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The aim of this study is to develop an indicator-based framework for assessing vulnerability to different types of droughts and generate spatio-temporal patterns of socioeconomic drought using machine learning models. The study is conducted in India, a country highly susceptible to drought due to its varied topography and tropical monsoon climate. Current literature lacks a comprehensive approach that integrates all four types of droughts (i.e., meteorological, agricultural, hydrological and socioeconomic) and assesses spatiotemporal dynamics of drought vulnerability from a science-policy perspective. Therefore, one of the objectives of this study is to propose an integrated indicator-based framework for assessing drought vulnerability that considers all four types of droughts and supports policymakers in drought management. The framework uses various methods such as Principal Component Analysis, Variance Inflation Factor, and Shannon Entropy to construct indicators. The study has identified several states that are highly vulnerable to one or more themes of drought, such as Jharkhand and Telangana. Rajasthan, Gujarat, Maharashtra, and the Bundelkhand region in Uttar Pradesh are the most drought-prone areas in India. These regions frequently experience drought due to a combination of climatic, geographical, and socio-economic factors. The central regions of India are particularly vulnerable to meteorological drought, while states like Uttar Pradesh, Manipur, Assam, and Meghalaya have shown increasing trend in agricultural drought vulnerability. Many states across India like Uttarakhand, Jharkhand, Uttar Pradesh, and Haryana are vulnerable to hydrological drought, and several states, including Telangana, Madhya Pradesh, Bihar, Rajasthan, Gujarat, Jharkhand, Assam, and Chhattisgarh are vulnerable to socioeconomic drought.
Uttar Pradesh and Uttarakhand are two states located in the northern India that rely heavily on the River Ganga as a critical source of water for agriculture, industry, and drinking purposes. One of the most important tributaries of the Ganga River that runs through these states is the Ramganga River. Unfortunately, the Ramganga River has been experiencing water deficit due to various reasons such as climate change, deforestation, and low water use efficiency both in irrigation and industry. The scarcity of water in the region has exacerbated the socio-economic vulnerability of both states. It has been observed that the existing literature focus on drought estimation using a variety of indicators, however there is currently no index that can be used to measure socioeconomic drought at different spatial scales. Moreover, these indices are difficult to calculate due to lack of data. By establishing a new socioeconomic drought index, this study seeks to overcome that constraint. The study aims at developing a new socioeconomic drought indicator-based framework that can be used to easily measure the socioeconomic impact of drought. The index is based on a composite approach that combines several indicators, including water demand and supply, economic activities, and social vulnerability. The index will help decision-makers to understand the extent of the socioeconomic impact of drought better and to develop effective measures to address it. In the Ramganga Basin, districts in Uttar Pradesh face a higher risk of socioeconomic drought than those in Uttarakhand, as indicated by a formula combining the water deficit index with the vulnerability index. This increased risk in Uttar Pradesh is attributed to factors like low literacy rates, high population density, limited tap water access, and a significant marginalized population. In contrast, Uttarakhand's challenges stem from its limited water resources and difficult terrain, although recent government efforts in water management have mitigated drought risks to some extent.
Many existing works focus exclusively on assessment and prediction of drought while the role of policy is missing in most of the studies. There is a need to develop a solution-based framework which could be used to identify drought through indicators, so that the concerned administrations can utilize it as guiding tool for drought management. AI-ML based approaches can reveal the association among various drought types through long term time series of the drought indicators. The study's third objective is to draw linkages among different types of droughts using AI/ML-based models. The study also identifies the trend of drought severity in the basin.
In conclusion, this study introduces a framework to assess socioeconomic drought and bridge the gap in the integrated assessment of different types of droughts. Observed drought events have been collected from various sources including government agencies, and media reports. Finally, the linkages among different types of droughts have been assessed using machine learning models. The results of this study offer crucial insights into the vulnerability and management of drought in India, and aid in effective policy making and strategies for drought mitigation and adaptation.
