Figure 1.1. Schematic diagram of this study 30

Figure 1.2. Previous IPCC assessments(SAR IS92a, AR4 SRES A1B, A2 and B1) are compared with RCP scenarios 32

Figure 1.3. Classification of statistical downscaling 38

Figure 1.4. Schematic diagram for Stationary Quantile Mapping 41

Figure 1.5. Statistical distribution obtained from stationary and nonstationary quantile mapping 42

Figure 1.6. Process for implementing Nonstationary Quantile Mapping 42

Figure 1.7. Uncertainty range of between 20 GCMs 45

Figure 1.8. Schematic diagram of Bayesian Model Averaging 48

Figure 1.9. Schematic diagram of EM algorithm 50

Figure 1.10. Conceptual diagram of MTSM method 52

Figure 1.11. Flowchart of flood frequency analysis 54

Figure 1.12. Schematic of SWAT model 59

Figure 1.13. Hydrologic cycle 60

Figure 1.14. HRU / Subbasin flow chart 65

Figure 1.15. Partitioning of nitrogen in SWAT 69

Figure 1.16. Partitioning of phosphorous in SWAT 70

Figure 1.17. Hydrologic response units(HRU) concept 71

Figure 1.18. Weather station 72

Figure 1.19. DEM 72

Figure 1.20. Soil 72

Figure 1.21. Land use 72

Figure 1.22. Response relationship between water quality factors of the QUALMEV(left) and QUAL2E(right) 81

Figure 1.23. Plan of database 82

Figure 1.24. Input procedures of the target data 83

Figure 1.25. Loading procedures of the database 84

Figure 1.26. Development Methodology 85

Figure 1.27. Perform procedures of project 86

Figure 2.1. Sangju-bo upstream basin area and rain gauge distribution 89

Figure 3.1. 20 GCMs grids in South Korea 97

Figure 3.2. A sample of GCM precipitation field throughout world(1.1≒122km) 98

Figure 3.3. Previous IPCC assessments(SAR IS92a, AR4 SRES A1B, A2 and B1) are compared with RCP scenarios 100

Figure 3.4. Probability distribution of HadGEM3-RA during 2006-2013(left : PDF, right : CDF) 104

Figure 3.5. Probability distribution of HadGEM3-RA during 2006-2013(left : PDF, right : CDF) 106

Figure 3.6. Probability distribution of 20 GCMs during 1981~2000(left : PDF, right : CDF) 108

Figure 3.7. Probability distribution after SQM of each gauge station during 1981~2000(left : PDF, right : CDF) 110

Figure 3.8. Comparison of seasonal precipitation results before and after the SQM of HadGEM3-RA in Sangju-bo upstream basin during 2006~2013 112

Figure 3.9. Comparison of seasonal precipitation results before and after the SQM of simple averaging 20 GCMs in Sangju-bo upstream basin during 1981~2000 112

Figure 3.10. Box-Whisker plot of seasonal precipitation before and after SQM in Sangju-bo upstream basin during 1981~2000 114

Figure 3.11. Histogram of Bayesian model weights of each rain gauge station during 1981~2000 117

Figure 3.12. Annual precipitation and uncertainty range with 95% confidence interval of each rain gauge station during 1981~2000 119

Figure 3.13. Frequency of occurrences for monthly precipitation of each rain gauge station during 1981~2000 124

Figure 3.14. Scatter plot of Bayesian ensemble percentile precipitation for the observation of each rain gauge station during 1981~2000 125

Figure 3.15. Histogram of seasonal precipitation of each rain gauge station during 1981~2000 127

Figure 3.16. Box-Whisker plot of Bayesian ensemble seasonal precipitation and uncertainty range of each rain gauge station 128

Figure 3.17. Comparison of temperature result befor and after bias correction in Sangju-bo upstream basin during 1981~2000(left : before, right : after) 129

Figure 3.18. Probability distribution after NSQM for RCP 4.5 of each gauge station during 2021~2100, HadGEM3-RA(left : PDF, right : CDF) 132

Figure 3.19. Probability distribution after NSQM for RCP 4.5 of each gauge station during 2021~2100, BMA Ensemble(left : PDF, right : CDF) 134

Figure 3.20. KMA RCM(HadGEM3-RA) RCP 4.5 scenario against baseline enhancement ratio map of annual precipitation(2021~2100) 137

Figure 3.21. BMA RCP 4.5 scenario against baseline enhancement ratio map of annual precipitation(2021~2100) 138

