 12-hour, etc. The graphs provide curves of the transpositioned maximized adjusted storm rainfall amounts for all area sizes. These DA curves represent the maximum rainfall potential based on standard procedure modifications of the largest observed historic storms in the region surrounding the basins. An enveloping curve is drawn using the largest rainfall values. All of the plotted rainfall amounts either lie on the enveloping curve or below it. The exception is in the case where there is reason to suspect that a value is larger than is reasonable and that rainfall value may be undercut, i.e. the envelop curve should be drawn beneath the value. Undercutting should rarely be done and each case needs to be justified. No undercutting was done in this study. In general, the enveloping curve should provide a smooth transition among the maximum rainfall values for various area sizes. This process of enveloping DA plots provides continuity in space for the rainfall amounts among various area sizes.

After enveloping curves are completed for each of the duration periods, DD curves are plotted on a linear-linear graph, with duration on one axis and depth on the other. Since there is only a single curve for each area size from the enveloped DA plots, all of DA curves can be plotted as a family of curves on a single graph. Enveloping of curves is completed for each area size. The enveloping curve should provide a smooth transition among the maximum rainfall values for various durations. This procedure of enveloping DD plots provides continuity in time for the rainfall amounts among various durations.

The final envelopment curves provide the maximum rainfall amounts that represent PMP values for each particular grid point. Rainfall amounts for each area size and each duration are taken from the curves and used to construct the PMP DAD table.

D- 6

Appendix E Storm Precipitation Analysis System (SPAS)

Description E- 1

INTRODUCTION The Storm Precipitation Analysis System (SPAS) is grounded on years of scientific research with a demonstrated reliability in hundreds of post-storm precipitation analyses.

It has evolved into a trusted hydrometeorological tool that provides accurate precipitation data at a high spatial and temporal resolution for use in a variety of sensitive hydrologic applications (Faulkner et al 2004, Tomlinson et al 2003-2012). Applied Weather Associates, LLC and METSTAT, Inc. initially developed SPAS in 2002 for use in producing Depth-Area-Duration values for Probable Maximum Precipitator (PMP) analyses. SPAS utilizes precipitation gauge data, basemaps and radar data (when available) to produce gridded precipitation at time intervals as short as 5-minutes, at spatial scales as fine as 1 km2 and in a variety of customizable formats. To date (April 2012) SPAS has been used to analyze over 230 storm centers across all types of terrain, among highly varied meteorological settings and some occurring over 100-years ago.

SPAS output has many applications including, but not limited to: hydrologic model calibration/validation, flood event reconstruction, storm water runoff analysis, forensic cases and PMP studies. Detailed SPAS-computed precipitation data allow hydrologists to accurately model runoff from basins, particularly when the precipitation is unevenly distributed over the drainage basin or when rain gauge data is limited or not available.

The increased spatial and temporal accuracy of precipitation estimates has eliminated the need for commonly made assumptions about precipitation characteristics (such as uniform precipitation over a watershed), thereby greatly improving the precision and reliability of hydrologic analyses.

In order to instill consistency in SPAS analyses, many of the core methods have remained consistent from beginning. However, SPAS is constantly evolving and improving through new scientific advancements and as new data and improvements are incorporated. This write-up describes the current inter-workings of SPAS, but the reader should realize SPAS can be customized on a case-by-case basis to account for special circumstances; these adaptations are documented and included in the deliverables. The over arching goal of SPAS is to combine the strengths of rain gauge data and radar data (when available) to provide sound, reliable and accurate spatial precipitation data.

Hourly precipitation observations are generally limited to a small number of locations, with many basins lacking observational precipitation data entirely. Meanwhile Next Generation Radar (NEXRAD) data provides valuable spatial and temporal information over data-sparse basins, it has historically lacked reliability for determining precipitation rates and reliable quantitative precipitation estimates (QPE). The improved reliability in SPAS is made possible by hourly calibration of the NEXRAD radar-precipitation relationship, combined with local hourly bias adjustments to force consistency between the final result and ground truth precipitation measurements. If NEXRAD radar data is available (generally for storm events since the mid-1990's), precipitation at temporal scales as frequent as 5-minutes is available, otherwise the precipitation data is available hourly. A summary of the general SPAS processes are shown in flow chart in Figure E.0.

E- 2

Figure E.0 SPAS flow chart.

SETUP Prior to a SPAS analysis careful definition of the storm analysis domain and time frame to be analyzed is established. Several considerations are made to ensure the domain (longitude-latitude box) and time frame are sufficient for the given application.

SPAS Analysis Domain For PMP applications it is important to establish an analysis domain that completely encompasses a storm center, meanwhile hydrologic modeling applications are more concerned about a specific basin, watershed or catchment. If radar data is available, then it is also important to establish an area large enough to encompass enough stations (minimum of ~30) to adequately derive reliable radar-precipitation intensity relationships E- 3

(discussed later). The domain is defined by evaluating existing documentation on the storm as well as plotting and evaluating initial precipitation gauge data on a map. The analysis domain is defined to include as many hourly recording gauges as possible given their importance in timing. The domain must include enough of a buffer to accurately model the nested domain of interest. The domain is defined as a longitude-latitude (upper left and lower right corner) rectangular region.

SPAS Analysis Time Frame Ideally, the analysis time frame, also referred to as the Storm Precipitation Period (SPP),

will extend from a dry period through the target wet period then back into another dry period. This is to ensure that total storm precipitation amounts can be confidently associated with the storm in question and not contaminated by adjacent wet periods. If this is not possible, a reasonable time period is selected that is bounded by relatively lighter precipitation. The time frame of the hourly data must be sufficient to capture the full range of daily gauge observational periods in order for the daily observations to be disaggregated into estimated incremental hourly values (discussed later). For example, if a daily gauge takes observations at 8:00 AM, then the hourly data must be available from 8:00 AM the day prior. Given the configuration of SPAS, the minimum SPP is 72 hours8.333333e-4 days <br />0.02 hours <br />1.190476e-4 weeks <br />2.7396e-5 months <br /> and aligns midnight to midnight.

The core precipitation period (CPP) is a sub-set of the SPP and represents the time period with the most precipitation and the greatest number of reporting gauges. The CPP represents the time period of interest and where our confidence in the results is highest.

DATA The foundation of a SPAS analysis is the ground truth precipitation measurements. In fact, the level of effort involved in data mining and quality control represent over half of the total level of effort needed to conduct a complete storm analysis. SPAS operates with three primary data sets: precipitation gauge data, a basemap and, if available, radar data. Table E.0 conveys the variety of precipitation gauges usable by SPAS. For each gauge, the following elements are gathered, entered and archived into to SPAS database:

Station ID Station name Station type (H=hourly, D=Daily, S=Supplemental, etc.)

Longitude in decimal degrees Latitude in decimal degrees Elevation in feet above MSL Observed precipitation Observation times Source If unofficial, the measurement equipment and/or method is also noted.

E- 4

Based on the SPP and analysis domain, hourly and daily precipitation gauge data are extracted from our in-house database as well as the Meteorological Assimilation Data Ingest System (MADIS). Our in-house database is contains data dating back to the late 1800s, while the MADIS system (described below) contains archived data back to 2002.

Hourly Precipitation Data Our hourly precipitation database is largely comprised of data from NCDC TD-3240, but also precipitation data from other mesnonets and meteorological networks (e.g. ALERT, Flood Control Districts, etc.) that we have collected and archived as part of previous studies. Meanwhile, MADIS provides data from a large number of networks across the U.S., including NOAAs HADS (Hydrometeorological Automated Data System),

numerous mesonets, the Citizen Weather Observers Program (CWOP), departments of transportation, etc. (see http://madis.noaa.gov/mesonet_providers.html for a list of providers). Although our automatic data extraction is fast, cost-effective and efficient, it never captures all of the available precipitation data for a storm event. For this reason, a thorough data mining effort is undertaken to acquire all available data from sources such as U.S. Geological Survey (USGS), Remote Automated Weather Stations (RAWS),

Community Collaborative Rain, Hail & Snow Network (CoCoRaHS), National Atmospheric Deposition Program (NADP), Clean Air Status and Trends Network (CASTNET), local observer networks, Climate Reference Network (CRN), Global Summary of the Day (GSD) and Soil Climate Analysis Network (SCAN). Unofficial hourly precipitation are gathered to give guidance on either timing or magnitude in areas otherwise void of precipitation data. The WeatherUnderground and MesoWest, two of the largest weather databases on the Internet, contain a good deal of official data, but also unofficial gauges.

Table E.0 Different precipitation gauge types used by SPAS.

Precipitation Gauge Type Description Hourly Hourly gauges with complete, or nearly complete, incremental hourly precipitation data.

Hourly estimated Hourly gauges with some estimated hourly values, but otherwise reliable.

Hourly pseudo Hourly gauges with reliable temporal precipitation data, but the magnitude is questionable in relation to co-located daily or supplemental gauge.

Daily Daily gauge with complete data and known observation times.

E- 5

Daily estimated Daily gauges with some or all estimated data.

Supplemental Gauges with unknown or irregular observation times, but reliable total storm precipitation data. (E.g. public reports, storms reports, Bucket surveys, etc.)

Supplemental estimated Gauges with estimated total storm precipitation values based on other information (e.g. newspaper articles, stream flow discharge, inferences from nearby gauges, pre-existing total storm isohyetal maps, etc.)

Daily Precipitation Data Our daily database is largely based on NCDCs TD-3206 (pre-1948) and TD-3200 (1948 through present) as well as SNOTEL data from NRCS. Since the late 1990s, the CoCoRaHS network of more than 15,000 observes in the U.S. has become a very important daily precipitation source. Other daily data is gathered from similar, but smaller gauge networks, for instance the High Spatial Density Precipitation Network in Minnesota.

As part of the daily data extraction process, the time of observation, as indicted in database (if available), accompanies each measured precipitation value. Accurate observation times are necessary for SPAS to disaggregate the daily precipitation into estimated incremental values (discussed later). Knowing the observation time also allows SPAS to maintain precipitation amounts within given time bounds, thereby retaining known precipitation intensities. Given the importance of observation times, efforts are taken to insure the observation times are accurate. Hardcopy reports of Climatological Data, scanned observational forms (available on-line) and/or gauge metadata forms have proven to be valuable and accurate resources for validating observation times.

Furthermore, erroneous observation times are identified in the mass-curve quality-control procedure (discussed later) and can be corrected at that point in the process.

Supplemental Precipitation Gauge Data For gauges with unknown or irregular observation times, the gauge is considered a supplemental gauge. A supplemental gauge can either be added to the storm database with a storm total and the associated SPP as the temporal bounds or as a gauge with the known, but irregular observation times and associated precipitation amounts. For instance, if all that is known is 3 fell between 0800-0900, then that information can be entered. Gauges or reports with nothing more than a storm total are often abundant, but in order to use them, it is important the precipitation is only from the storm period in question. Therefore, it is ideal to have the analysis time frame bounded by dry periods.

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Perhaps the most important source of data, if available, is from bucket surveys, which provide comprehensive lists of precipitation measurements collected during a post-storm field exercise. Although some bucket survey amounts are not from conventional precipitation gauges, they provide important information, especially in areas lacking data.

Particularly for PMP-storm analysis applications, it is customary to accept extreme, but valid non-measured precipitation values in order to capture the highest precipitation values.

Basemap Basemaps are independent grids of spatially distributed weather or climate variables that are used to govern the spatial patterns of the hourly precipitation. The basemap also governs the spatial resolution of the final SPAS grids, unless radar data is available/used to govern the spatial resolution. Note that a base map is not required as the hourly precipitation patterns can be based on a station characteristics and an inverse distance weighting technique (discussed later). Basemaps in complex terrain are often based on the PRISM mean monthly precipitation (Figure E.1a) or Hydrometeorological Design Studies Center precipitation frequency grids (Figure E.1b) given they resolve orographic enhancement areas and micro-climates at a spatial resolution of 30-seconds (about 800 m). Basemaps of this nature in flat terrain are not as effective given the small terrain forced precipitation gradients. Therefore, basemaps for SPAS analyses in flat terrain are often developed from pre-existing (hand-drawn) isohyetal patterns (Figure E.1c),

composite radar imagery or a blend of both.

b) a) c)

Figure E.1 Sample SPAS basemaps: (a) A pre-existing (USGS) isohyetal pattern across flat terrain (SPAS 1209), (b) PRISM mean monthly (October) precipitation (SPAS 1192) and (c) A 100-year 24-hour precipitation grid from NOAA Atlas 14 (SPAS 1138).

Radar Data For storms occurring since approximately the mid-1990's, weather radar data is available to supplement the SPAS analysis. A fundamental requirement for high quality radar-estimated precipitation is a high quality radar mosaic, which is a seamless collection of E- 7

concurrent weather radar data from individual radar sites, however in some cases a single radar is sufficient (i.e. for a small area size storm event such as a thunderstorm). Weather radar data has been in use by meteorologists since the 1960s to estimate precipitation depths, but it was not until the early 1990s that new, more accurate NEXRAD Doppler radar (WSR88D) was placed into service across the United States. Currently efforts are underway to convert the WSR88D radars to dual polarization (DualPol) radar. Today, NEXRAD radar coverage of the contiguous United States is comprised of 159 operational sites and 30 in Canada. Each U.S. radar covers an approximate 285 mile (460 km) radial extent and while Canadian radars have approximately 256 km (138 nautical miles) radial extent over which the radar can detect precipitation. (see Figure E.2) The primary vendor of NEXRAD weather radar data for SPAS is Weather Decision Technologies, Inc. (WDT), who accesses, mosaics, archives and quality-controls NEXRAD radar data from NOAA and Environment Canada. SPAS utilizes Level II NEXRAD radar reflectivity data in units of dBZ, available every 5-minutes in the U.S.

and 10-minutes in Canada.

Figure E.2 U.S. radar locations and their radial extents of coverage below 10,000 feet above ground level (AGL). Each U.S. radar covers an approximate 285 mile radial extent over which the radar can detect precipitation.

