Department of Remote Sensing, Birla Institute of Technology (BIT), Mesra, Ranchi, 835215, India.
Environ Monit Assess. 2022 Oct 21;195(1):8. doi: 10.1007/s10661-022-10557-z.
Environmental hazards like drought lead to degrading food production and adversely impact the agro-economy. This study investigates the contributions of different climatic and socio-economic variables to agricultural drought in Jharkhand. The three primary criteria, i.e., exposure (E), sensitivity (S), and adaptive capacity (AC), responsible for agricultural drought vulnerability, were examined to identify the drought-prone areas. Long-term (1958-2020) gridded climatic datasets obtained from the Terra-climate global dataset, MODIS vegetation index dataset (MOD13Q1) for the years 2001-2020, different soil parameters obtained from the ISRIC global soil database and state agricultural portal of Jharkhand, and different socio-economic datasets obtained from census data (2011) provided by Govt. of India, were utilized for this study. Analytic Hierarchy Process (AHP) was used to estimate the weighted contribution of the indicator variables falling under each criterion (E, S, and AC), and three criteria index maps were generated. These separate maps were further integrated to generate the final vulnerability index map. Finally, the study area was categorized into different zones based on the drought vulnerability index value ranging from 0 to 1, according to the severity of the drought. It was observed that about 4.05%, 28.12%, and 37.07% of the total geographical area is very highly, highly, and moderately vulnerable to agricultural drought, respectively. Amongst the three primary criteria, exposure showed a significant positive correlation (R = 0.61), and sensitivity showed a strong positive correlation (R = 0.55) with vulnerability. The adaptive capacity was negatively correlated (R = -0.75) with the vulnerability. However, putting equal weights to the variables to calculate the vulnerability, the exposure and sensitivity indicators showed a significant positive correlation with the vulnerability, with an R-value of 0.82 and 0.79, respectively. In contrast, the adaptive capacity showed a negative correlation with the vulnerability with R = -0.75.
环境危害,如干旱,导致粮食生产恶化,并对农业经济产生不利影响。本研究探讨了不同气候和社会经济变量对贾坎德邦农业干旱的贡献。本研究考察了导致农业干旱脆弱性的三个主要标准,即暴露(E)、敏感性(S)和适应能力(AC),以确定易受干旱影响的地区。本研究使用了来自 Terra-climate 全球数据集的长期(1958-2020 年)网格化气候数据集、2001-2020 年 MODIS 植被指数数据集(MOD13Q1)、来自 ISRIC 全球土壤数据库和贾坎德邦州农业门户网站的不同土壤参数以及来自印度政府人口普查数据(2011 年)的不同社会经济数据集。层次分析法(AHP)用于估计属于每个标准(E、S 和 AC)的指标变量的加权贡献,并生成三个标准指数图。然后,将这些单独的地图进一步整合,生成最终的脆弱性指数地图。最后,根据干旱脆弱性指数值(范围为 0 到 1),将研究区域划分为不同的区域,该值表示干旱的严重程度。结果表明,约 4.05%、28.12%和 37.07%的总地理区域分别非常高度、高度和中度易受农业干旱影响。在三个主要标准中,暴露与脆弱性呈显著正相关(R=0.61),敏感性与脆弱性呈强正相关(R=0.55)。适应能力与脆弱性呈负相关(R=-0.75)。然而,在计算脆弱性时,对变量赋予相同的权重,暴露和敏感性指标与脆弱性呈显著正相关,R 值分别为 0.82 和 0.79。相比之下,适应能力与脆弱性呈负相关,R 值为-0.75。