Figure 3.22. Annual precipitation and uncertainty range from 95% confidence interval of each rain gauge station under RCP 4.5 scenario 139

Figure 3.23. Histogram of seasonal precipitation of each rain gauge station under RCP 4.5 scenario(HadGEM3-RA) 141

Figure 3.24. Histogram of seasonal precipitation of each rain gauge station under RCP 4.5 scenario(BMA) 142

Figure 3.25. Time series graph under RCP 4.5 scenario(HadGEM3-RA) 143

Figure 3.26. Time series graph under RCP 4.5 scenario(BMA) 144

Figure 3.27. Box-Whisker plot of RCP 4.5 precipitation and uncertainty range by projection periods in Sangju-bo upstream basin(BMA) 146

Figure 3.28. Projected annual mean air temperature in Sangju-bo upstream basin during 2021~2100 147

Figure 3.29. Time series graph under RCP 4.5 scenario(CanESM2) 152

Figure 3.30. Time series graph under RCP 4.5 scenario(GFDL-ESM2G) 153

Figure 4.1. GEV distribution during 1981~2000(left: 1-day BMA precipitation, right: 24hr duration BMA precipitation) 165

Figure 4.2. GEV distribution of 24hr BMA precipitation of each rain gauge station during 1981~2000 167

Figure 4.3. Non-central moment(1~5 order) of each rain gauge station 171

Figure 4.4. Scatter plot Scale exponent for the moment of each rain gauge station 173

Figure 4.5. IDF curve from general flood frequency analysis result 179

Figure 4.6. IDF curve from GEV scaling using observed data 179

Figure 4.7. IDF curve from GEV scaling using downscaled data 180

Figure 5.1. Hydrology response urits(HRU) concept 184

Figure 5.2. Weather station 185

Figure 5.3. DEM 185

Figure 5.4. Soil 185

Figure 5.5. Land use 185

Figure 5.6. Calibration(a) and validation(b) of flow in Dosan 188

Figure 5.7. Calibration(a) and validation(b) of flow in Andong Dam 189

Figure 5.8. Calibration(a) and validation(b) of flow in Youngyang 190

Figure 5.9. Calibration(a) and Validation(b) of Flow in Imha Dam 191

Figure 5.10. Calibration of flow in Jukjeon 192

Figure 5.11. Validation of flow in Jukjeon 193

Figure 5.12. Cross-section grid construction of horizontal and vertical in Andong Dam 195

Figure 5.13. Water level-Storage capacity relationship in Andong reservoir 196

Figure 5.14. Water quality station in Andong Dam 196

Figure 5.15. Andong Dam operation data in calibration and validation period 197

Figure 5.16. Weather condition in Andong Dam watershed 199

Figure 5.17. Cross-section grid construction of horizontal and vertical in Imha Dam 200

Figure 5.18. Water level-Storage capacity relationship in Imha reservoir 201

Figure 5.19. Water quality station in Imha Dam 202

Figure 5.20. Imha Dam operation data in calibration and validation period 203

Figure 5.21. Weather condition in Imha Dam watershed 205

Figure 5.22. Comparison of observation and simulation in Andong Dam(reservoir water surface) 206

Figure 5.23. Comparison of observation and simulation water temperature in Andong Dam 1 208

Figure 5.24. Comparison of time series concentration change of TN, TP, Chl-a in Andong Dam 1(2011, 2010) 210

Figure 5.25. Comparison of observation and simulation in Imha Dam(reservoir water surface) 211

Figure 5.26. Comparison of observation and simulation water temperature in Imha Dam intake station 212

Figure 5.27. Comparison of time series concentration change of TN, TP, Chl-a in Imha Dam 1(2006, 2011) 213

