Reddy Nagireddy Masthan, Saravanan Subbarayan
Department of Civil Engineering, National Institute of Technology, Tiruchirappalli, India.
Environ Sci Pollut Res Int. 2023 Apr;30(16):47119-47143. doi: 10.1007/s11356-023-25649-7. Epub 2023 Feb 3.
In this study, a comparison of 17 Coupled Model Intercomparison Project Phase 6 (CMIP6) data sets with Indian Meteorological Department (IMD) data in terms of finding out extreme precipitation indices obtained by the Expert Team on Climate Change Detection and Indices (ETCCDI) is done. The extreme indices considered were the consecutive dry days (CDD), consecutive wet days (CWD), maximum 1-day precipitation (RX1DAY), maximum 5-day precipitation (RX5DAY), precipitation > 2.5 mm (RR2.5), heavy precipitation > 10 mm (R10MM), very heavy precipitation > 20 mm (R20MM), and simple daily intensity (SDII) that have been calculated for 17 CMIP6 datasets from 1950 to 2014 with IMD-gridded precipitation datasets over India. Rankings were assigned using the TOPSIS method with evaluation metrics as CC and RMSE as conditions. Almost all datasets performed well for indices, i.e., CWD, R10MM, R20MM, and RR2.5. The top 5 performing models are EC-Earth3, EC-Earth3-Veg, MRI-ESM2-0, GFDL-ESM4, and MIROC6 which were ensembled and projected for future periods in the near (2015-2040), middle (2041-2070), and far future (2070-2100), and extreme indices were calculated under Shared Socioeconomic Pathways 126 (SSP126), SSP245, SSP370, and SSP585 scenarios. The ensemble mean shows that RX1DAY, RX5DAY, R10MM, R20MM, and CWD are observed to increase in western ghats and northeastern regions of India. Central India exhibits a dynamic influence on precipitation indices in different climate change scenarios. The temporal variation for the SSP585 scenario predicts significant increases of about 45.41%, 149.40%, 52.26%, and 45.92% in R10MM, R20MM, RX1DAY, and RX5DAY over the current climate. Future extreme precipitation indices help flood modelers and hydrologists with watershed management.
在本研究中,对17个耦合模式比较计划第六阶段(CMIP6)数据集与印度气象部门(IMD)的数据进行了比较,以找出气候变化检测与指数专家团队(ETCCDI)得出的极端降水指数。所考虑的极端指数包括连续干旱天数(CDD)、连续湿润天数(CWD)、1日最大降水量(RX1DAY)、5日最大降水量(RX5DAY)、降水量>2.5毫米(RR2.5)、强降水量>10毫米(R10MM)、极强降水量>20毫米(R20MM)以及简单日强度(SDII),这些指数是根据1950年至2014年期间17个CMIP6数据集以及印度IMD网格化降水数据集计算得出的。使用TOPSIS方法进行排名,评估指标为相关系数(CC)和均方根误差(RMSE)。几乎所有数据集在CWD、R10MM、R20MM和RR2.5等指数方面表现良好。表现最佳的前5个模式分别是EC - Earth3、EC - Earth3 - Veg、MRI - ESM2 - 0、GFDL - ESM4和MIROC6,对这些模式进行集合,并针对近期(2015 - 2040年)、中期(2041 - 2070年)和远期(2070 - 2100年)进行未来预测,同时在共享社会经济路径126(SSP126)、SSP245、SSP370和SSP585情景下计算极端指数。集合平均值显示,在印度西高止山脉和东北地区,RX1DAY、RX5DAY、R10MM、R20MM和CWD呈上升趋势。印度中部在不同气候变化情景下对降水指数呈现出动态影响。SSP585情景下的时间变化预测,与当前气候相比,R10MM、R20MM、RX1DAY和RX5DAY将分别显著增加约45.41%、149.40%、52.26%和45.92%。未来的极端降水指数有助于洪水模型构建者和水文工作者进行流域管理。