Towards use of extreme rainfall forecast and socio-economic data to generate Impact-based forecasts

Each year India witnesses numerous casualties, economic losses and vast displacement of people due to extreme rainfall events (EREs). One of the reasons for such losses is that the weather warnings associated with the EREs are not properly communicated to the general public. It is essential that the expected impacts are communicated well in advance so that appropriate remedial actions can be taken and losses can be minimized. Several national and regional rainfall forecasting agencies have started issuing risk-based warnings which includes the potential impacts arising due to the EREs. This framework of providing forecast information based on the potential impacts of a hazard is called Impact-Based Forecasting (IBF). In this study, we develop a framework for generating the impact-based forecasts and associated warning matrices for the districts of eastern Uttar Pradesh, India, by integrating the rainfall forecasts and the socio-economic characteristics of the region. The region is densely populated, has relatively poor socio-economic conditions and is prone to frequent EREs. We take into account various sectors such as population, economy and agriculture where maximum impacts are expected to take place. Moreover, we identify the vulnerable districts based on the frequency of the number of extreme rainfall forecasts in the past four years (2017-2020) and the nature of socio-economic conditions. The vulnerable districts are categorized in three categories (low, medium and high) based on the expected impacts. For each of the vulnerable districts, sample IBFs and warning matrices are generated. IBFs inform about the possible impacts different sectors in each district may face on a given day due to the forecasted ERE. On the other hand, warning matrices provide updated information regarding the category of risk for the district a few days in advance. The study is significant since it follows a methodological framework to generate impact-based forecasts and warnings which includes analysis of rainfall forecasts, identification of possible impacts and suggestion of appropriate mitigation actions.