SABER: A Model-Agnostic Postprocessor for Bias Correcting Discharge from Large Hydrologic Models

Hydrologic modeling is trending toward larger spatial and temporal domains, higher resolutions, less extensive local calibration validation. Thorough validation are difficult because the quantity of observations needed for such scales do not exist or inaccessible to modelers. We present Stream Analysis Bias Estimation Reduction (SABER) method bias correction targeting large models. SABER intended model consumers apply a subset domain at gauged ungauged locations address issues with data size availability. extends frequency-matching postprocessing techniques using flow duration curves (FDC) subbasins be applied clustering analysis. uses “scalar” FDC (SFDC), ratio simulated observed FDC, characterize biases spatially, temporally, varying exceedance probabilities make corrections subbasins. Biased flows corrected scalar values from SFDC. Corrected refined fit Gumbel Type 1 distribution. theory, procedure, study in Colombia. reduces improves composite metrics, including Nash Sutcliffe Kling Gupta Efficiency. Recommendations future work discussion limitations provided.