Drought is a phenomenon that is caused by several factors and can be divided into meteorological drought, agricultural drought, hydrological drought, and socioeconomic drought. In this study, the characteristics of propagating from meteorological drought to agricultural (or hydrological) drought in the Andong Dam basin and Hapcheon Dam basin located in the Nakdong River basin in Korea were investigated. Standardized precipitation index (SPI), standardized soil moisture index (SMI), and standardized runoff index (SRI) were used to characterize meteorological, agricultural, and hydrological droughts, respectively. SPI-m (1–12) on various timescales and SMI-1 (or SRI-1) were selected as drought propagation timeseries, such that a correlation analysis was performed to evaluate the correlation and propagation time between meteorological and agricultural (or hydrological) drought. Propagation probability was quantified using a copula-based model. The correlation between meteorological and agricultural (or hydrological) droughts was not significantly affected by seasons. A relatively strong correlation was found in summer. A relatively weak correlation was shown in autumn. In addition, it was found that there was a difference in correlation between the Andong Dam basin and the Hapcheon Dam basin. On the other hand, in both watersheds, the propagation time was as long as 2 to 4 months in spring and decreased to 1 month in summer.

Floods are a major natural disaster that occurs suddenly, causing loss of life and significant socioeconomic damage, making proactive response through accurate forecasting essential. River water levels are a key indicator for determining flood occurrence, but forecasting accuracy varies depending on hydrological, meteorological, and watershed characteristics. In particular, small-scale rivers have higher water level variability compared to large-scale rivers, limiting their practical flood response capabilities. To overcome these limitations, this study aimed to develop an AI-based river water level forecasting model and improve its forecasting performance. The forecast model is configured to forecast river water levels three hours ahead using time-series data from the previous 24 hours as input, based on the forecast time point. Four locations among existing AI-forecast rivers where flood forecasting is difficult were selected, and input variables reflecting each location's hydrological, meteorological, and oceanographic characteristics were configured. As a result, this study confirmed a trend toward improved flood forecasting performance through the configuration of input variables tailored to each site's characteristics and the adoption of the latest AI models.AcknowledgmentsThis work was supported by Korea Environment Industry & Technology Institute(KEITI) through Research and Development on the Technology for Securing the Water Resources Stability in Response to Future Change Project, funded by Korea Ministry of Climate, Energy, Environment(MCEE)(RS-2024-00332300) and by the National Research Foundation of Korea(NRF) grant funded by the Korea government(MSIT)(RS-2025-00563294).

Despite the rapid intensification of extreme rainfall due to recent climate change, the Probable Maximum Precipitations (PMPs), key design standards for hydraulic structures in Korea, have not been updated for a long period. This lack of updates raises concerns that the current standards fail to reflect actual climate risks. To address this, this study proposes a future PMPs estimation procedure tailored to Korean conditions based on the WMO (2009) hydro-meteorological method. The procedure was applied to the SSP2-4.5 and SSP5-8.5 scenarios of the EC-Earth3-Veg and HadGEM3-RA models to project future ensemble-based PMPs. In particular, conventional methods calculate the moisture maximization ratio by fixing the representative precipitable water to historical observed values. This approach tends to over-reflect the increase in future maximum precipitable water, leading to an overestimation of PMPs. To overcome this limitation, this study estimated future annual representative precipitable water based on a Copula model. By applying this to the moisture maximization ratio calculation, the issue of PMPs overestimation was effectively mitigated. However, since this study relies on the results of two climate models, inherent uncertainties exist. Therefore, future research needs to project future PMPs using a multi-model ensemble with a broader range of models to enhance reliability.AcknowledgmentThis work was supported by Korea Environment Industry & Technology Institute(KEITI) through Research and Development on the Technology for Securing the Water Resources Stability in Response to Future Change Project, funded by Korea Ministry of Climate, Energy, Environment(MCEE)(RS-2024-00332300) and by the National Research Foundation of Korea(NRF) grant funded by the Korea government(MSIT)(RS-2025-00563294).

Compound drought–heat events are increasing under climate change, yet quantitative assessment of how antecedent drought alters extreme heat risk remains limited. This study examines the relationship between antecedent moisture conditions and extreme summer temperatures in Korea by combining correlation analysis with copula-based probabilistic modeling. Spearman’s rank correlation analysis reveals a consistent negative association between antecedent Standardized Precipitation Index (SPI) and daily maximum temperature (Tmax), indicating that drier conditions are systematically linked to higher summer temperatures. Copula models are then used to characterize the joint dependence between SPI and Tmax and to estimate conditional exceedance probabilities and return periods under different moisture states. The results show that dry and extremely dry conditions increase the probability of exceeding high Tmax thresholds and substantially shorten return periods, whereas wet conditions suppress extreme heat risk. The influence of antecedent drought becomes more pronounced for longer return periods, highlighting enhanced sensitivity in the extreme tail of the temperature distribution. These findings suggest that extreme heat risk is dynamically conditioned by prior hydrological states, emphasizing the importance of accounting for antecedent drought in interpreting and anticipating high-impact temperature extremes.AcknowledgmentsThis work was supported by Korea Environment Industry & Technology Institute(KEITI) through Research and Development on the Technology for Securing the Water Resources Stability in Response to Future Change Project, funded by Korea Ministry of Climate, Energy, Environment(MCEE)(RS-2024-00332300) and by the National Research Foundation of Korea(NRF) grant funded by the Korea government(MSIT)(RS-2025-00563294).