Abstract
Abstract. Ideally, semi-distributed hydrologic models should provide better streamflow simulations than lumped models, along with spatially-relevant water resources management solutions. However, the spatial distribution of model parameters raises issues related to the calibration strategy and to the identifiability of the parameters. To analyse these issues, we propose to base the evaluation of a semi-distributed model not only on its performance at streamflow gauging stations, but also on the spatial and temporal pattern of the optimised value of its parameters. We implemented calibration over 21 rolling periods and 64 catchments, and we analysed how well each parameter is identified in time and space. Performance and parameter identifiability are analysed comparatively to the calibration of the lumped version of the same model. We show that the semi-distributed model faces more difficulties to identify stable optimal parameter sets. The main difficulty lies in the identification of the parameters responsible for the closure of the water balance (i.e. for the particular model investigated, the intercatchment groundwater flow parameter).
Highlights
1.1 What hydrological good sense suggestsDeveloping modelling tools that help to understand the spatial distribution of water resources is a key issue for better management
This paper investigates the procedure of parameter identifiability in a semi-distributed model by comparing model calibration schemes and results with a lumped model on which it is based
We address two main questions: (1) Does spatial distribution of parameters interfere with parameters identifiability? one could hope that applying parameters to a more geographically-limited area tends to facilitate their identification
Summary
1.1 What hydrological good sense suggestsDeveloping modelling tools that help to understand the spatial distribution of water resources is a key issue for better management. The dynamics of streamflow depends on (i) the spatial variability of precipitation (which, a priori, should be better handled by a semi-distributed hydrological model), (ii) the heterogeneity of catchment behavior (which can be dealt explicitly with by spatially-variable model parameters), and, increasingly, (iii) localized human regulations (for instance, water reservoirs). The authors state that the impact of spatial variability could become “virtually non-detectable by conventional performance measures by the time the water reaches the catchment outlet”. This raises the need to better understand how well parameters are identified in a semi-distributed model compared to a lumped model. Optimised parameter values should not be overly sensitive to changes of climatic conditions, and one would expect a semi-distributed model to be more stable than a lumped one (because the parameters of the lumped model would have to account implicitly for changing spatial precipitation patterns)
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