Tayyaba Syed, Puppala Harish, Arora Manoj Kumar
Department of Civil Engineering, SRM University-AP, Amaravati, 522240, Andhra Pradesh, India.
Centre for Geospatial Technology, SRM University-AP, Amaravati, 522240, Andhra Pradesh, India.
Environ Monit Assess. 2025 Jul 5;197(8):866. doi: 10.1007/s10661-025-14324-8.
Droughts are one of the most severe natural hazards, and its occurrences are increasingly exacerbated due to climate change. While numerous studies have analyzed drought occurrences using multi-model ensembles (MME) developed considering uniform weights to general circulation models (GCMs), biases inherent in these models impeded the attainment of reliable predictions. Also, studies conducted were region specific and were limited to considering a specific socio-economic pathway (SSP). The inconsistency in findings drawn across different SSPs limits the applicability of these results to implement best management practices to combat drought effectively. In this study, Drought Prone Index (DPI) built on the mathematical framework of Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS) has been proposed. This index represents the frequency and severity of the possible drought events considering near future (2024-2060) and far future (2061-2100). Further, to overcome the limitation of bias, a multi-criteria decision-making (MCDM) framework integrating CRiteria Importance Through Intercriteria Correlation (CRITIC) and analytical hierarchy process (AHP) methods has been proposed to create differential weighted multi-model ensemble. The proposed framework is demonstrated considering India as study area. Findings of our study indicate a significant increase in rainfall and temperature ranging between 100-440 mm, and 0.75-3.5 °C across different SSP scenarios. Alongside a decline in rainfall in certain regions of Northeast India and the Western Ghats is observed from the derived spatial maps created using the data of developed MME. Spatial variation of DPI computed at a district level indicates that though the frequency of drought occurrences in the near and far future periods does not substantially increase, the severity of droughts is found to be intense. Findings highlight that it is imperative to consider the influence of climate change while assessing the droughts. These findings can assist policymakers and stakeholders in prioritizing resource allocation and implementing targeted mitigation strategies.
干旱是最严重的自然灾害之一,由于气候变化,其发生频率日益加剧。虽然许多研究使用考虑统一权重的多模型集合(MME)对通用环流模型(GCM)进行分析以研究干旱发生情况,但这些模型固有的偏差阻碍了获得可靠的预测结果。此外,已开展的研究是针对特定区域的,并且仅限于考虑特定的社会经济路径(SSP)。不同SSP得出的研究结果不一致,限制了这些结果在有效实施最佳管理实践以应对干旱方面的适用性。在本研究中,提出了基于逼近理想解排序法(TOPSIS)数学框架构建的干旱易发性指数(DPI) 。该指数表示考虑近期(2024 - 2060年)和远期(2061 - 2100年)可能发生干旱事件的频率和严重程度。此外,为克服偏差限制,提出了一个多标准决策(MCDM)框架,该框架整合了基于准则间相关性确定准则重要性(CRITIC)和层次分析法(AHP)方法,以创建差异加权多模型集合。以印度为研究区域对所提出框架进行了验证。我们研究结果表明,在不同SSP情景下降雨和温度显著增加,增幅分别在100 - 440毫米和0.75 - 3.5摄氏度之间。同时,根据使用已开发的MME数据创建的空间地图观察到印度东北部某些地区和西高止山脉降雨量有所下降。在地区层面计算的DPI空间变化表明,虽然近期和远期干旱发生频率没有大幅增加,但干旱的严重程度却很高。研究结果强调,在评估干旱时必须考虑气候变化的影响。这些结果可以帮助政策制定者和利益相关者确定资源分配的优先级并实施有针对性的缓解策略。