Abstract
Precipitation is essential for understanding hydrological processes and identifying the characteristics that must be considered to protect human lives and property from natural disasters. Hydrological analyses assume that precipitation shows stationarity. However, because of the recent changes in climate, the stationarity of climate data has been widely debated, and a need has arisen to analyze its nonstationary nature. In this study, we reviewed a method to analyze the stationarity of annual precipitation data from 37 meteorological stations that have recorded data for more than 45 years. Six stations that showed abnormal precipitation during the previous year were selected to evaluate the normality of future precipitation. The results showed that a significant trend was present in four out of 37 stations with unstable precipitation in 22 stations and persistent precipitation in 4 stations. The stationarity analysis of future annual precipitation using climate change scenarios suggested that no trend would be present in 11 stations and that unstable precipitation would be present in six stations. Persistent precipitation was identified in four stations. A comparison between the historical and predicted precipitation data conducted with the climate change scenarios showed that an increasing number of stations presented nonstationarity. Therefore, both stationarity and nonstationarity should be considered when performing hydrological analyses using annual precipitation data in Korea. Accordingly, prior to conducting any such analyses, the effect of climate change on annual precipitation should also be considered.
Highlights
Climate change is altering precipitation patterns in South Korea during summer by increasing the intensity and frequency of precipitation events
We addressed the necessity of determining stationarity based on a time series analysis this study,data we addressed the necessity of determining stationarity based on a The timeanalysis series of the In precipitation of South Korea by considering persistence, trends, and stability
The spatial disaggregation with quantile delta mapping (SDQDM) method interpolates with the inverse distance weighted using the values of the surrounding global climate model (GCM) grids inverse distance weighted using the values of the surrounding global climate model (GCM) grids before performing a bias correction, and the rate of change for the future period is estimated before performing a bias correction, and the rate of change for the future period is estimated from the original GCM for each quantile of the data
Summary
Climate change is altering precipitation patterns in South Korea during summer by increasing the intensity and frequency of precipitation events. Ref./Vaskar, D et al, analyzed the trends, persistence, and stationarity of the annual precipitation data from 61 stations to evaluate time series characteristics and provide a review of analytical methods, [2]. Ref./Kim et al.,considered the characteristics of a Generalized Pareto distribution (GPA) and climate change and used a Monte Carlo simulation to generate data for a trend analysis to evaluate extreme hydrological event trends in the context of a changing climate, [7] They conducted simulations based on the trend analyses using the probability distribution and suggested that an increasing trend was present with more data and a higher level of extortion after conducting the trend analysis for each of the conditions. With regard to stationarity studies, Ref./Kang et al, used the augmented Dickey–Fuller test and analysis of variance to analyze 30 years of precipitation data and provided a more objective trend analysis by evaluating stationarity and homogeneity to identify the variability of the hydrological factors of the Korean Peninsula, [8]. Persistence and stability must be considered in the trend analysis
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