India-wide Extreme Rainfall Driven Flood Hazard Forecasting

In recent decades, the frequency and intensity of extreme precipitation events has increased worldwide as a result of climate change. It is necessary to set up early warning systems to enable effective disaster prevention in flood-prone areas. Despite efforts to develop modern forecasting systems for extreme precipitation, there are still problems such as low hit rates, high false alarms and spatio-temporal distortions. In particular, the crucial aspect of forecasting rainfall risk at the national level for India has not been addressed in the existing literature. In this study, an attempt is made to predict the flood hazards caused by extreme rainfall by estimating the probability of occurrence of an extreme rainfall event based on the predicted rainfall values with a certain lead time. The hazard model is based on the conditional probability of historical observed and predicted rainfall data. In applying the method in India, reliable data sources are used, including observed gridded precipitation data from the India Meteorological Department (IMD) and forecast precipitation from the Global Ensemble Forecast System (GEFS) Reforecast Version 2 for the period from 1985 to 2018 (34 years). Extreme precipitation days are identified as those that exceed the 95^th percentile value for a given grid. Hazard assessments are carried out at grid level for lead times of 1, 3, 5, 10 and 15 days. The resulting hazard maps are consistent with the observed rainfall patterns confirmed by the recent rainfall-induced floods in India. This model gives stakeholders the ability to identify regions that are at risk in the near future (weeks). This facilitates proactive evacuation and mitigation planning prior to the occurrence of extreme rainfall events.