Nonstationary Flood Frequency Analysis for Annual Flood Peak Series, Adopting Climate Indices and Check Dam Index as Covariates

Traditionally, flood frequency analysis under the assumption of stationarity has been a cornerstone and there is mature technology applied in practice. However, recent evidences of the impact of climate variability and anthropogenic factors have thrown into question the applicability of stationary hypothesis. In this study, Kendall’s tau and Spearman’s rho correlation test were adopted to detect the relationship between climate indices (PDO, NAO, AO, NPO and ENSO) and annual flood peak data. The test results showed that NPO and Niño3 had significant correlations with the flood peak which could prove the climate cause of non-stationarity. Niño3 is used herein to describe ENSO. We also proposed a check dam index (CDI_p) to represent the effect of human activities that caused nonstationarity on flood. The CDI_p was based on the estimated storage capacity and drainage area of large number of check dams and small hydraulic structures. A framework for nonstationary flood frequency analysis was developed through Generalized Additive Models in Location, Scale and Shape (GAMLSS), and two models based on GAMLSS were applied to the annual flood peak. The model results that incorporated climate indices (NPO and Niño3) and CDI_p as covariates in the parameters of the selected distribution exhibited an undulate behavior, which could better describe nonstationarity than the model with only time dependence. For a reservoir index (RI) proposed by López and Francés (2013) which is similar to CDI_p, we established two contrast models and the result revealed that CDI_p is superior to RI. These results highlight the necessity of flood frequency analysis under nonstationary conditions, and alternative definitions of return period should be adapted.