ML17263A007
| ML17263A007 | |
| Person / Time | |
|---|---|
| Issue date: | 08/29/1979 |
| From: | Levine S Office of Nuclear Regulatory Research |
| To: | Harold Denton Office of Nuclear Reactor Regulation |
| References | |
| RIL-0058 | |
| Download: ML17263A007 (7) | |
Text
UNITED STATES NUCLEAR REGULATORY COMMISSION WASHINGTON, D. C. 20555 MEMORANDµM FOR:
Harold R. Denton, Director AUG 291f1S Office of Nuclear Reactor Regulation FROM:
Saul Levine, Director Office of Nuclear Regulatory Research
SUBJECT:
RESEARCH INFORMATION LETTER # 58 -
COMPARISON OF SIMULATION MODELS USED IN ASSESSING THE EFFECTS OF POWER PLANT INDUCED MORTALITY ON FISH POPULATIONS Introduction and Su1T111ary This memorandum transmits the results of completed research on comparison of simulation models used in assessing the effects of power-plant-induced mortality on fish populationsl. This work was performed by the Center for Quantitative Science at the University of Washington*s College of Fisheries under the direction of the Environmental Effects*Branch of RES.
Research Request NRR 78-7, 11Evaluation of Ecosystem Simulation Models as Tools for Confirmatory Assessment of Power Plant Impacts,
11 stated that the NRR staff lacks quantitative methodologies for predicting and assessing potential impacts on fisheries resources which may result from power plant effects. It also stated that theoretical models and computer simulations provide a possible approach to resolving these inadequacies. This report provides information on the currently available models and simulations, documents their underlying assumptions, specifies data input and parameter estimation requirements and discusses their theoretical limitations and verification procedures.
Methodology The approach used to review the models for predicting the impact of power plant operation on economically important fish species involved several steps. The model equations and underlying assumptions were compared.
Para-meter values were compared and the data sources used in obtaining them were investigated. Since many of the models had differing assumptions, parameter values or 'both, general simulators were developed to evaluate the relative predictive ability of the various models.
lNUREG/CR-0474, 11Comparison of Simulation Models Used in Assessing the Effects of Power-Plant-Induced Mortality Qn Fish Populations 11 I /
~ Harold R. Denton *
- The eight models reviewed were partitioned into two submodels: A young-of-the-year model which simulates the annual effect of plant entrainment and impinge-ment on recruitment of young-of-the-year into the adult population, and a life-cycle model, which simulates the subsequent, long-tenn effect of reduced recruitment on the adult population. The interactive life-cycle model simulator developed to compare the available models is diagramed in Figure 1. This model can accept density-dependent assumptions for both. young-of-the-year and fishing survival.
It allows parameters to be varied easily from run to run and allows plant operation to go on or off at any time.
Results Table l summarizes the predictions of percentage reduction young-of-the-year of the various models and Table 2 summarizes the predictions of impact on adult fish populations of the various life-cycle models.
As shown in Table 1, the percentage reduction values for the ORNL 1-D and LMS models differ greatly for similar cases. These models are complex and are the only models reviewed that consider migration explicitly. Therefore a large proportion of the text is devoted to an analysis of them.
Because the predictions given in Table 2 are not directly comparable, the authors developed their own life-cycle model simulator. Sensitivity studies and results are given for sex ratio, compensatory mortality, life-cycle parameters, and entrainment factors.
Conclusions and Recommendations Major differences between the models include the life stage lengths, density-dependent or density~independent young-of-the-year mortality, density-dependent or density-independent fishing mortality, and the method for computing recruit-ment of young-of-the-year fish into the adult population. Major differences in parameter values include entrainment factors, total egg production, equilibrium population size, and survival probabilities for the life-cycle models.
No presently existing impact model can be used to make quantitative predictions due to the large year-to-year variability in young-of-the-year densities and spatial distribution and the sensitivity of results to uncertainties in the parameters used in the density-dependent mortality function.
We reconmend that additional research be carried out to develop a better model for predicting the impact of power plant operation on fisheries.
In the mean-time NUREG/CR-0474 can be used to evaluate the limitations of presently available models.
If you have any questions with regatd to this report, please contact Mr. Frank Swanberg, Jr., Chief, Environmental Effects Branch (427-4358).
l.1~
~~e, Director Office of ~uclear Fegulatory Research
Enclosure:
START ENTER DATA SURVIVAL FRACTIONS, FECUNDITY, FISHING MORTALITY PARAMETERS ENTER INITIAL (EQUILIBRIUM) CONDITIONS ENTER FISHING SURVIVAL PROBABILITY ENTER PARAMETERS FOR SURVIVAL PROBABILITY AS FECUNDITY OF NUMBER OF EGGS ENTER PARAMETERS FOR PR AS FUNCTION OF NUMBER OF EGGS COMPUTE d*d FISHING PROBABILITY ANO/OR SURVIVAL PROBABILITY (IF APPLICABLE)
FOR y-o-y ANO FECUNDITIES COMPUTE NUMBER IN EACH AGE CLASS AT TIME t BY MATRIX "ULTIPLICATION R(t)
- A*N(t - 1)
PRINT YIELD, FISHING SURVIVAL PROBABILITY, TOTAL ADULT POPULATION All~-_.;~
C==:J Information required for model operation A. Question prompted V
by the mode I COMPUTE FECUNDITIES FROM REGRESSION EQUATION PARAMETERS PRINT NUMBER IN EACH AGE CLASS NO
,............., Operation per-formed by mode I f-'i gure *1.
