Simultaneous calibration of hydrological models in geographical space

Hydrological models are usually calibrated for selected catchments individually using specific performance criteria. This procedure assumes that the catchments show individual behavior. As a consequence, the transfer of model parameters to other ungauged catchments is problematic. In this paper, the possibility of transferring part of the model parameters was investigated. Three different conceptual hydrological models were considered. The models were restructured by introducing a new parameter η which exclusively controls water balances. This parameter was considered as individual to each catchment. All other parameters, which mainly control the dynamics of the discharge (dynamical parameters), were considered for spatial transfer. Three hydrological models combined with three different performance measures were used in three different numerical experiments to investigate this transferability. The first numerical experiment, involving individual calibration of the models for 15 selected MOPEX catchments, showed that it is difficult to identify which catchments share common dynamical parameters. Parameters of one catchment might be good for another catchment but not the opposite. In the second numerical experiment, a common spatial calibration strategy was used. It was explicitly assumed that the catchments share common dynamical parameters. This strategy leads to parameters which perform well on all catchments. A leave-one-out common calibration showed that in this case a good parameter transfer to ungauged catchments can be achieved. In the third numerical experiment, the common calibration methodology was applied for 96 catchments. Another set of 96 catchments was used to test the transfer of common dynamical parameters. The results show that even a large number of catchments share similar dynamical parameters. The performance is worse than those obtained by individual calibration, but the transfer to ungauged catchments remains possible. The performance of the common parameters in the second experiment was better than in the third, indicating that the selection of the catchments for common calibration is important

A framework for space–time modelling of rainfall events for hydrological applications of weather radar

\ua9 2024 The Author(s). High-resolution rainfall fields are a crucial tool for many hydrological and hydrodynamic applications, including flood forecasting and urban drainage design. The aim of this study is to explore and exploit the space–time properties of rainfall using Fast-Fourier transforms, to provide a new method for the generation of high-resolution synthetic rainfall grids. These fields have realistic spatio-temporal properties, parametrised using historical radar rainfall events, matching the resolution of weather radar data (1km, 5 min), for events with a duration of 0.5–6 h. Utilising spectral random field theory, simulated rainfall fields are generated with a prescribed correlation structure, anisotropy, advection and marginal rainfall rate proportions and distributions. A model for rainfall generation is demonstrated, with an enriched model parameter sampling architecture using meaningful event clustering, based on space–time event properties. This model framework performs well at recreating short-duration spatio-temporal rainfall events, both visually and statistically. The extension of a clustered rainfall model allows for larger-scale sampling of synthetic event parameters, with specific rainfall event types. There are numerous potential uses for this rainfall model, such as design storms or test cases for applications of radar rainfall estimates. These include but are not limited to nowcasting, numerical weather prediction, flash flood forecasting and machine learning model training data generation

Diagnostic statistics of daily rainfall variability in an evolving climate

International audienceTo investigate the character of daily rainfall variability under present and future climate described via global warming a suite of diagnostic statistics was used. The rainfall was modeled as a stochastic process coupled with atmospheric circulation. In this study we used an automated objective classification of daily patterns based on optimized fuzzy rules. This kind of classification method provided circulation patterns suitable for downscaling of General Circulation Model (GCM)-generated precipitation. The precipitation diagnostics included first and second order moments, wet and dry-day renewal process probabilities and spell lengths as well as low-frequency variability via the standard deviation of monthly totals. These descriptors were applied to nine elevation zones and entire area of the Mesochora mountainous catchment in Central Greece for observed, 1×CO2 and 2×CO2 downscaled precipitation. The statistics' comparison revealed significant differences in the most of the daily diagnostics (e.g. mean wet-day amount, 95th percentile of wet-day amount, dry to wet probability), spell statistics (e.g. mean wet/dry spell length), and low-frequency diagnostic (standard deviation of monthly precipitation total) between warm (2×CO2) and observed scenario in a progressive rate from lower to upper zone. The differences were very greater for the catchment area. In the light of these results, an increase in rainfall occurrence with diminished rainfall amount and a sequence of less consecutive dry days could describe the behaviour of a possible future climate on the examined catchment