Comparison of estimation methods for a nonstationary index-flood model in flood frequency analysis using peaks over threshold

Abstract

Accurate estimation of flood frequency is crucial for designing safe infrastructures. To reduce model uncertainty, threshold modeling techniques are often useful in bringing more valuable flood information into the analysis than traditional models based on annual maximum 15 discharges. Due to climatic or anthropogenic causes, changes in flood magnitudes in many parts of the world have been observed and are expected to continue in the future. To characterize such changes, nonstationary models have focussed on the modeling of stations with long records, but in practice such models may be needed to improve the evaluation of flood risk for stations having shorter records. In this study, a nonstationary index-flood model for peaks over threshold 20is investigated to reduce model uncertainty. The assumption of an index-flood model is used to define a probability structure that is stable in time. This allows to extend existing (stationary) procedures to automatically calibrate the proposed model in an at-site and regional context. As part of this procedure, four estimators are investigated in a simulation study to determine which 3 perform best in different situations. Two methods are based on the combination of regression 25 techniques and L-moments, while the other two methods employ likelihood-based techniques. A case study of 425 stations in Canada is considered to verify if a nonstationary index-flood model using pooling groups that combine stationary and nonstationary stations can reduce the uncertainty of design levels associated with a finite reference period.

Peaks-over-threshold model in flood frequency analysis: a scoping review

“…However, this study suggested further research on automated procedure in POT data construction under stationary condition with additional statistical arguments. Nonstationary POT based FFA has attracted more attention recently (Durocher et al 2019;Mostofi Zadeh et al 2019); however, the findings of these studies are not conclusive, and further research is warranted on nonstationary POT based FFA.…”

“…Quantile regression technique has been widely employed under AM model . Durocher et al (2019) compared four estimators based on index flood method and quantile regression technique including regression analysis, L-moments and likelihood method using POT data. Gupta et al (1994) noted that the coefficient of variation of AM flows should not vary with catchment area in a proposed region/group.…”

“…They noted the challenge of forming a homogenous group, which was due to several sites presenting significant level of nonstationarity. Durocher et al (2019) compared several estimators under nonstationary condition using index flood method for a data set of 425 Canadian stations and found that the L moments approach was more robust and less biased than ML estimator. This study also found that a hybrid pooling group approach, which included sites with stationary and nonstationary conditions, improved the accuracy of the quantile estimates.…”

“…Later, Langousis et al (2016) performed a critical review on threshold selection. Recently, the automated threshold detection techniques have been compared (Curceac et al 2020;Durocher et al 2019;Durocher et al 2018a, b). To the best of our knowledge, scoping review on POT approach is limited and the literature on applying POT model remains sparse as compared to the AM model.…”

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