EVALUATION OF WATERSHED SCALE AND DATA RESOLUTION ON PERFORMANCE OF A DISTRIBUTED MODEL

The demand for reliable estimates of streamflow has increased as society becomes more susceptible to climatic extremes such as droughts and flooding, especially at small scales where local population centers and infrastructure can be affected by rapidly occurring events. With critical hydrologic observation networks in decline worldwide, future expansion of existing networks into current ungauged locations may be limited. Spatially distributed models can help improve hydrologic predictions in ungauged basins because of their ability to model hydrologic processes at small scales, thus providing estimates at multiple subbasin locations. The Hydrology Laboratory-Research Distributed Hydrologic Model (HL-RDHM) is used to explore the accuracy of a distributed hydrologic model to simulate discharge at interior points representing various watershed scales. Basin sizes range from 20 – 2500 km², with subbasins nested in three National Weather Service (NWS) forecast basins in the upper Midwest. The model is calibrated and validated using USGS observed discharge data at the basin outlets, and subbasin discharge is then evaluated. Two different precipitation products, NLDAS-2 with a nominal 12.5 km resolution and Stage IV with an approximate 4 km resolution, were tested to characterize the role of input uncertainty and resolution on the discharge simulations at the various scales. In general, model performance decreased as basin size decreased across study basins and yielded correlation coefficients of 0.65 and 0.04 for NLDAS-2 and Stage IV forced simulations, respectively. Once basin area was less than 250 km² or 30% of the total watershed area, model performance became poor. Simulations forced with the NLDAS-2 product had Nash-Sutcliffe efficiency (NSE) scores, ranging from 0.50 and 0.75 during calibration for basin outlets and from 0.11 to 0.4 for subbasins less than 250 km². Subbasins located further away from the watershed outlet typically had poorer model performance. Simulated discharge using Stage IV performed better for low flow periods leading to better Mean Absolute Error (MAE) scores.