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The WDT and National Severe Storms Lab (NSSL) Radar Data Quality Control Algorithm (RDQC) removes non-precipitation artifacts from base Level-II radar data and remaps the data from polar coordinates to a Cartesian (latitude/longitude) grid. Non-precipitation artifacts include ground clutter, bright banding, sea clutter, anomalous propagation, sun strobes, clear air returns, chaff, biological targets, electronic interference and hardware test patterns. The RDQC algorithm uses sophisticated data processing and a Quality Control Neural Network (QCNN) to delineate the precipitation echoes caused by radar artifacts (Lakshmanan and Valente 2004). Beam blockages due to terrain are mitigated by using 30 meter DEM data to compute and then discard data from a radar beam that clears the ground by less than 50 meters and incurs more than 50% power blockage. A clear-air echo removal scheme is applied to radars in clear-air mode when there is no precipitation reported from observation gauges within the vicinity of the radar.

In areas of radar coverage overlap, a distance weighting scheme is applied to assign reflectivity to each grid cell, for multiple vertical levels. This scheme is applied to data from the nearest radar that is unblocked by terrain.

Once the data from individual radars have passed through the RDQC, they are merged to create a seamless mosaic for the United States and southern Canada as shown in Figure E.3. A multi-sensor quality control can be applied by post-processing the mosaic to remove any remaining false echoes. This technique uses observations of infra-red cloud top temperatures by GOES satellite and surface temperature to create a precipitation/no-precipitation mask. Figure 4 shows the impact of WDTs quality control measures.

Upon completing all QC, WDT converts the radar data from its native polar coordinate projection (1 degree x 1.0 km) into a longitude-latitude Cartesian grid (based on the WGS84 datum), at a spatial resolution of ~1/3rd-square mile for processing in SPAS.

a) b)

Figure E.3 (a) Level-II radar mosaic of CONUS radar with no quality control, (b) WDT quality controlled Level-II radar mosaic.

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SPAS conducts further QC on the radar mosaic by infilling areas contaminated by beam blockages. Beam blocked areas are objectively determined by evaluating total storm reflectivity grid which naturally amplifies areas of the SPAS analysis domain suffering from beam blockage as shown in Figure E.4.

a) b)

Figure E.4 Illustration of SPAS-beam blockage infilling where (a) is raw, blocked radar and (b) is filled for a 42-hour storm event.

METHODOLOGY Daily and Supplemental Precipitation to Hourly To obtain one hour temporal resolutions and utilize all gauge data, it is necessary to disaggregate the daily and supplemental precipitation observations into estimated hourly amounts. This process has traditionally been accomplished by distributing (temporally) the precipitation at each daily/supplemental gauge in accordance to a single nearby hourly gauge (Thiessen polygon approach). However, this may introduce biases and not correctly represent hourly precipitation at daily/supplemental gauges situated in-between hourly gauges. Instead, SPAS uses a spatial approach by which the estimated hourly precipitation at each daily and supplemental gauge is governed by a distance weighted algorithm of all nearby true hourly gauges.

In order to disaggregate (i.e. distribute) daily/supplemental gauge data into estimate hourly values, the true hourly gauge data is first evaluated and quality controlled using synoptic maps, nearby gauges, orographic effects, gauge history and other documentation on the storm. Any problems with the hourly data are resolved, and when possible/necessary accumulated hourly values are distributed. If an hourly value is missing, the analyst can choose to either estimate it or leave it missing for SPAS to estimate later based on nearby hourly gauges. At this point in the process, pseudo (hourly) gauges can be added to represent precipitation timing in topographically E- 10

complex locations, areas with limited/no hourly data or to capture localized convention.

In order to adequately capture the temporal variations of the precipitation a pseudo hourly gauge is sometimes necessary. A pseudo gauge is created by distributing the precipitation at a co-located daily gauge or by creating a completely new pseudo gauge from other information such as inferences from COOP observation forms, METAR visibility data (if hourly precipitation isnt already available), lightning data, satellite data, or radar data. Often radar data is the best/only choice for creating pseudo hourly gauges, but this is done cautiously given the potential differences (over-shooting of the radar beam equating to erroneous precipitation) between radar data and precipitation. In any case, the pseudo hourly gauge is flagged so SPAS only uses it for timing and not magnitude. Care is taken to ensure hourly pseudo gauges represent justifiably important physical and meteorological characteristics before being incorporated into the SPAS database. Although pseudo gauges provide a very important role, their use is kept to a minimum. The importance of insuring the reliability of every hourly gauge cannot be over emphasized. All of the final hourly gauge data, including pseudos, are included in the hourly SPAS precipitation database.

Using the hourly SPAS precipitation database, each hourly precipitation value is converted into a percentage that represents the incremental hourly precipitation divided by the total SPP precipitation. The GIS-ready x-y-z file is constructed for each hour that contains the latitude (x), longitude(y) and percent of precipitation (z) for a particular hour. Using the GRASS GIS, an inverse-distance-weighting squared (IDW) interpolation technique is applied to each of the hourly files. The result is a continuous grid with percentage values for the entire analysis domain, keeping the grid cells on which the hourly gauge resides faithful to the observed/actual percentage. Since the percentages typically have a high degree of spatial autocorrelation, the spatial interpolation has skill in determining the percentages between gauges, especially since the percentages are somewhat independent of the precipitation magnitude. The end result is a GIS grid for each hour that represents the percentage of the SPP precipitation that fell during that hour.

After the hourly percentage grids are generated and QCed for the entire SPP, a program is executed that converts the daily/supplemental gauge data into incremental hourly data.

The timing at each of the daily/supplemental gauges is based on (1) the daily/supplemental gauge observation time, (2) daily/supplemental precipitation amount and (3) the series of interpolated hourly percentages extracted from grids (described above).

This procedure is detailed in Figure E.5 below. In this example, a supplemental gauge reported 1.40" of precipitation during the storm event and is located equal distance from the three surrounding hourly recording gauges. The procedure steps are:

Step 1. For each hour, extract the percent of SPP from the hourly gauge-based percentage at the location of the daily/supplemental gauge. In this example, assume these values are the average of all the hourly gauges.

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Step 2. Multiply the individual hourly percentages by the total storm precipitation at the daily/supplemental gauge to arrive at estimated hourly precipitation at the daily/supplemental gauge. To make the daily/supplemental accumulated precipitation data faithful to the daily/supplemental observations, it is sometimes necessary to adjust the hourly percentages so they add up to 100% and account for 100% of the daily observed precipitation.

Figure E.5 Example of disaggregation of daily precipitation into estimated hourly precipitation based on three (3) surrounding hourly recording gauges.

In cases where the hourly grids do not indicate any precipitation falling during the daily/supplemental gauge observational period, yet the daily/supplemental gauge reported precipitation, the daily/supplemental total precipitation is evenly distributed throughout the hours that make up the observational period; although this does not happen very often, this solution is consistent with NWS procedures. However, the SPAS analyst is notified of these cases in a comprehensive log file, and in most cases they are resolvable, sometimes with a pseudo hourly gauge.

GAUGE QUALITY CONTROL Exhaustive quality control measures are taken throughout the SPAS analysis. Below are a few of the most significant QC measures taken.

Mass Curve Check A mass curve-based QC-methodology is used to ensure the timing of precipitation at all gauges is consistent with nearby gauges. SPAS groups each gauge with the nearest four gauges (regardless of type) into a single file. These files are subsequently used in software for graphing and evaluation. Unusual characteristics in the mass curve are E- 12

investigated and the gauge data corrected, if possible and warranted. See Figure E.6 for an example.

Figure E.6 Sample mass curve plot depicting a precipitation gauge with an erroneous observation time (blue line). X-axis is the SPAS index hour and the y-axis is inches. The statistics in the upper left denote gauge type, distance from target gauge (in km), and gauge ID. In this example, the center gauge (blue line) was found to have an observation error/shift of 1 day.

Gauge Mis-location Check Although the gauge elevation is not explicitly used in SPAS, it is however used as a means of QCing gauge location. Gauge elevations are compared to a high-resolution 15-second DEM to identify gauges with large differences, which may indicate erroneous longitude and/or latitude values.

Co-located Gauge QC Care is also taken to establish the most accurate precipitation depths at all co-located gauges. In general, where a co-located gauge pair exists, the highest precipitation is accepted (if accurate). If the hourly gauge reports higher precipitation, then the co-located daily (or supplemental) is removed from the analysis since it would not add anything to the analysis. Often daily (or supplemental) gauges report greater precipitation than a co-located hourly station since hourly tipping bucket gauges tend to suffer from gauge under-catch, particularly during extreme events, due to loss of precipitation during tips. In these cases the daily/supplemental is retained for the E- 13

magnitude and the hourly used as a pseudo hourly gauge for timing. Large discrepancies between any co-located gauges are investigated and resolved since SPAS can only utilize a single gauge magnitude at each co-located site.

SPATIAL INTERPOLATION At this point the QCed observed hourly and disaggregated daily/supplemental hourly precipitation data are spatially interpolated into hourly precipitation grids. SPAS has three options for conducting the hourly precipitation interpolation, depending on the terrain and availability of radar data, thereby allowing SPAS to be optimized for any particular storm type or location. Figure E.7 depicts the results of each spatial interpolation methodology based on the same precipitation gauge data.

Figure E.7 Depictions of total storm precipitation based on the three SPAS interpolation methodologies for a storm (SPAS 1177, Vanguard, Canada) across flat terrain: (a) no basemap, (b) basemap-aided and (3) radar.

Basic Approach The basic approach interpolates the hourly precipitation point values to a grid using an inverse distance weighting squared GIS algorithm. This is sometimes the best choice for convective storms over flat terrain when radar data is not available, yet high gauge density instills reliable precipitation patterns. This approach is rarely used.

Basemap Approach Another option includes the use of a basemap, also known as a climatologically-aided interpolation (Hunter 2005). As noted before, the spatial patterns of the basemap govern the interpolation between points of hourly precipitation estimates, while the actual hourly precipitation values govern the magnitude. This approach to interpolating point data across complex terrain is widely used. In fact, it was used extensively by the NWS during their storm analysis era from the 1940s through the 1970s.

In application, the hourly precipitation gauge values are first normalized by the corresponding grid cell value of the basemap before being interpolated. The normalization allows information and knowledge from the basemap to be transferred to E- 14

the spatial distribution of the hourly precipitation. Using an IDW squared algorithm, the normalized hourly precipitation values are interpolated to a grid. The resulting grid is then multiplied by the basemap grid to produce the hourly precipitation grid. This is repeated each hour of the storm.

Radar Approach The coupling of SPAS with NEXRAD provides the most accurate method of spatially and temporally distributing precipitation. To increase the accuracy of the results however, quality-controlled precipitation observations are used for calibrating the radar reflectivity to rain rate relationship (Z-R relationship) each hour instead of assuming a default Z-R relationship. Also, spatial variability in the Z-R relationship is accounted for through local bias corrections (described later). The radar approach involves several steps, each briefly described below. The radar approach cannot operate alone - either the basic or basemap approach must be completed before radar data can be incorporated.

Z-R Relationship SPAS derives high quality precipitation estimates by relating quality controlled level-II NEXRAD radar reflectivity radar data with quality-controlled precipitation gauge data in order to calibrate the Z-R (radar reflectivity, Z, and precipitation, R) relationship.

Optimizing the Z-R relationship is essential for capturing temporal changes in the Z-R.

Most current radar-derived precipitation techniques rely on a constant relationship between radar reflectivity and precipitation rate for a given storm type (e.g. tropical, convective), vertical structure of reflectivity and/or reflectivity magnitudes. This non-linear relationship is described by the Z-R equation below:

Z = A Rb (1)

Where Z is the radar reflectivity (measured in units of dBZ), R is the precipitation (precipitation) rate (millimeters per hour), A is the multiplicative coefficient and b is the power coefficient. Both A and b are directly related to the rain drop size distribution (DSD) and rain drop number distribution (DND) within a cloud (Martner and Dubovskiy 2005).

The variability in the results of Z versus R is a direct result of differing DSD, DND and air mass characteristics (Dickens 2003). The Figure E.8 Example SPAS (denoted DSD and DND are determined by complex as Exponential) vs. default Z-R interactions of microphysical processes that relationship (SPAS #1218, Georgia fluctuate regionally, seasonally, daily, hourly, September 2009). and even within the same cloud. For these E- 15

reasons, SPAS calculates an optimized Z-R relationship across the analysis domain each hour based on observed precipitation rates and radar reflectivity (see Figure E.8).

The National Weather Service (NWS) utilizes different default Z-R algorithms, depending on the precipitation-causing event, to estimate precipitation through the use of NEXRAD radar reflectivity data across the United States (see Figure E.90) (Baeck and Smith 1998 and Hunter 1999). A default Z-R relationship of Z = 300R1.4 is the primary algorithm used throughout the continental U.S. However, it is widely known that this, compared to unadjusted radar-aided estimates of precipitation, suffers from deficiencies that may lead to significant over or under-estimation of precipitation.

Figure E.9 Commonly used Z-R algorithms used by the NWS.

Instead of adopting a standard Z-R, SPAS utilizes a least squares fit procedure for optimizing the Z-R relationship each hour of the SPP. The process begins by determining if sufficient (minimum 12) observed hourly precipitation and radar data pairs are available to compute a reliable Z-R. If insufficient (<12) gauge pairs are available, then SPAS adopts the previous hour Z-R relationship, if available, or applies a user-defined default Z-R algorithm from Figure 9. If sufficient data are available, the one hour sum of NEXRAD reflectivity (Z) is related to the 1-hour precipitation at each gauge.

A least-squares-fit exponential function using the data points is computed. The resulting best-fit, one hour-based Z-R is subjected to several tests to determine if the Z-R relationship and its resulting precipitation rates are within a certain tolerance based on the R-squared fit measure and difference between the derived and default Z-R precipitation results. Experience has shown the actual Z-R versus the default Z-R can be significantly different (Figure E.10).

