• 한국공간정보시스템학회:학술대회논문집
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• 2005.11a
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• pp.225-229
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• 2005

The objective of this study is to evaluate snowmelt impact on watershed hydrology using climate change scenarios on Soyanggang-dam and Chungju-dam watershed. SLURP model was used for analyzing hydrological changes based on climate changes. The results (in years 2050 and 2100) of climate changes scenarios was CCCma CGCM2 of SRES suggested by IPCC and the snow cover map and snow depth was derived from NOAA/AVHRR images. The model was calibrated and verified for dam inflow data from 1998 to 2001.

• Proceedings of the KSRS Conference
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• 2006.03a
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• pp.198-201
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• 2006

The objective of this study is to evaluate snowmelt impact on watershed hydrology using climate change scenarios on Soyanggang-dam and Chungju-dam watershed. SLURP model was used for analyzing hydrological changes based on climate changes. The results (in years 2050 and 2100) of climate changes scenarios was CCCma CGCM2 of SRES suggested by IPCC and the snow cover map and snow depth was derived from NOAA/AVHRR images. The model was calibrated and verified for dam inflow data from 1998 to 2001.

• Journal of the Korean Association of Geographic Information Studies
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• v.10 no.3
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• pp.58-69
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• 2007

The vegetation area that occupies 76% in land surface of the earth can give a considerable impact on water resources, environment and ecological system by future climate change. The purpose of this study is to predict future vegetation cover information from NDVI (Normalized Difference Vegetation Index) extracted from satellite images. Current vegetation information was prepared from monthly NDVI (March to November) extracted from NOAA AVHRR (1994 - 2004) and Terra MODIS (2000 - 2004) satellite images. The NDVI values of MODIS for 5 years were 20% higher than those of NOAA. The interrelation between NDVIs and monthly averaged climate factors (daily mean, maximum and minimum temperature, rainfall, sunshine hour, wind velocity, and relative humidity) for 5 river basins of South Korea showed that the monthly NDVIs had high relationship with monthly averaged temperature. By linear regression, the future NDVIs were estimated using the future mean temperature of CCCma CGCM2 A2 and B2 climate change scenario. The future vegetation information by NOAA NDVI showed little difference in peak value of NDVI, but the peak time was shifted from July to August and maintained high NDVIs to October while the present NDVI decrease from September. The future MODIS NDVIs showed about 5% increase comparing with the present NDVIs from July to August.

• Korean Journal of Remote Sensing
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• v.24 no.1
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• pp.25-33
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• 2008

To investigate the hydrologic impacts of climate changes on dam inflow for Soyanggangdam watershed $(2694.4km^2)$ of northeastern South Korea, SLURP (Semi-distributed Land Use-based Runoff Process) model and the climate change results of CCCma CGCM2 based on SRES A2 and B2 were adopted. By the CA-Markov technique, future land use changes were estimated using the three land cover maps (1985, 1990, 2000) classified by Landsat TM satellite images. NDVI values for 2050 and 2100 land uses were estimated from the relationship of NDVI-Temperature linear regression derived from the observed data (1998-2002). Before the assessment, the SLURP model was calibrated and verified using 4 years (1998-2001) dam inflow data with the Nash-Sutcliffe efficiencies of 0.61 to 0.77. In case of A2 scenario, the dam inflows of 2050 and 2100 decreased 49.7 % and 25.0 % comparing with the dam inflow of 2000, and in case of B2 scenario, the dam inflows of 2050 and 2100 decreased 45.3 % and 53.0 %, respectively. The results showed that the impact of land use change covered 2.3 % to 4.9 % for the dam inflow change.

