Gauging the Ungauged: Regionalization of Flow Indices at Grid Level

Despite advances in data collection and modeling, there still remains a significant gap in predicting flows in ungauged locations. This study presents an approach for using gridded data to regionalize flow indices (long-term average streamflows and flows that are equaled or exceeded for 95% of the time) along an entire streamflow network grid. The methodology is based on using the Terrain Analysis Using Digital Elevation Model (TauDEM) toolset to obtain input variables for the regionalization model as averaged to the catchment area of each pixel in the streamflow network grid. The variables used as input for the regionalization regression were the catchment area of each pixel in the streamflow network grid, average slope, annual rainfall, and annual evapotranspiration of the corresponding catchment area. These variables were downscaled to the resolution of the Multi-Error-Removed Improved-Terrain (MERIT) digital elevation model (DEM) ($90\times 90\mathrm{m}$). The result was a $90\times 90\text{-}\mathrm{m}$ stream network grid with corresponding values for the modeled flow indices. Thus, the use of satellite-derived or gridded products improved the capacity of the grid to capture spatial patterns that fail to be captured in poorly gauged watersheds. The methodology can be adapted to other cases, for example, to model the parameters of hydrological models along the streamflow network grid. The prediction of the streamflow/flow indices over the entire basin can provide water managers with greater confidence in granting water rights and in sustainably managing the resources in ungauged catchments.