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Technical Memorandum
December 21, 2007
Authors: Jody B. Lando, Cameron Turner, Paul Bergman
Advances in Winter-run Chinook modeling
Recent developments in winter-run Chinook modeling, contracted by the California Department
of Water Resources (DWR), have advanced model function and biological realism within the
model. To achieve these advances and increase model transparency, the updated model now
operates within simulation modeling software called GoldSim (www.goldsim.com). The DWR
contract was explicitly scoped to evaluate the potential effects and design alternatives for the
North-of-Delta-Offstream-Storage project. While the DWR objectives are specific, the
advanced model function and realism are broadly applicable.
1. Spawning distribution
Spawning functionality in the Integrated Modeling Framework (IMF) did not include any
spatial component but was instead modeled as a single, geographically unspecified event.
Our recent modeling efforts mimic a more biological process that distributes spawning
amongst the uppermost reaches in response to the status of migration barriers, upstream
ranking, and spawning capacity. Modeling the location of spawning is an important
advancement because it allows spawning success and egg/alevin incubation to respond
appropriately to physical conditions that are spatially variable (e.g., habitat availability,
temperature, flow). The response to these conditions propagates through the life cycle by
modifying the number of fry that emerge in a brood year.
Our latest life cycle model distributes migrating adults hierarchically, beginning with a
response to migration barriers (the RBD and ACID dams on the Sacramento R.). If the
RBDD is closed, the escapement divides above and below according to one regime. If the
RBDD is open, all fish move past it. Subsequently, the status of the ACID dam determines
whether or not the uppermost reach will receive its full spawning capacity. The spawning
capacity of each reach is determined by the area of available spawning gravel and the
average redd size (a constant). Spawning fish first fill the capacity of the uppermost
available reach. The remainder fill the next uppermost reach, continuing downstream until
the entire escapement is distributed.
2. Redd dewatering
Although redd dewatering was not a part of the IMF, this source of egg and alevin mortality
can have a large effect on some salmonid species. In our recent life cycle modeling efforts
we have incorporated redd dewatering based on simulations performed by the USFWS for
Fish Biology • Biostatistics • Simulation Modeling • Ecology • Genetics • Hydrology • Geomorphology • Estuarine and Marine
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the Sacramento River (USFWS, 2006). In this approach redd dewatering is dependent on the
relationship between the river flow during spawning and the lowest river flow during
incubation: the greater the drop in flow after spawning, the greater the proportion of redds
that are dewatered. This relationship is shown in Fig. 1.
Spawning Flow (cfs)
% of Redds Dewatered
80
3500
4500
4000
5000
70
5500
7500
6500
9000
60
11000
15000
23000
13000
19000
27000
50
31000
40
30
20
10
29000
25000
21000
17000
14000
12000
10000
8000
7000
6000
5250
4750
4250
3750
3250
0
Incubation Flow (cfs)
Figure 1. Redd dewatering relationship between the river flow during spawning and the lowest river flow during
incubation
3. Incubation time
The duration of incubation was not a part of the IMF. However, modeling this important
feature of the salmonid life history allows the correct determination of egg/alevin mortality
by redd dewatering and specifies when fry emerge from the gravel. The timing of emergence
determines which river conditions fry will encounter and thus functions as a critical factor in
modeling juvenile survival and migration. Our most recent work models the duration of egg
and alevin incubation as a function of temperature and calendar time of spawning. This
approach was crafted using published analyses of salmonid incubation experiments.
The duration of egg and alevin incubation is determined by the temperature on the day of
egg deposition or hatching, respectively. The relationship between temperature and
incubation time is determined by the following power function (Beacham and Murray,
1990):
logeD = logea + bloge(T - c) + dlogeS
where:
D = hatching or emergence time after fertilization
T = Temperature (°C)
S = timing of spawning (Day of Year when spawning begins)
a, b, c, d = coefficients
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We model the time period from fertilization to hatch (eggs) and the time from hatch to
emergence (alevin) separately. Thus, for a given temperature the egg incubation time is
Dhatching and the alevin incubation time is Demergence – Dhatching. These TemperatureIncubation Time relationships are shown in Figure 2.
500
Hatch Time (Egg Incubation Time)
Length of Incubation (days)
450
400
Emergence Time
350
300
Hatch to Emergence Time (Alevin Incubation Time)
250
200
150
100
50
0
0
5
10
15
20
25
30
Mean Temperature (°C)
Figure 2. Relationship between temperature and incubation time for Eggs and Alevin. Alevin incubation time is the
difference between Hatch Time and Emergence Time.
