Soojun Kim

Hydrological Drought Analysis in Namhan River Basin, Korea

AbstractAnalysis 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.

Scrub Typhus Incidence Modeling with Meteorological Factors in South Korea.

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.

An impact assessment of climate and landuse change on water resources in the Han river.

As climate changes and abnormal climates have drawn research interest recently, many countries utilize the GCM, which is based on SRES suggested by IPCC, to obtain more accurate forecast for future climate changes. Especially, many research attempts have been made to simulate localized geographical characteristics by using RCM with the high resolution data globally. To evaluate the impacts of climate and landuse change on water resources in the Han-river basin, we carried out the procedure consisting of the CA-Markov Chain, the Multi-Regression equation using two independent variables of temperature and rainfall, the downscaling technique based on the RegCM3 RCM, and SLURP. From the CA-Markov Chain, the future landuse change is forecasted and the future NDVI is predicted by the Multi-Regression equation. Also, RegCM3 RCM 50 sets were generated by the downscaling technique based on the RegCM3 RCM provided by KMA. With them, 90 year runoff scenarios whose period is from 2001 to 2090 are simulated for the Han-river basin by SLURP. Finally, the 90-year simulated monthly runoffs are compared with the historical monthly runoffs for each dam in the basin. At Paldang dam, the runoffs in September show higher increase than the ones in August which is due to the change of rainfall pattern in future. Additionally, after exploring the impact of the climate change on the structure of water circulation, we find that water management will become more difficult by the changes in the water circulation factors such as precipitation, evaporation, transpiration, and runoff in the Han-river basin.

Impact of Climate Change on Habitat of the Rhynchocypris Kumgangensis in Pyungchang River

Abstract This study tried to analyze the impact of climate change on ecological habitat. In this regard, the Rhynchocypris Kumgangensis was selected among the CBIS(Climate-sensitive Biologocal Indicator Species) suggested by the Ministry of Environment. And ecological habitat and restrictive conditions for its survival was surveyed. Future runoff and water quality in the upstream of Pyungchang river were simulated by appling climate change scenarios to SWAT model which is able to simulate water quality. The estimated results explained characteristics on the increase of runoff, BOD, and water temperature and the decrease of DO in the future. The restrictive condition on ecological habitat of the Rhynchocypris Kumgangensis was used water quality during the April to May spawning season since BOD and DO were satisfactory as the first grade of water criteria in the estimated result of future water quality. As a result, it was analyzed that habitat of the Rhynchocypris Kumgangensis in the present was possible about 50~60% of the river. But the habitat would be decreased gradually in the future and would be possible in a very small part of the river in the long term.

Long-Term Simulation of Daily Streamflow Using Radar Rainfall and the SWAT Model: A Case Study of the Gamcheon Basin of the Nakdong River, Korea

In recent years, with the increasing need for improving the accuracy of hydrometeorological data, interests in rain-radar are also increasing. Accordingly, with high spatiotemporal resolution of rain-radar rainfall data and increasing accumulated data, the application scope of rain-radar rainfall data into hydrological fields is expanding. To evaluate the hydrological applicability of rain-radar rainfall data depending on the characteristics of hydrological model, this study applied and to a SWAT model in the Gamcheon stream basin of the Nakdong River and analyzed the effect of rainfall data on daily streamflow simulation. The daily rainfall data for , , and were utilized as input data for the SWAT model. As a result of the daily runoff simulation for analysis periods using and , the simulation which utilized reflected the rainfall-runoff characteristics better than the simulations which applied or . However, in the rainy or wet season, the simulations which utilized or were similar to or better than the simulation that applied . This study reveals that analysis results and degree of accuracy depend significantly on rainfall characteristics (rainy season and dry season) and QPE algorithms when conducting a runoff simulation with radar.

