Oregon • California • Washington • Idaho • Alaska 600 NW Fariss Road Gresham, OR 97030-2434 V: 503.491.9577 F: 503.465.1940 www.fishsciences.net 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 1 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 Fish Biology • Biostatistics • Simulation Modeling • Ecology • Genetics • Hydrology • Geomorphology • Estuarine and Marine 2 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. Fish Biology • Biostatistics • Simulation Modeling • Ecology • Genetics • Hydrology • Geomorphology • Estuarine and Marine 3 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 4