Flow chart for life cycle model simulator.
. A Table
- 1. Comparison of predictions of percentage reduction (PR) for various models.
Model LMS 1-D 1967 LMS 1-D 1973 LMS 2-D ORNL 1-D ORNL Sununit JHU Delmarva Compensation*
High High High Low High Low Low None None None Entrainment factors Best estimate Maximum Best estimate Best estimate*
Best estimate Best estimate Minimum Minimum Best estimate Maximum PR Plants operating 2.5 Indian Point Units 1 & 2 4.0
- 2. 77 Indian Point Units 1, 2, 4.88
& 3 and Cornwall 1.257 Indian Point Units 1, 2, 3.138
& 3 2.44 18.0 Bowline Unit 2, Indian 34.0 Point Units 1, 2, & 3, 42.0 Roseton Units 1 & 2 4.5 Summit 1.0-5.0 Summit 0.71-5.53 Summit
~':""'
Table 2.
Comparison of life cycle model impact predictions.
y-o-y PR in Model PR compensation PR in total adults 1-year-old fish Number of years Number of years 5
10 5
10 LMS l-D(67) 2.07 High 2.52 3.93
- 2. 71 4.01 3.42 Low
'*
- 93 9.74 5.68 7.4.13 3.13 None 4.82 11.39 5.55 12.00 Number of years Number of years e
7 10 40 7
10 40 LMS 2-D 1.21 High 1.29 1.64 2.18 1.33 1.68 2.18 1.26 High 1.34 1.70 2.26 1.38
- 1. 75 2.26 2.44 Low 2~64 3.70 6.82 2.81 3.91 6.99 3.14 Low 3.46 4.86 8.95 3.61 5.03 8.99 4.47 Low 4.93 6.88 12.42 5.13 7.11 12.46
"" t Model PR y-o-y Relative yield PR in compensation 1.:.year-old fish Number of years Number of years 5
10 20 40 5
10 20 40 ORNL 10 None 0.96 0.90 0.85 0.83 10 14 17 18 25 None 0.88 0.75 0.64 0.60 25 33 38 42 50 None 0.78 0.52 0.35 0.26 50 62 70 75 e
Table 2.
Comparison of life cycle modal impact predictions -
(Continued),
y-o-y Model PR compensation PR in a.nnual yield ORNL 0.5 None 0.03 Summit 2.75 None
- o. 77 5.0 None 3.7 JHU 2,5 None 0.45 5.0 None
- 1. 7 y-o-y Model PR compensation PR in total adults 35 years Wint!er 1.0 Best estirr.ate 6.0 Flounder 1.0 None 9.0
Harold '
. The ef ght models reviewed were partit10ned into two submode ls: A young-of-the-year model which simulates the annual effect of plant entrainment and impinge-ment on recruitment of young-of-the-year into the adult population, and a Hfe-cycle mde111 which simulates the subsequent, long-tenn effect of reduced recruitment on the adult population. The interactive life-cycle model simulator developed to cmpare the available models 1s diagramed 1n Figure 1. This model can accept density-dependent assumptions for both young-of-the-year and fishing survival. It allows parameters to be varied easily from run to nm and allows plant operation to go on or off at any time.
Results
~
Table 1 surmiar1zes the predictions of percentage reductlon yountr-of-the-year of the various models and Table 2 summarizes the predictions of impact on adult f1sh populations of the various life-cycle models. As shown 1n Table 1, the percentage reduction values for the ORNL 1-D and LMS models differ greatly for s1mf 1ar cases. These models are complex and are the only models Nv1ewed that consfder migration expl 1c1t1y. Therefore a large proportion of the text is devoted to an analysis of them. Because the predictions given 1n Table 2 are not directly comparable, the authors developed their wwn life-cycle model simulator. Sensitivity studies and results are gfven for sex ratio, compensatory morta11tys life-cycle parameters, and entrainment factors.
Conclusions and Recoumendations Major differences between the models include the life stage lengths, densfty-dependent or densftycindependent young-of-the-year mortality, density-dependent or density-independent fishing mortality, and the method for computing recn.tit-ment of young-of-the-year fish 1nto the adult population. Major differences 1n parameter values include entrainment factors, total egg production. equiHbrillft population size. and survival probabilities for the life-cycle models.
No presently existing impact model can be used to make quantitative predictions due to the large year-to-year variability in young-of-the-year densities and spatial distrtbutf on and the sensitfvfty of results to uncertainties in the parameters used in the density-dependent mortality functfon.
We recontnend that additional research.be carried out to develop a better model for predicting the impact of power plant operation on fisheries. In the mean-
- time NUREG/CR-0474 can be used to evaluate the limitations of presently ava11able
.models.
~
- If you have any questions with regard to th1s report, please contact
- Mr. *frank Swanberg. Jr ** Chief, Environmental Effects Branch {427-4358).
DISTRIBUTION:
Central:.,file Davis Levine CHRONO:* <>
Bassett CI RC"'._.:;. :. * ::
- Arsenault Fou i'ke-~:-*~\\*'2::£.~.,
Larkins Swanberg*~ Jr.
Budnitz
Enclosure:
NUREG/CR-0474 Orfgin"J Sfgned br.
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Saul lev1ne~ Director Office of Nuclear Regulatory Research ifa;-----.------1+-~-------,------~------.--------,-----*
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