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Figure E.10 Comparison of the SPAS optimized hourly Z-R relationships (black lines) versus a default Z=75R2.0 Z-R relationship (red line) for a period of 99 hours0.00115 days <br />0.0275 hours <br />1.636905e-4 weeks <br />3.76695e-5 months <br /> for a storm over southern California.

Radar-aided Hourly Precipitation Grids Once a mathematically optimized hourly Z-R relationship is determined, it is applied to the total hourly Z grid to compute an initial precipitation rate (inches/hour) at each grid cell. To account for spatial differences in the Z-R relationship, SPAS computes residuals, the difference between the initial precipitation analysis (via the Z-R equation) and the actual ground truth precipitation (observed - initial analysis), at each gauge. The point residuals, also referred to as local biases, are normalized and interpolated to a residual grid using an inverse distance squared weighting algorithm. A radar-based hourly precipitation grid is created by adding the residual grid to the initial grid; this allows the precipitation at the grid cells for which gauges are on to be true and faithful to the gauge measurement. The pre-final radar-aided precipitation grid is subject to some final, visual QC checks to ensure the precipitation patterns are consistent with the terrain; these checks are particularly important in areas of complex terrain where even QCed radar data can be unreliable. The next incremental improvement with SPAS program will come as the NEXRAD radar sites are upgraded to dual-polarimetric capability.

Radar- and Basemap-Aided Hourly Precipitation Grids At this stage of the radar approach, a radar- and basemap-aided hourly precipitation grid exists for each hour. At locations with precipitation gauges, the grids are equal, however elsewhere the grids can vary for a number of reasons. For instance, the basemap-aided hourly precipitation grid may depict heavy precipitation in an area of complex terrain, blocked by the radar, whereas the radar-aided hourly precipitation grid may suggest little, if any, precipitation fell in the same area. Similarly, the radar-aided hourly precipitation E- 17

grid may depict an area of heavy precipitation in flat terrain that the basemap-approach missed since the area of heavy precipitation occurred in an area without gauges. SPAS uses an algorithm to compute the hourly precipitation at each pixel given the two results.

Areas that are completely blocked from a radar signal are accounted for with the basemap-aided results (discussed earlier). The precipitation in areas with orographically effective terrain and reliable radar data are governed by a blend of the basemap- and radar-aided precipitation. Elsewhere, the radar-aided precipitation is used exclusively.

This blended approach has proven effective for resolving precipitation in complex terrain, yet retaining accurate radar-aided precipitation across areas where radar data is reliable.

Figure E.11 illustrates the evolution of final precipitation from radar reflectivity in an area of complex terrain in southern California.

a) b) c) d)

Figure E.11 A series of maps depicting 1-hour of precipitation utilizing (a) inverse distance weighting of gauge precipitation, (b) gauge data together with a climatologically-aided interpolation scheme, (c) default Z-R radar-estimated interpolation (no gauge correction) and (d) SPAS precipitation for a January 2005 storm in southern California, USA.

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SPAS versus Gauge Precipitation Performance measures are computed and evaluated each hour to detect errors and inconsistencies in the analysis. The measures include: hourly Z-R coefficients, observed hourly maximum precipitation, maximum gridded precipitation, hourly bias, hourly mean absolute error (MAE), root mean square error (RMSE), and hourly coefficient of determination (r2).

Figure E.12 Z-R plot (a), where the blue line is the SPAS derived Z-R and the black line is the default Z-R, and the (b) associated observed versus SPAS scatter plot at gauge locations.

Comparing SPAS-calculated precipitation (Rspas) to observed point precipitation depths at the gauge locations provides an objective measure of the consistency, accuracy and bias.

Generally speaking SPAS is usually within 5% of the observed precipitation (see Figure E.12). Less-than-perfect correlations between SPAS precipitation depths and observed precipitation at gauged locations could be the result of any number of issues, including:

Point versus area: A rain gauge observation represents a much smaller area than the area sampled by the radar. The area that the radar is sampling is approximately 1 km2, whereas a rain gauge only samples approximately 8.0x10-9 km2. Furthermore, the radar data represents an average reflectivity (Z) over the grid cell, when in fact the reflectivity can vary across the 1 km 2 grid cell.

Therefore, comparing a grid cell radar derived precipitation value to a gauge (point) precipitation depth measured may vary.

Precipitation gauge under-catch: Although we consider gauge data ground truth, we recognize gauges themselves suffer from inaccuracies. Precipitation E- 19

gauges, shielded and unshielded, inherently underestimate total precipitation due to local airflow, wind under-catch, wetting, and evaporation. The wind under-catch errors are usually around 5% but can be as large as 40% in high winds (Guo et al 2001, Duchon and Essenberg 2001, Ciach 2003, Tokay et al 2010). Tipping buckets miss a small amount of precipitation during each tip of the bucket due to the bucket travel and tip time. As precipitation intensities increase, the volumetric loss of precipitation due to tipping tends to increase. Smaller tipping buckets can have higher volumetric losses due to higher tip frequencies, but on the other hand capture higher precision timing.

Radar Calibration: NEXRAD radars calibrate reflectivity every volume scan, using an internally generated test. The test determines changes in internal variables such as beam power and path loss of the receiver signal processor since the last off-line calibration. If this value becomes large, it is likely that there is a radar calibration error that will translate into less reliable precipitation estimates.

The calibration test is supposed to maintain a reflectivity precision of 1 dBZ. A 1 dBZ error can result in an error of up to 17% in Rspas using the default Z-R relationship Z=300R1.4. Higher calibration errors will result in higher Rspas errors.

However, by performing correlations each hour, the calibration issue is minimized in SPAS.

Attenuation: Attenuation is the reduction in power of the radar beams energy as it travels from the antenna to the target and back. It is caused by the absorption and the scattering of power from the beam by precipitation. Attenuation can result in errors in Z as large as 1 dBZ especially when the radar beam is sampling a large area of heavy precipitation. In some cases, storm precipitation is so intense (>12 inches/hour) that individual storm cells become opaque and the radar beam is totally attenuated. Armed with sufficient gauge data however, SPAS will overcome attenuation issues.

Range effects: The curvature of the Earth and radar beam refraction result in the radar beam becoming more elevated above the surface with increasing range.

With the increased elevation of the radar beam comes a decrease in Z values due to the radar beam not sampling the main precipitation portion of the cloud (i.e.

over topping the precipitation and/or cloud altogether). Additionally, as the radar beam gets further from the radar, it naturally samples a larger and larger area, therefore amplifying point versus area differences (described above).

Radar Beam Occultation/Ground Clutter: Radar occultation (beam blockage) results when the radar beams energy intersects terrain features as depicted in Figure E.13. The result is an increase in radar reflectivity values that can result in higher than normal precipitation estimates. The WDT processing algorithms account for these issues, but SPAS uses GIS spatial interpolation functions to infill areas suffering from poor or no radar coverage.

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Anomalous Propagation (AP) - AP is false reflectivity echoes produced by unusual rates of refraction in the atmosphere. WDT algorithms remove most of the AP and false echoes, however in extreme cases the air near the ground may be so cold and dense that a radar beam that starts out moving upward is bent all the way down to the ground. This produces erroneously strong echoes at large distances from the radar. Again, equipped with sufficient gauge data, the SPAS bias corrections will overcome AP issues.

Figure E.13 Depiction of radar artifacts. (Source: Wikipedia)

SPAS is designed to overcome many of these short-comings by carefully using radar data for defining the spatial patterns and relative magnitudes of precipitation, but allowing measured precipitation values (ground truth) at gauges to govern the magnitude. When absolutely necessary, the observed precipitation values at gauges are nudged up (or down) to force the SPAS results to be consistent with observed gauge values. Nudging gauge precipitation values helps to promote better consistency between the gauge value and the gridcell value, even though these two values sometimes should not be the same since they are sampling different area sizes. For reasons discussed in the "SPAS versus Gauge Precipitation" section, the gauge value and gridcell value can vary. Plus, SPAS is designed to toss observed individual hourly values that are grossly inconsistent with the radar data, hence driving a difference between the gauge and gridcell. In general, when the gauge and gridcell value differ by more than 15% and/or 0.50 inches, and the gauge data has been validated, then it is justified to nudge (artificially increase or decrease) the observed gauge value to "force" SPAS to derive a gridcell value equal to the observed value. Sometimes simply shifting the gauge location to an adjacent gridcell resolves the problems. Regardless, a large gauge versus gridcell difference is a "red flag" and sometimes the result of an erroneous gauge value or a mis-located gauge, but in some cases the difference can only be resolved by nudging the precipitation value.

Before final results are declared, a precipitation intensity check is conducted to ensure the spatial patterns and magnitudes of the maximum storm intensities at 1-, 6-, 12-, etc. hours E- 21

are consistent with surrounding gauges and published reports. Any erroneous data are corrected and SPAS re-run. Considering all of the QA/QC checks in SPAS, it typically requires 5-15 basemap SPAS runs and, if radar data is available, another 5-15 radar-aided runs, to arrive at the final output.

Test Cases To check the accuracy of the DAD software, three test cases were evaluated.

"Pyramidville Storm The first test was that of a theoretical storm with a pyramid shaped isohyetal pattern.

This case was called the Pyramidville storm. It contained 361 hourly stations, each occupying a single grid cell. The configuration of the Pyramidville storm (see Figure E.14) allowed for uncomplicated and accurate calculation of the analytical DA truth independent of the DAD software. The main motivation of this case was to verify that the DAD software was properly computing the area sizes and average depths.

1. Storm center: 39°N 104°W

2. Duration: 10-hours

3. Maximum grid cell precipitation: 1.00

4. Grid cell resolution: 0.06 sq.-miles (361 total cells)

5. Total storm size: 23.11 sq-miles

6. Distribution of precipitation:

Hour 1: Storm drops 0.10 at center (area 0.06 sq-miles)

Hour 2: Storm drops 0.10 over center grid cell AND over one cell width around hour 1 center Hours 3-10:

1. Storm drops 0.10 per hour at previously wet area, plus one cell width around previously wet area

2. Area analyzed at every 0.10

3. Analysis resolution: 15-sec (~.25 square miles)

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Figure E.14 "Pyramidville Total precipitation. Center = 1.00, Outside edge = 0.10.

The analytical truth was calculated independent of the DAD software, and then compared to the DAD output. The DAD software results were equal to the truth, thus demonstrating that the DA estimates were properly calculated (Figure E.15).

Depth-Area Curves for 10-hr Storm "Pyramidville" - 39.5N 104.5W & 39N 104W 100.0000 10.0000 Area (sq. mi.)

DAD Software Analytical truth 1.0000 0.1000 0 0.2 0.4 0.6 0.8 1 1.2 Maximum Average Precipitation Depth (inches)

Figure E.15 10-hour DA results for Pyramidville; truth vs. output from DAD software.

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The Pyramidville storm was then changed such that the mass curve and spatial interpolation methods would be stressed. Test cases included:

Two-centers, each center with 361 hourly stations A single center with 36 hourly stations, 0 daily stations A single center with 3 hourly stations and 33 daily stations As expected, results began shifting from the truth, but minimally and within the expected uncertainty.

Ritter, Iowa Storm, June 7, 1953 Ritter, Iowa was chosen as a test case for a number of reasons. The NWS had completed a storm analysis, with available DAD values for comparison. The storm occurred over relatively flat terrain, so orographics was not an issue. An extensive bucket survey provided a great number of additional observations from this event. Of the hundreds of additional reports, about 30 of the most accurate reports were included in the DAD analysis.

The DAD software results are very similar to the NWS DAD values (Table E.1).

Table E.1 The percent difference [(AWA-NWS)/NWS] between the AWA DA results and those published by the NWS for the 1953 Ritter, Iowa storm.

Difference Duration (hours)

Area (sq.mi.) 6 12 24 total 10 -15% -7% 2% 2%

100 -7% -6% 1% 1%

200 2% 0% 9% 9%

1000 -6% -7% 4% 4%

5000 -13% -8% 2% 2%

10000 -14% -6% 0% 0%

Westfield, Massachusetts Storm, August 8, 1955 Westfield, Massachusetts was also chosen as a test case for a number of reasons. It is a probable maximum precipitation (PMP) driver for the northeastern United States. Also, the Westfield storm was analyzed by the NWS and the DAD values are available for E- 24

comparison. Although this case proved to be more challenging than any of the others, the final results are very similar to those published by the NWS (Table E.2).

Table E.2 The percent difference [(AWA-NWS)/NWS] between the AWA DA results and those published by the NWS for the 1955 Westfield, Massachusetts storm.

Difference Duration (hours)

Area (sq.

mi.) 6 12 24 36 48 60 total 10 2% 3% 0% 1% -1% 0% 2%

100 -5% 2% 4% -2% -6% -4% -3%

200 -6% 1% 1% -4% -7% -5% -5%

1000 -4% -2% 1% -6% -7% -6% -3%

5000 3% 2% -3% -3% -5% -5% 0%

10000 4% 9% -5% -4% -7% -5% 1%

20000 7% 12% -6% -3% -4% -3% 3%

The principal components of SPAS are: storm search, data extraction, quality control (QC), conversion of daily precipitation data into estimated hourly data, hourly and total storm precipitation grids/maps and a complete storm-centered DAD analysis.

OUTPUT Armed with accurate, high-resolution precipitation grids, a variety of customized output can be created (see Figures E.16A-D). Among the most useful outputs are sub-hourly precipitation grids for input into hydrologic models. Sub-hourly (i.e. 5-minute) precipitation grids are created by applying the appropriate optimized hourly Z-R (scaled down to be applicable for instantaneous Z) to each of the individual 5-minute radar scans; 5-minutes is often the native scan rate of the radar in the US. Once the scaled Z-R is applied to each radar scan, the resulting precipitation is summed up. The proportion of each 5-minute precipitation to the total 1-hour radar-aided precipitation is calculated.

