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Title Page
Contents
ABSTRACT 9
1. INTRODUCTION 11
2. Description of the study area and datasets 21
2.1. Study area 21
2.1.1. Korean peninsula 21
2.1.2. Mongolia 25
2.1.3. Australia 27
2.2. Ground Measurements Datasets 29
2.3. Global Land Datasets 30
2.3.1. Moderate resolution imaging Spectroradiometer (MODIS) 30
2.3.2. Global Land Data Assimilation System (GLDAS) 34
3. Methods 35
3.1. Satellite-based Evapotranspiration Algorithm 35
3.1.1. Modified Remote Sensing-based Penman-Monteith Algorithm 35
3.1.2. Model Improvement under All Sky Condition 42
3.2. Drought Indices 44
3.2.1. Standalone MODIS-based Evaporative Stress Index(stMOD_ESI) 44
3.2.2. Energy-based Water Deficit Index (EWDI) 45
3.2.3. Standardized Precipitation Index (SPI) 47
3.2.4. Palmer Drought Severity Index (PDSI) 48
4. Results and Discussions 49
4.1. Spatio-Temporal Distributions of Various Variables 49
4.1.1. Radiation Energy 49
4.1.2. Carbon Circulation Variables 59
4.1.3. MOD16 Evapotranspiration Product 63
4.1.4. Evapotranspiration 67
4.2. Evaluation of Drought Indices 75
4.2.1. Spatial Distributions of Drought Indices 75
4.2.2. Temporal Distributions of Drought Indices 82
4.2.3. Validation with Other Drought Indices 86
4.2.4. Large-scale Drought Analysis 93
5. Summary and Conclusions 112
References 114
초록 133
Figure 1. Geographic location of the Korean peninsula and validation sites 22
Figure 2. Geographic locations of Mongolia and validation sites 25
Figure 3. Geographic location of Australia and validation sites 28
Figure 4. Validation results for each Rld model(이미지참조) 54
Figure 5. Spatial distributions of Rld based on Abramowitz's equation(이미지참조) 57
Figure 6. Spatial distribution of GPP in South Korea 60
Figure 7. Time series of observed and estimated GPP 61
Figure 8. Relationship between MODIS-based GPP and fluxtower measurement GPP 62
Figure 9. Spatial distribution of ET in South Korea 64
Figure 10. Time series of observed and estimated ET 65
Figure 11. Relationship between MODIS-based ET and fluxtower measurement GPP 66
Figure 12. Temporal variations of daily estimated ET in 2012 69
Figure 13. Comparison between observed and estimated ET 70
Figure 14. Spatial distribution of monthly estimated ET under all sky condition in 2012 73
Figure 15. Seasonal anomalies in EWDI for 2004-2013 77
Figure 16. Seasonal anomalies of various drought indices 80
Figure 17. Temporal variations of various drought indices for the Uisung region 84
Figure 18. Temporal variations of various drought indices for the Boeun region 84
Figure 19. Temporal variations of various drought indices for the Imsil region 85
Figure 20. Scatterplots among anomaly drought indices for the Uisung region 88
Figure 21. Scatterplots among anomaly drought indices for the Boeun region 89
Figure 22. Scatterplots among anomaly drought indices for the Imsil region 90
Figure 23. Spatial distributions of EWDI and stMOD_ESI for the Korean peninsula in 2010 94
Figure 24. Spatial distributions of EWDI and stMOD_ESI for Australia in 2010 95
Figure 25. Spatial distributions of EWDI and stMOD_ESI for Mongolia in 2010 95
Figure 26. Temporal variations of various drought indices for the Anju site 98
Figure 27. Temporal variations of various drought indices for the Hamheung site 98
Figure 28. Temporal variations of various drought indices for the Kimchaek site 99
Figure 29. Temporal variations of various drought indices for the Perth site 100
