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.

Analysis and forecasting of droughts are important for water resources management. The objective of this study is to analyze hydrological droughts using the joint drought probability distribution derived from the copula theory and analyze drought return periods upstream of Namhan River in the upper Han River basin, Korea. Results of the study shows that the severest drought that occurred from 1981 to 1982 has a 110-year return period.

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.

As a significant portion of the available water resources in volcanic terrains such as Jeju Island are dependent on groundwater, reliable groundwater level forecasting is one of the important tasks for efficient water resource management. This study aims to propose deep-learning-based methods for groundwater level forecasting that can be utilized in actual management works and to assess their applicability. The study suggests practical forecasting methodologies through the Gyorae area of Jeju Island, where the groundwater level is highly volatile and unpredictable. To this end, the groundwater level data of the JH Gyorae-1 point and a total of 12 kinds of daily hydro-meteorological data from 2012 to 2021 were collected. Subsequently, five factors (i.e., mean wind speed, sun hours, evaporation, minimum temperature, and daily precipitation) were selected as hydro-meteorological data for groundwater level forecasting through cross-wavelet analysis between the collected hydro-meteorological data and groundwater level data. The study simulated the groundwater level of the JH Gyorae-1 point using the long short-term memory (LSTM) model, a representative deep-learning technique, with the selected data to show that the methodology is adequately applicable. In addition, for its better utilization in actual practice, the study suggests and analyzes (i) a derivatives-based groundwater level learning model which is defined as derivatives-based learning to forecast derivatives (gradients) of the groundwater level, not the target groundwater time series itself, and (ⅱ) an ensemble forecasting methodology in which groundwater level forecasting is performed repetitively with short time intervals.