{"title":"水文气候变化对流域管理中具有气候复原力的农业的影响","authors":"C. Singha, Satiprasad Sahoo, Ajit Govind, Biswajeet Pradhan, Shatha Alrawashdeh, Taghreed Hamdi Aljohani, Hussein Almohamad, Abu Reza Md Towfiqul Islam, Hazam Ghassan Abdo","doi":"10.2166/wcc.2023.656","DOIUrl":null,"url":null,"abstract":"\n \n This paper focuses on exploring the potential of climate-resilient agriculture (CRA) for river basin-scale management. Our analysis is based on long-term historical and future climate and hydrological datasets within a GIS environment, focusing on the Ajoy River basin in West Bengal, Eastern India. The standardized anomaly index (SAI) and slope of the linear regression (SLR) methods were employed to analyze the spatial pattern of the climate variables (precipitation, Tmax, and Tmin) and hydrological variables (actual evapotranspiration (AET), runoff (Q), vapor pressure deficit (VPD), potential evapotranspiration (PET), and climate water deficit (DEF)) using the TerraClimate dataset spanning from 1958 to 2020. Future climate trend analysis spanning 2021–2100 was conducted using the CMIP6-based GCMs (MIROC6 and EC-Earth3) dataset under shared socio-economic pathway SSP2-4.5, SSP5-8.5, and historical). For spatiotemporal water storage analysis, we relied on Gravity Recovery and Climate Experiment (GRACE) from CSR and JPL data, covering the period from 2002 to 2021. Validation was performed using regional groundwater level data, employing various machine learning classification models. Our findings revealed a negative precipitation trend (approximately −0.04 mm/year) in the southern part, whereas the northern part exhibited a positive trend (approximately 0.10 mm/year).","PeriodicalId":49150,"journal":{"name":"Journal of Water and Climate Change","volume":"29 11","pages":""},"PeriodicalIF":2.7000,"publicationDate":"2023-12-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Impacts of hydroclimate change on climate-resilient agriculture at the river basin management\",\"authors\":\"C. Singha, Satiprasad Sahoo, Ajit Govind, Biswajeet Pradhan, Shatha Alrawashdeh, Taghreed Hamdi Aljohani, Hussein Almohamad, Abu Reza Md Towfiqul Islam, Hazam Ghassan Abdo\",\"doi\":\"10.2166/wcc.2023.656\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"\\n \\n This paper focuses on exploring the potential of climate-resilient agriculture (CRA) for river basin-scale management. Our analysis is based on long-term historical and future climate and hydrological datasets within a GIS environment, focusing on the Ajoy River basin in West Bengal, Eastern India. The standardized anomaly index (SAI) and slope of the linear regression (SLR) methods were employed to analyze the spatial pattern of the climate variables (precipitation, Tmax, and Tmin) and hydrological variables (actual evapotranspiration (AET), runoff (Q), vapor pressure deficit (VPD), potential evapotranspiration (PET), and climate water deficit (DEF)) using the TerraClimate dataset spanning from 1958 to 2020. Future climate trend analysis spanning 2021–2100 was conducted using the CMIP6-based GCMs (MIROC6 and EC-Earth3) dataset under shared socio-economic pathway SSP2-4.5, SSP5-8.5, and historical). For spatiotemporal water storage analysis, we relied on Gravity Recovery and Climate Experiment (GRACE) from CSR and JPL data, covering the period from 2002 to 2021. Validation was performed using regional groundwater level data, employing various machine learning classification models. Our findings revealed a negative precipitation trend (approximately −0.04 mm/year) in the southern part, whereas the northern part exhibited a positive trend (approximately 0.10 mm/year).\",\"PeriodicalId\":49150,\"journal\":{\"name\":\"Journal of Water and Climate Change\",\"volume\":\"29 11\",\"pages\":\"\"},\"PeriodicalIF\":2.7000,\"publicationDate\":\"2023-12-15\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Journal of Water and Climate Change\",\"FirstCategoryId\":\"93\",\"ListUrlMain\":\"https://doi.org/10.2166/wcc.2023.656\",\"RegionNum\":4,\"RegionCategory\":\"环境科学与生态学\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q2\",\"JCRName\":\"WATER RESOURCES\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Journal of Water and Climate Change","FirstCategoryId":"93","ListUrlMain":"https://doi.org/10.2166/wcc.2023.656","RegionNum":4,"RegionCategory":"环境科学与生态学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q2","JCRName":"WATER RESOURCES","Score":null,"Total":0}
Impacts of hydroclimate change on climate-resilient agriculture at the river basin management
This paper focuses on exploring the potential of climate-resilient agriculture (CRA) for river basin-scale management. Our analysis is based on long-term historical and future climate and hydrological datasets within a GIS environment, focusing on the Ajoy River basin in West Bengal, Eastern India. The standardized anomaly index (SAI) and slope of the linear regression (SLR) methods were employed to analyze the spatial pattern of the climate variables (precipitation, Tmax, and Tmin) and hydrological variables (actual evapotranspiration (AET), runoff (Q), vapor pressure deficit (VPD), potential evapotranspiration (PET), and climate water deficit (DEF)) using the TerraClimate dataset spanning from 1958 to 2020. Future climate trend analysis spanning 2021–2100 was conducted using the CMIP6-based GCMs (MIROC6 and EC-Earth3) dataset under shared socio-economic pathway SSP2-4.5, SSP5-8.5, and historical). For spatiotemporal water storage analysis, we relied on Gravity Recovery and Climate Experiment (GRACE) from CSR and JPL data, covering the period from 2002 to 2021. Validation was performed using regional groundwater level data, employing various machine learning classification models. Our findings revealed a negative precipitation trend (approximately −0.04 mm/year) in the southern part, whereas the northern part exhibited a positive trend (approximately 0.10 mm/year).
期刊介绍:
Journal of Water and Climate Change publishes refereed research and practitioner papers on all aspects of water science, technology, management and innovation in response to climate change, with emphasis on reduction of energy usage.