Figure 5.28. Schematic of upper Sangju weir watershed 214

Figure 5.29. Monthly analysis of water quality in upper Sanju weir 215

Figure 5.30. Annual analysis of water quality in upper Sanju weir 215

Figure 5.31. Calibration(BOD) 216

Figure 5.32. Validation(T-P) 216

Figure 5.33. Combinated Impact of Climate and Land Use Change 219

Figure 5.34. Trend Analysis of Naeseongchun 220

Figure 5.35. Monthly Analysis of Streamflow in Naeseongchun 220

Figure 5.36. Streamflow Change Rate of Naeseongchun 221

Figure 5.37. Flow Change in Naeseongchun 222

Figure 5.38. Precipitation vs. Total Streamflow in Naeseongchun 223

Figure 5.39. Monthly Analysis of SS in Naeseongchun 224

Figure 5.40. SS Change Rate of Naeseongchun 224

Figure 5.41. Monthly Analysis of T-N in Naeseongchun 226

Figure 5.42. T-N Change Rage of Naeseongchun 226

Figure 5.43. Monthly Analysis of T-P in Naeseongchun 227

Figure 5.44. T-N Change Rate of Naeseongchun 228

Figure 6.1. Observation Data - Dam basin 229

Figure 6.2. Observation Data - River basin 230

Figure 6.3. Observation Data - Bo upstream basin 230

Figure 6.4. Prospect Data - Dam basin 230

Figure 6.5. Prospect Data - River basin 230

Figure 6.6. Prospect Data - Bo upstream basin 230

Figure 6.7. Prospect Data - model results 231

Figure 6.8. Prospect Data - Water quality monitoring network 231

Figure 6.9. Water Resources Data - Multipurpose Dam 231

Figure 6.10. Water Resources Data - Water dam 232

Figure 6.11. Water Resources Data - Multi-function Bo 232

Figure 6.12. Water Resources Data - Water level stations 232

Figure 6.13. Water Resources Data - Rainfall stations 232

Figure 6.14. Water Resources Data - Water quality monitoring network 232

Figure 6.15. GIS Data 233

Figure 6.16. Main screen 244

Figure 6.17. System Configuration 245

Figure 6.18. System Configuration screen - Main 246

Figure 6.19. System Configuration screen - Observation Information Status lookup by period 247

Figure 6.20. System Configuration screen - The chart zoom 248

[부록 1] RCP 기반 기후변화시나리오 분석 및 미래기후 전망 검토 269

Fig. 2.1. Schematic illustration of SRES scenario. Four qualitative storylines yield four sets of scenarios called "families": A1, A2, B1, and B2. Altogether 40 SRES scenarios have been developed by six modeling teams. All are... 269

Fig. 2.2. Approaches to the development of global scenarios: (a) previous sequential approach; (b) proposed parallel approach. Numbers indicate analytical steps(2a and 2b proceed concurrently). Arrows indicate transfers of information... 270

Fig. 3.1. Main features of the Atmosphere-Ocean General Circulation Models(AOGCMs) and Earth System Models(ESMs) participating in Coupled Model Intercomparison Project 5(CMIP5), and a comparison with CMIP3 in AR4... 274

Fig. 3.2. Main features of the ESMICs assessed in the AR5, including components and complexity of the models. Model complexity for four components is indicated by colour shading.(Adapted from Flato et al., 2013) 275

Fig. 3.3. Left: Schematic summary of CMIP5 short-term experiment with tier 1 experiments(yellow background) organized around a central core(pink background). Right: Schematic summary of CMIP5 long-term experiments with... 276

Fig. 3.4. Annual mean surface air temperature for the period 1980-2005. (a) Multi-model (ensemble) mean constructed with one realization of all available model used in the CMIP5 historical experiment. (b) Multi-model mean bias as... 280

Fig. 3.5. Annual-mean precipitation rate (mm/day) for the period 1980-2005. (a) Multi-model mean constructed with one realization of all available AOGCMs used in the CMIP5 historical experiment. (b) Difference between multi-model... 281

Fig. 3.6. Relative error measures of CMIP5 model performance, based on the global seasonal-cycle climatology (1980-2005) computed from the historical experiments. Rows and columns represent individual variables and models... 282

Fig. 4.1. Time series of global annual mean surface air temperature anomalies (relative to 1986-2005) from CMIP5 concentration-driven experiments. Projections are shown for each RCP for the multi-model (solid... 284

Fig. 4.2. CMIP5 multi-model ensemble average of DJF (top row) and JJA (bottom row) mean sea level pressure change (2081-2100 minus 1986-2005) for, from left to right, RCP2.6, 4.5, and 8.5. Hatching indicates regions where the multi-model... 287

Fig. 4.3. CMIP5 multi-model ensemble average of zonal and annual mean wind change (2081-2100 minus 1986-2005) for from left to right, RCP2.6, 4.5, and 8.5. Black contour represent the multi-model average for 1986-2005 base period... 288

Fig. 4.4. Projected changes in near-surface relative humidity from the CMIP5 models under RCP8.5 for the DJF (left), JJA (middle), and annual mean (right) averages relative to 1986-2005 for the periods, 2046-2065 (top-row), 2081-2100... 290

