PNNL

Abstract

Meteorological forcing plays a critical role in accurately simulating the watershed hydrological cycle. With the advancement of high-performance computing and the development of integrated watershed models, simulating the watershed hydrological cycle at relatively high temporal (hourly to daily) and spatial resolution (10s of meters) has become efficient and computationally affordable. These hyperresolution watershed models require relatively high resolution of meteorological forcing as model input to ensure the fidelity and accuracy of simulated responses. In this study, we utilized the Advanced Terrestrial Simulator (ATS), an integrated watershed model, to simulate surface and subsurface flow and land surface processes using unstructured meshes at the Coal Creek Watershed near Crested Butte (Colorado). We compared simulated watershed hydrologic responses including streamflow, ET, and SWE driven by three publically available, gridded meteorological forcing (GMF) -- Daymet, PRISM, and NLDAS. The performance of different meteorological forcing on simulated variables depends on the quantity of interest and its spatial and temporal scale. By comparing various spatial resolutions of PRISM ranging from 400 m to 4 km, the simulated discharge only becomes noticeably worse when spatial resolution of meteorological forcing is coarsened to 4 km (or 30 % of the watershed area). However, the spatial resolution becomes more important when comparing the spatially distributed hydrologic variables such as snow water equivalent. Using a different temporal resolution of NLDAS (hourly to daily), the simulated discharge shows better performance with daily resolution compared to 12-hourly and hourly resolution. However, models forced by the sub-daily resolution preserve the dynamic watershed responses (e.g., diurnal fluctuation of streamflow) that are absent in results forced by daily resolution. Our findings suggest that watershed hydrologic responses are highly dependent on a given spatiotemporal resolution of the GMF, which may have important implications on model calibration and watershed management decisions.