Development of understanding in hydro-climate services in India to inform food and water security

This project aims to improve understanding of hydro-climate services in India in order to inform food and water security. It involves collaboration between UCL and the Centre for Ecology and Hydrology (CEH) in the UK and the National Institute of Hydrology (NIH), Roorkee and Indian Institute of Technology (IIT), Bombay in India. This report is structured around the three main themes of the project: catchment hydrological modelling, assessment of environmental flows under climate change, and a feasibility study to assess the potential of developing guidance for India similar to that of the Flood Estimation Handbook for the UK

Flood frequency estimation in data-sparse Maharashtra, India

Monsoon-related extreme flood events are experienced regularly in the Godavari and Krishna river basins in peninsular India, causing costly damage and disruption to local communities. Being able to estimate the likely magnitude of the 1-in-100 year flood, say, would allow hydrological practitioners to design new structures to prevent such damage or at least withstand it. To this end, catchment descriptor equations were developed through 10 multiple linear regression and stepwise model selection to estimate the median of the annual maximum flow series (QMED), making use of open source and freely available datasets. To allow the estimation of floods with other specified return periods, Hosking-Wallis distribution tests were performed to select the most appropriate distribution to model the annual maxima series; the Generalised Pareto was highlighted as the most able to accurately describe specific stations, and the Pearson Type III was seen to be the distribution most useful at being 15 able to describe extreme flow behaviour across the entire region

Comparison of nonstationary regional flood frequency analysis techniques based on the index-flood approach

Regional flood frequency analysis (RFFA) techniques are used in hydrological applications for estimation of design quantiles at ungauged sites or catchments with sparse observational records. The index-flood method, a popular approach for RFFA, is based on the assumption that the flood records within a homogeneous region are identically distributed, except for a site-specific index flood. Because of rapidly changing land-use patterns, human interventions, and climate change, recent studies proposed extension of the index-flood method to account for nonstationarity in flood records. This work compared index-flood–based nonstationary RFFA techniques, in both synthetically generated and real-world homogeneous regions, with sites marked by significant trends in flood records. From the data used in the analysis, it is evident that two recently proposed transformation-based approaches are more suitable compared to other methods, and can capture time-varying behavior of floods effectively