Although the river basin is the organizing principal of the terrestrial water cycle research, until recently, models which couple the complexity of climate, terrain, ecology, and geology at this scale to the particular needs of a water resource forecast, have not been considered practical. The present research proposes to investigate the multi-scale dynamics of precipitation-recharge-runoff, and the partitioning of water and energy budgets over complex terrain and hydrogeological conditions. The approach maintains the natural coupling between surface and subsurface processes at each scale of interest but recognizes that surface water basins and groundwater basins may have distinct delineations. The modeling approach is based on a finite volume representation, where conservation and constitutive equations are averaged over a specified support scale. The model is multi-scale in the sense that climatic, vegetative, topographic and hydrogeologic elements of the landscape are resolved in such a way as to preserve the necessary space-time scales for a particular water resource forecast (flood dynamics, stream-aquifer response to drought, etc.). The research addresses the tradeoff between the scale of computing and the need to include fine-scale material properties in water resource predictions.
Long-term flow forecasts for the SRB must account for the competing time scales of the surface and subsurface processes governing the basin's response to climatologic or landuse forcing. Our strategy is to condition forecasts on both rapid surface responses as well as slower subsurface responses using multiobjective evolutionary algorithms. This multiobjective framework will adapt model parameters and resolution to account for the disparate time-scales and physical complexities of surface and subsurface flow regimes. This research will initially focus on developing regional conceptual surface-groundwater models for component watersheds within the SRB. The next phase of the research will develop a strategy for synthesis of the regional conceptual models with geospatial data as input to a large-scale model for the surface-groundwater dynamics of the entire SRB. The final phase of the research will develop a decision-support system that will assimilate new climatologic data into long-range runoff predictions. These long-range predictions will support the development of improved water management policies for the entire SRB.
The research addresses four fundamental questions: 1) What role does hydrogeology play in long-term and short-term runoff and what is the relation to climate and landuse dynamics? 2) When does small-scale soil and subsurface variability control runoff and how can models "adapt" to changing external (climate) and internal conditions (landuse)? 3) What are the space-time scales at which tributaries of the river basin are dynamically coupled? 4) How can evolutionary computing strategies and "qualitative" conceptual models be incorporated to better resolve model dimensionality, parameterization, and prediction at the river basin scale?
