Abstract
AbstractMost of previous assessments of hydrologic model performance are fragmented, based on small number of catchments, different methods or time periods and do not link the results to landscape or climate characteristics. This study uses large‐sample hydrology to identify major catchment controls on daily runoff simulations. It is based on a conceptual lumped hydrological model (GR6J), a collection of 29 catchment characteristics, a multinational set of 1103 catchments located in Austria, France, and Germany and four runoff model efficiency criteria. Two analyses are conducted to assess how features and criteria are linked: (i) a one‐dimensional analysis based on the Kruskal‐Wallis test and (ii) a multidimensional analysis based on regression trees and investigating the interplay between features. The catchment features most affecting model performance are the flashiness of precipitation and streamflow (computed as the ratio of absolute day‐to‐day fluctuations by the total amount in a year), the seasonality of evaporation, the catchment area, and the catchment aridity. Nonflashy, nonseasonal, large, and nonarid catchments show the best performance for all the tested criteria. We argue that this higher performance is due to fewer nonlinear responses (higher correlation between precipitation and streamflow) and lower input and output variability for such catchments. Finally, we show that, compared to national sets, multinational sets increase results transferability because they explore a wider range of hydroclimatic conditions.
Highlights
Achieving accurate streamflow simulations is a common objective to most hydrological modelers
The main objective of this study is to investigate the link between daily runoff simulations and climate and landscape characteristics using a large multinational data set
We considered GR6J to be a good candidate for this experiment
Summary
Achieving accurate streamflow simulations is a common objective to most hydrological modelers To this end, modelers typically focus on: (i) the quality of model inputs [Gupta and Sorooshian, 1985; Oudin et al, 2006; Arheimer et al, 2012], (ii) the improvement of model structures [Perrin et al, 2003; Das et al, 2008; Fenicia et al, 2011] or (iii) model calibration [Duan et al, 2006; Kuzmin et al, 2008; Efstratiadis and Koutsoyiannis, 2010] or regionalization [Hrachowitz et al, 2011; Parajka et al, 2013]. These results correspond well with previous spatial performance patterns of Clark et al [2008] who applied many conceptual models to a subset of the MOPEX basin set and found poor performance in arid regions
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