4. Juvenile growth and maturation
Previous IMF modeling efforts used stock-recruitment functions to instantaneously mature
juveniles from one lifestage to another and did not grow cohorts through time and space.
New model advancements allow us to grow cohorts of fish at a time-dependent and
lifestage-dependent rate, so that juveniles will be at the right size at the right time, in order
for river conditions to influence them appropriate to their lifestage. Also, by explicitly
modeling temperature-dependent growth we will be able to model any changes in growth
rate and maturation rate that occur as a result of changing water temperatures.
We modeled Fry and parr growth as a function of water temperature as reported by classic
salmon growth experiments conducted by Shelbourne et al. (1973) and Brett et al. (1969).
The experimental growth studies modeled temperature-dependent growth rate for three
different weight ranges of fish that correspond well with size cut-offs used to separate fry,
parr and smolt lifestages in other salmon modeling efforts. Therefore, the experimental data
we used to model juvenile growth informed our size distinctions used for each juvenile
lifestage.
Maturation rate, the rate at which juveniles transition from one lifestage to the next, is
dependent on the mean size of the lifestage population in each reach. For example, the
maturation rate from fry to parr is zero percent per day until the mean size of fry reaches the
midpoint between the minimum and maximum sizes for fry. Once the mean size reaches
this midpoint the maturation rate increases exponentially to a maximum of 100% per day.
This functionality assumes that juvenile growth is exponential and that there is a constant
influx of new members to a life stage at the minimum size.
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5. Juvenile migration
The main advancement in our efforts to model juvenile migration is our ability to realistically
model time-dependent spatial movement of juvenile salmon and ensure that they are
influenced by river conditions at the right time, in the right reach. Previous IMF modeling
efforts did not include spatial specificity, and time-specific river conditions only affected
juveniles upon entering the Delta. However, our new modeling efforts will more accurately
represent juvenile movement and model the influence of reach-specific and time-specific
river conditions on juveniles.
Downstream migration of juveniles is modeled as the sum of volitional, flow-induced, and
capacity-induced movement. Although many environmental stimuli (i.e. water temperature,
photoperiod, flow) likely play a key role in influencing juvenile Chinook to volitionally
migrate, we found little supporting evidence in literature that documented the magnitude of
the effect of environmental stimuli on migration. Therefore, we took a simplified approach
for volitional migration and estimated the percent of each juvenile life stage that migrate
each day using a combination of historical trapping information in the Sacramento River and
experimental lab studies examining juvenile Chinook migration. During model calibration,
volitional migration values will be fine-tuned by matching historic trapping observations in
the Sacramento River with model migration timing.
For flow-induced migration, we assumed that juveniles get swept downstream during very
high flow events (e.g. redd-scouring flows). Therefore, using results from Sacramento River
gravel restoration studies, we set flow-induced migration of juveniles to start at flows capable of
displacing gravel and increase to a maximum when flows are observed to cause significant
bed-changing events. Lastly, as capacity of for each lifestage is reached (determined by
available habitat), juveniles are forced to migrate to the nearest downstream reach.
References
Beacham, T. D. and C. B. Murray, 1990. Temperature, egg size, and development of embryos
and alevins of 5 species of pacific salmon—a comparative analysis. Transactions of the
American Fisheries Society 119:927-945.
Brett, J. R., Shelbourn, J. E. and C. T. Shoop, 1969. Growth rate and body composition of sockey
salmon, Oncorhynchus nerka, in relation to temperature and ration size. Journal of the
Fisheries Research Board of Canada 26:2363-2394.
Shelbourn, J. E., Brett, J. R. and S. Shirahat, 1973. Effect of temperature and feeding regime on
specific growth-rate of sockeye salmon fry (Oncorhynchus nerka), with a consideration of
size effect. Journal of the Fisheries Research Board of Canada 30:1191-1194.
USFWS. 2006. Relationships between flow fluctuations and redd dewatering and juvenile
stranding for Chinook salmon and steelhead in the Sacramento River between Keswick Dam
and Battle Creek. (Gard, M., ed.). Sacramento, CA: U. S. Fish and Wildlife Service.
Fish Biology • Biostatistics • Simulation Modeling • Ecology • Genetics • Hydrology • Geomorphology • Estuarine and Marine
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