On evaluating the robustness of spatial-proximity-based regionalization methods

In absence of streamflow data to calibrate a hydrological model, its parameters are to be inferred by a regionalization method. In this technical note, we discuss a specific class of regionalization methods, those based on spatial proximity, which transfers hydrological information (typically calibrated parameter sets) from neighbor gauged stations to the target ungauged station. The efficiency of any spatial proximity-based regionalization method will depend on the density of the available streamgauging network, and the purpose of this note is to discuss how to assess the robustness of the regionalization method (i.e., its resilience to an increasingly sparse hydrometric network). We compare two options: (i) the random hydrometrical reduction (HRand) method, which consists in sub-sampling the existing gauging network around the target ungauged station, and (ii) the hydrometrical desert method (HDes), which consists in ignoring the closest gauged stations. Our tests suggest that the HDes method should be preferred, because it provides a more realistic view on regionalization performance. (C) 2016 Elsevier B.V. All rights reserved.