• Ocean and Polar Research
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• v.37 no.4
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• pp.265-278
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• 2015

We analyzed the North Pacific Intermediate Water (NPIW) that was simulated in 25 coupled general circulation models (CGCMs) using historical and Representative Concentration Pathway 4.5 (RCP4.5) scenario experiments of Coupled Model Intercomparison Project Phase 5 (CMIP5), focusing on the evaluation of the performance of HadGEM2-AO. A large inter-model diversity in salinity, density, and depth of the NPIW exists even though the multi-model ensemble mean (MME) is comparable to observations. It was found that the depth of the NPIW tends to be deeper in the models in which the NPIW is relatively saltier. HadGEM2-AO simulates the lightest NPIW having the lowest salinity at shallower depth, compared with other CGCMs. Future projections of the NPIW show that the temperature of the NPIW increases, but the density decreases in all CMIP5 models. It was shown that the salinity of the NPIW decreases in most models and the decrease tends to be larger in models simulating the lighter NPIW. The HadGEM2-AO projects moderate changes in the temperature and density of the NPIW out of the CMIP5 models.

• Journal of The Korean Society of Agricultural Engineers
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• v.52 no.6
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• pp.27-37
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• 2010

The effect of potential future climate change on the storage rate of paddy field during storm periods (June - September) was assessed using the daily paddy water balance model. 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 2020s, 2050s and 2080s was downscaled by Change Factor method through bias-correction using 30 years weather data. The future (2020s, 2050s and 2080s) rainfall, storage and irrigation of paddy field, runoff in paddy levee and ponding depth were analyzed for the A2 and B2 climate change scenarios based on a base year (2005). The future irrigation change of paddy field was projected to increase by decrease in rainfall. So, runoff change in paddy levee was decrease slightly, future storage change of paddy was projected to increase.

• Journal of Korea Water Resources Association
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• v.43 no.10
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• pp.865-881
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• 2010

To evaluate influence of the future climate change on water environment, it is necessary to use a rainfall-runoff model, or a basin model allowing us to simultaneously simulate water quality factors such as sediment and nutrient material. Thus, SWAT is selected as a watershed-based model and Nakdong river basin is chosen as a target basin for this study. To apply climate change scenarios as input data to SWAT, Australian model (CSIRO: Mk3.0, CSMK) and Canadian models (CCCma: CGCM3-T47, CT47) of GCMs are used. Each GCMs which have A2, B1, and A1B scenarios effectively represent the climate characteristics of the Korean peninsula. For detecting climate change in Nakdong river basin, precipitation and temperature, increasing rate of these were analyzed in each scenarios. By simulation results, flow and increasing rate of these were analyzed at particular points which are important in the object basin. Flow and variation of flow in the scenarios for present and future climate changes were compared and analyzed by years, seasons, divided into mid terms. In most of the points temperature and flow rate are increased, because climate change is expected to have a significant effect on rising water temperature and flow rate of river and lake, further on the basis of this study result should set enhancing up water control project of hydraulic structures caused by increasing outer discharge of the Nakdong River Basin due to climate change.

• Proceedings of the Korea Water Resources Association Conference
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• 2009.05a
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• pp.1250-1253
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• 2009

기후변화가 수자원에 미치는 영향을 평가하는데 있어서 주로 기후모형인 Global Climate Model (GCM)이 사용되고 있다. 그러나 이러한 기후모형의 공간적 해상도는 $3^{\circ}{\sim}4^{\circ}$ 정도로 한반도의 경우 바다로 묘사되기도 한다. 따라서 GCM을 이용해서 기후변화가 유역단위 수자원에 미치는 영향을 평가하기 위해서는 일반적으로 축소기법이 사용되고 있다. 현재까지 다양한 축소기법이 개발되었으며, 대표적인 모형으로는 SDSM(Statistical Down-Scaling Model)과 LARS-WG(The Long Ashton Research Station Weather Generator)이 있다. 이에 본 연구에서는 SDSM, LARS-WG와 함께 최근에 축소기법으로 사용되고 있는 인공신경망 기법을 이용해서 CCCMA(Canadian Centre for Climate Modeling and Analysis)에서 일 단위로 모의한 CGCM3 A2 시나리오를 기반으로 우포늪의 강우 및 온도시나리오를 구축하였다. 대상 지점인 우포늪은 경상남도 창녕군 우포늪(위도 $35^{\circ}$33', 경도 $128^{\circ}$25')에 위치하고 있으며, 모의 기간은 CASE1의 경우 현재, CASE2는 2050$^{\sim}$ 2080년, CASE3는 2080년$^{\sim}$2100년으로 각각 구분하여 축소기법을 적용하였다. 축소결과 축소기법에 따라 일정정도 차이를 보이기는 하였으나 강우와 온도 모두 증가하게 됨을 확인하였다.

