Impact Evaluation Using Nonstationary Parameters for Historical and Projected Extreme Precipitation

Abstract

Recent improvements in time series studies of hydro-climatological variables have led to the belief that the effects of nonstationarity are substantial enough to call the idea of traditional stationary approaches into doubt. The mean and variability of annual and seasonal rainfall in Pakistan are changing due to anthropogenic climate change. With the use of stationary and nonstationary frequency analysis techniques, this study set out to assess the impacts of nonstationarity in Southern Punjab, Pakistan, over the historical period of 1970–2015 and the future periods of 2020–2060 and 2060–2100. Four frequency distributions, namely Generalized Extreme Value (GEV), Gumbel, normal, and lognormal, were used. The findings of the nonstationarity impact across Southern Punjab showed different kinds of impacts, such as an increase or reduction in the return level of extreme precipitation. In comparison to other distributions, GEV provided the finest fit. In Bahawalnagar, Bahawalpur, Multan, Rahim Yar Khan and DG. Khan, the annual nonstationarity impacts for the 100-year return level were increased up to 15.2%, 8.7%, 58.3%, 18.7%, and 20%, respectively. Moreover, extreme precipitation was found to be increasing during the historical and projected periods, which may increase floods, while less water availability appeared at a seasonal scale (summer) during 2061–2100. The increased nonstationarity effects emphasized adapting these nonstationarities induced by climate change into the design of water resource structures.

Modeling Extreme Values of Non-Stationary Precipitation Data with Effects of Covariates

“…Kumar et al (2023) (8) investigated the risk of extreme precipitation/discharge indicators using non-stationary GEV models with covariates, then the machine learning models and a conceptual water balance model were calibrated on a monthly measure for discharge estimation. Khan et al (2023) (9) intended to analyze the effects of nonstationarity in precipitation of Southern Punjab, Pakistan on employing the four frequency distributions: GEV, Gumbel, normal, and lognormal. The results of the non-stationarity revealed a variety of impacts across regions, including an increase or decrease in the return amount of intense precipitation.…”