Meteorological Driving Datasets for the U.S. Midwest and Great Lakes Region Incorporating Precipitation Gauge Undercatch Corrections

Abstract

Abstract Precipitation (P) gauge undercatch (PUC) is an important source of error when using observed meteorological datasets for hydrologic modeling studies in regions with cold and windy winters. Preliminary simulations using the Variable Infiltration Capacity (VIC) hydrological model forced with different meteorological datasets showed significant underprediction of simulated streamflow throughout the domain. A new hybrid gridded meteorological dataset at 1/16° resolution based on observed station data was assembled over the U.S. Midwest and Great Lakes region from 1915 to 2021 at a daily time step. Correction of primary station data using existing techniques is generally difficult or infeasible in the United States due to missing station metadata and lack of local wind speed (WS) measurements. We developed and tested several different postprocessing adjustment techniques using regridded WS obtained from the NCEP–NCAR reanalysis. The most effective approach corrected rain or mixed P using WS alone, and P as snow using a regressed snow-to-P ratio from a group of wind-shielded reference stations (to account for different and generally unknown snow measurement techniques). The PUC-corrected gridded products were validated against high-quality shielded stations and corrected Global Historical Climatology Network stations with in situ WS, showing good overall agreement. Observed monthly streamflow at 40 river basins was also compared to hydrologic model simulations forced by datasets with and without PUC corrections. The best PUC-corrected dataset produced improvements in streamflow simulations in at least 80% of the streamflow locations for three validation metrics (r2, Nash–Sutcliff efficiency, bias in the mean), demonstrating its value for hydrometeorological studies in the greater Midwest region. Significance Statement Many applications in hydrology require in situ precipitation (P) measurements, which are known to have a systematic low bias due to the effects of wind, also known as precipitation undercatch (PUC). Addressing PUC is problematic in the United States due to limited access to detailed station metadata (SMD) and local wind speed (WS) measurements. In this paper we develop a set of procedures to create gridded precipitation datasets for the U.S. Midwest region that incorporate corrections for PUC without needing either (i) detailed SMD or (ii) local WS measurements. Among other tests, results in 40 test basins throughout the Midwest show substantial improvements in simulated streamflow in 32 out of 40 basins when PUC corrections are included in meteorological driving datasets.