Each 5-minute proportion (%) is then applied to the quality controlled, bias corrected 1-hour total precipitation (created above) to arrive at the final 5-minute precipitation for each scan. This technique ensures the sum of 5-minute precipitation equals that of the quality controlled, bias corrected 1-hour total precipitation derived initially.

Depth-area-duration (DAD) tables/plots, shown in Figure E.16d, are computed using a highly-computational extension to SPAS. DADs provide an objective three dimensional (magnitude, area size, and duration) perspective of a storms precipitation. SPAS DADs are computed using the procedures outlined by the NWS Technical Paper 1 (1946).

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a) b) c)

d)

Figure E.16 Various examples of SPAS output, including (a) total storm map and its associated (b) basin average precipitation time series, (c) total storm precipitation map, (d) depth-area-duration (DAD) table and plot, and (e) precipitation gauge catalog with total storm statistics.

SUMMARY

Grounded on years of scientific research with a demonstrated reliability in post-storm analyses, SPAS is a hydro-meteorological tool that provides accurate precipitation analyses for a variety of applications. SPAS has the ability to compute precise and accurate results by using sophisticated timing algorithms, basemaps, a variety of E- 26

precipitation data and most importantly NEXRAD weather radar data (if available). The approach taken by SPAS relies on hourly, daily and supplemental precipitation gauge observations to provide quantification of the precipitation amounts while relying on basemaps and NEXRAD data (if available) to provide the spatial distribution of precipitation between precipitation gauge sites. By determining the most appropriate coefficients for the Z-R equation on an hourly basis, the approach anchors the precipitation amounts to accepted precipitation gauge data while using the NEXRAD data to distribute precipitation between precipitation gauges for each hour of the storm.

Hourly Z-R coefficient computations address changes in the cloud microphysics and storm characteristics as the storm evolves. Areas suffering from limited or no radar coverage, are estimated using the spatial patterns and magnitudes of the independently created basemap precipitation grids. Although largely automated, SPAS is flexible enough to allow hydro-meteorologists to make important adjustments and adapt to any storm situation.

REFERENCES Baeck M.L., Smith J.A., 1998: Precipitation Estimation by the WSR-88D for Heavy Precipitation Events, Weather and Forecasting: Vol. 13, No. 2, pp. 416-436.

Ciach, G.J., 2003: Local Random Errors in Tipping-Bucket Rain Gauge Measurements.

J. Atmos. Oceanic Technol., 20, 752-759.

Dickens, J., 2003: On the Retrieval of Drop Size Distribution by Vertically Pointing Radar, American Meteorological Society 32nd Radar Meteorology Conference, Albuquerque, NM, October 2005.

Duchon, C.E., and G.R. Essenberg, 2001: Comparative Precipitation Observations from Pit and Above Ground Rain Gauges with and without Wind Shields, Water Resources Research, Vol. 37, N. 12, 3253-3263.

Faulkner, E., T. Hampton, R.M. Rudolph, and Tomlinson, E.M., 2004: Technological Updates for PMP and PMF - Can They Provide Value for Dam Safety Improvements? Association of State Dam Safety Officials Annual Conference, Phoenix, Arizona, September 26-30, 2004.

Guo, J. C. Y., Urbonas, B., and Stewart, K., 2001: Rain Catch under Wind and Vegetal Effects. ASCE, Journal of Hydrologic Engineering, Vol. 6, No. 1.

Hunter, R.D. and R.K. Meentemeyer, 2005: Climatologically Aided Mapping of Daily Precipitation and Temperature, Journal of Applied Meteorology, October 2005, Vol. 44, pp. 1501-1510.

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Hunter, S.M., 1999: Determining WSR-88D Precipitation Algorithm Performance Using The Stage III Precipitation Processing System, Next Generation Weather Radar Program, WSR-88D Operational Support Facility, Norman, OK.

Lakshmanan, V. and M. Valente, 2004: Quality control of radar reflectivity data using satellite data and surface observations, 20th Intl Conf. on Inter. Inf. Proc. Sys.

(IIPS) for Meteor., Ocean., and Hydr., Amer. Meteor. Soc., Seattle, CD-ROM, 12.2.

Martner, B.E, and V. Dubovskiy, 2005: Z-R Relations from Raindrop Disdrometers:

Sensitivity To Regression Methods And DSD Data Refinements, 32nd Radar Meteorology Conference, Albuquerque, NM, October, 2005 Tokay, A., P.G. Bashor, and V.L. McDowell, 2010: Comparison of Rain Gauge Measurements in the Mid-Atlantic Region. J. Hydrometeor., 11, 553-565.

Tomlinson, E.M., W.D. Kappel, T.W. Parzybok, B. Rappolt, 2006: Use of NEXRAD Weather Radar Data with the Storm Precipitation Analysis System (SPAS) to Provide High Spatial Resolution Hourly Precipitation Analyses for Runoff Model Calibration and Validation, ASDSO Annual Conference, Boston, MA.

Tomlinson, E.M., and T.W. Parzybok, 2004: Storm Precipitation Analysis System (SPAS), proceedings of Association of Dam Safety Officials Annual Conference, Technical Session II, Phoenix, Arizona.

Tomlinson, E.M., R.A. Williams, and T.W. Parzybok, September 2003: Site-Specific Probable Maximum Precipitation (PMP) Study for the Great Sacandaga Lake /

Stewarts Bridge Drainage Basin, Prepared for Reliant Energy Corporation, Liverpool, New York.

Tomlinson, E.M., R.A. Williams, and T.W. Parzybok, September 2003: Site-Specific Probable Maximum Precipitation (PMP) Study for the Cherry Creek Drainage Basin, Prepared for the Colorado Water Conservation Board, Denver, CO.

Tomlinson, E.M., Kappel W.D., Parzybok, T.W., Hultstrand, D., Muhlestein, G., and B.

Rappolt, May 2008: Site-Specific Probable Maximum Precipitation (PMP) Study for the Wanahoo Drainage Basin, Prepared for Olsson Associates, Omaha, Nebraska.

Tomlinson, E.M., Kappel W.D., Parzybok, T.W., Hultstrand, D., Muhlestein, G., and B.

Rappolt, June 2008: Site-Specific Probable Maximum Precipitation (PMP)

Study for the Blenheim Gilboa Drainage Basin, Prepared for New York Power Authority, White Plains, NY.

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Tomlinson, E.M., Kappel W.D., and T.W. Parzybok, February 2008: Site-Specific Probable Maximum Precipitation (PMP) Study for the Magma FRS Drainage Basin, Prepared for AMEC, Tucson, Arizona.

Tomlinson, E.M., Kappel W.D., Parzybok, T.W., Hultstrand, D., Muhlestein, G., and P.

Sutter, December 2008: Statewide Probable Maximum Precipitation (PMP)

Study for the state of Nebraska, Prepared for Nebraska Dam Safety, Omaha, Nebraska.

Tomlinson, E.M., Kappel, W.D., and Tye W. Parzybok, July 2009: Site-Specific Probable Maximum Precipitation (PMP) Study for the Scoggins Dam Drainage Basin, Oregon.

Tomlinson, E.M., Kappel, W.D., and Tye W. Parzybok, February 2009: Site-Specific Probable Maximum Precipitation (PMP) Study for the Tuxedo Lake Drainage Basin, New York.

Tomlinson, E.M., Kappel, W.D., and Tye W. Parzybok, February 2010: Site-Specific Probable Maximum Precipitation (PMP) Study for the Magma FRS Drainage Basin, Arizona.

Tomlinson, E.M., Kappel W.D., Parzybok, T.W., Hultstrand, D.M., Muhlestein, G.A.,

March 2011: Site-Specific Probable Maximum Precipitation Study for the Tarrant Regional Water District, Prepared for Tarrant Regional Water District, Fort Worth, Texas.

Tomlinson, E.M., Kappel, W.D., Hultstrand, D.M., Muhlestein, G.A., and T. W.

Parzybok, November 2011: Site-Specific Probable Maximum Precipitation (PMP) Study for the Lewis River basin, Washington State.

Tomlinson, E.M., Kappel, W.D., Hultstrand, D.M., Muhlestein, G.A., and T. W.

Parzybok, December 2011: Site-Specific Probable Maximum Precipitation (PMP) Study for the Brassua Dam basin, Maine.

U.S. Weather Bureau, 1946: Manual for Depth-Area-Duration analysis of storm precipitation. Cooperative Studies Technical Paper No. 1, U.S. Department of Commerce, Weather Bureau, Washington, D.C., 73pp.

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Appendix F PMP Short Storm List Storm Data F- 1

Appendix F:

All Season Short Storm List Storm Analyses Appendix F: Table F.1: List of storm used in the All Season PMP development Page 1 of 346

Boulder, CO, AWA 86 September 8, 2013 Storm Type: Frontal Grid Point Used: 21-22 Page 2 of 346

Appendix F: Table F.2: Storm spreadsheet for Boulder, CO September 8, 2013 Page 3 of 346

Appendix F: Table F.3: Depth-area-duration values for Boulder, CO September 8, 2013 Appendix F: Figure F.1: Depth-area-duration chart for Boulder, CO September 8, 2013 Page 4 of 346

Appendix F: Figure F.2: Mass curve chart for Boulder, CO September 8, 2013 Page 5 of 346

Appendix F: Figure F.3: Total storm isohyetal analysis for Boulder, CO September 8, 2013 Page 6 of 346

Cheyenne Mountain, CO, AWA 85 September 8, 2013 Storm Type: Frontal Grid Point Used: 14, 21-22 Page 7 of 346

Appendix F: Table F.4: Storm spreadsheet for Cheyenne Mountain, CO September 8, 2013 Page 8 of 346

Appendix F: Table F.5: Depth-area-duration values for Cheyenne Mountain, CO September 8, 2013 Appendix F: Figure F.4: Depth-area-duration chart for Cheyenne Mountain, CO September 8, 2013 Page 9 of 346

Appendix F: Figure F.5: Mass curve chart for Cheyenne Mountain, CO September 8, 2013 Page 10 of 346

Appendix F: Figure F.6: Total storm isohyetal analysis for Cheyenne Mountain, CO September 8, 2013 Page 11 of 346

Dubuque, IA, AWA 1 July 27, 2011 Storm Type: Frontal/MCC Grid Point Used: 8-10, 16-18 Page 12 of 346

Appendix F: Table F.6: Storm spreadsheet for Dubuque, IA July 27, 2011 Page 13 of 346

Appendix F: Table F.7: Depth-area-duration values for Dubuque, IA July 27, 2011 Appendix F: Figure F.7: Depth-area-duration chart for Dubuque, IA July 27, 2011 Page 14 of 346

Appendix F: Figure F.8: Mass curve chart for Dubuque, IA July 27,2011 Page 15 of 346

Appendix F: Figure F.9: Total storm isohyetal analysis for Dubuque, IA July 2011 Page 16 of 346

Warner Park, TN, AWA 2 April 30, 2010 Storm Type: Frontal/MCC Grid Points Used: 1-3, 8-10, 16-17 Page 17 of 346

Appendix F: Table F.8: Storm spreadsheet for Warner Park, TN May 30, 2010 Page 18 of 346

Appendix F: Table F.9: Depth-area-duration values for Warner Park, TN, May 30, 2010 Appendix F: Figure F.10: Depth-area-duration chart for Warner Park, TN, May 30, 2010 Page 19 of 346

Appendix F: Figure F.11: Mass curve chart for Warner Park, TN, May 30, 2010 Page 20 of 346

Appendix F: Figure F.12: Total storm isohyetal analysis for Warner Park, TN, May 30, 2010 Page 21 of 346

Alley Spring, MO, AWA 3 March 17, 2008 Storm Type: Frontal Grid Points Used: 8-10, 16-17 Page 22 of 346

Appendix F: Table F.10: Storm spreadsheet for Alley Spring, MO, March 17, 2008 Page 23 of 346

Appendix F: Table F.11: Depth-area-duration values for Alley Spring, MO March 17, 2008 Appendix F: Figure F.13: Depth-area-duration chart for Alley Spring, MO March 17, 2008 Page 24 of 346

Appendix F: Figure F.14: Mass curve chart for Alley Spring, MO March 17, 2008 Page 25 of 346

Appendix F: Figure F.15: Total storm isohyetal analysis for Alley Spring, MO March 2008 Page 26 of 346

Larto Lake, LA, AWA 4 September 1, 2008 Storm Type: Tropical Grid Points Used: 1-2 Page 27 of 346

Appendix F: Table F.12: Storm spreadsheet for Larto Lake, LA, September 1, 2008 Page 28 of 346

Appendix F: Table F.13: Depth-area-duration values for Larto Lake, LA, September 1, 2008 Appendix F: Figure F.16: Depth-area-duration chart for Larto Lake, LA, September 1, 2008 Page 29 of 346

Appendix F: Figure F.17: Mass curve chart for Larto Lake, LA, September 1, 2008 Page 30 of 346

Appendix F: Figure F.18 Total storm isohyetal analysis for Larto Lake, LA, September 1, 2008 Page 31 of 346

Fall River, KS, AWA 5 June 30, 2007 Storm Type: Frontal/MCC Grid Points Used: 1-4, 8-11, 16-18 Page 32 of 346

Appendix F: Table F.14: Storm spreadsheet for Fall River, KS, June 30, 2007 Page 33 of 346

Appendix F: Table F.15: Depth-area-duration values for Fall River, KS June 30, 2007 Appendix F: Figure F.19: Depth-area-duration chart for Fall River, KS June 30, 2007 Page 34 of 346

Appendix F: Figure F.20: Mass curve chart for Fall River, KS June 30, 2007 Page 35 of 346

Appendix F: Figure F.21: Total storm isohyetal analysis for Fall River, KS June 30, 2007 Page 36 of 346

Hokah, MN, AWA 6 August 18, 2007 Storm Type: Frontal/MCC Grid Points Used: 8-11, 16-18 Page 37 of 346

Appendix F: Table F.16: Storm spreadsheet for Hokah, MN, August 18, 2007 Page 38 of 346

