Design of water control structures, reservoir management, economic
evaluation of flood protection projects, land use planning and
management, flood insurance assessment, and other projects rely on
knowledge of magnitude and frequency of floods. Often, estimation of
floods is not easy because of lack of flood records at the target sites.
Regional flood frequency analysis (RFFA) alleviates this problem by
utilizing flood records pooled from other watersheds, which are similar
to the watershed of the target site in flood characteristics.
Clustering techniques are used to identify group(s) of watersheds which
have similar flood characteristics. This book is a comprehensive
reference on how to use these techniques for RFFA and is the first of
its kind. It provides a detailed account of several recently developed
clustering techniques, including those based on fuzzy set theory and
artificial neural networks. It also documents research findings on
application of clustering techniques to RFFA that remain scattered in
various hydrology and water resources journals.
The optimal number of groups defined in an area is based on cluster
validation measures and L-moment based homogeneity tests. These form the
bases to check the regions for homogeneity.
The subjectivity involved and the effort needed to identify homogeneous
groups of watersheds with conventional approaches are greatly reduced by
using efficient clustering techniques discussed in this book.
Furthermore, better flood estimates with smaller confidence intervals
are obtained by analysis of data from homogeneous watersheds.
Consequently, the problem of over- or under-designing by using these
flood estimates is reduced. This leads to optimal economic design of
structures. The advantages of better regionalization of watersheds and
their utility are entering into hydrologic practice.
Audience:
This book will be of interest to researchers in stochastic hydrology,
practitioners in hydrology and graduate students.
