A Comparative Study of Geometric and Exponential Laws in Modelling the Distribution of Daily Precipitation Durations

Abstract

Wet and dry duration modelling is critical in engineering hydrology. The goal of this study is to model and analyze the wet and dry duration distributions. For this intent, daily rainfall data for Kenitra station were used on the period from 1967 to 2017. To represent the distribution of wet and dry durations, First-order Markov chain, Second-order Markov chain, and truncated negative binomial distribution are applied to represent the distribution of wet and dry durations. To assess the data adherence to the proposed models, the Chi-square and Kolmogorov-Smirnov tests have been used. The Akaike information criterion is applied to determine the most effective model distribution. We go further to investigate the distribution of the number of wet and dry days over a k-day period. This law is implemented using an algorithm based on conditional laws. This work is completed by comparing the calculated moments of the three estimated models to the observed moments of the number of wet/dry days over k consecutive days. The study demonstrates the effectiveness of our method for modelling wet and dry daily precipitation durations.