Spatiotemporal precipitation data is one of the essential components in modeling hydrological problems. Although the estimation of these data has achieved remarkable accuracy owning to the recent advances in remote-sensing technology, gaps remain between satellite-based precipitation and observed data due to the dependence of precipitation on the spatiotemporal distribution and the specific characteristics of the area. This paper presents an efficient approach based on a combination of the convolutional neural network and the autoencoder architecture, called the convolutional autoencoder (ConvAE) neural network, to correct the pixel-by-pixel bias for satellite-based products. The two daily gridded precipitation datasets with a spatial resolution of 0.25° employed are Asian Precipitation-Highly Resolved Observational Data Integration towards Evaluation (APHRODITE) as the observed data and Precipitation Estimation from Remotely Sensed Information using Artificial Neural Networks-Climate Data Record (PERSIANN-CDR) as the satellite-based data. Furthermore, the Mekong River basin was selected as a case study, because it is one of the largest river basins, spanning six countries, most of which are developing countries. In addition to the ConvAE model, another bias correction method based on the standard deviation method was also introduced. The performance of the bias correction methods was evaluated in terms of the probability distribution, temporal correlation, and spatial correlation of precipitation. Compared with the standard deviation method, the ConvAE model demonstrated superior and stable performance in most comparisons conducted. Additionally, the ConvAE model also exhibited impressive performance in capturing extreme rainfall events, distribution trends, and described spatial relationships between adjacent grid cells well. The findings of this study highlight the potential of the ConvAE model to resolve the precipitation bias correction problem. Thus, the ConvAE model could be applied to other satellite-based products, higher-resolution precipitation data, or other issues related to gridded data.

To investigate the recent effects of climate change in the upper Geum River basin in South Korea, a detailed trend analysis of 17 extreme climate indices based on 33 years (1988–2020) of daily precipitation, and daily (minimum and maximum) temperature data has been analyzed in this study. Out of the 17 extreme climate indices, nine (eight) indices were based on temperature (precipitation) data. Trend analysis based on detailed temporal scales (annual, seasonal, monthly) were performed through the Mann–Kendall trend test and the Theil–Sen slope method. Furthermore, the Mann–Whitney–Pettit test was also applied in this study, to detect abrupt changes in the extreme climate indices. Based on the results of this study, the climate conditions at the upper Geum River basin for the past three decades can be summarized as follows: general increase in temperature intensity, decrease in cold duration, increased heat duration, increased precipitation intensity, and increased consecutive wet and dry durations. Furthermore, a prolonged summer season (shorter spring, and autumn periods) and precipitation shifts, were detected based on trend analysis results of seasonal, and monthly time scales. The results presented in this study can provide supplementary data for improving watershed management strategies in the upper Geum River basin.

The calculation of hydrologic frequency is an important basic step in the planning and design stage of any water conservancy project. The purpose of the frequency analysis is to deduce the hydrologic variables under different guarantee rates, and to provide hydrologic information for water conservancy project planning and design. The calculation of hydrologic frequency requires that the sample size is large enough, as only then can the statistical characteristics of samples take the place of the total statistical eigenvalues. This means that the samples can reveal the statistical characteristics of hydrologic variables and identify the randomness rule of hydrologic phenomena. Many countries in the East Asian monsoon climate zone (China, Japan and South Korea) have stipulated a sample size of 30 years for hydrologic frequency analysis. In this paper the rationality of the 30-year sample size is proved by analyzing the periodic and random rules of hydrologic phenomenon and the influencing mechanism of solar activity, and by adopting the general conclusion of the sampling theorem. Then, using the wavelet analysis method to examine annual precipitation data in a long series generated from representative precipitation observation stations in China, the strong-weak cycle of solar activity is proved to be 10 years, which is consistent with the wet-dry cycle of the representative precipitation stations (10–12 years). Finally, adopting numerical modeling to analyze the normal distribution of randomly generated samples and long-range annual precipitation data collected from representative stations, hypothesis testing (u, F and t) is used to prove that a 30-year sample size is reasonable. This research provides a reference as to how to prove the necessary sample size for relevant statistical analyses (for example, how large the sample should be for analyzing hydrologic factors trend evolution, hydrologic data consistency and ergodicity of statistical samples), thus ensuring the reliability of the analytical results.

Low Impact Development (LID) is one of the sustainable approaches to urban stormwater management in areas with rapid urbanization. Although LID has been shown to have a positive effect in flood reduction, the hydrological balance regulation effect of LID under a variety of rainfall events is not fully understood. In this study, we assessed the hydrological efficiency of LID at two residential–commercial mixed sites in Korea to investigate the main function of LID in terms of diverse rainfall characteristics. Storm Water Management Model (SWMM) was constructed to simulate the hydrological process numerical simulations in the pre-development, post-development and LID design scenarios, respectively. The model was calibrated and validated by using five observed rainfall–runoff events. Then, four single and four multiple LID practices (LIDs) were used to estimate their effectiveness under seven different designed rainfall events. The results indicate that LIDs substantially influence the hydrology cycle system, while the regulating effect varies with rainfall amounts. The efficiency of LIDs in flood reduction is proved to be more effective during lower storm events. However, LIDs should be designed to primarily prioritize the restoration of hydrological balance when the rainfall return period is longer.