26 Oct 2023
 | 26 Oct 2023
Status: this preprint is currently under review for the journal HESS.

Global scale evaluation of precipitation datasets for hydrological modelling

Solomon Hailu Gebrechorkos, Julian Leyland, Simon J. Dadson, Sagy Cohen, Louise Slater, Michel Wortmann, Philip J. Ashworth, Georgina L. Bennett, Richard Boothroyd, Hannah Cloke, Pauline Delorme, Helen Griffith, Richard Hardy, Laurence Hawker, Stuart McLelland, Jeffrey Neal, Andrew Nicholas, Andrew J. Tatem, Ellie Vahidi, Yinxue Liu, Justin Sheffield, Daniel R. Parsons, and Stephen E. Darby

Abstract. Precipitation is the most important driver of the hydrological cycle but is challenging to estimate over large scales from satellites and models. Here, we assessed the performance of six global and quasi-global high-resolution precipitation datasets (ERA5 global reanalysis (ERA5), Climate Hazards group Infrared Precipitation with Stations version 2.0 (CHIRPS), Multi-Source Weighted-Ensemble Precipitation version 2.80 (MSWEP), TerraClimate (TERRA), Climate Prediction Centre Unified version 1.0 (CPCU) and Precipitation Estimation from Remotely Sensed Information using Artificial Neural Networks-Cloud Classification System-Climate Data Record (PERCCDR)) for hydrological modelling globally and quasi-globally. We forced the WBMsed global hydrological model with the precipitation datasets to simulate river discharge from 1983 to 2019 and evaluated the predicted discharge against more than 1800 hydrological stations worldwide, using a range of statistical methods. The results show large differences in the accuracy of discharge predictions when using different precipitation input datasets. Based on evaluation at annual, monthly and daily time scales, MSWEP followed by ERA5 demonstrated a higher CC and KGE than other datasets for more than 50 % of the stations. Whilst, ERA5 was the second-highest performing dataset, it showed the highest error and bias in about 20 % of the stations. The PERCCDR is the least well performing dataset with large bias (percentage of bias up to 99 %) and errors (normalised root mean square error up  to 247 %) with a higher KGE and CC than the other products in less than 10 % of the stations. Even though MSWEP provided the highest performance overall, our analysis reveals high spatial variability, meaning that it is important to consider other datasets in areas where MSWEP showed a lower performance. The results of this study provide guidance on the selection of precipitation datasets for modelling river discharge for a basin, region or climatic zone as there is no single best precipitation dataset globally. Finally, the large discrepancy in the performance of the datasets in different parts of the world highlights the need to improve global precipitation data products. 

Solomon Hailu Gebrechorkos et al.

Status: open (until 21 Dec 2023)

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Solomon Hailu Gebrechorkos et al.

Solomon Hailu Gebrechorkos et al.


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Short summary
Our global precipitation data evaluation for hydrological modelling revealed variations in dataset accuracy. The Multi-Source Weighted-Ensemble Precipitation version 2.80 (MSWEP) followed by ERA5 performed well in some areas but had limitations in others. This informs dataset choice for river discharge modelling and highlights the need for improved global precipitation data quality, especially for daily and extreme values.