Appendix F: Table F.17: Depth-area-duration values for Hokah, MN, June 2007 Appendix F: Figure F.22: Depth-area-duration chart for Hokah, MN, June 2007 Page 39 of 346

Appendix F: Figure F.23: Mass curve chart for Hokah, MN, June 2007 Page 40 of 346

Appendix F: Figure F.24: Total storm isohyetal analysis for Hokah, MN, June 2007 Page 41 of 346

Ogallala, NE, AWA 7 July 6, 2002 Storm Type: MCC Grid Points Used: 5, 12, 19-20 Page 42 of 346

Appendix F: Table F.18: Storm spreadsheet for Ogallala, NE, July 6, 2002 Page 43 of 346

Appendix F: Table F.19: Depth-area-duration values for Ogallala, NE, July 6, 2002 Appendix F: Figure F.25: Depth-area-duration chart for Ogallala, NE, July 6, 2002 Page 44 of 346

Appendix F: Figure F.26: Mass curve chart for Ogallala, NE, July 6, 2002 Page 45 of 346

Appendix F: Figure F.27: Total storm isohyetal analysis for Ogallala, NE, July 6, 2002 Page 46 of 346

Fort Collins, CO, AWA 8 July 28, 1997 Storm Type: MCC Grid Points Used: 6, 13, 15, 21 Page 47 of 346

Appendix F: Table F.20: Storm spreadsheet for Fort Collins, CO, July 28, 1997 Page 48 of 346

Appendix F: Table F.21: Depth-area-duration values for Fort Collins, CO, July 28, 1997 Appendix F: Figure F.28: Depth-area-duration chart for Fort Collins, CO, July 28, 1997 Page 49 of 346

Appendix F: Figure F.29: Mass curve chart for Fort Collins, CO, July 28, 1997 Page 50 of 346

Appendix F: Figure F.30: Total storm isohyetal analysis for Fort Collins, CO, July 28, 1997 Page 51 of 346

Pawnee Creek, CO, AWA 9 July 29, 1997 Storm Type: MCC Grid Points Used: 5-6, 12, 15, 19-20 Page 52 of 346

Appendix F: Table F.22: Storm spreadsheet for Pawnee Creek, CO, July 29, 1997 Page 53 of 346

Appendix F: Table F.23: Depth-area-duration values for Pawnee Creek, CO, July 29, 1997 1-SPAS #1036 DAD Curves hour Pawnee Creek, CO July 29 - 30, 1997 10,000 2-hour 1,000 3-hour Area (mi2) 100 4-hour 10 5-hour 1

0 2 4 6 8 10 12 14 16 Maximum Average Depth of Precipitation (inches)

Appendix F: Figure F.31: Depth-area-duration chart for Pawnee Creek, CO, July 29, 1997 Page 54 of 346

Appendix F: Figure F.32: Mass curve chart for Pawnee Creek, CO, July 29, 1997 Page 55 of 346

Appendix F: Figure F.33: Total storm isohyetal analysis for Pawnee Creek, CO, July 29, 1997 Page 56 of 346

Aurora College, IL, AWA 10 July 16, 1996 Storm Type: Frontal/MCC Grid Points Used: 1-3, 8-10, 16-17 Page 57 of 346

Appendix F: Table F.24: Storm spreadsheet for Aurora College, IL, July 16, 1996 Page 58 of 346

Appendix F: Table F.25: Depth-area-duration values for Aurora College, IL, July 16, 1996 Appendix F: Figure F.34: Depth-area-duration chart for Aurora College, IL, July 16, 1996 Page 59 of 346

Appendix F: Figure F.35: Mass curve chart for Aurora College, IL, July 16, 1996 Page 60 of 346

Appendix F: Figure F.36: Total storm isohyetal analysis for Aurora College, IL July 1996 Page 61 of 346

Minneapolis, MN, AWA 11 July 23, 1987 Storm Type: MCC Grid Points Used: 8-11, 16-18 Page 62 of 346

Appendix F: Table F.26: Storm spreadsheet for Minneapolis, MN July 23, 1987 Page 63 of 346

Appendix F: Table F.27: Depth-area-duration values for Minneapolis, MN July 23, 1987 Appendix F: Figure F.37: Depth-area-duration chart for Minneapolis, MN July 23, 1987 Page 64 of 346

Appendix F: Figure F.38: Mass curve chart for Minneapolis, MN July 23, 1987 Page 65 of 346

Appendix F: Figure F.39: Total storm isohyetal analysis Minneapolis, MN July 23, 1987 Page 66 of 346

Big Rapids, MI, AWA 12 September 9, 1986 Storm Type: Frontal Grid Points Used: 8-11, 16-18 Page 67 of 346

Appendix F: Table F.28: Storm spreadsheet for Big Rapids, MI September 9, 1986 Page 68 of 346

Appendix F: Table F.29: Depth-area-duration values for Big Rapids, MI September 9, 1986 Appendix F: Figure F.40: Depth-area-duration chart for Big Rapids, MI September 9, 1986 Page 69 of 346

Appendix F: Figure F.41: Mass curve chart for Big Rapids, MI September 9, 1986 Page 70 of 346

Appendix F: Figure F.42: Total storm isohyetal analysis for Big Rapids, MI September 9, 1986 Page 71 of 346

Cheyenne, WY, AWA 13 August 1, 1985 Storm Type: Thunderstorm Grid Points Used: 6, 13, 15, 21 Page 72 of 346

Appendix F: Table F.30: Storm spreadsheet for Cheyenne, WY, August 1, 1985 Page 73 of 346

Appendix F: Table F.31: Depth-area-duration values for Cheyenne, WY, August 1, 1985 Appendix F: Figure F.43: Depth-area-duration chart for Cheyenne, WY, August 1, 1985 Page 74 of 346

Appendix F: Figure F.44: Mass curve chart for Cheyenne, WY, August 1, 1985 Page 75 of 346

Appendix F: Figure F.45: Total storm isohyetal analysis for Cheyenne, WY, August 1, 1985 Page 76 of 346

Forest City, MN, AWA 14 June 20, 1983 Storm Type: MCC Grid Points Used: 8-11, 16-18 Page 77 of 346

Appendix F: Table F.32: Storm spreadsheet for Forest City, MN, June 20, 1983 Page 78 of 346

Appendix F: Table F.33 and Figure F.46: Depth-area-duration values and Depth-area-duration chart for Forest City, MN, June 20, 1983 Page 79 of 346

Appendix F: Figure F.47: Mass curve chart for Forest City, MN, June 20, 1983 Page 80 of 346

Appendix F: Figure F.48: Total storm isohyetal analysis for Forest City, MN June 20, 1983 Page 81 of 346

Big Fork, AR, AWA 15 December 1, 1982 Storm Type: Frontal Grid Points Used: 1-3, 8-10, 16-17 Page 82 of 346

Appendix F: Table F.34: Storm spreadsheet for Big Fork, AR, December 1, 1982 Page 83 of 346

Appendix F: Table F.35: Depth-area-duration values for Big Fork, AR, December 1, 1982 Appendix F: Figure F.49: Depth-area-duration chart for Big Fork, AR, December 1, 1982 Page 84 of 346

Appendix F: Figure F.50: Mass curve chart for Big Fork, AR, December 1, 1982 Page 85 of 346

Appendix F: Figure F.51: Total storm isohyetal analysis for Big Fork, AR, December 1, 1982 Page 86 of 346

Clyde, TX, AWA 65, SPAS 1184 October 10, 1981 Storm Type: Frontal/Tropical Grid Points Used: 8, 10-11, 16-18 Page 87 of 346

Appendix F: Table F.36: Storm spreadsheet for Clyde, TX, October 10, 1981 Page 88 of 346

Appendix F: Table F.37: Depth-area-duration values for Clyde, TX, October 10, 1981 Appendix F: Figure F.52: Depth-area-duration chart for Clyde, TX, October 10, 1981 Page 89 of 346

Appendix F: Figure F.53: Mass curve chart for Clyde, TX, October 10, 1981 Page 90 of 346

Appendix F: Figure F.54: Total storm isohyetal analysis for Clyde, TX, October 10, 1981 Page 91 of 346

Frijole Creek, CO, AWA 17 July 3, 1981 Storm Type: Thunderstorm Grid Points Used: 6, 13, 15, 21 Page 92 of 346

Appendix F: Table F.38: Storm spreadsheet for Frijole Creek, CO, July 3, 1981 Page 93 of 346

Appendix F: Table F.39: Depth-area-duration values for Frijole Creek, CO, July 3, 1981 Appendix F: Figure F.55: Depth-area-duration chart for Frijole Creek, CO, July 3, 1981 Page 94 of 346

Appendix F: Figure F.56: Mass curve chart for Frijole Creek, CO, July 3, 1981 Page 95 of 346

Appendix F: Figure F.57: Total storm isohyetal analysis for Frijole Creek, CO, July 3, 1981 Page 96 of 346

Albany, TX AWA 18 August 3, 1978 Storm Type: Frontal/Tropical Grid Points Used: 2-4 Page 97 of 346

Appendix F: Table F.40: Storm spreadsheet for Albany, TX, August 3, 1978 Page 98 of 346

Appendix F: Table F.41: Depth-area-duration values for Albany, TX, August 3, 1978 Appendix F: Figure F.58: Depth-area-duration chart for Albany, TX, August 3, 1978 Page 99 of 346

Appendix F: Figure F.59: Mass curve chart for Albany, TX, August 3, 1978 Page 100 of 346

Appendix F: Figure F.60. Total storm isohyetal analysis for Albany, TX, August 3, 1978 Page 101 of 346

Big Thompson Canyon, CO, AWA 19 July, 31, 1976 Storm Type: 7, 13-14, 21 Page 102 of 346

Appendix F: Table F.42: Storm spreadsheet for Big Thompson Canyon, CO, July 31, 1976 Page 103 of 346

Appendix F: Table F.43: Depth-area-duration values for Big Thompson Canyon, CO, July 31, 1976 Appendix F: Figure F.61: Depth-area-duration chart for Big Thompson Canyon, CO, July 31, 1976 Page 104 of 346

Appendix F: Figure F.62: Mass curve chart for Big Thompson Canyon, CO, July 31, 1976 Page 105 of 346

Appendix F: Figure F.63: Total storm isohyetal analysis for Big Thompson Canyon, CO, July 31, 1976 Page 106 of 346

Waterton Red Rock, Alberta, AWA 20 June 14, 1921 Storm Type: Frontal Grid Points Used: 7, 14, 21-22 Page 107 of 346

Appendix F: Table F.45: Storm spreadsheet for Waterton Red Rocks, AB, June 14, 1975 Page 108 of 346

Appendix F: Table F.46: Depth-area-duration values for Waterton Red Rocks, AB, June 14, 1975 Appendix F: Figure F.64: Depth-area-duration chart for Waterton Red Rocks, AB, June 14, 1975 Page 109 of 346

Appendix F: Figure F.65: Mass curve chart for Waterton Red Rocks, AB, June 14, 1975 Page 110 of 346

Appendix F: Figure F.66: Total storm isohyetal analysis for Waterton Red Rocks, AB, June 14, 1975 Page 111 of 346

Enid, OK, AWA 21 October 10, 1973 Storm Type: MCC Grid Points Used: 1-4, 8-11, 16-18 Page 112 of 346

Appendix F: Table F.47: Storm spreadsheet for Enid, OK, October 10, 1973 Page 113 of 346

Appendix F: Table F.48: Depth-area-duration values Appendix F: Figure F.63: Depth-area-duration chart for Enid, OK, October 10, 1973 Page 114 of 346

Appendix F: Figure F.67: Mass curve chart for Enid, OK, October 10, 1973 Page 115 of 346

Appendix F: Figure F.68: Total storm isohyetal analysis for Enid, OK, October 10, 1973 Page 116 of 346

Bayfield, CO, AWA 22 September 3, 1970 Storm Type: Remnant Tropical Grid Points Used: 22 Page 117 of 346

Appendix F: Table F.49 Page 118 of 346

Appendix F: Table F.50: Depth-area-duration values for Bayfield, CO, September 3, 1970 Appendix F: Figure F.69: Depth-area-duration chart for Bayfield, CO, September 3, 1970 Page 119 of 346

Appendix F: Figure F.70: Mass curve chart for Bayfield, CO, September 3, 1970 Page 120 of 346

Appendix F: Figure F.71: Total storm isohyetal analysis for Bayfield, CO, September 3, 1970 Page 121 of 346

Big Elk Meadow, CO, AWA 23 May 4, 1969 Storm Type: Frontal Grid Points Used: 7, 13-14, 21 Page 122 of 346

Appendix F: Table F.51: Storm spreadsheet for Big Elk Meadow, CO, May 4, 1969 Page 123 of 346

Appendix F: Table F.52: Depth-area-duration values for Big Elk Meadow, CO, May 4, 1969 Appendix F: Figure F.72: Depth-area-duration chart for Big Elk Meadow, CO, May 4, 1969 Page 124 of 346

Appendix F: Figure F.73: Mass curve chart for Big Elk Meadow, CO, May 4, 1969 Page 125 of 346

Appendix F: Figure F.74: Total storm isohyetal analysis for Big Elk Meadow, CO, May 4, 1969 Page 126 of 346

Wooster, OH, AWA 24 July 4, 1969 Storm Type: Frontal/MCC Grid Points Used: 8-11, 16-18 Page 127 of 346

Appendix F: Table F.53: Storm spreadsheet for Wooster, OH July 4, 1969 Page 128 of 346

Appendix F: Table F.54: Depth-area-duration values for Wooster, OH July 4, 1969 Appendix F: Figure F.74: Depth-area-duration chart for Wooster, OH July 4, 1969 Page 129 of 346

Appendix F: Figure F.75: Mass curve chart for Wooster, OH July 4, 1969 Page 130 of 346

Appendix F: Figure F.76: Total storm isohyetal analysis for Wooster, OH July 4, 1969 Page 131 of 346

Gladewater, TX, AWA 25 April 27, 1966 Storm Type: Frontal Grid Points Used: 1-3, 8-10, 16-17 Page 132 of 346

