Evaluating precipitation datasets for large-scale distributed hydrological modelling

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Authors

Maurizio Mazzoleni , Luigia Brandimarte, Alessandro Amaranto 

Abstract

Over the past decades, a variety of valuable research studies has helped to advance our understanding of the advantages and limitations of satellite derived precipitation datasets as a forcing to hydrological models, in combination with or as an alternative to gauge data.
However, most studies have assessed the performance of only one single dataset (or a few), have used global precipitation datasets to force lumped models on regional/large-scale basins or have tested more complex distributed models only at small-scale basins. In addition, only few studies have re-calibrated the model for each precipitation dataset or have investigated reanalysis-based precipitation datasets.
We aimed at addressing these gaps in the literature: in particular, we compared the performance of 18 different precipitation datasets when used as main forcing in a grid-based distributed hydrological model to assess streamflow in medium to large-scale river basins. These datasets are classified as Uncorrected Satellites (Class 1), Corrected Satellites (Class 2) and Reanalysis - Gauges based datasets (Class 3). To provide a broad-based analysis, 8 large-scale river basins (Amazon, Brahmaputra, Congo, Danube, Godavari, Mississippi, Rhine and Volga) having different sizes, hydrometeorological characteristics, and human influence were selected. The distributed hydrological model was recalibrated for each precipitation dataset individually.
We found that there is not a unique best performing precipitation dataset for all basins and that results are very sensitive to the basin characteristics. However, a few datasets persistently outperform the others: SM2RAIN-ASCAT for Class 1, CHIRPS V2.0, MSWEP V2.1, and CMORPH-CRTV1.0 for Class 2, GPCC and WFEDEI GPCC for Class 3. Surprisingly, precipitation datasets showing the highest model accuracy at basin outlets do not show the same high performance in internal locations, supporting the use of distributed modelling approach rather than lumped.

DOI

https://doi.org/10.31223/osf.io/v2r7c

Subjects

Earth Sciences, Hydrology, Physical Sciences and Mathematics

Keywords

remote sensing, Distributed hydrological modelling, Largescale hydrology, Precipitation datasets

Dates

Published: 2019-07-18 09:26

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License

CC BY Attribution 4.0 International

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