Comparison of Estimation Methods for a Nonstationary Index‐Flood Model in Flood Frequency Analysis Using Peaks Over Threshold

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 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 focused 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 is 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 perform best in different situations. Two methods are based on the combination of regression 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.