An Evaluation Method of the Probability of Heavy Rainfall Intensity Based on Projection Uncertainties of Climate Scenarios

Abstract

We have proposed a generation method for heavy rainfall that reflects the uncertainty of climate projection extracted from a number of climate scenarios. First, we extracted heavy rainfall events from each collected climate scenario, and determined their occurrence frequency, average amount of rainfall, and coefficient of variation. These statistical values were used as estimates for the characteristics of heavy rainfall in each scenario. Next, variability of the values among climate scenarios for each estimate was defined as the uncertainty of the climate projection, and we applied the normal distribution to express the probability of occurrence for each value. The values extracted from the distributions, defined as the change of parameters of the normal distribution, were combined to generate a quasi-climate scenario. This process was repeated many times to create a variety of scenarios, each with different characteristics; the results are based on the uncertainties considering the distributions. The methodology was applied to both present and future climate scenarios. Heavy rainfall groups were generated under the quasi-scenario and used for estimation of present and future rainfall probability distributions. The results show that the rainfall intensity will increase in the future, and the proposed method could express distributions of probability rainfalls in the future with uncertainty of climate projection. The results are expected to be useful for climate change risk assessment for flooding and safety assessment of reservoirs and sediment-related disasters caused by heavy rainfall.