• Korean Journal of Remote Sensing
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• v.24 no.1
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• pp.45-55
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• 2008

The impact on streamflow and groundwater recharge considering future potential climate and land use change was assessed using SLURP (Semi-distributed Land-Use Runoff Process) continuous hydrologic model. The model was calibrated and verified using 4 years (1999-2002) daily observed streamflow data for a $260.4km^2$ which has been continuously urbanized during the past couple of decades. The model was calibrated and validated with the coefficient of determination and Nash-Sutcliffe efficiency ranging from 0.8 to 0.7 and 0.7 to 0.5, respectively. The CCCma CGCM2 data by two SRES (Special Report on Emissions Scenarios) climate change scenarios (A2 and B2) of the IPCC (Intergovemmental Panel on Climate Change) were adopted and the future weather data was downscaled by Delta Change Method using 30 years (1977 - 2006, baseline period) weather data. The future land uses were predicted by CA (Cellular Automata)-Markov technique using the time series land use data of Landsat images. The future land uses showed that the forest and paddy area decreased 10.8 % and 6.2 % respectively while the urban area increased 14.2 %. For the future vegetation cover information, a linear regression between monthly NDVI (Normalized Difference Vegetation Index) from NOAA/AVHRR images and monthly mean temperature using five years (1998 - 2002) data was derived for each land use class. The future highest NDVI value was 0.61 while the current highest NDVI value was 0.52. The model results showed that the future predicted runoff ratio ranged from 46 % to 48 % while the present runoff ratio was 59 %. On the other hand, the impact on runoff ratio by land use change showed about 3 % increase comparing with the present land use condition. The streamflow and groundwater recharge was big decrease in the future.

• Journal of The Korean Society of Agricultural Engineers
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• v.54 no.2
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• pp.77-86
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• 2012

This study was aim to predict future land-cover changes and to analyze regional land-cover changes in irrigation areas and agricultural reservoir watersheds under climate change scenario. To simulate the future land-cover under climate change scenario - A1B of the SRES (Special Report on Emissions Scenarios), Dyna-CLUE (Conversion of Land Use Change and its Effects) was applied for modeling of competition among land-use types in relation to socioeconomic and biophysical driving factors. For the study areas, 8 agricultural reservoirs were selected from 8 different provinces covering all around nation. The simulation results from 2010 to 2100 suggested future land-cover changes under the scenario conditions. For Madun reservoir in Gyeonggi-do, total decrease amount of paddy area was a similar amount of 'Base demand scenario' of Water Vision 2020 published by MLTMA (Ministry of Land, Transport and Maritime Affairs), while the decrease amounts of paddy areas in other sites were less than the amount of 'High demand scenario' of Water Vision 2020. Under A1B scenario, all the land-cover results showed only slight changes in irrigation areas of agricultural reservoirs and most of agricultural reservoir watersheds will be increased continuously for forest areas. This approach could be useful for evaluating and simulating agricultural water demand in relation to land-use changes.

• Journal of The Korean Society of Agricultural Engineers
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• v.55 no.4
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• pp.55-63
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• 2013

This study investigates the trends and uncertainty of the impacts of climate change on paddy rice production in the Geumho river basin. The Long Ashton Research Station stochastic Weather Generator (LARS-WG) was used to derive future climate data for the Geumho river basin from 15 General Circulation models (GCMs) for 3 Special Report on Emissions Scenarios (SRES) (A2, A1B and B1) included in the Intergovernmental Panel on Climate Change (IPCC) 4th assessment report. The Food and Agricultural Organization (FAO) AquaCrop, a water-driven crop model, was statistically calibrated for the 1982 to 2010 climate. The index of agreement (IoA), prediction efficiency ($R^2$), percent bias (PBIAS), root mean square error (RMSE) and a visual technique were used to evaluate the adjusted AquaCrop simulated yield values. The adjusted simulated yields showed RMSE, NSE, IoA and PBIAS of 0.40, 0.26, 0.76 and 0.59 respectively. The 5, 9 and 15 year central moving averages showed $R^2$ of 0.78, 0.90 and 0.96 respectively after adjustment. AquaCrop was run for the 2020s (2011-2030), 2050s (2046-2065) and 2090s (2080-2099). Climate change projections for Geumho river basin generally indicate a hotter and wetter future climate with maximum increase in the annual temperature of $4.5^{\circ}C$ in the 2090s A1B, as well as maximum increase in the rainfall of 45 % in the 2090s A2. The means (and ranges) of paddy rice yields are projected to increase by 21 % (17-25 %), 34 % (27-42 %) and 43 % (31-54 %) for the 2020s, 2050s and 2090s, respectively. The A1B shows the largest rice yield uncertainty in all time slices with standard deviation of 0.148, 0.189 and $0.173t{\cdot}ha^{-1}$ for the 2020s, 2050s and 2090s, respectively.

