Abstract
Abstract. For the analysis of climate impact on flood flows and flood frequency in macroscale river basins, hydrological models can be forced by several sets of hourly long-term climate time series. Considering the large number of model units, the small time step and the required recalibrations for different model forcing an efficient calibration strategy and optimisation algorithm are essential. This study investigates the impact of different calibration strategies and different optimisation algorithms on the performance and robustness of a semi-distributed model. The different calibration strategies were (a) Lumped, (b) 1-Factor, (c) Distributed and (d) Regionalisation. The latter uses catchment characteristics and estimates parameter values via transfer functions. These methods were applied in combination with three different optimisation algorithms: PEST, DDS, and SCE. In addition to the standard temporal evaluation of the calibration strategies, a spatial evaluation was applied. This was done by transferring the parameters from calibrated catchments to uncalibrated ones and validating the model performance of these uncalibrated catchments. The study was carried out for five sub-catchments of the Aller-Leine River Basin in Northern Germany. The best result for temporal evaluation was achieved by using the combination of the DDS optimisation with the Distributed strategy. The Regionalisation method obtained the weakest performance for temporal evaluation. However, for spatial evaluation the Regionalisation indicated more robust models, closely followed by the Lumped method. The 1-Factor and the Distributed strategy showed clear disadvantages regarding spatial parameter transferability. For the parameter estimation based on catchment descriptors as required for ungauged basins, the Regionalisation strategy seems to be a promising tool particularly in climate impact analysis and for hydrological modelling in general.
Highlights
The use of hydrological models for answering questions in water resources management is nowadays the technical standard, in sciences
In addition to the evaluation of the model performance, the efficiency of the different combinations of calibration strategies and optimisation algorithms were investigated. This was done by limiting the number of iterations to 1000 for each catchment and comparing the model performance after applying the different optimisation algorithms
The Lumped method as well as the 1-Factor method showed good results for all three optimisation algorithms in the calibration period. Considering that both methods handle the same dimension of the parameter search space (42 dimensions for all three catchments) the slightly better performance of the 1-Factor method might be due to a good estimation of the spatial variability of the initial parameter set
Summary
The use of hydrological models for answering questions in water resources management is nowadays the technical standard, in sciences. In many cases the modeller has to handle catchments on large scales ranging from 100 km to more than 10 000 km. There is a number of different process oriented models (some are called physically based), whose parameters are closely related to the physical properties of the catchment. For example it is hardly possible to get a sufficiently detailed description of the soils In practice this means that at least some of the parameters have to be estimated via calibration (Beven, 2001) what is done automatically in this study. Taking all parameters of a distributed- or semi-distributed-model into account the dimension of the parameter search space can be described as:
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