Fig. 4.5. Changes in annual mean soil moisture relatevbe to the reference period 1986-2005 projectd for 2081-2100 from the CMIP5 ensemble. Hatching indicates regions where the multi-model mean change is less than one standard deviation... 291

Fig. 5.1. Schematic diagram illustrates the main ways that human activity influences monsoon rainfall.(Adapted from Christensen et al., 2013) 292

Fig. 5.2. Maps of precipitation changes for Central, North, East and South Asia in 2080-2099 with respect to 1986-2005 in JJAS (above) and DJFM (below) in the SRES A1B scenario with 24 CMIP3 models (left), in the RCP4.5 scenario with 39... 294

Fig. 5.3. Time series of temperature change relative to 1986-2005 averaged over land grids over eastern Asia in DJF(top left) and the same over Tibetan Plateau(top right). Individual lines indicate one ensemble member per model, thick lines... 296

Fig. 5.4. Same as in the Fig. 5.3 except for the June-July-August (JJA).(Adapted from IPCC, 2013) 297

Fig. 5.5. The same as in Fig. 5.3 except for the precipitation. Hatching denotes areas where the 20-year mean differences of the percentiles are less than the standard deviation of model-estimated present-day natural... 298

Fig. 5.6. The same as in Fig. 5.5 except for the JJA season.(Adapted from IPCC, 2013) 298

Fig. 5.7. Time series of temperature (a) and precipitation anomalies (b) with respect to the period of 1971-2000 averaged over the region of Korea peninsula from the 16 CMIP5 models. Shading areas indicate the one standard deviation... 299

Fig. 5.8. (Top) Annual mean temperature from ERA-interim reanalysis for 1979-2005 (a), differences between HadGEM2-AO and ERA-interim reanalysis (b), and differences between HadGEM3-RA and ERA-interim reanalysis. (Bottom) The... 301

Fig. 5.9. (Top) Present-day (1981-2005) precipitation amount simulated by HadGEM3-RA (a) and differences between future (2076-2100) and present for RCP2.6 (b), RCP4.5 (c), RCP6.0 (d), and RCP8.5 (e). (Bottom) THe same as in the... 303

Fig. 6.1. Schematic of the proposed experiment design for the phase 6 of the CMIP6. The inner ring and surrounding grey ring invove standardized functions of all CMIP, including... 305

Fig. 6.2. Domains for the CORDEX-South Asia (Orange), CORDEX-East Asia Phase Ⅰ(Pink dashed), Phase Ⅱ(Red), CORDEX-Southeast Asia (Sky blue), and CORDEX-Australia (Green) 306

[부록 2] 기후변화 시나리오 수자원분야 적용 및 관리방안 제안 317

그림 1. 기후재해, 기상재해, 수재해, 지구물리재해, 생물재해로 나누어 본 전세계적인 재해 증가를 보여주는 그림(출처는 본문에 기재) 317

그림 2. 구글 트렌드 빅데이터 시계열 분석(영어) 318

그림 3. 구글 트렌드 빅데이터 시계열 분석(한글) 319

그림 4. 구글 트렌드 빅데이터 시계열 분석(영어) 320

그림 5. 구글 트렌드 빅데이터 시계열 분석(한글) 320

그림 6. 지구상의 물 순환을 정량적으로 도해한 그림(Trenberth et al., 2007) 322

그림 7/그림 8. 해외 수자원관련 키워드별 연구 현황 323

그림 8. 국내 수자원관련 키워드별 연구 현황 324

그림 9. 미래기후 변화 시나리오(RCP 4.5)에 따른 CMIP5 20개 모델을 통한 한반도(33˚N-43˚N, 124˚E-131˚E)의 (a) 겨울철과 (b) 여름철 강수 단기(2006~2049) 및 장기(2050~2099) 미래 변화(문혜진 등, 2014) 325

그림 10. 미래기후 변화 시나리오(RCP 4.5) 에 따른 CMIP5 20개 모델을 통한 한반도(33˚N-43˚N, 124˚E-131˚E)의 (a,b) 겨울철과 (c,d) 여름철 수분수렴과 역학적, 열역학적, 에디 항들의 단기(2025-2049) 및 장기... 327

그림 11. 빅데이터 분석을 통한 분야별 기후변화 영향 개념도 334

그림 12. 기후변화에 의한 2100년 부근의 전 지구적 수문변수의 변화량(Wang, 2012) 335

그림 13. AR5 모형 예측 자료를 이용한 글로벌 몬순 지역별 21세기 강수와 순환장 강도 변화(Wang et al., 2014) 349