Statistical analysis of flash flood events for designing water harvesting systems in an extremely arid environment

Abstract

A water harvesting system for research purposes has been established in the Lisan Peninsula of the Dead Sea in the middle of the Jordan Rift Valley, where no authorized guideline is available for designing water harvesting systems. Rainfall and runoff, which occurred as flash floods, were observed at the downstream end of a gorge with a 1.12 km 2 barren catchment area from October 2014 through July 2019. Due to the extremely arid environment, runoff from the catchment is ephemeral, and the flash flood events can be clearly distinguishable from each other. Thirteen flash flood events with a total runoff volume of more than 100 m 3 were successfully recorded during the five rainy seasons. Pearson and Spearman correlations between duration, total rainfall depths at two points, total runoff volume, maximum runoff discharge, bulk runoff coefficient, total variation in runoff discharge and maximum variation in runoff discharge of each flash flood event were examined, revealing no straightforward relationship between rainfall and runoff. The performance of the conventional SCS runoff curve number method was also deficient in reproducing any rainfall-runoff relationship. Therefore, probability distribution fitting was performed for each random variable, focusing on the lognormal distribution with three parameters and the generalized extreme value distribution. The maximum goodness-of-fit estimation turns out to be a more rational and efficient method in obtaining the parameter values of those probability distributions rather than the standard maximum likelihood estimation, which has known disadvantages. Results support the design of the water harvesting system and provide quantitative information for designing and operating similar systems in the future.

Iso-severity flood hazard patterns in diverse relief and climatic regions to support flood management

“…After specifying the distribution tting process, EasyFit presents probability density function, summary and goodness of t. In the goodness of t section, the results of the distributions were displayed. In the present study, the appropriate statistical distributions were chosen based on Kolmogorov-Smirnov tests (Acharya and Joshi 2020; Lee et al 2020; Samantaray and Sahoo 2020; Unami et al 2021). After determining the best rank for the statistical distribution, the output ow rate was calculated for different return periods and the probability of occurrence in the selected statistical distribution (Romali and Yusop 2017b).…”

“…Accordingly, the maximum annual return period was the average interval between years, which included ood ow with a speci ed minimum. Therefore, based on AMM, it was determined that after determining the best distribution and also determining the maximum ow discharge in different return periods, the ood probability values was selected based on the probability values (Romali and Yusop 2017a; Acharya and Joshi 2020; Unami et al 2021). In this way, the threshold for selecting the number of oods in each RGS was the maximum discharge values in the same return period that in RGS, values higher than this value were considered as the ood threshold.…”