Assessing the applicability of six precipitation probability distribution models on the Loess Plateau of China

Stochastic modelling of daily precipitation is useful for many hydrological and agricultural applications; however, the ability of the precipitation generator should be assessed to ensure accurate precipitation simulation. In particular, the appropriate choice of a precipitation probability distribution is of utmost importance. The Loess Plateau in China has a semi‐arid climate with strong monsoon influence and contains some of the most erodible soils in the world. The large annual variability in precipitation and the common occurrence of very large rainfall events makes this region very challenging for stochastic generation of precipitation. Accordingly, the objective of this study is to compare the performances of six precipitation probability distributions (exponential, gamma, Weibull, skewed normal, mixed exponential and hybrid exponential/generalized Pareto distributions) on the Loess Plateau of China based on daily precipitation data of 47 stations during 1961–2009. Results indicate that using increasingly more complex precipitation distributions contribute to more accurate precipitation simulation. However, none of the tested distributions is able to simulate all the observed statistical characteristics of precipitation. The three‐parameter models are superior to simulating the observed mean and variance. The hybrid exponential/generalized Pareto distribution is the best at simulating the frequency distributions and interannual variations of precipitation while the skewed normal distribution performs the best in reproducing extreme precipitation events. Overall, as erosion on the Loess Plateau is highly dependent on extreme precipitation, the skewed normal distribution may be the best candidate and therefore is recommended on the Loess Plateau.