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Towards an Integrated Drought Monitoring with Multi-satellite Data Products Over Korean Peninsular

위성자료를 활용한 한반도 전역의 가뭄 통합 모니터링 방안

  • Received : 2017.11.06
  • Accepted : 2017.12.18
  • Published : 2017.12.31

Abstract

Drought is a worldwide natural disaster with extensively adverse impacts on natural ecosystems, agricultural products, social communities and regional economy. Various global satellite observations, including SMAP soil moisture, GRACE terrestrial water storage, Terra and Aqua vegetation productivity, evapotranspiration, and satellite precipitation measures are currently used to characterize seasonal timing and inter-annual variations of regional water supply pattern, vegetation growth, drought events, and its associated influence ecosystems and human society. We suggest the satellite monitoring system development to quantify meteorological, eco-hydrological, and socio-ecological factors related to drought events, and characterize spatial and temporal drought patterns in Korea. The combination of these complementary remote sensing observations(visible to microwave bands) provide an effective means for evaluating regional variations in the timing, frequency, and duration of drought, and availability of water supply influencing vegetation and crop growth. This integrated drought monitoring could help national capacity to deal with natural disasters.

가뭄은 전 지구차원의 다방면에 걸쳐 피해를 주는 자연 재해이다. 21세기 들어 최근까지 다양한 위성관측 기기를 활용하여 다양한 가뭄의 유형을 모니터링 할 수 있게 되었는데, SMAP위성의 토양수분, GRACE 위성의 생태계 물 저장량, Terra & Aqua의 생태계 생산량과 증발산량 그리고 위성 강우 관측 등이 그 예이다. 이들 위성 자료의 분석을 통해 지역적 수자원 현황 및 가뭄과 이로 인한 (수)생태계 영향, 농업 등의 산업, 그리고 인간사회의 영향을 시공간적으로 파악할 수 있다. 가시광선부터 마이크로파까지 채널 (밴드)마다 관측할 수 있는 기상 및 환경 변수가 다르기 때문에 다양한 센서로부터 획득할 수 있는 원격탐사자료는 한반도 전역을 대상으로 가뭄에 대한 수자원 변화와 연관된 피해를 시공간적으로 파악하는 데에 상호 보완적이며 효과적인 수단이다. 따라서 이러한 위성자료들을 통해 국가 재난 대응 차원의 활용방안을 제안하고자 한다.

