Models, Data, Information and Hypotheses: Towards a More effective use of Simulation Models in Hydrology
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|Created:||Dec 14, 2017 at 4 p.m.|
|Last updated:||Dec 14, 2017 at 4:28 p.m. by Richard Hooper|
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Water balance provides an organizing principle for rainfall-runoff models, both as a constraint (conservation of mass) but also as a perspective that structures models as fluxes among various compartments and states. Given that fewer tunable model parameters provides a more powerful quantitative test of our understanding of hydrologic processes, a dilemma arises between the use of lumped conceptual models and fully distributed models: lumped models generally have fewer parameters than distributed models, but translating data collected at a physical location in the field into information that is meaningful to a lumped model is not straightforward. By contrast, physically distributed models using spatially explicit computational grids or meshes can more directly relate internal model states to field data, yet data are quite sparse relative to the information requirements of the model.
Thus, a central challenge emerges of extracting information from data, whether point in situ measurements or remotely sensed data based upon grids or an eddy covariance measurement of uncertain footprint, to inform model parameter values or the dynamics of model state variables. The new National Water Model (NWM), developed by NOAA’s Office of Water Prediction, offers an interesting test case for addressing this challenge. As currently constructed, the NWM translates between a gridded landscape structure and a geofabric of reach catchments defined by the NHDPlus. We are examining the application of the NWM to three different Critical Zone Observatory catchments (Shavers Creek (PA), Upper Sangamon River (IL) , and Clear Creek (IA)) to explore the representation of groundwater-surface water exchange in these well-characterized basins. We contrast the effectiveness of different modeling approaches in extracting information from the available data sets.
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