Uncertainty analysis in parameter regionalization for streamflow prediction in ungauged semi-arid catchments
Ályson Brayner Sousa Estácio, Alexandre Cunha Costa, Francisco Assis Souza Filho, Renan Vieira Rocha
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.