Abstract
Recent evidence of regional climate change associated with the intensification of human activities has led hydrologists to study a flood regime in a non-stationarity context. This study utilized a Bayesian framework with informed priors on shape parameter for a generalized extreme value (GEV) model for the estimation of design flood quantiles for “at site analysis” in a changing environment, and discussed its implications for flood management in the Kabul River basin (KRB), Pakistan. Initially, 29 study sites in the KRB were used to evaluate the annual maximum flood regime by applying the Mann–Kendall test. Stationary (without trend) and a non-stationary (with trend) Bayesian models for flood frequency estimation were used, and their results were compared using the corresponding flood frequency curves (FFCs), along with their uncertainty bounds. The results of trend analysis revealed significant positive trends for 27.6% of the gauges, and 10% showed significant negative trends at the significance level of 0.05. In addition to these, 6.9% of the gauges also represented significant positive trends at the significance level of 0.1, while the remaining stations displayed insignificant trends. The non-stationary Bayesian model was found to be reliable for study sites possessing a statistically significant trend at the significance level of 0.05, while the stationary Bayesian model overestimated or underestimated the flood hazard for these sites. Therefore, it is vital to consider the presence of non-stationarity for sustainable flood management under a changing environment in the KRB, which has a rich history of flooding. Furthermore, this study also states a regional shape parameter value of 0.26 for the KRB, which can be further used as an informed prior on shape parameter if the study site under consideration possesses the flood type “flash”. The synchronized appearance of a significant increase and decrease of trends within very close gauge stations is worth paying attention to. The present study, which considers non-stationarity in the flood regime, will provide a reference for hydrologists, water resource managers, planners, and decision makers.
Highlights
The comprehensive understanding of flood regimes is an important challenge in hydrology
The main objectives of the study were: (1) to analyze temporal and spatial trends in the annual maximum flood regime for the Kabul River basin (KRB), Pakistan, because no study has yet been reported in the literature to study the trends in annual extreme data of flood in detail, and (2) to address the non-stationary modeling of the flood regime in the KRB and its implications for flood management in a changing environment
We explored the differences between stationary and non-stationary flood quantile estimates for a given return period using flood frequency curves (FFCs), along with their uncertainty bounds for risk assessment, to analyze the importance of non-stationary models for improving flood management in the study area
Summary
The comprehensive understanding of flood regimes is an important challenge in hydrology.Hydrologists and engineers customarily use flood frequency analysis (FFA) as a tool to understandWater 2019, 11, 1246; doi:10.3390/w11061246 www.mdpi.com/journal/waterWater 2019, 11, 1246 flood regimes throughout the world. Keeping this point of view, several studies have tried to explore the validity of this hypothesis in flood regimes in many regions around the world, considering the effect of natural climate variability [6,7,8,9,10,11,12] or land use changes [13,14,15] The results of these studies have shown clear violations of the assumption of stationarity, which is consistent with studies that indicate an intensification of the hydrologic cycle [16,17]
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