用机器学习选气候模型，精准预测印度河支流区未来降水变化。
Selection of CMIP6 Models for Regional Precipitation Projection and Climate Change Assessment in the Jhelum and Chenab River Basins
- 基于包络法与机器学习筛选适合的CMIP6模型，无需实测数据。
- 选定NorESM2 LM和FGOALS g3为杰赫勒姆与奇纳布河流域最优模型。
- 揭示旁遮普、查谟、克什米尔部分地区受气候变化影响最严重。
准确预测水道径流量依赖于气候数据的可靠投影。不同全球气候模型（GCM）会给出差异显著的结果。本研究针对最新一代CMIP6数据，提出一种基于包络法的GCM筛选方法，融合机器学习技术，可在无实测参考数据情况下完成模型选择。据我们所知，首次对CMIP6共享社会经济路径（SSP）情景数据进行了此类比较。同时分析了不同SSP情景下的气候变化影响，并计算极端气候指数。最后，对比了CMIP5与CMIP6数据在时空上的差异。结果表明，诺尔森2 LM和FGOALS g3是杰赫勒姆与奇纳布河流域的优选模型。空间图显示旁遮普、查谟及克什米尔部分区域气候脆弱性高。相较于CMIP5的RCP情景，当前的SSP情景降水投影未表现出明显差异。未来可进一步开展更细致的统计比较以强化结论。
原文摘要 · Abstract (English)
Effective water resource management depends on accurate projections of flows in water channels. For projected climate data, use of different General Circulation Models (GCM) simulates contrasting results. This study shows selection of GCM for the latest generation CMIP6 for hydroclimate change impact studies. Envelope based method was used for the selection, which includes components based on machine learning techniques, allowing the selection of GCMs without the need for in-situ reference data. According to our knowledge, for the first time, such a comparison was performed for the CMIP6 Shared Socioeconomic Pathway (SSP) scenarios data. In addition, the effect of climate change under SSP scenarios was studied, along with the calculation of extreme indices. Finally, GCMs were compared to quantify spatiotemporal differences between CMIP5 and CMIP6 data. Results provide NorESM2 LM, FGOALS g3 as selected models for the Jhelum and Chenab River. Highly vulnerable regions under the effect of climate change were highlighted through spatial maps, which included parts of Punjab, Jammu, and Kashmir. Upon comparison of CMIP5 and CMIP6, no discernible difference was found between the RCP and SSP scenarios precipitation projections. In the future, more detailed statistical comparisons could further reinforce the proposition.
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