Abstract:The present study discusses the application of the physically based distributed MIKE-SHE code to a medium size catchment using different grid sizes to investigate scale effects on the model results. First a 600 m grid-square model was calibrated. This was then subjected to a multi-resolution (MR) validation test by using the effective parameters of the calibrated model in a 300 m and a 1200 m grid-square model. The MR test indicated that the models for the resolutions analysed only differ marginally. Secondly, the effect of grid size on both the calibrated effective model parameters and the model performance was analysed in the scope of a multi-calibration test in which the calibration and validation processes were kept as unique and standard as possible for every grid size. The model was calibrated and validated for every grid size against both daily catchment discharge measurements and observed water levels using both a split sample procedure and a multi-site validation test. The investigation indicated that the best validation results, in terms of river discharge, were obtained with a 600 m grid-resolution, independent of the stream-flow station. This, together with the exponential increase in computation time when reducing the grid size, indicates that, for the given level of data input and quality, the model type and structure, and the time step, a 600 m grid-resolution is most appropriate for the catchment under study.

Abstract. Physically based distributed models are rarely calibrated and validated thoroughly because of lack of data. In practice, validation is limited to comparison of simulated and predicted discharges in a catchment, or of simulated and observed piezometric levels in some calibrated wells. Rarely, internal noncalibrated wells or discharge stations are included in model evaluation. In this study, the fully distributed physically based MIKE SHE model was applied to the 600-km2 catchment of the Grote and the Kleine Gete, Belgium. Firstly, the MIKE SHE model was calibrated against both daily discharge measurements and observed water levels and then validated using a simple split-sample test. The observed discharges were simulated successfully in both the calibration and the validation period, while results for the piezometric levels differed considerably among the wells. In addition, a multi-site validation test for 2 internal discharge stations and 6 observation wells showed inferior results for the discharge stations and comparable results for the water table wells. As in the calibration and the split-sample test validation, water table fluctuations were predicted well in some wells, but with little agreement in others. This may be due to scale effects and to the poor quality of the data in certain areas of the catchment. Mainly, the lack of data made it difficult to simulate time series of internal catchment variables with acceptable accuracy so that even the calibrated and validated model could not provide reliable predictions of the water table over the entire catchment. Keywords: integral hydrological modelling; distributed code; MIKE-SHE; model performance; model calibration; model validation

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Epidemiological analyses of health risks associated with non-optimal temperature are traditionally based on ground observations from weather stations that offer limited spatial and temporal coverage. Climate reanalysis represents an alternative option that provide complete spatio-temporal exposure coverage, and yet are to be systematically explored for their suitability in assessing temperature-related health risks at a global scale. Here we provide the first comprehensive analysis over multiple regions to assess the suitability of the most recent generation of reanalysis datasets for health impact assessments and evaluate their comparative performance against traditional station-based data. Our findings show that reanalysis temperature from the last ERA5 products generally compare well to station observations, with similar non-optimal temperature-related risk estimates. However, the analysis offers some indication of lower performance in tropical regions, with a likely underestimation of heat-related excess mortality. Reanalysis data represent a valid alternative source of exposure variables in epidemiological analyses of temperature-related risk.

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