DOWNSCALING FROM REGIONAL CLIMATE MODELS TO RIVER HYDRODYNAMICS FOR USE IN MULTI-SCALE MODELING OF RIVERINE ECOSYSTEMS AND RESPONSES OF FISH POPULATIONS

Changes in discharge, channel morphology, water quality, and biota have been implicated as causative agents in dramatic declines in Missouri River native fishes, in particular, pallid sturgeon (Scaphirhynchus albus) (Hesse and Sheets, 1993). Critical to any attempts at recovery of the Missouri River ecosystem, will be understanding potential impact that climate change may have on sustainable fish populations resulting from changes in river dynamics.

Downscaling regional climate models to river systems requires translating climate variables such as air temperature and precipitation to water temperature and discharge, commonly through use of hydrologic models. Reliance on a single model often leads to predictions that represent some phenomenon well at the expense of others (Duan and others, 2007). Varying strengths and weaknesses of individual models in capturing physical processes in the catchment prevent the ability to convincingly declare any one model to be the “best” [Smith and others, 2004; Beven, 2006]. To account for the uncertainty introduced by structural errors inherent in any model, we will use several hydrologic models to produce watershed runoff at key locations along the Missouri River. Models will range in complexity from simple watershed-based models available in the US Army Corps of Engineers Hydrologic Modeling System to the grid-based Variable Infiltration Capacity model (Wood and others, 2007).

Multiple hydrologic models will provide an ensemble of runoff simulations to link with a hydraulic model for stream-level simulations. Translating watershed run-off into river temperature and discharge is being done through 1-D models such as CHARIMA (Wright and others, 1999), statistical downscaling (Vrac and others, 2007), or development of empirical relationships between watershed run-off and temperature and mainstem discharge and temperature (Larson and Schwein, 2004). We are developing discharge distribution uncertainties based on runoff information from the previous stage using a deterministic model for river depth and velocity.

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Smith, M. B., Seo, D-J, Koren, V.I, Reed, S.M., Zhang, Z., and Duan, Q., 2004, The distributed model intercomparison project (DMIP): motivation and experiment design: Journal of Hydrology, v. 298, no. 1-4, 4-26.

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