Appendix F: Table F.55: Storm spreadsheet for Gladewater, TX, April 27, 1966 Page 133 of 346

Appendix F: Table F.56: Depth-area-duration values for Gladewater, TX, April 27, 1966 Appendix F: Figure F.77: Depth-area-duration chart for Gladewater, TX, April 27, 1966 Page 134 of 346

Appendix F: Figure F.78: Mass curve chart for Gladewater, TX, April 27, 1966 Page 135 of 346

Appendix F: Figure F.79: Total storm isohyetal analysis for Gladewater, TX, April 27, 1966 Page 136 of 346

Edgerton, MO, AWA 26 July 18, 1965 Storm Type: Frontal/MCC Grid Points Used: 1-4, 8-11, 16-18 Page 137 of 346

Appendix F: Table F.57: Storm spreadsheet for Edgerton, MO July 18, 1965 Page 138 of 346

Appendix F: Table F.58: Depth-area-duration values for Edgerton, MO July 18, 1965 Appendix F: Figure F.80: Depth-area-duration chart for Edgerton, MO July 18, 1965 Page 139 of 346

Appendix F: Figure F.81: Mass curve chart for Edgerton, MO July 18, 1965 Page 140 of 346

Appendix F: Figure F.82: Total storm isohyetal analysis for Edgerton, MO July 18, 1965 Page 141 of 346

Holly, CO, AWA 27 June 16, 1965 Storm Type: MCC Grid Points Used: 5-6, 12, 15, 19-20 Page 142 of 346

Appendix F: Table F.59: Storm spreadsheet for Holly, CO, June 16, 1965 Page 143 of 346

Appendix F: Table F.60: Depth-area-duration values for Holly, CO, June 16, 1965 Appendix F: Figure F.83: Depth-area-duration chart for Holly, CO, June 16, 1965 Page 144 of 346

Appendix F: Figure F.84: Mass curve chart for Holly, CO, June 16, 1965 Page 145 of 346

Appendix F: Figure F.85: Total storm isohyetal analysis for Holly, CO, June 16, 1965 Page 146 of 346

Plum Creek, CO, AWA 28 June 15, 1965 Storm Type: MCC Grid Points Used: 6, 13, 15, 21 Page 147 of 346

Appendix F: Table F.61: Storm spreadsheet for Plum Creek, CO, June 15, 1965 Page 148 of 346

Appendix F: Table F.62: Depth-area-duration values for Plum Creek, CO, June 15, 1965 Appendix F: Figure F.86: Depth-area-duration chart for Plum Creek, CO, June 15, 1965 Page 149 of 346

Appendix F: Figure F.87: Mass curve chart for Plum Creek, CO, June 15, 1965 Page 150 of 346

Appendix F: Figure F.88: Total storm isohyetal analysis for Plum Creek, CO, June 15, 1965 Page 151 of 346

College Hill, OH, AWA 30 June 3, 1963 Storm Type: MCC Grid Points Used: 8 Page 152 of 346

Appendix F: Table F.63: Storm spreadsheet for College Hill, OH, June 3, 1963 Page 153 of 346

Appendix F: Table F.64: Depth-area-duration values for College Hill, OH June 3, 1963 Appendix F: Figure F.89: Depth-area-duration chart for College Hill, OH, June 3, 1963 Page 154 of 346

Appendix F: Figure F.90: Mass curve chart for College Hill, OH, June 3, 1963 Page 155 of 346

Appendix F: Figure F.91: Total storm isohyetal analysis for College Hill, OH June 1963 Page 156 of 346

David City, NE, AWA 31 June 24, 1963 Storm Type: MCC Grid Points Used:

Page 157 of 346

Appendix F: Table F.65: Storm spreadsheet for David City, NE June 24, 1963 Page 158 of 346

Appendix F: Table F.66: Depth-area-duration values David City, NE June 24, 1963 Appendix F: Figure C.92: Depth-area-duration chart for David City, NE June 24, 1963 Page 159 of 346

Appendix F: Figure F.93: Mass curve chart for David City, NE June 24, 1963 Page 160 of 346

Appendix F: Figure F.94: Total storm isohyetal analysis for David City, NE June 24, 1963 Page 161 of 346

Ida Grove, IA, AWA 32 August 30, 1962 Storm Type: Frontal/MCC Grid Points Used: 1-4, 8-11, 16-18 Page 162 of 346

Appendix F: Table F.67: Storm spreadsheet for Ida Grove, IA August 30, 1962 Page 163 of 346

Appendix F: Figure F.95 and Table F.68: Total storm isohyetal analysis and Depth-area-duration values for Ida Grove, IA August 30, 1962 Page 164 of 346

Prague, NE, AWA 33 August 1, 1959 Storm Type: Frontal/MCC Grid Points Used: 2-4, 8-11, 16-18 Page 165 of 346

Appendix F: Table F.69: Storm spreadsheet for Prague, NE August 1, 1959 Page 166 of 346

Appendix F: Table F.70 and Figure F.96: Depth-area-duration values and Depth-area-duration chart for Prague, NE August 1, 1959 Page 167 of 346

Appendix F: Figure F.97: Mass curve chart for Prague, NE August 1, 1959 Page 168 of 346

Appendix F: Figure F.98: Total storm isohyetal analysis for Prague, NE August 1, 1959 Page 169 of 346

Paris Waterworks, IN, AWA 34 June 27, 1957 Storm Type: Frontal/Tropical Grid Points Used: 1-2, 8-9, 16 Page 170 of 346

Appendix F: Table F.71: Storm spreadsheet for Paris Waterworks, IN June 27, 1957 Page 171 of 346

Appendix F: Table F.72: Depth-area-duration values for Paris Waterworks, IN June 27, 1957 Page 172 of 346

Table F.73 and Figure F.99: Depth-area-duration Table and synoptic analysis for Paris Waterworks, IN June 27, 1957 Page 173 of 346

Lake Maloya, NM, AWA 35 May 19, 1955 Storm Type: Frontal Grid Points Used: 7, 13-14, 21 Page 174 of 346

Appendix F: Table F.74: Storm spreadsheet for Lake Maloya, NM, May 19, 1955 Page 175 of 346

Appendix F: Table F.75: Depth-area-duration values for Lake Maloya, NM, May 19, 1955 Appendix F: Figure F.100: Depth-area-duration chart for Lake Maloya, NM, May 19, 1955 Page 176 of 346

Appendix F: Figure F.101: Mass curve chart for Lake Maloya, NM, May 19, 1955 Page 177 of 346

Appendix F: Figure F.102: Total storm isohyetal analysis Lake Maloya, NM, May 19, 1955 Page 178 of 346

Ritter, IA, AWA 36 June 7, 1953 Storm Type: MCC Grid Points Used: 1-4, 8-11, 16-18 Page 179 of 346

Appendix F: Table F.76: Storm spreadsheet for Ritter, IA June 7, 1953 Page 180 of 346

Appendix F: Table F.77: Depth-area-duration values for Ritter, IA June 7, 1953 Page 181 of 346

Appendix F: Figure F.103 and Figure F.104: Total storm isohyetal analysis and Mass curve chart for Ritter, IA June 7, 1953 Page 182 of 346

Kelso, MO, AWA 37 August 11, 1952 Storm Type: MCC Grid Points Used: 1-3, 8-10, 16-17 Page 183 of 346

Appendix F: Table F.78: Storm spreadsheet for Kelso, MO August 11, 1952 Page 184 of 346

Appendix F: Table F.79: Depth-area-duration chart for Kelso, MO August 11, 1952 Page 185 of 346

Appendix F: Figure F.105 and Figure F.106: Total storm isohyetal and Mass curve chart for Kelso, MO August 11, 1952 Page 186 of 346

Council Grove, KS, AWA 38 July 9, 1951 Storm Type: Frontal Grid Pints Used: 1-4, 8-11, 16-18 Page 187 of 346

Appendix F: Table F.80: Storm spreadsheet for Council Grove, KS July 9, 1951 Page 188 of 346

Appendix F: Table F.81: Depth-area-duration values for Council Grove, KS July 9, 1951 Page 189 of 346

Appendix F: Figure F.107 and Figure F.108: Total storm isohyetal analysis and mass curve chart for Council Grove, KS July 9, 1951 Page 190 of 346

Dumont, IA, AWA 39 June 25, 1951 Storm Type: MCC Grid Points Used: 1-4, 8-11, 16-18 Page 191 of 346

Appendix F: Table F.82: Storm spreadsheet for Dumont, IA June 25, 1951 Page 192 of 346

Appendix F: Table F.83: Depth-area-duration chart for Dumont, IA June 25, 1951 Page 193 of 346

Appendix F: Figure F.109 and Figure F.110: Total storm isohyetal and Mass curve chart for Dumont, IA June 25, 1951 Page 194 of 346

Holt, MO, AWA 40 June 18, 1947 Storm Type: MCC Grid Points Used: 1-4, 8-11, 16-18 Page 195 of 346

Appendix F: Table F.84: Storm spreadsheet for Holt, MO June 18, 1947 Page 196 of 346

Appendix F: Table F.85: Depth-area-duration chart for Holt, MO June 18, 1947 Page 197 of 346

Appendix F: Figure F.111 and Figure F.112: Total storm isohyetal and Mass curve chart for Holt, MO June 1947 Page 198 of 346

Cole Camp, MO, AWA 41 August 12, 1946 Storm Type: Frontal Grid Points Used: 1-3, 8-10, 16-17 Page 199 of 346

Appendix F: Table F.86: Storm spreadsheet for Cole Camp, MO August 12, 1946 Page 200 of 346

Appendix F: Table F.87: Depth-area-duration values for Cole Camp, MO August 12, 1946 Page 201 of 346

Appendix F: Figure F.113 and Figure F.114: Isohyetal map and mass curve chart for Cole Camp, MO August 12, 1946 Page 202 of 346

Collinsville, IL, AWA 42 August 12, 1946 Storm Type: Frontal Grid Points Used: 1-3, 8-10, 16-17 Page 203 of 346

Appendix F: Table F.88: Storm spreadsheet for Collinsville, IL August 12, 1946 Page 204 of 346

Appendix F: Table F.89: Depth-area-duration values for Collinsville, IL August 12, 1946 Page 205 of 346

Appendix F: Figure F.115 and Figure F.116: Isohyetal map and mass curve chart for Collinsville, IL August 12, 1946 Page 206 of 346

Stanton, NE, AWA 43 June 10, 1944 Storm Type: MCC Grid Points Used: 2-4, 8-11, 16-18 Page 207 of 346

Appendix F: Table F.89: Storm spreadsheet for Stanton, NE June, 10, 1944 Page 208 of 346

Appendix F: Table F.90: Depth-area-duration chart for Stanton, NE June 10, 1944 Page 209 of 346

Appendix F: Figure F.117 and Figure F.118: Isohyetal map and mass curve chart for Stanton, NE June 10, 1944 Page 210 of 346

Mounds, OK, AWA 44 May 16, 1943 Storm Type: MCC Grid Points Used: 1-4, 8-11, 16-18 Page 211 of 346

Appendix F: Table F.91: Storm spreadsheet for Mounds, OK May 16, 1943 Page 212 of 346

Appendix F: Table F.92: Depth-area-duration chart for Mounds, OK May 16, 1943 Page 213 of 346

Appendix F: Figure F.119 and Figure F.120: Isohyetal map and mass curve chart for Mounds, OK May 16, 1943 Page 214 of 346

Silver Lake, TX AWA 45 June 5, 1943 Storm Type: MCC Grid Points Used: 1-3, 8-10, 16-17 Page 215 of 346

Appendix F: Table F.92: Storm spreadsheet for Silver Lake, TX June 5, 1943 Page 216 of 346

Appendix F: Table F.93: Depth-area-duration chart for Silver Lake, TX June 5, 1943 Page 217 of 346

Appendix F: Figure F.121 and Figure F.122: Total storm isohyetal and mass curve chart for Silver Lake, TX June 5, 1943 Page 218 of 346

Warner, OK, AWA 46 May 6, 1943 Storm Type: Frontal Grid Points Used: 1-3, 8-10, 16-17 Page 219 of 346

Appendix F: Table F.94: Storm spreadsheet for Warner, OK May 6, 1943 Page 220 of 346

Appendix F: Table F.95: Depth-area-duration chart for Warner, OK May 6, 1943 Page 221 of 346

Appendix F: Figure F.123 and Figure F.124: Total storm isohyetal and mass curve chart for Warner, OK May 6, 1943 Page 222 of 346

Rancho Grande, NM, AWA 47 August 29, 1942 Storm Type: Tropical Grid Points Used: 6, 13 Page 223 of 346

Appendix F: Table F.96: Storm spreadsheet for Rancho Grande, NM August, 29, 1942 Page 224 of 346

Appendix F: Table F.97: Depth-area-duration values for Rancho Grande, NM August, 29, 1942 Page 225 of 346

Appendix F: Figure F.125 and Figure F.126: Total storm isohyetal analysis Mass curve chart for Rancho Grande, NM August, 29, 1942 Page 226 of 346

Hayward, WI, AWA 48 August 28, 1941 Storm Type: MCC Grid Points Used: 8-11, 16-18 Page 227 of 346

Appendix F: Table F.98: Storm spreadsheet for Hayward, WI August 28, 1941 Page 228 of 346

Appendix F: Table F.99: Depth-area-duration values for Hayward, WI August 28, 1941 Page 229 of 346

Appendix F: Figure F.127 and Figure F.128: Total storm isohyetal analysis and mass curve chart for Hayward, WI August 28, 1941 Page 230 of 346

McColleum Ranch, NM, AWA 49 September 20, 1941 Storm Type: Frontal Grid Points Used: 6, 13 Page 231 of 346

Appendix F: Table F.100: Storm spreadsheet for McColleum Ranch, NM September 20, 1941 Page 232 of 346

Appendix F: Table F.101: Depth-area-duration values for McColleum Ranch, NM September 20, 1941 Page 233 of 346

