Gebrechorkos, Solomon H., Leyland, Julian, Dadson, Simon J., Cohen, Sagy, Slater, Louise, Wortmann, Michel, Ashworth, Philip Anthony, Bennett, Georgina, Boothroyd, Richard, Cloke, Hannah, Delorme, Pauline, Griffith, Helen, Hardy, Richard J., Hawker, Laurence, McLelland, Stuart, Neal, Jeffrey, Nicholas, Andrew, Tatem, Andrew, Vahidi, Ellie, Liu, Yinxue, Sheffield, Justin, Parsons, Daniel and Darby, Steve (2024) Global scale evaluation of precipitation datasets for hydrological modelling. Hydrology and Earth System Sciences. (In Press)
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 (European Center for Medium-range Weather Forecast (ECMWF) Reanalysis version 5 (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 1825 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 correlation (CC) and Kling-Gupta Efficiency (KGE) than other datasets for more than 50% of the stations. Whilst ERA5 was the second-highest performing dataset and it showed the highest error and bias in about 20% of the stations. The PERCCDR is the least well-performing dataset with bias of up to 99% and a normalised root mean square error of up to 247%. PERCCDR only show 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.
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