• KSCE Journal of Civil and Environmental Engineering Research
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• v.30 no.2B
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• pp.107-120
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• 2010

The effect of potential future climate change on the inflow of agricultural reservoir and its impact to downstream streamflow by reservoir operation for paddy irrigation water was assessed using the SLURP (semi-distributed land use-based runoff process), a physically based hydrological model. The fundamental input data (elevation, meteorological data, land use, soil, vegetation) was collected to calibrate and validate of the SLURP model for a 366.5 $km^2$ watershed including two agricultural reservoirs (Geumgwang and Gosam) located in Anseongcheon watershed. Then, the CCCma CGCM2 data by SRES (special report on emissions scenarios) A2 and B2 scenarios of the IPCC (intergovernmental panel on climate change) was used to assess the future potential climate change. The future weather data for the year, m ms, m5ms and 2amms was downscaled by Change Factor method through bias-correction using 3m years (1977-2006) weather data of 3 meteorological stations of the watershed. In addition, the future land uses were predicted by modified CA (cellular automata)-Markov technique using the time series land use data fromFactosat images. Also the future vegetation cover information was predicted and considered by the linear regression between monthly NDVI (normalized difference vegetation index) from NOAA AVHRR images and monthly mean temperature using eight years (1998-2006) data.

• Proceedings of the Korea Water Resources Association Conference
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• 2008.05a
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• pp.1058-1062
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• 2008

지구 온난화에 의한 대기 순환의 변화와 이에 따른 수증기 수송 및 강수량의 변화는 전지구 및 지역적인 수문환경의 변화를 초래하므로 장기적인 차원의 수자원 계획 수립에는 반드시 기후 변화에 따른 영향이 제대로 반영되어야 한다. 그러나 개별 모델이 사용하는 역학과정과 물리과정의 모수화 및 분해능이 다르고 이에 따른 모의 결과도 다르게 나타나는 등의 상당한 불확실성이 내재되어 있다. 따라서 본 연구에서는 기후변화에 관한 정부간 패널인 IPCC(Intergovernmental Panel on Climate Change)에 참여한 대기해양결합 대순환모델(AOGCMs)이 온실가스 배출 시나리오를 바탕으로 생산한 기온과 강수의 불확실성을 동아시아에 대해 평가하고 이를 바탕으로 미래 기후를 전망하였다. 국립기상연구소 ECHO-G/S 모델과 IPCC 23개 모델의 배출 시나리오(Special Report on Emissions Scenarios, SRES) 자료는 20세기(1900-1999년)와 21세기(2000-2099년)의 200년 동안이고, 관측자료는 영국 CRU(Climate Research Unit) 월평균 2m 기온의 30년(1961-1990년) 평균값과 CMAP 월 평균 강수량의 21년간(1979-1990년) 평균값을 이용하였다. 동아시아지역 기온과 강수의 불확실성을 평가하기 위해서 모델과 관측간 편이, 평균제곱근오차(RMSE) 등의 통계적인 방법을 사용하였다. 동아시아 지역의 연평균 기온은 대체로 모델의 기온이 관측보다 적게 모의되는 음의 편이를 나타내고, 강수는 모델이 관측보다 더 크게 모의 되는 양의 편이를 나타냈다. 계절적으로는 여름철 강수와 봄철 기온의 편이가 크게 나타났다. 연평균 및 겨울철 강수와 기온의 RMSE는 비례하는데 이는 기온 모의성능이 좋은 모델이 강수 모의성능도 좋게 나타나는 것을 의미한다.

• Proceedings of the Korea Water Resources Association Conference
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• 2010.05a
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• pp.938-942
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• 2010

