Since its recurrence in 1986, scrub typhus has been occurring annually and it is considered as one of the most prevalent diseases in Korea. Scrub typhus is a 3rd grade nationally notifiable disease that has greatly increased in Korea since 2000. The objective of this study is to construct a disease incidence model for prediction and quantification of the incidences of scrub typhus. Using data from 2001 to 2010, the incidence Artificial Neural Network (ANN) model, which considers the time-lag between scrub typhus and minimum temperature, precipitation and average wind speed based on the Granger causality and spectral analysis, is constructed and tested for 2011 to 2012. Results show reliable simulation of scrub typhus incidences with selected predictors, and indicate that the seasonality in meteorological data should be considered.

It is accepted that human-induced climate change is unavoidable and it will have effects on physical, chemical, and biological properties of aquatic habitats. This will be especially important for cold water fishes such as trout. The objective of this study is to simulate water temperature for future periods under the climate change situations. Future water temperature in the Fourchue River (St-Alexandre-de-Kamouraska, QC, Canada) were simulated by the CEQUEAU hydrological and water temperature model, using meteorological inputs from the Coupled Model Intercomparison Project Phase 5 (CMIP5) Global Circulation Models (GCMs) with Representative Concentration Pathway (RCP) 2.6, 4.5 and 8.5 climate change scenarios. The result of the study indicated that water temperature in June will increase 0.2–0.7 °C and that in September, median water temperature could decrease by 0.2–1.1 °C. The rise in summer water temperature may be favorable to brook trout (Salvelinus fontinalis) growth, but several days over the Upper Incipient Lethal Temperature (UILT) are also likely to occur. Therefore, flow regulation procedures, including cold water releases from the Morin dam may have to be considered for the Fourchue River.

Long-term streamflow data are vital for analysis of hydrological droughts. Using an artificial neural network (ANN) model and nine tree-ring indices, this study reconstructed the annual streamflow of the Sacramento River for the period from 1560 to 1871. Using the reconstructed streamflow data, the copula method was used for bivariate drought analysis, deriving a hydrological drought return period plot for the Sacramento River basin. Results showed strong correlation among drought characteristics, and the drought with a 20-year return period (17.2 million acre-feet (MAF) per year) in the Sacramento River basin could be considered a critical level of drought for water shortages.

Generalized likelihood uncertainty estimation (GLUE) is one of the widely-used methods for quantifying uncertainty in flood inundation mapping. However, the subjective nature of its application involving the definition of the likelihood measure and the criteria for defining acceptable versus unacceptable models can lead to different results in quantifying uncertainty bounds. The objective of this paper is to perform a sensitivity analysis of the effect of the choice of likelihood measures and cut-off thresholds used in selecting behavioral and non-behavioral models in the GLUE methodology. By using a dataset for a reach along the White River in Seymour, Indiana, multiple prior distributions, likelihood measures and cut-off thresholds are used to investigate the role of subjective decisions in applying the GLUE methodology for uncertainty quantification related to topography, streamflow and Manning’s n. Results from this study show that a normal pdf produces a narrower uncertainty bound compared to a uniform pdf for an uncertain variable. Similarly, a likelihood measure based on water surface elevations is found to be less affected compared to other likelihood measures that are based on flood inundation area and width. Although the findings from this study are limited due to the use of a single test case, this paper provides a framework that can be utilized to gain a better understanding of the uncertainty while applying the GLUE methodology in flood inundation mapping.

This study develops a high-resolution, grid-based explainable machine learning (XAI) framework to systematically identify dominant flood-influencing factors across Jeju Island, Korea, by integrating historical flood trace maps with multi-source spatial datasets. Flood occurrence was classified at a 100 m grid resolution using four state-of-the-art tree-based ensemble algorithms, enabling robust modeling of nonlinear interactions between hydro-meteorological, geomorphological, and infrastructural variables. Model performance was rigorously evaluated across multiple subregions to quantify spatial heterogeneity in predictive skill and controlling mechanisms. The models achieved moderate to high classification performance, with maximum recall and F1-scores reaching 0.81 and 0.75, respectively, demonstrating strong capability in detecting flood-prone conditions.Explainability analyses based on feature-importance metrics consistently identified short- and long-duration extreme rainfall (3-hour and 12-hour maxima), 5-day antecedent precipitation, maximum wind speed, groundwater level, and proximity to detention facilities and river networks as the most influential predictors of flood occurrence. Notably, their relative contributions exhibited pronounced spatial variability. In inland and high-elevation basins, flood dynamics were primarily governed by rainfall persistence and subsurface hydrological responses, whereas in coastal and highly urbanized zones, flood occurrence was more strongly modulated by drainage connectivity and proximity to hydraulic infrastructure.These spatially differentiated controls reflect the complex volcanic hydro-geomorphological setting of Jeju Island and highlight the limitations of uniform flood warning criteria. The findings underscore the necessity of region-specific, dynamically adaptive warning thresholds that explicitly account for local hydrological processes and infrastructure configurations.Overall, this study demonstrates the methodological advantages of grid-based explainable machine learning for physically interpretable and spatially adaptive flood risk assessment. The proposed framework provides a transferable blueprint for data-driven disaster risk management in volcanic island environments and other hydrogeomorphologically complex regions under intensifying climate extremes.

The water stress indicator represents the water withdrawal rate with respect to the available water excluding the environmental flow and is designed to provide an overview of a country's water management. To use this indicator in various regions during the dry season when water problems may arise, items are modified and applied with a low flow, an environmental flow, and stream water use. This method conforms to Korea's water use permit and instream flow system thus, it can be used practically. In addition, since stream water during the dry season is more vulnerable to use than water from reservoirs, a water resource development indicator is introduced. Based on this water resource development indicator and the water stress indicator, the Yeongsangang River area is the most susceptible to water shortage during the dry season. These two indicators are effective because only a few data types are needed to identify areas that are vulnerable during the dry season.