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https://doi.org/10.5194/hess-2020-68
© Author(s) 2020. This work is distributed under
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/hess-2020-68
© Author(s) 2020. This work is distributed under
the Creative Commons Attribution 4.0 License.

Submitted as: research article 28 Apr 2020

Submitted as: research article | 28 Apr 2020

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This preprint is currently under review for the journal HESS.

Suitability of 17 rainfall and temperature gridded datasets for largescale hydrological modelling in West Africa

Moctar Dembélé1, Bettina Schaefli1,a, Nick van de Giesen2, and Grégoire Mariéthoz1 Moctar Dembélé et al.
  • 1Institute of Earth Surface Dynamics, Faculty of Geosciences and Environment, University of Lausanne, CH-1015 Lausanne, Switzerland
  • 2Water Resources Section, Faculty of Civil Engineering and Geosciences, Delft University of Technology, Stevinweg 1, 2628 CN Delft, The Netherlands
  • anow at: Institute of Geography, Faculty of Science, University of Bern, CH-3012, Switzerland

Abstract. This study evaluates the ability of different gridded rainfall datasets to plausibly represent the spatiotemporal patterns of multiple hydrological processes (i.e. streamflow, actual evaporation, soil moisture and terrestrial water storage) for large-scale hydrological modelling in the predominantly semi-arid Volta River Basin (VRB) in West Africa. Seventeen precipitation products based on satellite data (TAMSAT, CHIRPS, ARC, RFE, MSWEP, GSMaP, PERSIANN-CDR, CMORPH-CRT, TRMM 3B42, TRMM 3B42RT) and on reanalysis (JRA-55, EWEMBI, WFDEI-GPCC, WFDEI-CRU, MERRA-2, PGF and ERA5) are compared as input for the fully distributed mesoscale Hydrologic Model (mHM). To assess the model sensitivity to meteorological forcing during rainfall partitioning into evaporation and runoff, six different temperature reanalysis datasets are used in combination with the precipitation datasets, which results in evaluating 102 combinations of rainfall-temperature input data. The model is recalibrated for each of the 102 input combinations, and the model responses are evaluated by using in-situ streamflow data and satellite remote sensing datasets from GLEAM evaporation, ESA CCI soil moisture, and GRACE terrestrial water storage. A bias-insensitive metric is used to assess the impact of meteorological forcing on the simulation of the spatial patterns of hydrological processes. The results of the process-based evaluation show that the rainfall datasets have contrasting performances across the four climatic zones present in the VRB, suggesting that, in general, basin-wide hydrological model performance might be misleading and invalid for a smaller spatial domain. No single rainfall or temperature dataset consistently ranks first in reproducing the spatiotemporal variability of all hydrological processes. A dataset that is best in reproducing the temporal dynamics is not necessarily the best for the spatial patterns. In addition, the results suggest that there is more uncertainty in representing the spatial patterns of hydrological processes than their temporal dynamics. Finally, some region-tailored datasets outperform the global datasets, thereby stressing the necessity and importance of regional evaluation studies for satellite and reanalysis meteorological datasets.

Moctar Dembélé et al.

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Short summary
This study evaluates 102 combinations of rainfall and temperature datasets from satellite and reanalysis sources, and used as input to a fully distributed hydrological model. The model is recalibrated for each input data, and the outputs are evaluated with streamflow, evaporation, soil moisture, and terrestrial water storage data. Results show that no single rainfall or temperature data consistently ranks first in reproducing the spatiotemporal variability of all hydrological processes.
This study evaluates 102 combinations of rainfall and temperature datasets from satellite and...
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