Spatial proximity, physical similarity, regression and ungaged catchments: A comparison of regionalization approaches based on 913 French catchments

Abstract

[1] Given the contradictory results from recent studies, this paper compares classical regionalization schemes of catchment model parameters over the wide range of hydroclimates found in France. To ensure the generality of the conclusions, we used two lumped rainfall-runoff models applied to daily data over a large set of 913 French catchments. Three types of approaches were considered: regionalization using regression, regionalization based on spatial proximity and regionalization based on physical similarity. This comparison shows that in France, where a dense network of gauging stations is available, spatial proximity provides the best regionalization solution. The regression approach is the least satisfactory, with results very close to those obtained using one median parameter set for the whole country. The physical similarity approach is intermediary. However, the results obtained with these three methods lag far behind those obtained by full model calibration. Our results also show that some improvement could be made by combining spatial proximity and physical similarity, and that there is still considerable room for progress in the field of ungaged catchment modeling.

Parameter regionalization for a process-oriented distributed model dedicated to flash floods

“…A comparison of the three methods mentioned above with two lumped conceptual models (GR4J and TOPMO) shows that in France, where the gauging network is relatively dense, spatial proximity provides the best regionalization results for a 913 catchments dataset (Oudin et al, 2008). It is argued that the failure of methods based on catchment descriptors might be attributable to the lack of key physical descriptors of soil hydrology, and that there is room for progress by learning how to merge the different methodologies.…”

“…Among the numerous techniques proposed for the regionalization of catchment model parameters, generally for continuous (conceptual) rainfall runoff models, three kinds of approaches can be distinguished with their specific advantages and inherent drawbacks (Oudin et al, 2008): regression based methods, geographical proximity, and similarity methods. Several regionalization studies mostly for rather large datasets are briefly presented in (Table 2).…”

“…Physical similarity outperforms regression methods Parajka et al (2005) 320 Austrian catchments, 11 parameters of the HBV model. Slightly better efficiency for similarity approach compared to krigging Oudin et al (2008) 913 French catchments, 4 parameters of GR4J model or 6 parameters of TOPMODEL. ''Lack of a key physical descriptor.…”

Forecasting and Providing Warnings of Flash Floods for Ungauged Mountainous Areas Based on a Distributed Hydrological Model

“…The calibrated parameters of the donor catchments are then transferred to the ungauged (receiver) catchments. The application of these methods in UK, Australia, and Indian, as well as other countries, has demonstrated that the former two approaches yield better results than the latter [22][23][24][25][26]. A combination of spatial proximity (distance) and physical similarity indices could therefore improve the accuracy of simulations [25].…”

“…The application of these methods in UK, Australia, and Indian, as well as other countries, has demonstrated that the former two approaches yield better results than the latter [22][23][24][25][26]. A combination of spatial proximity (distance) and physical similarity indices could therefore improve the accuracy of simulations [25]. For the stream flow prediction of ungauged catchments, the presence of well-gauged catchments in proximity is more beneficial than having physically similar catchments [24].…”