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Percentile Approach of Drought Severity Classification in Evaporative Stress Index for South Korea

Evaporative Stress Index (ESI)의 국내 가뭄 심도 분류 기준 제시

  • Lee, Hee-Jin (Department of Bioresources and Rural Systems Engineering, Hankyong National University) ;
  • Nam, Won-Ho (Department of Bioresources and Rural Systems Engineering, Institute of Agricultural Environmental Science, National Agricultural Water Research Center, Hankyong National University) ;
  • Yoon, Dong-Hyun (Department of Bioresources and Rural Systems Engineering, Hankyong National University) ;
  • Hong, Eun-Mi (School of Natural Resources and Environmental Science, Kangwon National University) ;
  • Kim, Taegon (Department of Bioproducts and Biosystems Engineering, University of Minnesota) ;
  • Park, Jong-Hwan (Rural Research Institute, Korea Rural Community Corporation) ;
  • Kim, Dae-Eui (Rural Research Institute, Korea Rural Community Corporation)
  • Received : 2020.01.02
  • Accepted : 2020.03.17
  • Published : 2020.03.31

Abstract

Drought is considered as a devastating hazard that causes serious agricultural, ecological and socio-economic impacts worldwide. Fundamentally, the drought can be defined as temporarily different levels of inadequate precipitation, soil moisture, and water supply relative to the long-term average conditions. From no unified definition of droughts, droughts have been divided into different severity level, i.e., moderate drought, severe drought, extreme drought and exceptional drought. The drought severity classification defined the ranges for each indicator for each dryness level. Because the ranges of the various indicators often don't coincide, the final drought category tends to be based on what the majority of the indicators show and on local observations. Evaporative Stress Index (ESI), a satellite-based drought index using the ratio of potential and actual evaporation, is being used as a index of the droughts occurring rapidly in a short period of time from studies showing a more sensitive and fast response to drought compared to Standardized Precipitation Index (SPI), and Palmer Drought Severity Index (PDSI). However, ESI is difficult to provide an objective drought assessment because it does not have clear drought severity classification criteria. In this study, U.S. Drought Monitor (USDM), the standard for drought determination used in the United States, was applied to ESI, and the Percentile method was used to classify drought categories by severity. Regarding the actual 2017 drought event in South Korea, we compare the spatial distribution of drought area and understand the USDM-based ESI by comparing the results of Standardized Groundwater level Index (SGI) and drought impact information. These results demonstrated that the USDM-based ESI could be an effective tool to provide objective drought conditions to inform management decisions for drought policy.

