Evaluation of a Hydrologic Model of the Rio Grande Using a Long-Term Dataset of Land Surface Fluxes and States

Researchers at the Desert Research Institute (DRI) are conducting SAHRA-related research aimed at (1) understanding the spatial and temporal distribution of snow and water balance above the mountain front and (2) developing and calibrating both operational and physically based numerical models that can be used to predict the quantity and timing of runoff in semi-arid regions where the majority of runoff originates in the seasonal snow pack. Unfortunately, observations of hydrologic variables (precipitation, streamflow, evapotranspiration, snow water equivalent, etc.) are sparse in the semi-arid regions of the western United States and, therefore, the evaluation of model accuracy (usually in terms of streamflow) is often very limited. However, comparisons of model output with newly developed high-resolution estimates of hydrologically based land surface fluxes and states may provide insight to model accuracy in areas with little or no observed information. In this study, we apply a hydrologic model to the Rio Grande (above El Paso, TX) and compare the model output to a dataset of estimated land surface fluxes and states. Specifically, the USGS Precipitation-Runoff Modeling System (PRMS), within the Modular Modeling System (MMS), is applied to the watershed at a daily time step with a spatial resolution of 1/8 degree. Many of the model parameters are derived directly from spatial information describing important hydrologic characteristics of the watershed (e.g., soils, vegetation, slope, aspect, etc.) using existing empirical relationships. Model estimates of land surface fluxes and states (e.g., streamflow, groundwater flow, evapotranspiration, snow water equivalent, soil moisture, etc.) are compared with a long-term dataset of land surface fluxes and states from a variety of different sources. From these comparisons, we hope to gain a better understanding of the role of basin scale, grid resolution and some of the uncertainties associated with current prediction methods - all important issues presented within the Prediction in Ungauged Basins (PUB) initiative.