Fuzzy Ensemble Clustering Approach to Address Regionalization Uncertainties in Flood Frequency Analysis

Various regionalization approaches are in use to delineate watersheds in an area into homogeneous groups/regions for regional frequency analysis of hydrologic extremes (floods, droughts). Each approach differs in the underlying assumptions and strategy for regionalization and yields regions that differ in composition. There is ambiguity in the choice of approaches, as none is established to be universally superior in yielding true regions that are unknown. This article proposes a novel fuzzy ensemble clustering (FEC) approach to deal with uncertainty in the composition of regions obtained using different regionalization approaches. It forms fuzzy meta‐regions by integrating information on similarities in watershed groupings found in an ensemble of regions derived using several regionalization approaches. The FEC approach's potential to form effective regions (than those found in the ensemble) and their utility in regional flood frequency analysis to predict flood quantiles at ungauged sites is demonstrated through Monte Carlo simulation experiments and a case study on peninsular India. An ensemble of regions for use with FEC is formed using the region of influence approach, clustering approach based on Gaussian mixture model (GMM), and a hybrid approach which combines canonical correlation analysis with GMM based clustering. The FEC is shown to be effective in grouping similar watersheds even when the ensemble comprises regions having some improperly/wrongly grouped watersheds. Key Points Proposes a novel fuzzy ensemble watershed regionalization approach to address regionalization uncertainty in flood frequency analysis It alleviates uncertainty in watershed regionalization by ameliorating information on watershed grouping patterns derived using different approaches Potential of the approach is demonstrated through Monte Carlo simulation experiments and a case study on watersheds in peninsular India