Using Remote Sensing Data-based Hydrological Model Calibrations for
Predicting Runoff in Ungauged or Poorly Gauged Catchments
Abstract
Because remote sensing (RS) data are spatially and temporally explicit
and available across the globe, they have the potential to be used for
predicting runoff in ungauged or poorly gauged catchments, a challenging
area of research in hydrology over the last several decades. There is
potential to use remotely sensed data for calibrating hydrological
models in regions with limited streamflow gauges. This study conducts a
comprehensive investigation on how to incorporate gridded remotely
sensed-evapotranspiration (AET) and water storage data for constraining
hydrological model calibration in order to predict daily and monthly
runoff in 30 catchments of Yalong River basin, China. To this end, seven
RS data calibration schemes are explored, compared to traditional
calibration against observed runoff and traditional regionalization
using spatial proximity. Our results show that using bias-corrected
remotely sensed AET (bias-corrected PML-AET data) for constraining model
calibration performs much better than using the non bias-corrected
remotely sensed AET data (non bias-corrected AET obtained from PML model
estimate). Using the bias-corrected PML-AET data in a gridded way is
much better than that in a lumped way, and outperforms the traditional
regionalization approach especially at upstream and large catchments.
Combining the bias-corrected PML-AET and GRACE water storage data
performs similarly to using the bias-corrected PML-AET data only. This
study demonstrates that and there is great potential to use RS-AET based
data for calibrating hydrological models in order to predict runoff in
data sparse regions with complex terrain conditions.