This study aims to examine the spatiotemporal variations of extreme rainfall events in Seoul, South Korea, utilizing subhourly rainfall data collected from an extensive observation network. Employing the Mann–Kendall trend detection test and Moran’s I index, this study analyzed temporal trends and spatial distributions in the extreme rainfall time series at 37 weather stations. The findings revealed that the annual maximum precipitation (AMP) series for various durations in Seoul generally exhibited a decreasing trend, with a marked decline observed for durations of 300 and 360 min. Furthermore, the study identified notable changes in the spatial patterns of AMP events, particularly for subhourly AMP events. The results indicate an intensification in the spatial concentration of AMP distribution. Consequently, despite the overall reduction in AMP, the likelihood of urban flooding may increase. This suggests that existing hydraulic design criteria, based on past concepts that temporal and spatial variability are constant, may no longer adequately represent extreme rainfall events. Therefore, revising these criteria to account for the evolving nature of extreme rainfall patterns is imperative.

The changes of agro-climate and heat extremes, and their impact on rice cultivation are assessed over South Korea in context of 2 and 3 °C global warming levels (GWL) compared to pre-industrial levels, with ensemble regional climate model projection produced under the Coordinated Regional Climate Downscaling Experiment–East Asia (CORDEX-EA) phase 2 protocols. It is found that the mean temperature increase under global warming has not only positive effects such as the extension of vegetable and crop periods and the widening of the cultivatable regions but also negative effects due to the shortening of the reproductive growth period. On the other hand, extreme heat changes in the future clearly show a negative effect on rice cultivation via the increase of hot days during heat-sensitive stages (27.16% under 2 °C GWL, 54.59% under 3 °C GWL) among rice phenology which determines the rice yield in tandem with rice flowering, ripening, and sterility problems. The major type of heat extreme is dominated by nationwide warm anomalies covering entire S. Korea, and the proportion of this type is projected to increase from 35.8% to 49.5% (57.4%) under 2 °C (3 °C) GWL in association with the thermal expansion of atmosphere which links to the favorable environment for occurring barotropic anti-cyclonic system.

Estimation and Comparison of Carbon Uptake in Rice Paddy, Dry Cropland and Grove in South Korea using Eddy Covariance Flux Data-Agricultural Ecosystem,Carbon Cycle,Eddy Covariance,Net Ecosystem Exchange

Climate change poses significant challenges to agriculture, leading to increased crop damage owing to extreme weather conditions. Detecting and analyzing such damage is crucial for mitigating its effects on crop yield. This study proposes a novel autoencoder (AE)-based model, termed “Memory Ganomaly,” designed to detect and analyze weather-induced crop damage under conditions of significant class imbalance. The model integrates memory modules into the Ganomaly architecture, thereby enhancing its ability to identify anomalies by focusing on normal (undamaged) states. The proposed model was evaluated using apple and peach datasets, which included both damaged and undamaged images, and was compared with existing robust Convolutional neural network (CNN) models (ResNet-50, EfficientNet-B3, and ResNeXt-50) and AE models (Ganomaly and MemAE). Although these CNN models are not the latest technologies, they are still highly effective for image classification tasks and are deemed suitable for comparative analyses. The results showed that CNN and Transformer baselines achieved very high overall accuracy (94–98%) but completely failed to identify damaged samples, with precision and recall equal to zero under severe class imbalance. Few-shot learning partially alleviated this issue (up to 75.1% recall in the 20-shot setting for the apple dataset) but still lagged behind AE-based approaches in terms of accuracy and precision. In contrast, the proposed Memory Ganomaly delivered a more balanced performance across accuracy, precision, and recall (Apple: 80.32% accuracy, 79.4% precision, 79.1% recall; Peach: 81.06% accuracy, 83.23% precision, 80.3% recall), outperforming AE baselines in precision and recall while maintaining comparable accuracy. This study concludes that the Memory Ganomaly model offers a robust solution for detecting anomalies in agricultural datasets, where data imbalance is prevalent, and suggests its potential for broader applications in agricultural monitoring and beyond. While both Ganomaly and MemAE have shown promise in anomaly detection, they suffer from limitations—Ganomaly often lacks long-term pattern recall, and MemAE may miss contextual cues. Our proposed Memory Ganomaly integrates the strengths of both, leveraging contextual reconstruction with pattern recall to enhance detection of subtle weather-related anomalies under class imbalance.