Two decades of ensemble flood forecasting: a state-of-the-art on past developments, present applications and future opportunities
Jew Das, Velpuri Manikanta, K. Nikhil Teja, N. V. Umamahesh
Abstract
In the present study a total of 201 research articles from around the globe over the review period from 2001 to 2021 are identified and investigated. The study has identified some of the grand challenges associated with ensemble flood forecasting (EFF). In addition, future opportunities are identified including the development of quality-controlled datasets for longer duration, new techniques to weigh the ensembles, inclusion of more physically based datasets in data assimilation techniques, simulation of more observable states by hydrological models to facilitate data assimilation, application of artificial intelligence and machine learning, and optimization of computational efficiency to issue timely flood warnings. Moreover, it is worth mentioning that in addition to the technical aspects, the effectiveness of the operational flood forecasting system depends on the non-technical aspects.