Keywords

References

  1. Coats, S., J. E. Smerdon, K. B. Katrnauskas, and R. Seager, 2016. The improbable but unexceptional occurrence of megadrought clustering in the American West during the Medieval Climate Anomaly, Environmental Research Letters, 11(7): 074025. https://doi.org/10.1088/1748-9326/11/7/074025
  2. Dee, D. P., S. M. Uppala, A. J. Simmons, P. Berrisford, P. Poli, S. Kobayashi, U. Andrae, M. A. Balmasewda, G. Balsamo, P. Bauer, P. Bechtold, A. C. M. Beljaars, L. van de Berg, J. Bidlot, N. Bormann, C. Delsol, R. Dragani, M. Fuentes, A. J. Geer, L. Haimberger, S. B. Healy, H. Hersbach, E. V. Holm, L. Isaksen, P. Kallberg, M. Kohler, M. Matricardi, A. P. McNally, B. M. Monge-Sanz, J. J. Morcrette, B. K. Park, C. Peubey, P. de Rosnay, C. Tavolato, J. N. Thepaut, and F. Vitart, 2011. The ERAInterim reanalysis: configuration and performance of the data assimilation system, Quarterly Journal of the Royal Meteorological Society, 137(656): 553-597. https://doi.org/10.1002/qj.828
  3. Ji, L. and A. J. Peters, 2003. Assessing vegetation response to drought in the northern Great Plains using vegetation and drought indices, Remote Sensing of Environment, 87(1): 85-98. https://doi.org/10.1016/S0034-4257(03)00174-3
  4. Jia, W., L. Zhang, Q. Chang, D. Fu, Y. Cen, and Q. Tong, 2016. Evaluation an enhanced vegetation condition index (VCI) based on VIUPD for drought monitoring in the Continental United States, Remote Sensing, 8(3): 224. https://doi.org/10.3390/rs8030224
  5. van Hoek, M., L. Jia, J. Zhou, C. Zheng, and M. Menenti, 2016. Early drought detection by spectral analysis of satellite time series of precipitation and normalized difference vegetation index (NDVI), Remote Sensing, 8(5): 422. https://doi.org/10.3390/rs8050422
  6. Kim, H., J. Park, J. Yoo, and T. Kim, 2015. Assessment of drought hazard, vulnerability, and risk: A case study for administrative districts in South Korea, Journal of Hydro-environmental Research, 9(1): 28-35. https://doi.org/10.1016/j.jher.2013.07.003
  7. Kim, B., J. H. Sung, B. H. Lee, and D. J. Kim, 2013. Evaluation on the impact of extreme droughts in South Korea using the SPEI and RCP8.5 climate change scenario, Journal of Korean Society of Hazard Mitigation, 13(2): 97-109 (in Korean with English abstract). https://doi.org/10.9798/KOSHAM.2013.13.2.097
  8. Kim, Y, 2013. Drought and elevation effects on MODIS vegetation indices in northern Arizona ecosystems, International Journal of Remote Sensing, 34(14): 4889-4899. https://doi.org/10.1080/2150704X.2013.781700
  9. Kim, D. H., C. Yoo, and T. Kim, 2011. Application of spatial EOF and multivariate time series model of for evaluating agricultural drought vulnerability in Korea, Advances in Water Resources, 34(3): 340-350. https://doi.org/10.1016/j.advwatres.2010.12.010
  10. Kim, D., H. Byun, and K. Choi, 2009. Evaluation, modification, and application of the Effective drought index to 200-year drought climatology of Seoul, Korea, Journal of Hydrology, 378(1-2): 1-12. https://doi.org/10.1016/j.jhydrol.2009.08.021
  11. Kim, S., B. Kim, T. J. Ahn, and H. S. Kim. 2009. Spatio-temporal characterization of Korean drought using severity-area-duration curve analysis, Water and Environment Journal, 25(1): 22-30.
  12. Keyamtash, J. and J. A. Dracup, 2002. The quantification of drought: An evaluation of drought indices, Bulletin of the American Meteorological Society, 83(8): 1167-1180. https://doi.org/10.1175/1520-0477-83.8.1167
  13. Korea Meteorological Administration (KMA), 2016. 2016 abnormal climate report.
  14. Kwak, J., D. Kim, S. Kim, and V. P. Singh, 2013. Hydrological drought analysis in Manhan river basin, Korea, Journal of Hydrologic Engineering, 19(8): 1-10.
  15. Lee, J., J. Seo, and C. Kim, 2012. Analysis on trends, periodicities and frequencies of Korean Drought using drought indices, Journal of Korea Water Resources Association, 45(1): 75-89 (in Korean with English abstract). https://doi.org/10.3741/JKWRA.2012.45.1.75
