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.

Drought has become one of the most important elements for water resources planning and management in Korea. The objective of this study is to estimate the spatial distribution of drought and change in the drought characteristics over time due to climate change. For the spatial characterization of drought, the standardized precipitation index (SPI) is calculated from the 45 observatories in Korea and the spatial distribution is also estimated based on the joint probability analysis using the copula method. To analyze the effect of climate change, spatial distribution of drought in the future is analyzed using the SPI time series calculated from Representative Concentration Pathways (RCPs) scenarios and HADGEM3-RA regional climate model. The results show that the Youngsan River and the northwest of Nakdong River basins in Korea have nearly doubled drought amount compared to the present and are most vulnerable to drought in near future (2016 to 2039 years).

No abstract

Abstract: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 water temperature are important for water-ecological management. The objective of this study is to compare models for water temperature during the summer season for an impounded river. In a case study, we consider hydro-climatic and water temperature data of the Fourchue River (St-Alexandre-de-Kamouraska, Quebec, Canada) from between 2011 to and 2014. Three different models were applied, which are broadly characterized as deterministic (CEQUEAU), stochastic (Auto-regressive Moving Average with eXogenous variables or ARMAX) and nonlinear (Nonlinear Autoregressive with eXogenous variables or NARX). The efficiency of each model was analyzed and compared. The rResults show that the ARMAX is the best performing water temperature model for the Fourchue River and the CEQUEAU model also simulated water temperature adequately without the overfitting issues that seem to plague the autoregressive models.