Figure 30. Temporal variations of various drought indices for the Melbourne site 101
Figure 31. Temporal variations of various drought indices for the Brisbane site 101
Figure 32. Temporal variations of various drought indices for the Adelaide site 102
Figure 33. Temporal variations of various drought indices for the Darwin site 102
Figure 34. Temporal variations of various drought indices for the Giles site 103
Figure 35. Temporal variations of various drought indices for the Tsetserleg site 104
Figure 36. Temporal variations of various drought indices for the Darkhan site 104
Figure 37. Temporal variations of various drought indices for the Sainshand site 105
Figure 38. Temporal variations of various drought indices for the Khovd site 105
Figure 39. Temporal variations of drought indices for the Dalanzadgad site 106
Figure 40. Temporal variations of various drought indices for the Murun site 106
Figure 41. The results of ROC analysis at the Korean peninsula study sites 107
Figure 42. The results of ROC analysis at the Australia study sites 108
Figure 43. The results of ROC analysis at the Mongolia study sites 109
가뭄은 농업, 경제 및 환경적인 피해를 초래하는 자연 재해 중 하나이다. 광역적인 피해를 주는 가뭄은 시작과 끝을 정확히 알기 어렵고, 진행 방향을 예측하기 힘들기 때문에 이에 대한 피해상황이나 피해대책을 강구하는 것이 쉽지 않다. 최근, 가뭄을 분석하기 위해 새로운 가뭄지수를 개발하거나 기존의 가뭄지수들을 개선하는 연구들이 많이 수행되고 있다. 대부분의 가뭄 연구들은 지상 관측 자료를 활용하여 가뭄현상을 분석하고 있지만 지상 관측 자료를 활용한 가뭄 분석 연구는 지상 관측 망이 조밀하지 않기 때문에 해당 지역을 대표하는 값을 설정하는 데에 한계가 따른다. 이러한 문제를 해결하기 위해 많은 연구들이 정확한 가뭄 상태를 공간적으로 파악할 수 있는 인공위성을 활용한 가뭄 지수 개발에 박차를 가하고 있다.
본 연구에서는 증발산, 토양수분, 태양복사에너지, 식생 활동 등과 같은 수문기상인자들을 활용하여 새로운 가뭄 지수(Energy-based Water Deficit Index(EWDI)를 개발하였고 이는 Moderate Resolution Imaging Spectroradiometer(MODIS)에서 제공되는 산출물들을 활용하였다. EWDI는 물의 순환과 탄소 순환을 동시에 고려하여 기존에 활용되는 다른 가뭄지수들보다 다양한 측면에서 가뭄을 분석할 수 있는 장점을 가지고 있으며 산정된 EWDI는 증발산 기반의 가뭄지수인 Stand-alone MODIS based Evaporative Stress Index(stMOD_ESI)와 함께 시공간적인 변동성을 비교하여 전 세계적으로 가뭄 피해가 심각한 지역인 몽골, 호주, 한반도 지역에 대해 2000년에서 2010년까지 적용성을 파악하였다. 또한, 본 연구에서는 각 지수들 간의 상관관계를 파악하고 수문기상 인자들과 가뭄 현상 사이에 관계성을 파악하기 위해 Receiver Operating Characteristics(ROC) 분석을 수행하였다. 위에서 언급한 여러 분석 결과를 토대로, EWDI와 stMOD_ESI는 기존에 많이 쓰였던 가뭄 지수인 표준강수지수(Standardized Precipitation Index, SPI)에 비해 가뭄 상태를 더욱 잘 파악할 수 있는 것으로 나타났으며 EWDI와 stMOD_ESI가 광역적인 범위에서의 적용성이 높음을 파악하였다. 본 연구를 통해 수문기상학 및 수자원 분야에서의 인공위성을 활용한 가뭄 분석 연구의 기틀이 마련되길 기대해 볼 수 있다.| 번호 | 참고문헌 | 국회도서관 소장유무 |
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| 131 | Estimating daily gross primary production of maize based only on MODIS WDRVI and shortwave radiation data ![]() |
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| 132 | Validation of the MODIS bidirectional reflectance distribution function and albedo retrievals using combined observations from the aqua and terra platforms ![]() |
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| 133 | An improved equation for estimating long‐wave radiation from the atmosphere ![]() |
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| 134 | 2002. First operational BRDF, Albedo and Nadir Reflectance Products from MODIS. Remote Sensing of Environment, 83, 135-148. | 미소장 |
| 135 | Operational retrieval of atmospheric temperature, moisture, and ozone from MODIS infrared radiances ![]() |
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| 136 | Spatial and Temporal Variability of Net Primary Productivity (NPP) over Terrestrial Biosphere of India Using NOAA-AVHRR Based GloPEM Model ![]() |
