Recently, Asia has experienced significant damage from extreme precipitation events caused by climate change. Improving the accuracy of quantitative precipitation forecasts over wide regions is essential to mitigate the damage caused by precipitation-related natural disasters. This study compared the predictive performances of a global model trained on the entire dataset and a clustered model that clustered precipitation types. The precipitation prediction model was constructed by combining convolutional long short-term memory with a U-Net structure. Research data consisted of precipitation events recorded at 10 min intervals from 2017 to 2021, utilizing radar data covering the entire Korean Peninsula. The model was trained on radar precipitation data from 30 min before the current time (t − 30 min, t − 20 min, t − 10 min, and t − 0 min) to predict the precipitation after 10 min (t + 10 min). The prediction performance was assessed using the root mean squared error and mean absolute error for continuous precipitation data and precision, recall, F1 score, and accuracy for the presence or absence of precipitation. The research findings indicate that, with sufficient training data for each precipitation type, models trained on clustered precipitation types outperform those trained on the entire dataset, particularly for predicting high-intensity precipitation events.

The characteristics of the watershed are important to reduce hydrologic disasters, such as the risk of dam flooding. In other words, quantitative precipitation estimation(QPE) is important to manage water resources in large regions. Both radar and rain gauged data are used to improve QPE. This study is dealt with suggesting the best location of additional rain gauged stations to be installed in order to improve QPE as entropy theory. Conditional entropy is used to quantitatively evaluate the location of additional gauged stations to be installed given the existing rainfall network. Because radar produces high-resolution precipitation estimates, it can be used to identify the high entropy points to reduce rainfall uncertainty. The data were collected from May 2018 to August 2019 in the Bukhan river dam basin. Road networks were also considered for the establishment for a practical approach. This work was supported by KOREA HYDRO & NUCLEAR POWER CO., LTD(No. 2018-Tech-20)

This study proposes a novel transformation method for analyzing block minima using the generalized extreme value distribution (GEVD). The negative power transformation (NPT), which includes a tunable hyperparameter and reduces to the reciprocal transformation (RT) when set to 1, improves the accuracy and robustness in estimating long-term return levels (RL). Compared to traditional methods, the NPT-GEVD demonstrates lower bias, standard errors, and root mean square errors in Monte Carlo simulations. Furthermore, the NPT-GEVD provides consistent RL estimates with improved robustness across varying parameterizations and sample sizes, mainly when using L-moments for small datasets. The application of the NPT-GEVD to rainfall data from South Korea revealed that the RLs for detecting hourly cumulative rainfall threshold levels varied from 30 min to over 4 h, depending on the location and threshold. This research underscores the value of advanced transformation techniques in environmental risk management, offering critical insights for flood prediction and mitigation strategies in climate change.

Uncertainty in the gauged network can lead to inaccuracies in dam operations. Entropy is a well-known measurement of uncertainty. Goesan Dam has a small basin area and is affected by a small amount of precipitation, and Hwacheon Dam is contained outside the territory of South Korea, making it difficult to observe the water flow. The observed gauged precipitation and radar data on rainy days were considered between 2018 and 2019. Choosing appropriate radar were performed based on the priority of the rainfall gauge network using conditional entropy. This is because the rainfall gauge network is the actual precipitation and it can only cover certain points. However, the radar is the cloud reflectivity of a large area. Therefore the location of additional rain spots was selected through conditional entropy of highly consistent radar data. Nevertheless, there might be difficulties in installing gauged equipment in reality. So the optimal rainfall network was designed in consideration of the road network. As a result, the uncertainty of precipitation in Goesan Dam and Hwachoen Dam could be decreased by 63.3% and 67.9% respectively when three additional potential rain points were operated without any restriction. The uncertainty in the Goesan Dam basin and Hwachoen Dam would be reduced up to 55.3% and 65.0% when three additional potential rain points were installed nearby the road network. Therefore, through the proposed method, an optimal rainfall network can be designed by balancing cost and uncertainty.This work was supported by KOREA HYDRO & NUCLEAR POWER CO., LTD (No. 2018-Tech-20)

This study models the spatiotemporal heterogeneity of extreme rainfall over the Korean Peninsula using the generalised additive extreme value models (EVGAM) to address the limitations of traditional stationary approaches under climate change. Analyzing 30 years of daily precipitation data (1995–2024), we conducted a comparative analysis between typhoon-inclusive and non-typhoon scenarios to isolate the meteorological drivers of extremes. The results revealed distinct covariate dependencies: while spatial location (latitude and longitude) governs rainfall variability in non-typhoon conditions, elevation emerged as the critical determinant for the scale parameter during typhoon events, highlighting the role of orographic effects. Furthermore, the shape parameter exhibited multi-decadal oscillations corresponding to climate variability indices. To ensure local accuracy, a dual fitting strategy was implemented, supplementing EVGAM with standalone generalized extreme value (GEV) estimation for stations exhibiting poor goodness-of-fit. The resulting 50-year and 100-year return level maps quantify regional risks, identifying the southern coast as a high-vulnerability zone driven by typhoons, while inland basins benefited from orographic shielding. This comprehensive framework provides a robust scientific basis for adaptive water resource management and infrastructure design.