Gaozhen Wang, Hongxiang Wang, Lintong Huang, Ning He, Bing Wang, Fengtian Hong, Yanhua Li, Handong Ye, Jiaqi Lan, Wenxian Guo
{"title":"基于 MLLR-Budyko 框架的径流驱动因素定量评估","authors":"Gaozhen Wang, Hongxiang Wang, Lintong Huang, Ning He, Bing Wang, Fengtian Hong, Yanhua Li, Handong Ye, Jiaqi Lan, Wenxian Guo","doi":"10.2166/ws.2024.129","DOIUrl":null,"url":null,"abstract":"\n \n Few evaluation frameworks investigate the mechanisms causing runoff alterations by quantifying the causes of runoff alterations across different time scales (wet/normal/dry seasons and months) and in-depth analysis of each meteorological indicator's contribution to runoff change. This study quantitatively evaluated the hydrological regime of the Jialing River before and after the abrupt change predicated on the indicators of hydrologic alteration and range of variability approach (IHA-RVA) and the Gini coefficient. Through the partial differential equation of runoff characteristics (MLLR-Budyko), and separated in detail the contribution of each meteorological indicator to runoff alterations. Additionally, the ABCD model expanded and validated the results of the partial differential equations on the runoff contribution on a time scale. The findings demonstrate that the overall hydrological regime changed moderately in the river (48.63%). Of the 14 meteorological indicators separated by MLLR-Budyko, the wet season precipitation contributed the most to the runoff alterations, with a contribution rate of −178.12% of the runoff changes driven by all the meteorological indicators, and the coefficient of variation of the annual precipitation contributed the least, with a contribution rate of 2.16%; use ABCD model reconstruction of natural runoff found significant differences in the contribution of drivers at different time scales.","PeriodicalId":509977,"journal":{"name":"Water Supply","volume":null,"pages":null},"PeriodicalIF":0.0000,"publicationDate":"2024-06-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Quantitative evaluation of runoff drivers based on the MLLR-Budyko framework\",\"authors\":\"Gaozhen Wang, Hongxiang Wang, Lintong Huang, Ning He, Bing Wang, Fengtian Hong, Yanhua Li, Handong Ye, Jiaqi Lan, Wenxian Guo\",\"doi\":\"10.2166/ws.2024.129\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"\\n \\n Few evaluation frameworks investigate the mechanisms causing runoff alterations by quantifying the causes of runoff alterations across different time scales (wet/normal/dry seasons and months) and in-depth analysis of each meteorological indicator's contribution to runoff change. This study quantitatively evaluated the hydrological regime of the Jialing River before and after the abrupt change predicated on the indicators of hydrologic alteration and range of variability approach (IHA-RVA) and the Gini coefficient. Through the partial differential equation of runoff characteristics (MLLR-Budyko), and separated in detail the contribution of each meteorological indicator to runoff alterations. Additionally, the ABCD model expanded and validated the results of the partial differential equations on the runoff contribution on a time scale. The findings demonstrate that the overall hydrological regime changed moderately in the river (48.63%). Of the 14 meteorological indicators separated by MLLR-Budyko, the wet season precipitation contributed the most to the runoff alterations, with a contribution rate of −178.12% of the runoff changes driven by all the meteorological indicators, and the coefficient of variation of the annual precipitation contributed the least, with a contribution rate of 2.16%; use ABCD model reconstruction of natural runoff found significant differences in the contribution of drivers at different time scales.\",\"PeriodicalId\":509977,\"journal\":{\"name\":\"Water Supply\",\"volume\":null,\"pages\":null},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2024-06-04\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Water Supply\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.2166/ws.2024.129\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Water Supply","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.2166/ws.2024.129","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Quantitative evaluation of runoff drivers based on the MLLR-Budyko framework
Few evaluation frameworks investigate the mechanisms causing runoff alterations by quantifying the causes of runoff alterations across different time scales (wet/normal/dry seasons and months) and in-depth analysis of each meteorological indicator's contribution to runoff change. This study quantitatively evaluated the hydrological regime of the Jialing River before and after the abrupt change predicated on the indicators of hydrologic alteration and range of variability approach (IHA-RVA) and the Gini coefficient. Through the partial differential equation of runoff characteristics (MLLR-Budyko), and separated in detail the contribution of each meteorological indicator to runoff alterations. Additionally, the ABCD model expanded and validated the results of the partial differential equations on the runoff contribution on a time scale. The findings demonstrate that the overall hydrological regime changed moderately in the river (48.63%). Of the 14 meteorological indicators separated by MLLR-Budyko, the wet season precipitation contributed the most to the runoff alterations, with a contribution rate of −178.12% of the runoff changes driven by all the meteorological indicators, and the coefficient of variation of the annual precipitation contributed the least, with a contribution rate of 2.16%; use ABCD model reconstruction of natural runoff found significant differences in the contribution of drivers at different time scales.