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Potential Use of Extreme Rainfall Forecast and Socio-Economic Data for Impact-Based Forecasting at the District Level in Northern India

Due to the increase in extreme rainfall events in India, there is an urgent need for prior communication of the expected impacts and appropriate responses in order to mitigate the losses of lives and damage to property. Extreme rainfall events cause numerous casualties, damage to property and infrastructure and vast displacement of people. Hence, the development of an approach where the rainfall forecasts are well analyzed, associated risks are identified, and the probable impacts are clearly communicated to relevant stakeholders is required. In this study, we aim to develop a framework for generating the impact-based forecasts (IBF) and associated warning matrices over the selected districts of eastern Uttar Pradesh, India, by integrating the rainfall forecasts and the socio-economic characteristics such as population, economy and agriculture. The selected districts lack proper infrastructure, have poor socio-economic conditions and have been historically prone to frequent extreme rainfall. The basic idea is to estimate the impacts that could occur over various sectors of population, economy and agriculture and suggest appropriate actions in order to mitigate the severity of the impacts. To this end, we identify the vulnerable districts based on the frequency of the number of extreme rainfall forecasts (ERFs) in the past four years (2017–2020) and the nature of socio-economic conditions. We selected three vulnerable districts based on the expected impacts, i.e., Shravasti (high category), Gorakhpur (medium category) and Jaunpur (low category) and subsequently, the corresponding IBFs are generated. Furthermore, a warning matrix is created for each district which provides updated information regarding the potential risk for a district a few days in advance. This study is significant since it identifies the different levels of potential impact over multiple sectors of society, presents a framework to generate impact-based forecasts and warnings, informs about the expected impacts, and suggests mitigation actions to reduce potential damage and losses.