Modeling All Exceedances Above a Threshold Using an Extremal Dependence Structure: Inferences on Several Flood Characteristics

TL;DR

This paper introduces a novel extremal dependence model for flood data that improves quantile estimation and enables inference on flood duration, especially useful with limited data and considering temporal dependence.

Contribution

The paper proposes a new model capturing extremal dependence in flood time series, enhancing flood quantile estimation and inference on flood duration.

Findings

The model yields more accurate flood peak quantile estimates.

Incorporating dependence improves flood duration inference.

Catchment characteristics influence flood duration prediction accuracy.

Abstract

Flood quantile estimation is of great importance for many engineering studies and policy decisions. However, practitioners must often deal with small data available. Thus, the information must be used optimally. In the last decades, to reduce the waste of data, inferential methodology has evolved from annual maxima modeling to peaks over a threshold one. To mitigate the lack of data, peaks over a threshold are sometimes combined with additional information - mostly regional and historical information. However, whatever the extra information is, the most precious information for the practitioner is found at the target site. In this study, a model that allows inferences on the whole time series is introduced. In particular, the proposed model takes into account the dependence between successive extreme observations using an appropriate extremal dependence structure. Results show that this model leads to more accurate flood peak quantile estimates than conventional estimators. In addition, as the time dependence is taken into account, inferences on other flood characteristics can be performed. An illustration is given on flood duration. Our analysis shows that the accuracy of the proposed models to estimate the flood duration is related to specific catchment characteristics. Some suggestions to increase the flood duration predictions are introduced.