Impacts of hydroclimate change on climate-resilient agriculture at the river basin management

被引:4
|
作者
Singha, Chiranjit [1 ]
Sahoo, Satiprasad [2 ]
Govind, Ajit [2 ]
Pradhan, Biswajeet [3 ,4 ]
Alrawashdeh, Shatha [5 ]
Aljohani, Taghreed Hamdi [6 ]
Almohamad, Hussein [7 ]
Islam, Abu Reza Md Towfiqul [8 ,9 ]
Abdo, Hazem Ghassan [10 ]
机构
[1] Visva Bharati Cent Univ, Inst Agr, Dept Agr Engn, Sriniketan 731236, Birbhum, India
[2] Int Ctr Agr Res Dry Areas ICARDA, 2 Port Said,Victoria Sq,Ismail El Shaaer Bldg, Cairo 11728, Egypt
[3] Univ Technol Sydney, Fac Engn & IT, Ctr Adv Modelling & Geospatial Informat Syst CAMGI, Sch Civil & Environm Engn, Sydney, NSW 2007, Australia
[4] Univ Kebangsaan Malaysia, Inst Climate Change, Earth Observat Ctr, Bangi 43600, Ukm, Malaysia
[5] Al Hussein Bin Talal Univ, Fac Art, Dept Geog, Maan, Jordan
[6] Taibah Univ, Fac Arts & Humanities, Geog Dept, Medina, Saudi Arabia
[7] Qassim Univ, Coll Arab Language & Social Studies, Dept Geog, Buraydah 51452, Saudi Arabia
[8] Begum Rokeya Univ, Dept Disaster Management, Rangpur 5400, Bangladesh
[9] Daffodil Int Univ, Dept Dev Studie, Dhaka 1216, Bangladesh
[10] Tartous Univ, Fac Arts & Humanities, Geog Dept, Tartous, Syria
关键词
agricultural adaptability; climate change; climate-resilient agriculture; food security; hydroclimatic change; VARIABILITY; REGRESSION;
D O I
10.2166/wcc.2023.656
中图分类号
TV21 [水资源调查与水利规划];
学科分类号
081501 ;
摘要
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, T-max,T- and T-min) 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).
引用
收藏
页码:209 / 232
页数:24
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