현재 기후변화가 심화되면서 기상변동성이 커지고 이에 따라 사막화 현상의 심화, 엘니뇨(El Nino), 라니냐(La Nina), 태풍, 집중호우 등의 이상기후 현상이 전 지구상에 걸쳐 광역적으로 나타나고 있는 실정이다. 이러한 기후변화는 앞서 말한 것과 같이 여러 기후인자들을 변화시켜 수자원의 양적변화 등 지속가능한 수자원 개발 관리에 큰 영향을 미치므로 이에 대한 연구가 국내외에서 활발히 진행되고 있다. 대표적으로 여러 가지 2CO2 시나리오에 대한 대기 순환 모형의 적용 결과를 이용하여, 이러한 기후변화가 수문순환에 영향을 미치는 기후인자인 기온, 강수량, 습도 및 풍속, 그리고 물의 수량 및 수질 등에 미치는 영향을 분석하고, 이를 기반으로 기후변화와 관련된 환경 및 수자원의 정책 개발에 대한 연구들이 주로 수행되고 있다. 국내 역시 기후 변화와 관련된 연구들이 수행되고는 있으나, 기후변화와 연계된 유량과 수질 예측에 대한 연구가 절실히 요구되고 있는 실정이다. 따라서 본 연구에서는 IPCC의 배출 시나리오(Special Report on Emissions Scenarios, 이하 SRES) 중 인구증가율이 높고 경제발달과 기술변화가 느리고 환경에 무관심한 극한현상을 나타내는 A2 시나리오와 청정 및 자원 효율적인 기술 등 급격히 발전하고 조사대상 유역특성과 유사한 B1 시나리오를 선정하고, 이에 대한 유역의 기온과 강우량을 GCM을 적용하여 모의하였다. 또한 향후의 기후변화가 유출 수질(BOD, TN, TP)에 미치는 영향을 2020년, 2050년, 2080년에 대하여 평가하기 위하여 GIS 기반의 유역 모형인 SWAT을 대상모형으로 선정하였다. 신뢰성 평가를 위해 현재 상태에서의 모의를 검 보정 하여 실제 A2, B1 기후변화 시나리오에 따른 기온 및 강우량 변화 등에 대한 영향을 평가하여 보았다.

• Journal of Korean Society for Atmospheric Environment
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• v.28 no.1
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• pp.22-38
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• 2012

Adaptation of climate change is necessary to avoid unexpected impacts of climate change caused by human activities. Vulnerability refers to the degree to which system cannot cope with impacts of climate change, encompassing physical, social and economic aspects. Therefore the quantification of climate change impacts and its vulnerability is needed to identify vulnerable regions and to setup the proper strategies for adaptation. In this study, climate change vulnerability is defined as a function of climate exposure, sensitivity, and adaptive capacity. Also, we identified regions vulnerable to ozone due to climate change in Korea using developed proxy variables of vulnerability of regional level. 18 proxy variables are selected through delphi survey to assess vulnerability over human health sector for ozone concentration change due to climate change. Also, we estimate the weighting score of proxy variables from delphi survey. The results showed that the local regions with higher vulnerability index in the sector of human health are Seoul and Daegu, whereas regions with lower one are Jeollanam-do, Gyeonggi-do, Gwangju, Busan, Daejeon, and Gangwon-do. The regions of high level vulnerability are mainly caused by their high ozone exposure. We also assessed future vulnerability according to the Intergovernmental Panel on Climate Change (IPCC) Special Report on Emissions Scenarios (SRES) A2, A1FI, A1T, A1B, B2, and B1 scenarios in 2020s, 2050s and 2100s. The results showed that vulnerability increased in all scenarios due to increased ozone concentrations. Especially vulnerability index is increased by approximately 2 times in A1FI scenarios in the 2020s. This study could support regionally adjusted adaptation polices and the quantitative background of policy priority as providing the information on the regional vulnerability of ozone due to climate change in Korea.

• Journal of Korea Water Resources Association
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• v.47 no.2
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• pp.145-159
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• 2014

This study is to evaluate the future climate change impact on turbidity water and eutrophication for Chungju Lake by using CE-QUAL-W2 reservoir water quality model coupled with SWAT watershed model. The SWAT was calibrated and validated using 11 years (2000~2010) daily streamflow data at three locations and monthly stream water quality data at two locations. The CE-QUAL-W2 was calibrated and validated for 2 years (2008 and 2010) water temperature, suspended solid, total nitrogen, total phosphorus, and Chl-a. For the future assessment, the SWAT results were used as boundary conditions for CE-QUAL-W2 model run. To evaluate the future water quality variation in reservoir, the climate data predicted by MM5 RCM(Regional Climate Model) of Special Report on Emissions Scenarios (SRES) A1B for three periods (2013~2040, 2041~2070 and 2071~2100) were downscaled by Artificial Neural Networks method to consider Typhoon effect. The RCM temperature and precipitation outputs and historical records were used to generate pollutants loading from the watershed. By the future temperature increase, the lake water temperature showed $0.5^{\circ}C$ increase in shallow depth while $-0.9^{\circ}C$ in deep depth. The future annual maximum sediment concentration into the lake from the watershed showed 17% increase in wet years. The future lake residence time above 10 mg/L suspended solids (SS) showed increases of 6 and 17 days in wet and dry years respectively comparing with normal year. The SS occupying rate of the lake also showed increases of 24% and 26% in both wet and dry year respectively. In summary, the future lake turbidity showed longer lasting with high concentration comparing with present behavior. Under the future lake environment by the watershed and within lake, the future maximum Chl-a concentration showed increases of 19 % in wet year and 3% in dry year respectively.