Recent studies strongly suggest the possibility of more frequent extreme events as a result of the changing climate. These weather extremes, such as excessive rainfall, result in debris flow, river overflow and urban flooding, which can pose a substantial threat to the community. An effective flood model is therefore a crucial tool in flood disaster control and mitigation. A number of flood models have been established in recent years. However, the major challenge in developing effective and accurate flood models is the disadvantage of running multiple models for separate, individual conditions. Among the solutions in recent research is the development of combined 1D–2D flood modeling. Coupled 1D–2D flood modeling allows the channel flows to be represented in 1D and the overbank flow to be modeled in 2D. In order to test the efficiency of the approach, this research aims to assess the capability of the U.S. Army Corps of Engineers Hydrologic Engineering Center River Analysis System (HEC-RAS) model’s implementation of the combined 1D–2D hydraulic computation in simulating river overflow inundation. For verification, the simulation is applied to the Baeksan river levee breach event in South Korea in 2011. The simulation results show similarities of the observed data and the outputs from widely used flood models. This proves the applicability of the HEC-RAS 1D–2D coupling method as a powerful tool in simulating accurate inundations for flood events.

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.

In Korea, landslide damage areas have increased significantly from the 1990s to the 2000s owing to increases in rainfall intensity and the number of rainy days in addition to indiscriminate land development. This study was conducted to predict shallow landslide-prone areas by using the transient rainfall infiltration and grid-based regional slope-stability model (TRIGRS). A landslide event that occurred on July 27, 2011, in Mt. Umyeon, Seoul, was modeled, and the stability results were compared with 140 observed landslide points in both time and location to evaluate the performance of TRIGRS. Simulated outcomes from five different raster cells of 5, 10, 15, 20, and 25 m with the same background information were compared to identify the ultimate grid scale. The results revealed that in location estimation, smaller grid size resulted in more accurate results. However, the 10 and 15 m grid sizes gave better results in the timing assessment When the best grid size was considered TRIGRS overestimated t...