Time-varying Intensity-Duration-Frequency relationship through climate-informed covariates

Abstract

The overwhelming increase and variations in the extreme rainfall events demand the use of a nonstationary Intensity-Duration-Frequency (IDF) curve for the design and management of water resource infrastructure. Generally, nonstationary IDF curves are developed by incorporating the trend in the distribution parameter using Generalized Extreme Value distribution (GEV) with time as a covariate. The physical processes influencing the variations in a hydrologic variable can be captured by utilizing relevant climatic variables (climate-informed) as covariates, since time alone cannot be the best covariate. Hence this study investigates the potential climate-informed covariates influencing the extreme rainfall and incorporates the best covariates to develop a realistic nonstationary IDF relationship. Unlike previous studies, a Time Sliding Window (TSW) approach is employed to detect the changing distribution parameters before performing Nonstationary Modeling (NSM). The proposed covariate based TSW-NSM is effectively used to construct IDF curves for seven major metropolitan cities of India. Several models are generated based on the detected changing parameters and combinations of covariates. Then, Bayesian Differential Evolutionary Monte Carlo (DE-MC) algorithm is employed to estimate the uncertainty bound of the nonstationary parameters, and the best model is chosen using the Deviance Information Criterion (DIC). The results reveal that the best covariate combinations for short duration events are dominated by local processes (i.e., Local temperature changes and diurnal temperature changes), whereas the same for longer duration events are dominated by the global processes (global warming, ENSO Modoki cycle, and IOD). However, the acceptable nonstationary models reveal that all the temperature-based covariates are capable of capturing the dynamic behavior. It is also observed that the local processes carry the signature of global processes. Finally, the return levels computed through the best nonstationary model show that the return periods are decreasing, and the short duration events have undergone drastic changes than the longer duration events. Thus, the results suggest that employing climate-informed covariates based nonstationary IDF curves is indispensable for devising long-term strategies to address the changing climate.