Bidroha Basu

Regional flood frequency analysis using kernel‐based fuzzy clustering approach

Regionalization approaches are widely used in water resources engineering to identify hydrologically homogeneous groups of watersheds that are referred to as regions. Pooled information from sites (depicting watersheds) in a region forms the basis to estimate quantiles associated with hydrological extreme events at ungauged/sparsely gauged sites in the region. Conventional regionalization approaches can be effective when watersheds (data points) corresponding to different regions can be separated using straight lines or linear planes in the space of watershed related attributes. In this paper, a kernel-based Fuzzy c-means (KFCM) clustering approach is presented for use in situations where such linear separation of regions cannot be accomplished. The approach uses kernel-based functions to map the data points from the attribute space to a higher-dimensional space where they can be separated into regions by linear planes. A procedure to determine optimal number of regions with the KFCM approach is suggested. Further, formulations to estimate flood quantiles at ungauged sites with the approach are developed. Effectiveness of the approach is demonstrated through Monte-Carlo simulation experiments and a case study on watersheds in United States. Comparison of results with those based on conventional Fuzzy c-means clustering, Region-of-influence approach and a prior study indicate that KFCM approach outperforms the other approaches in forming regions that are closer to being statistically homogeneous and in estimating flood quantiles at ungauged sites. Key Points Kernel-based regionalization approach is presented for flood frequency analysis Kernel procedure to estimate flood quantiles at ungauged sites is developed A set of fuzzy regions is delineated in Ohio, USA

Evaluation of the Index-Flood Approach Related Regional Frequency Analysis Procedures

AbstractIndex-flood related regional frequency analysis (RFA) procedures are in use by hydrologists to estimate design quantiles of hydrological extreme events at data sparse/ungauged locations in river basins. There is a dearth of attempts to establish which among those procedures is better for RFA in the L-moment framework. This paper evaluates the performance of the conventional index flood (CIF), the logarithmic index flood (LIF), and two variants of the population index flood (PIF) procedures in estimating flood quantiles for ungauged locations by Monte Carlo simulation experiments and a case study on watersheds in Indiana in the U.S. To evaluate the PIF procedure, L-moment formulations are developed for implementing the procedure in situations where the regional frequency distribution (RFD) is the generalized logistic (GLO), generalized Pareto (GPA), generalized normal (GNO) or Pearson type III (PE3), as those formulations are unavailable. Results indicate that one of the variants of the PIF procedu...

A Relook at Ohio Watershed Regions for Homogeneity in Flood Frequency Analysis

Regionalization approaches are widely used in hydrology to identify homogeneous groups (regions) of watersheds to facilitate prediction of hydrometeorological extreme events in ungauged basins for various applications. This paper evaluates statistical homogeneity of watersheds in each of the regions of Ohio that were delineated in the past studies (Koltun, 2003; Koltun and Roberts, 1990) to arrive at regression relationships for estimating flood-peak discharges at rural and unregulated streams. Results based on L-moment homogeneity tests indicated that all the Ohio regions are heterogeneous. Consequently a new set of homogeneous regions are proposed in Ohio based on conventional and global fuzzy cluster analysis procedures. Regional growth curves are developed for each of the newly formed regions and it is shown that their use results in effective quantile estimates at ungauged sites in watersheds of Ohio when compared to those resulting from the use of regression relationships that are being used for quantile estimation by United States Geological Survey (USGS).