Uncertainty analysis in parameter regionalization for streamflow prediction in ungauged semi-arid catchments

Abstract

Predicting in ungauged basins (PUB) depends on the modelling uncertainties in the donor catchments (DCs). However, PUB is normally limited to a unique outcome, which may be quite uncertain, mainly in semi-arid basins where streamflow variability is high. Our goal is to assess the uncertainty in the parameter regionalization for streamflow prediction in semi-arid ungauged basins (UB). We used Differential Evolution Adaptive Metropolis (DREAM) for a parameter calibration that considers its intrinsic uncertainty. A basin similarity regionalization was performed to transfer the parameters to UB, considering one, three and five DCs. Leave-one-out cross-validation was applied for 28 gauged catchments. Regionalization performance had average Nash-Sutcliffe efficiency above 0.50. The approach considering one DC performed better than the others. The model of a poorly monitored catchment performed better using the transferred parameters from long-recorded, similar catchments than using those calibrated in the catchment itself. The developed regionalization may be a relevant tool for water management in drylands.