Abstract
ABSTRACTThis paper investigates the transferability of calibrated HBV model parameters under stable and contrasting conditions in terms of flood seasonality and flood generating processes (FGP) in five Norwegian catchments with mixed snowmelt/rainfall regimes. We apply a series of generalized (differential) split-sample tests using a 6-year moving window over (i) the entire runoff observation periods, and (ii) two subsets of runoff observations distinguished by the seasonal occurrence of annual maximum floods during either spring or autumn. The results indicate a general model performance loss due to the transfer of calibrated parameters to independent validation periods of −5 to −17%, on average. However, there is no indication that contrasting flood seasonality exacerbates performance losses, which contradicts the assumption that optimized parameter sets for snowmelt-dominated floods (during spring) perform particularly poorly on validation periods with rainfall-dominated floods (during autumn) and vice versa.
Highlights
Climate change impact assessments are usually based on multi-model/multi-parameter ensembles which lead to a cascade of uncertainty (Wilby and Dessai 2010)
For 16 calibration periods that cover the years with annual maximum floods (AMFs) during autumn, the level of dominance ranges from 25 to 51%, with eight periods showing larger than 40% dominant autumn floods
To further investigate the role of contrasting flood seasonality conditions on the model performance losses, we show in Figure 7 the relationship between the dominance of AMF seasonality per calibration period and the maximum performance loss in terms of model robustness criterion (MRC) values estimated by the differential split-sample test (DSST)
Summary
Climate change impact assessments are usually based on multi-model/multi-parameter ensembles which lead to a cascade of uncertainty (Wilby and Dessai 2010). There is increasing concern that the hydrological models used in climate change impact assessments are not perfectly suited to dealing with changes in the hydro-meteorological conditions and their related catchment processes due to the conceptual representation and parameterization of the hydrological system (Thirel et al 2015a). We need to thoroughly verify the transferability of both model structures and calibrated model parameters under transient hydro-climatological conditions. The performance of process-based models should be robust against changing conditions. Model structures can become invalid if the dominant processes fundamentally change. Hydrological model parameters related to a specific process may become invalid if the process is not well represented during the calibration period. Different calibration periods showing contrasting hydro-meteorological conditions may already yield different best-fit parameter sets, highlighting a lack of parameter robustness over time (Wagener et al 2003, Merz et al 2011)
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