A Strategy on Optimizing a Stream Gauging Network to Mitigate the Flood Risk in a Disaster Risk Area

최근 전세계적으로 이상기후 현상에 의한 대규모 재해의 발 생이 빈발하고 있다. IPCC(Intergovernmental Panel on Climate Change, 2012)에 의하면 기후변화에 의하여 극한기상 현상의 발생이 더욱 강력하고 빈번하게 발생할 것으로 보고되고 있 다. 즉, 미래에 재해의 위험성은 더욱 크게 증가할 것이다. 이 러한 사실에 기반할 때 재해에 취약한 구조를 가지고 있는 재 해위험지구의 중점적인 관리가 요구된다. 재해관리에 있어 무 엇보다 중요한 것은 정확한 데이터를 기반으로 위험요소를 추정하는 것이다. 이에 따라 수문관측의 정확도가 더욱 중요 하게 되었으며 전국적으로 우량관측소와 수위관측소를 포함 하는 상당히 많은 관측소가 신설되고 있다. 여기에서 각 수문 관측소는 유역의 수문특성을 가장 잘 대표할 수 있는 위치에 경제적으로 설치되어야 한다. 따라서 유역내에서 최적화된 수 문관측망과 함께 정보이론 측면에서 각 수문관측망이 얼마나 Abstract

Future Climate Data from RCP 4.5 and Occurrence of Malaria in Korea

Since its reappearance at the Military Demarcation Line in 1993, malaria has been occurring annually in Korea. Malaria is regarded as a third grade nationally notifiable disease susceptible to climate change. The objective of this study is to quantify the effect of climatic factors on the occurrence of malaria in Korea and construct a malaria occurrence model for predicting the future trend of malaria under the influence of climate change. Using data from 2001–2011, the effect of time lag between malaria occurrence and mean temperature, relative humidity and total precipitation was investigated using spectral analysis. Also, a principal component regression model was constructed, considering multicollinearity. Future climate data, generated from RCP 4.5 climate change scenario and CNCM3 climate model, was applied to the constructed regression model to simulate future malaria occurrence and analyze the trend of occurrence. Results show an increase in the occurrence of malaria and the shortening of annual time of occurrence in the future.

Evaluation for Snowfall Depth Forecasting using Neural Network and Multiple Regression Models

Since snowfall is related to various meteorological variables such as temperature and precipitation, it is generated in nonlinear manner. Therefore this study constructs snowfall forecasting model using neural networks and multiple regression which can consider nonlinear process of snowfall. The study constructs the forecasting models for each station using temperature, precipitation, and snowfall depth observed from starting time of observation to 1999. And snowfalls are calculated for all stations by using temperature and precipitation in the period of 2000 to 2011. From the statistical analysis of the calculated snowfall, the proper model is selected. The selected models show the correlation coefficients of 0.700 to 0.949 and the adjusted determination coefficients of 41.7% to 89.8%. The applicability of neural network models is superior to other model at almost every station. But in some cases multiple regression models show better results than neural network models due to the lack of observational data during learning period and the extreme peak values which are not learned during forecasting period. According to the study, the results of the models confirm the predicting snowfall depth by using temperature and precipitation is possible and show neural network model is better than the existing statistical models.

Evaluation of Drought Severity with a Bayesian Network Analysis of Multiple Drought Indices

AbstractDrought indices assimilate meteorological and/or hydrological information to come up with a comprehensible index. Over the last few decades, hundreds of drought indices have been developed ...

Hydrological Drought Analysis Based on Copula Theory

Drought has been a more frequent phenomenon of major concern all over the world. From the perspective of water resources management, one of the biggest problems associated with drought analyses is a lack of quantitative estimation for the target drought amount. The objective of this study is to examine the establishing process for the severity-duration-frequency (hereafter referred as “SDF”) curves on climate change. The standardized truncation level that defines hydrological drought was estimated and a bivariate frequency analysis for drought duration and severity was derived. The SDF curves were also estimated. The methodology suggested in this study could be used as elementary data for water resources managements.