  16. Marj, A. F. and A. M. J. Meijerink, 2011. Agricultural drought forecasting using satellite images, climate indices and artificial neural network, International Journal of Remote Sensing, 32(24): 9709-9719.
  17. Mu, Q., M. Zhao, and S. W. Running, 2011. Improvements to a MODIS global terrestrial evapotranspiration algorithm, Remote Sensing of Environment, 115(8): 1781-1800. https://doi.org/10.1016/j.rse.2011.02.019
  18. Pachepsky, Y., D. E. Radcliffe, and H. M. Selim, 2003. Scaling methods in soil physics, CRC Press, Boca Raton, FL, USA.
  19. Schlaepfer, D. R., J. B. Bradford, W. K. Lauenroth, S. M. Munson, B. Tietjen, S. A. Hall, S. D. Wilson, M. C. Duniway, G. Jia, D. A. Pyke, A. Lkhagva, and K. Jamiyansharav, 2017. Climate change reduces extent of temperature drylands and intensified drought in deep soils, Nature Communications, 8: 14196. https://doi.org/10.1038/ncomms14196
  20. Schlesinger, W. H., M. C. Dietze, R. B. Jackson, R. P. Phillips, C. C. Rhoades, L. E. Rustad, and J. M. Vose, 2016. Forest biogeochemistry in response to drought, Global Change Biology, 22(7): 2318-2328. https://doi.org/10.1111/gcb.13105
  21. Schroeder, R., K. C. McDonald, M. Azarderakhsh, and R. Zimmermann, 2016. ASCAT MetOp-A diurnal backscatter observations of recent vegetation drought patterns over the continuous U.S.: An assessment of spatial extent and relationship with precipitation and crop yield, Remote Sensing of Environment, 177: 153-159. https://doi.org/10.1016/j.rse.2016.01.008
  22. Sur, C., J. Hur, K. Kim, W. Choi, and M. Choi, 2015. An evaluation of satellite-based drought indices on a regional scale, International Journal of Remote Sensing, 36(22): 5593-5612. https://doi.org/10.1080/01431161.2015.1101653
  23. Thomas, B. F., J. S. Famiglietti, F. W. Landerer, D. N. Wiese, N. P. Molotch, and D. F. Argus, 2017. GRACE Groundwater drought index: Evaluation of California Central Valley groundwater drought, Remote Sensing of Environment, 198: 384-392. https://doi.org/10.1016/j.rse.2017.06.026
  24. Vicca, S., M. Balzarolo, I. Filella, A. Granier, M. Herbst, A. Knohl, B. Longdoz, M. Mund, Z. Nagy, K. Pinter, S. Rambal, J. Verbesselt, A. Verger, A. Zeileis, C. Zhang, and J. Penuelas, 2016. Remote-sensed detection of effects of extreme droughts on gross primary production, Scientific Reports, 6: 28269. https://doi.org/10.1038/srep28269
  25. Viltard, N., C. Burlaud, and C. D. Kummerow, 2006. Rain retrieval from TMI Brightness temperature measurements using a TRMM PR-based database, Journal of Applied Meteorology and Climatology, 45(3): 455-466. https://doi.org/10.1175/JAM2346.1
  26. Wang, J., A. L. Kessner, C. Aegerter, A. Sharma, L. Judd, B. Wardlow, J. You, M. Shulski, S. Irmak, A. Kilic, and J. Zeng, 2016. A multi-sensor view of the 2012 central plains drought from space, Frontiers in Environmental Science, 4: 45.
  27. Westerling, A. L, 2015. Increasing western US forest wildfire activity: sensitivity to changes in the timing of spring, Philosophical Transactions B, 371(1696): 20150178. https://doi.org/10.1098/rstb.2015.0178
  28. Wilhite, D.A. and M.H. Glantz, 1985. Understanding the Drought Phenomenon: The Role of Definitions, Water International, 10(3): 111-120. https://doi.org/10.1080/02508068508686328
  29. Wu, D., J. J. Qu, and X. Hao, 2015. Agricultural drought monitoring using MODIS-based drought indices over the USA Corn Belt, International Journal of Remote Sensing, 36(21): 5403-5425. https://doi.org/10.1080/01431161.2015.1093190
  30. Zhang, L., W. Jiao, H. Zhang, C. Huang, and Q. Tong, 2017. Studying drought phenomena in the Continental United States in 2011 and 2012 using various drought indices, Remote Sensing of Environment, 190: 96-106. https://doi.org/10.1016/j.rse.2016.12.010