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Multi-model averaging for continuous streamflow prediction in ungauged basins
Authors:Richard Arsenault  François Brissette
Institution:1. école de technologie supérieure, Département de génie de la construction, Montréal, QC H3C 1K3, Canadarichard.arsenault.1@ens.etsmtl.ca;3. école de technologie supérieure, Département de génie de la construction, Montréal, QC H3C 1K3, Canada
Abstract:ABSTRACT

This paper assesses the possibility of using multi-model averaging techniques for continuous streamflow prediction in ungauged basins. Three hydrological models were calibrated on the Nash-Sutcliffe Efficiency metric and were used as members of four multi-model averaging schemes. Model weights were estimated through optimization on the donor catchments. The averaging methods were tested on 267 catchments in the province of Québec, Canada, in a leave-one-out cross-validation approach. It was found that the best hydrological model was practically always better than the others used individually or in a multi-model framework, thus no averaging scheme performed statistically better than the best single member. It was also found that the robustness and adaptability of the models were highly influential on the models’ performance in cross-verification. The results show that multi-model averaging techniques are not necessarily suited for regionalization applications, and that models selected in such studies must be chosen carefully to be as robust as possible on the study site.
Editor M.C. Acreman; Associate editor S. Grimaldi
Keywords:multi-model  model averaging  regionalization  streamflow prediction  PUB  physical similarity
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