Spatio-temporal accuracy evaluation of MSWEP daily precipitation over the Huaihe River Basin, China: A comparison study with representative satellite- and reanalysis-based products

Li, Lingjie; Wang, Yintang; Wang, Leizhi; Hu, Qizhou; Zhu, Zhenduo; Li, Liping; Li, Chengxi

doi:10.1007/s11442-022-2047-9

Cited by 9 publications

(2 citation statements)

References 40 publications

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“…A comparative study of 22 global-scale precipitation datasets shows that MSWEP combines the strengths of several source datasets and generally has the best accuracy [84]. Studies at regional scales such as in the Qinghai-Tibet Plateau [85], the Huaihe River Basin (China) [86], India [87], and the Highlands of Indo-Pak [88] have also shown good performance of MSWEP. In particular, studies by Tang et al [89,90] and Tian et al [91] in the LMRB also demonstrate the applicability of MSWEP.…”

Section: Data Descriptionmentioning

confidence: 99%

Spatiotemporal Projections of Precipitation in the Lancang–Mekong River Basin Based on CMIP6 Models

Sun,

Liu,

Zhang

et al. 2023

Remote Sensing

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The Lancang–Mekong River Basin (LMRB) is the largest international river in Southeast Asia, supporting a population of about 70 million people. Precipitation is the main source of water resources in the basin, with significant impacts on ecology, production, and livelihoods in the basin. In this study, future precipitation was projected using the Coupled Model Intercomparison Project Phase 6 (CMIP6) climate models. The initial bias of each model was corrected using the daily bias-correction (DBC) method, and then the models were ensembled using the Bayesian model-averaging (BMA) method. The evaluation, based on metrics such as climatology bias, root-mean-square error (RMSE), mean absolute error (MAE), and correlation coefficient (COR), showed that the ensemble precipitation performs better than the individual models. Precipitation under four future Shared Socioeconomic Pathway scenarios (SSP126, SSP245, SSP370, SSP585) displayed an increasing trend throughout the LMRB. The anomalies in annual precipitation in 2061–2090 under each scenario are 136 mm, 142 mm, 114 mm, and 227 mm, in that order. Precipitation in spring and winter shows a trend of increasing in the northern LMRB and decreasing in the southern LMRB, and precipitation in summer and autumn shows a significant trend of increasing in almost the whole basin (significance level 0.05). Spring precipitation in the Mekong Delta decreases in all scenarios. The ratio of wet-season precipitation to dry-season precipitation shows an increasing trend for all scenarios, indicating that the difference between wet-season precipitation and dry-season precipitation will increase in the future. For daily precipitation, the Lancang River Basin (LRB) is dominated by a 3–5% increase in the number of days with 5–10 mm/d of precipitation and the Mekong River Basin (MRB) by a 3–5% increase in the number of days with 10–20 mm/d of precipitation under four SSP scenarios in 2061–2090. There are important changes in the spatial distribution of future precipitation, with the 2500 mm isohyet expanding outwards in a circular pattern and the center of the 1500 mm isohyet moving westwards; i.e., areas with annual precipitation exceeding 2500 mm and 1500 mm will expand. For dry-season precipitation, the 500 mm isohyet shrinks, mainly in a circular pattern towards the center, while the 300 mm isohyet moves mainly towards the east, indicating that areas of dry-season precipitation below 500 mm and 300 mm will expand. In the future, the LMRB will generally become wetter in the wet season and drier in the dry season.

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Section: Data Descriptionmentioning

confidence: 99%

Spatiotemporal Projections of Precipitation in the Lancang–Mekong River Basin Based on CMIP6 Models

Sun,

Liu,

Zhang

et al. 2023

Remote Sensing

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“…In addition to the uncertainty in the precipitation datasets, the poorer performance in some regions presented in this and previous studies (Beck et al, 2017a;Lin et al, 2019;Harrigan et al, 2020) observations from field-based meteorological stations, in addition to a large set of satellite and reanalysis datasets (Beck et al, 2017a(Beck et al, , 2019a. Other studies have also shown the good performance of MSWEP for hydrological modelling in different parts of the world (Beck et al, 2017a;Lakew, 2020;Li et al, 2022a;Reis et al, 2022;Gu et al, 2023;López López et al, 2017;Satgé et al, 2019;Ibrahim et al, 2022). For example, Satgé et al (2019) evaluated 12 satellite-based precipitation estimates such as MSWEP, CHIRPS and PERSIANN-CDR in South America (Lake Titicaca region) and found MSWEP was the best precipitation dataset for realistic simulation of river discharge.…”

Section: Discussionmentioning

confidence: 64%

Global scale evaluation of precipitation datasets for hydrological modelling

Gebrechorkos,

Leyland,

Dadson

et al. 2023

Preprint

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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.

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Improving categorical and continuous accuracy of precipitation forecasts by integrating Empirical Quantile Mapping and Bernoulli-Gamma-Gaussian distribution

Li,

Yun,

Liu

et al. 2024

Atmospheric Research

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Spatio-temporal accuracy evaluation of MSWEP daily precipitation over the Huaihe River Basin, China: A comparison study with representative satellite- and reanalysis-based products

Cited by 9 publications

References 40 publications

Spatiotemporal Projections of Precipitation in the Lancang–Mekong River Basin Based on CMIP6 Models

Spatiotemporal Projections of Precipitation in the Lancang–Mekong River Basin Based on CMIP6 Models

Global scale evaluation of precipitation datasets for hydrological modelling

Improving categorical and continuous accuracy of precipitation forecasts by integrating Empirical Quantile Mapping and Bernoulli-Gamma-Gaussian distribution

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