Department of Geography, The University of Burdwan, Bardhaman, West Bengal, 713104, India.
Department of Geography, The University of Burdwan, Bardhaman, West Bengal, 713104, India.
Sci Total Environ. 2022 Nov 25;849:157850. doi: 10.1016/j.scitotenv.2022.157850. Epub 2022 Aug 5.
The problem of drought in India is a major issue in terms of various adverse impacts on livelihood of society. Drought Early Warning System (DEWS), a real-time drought-monitoring tool, has reported that over a fifth of India's geographical area (21.06 %) is suffering drought-like situations. This is 62 % larger than the drought-affected area during the same period last year, which was 7.86 %. Drought affects 21.06 %, with conditions ranging from unusually dry to extremely dry. While 1.63 % and 1.73 % of the area are experiencing 'extreme' or 'exceptional' dry conditions, 2.17 % is experiencing 'severe' dry conditions. Under 'moderate' dry circumstances, up to 8.15 % is possible. In this perspective groundwater vulnerability assessment in the overall country is needed for implementing the sustainable and long-term strategies for escaping from this type of hazardous situation. The main objective of this study is to estimate the drought vulnerability in changing climate which eventually influences the food security of India. The groundwater overdraft is one of the crucial elements in agricultural drought vulnerability. Various related parameters have been selected for estimating the drought vulnerability and its impact to food security in India. Here, MaxEnt (maximum entropy) and ANN (analytical neural network) has been considered in this perspective. The AUC values for the training datasets in the ANN and MaxEnt model are 0.891 and 0.921, respectively. The AUC values in ANN and MaxEnt model for the validation datasets are 0.876 and 0.904, respectively. Here MaxEnt model is most optimal than ANN considering predictive accuracy. From this study analysis it is established that western, south and middle portion of country is very much prone to drought vulnerability. So, special emphases in terms of the regional planning have to be taken into consideration for sustainable planning.
印度的干旱问题是一个重大问题，因为它对社会生计造成了各种不利影响。干旱预警系统（DewS）是一种实时干旱监测工具，该系统报告称，印度超过五分之一的地区（21.06%）正处于类似干旱的情况。这比去年同期受干旱影响的地区（7.86%）大 62%。干旱影响了 21.06%的地区，情况从异常干燥到极度干燥不等。虽然 1.63%和 1.73%的地区正经历“极端”或“异常”干旱状况，但 2.17%的地区正经历“严重”干旱状况。在“中度”干旱情况下，最高可达 8.15%。从这个角度来看，需要对全国地下水脆弱性进行评估，以实施可持续和长期的战略，摆脱这种危险情况。本研究的主要目的是评估气候变化下的干旱脆弱性，这最终会影响印度的粮食安全。地下水超采是农业干旱脆弱性的关键因素之一。为了估计印度干旱脆弱性及其对粮食安全的影响，选择了各种相关参数。在此，从这个角度考虑了最大熵（Maximum Entropy）和人工神经网络（Artificial Neural Network）。ANN 和 MaxEnt 模型在训练数据集上的 AUC 值分别为 0.891 和 0.921。ANN 和 MaxEnt 模型在验证数据集上的 AUC 值分别为 0.876 和 0.904。考虑到预测精度，MaxEnt 模型比 ANN 模型更为最优。从这项研究的分析可以看出，该国的西部、南部和中部地区非常容易受到干旱脆弱性的影响。因此，必须在区域规划方面给予特别重视，以进行可持续规划。