• Proceedings of the Korea Water Resources Association Conference
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• 2011.05a
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• pp.371-371
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• 2011

본 연구에서는 한강유역 내 관측기간이 충분한 기상청 지상관측소 10개소를 선정하고 CCCma(Canadian Century for Climate modeling and analysis)에서 제공하는 자료에 대한 인공신경망기법 상세화 적용을 실시하였다. 인공신경망의 학습을 위해 CGCM3.1/T63 20C3M시나리오(reference scenario)의 22개 2D변수 중 물리적으로 민감도가 높다고 판단되는 GCM_Prec, huss, ps를 입력변수로 선정하였으며 인공신경망 학습기간은 1991년~1995년, 검증기간은 1996년~2000년, 예측기간은 2011년~2100년으로 A1B, A2 B1 시나리오 등 다양한 기후변화 시나리오를 통해 예측band를 제시하고자 하였다. 하지만 공간상관을 고려하기 위하여 각 관측소에 대하여 인공신경망 학습을 하는 경우 관측소간 spatial correlation 및 spatial cluster구현이 어렵기 때문에 Spatial Rectangular Pulse모형을 이용하고자 하였으나, 강수면적에 대한 scale의 결정이 어렵다는 단점을 확인 하고 본 연구에서는 Random Cascade 모형을 이용하여 ${\beta}$를 통한 강수면적 scale(rainy area fraction)을 결정하고자 하였다. Random Cascade모형의 기법은 격자단위의 downscaling기법으로 강수대의 공간적 형상을 재현하며 스케일에 비종속적인(scale-invariant)프랙탈 특성을 이용하여 매개변수를 최소화 할 수 있는 장점을 가진 기법으로 한강유역 1Km내외 강우장을 만들어 topographic effect를 첨가하고자 한다.

• Proceedings of the Korea Water Resources Association Conference
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• 2008.05a
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• pp.224-228
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• 2008

2007년 세계경제포럼(WEF)은 우리가 직면한 최우선 해결과제로 기후변화를 언급하였다. 최저 기온 상승과 가뭄 영향 지역 확대, 폭염일수와 지역적 홍수 위험 증가 등 각종 이상기상이 야기하는 피해 확대에 대한 예상과 우려 때문이다(IPCC, 2007). 세계적으로 고온극한과 호우빈도 증가, 태풍 세기가 강화될 것으로 전망되고 있으며(IPCC, 2007), 국내의 경우 겨울철 한파 감소와 대설 피해 증가, 여름철 집중호우의 강도 심화, 가을철 초대형 태풍 발생으로 인한 피해 가능성이 예측 되고 있다(기상연구소, 2007). 현재, 이러한 현상들을 가시화하고 대처방안을 마련하기 위한 일환으로 기후변화 시나리오(GCM)가 작성되어 연구에 이용되고 있다. 그러나 GCM의 경우, 공간적 해상도가 낮아 지형학적 특성 등을 충분히 반영하지 못하는 단점이 있어 최근에는 공간 해상도가 GCM보다 높은 RCM(Regional Climate Model, 지역기후모델)자료를 적용한 연구도 진행되고 있다. 본 논문에서는 SRES A2 온난화가스시나리오 기반의 기상청 RegCM3 RCM($27km{\times}27km$)로 부터 일(daily)단위 자료를 각각 모의하여 비교하고, BLRPM을 이용하여 일(daily)단위 자료를 시(hourly)단위로 분해(disaggregation)하였다. 그리고 이들을 이용하여 지속기간별 확률강우량을 산정하여 미래 기후변화가 극한 강우에 미치는 영향을 평가하였다.