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| 137 | On the development of a simple downwelling longwave radiation scheme ![]() |
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| 138 | Utility of Penman–Monteith, Priestley–Taylor, reference evapotranspiration, and pan evaporation methods to estimate pasture evapotranspiration ![]() |
미소장 |
| 139 | Evaluating Ecohydrological Impacts of Vegetation Activities on Climatological Perspectives Using MODIS Gross Primary Productivity and Evapotranspiration Products at Korean Regional Flux Network Site ![]() |
미소장 |
| 140 | Long-wave radiation from clear skies ![]() |
미소장 |
| 141 | Estimation of instantaneous net surface longwave radiation from MODIS cloud-free data ![]() |
미소장 |
| 142 | An Approach toward a Rational Classification of Climate ![]() |
미소장 |
| 143 | Model estimates of net primary productivity, evapotranspiration, and water use efficiency in the terrestrial ecosystems of the southern United States during 1895–2007 ![]() |
미소장 |
| 144 | Regional Drought Assessment Based on the Reconnaissance Drought Index (RDI) ![]() |
미소장 |
| 145 | Validating modelled NPP using statistical yield data ![]() |
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| 146 | Correcting eddy-covariance flux underestimates over a grassland ![]() |
미소장 |
| 147 | 2006. Soil Moisture Retrieval Using Thermal Inertia, Determined with Visible and Thermal Spaceborne Data, Validated for European Forests. Remote Sensing of Environment, 101, 299-314. | 미소장 |
| 148 | The ASCAT Soil Moisture Product: A Review of its Specifications, Validation Results, and Emerging Applications ![]() |
미소장 |
| 149 | Estimation of high-spatial resolution clear-sky longwave downward and net radiation over land surfaces from MODIS data ![]() |
미소장 |
| 150 | Primary Productivity and Water Use in Native Forest, Grassland, and Desert Ecosystems ![]() |
미소장 |
| 151 | A Self-Calibrating Palmer Drought Severity Index ![]() |
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| 152 | 1993. Drought Assessment, Management, and Planning: Theory and Case Studies. Natural Resource Management and Policy Series, Vol. 2, Kluwer, 293. | 미소장 |
| 153 | Understanding: the Drought Phenomenon: The Role of Definitions ![]() |
미소장 |
| 154 | Satellite-based modeling of gross primary production in an evergreen needleleaf forest ![]() |
미소장 |
| 155 | Prediction of Continental-Scale Evapotranspiration by Combining MODIS and AmeriFlux Data Through Support Vector Machine ![]() |
미소장 |
| 156 | On downward shortwave and longwave radiations over high altitude regions: Observation and modeling in the Tibetan Plateau ![]() |
미소장 |
| 157 | Monitoring Drought over the Conterminous United States Using MODIS and NCEP Reanalysis-2 Data ![]() |
미소장 |
| 158 | Water‐use efficiency of forest ecosystems in eastern China and its relations to climatic variables ![]() |
미소장 |
| 159 | Global estimates of evapotranspiration and gross primary production based on MODIS and global meteorology data ![]() |
미소장 |
| 160 | 2012. Spatial and temporal variation of precipitation in Sudan and their possible cause during 1948-2005. Stochastic Environmental Research and Risk Assessment, 26, 429-441. | 미소장 |
| 161 | Improvements of the MODIS terrestrial gross and net primary production global data set ![]() |
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| 162 | Drought-Induced Reduction in Global Terrestrial Net Primary Production from 2000 Through 2009 ![]() |
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