Affiliation:
1. State Key Laboratory of Hydrology‐Water Resources and Hydraulic Engineering National Key Laboratory of Water Disaster Prevention Hohai University Nanjing China
2. College of Hydrology and Water Resources Hohai University Nanjing China
3. School of Geographic Information and Tourism Chuzhou University Chuzhou China
4. College of Civil Engineering Nanjing Forestry University Nanjing China
Abstract
AbstractThe fifth generation European Centre for Medium‐Range Weather Forecasts Reanalysis on global land surface (ERA5‐Land), the Multi‐Source Weighted‐Ensemble Precipitation (MSWEP), and the Climate Hazards Group Infrared Precipitation with Stations (CHIRPS) are three representative precipitation estimates with quasi‐global coverage, high‐resolution and long‐term record. This study concentrates on investigating, for the first time, the long‐term spatiotemporal accuracy and regional applicability of these precipitation estimates at a daily scale in the Yellow River basin (YRB) using 39 complete years of data record (1981–2019), with a special focus on their capability on monitoring the extreme precipitation events with short duration and the continuous heavy precipitation events. Results indicate that MSWEP generally performs better than ERA5‐Land and CHIRPS in almost all seasons and subregions, with the highest Pearson correlation coefficient and critical success index, lowest root mean square error and false alarm ratio. ERA5‐Land presents a severe overestimation of precipitation amount, particularly in the plateau climate region (BIAS = 52.27%), but well reflects its spatial–temporal patterns in the YRB. As for the detecting capability, MSWEP shows the best accuracy in detecting extreme precipitation, particularly in maximum consecutive 5‐day precipitation (RX5day). The MSWEP better represents the spatial distribution of maximum 1‐day precipitation and maximum consecutive 5‐day precipitation in the YRB, but it shows a significant overestimation in zone Southern Qinghai. MSWEP and CHIRPS have better performance of temporal variation consistency in annual precipitation with ground reference than ERA5‐Land, while ERA5‐Land performs well in capturing extreme precipitation temporal variation, especially for continuous heavy precipitation events. This study can provide useful guidance when choosing long‐term precipitation products for hydrometeorological applications and climate‐related studies in the YRB.
Funder
National Natural Science Foundation of China