Appendix F: Figure F.129 and Figure F.130: Total storm isohyetal analysis Mass curve chart for McColleum Ranch, NM September 20, 1941 Page 234 of 346

Prairieview, NM, AWA 50 May 20, 1941 Storm Type: Frontal Grid Points Used: 5-6, 12 Page 235 of 346

Appendix F: Table F.102: Storm spreadsheet for Prairieview, NM May 20, 1941 Page 236 of 346

Appendix F: Table F.103: Depth-area-duration values for Prairieview, NM May 20, 1941 Page 237 of 346

Appendix F: Figure F.131 and Figure F.132: Total storm isohyetal analysis and mass curve chart for Prairieview, NM May 20, 1941 Page 238 of 346

Grant Township, NE, AWA 51 June 3, 1940 Storm Type: MCC Grid Points Used: 2-4, 8-11, 16-18 Page 239 of 346

Appendix F: Table F.104: Storm spreadsheet for Grant Township, NE June 3, 1940 Page 240 of 346

Appendix F: Table F.105: Depth-area-duration values for Grant Township, NE June 3, 1940 Page 241 of 346

Appendix F: Figure F.133 and Figure F.134: Total storm isohyetal analysis and mass curve chart for Grant Township, NE June 3, 1940 Page 242 of 346

Hallett, OK AWA 52 September 2, 1940 Storm Type: MCC Grid Points Used: 1-4, 8-11, 16-18 Page 243 of 346

Appendix F: Table F.106: Storm spreadsheet for Hallett, OK September 2, 1940 Page 244 of 346

Appendix F: Table F.107: Depth-area-duration values for Hallett, OK September 2, 1940 Page 245 of 346

Appendix F: Figure F.135 and Figure F.136: Total storm isohyetal analysis and mass curve chart for Hallett, OK September 2, 1940 Page 246 of 346

Hempstead, TX AWA 53 November 22, 1940 Storm Type: Frontal/MCC Grid Points Used: 1-3, 8-10 Page 247 of 346

Appendix F: Table F.108: Storm spreadsheet for Hempstead, TX November 22, 1940 Page 248 of 346

Appendix F: Table F.109: Depth-area-duration values for Hempstead, TX November 22, 1940 Page 249 of 346

Appendix F: Figure F.137 and Figure F.138: Total storm isohyetal analysis and mass curve chart for Hempstead, TX November 22, 1940 Page 250 of 346

Index, AR, AWA 54 June 30, 1940 Storm Type: MCC Grid Points Used: 13-, 8-10, 16-17 Page 251 of 346

Appendix F: Table F.110: Storm spreadsheet for Index, AR June 30, 1940 Page 252 of 346

Appendix F: Table F.111: Depth-area-duration values for Index, AR June 30, 1940 Page 253 of 346

Appendix F: Figure F.139 and Figure F.140: Total storm isohyetal analysis and mass curve chart for Index, AR June 30, 1940 Page 254 of 346

Elbert, CO, AWA 55 May 30, 1935 Storm Type: MCC Grid Points Used: 6, 13, 15, 21 Page 255 of 346

Appendix F: Table F.112: Storm spreadsheet for Cherry Creek-Elbert, CO May 30, 1935 Page 256 of 346

Appendix F: Table F.113: Depth-area-duration values for Cherry Creek-Elbert, CO May 30, 1935 Appendix F: Figure F.141: Depth-area-duration chart for Cherry Creek-Elbert, CO May 30, 1935 Page 257 of 346

Appendix F: Figure F.142: Mass curve chart for Cherry Creek-Elbert, CO May 30, 1935 Page 258 of 346

Appendix F: Figure F.143 Total storm isohyetal analysis Cherry Creek-Elbert, CO May 30, 1935 Page 259 of 346

Hale, CO, AWA 56 May 30, 1935 Storm Type: MCC Grid Points Used: 5-6, 12, 15, 19-20 Page 260 of 346

Appendix F: Table F.114: Storm spreadsheet for Hale, CO May 30, 1935 Page 261 of 346

Appendix F: Table F.115: Depth-area-duration values for Hale, CO May 30, 1935 Appendix F: Figure F.144: Total storm isohyetal analysis for Hale, CO May 30, 1935 Page 262 of 346

Appendix F: Figure F.145: Mass curve chart for Hale, CO May 30, 1935 Page 263 of 346

Appendix F: Figure F.146 Total storm isohyetal analysis Hale, CO May 30, 1935 Page 264 of 346

Cheyenne, OK, AWA 57 April 3, 1934 Storm Type: MCC Grid Points Used: 8, 10-11, 16-18 Page 265 of 346

Appendix F: Table F.116: Storm spreadsheet for Cheyenne, OK April 3, 1934 Page 266 of 346

Appendix F: Table F.117: Depth-area-duration values for Cheyenne, OK April 3, 1934 Page 267 of 346

Appendix F: Figure F.147 and Figure F.148: Total storm isohyetal analysis and mass curve chart for Cheyenne, OK April 3, 1934 Page 268 of 346

Fairfield, TX, AWA 58 August 30, 1932 Storm Type: MCC Grid Points Used: 1-3, 8-10 Page 269 of 346

Appendix F: Table F.118: Storm spreadsheet for Fairfield, TX August 30, 1932 Page 270 of 346

Appendix F: Table F.119: Depth-area-duration values for Fairfield, TX August 30, 1932 Page 271 of 346

Appendix F: Figure F.149 and Figure F.150: Total storm isohyetal analysis and mass curve chart for Fairfield, TX August 30, 1932 Page 272 of 346

Porter, NM, AWA 59 October 9, 1930 Storm Type: Frontal/Tropical Grid Points Used: 5-6, 12-13 Page 273 of 346

Appendix F: Table F.120: Storm spreadsheet for Porter, NM October 9, 1930 Page 274 of 346

Appendix F: Table F.121: Depth-area-duration values for Porter, NM October 9, 1930 Page 275 of 346

Appendix F: Figure F.151 and Figure F.152: Total storm isohyetal analysis and mass curve chart for Porter, NM October 9, 1930 Page 276 of 346

Boyden, IA, AWA 60, MR 4-24 September 17, 1926 Storm Type: MCC Grid Points Used: 1-4, 8-11, 16-18 Page 277 of 346

Appendix F: Table F.122: Storm spreadsheet for Boyden, IA September 17, 1926 Page 278 of 346

Appendix F: Table F.123: Depth-area-duration values for Boyden, IA September 17, 1926 Page 279 of 346

Appendix F: Figure F.153 and Figure F.154: Total storm isohyetal analysis and mass curve chart for Boyden, IA September 17, 1926 Page 280 of 346

Neosho Falls, KS, AWA 61 September 12, 1926 Storm Type: MCC Grid Points Used: 1-4, 8-11, 16-18 Page 281 of 346

Appendix F: Table F.124: Storm spreadsheet for Neosho Falls, KS September 12, 1926 Page 282 of 346

Appendix F: Table F.125: Depth-area-duration values for Neosho Falls, KS September 12, 1926 Page 283 of 346

Appendix F: Figure F.155 and Figure F.156: Total storm isohyetal analysis and mass curve chart for Neosho Falls, KS September 12, 1926 Page 284 of 346

Penrose, CO, AWA 62 June 2, 1921 Storm Type: MCC Grid Points Used: 6, 13, 15, 21 Page 285 of 346

Appendix F: Table F.126: Storm spreadsheet for Penrose, CO June 2, 1921 Page 286 of 346

Appendix F: Table F.127: Depth-area-duration values for Penrose, CO June 2, 1921 Appendix F: Figure F.157: Depth-area-duration chart for Penrose, CO June 2, 1921 Page 287 of 346

Appendix F: Figure F.158: Mass curve chart for Penrose, CO June 2, 1921 Page 288 of 346

Appendix F: Figure F.159: Total storm isohyetal analysis for Penrose, CO June 2, 1921 Page 289 of 346

Meek, NM, AWA 63 September 15, 1919 Storm Type: Frontal Grid Points Used: 6, 13 Page 290 of 346

Appendix F: Table F.128: Storm spreadsheet for Meek, NM September 15, 1919 Page 291 of 346

Appendix F: Table F.129: Depth-area-duration values for Meek, NM September 15, 1919 Page 292 of 346

Appendix F: Figure F.160 and Figure F.161: Total storm isohyetal analysis and mass curve chart for Meek, NM September 15, 1919 Page 293 of 346

Clayton, NM, AWA 64 April 29, 1914 Storm Type: Frontal Grid Points Used: 5-6, 12-13 Page 294 of 346

Appendix F: Table F.130: Storm spreadsheet for Clayton, NM April 29, 1914 Page 295 of 346

Appendix F: Table F.131: Depth-area-duration values for Clayton, NM April 29, 1914 Page 296 of 346

Appendix F: Figure F.162 and Figure F.163: Total storm isohyetal analysis and mass curve chart for Clayton, NM April 29, 1914 Page 297 of 346

Cooper, MI, AWA 65 August 31, 1914 Storm Type: MCC Grid Points Used: 8-10, 16-18 Page 298 of 346

Appendix F: Table F.132: Storm spreadsheet for Cooper, MI August 31, 1914 Page 299 of 346

Appendix F: Table F.133: Depth-area-duration values for Cooper, MI August 31, 1914 Page 300 of 346

Appendix F: Figure F.164 and Figure F.165: Total storm isohyetal analysis and mass curve chart for Cooper, MI August 31, 1914 Page 301 of 346

Wagon Wheel, CO, AWA 66 October 3, 1911 Storm Type: Remnant Tropical Grid Points Used: 22 Page 302 of 346

Appendix F: Table F.134: Storm spreadsheet for Wagon Wheel, CO October 3, 1911 Page 303 of 346

Appendix F: Table F.135: Depth-area-duration values for Wagon Wheel, CO October 3, 1911 Appendix F: Figure F.166: Depth-area-duration for Wagon Wheel, CO October 3, 1911 Page 304 of 346

Appendix F: Figure F.167: Mass curve chart for Wagon Wheel, CO October 3, 1911 Page 305 of 346

Appendix F: Figure F.168: Total storm isohyetal analysis for Wagon Wheel, CO October 3, 1911 Page 306 of 346

Beaulieu, MN, AWA 67 July 18, 1909 Storm Type: MCC Grid Points Used: 8-11, 16-18 Page 307 of 346

Appendix F: Table F.136: Storm spreadsheet for Beaulieu, MN July 18, 1909 Page 308 of 346

Appendix F: Table F.137: Depth-area-duration values for Beaulieu, MN July 18, 1909 Page 309 of 346

Appendix F: Figure F.169 and Figure F.170: Total storm isohyetal analysis and mass curve chart for Beaulieu, MN July 18, 1909 Page 310 of 346

Ironwood, MI, AWA 68 July 21, 1909 Storm Type: Frontal Grid Points Used:

Page 311 of 346

Appendix F: Table F.138: Storm spreadsheet for Ironwood, MI July 21, 1909 Page 312 of 346

Appendix F: Table F.139: Depth-area-duration values for Ironwood, MI July 21, 1909 Page 313 of 346

Appendix F: Figure F.171 and Figure F.172: Total storm isohyetal analysis and mass curve chart for Ironwood, MI July 21, 1909 Page 314 of 346

Meeker, OK, AWA 69 October 19, 1908 Storm Type: Frontal Page 315 of 346

Appendix F: Table F.140: Storm spreadsheet for Meeker, OK October 19, 1908 Page 316 of 346

Appendix F: Table F.141: Depth-area-duration values for Meeker, OK October 19, 1908 Page 317 of 346

Appendix F: Figure F.173 and Figure F.174: Total storm isohyetal analysis and mass curve chart for Meeker, OK October 19, 1908 Page 318 of 346

Bonaparte, IA, AWA 70 June 10, 1905 Storm Type: MCC Page 319 of 346

Appendix F: Table F.142: Storm spreadsheet for Bonaparte, IA June 10, 1905 Page 320 of 346

Appendix F: Table F.143: Depth-area-duration values for Bonaparte, IA June 10, 1905 Page 321 of 346

Appendix F: Figure F.175 and Figure F.176: Total storm isohyetal analysis and mass curve chart for Bonaparte, IA June 10, 1905 Page 322 of 346

Medford, WI, AWA 71 June 4, 1905 Storm Type: Frontal/MCC Grid Points Used: 8-11, 16-18 Page 323 of 346

Appendix F: Table F.144: Storm spreadsheet for Medford, WI June 4, 1905 Page 324 of 346

Appendix F: Table F.145: Depth-area-duration values for Medford, WI June 4, 1905 Page 325 of 346

Appendix F: Figure F.177 and Figure F.178: Total storm isohyetal analysis and mass curve chart for Medford, WI June 4, 1905 Page 326 of 346

Rociada, NM, AWA 72 September 26, 1904 Storm Type: Frontal Grid Points Used: 7, 13-14 Page 327 of 346

Appendix F: Table F.146: Storm spreadsheet for Rociada, NM September 26, 1904 Page 328 of 346

Appendix F: Table F.147: Depth-area-duration values for Rociada, NM September 26, 1904 Page 329 of 346

Appendix F: Figure F.179 and Figure F.180: Total storm isohyetal analysis and mass curve chart for Rociada, NM September 26, 1904 Page 330 of 346

Woodburn, IA, AWA 73 August 24, 1903 Storm Type: Frontal/MCC Grid Points Used: 1-4, 8-11, 16-18 Page 331 of 346

Appendix F: Table F.148: Storm spreadsheet for Woodburn, IA August 24, 1903 Page 332 of 346

Appendix F: Table F.149: Depth-area-duration values for Woodburn, IA August 24, 1903 Page 333 of 346

Appendix F: Figure F.181 and Figure F.182: Total storm isohyetal analysis and mass curve chart for Woodburn, IA August 24, 1903 Page 334 of 346

Lambert, MN, AWA 75, UMV 1-2 July 18, 1897 Storm Type: Frontal/MCC Page 335 of 346

Appendix F: Table F.150: Storm spreadsheet for Lambert, MN July 18, 1897 Page 336 of 346

