Abstract

Estimating spatially distributed parameters remains one of the biggest challenges for large-domain hydrologic modeling. Many large-domain modeling efforts rely on spatially inconsistent parameter fields, e.g., patchwork patterns resulting from individual basin calibrations, parameter fields gener... Show moreEstimating spatially distributed parameters remains one of the biggest challenges for large-domain hydrologic modeling. Many large-domain modeling efforts rely on spatially inconsistent parameter fields, e.g., patchwork patterns resulting from individual basin calibrations, parameter fields generated through default transfer functions that relate geophysical attributes to model parameters, or spatially constant, default parameter values. This paper provides an initial assessment of a multiscale parameter regionalization (MPR) method over large geographical domains to derive seamless parameters in a spatially consistent manner. MPR applies transfer functions at the native scale of the geophysical data, and then scales these model parameters to the desired model resolution. We developed a stand-alone framework called MPR-flex for multimodel use and applied MPR-flex to the variable infiltration capacity model to produce hydrologic simulations over the contiguous United States (CONUS). We first independently calibrate 531 basins across CONUS to obtain a performance benchmark for each basin. To derive the CONUS parameter fields, we perform a joint MPR calibration using all but the poorest behaved basins to obtain a single set of transfer function parameters that are applied to the entire CONUS. Results show that CONUS-wide calibration has similar performance compared to previous simulations using a patchwork quilt of partially calibrated parameter sets, but without the spatial discontinuities in parameters that characterize some previous CONUS-domain model simulations. Several avenues to improve CONUS-wide calibration remain, including selection of calibration basins, objective function formulation, as well as MPR-flex improvements including transfer function formulations and scaling operator optimization. Show less