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A Non-Parametric Circular Statistics-Based Framework for Predicting Peakflow Seasonality at Ungauged Sites
Journal
Water Resources Research
ISSN
00431397
Date Issued
2022-08-01
Author(s)
Kalai, Chingka
Mondal, Arpita
Abstract
Regional frequency analysis (RFA) serves as a useful tool for flood estimation at a regional level, to improve the precision of estimated flood quantiles or for prediction in ungauged basins. RFA is used extensively in hydrologic literature for estimation of the magnitude of the hydrologic variable (annual maximum flows), pooling data from several sites within a homogeneous region. However, in areas that experience a strong seasonality of the climate, the timing of a hazardous event, such as flood, may be equally important. Therefore, in such regions, improved estimation of annual peakflow seasonality is imperative for efficient management of water infrastructure or for undertaking timely preventive measures against floods. In this study, we propose a novel RFA framework for estimation of annual peakflow seasonality based on circular statistics. The framework consists of (a) selection of attributes based on catchment similarity using seasonality descriptors as a response, (b) formation of homogeneous regions based on the region of influence method, (c) homogeneity tests adapted to directional data on timing of annual peakflows, and (d) probabilistic prediction of annual peakflow seasonality using the non-parametric regional circular density. Applicability of the proposed approach is first demonstrated on two synthetically generated homogeneous regions, with unimodal and bimodal annual peakflow seasonality, respectively. The proposed homogeneity test outperforms the existing test yielding H value < 1 for homogeneous regions. Further, real-world applications are illustrated on prediction of flood peak timing in the Northwest USA, revealing nearly 75% of the sites with absolute bias <16 days.
Subjects