Appendix F: Table F.151: Depth-area-duration values for Lambert, MN July 18, 1897 Page 337 of 346

Appendix F: Figure F.183 and Figure F.184: Total storm isohyetal analysis and mass curve chart for Lambert, MN July 18, 1897 Page 338 of 346

Greeley, NE, AWA 76 June 4, 1896 Storm Type: MCC Grid Points Used: 3-4, 8-11, 16-18 Page 339 of 346

Appendix F: Table F.152: Storm spreadsheet for Greeley, NE June 4, 1896 Page 340 of 346

Appendix F: Table F.153: Depth-area-duration values for Greeley, NE June 4, 1896 Page 341 of 346

Appendix F: Figure F.185 and Figure F.186: Total storm isohyetal analysis and mass curve chart for Greeley, NE June 4, 1896 Page 342 of 346

Larrabee, IA, AWA 77 September 10, 1891 Storm Type: MCC Grid Points Used: 1-4, 8-11, 16-18 Page 343 of 346

Appendix F: Table F.154: Storm spreadsheet for Larrabee, IA September 10, 1891 Page 344 of 346

Appendix F: Table F.155: Depth-area-duration values for Larrabee, IA September 10, 1891 Page 345 of 346

Appendix F: Figure F.187 and Figure F.188: Total storm isohyetal analysis and mass curve chart for Larrabee, IA September 10, 1891 Page 346 of 346

Appendix G LIP Short Storm List Storm Data G- 1

Appendix G:

Local Intense Precipitation Short Storm Analyses Appendix G: Table G.1: List of storms used in the Local Intense Precipitation PMP development Page 1 of 96

Warner Park, TN, AWA 2 April 30, 2010 Storm Type: Frontal/MCC Page 2 of 96

Appendix G: Table G.2: Storm spreadsheet for Warner Park, TN May 30, 2010 Page 3 of 96

Appendix G: Table G.3: Depth-area-duration values for Warner Park, TN, May 30, 2010 Appendix G: Figure G.1: Depth-area-duration chart for Warner Park, TN, May 30, 2010 Page 4 of 96

Appendix G: Figure G.2: Mass curve chart for Warner Park, TN, May 30, 2010 Appendix G: Figure G.3: Total storm isohyetal analysis for Warner Park, TN, May 30, 2010 Page 5 of 96

Larto Lake, LA, AWA 4 September 1, 2008 Storm Type: Tropical Page 6 of 96

Appendix G: Table G.4: Storm spreadsheet for Larto Lake, LA, September 1, 2008 Page 7 of 96

Appendix G: Table G.5: Depth-area-duration values for Larto Lake, LA, September 1, 2008 Appendix G: Figure G.4: Depth-area-duration chart for Larto Lake, LA, September 1, 2008 Page 8 of 96

Appendix G: Figure G.5: Mass curve chart for Larto Lake, LA, September 1, 2008 Page 9 of 96

Appendix G: Figure G.6: Total storm isohyetal analysis for Larto Lake, LA, September 1, 2008 Page 10 of 96

Fall River, KS, AWA 5 June 30, 2007 Storm Type: Frontal/MCC Page 11 of 96

Appendix G: Table G.6: Storm spreadsheet for Fall River, KS, June 30, 2007 Page 12 of 96

Appendix G: Table G.7: Depth-area-duration values for Fall River, KS June 30, 2007 Appendix G: Figure G.7: Depth-area-duration chart for Fall River, KS June 30, 2007 Page 13 of 96

Appendix G: Figure G.8: Mass curve chart for Fall River, KS June 30, 2007 Appendix G: Figure G.9: Total storm isohyetal analysis for Fall River, KS June 30, 2007 Page 14 of 96

Albany, TX AWA 18 August 3, 1978 Storm Type: Tropical Page 15 of 96

Appendix G: Table G.8: Storm spreadsheet for Albany, TX, August 3, 1978 Page 16 of 96

Appendix G: Table G.9: Depth-area-duration values for Albany, TX, August 3, 1978 Appendix G: Figure G.10: Depth-area-duration chart for Albany, TX, August 3, 1978 Page 17 of 96

Appendix G: Figure G.11: Mass curve chart for Albany, TX, August 3, 1978 Page 18 of 96

Appendix G: Figure G.12: Total storm isohyetal analysis for Albany, TX, August 3, 1978 Page 19 of 96

Enid, OK, AWA 21 October 10, 1973 Storm Type: MCC Page 20 of 96

Appendix G: Table G.10: Storm spreadsheet for Enid, OK, October 10, 1973 Page 21 of 96

Appendix G: Table G.11: and Appendix G: Figure G.13: Depth-area-duration values Depth-area-duration chart for Enid, OK, October 10, 1973 Page 22 of 96

Appendix G: Figure G.14: Mass curve chart for Enid, OK, October 10, 1973 Page 23 of 96

Appendix G: Figure G.15: Total storm isohyetal analysis for Enid, OK, October 10, 1973 Page 24 of 96

Wooster, OH, AWA 24 July 4, 1969 Storm Type: Frontal/MCC Page 25 of 96

Appendix G: Table G.12: Storm spreadsheet for Wooster, OH July 4, 1969 Page 26 of 96

Appendix G: Table G.13: Depth-area-duration values for Wooster, OH July 4, 1969 Appendix G: Figure G.16: Depth-area-duration chart for Wooster, OH July 4, 1969 Page 27 of 96

Appendix G: Figure G.17: Mass curve chart for Wooster, OH July 4, 1969 Page 28 of 96

Appendix G: Figure G.18: Total storm isohyetal analysis for Wooster, OH July 4, 1969 Page 29 of 96

Gladewater, TX, AWA 25 April 27, 1966 Storm Type: Frontal Page 30 of 96

Appendix G: Table G.14: Storm spreadsheet for Gladewater, TX, April 27, 1966 Page 31 of 96

Appendix G: Table G.15: Depth-area-duration values for Gladewater, TX, April 27, 1966 Appendix G: Figure G.19: Depth-area-duration chart for Gladewater, TX, April 27, 1966 Page 32 of 96

Appendix G: Figure G.20: Mass curve chart for Gladewater, TX, April 27, 1966 Page 33 of 96

Appendix G: Figure G.21: Total storm isohyetal analysis for Gladewater, TX, April 27, 1966 Page 34 of 96

Edgerton, MO, AWA 26 July 18, 1965 Storm Type: Frontal/MCC Page 35 of 96

Appendix G: Table G.16: Storm spreadsheet for Edgerton, MO July 18, 1965 Page 36 of 96

Appendix G: Table G.17: Depth-area-duration values for Edgerton, MO July 18, 1965 Appendix G: Figure G.22: Depth-area-duration chart for Edgerton, MO July 18, 1965 Page 37 of 96

Appendix G: Figure G.23: Mass curve chart for Edgerton, MO July 18, 1965 Page 38 of 96

Appendix G: Figure G.24: Total storm isohyetal analysis for Edgerton, MO July 18, 1965 Page 39 of 96

College Hill, OH, AWA 30 June 3, 1963 Storm Type: MCC Page 40 of 96

Appendix G: Table G.18: Storm spreadsheet for College Hill, OH, June 3, 1963 Page 41 of 96

Appendix G: Table G.19: Depth-area-duration values for College Hill, OH June 3, 1963 Appendix G: Figure G.25: Depth-area-duration chart for College Hill, OH, June 3, 1963 Page 42 of 96

Appendix G: Figure G.26: Mass curve chart for College Hill, OH, June 3, 1963 Page 43 of 96

Appendix G: Figure C.27: Total storm isohyetal analysis for College Hill, OH June 1963 Page 44 of 96

Camp Polk, LA, AWA 81 April 23, 1953 Storm Type: Frontal Page 45 of 96

Appendix G: Table G.20: Storm spreadsheet for Camp Polk, LA April 23, 1953 Page 46 of 96

Appendix G: Table G.21: Depth-area-duration values for Camp Polk, LA April 23, 1953 Page 47 of 96

Appendix G: Figure G.28 and Figure G.29: Total storm isohyetal analysis and mass curve chart for Camp Polk, LA April 23, 1953 Page 48 of 96

Harrisonburg Dam, LA, AWA 79 May 11, 1953 Storm Type: MCC Page 49 of 96

Appendix G: Table G.22: Storm spreadsheet for Harrisonburg Dam, LA May 11, 1953 Page 50 of 96

Appendix G: Table G.23: Depth-area-duration values for Harrisonburg Dam, LA May 11, 1953 Page 51 of 96

Appendix G: Figure G.30 and Figure G.31: Total storm isohyetal analysis and mass curve chart for Harrisonburg Dam, LA May 11, 1953 Page 52 of 96

Kelso, MO, AWA 37 August 11, 1952 Storm Type: MCC Page 53 of 96

Appendix G: Table G.24: Storm spreadsheet for Kelso, MO August 11, 1952 Page 54 of 96

Appendix G: Table G.25: Depth-area-duration chart for Kelso, MO August 11, 1952 Page 55 of 96

Appendix G: Figure G.32 and Figure G.33: Total storm isohyetal and Mass curve chart for Kelso, MO August 11, 1952 Page 56 of 96

Holt, MO, AWA 40 June 18, 1947 Storm Type: MCC Page 57 of 96

Appendix G: Table G.26: Storm spreadsheet for Holt, MO June 18, 1947 Page 58 of 96

Appendix G: Table G.27: Depth-area-duration chart for Holt, MO June 18, 1947 Page 59 of 96

Appendix G: Figure G.34 and Figure G.35: Total storm isohyetal and Mass curve chart for Holt, MO June 1947 Page 60 of 96

Collinsville, IL, AWA 42 August 12, 1946 Storm Type: Frontal Page 61 of 96

Appendix G: Table G.28: Storm spreadsheet for Collinsville, IL August 12, 1946 Page 62 of 96

Appendix G: Table G.29: Depth-area-duration values for Collinsville, IL August 12, 1946 Page 63 of 96

Appendix G: Figure G.36 and Figure G.37: Isohyetal map and mass curve chart for Collinsville, IL August 12, 1946 Page 64 of 96

Mounds, OK, AWA 44 May 16, 1943 Storm Type: MCC Page 65 of 96

Appendix G: Table G.30: Storm spreadsheet for Mounds, OK May 16, 1943 Page 66 of 96

Appendix G: Table G.31: Depth-area-duration chart for Mounds, OK May 16, 1943 Page 67 of 96

Appendix G: Figure G.38 and Figure G.39: Isohyetal map and mass curve chart for Mounds, OK May 16, 1943 Page 68 of 96

Silver Lake, TX AWA 45 June 5, 1943 Storm Type: MCC Page 69 of 96

Appendix G: Table G.32: Storm spreadsheet for Silver Lake, TX June 5, 1943 Page 70 of 96

Appendix G: Table G.33: Depth-area-duration chart for Silver Lake, TX June 5, 1943 Page 71 of 96

Appendix G: Figure G.40 and Figure G.41: Total storm isohyetal and mass curve chart for Silver Lake, TX June 5, 1943 Page 72 of 96

Hallett, OK AWA 52 September 2, 1940 Storm Type: MCC Page 73 of 96

Appendix G: Table G.34: Storm spreadsheet for Hallett, OK September 2, 1940 Page 74 of 96

Appendix G: Table G.35: Depth-area-duration values for Hallett, OK September 2, 1940 Page 75 of 96

Appendix G: Figure G.42 and Figure G.43: Total storm isohyetal analysis and mass curve chart for Hallett, OK September 2, 1940 Page 76 of 96

Engle, TX, AWA 83 June 29, 1940 Storm Type: MCC Page 77 of 96

Appendix G: Table G.36: Storm spreadsheet for Engle, TX June 29, 1940 Page 78 of 96

Appendix G: Table G.37: Depth-area-duration values for Engle, TX June 29, 1940 Page 79 of 96

Appendix G: Figure G.44 and Figure G.45: Total storm isohyetal analysis and mass curve chart for Engle, TX June 29, 1940 Page 80 of 96

Bebe, TX, AWA 84 June 30, 1936 Storm Type: MCC Page 81 of 96

Appendix G: Table G.38: Storm spreadsheet for Bebe, TX June 30, 1936 Page 82 of 96

Appendix G: Table G.39: Depth-area-duration values for Bebe, TX June 30, 1936 Page 83 of 96

Appendix G: Figure G.46 and Figure G.47: Total storm isohyetal analysis and mass curve chart for Bebe, TX June 30, 1936 Page 84 of 96

Neosho Falls, KS, AWA 61 September 12, 1926 Storm Type: MCC Page 85 of 96

Appendix G: Table G.40: Storm spreadsheet for Neosho Falls, KS September 12, 1926 Page 86 of 96

Appendix G: Table G.41: Depth-area-duration values for Neosho Falls, KS September 12, 1926 Page 87 of 96

Appendix G: Figure G.48 and Figure G.49: Total storm isohyetal analysis and mass curve chart for Neosho Falls, KS September 12, 1926 Page 88 of 96

THRALL, TX AWA 77 September 9, 1921 Storm Type: Tropical Page 89 of 96

Appendix G: Table G.42: Storm spreadsheet for Thrall, TX September 9, 1921 Page 90 of 96

Appendix G: Table G.43: Depth-area-duration values for Thrall, TX September 9, 1921 Page 91 of 96

Appendix G: Figure G.50 and Figure G.51: Total storm isohyetal analysis and mass curve chart for Thrall, TX September 9, 1921 Page 92 of 96

Bonaparte, IA, AWA 70 June 10, 1905 Storm Type: MCC Page 93 of 96

Appendix G: Table G.44: Storm spreadsheet for Bonaparte, IA June 10, 1905 Page 94 of 96

Appendix G: Table G.45: Depth-area-duration values for Bonaparte, IA June 10, 1905 Page 95 of 96

Appendix G: Figure G.52 and Figure G.53: Total storm isohyetal analysis and mass curve chart for Bonaparte, IA June 10, 1905 Page 96 of 96