Acknowledgement

Supported by : 농림식품기술기획평가원

References

  1. Allen, R. G., L. S. Pereira, D. Raes, and M. Smith, 1998. Crop evapotranspiration: Guidelines for computing crop water requirements. FAO Irrigation and Drainage Paper 56, Food and Agriculture Organization, Rome.
  2. Anderson, M. C., J. M. Norman, J. R. Mecikalski, J. A. Otkin, and W. P. Kustas, 2007. A climatological study of evapotranspiration and moisture stress across the continental U.S. based on thermal remote sensing: I. model formulation. Journal of Geophysical Research 112: D10117. doi:10.1029/2006JD007506.
  3. Anderson, M. C., C. R. Hain, B. Wardlow, A. Pimstein, J. R. Mecikalski, and W. P. Kustas, 2011. Evaluation of drought indices based on thermal remote sensing of evapotranspiration over the continental United States. Journal of Climate 24: 2025-2044. doi:10.1175/2010JCLI3812.1. https://doi.org/10.1175/2010JCLI3812.1
  4. Anderson, M. C., C. Hain, J. Otkin, X. Zhan, K. Mo, M. Svoboda, B. Wardlow, and A. Pimstein, 2013. An intercomparison of drought indicators based on thermal remote sensing and NLDAS-2 simulations with U.S. drought monitor classifications. Journal of Hydrometeorology 14:1035-1056. doi:110.1175/JHM-D-12-0140.1. https://doi.org/10.1175/JHM-D-12-0140.1
  5. Anderson, M. C., C. A. Zolin, C. R. Hain, K. Semmens, M. T. Yilmaz, and F. Gao, 2015. Comparison of satellitederived LAI and precipitation anomalies over Brazil with a thermal infrared-based evaporative stress index for 2003-2013. Journal of Hydrology 526: 287-302. doi:10.1016/j.jhydrol.2015.01.005. https://doi.org/10.1016/j.jhydrol.2015.01.005
  6. Anderson, M. C., C. A. Zolin, P. C. Sentelhas, C. R. Hain, K. Semmens, M. T. Yilmaz, F. Gao, J. A. Otkin, and R. Tetrault, 2016. The evaporative stress index as an indicator of agricultural drought in Brazil: An assessment based on crop yield impacts. Remote Sensing of Environment 174(1):82-99. doi:10.1016/j.rse.2015.11.034. https://doi.org/10.1016/j.rse.2015.11.034
  7. Bloomfield, J. P., and B. P. Marchant, 2013. Analysis of groundwater drought using a variant of the standarised precipitation index. Hydrology and Earth System Sciences Discussions 10(6): 7537-7574. doi:10.5194/hessd-10-7537-2013. https://doi.org/10.5194/hessd-10-7537-2013
  8. Du Pisani, L. G., H. J. Fouche, and J. C. Venter, 1998. Assessing rangeland drought in South Africa. Agricultural Systems 57(3): 367-380. doi:10.1016/S0308-521X(98)00024-9. https://doi.org/10.1016/S0308-521X(98)00024-9
  9. Hayes, M. J., O. V. Wilhelmi, and C. L. Knutson, 2004. Reducing drought risk: Bridging theory and practice. Natural Hazards Review 5(2): 106-113. doi:10.1061/(ASCE)1527-6988(2004)5:2(106).
  10. Heim, R. R., 2002. A review of twentieth-century drought indices used in the United States. Bulletin of the American Meteorological Society 83(8): 1149-1165. doi:10.1175/1520-0477-83.8.1149. https://doi.org/10.1175/1520-0477-83.8.1149
  11. Hong, E. M., W. H. Nam, and J. Y. Choi, 2015. Climate change impacts on agricultural drought for major upland crops using soil moisture model -Focused on the Jeollanam-do-. Journal of the Korean Society of Agricultural Engineers 57(3): 65-76. doi:10.5389/KSAE.2015.57.3.065. https://doi.org/10.5389/KSAE.2015.57.3.065
  12. Keyantash, J., and Dracup, J, 2002. The quantification of drought: An evaluation of drought indices. Bulletin of the American Meteorological Society 83(8): 1167-1180. doi:10.1175/1520-0477-83.8.1167. https://doi.org/10.1175/1520-0477-83.8.1167
  13. Kim, S. J., M. I. Kim, C. H. Lim, W. K. Lee, and B. J. Kim, 2017. Applicability analysis of FAO56 penman-monteith methodology for estimating potential evapotranspiration in Andong dam watershed using limited meteorological data. Journal of Climate Change Research 8(2): 125-143. doi:10.15531/KSCCR.2017.8.2.125. https://doi.org/10.15531/ksccr.2017.8.2.125
  14. Korea Meteorological Administration (KMA), 2018. 2017 abnormal climate report. Korea Meteorological Administration, Seoul, Korea.
  15. Lee, J. J., S. U. Shin, J. H. Jeong, and G. I. Chun, 2018. Development of groundwater level monitoring and forecasting technique for drought analysis (I) - Groundwater drought monitoring using standardized groundwater level index (SGI). Journal of Korea Water Resources Association 51(11): 1011-1020. doi:10.3741/JKWRA.2018.51.11.1011. https://doi.org/10.3741/JKWRA.2018.51.11.1011
  16. Lee, H. J., W. H. Nam, D. H. Yoon, E. M. Hong, D. E. Kim, M. D. Svoboda, T. Tadesse, and B. D. Wardlow, 2019. Satellite-based evaporative stress index (ESI) as an indicator of agricultural drought in North Korea. Journal of the Korean Society of Agricultural Engineers 61(3): 1-14. doi:10.5389/KSAE.2019.61.3.001. https://doi.org/10.5389/KSAE.2019.61.3.001
  17. McKee, T. B., N. J. Doesken, and J. Kliest, 1993. The relationship of drought frequency and duration to time scales. In Proceedings of the 8th Conference of Applied Climatology, 17-22 January, Anaheim, CA. American Meteorological Society, Boston, MA. 179-184.
  18. Mun, Y. S., W. H. Nam, M. G. Jeon, T. Kim, E. M. Hong, M. J. Hayes, and T. Tadesse, 2019. Application of meteorological drought index using Climate Hazards Group InfraRed Precipitation wigh Station (CHIRPS) based on global satellite-assisted precipitation products in Korea. Journal of the Korean Society of Agriculture Engineers 61(2): 1-11. doi:10.5389/KSAE.2019.61.2.001.
  19. Nam, W. H., J. Y. Choi, S. H. Yoo, and B. A. Engel, 2012. A real-time online drought broadcast system for monitoring soil moisture index. KSCE Journal of Civil Engineering 16(3): 357-365. doi:10.1007/s12205-012-1357-3. https://doi.org/10.1007/s12205-012-1357-3
  20. Nam, W. H., M. J. Hayes, D. A. Wilhite, T. Tadesse, M. D. Svoboda, and C. L. Knutson, 2014. Drought management and policy based on risk assessment in the context of climate change. Magazine of the Korean Society of Agricultural Engineers 56(2): 2-15.
  21. Nam, W. H., T. Tadesse, B. D. Wardlow, M. W. Jang, and S. Y. Hong, 2015a. Satellite-based hybrid drought assessment using Vegetation Drought Response Index in South Korea (VegDRI-SKorea). Journal of the Korean Society of Agricultural Engineers 57(4): 1-9. doi:10.5389/KSAE.2015.57.4.001. https://doi.org/10.5389/KSAE.2015.57.4.001
  22. Nam, W. H., M. J. Hayes, M. D. Svoboda, T. Tadesse, and D. A. Wilhite, 2015b. Drought hazard assessment in the context of climate change for South Korea. Agricultural Water Management 160: 106-117. doi:10.1016/j.agwat.2015.06.029. https://doi.org/10.1016/j.agwat.2015.06.029
  23. Nam, W. H., E. M. Hong, J. Y. Choi, T. G. Kim, M. J. Hayes, and M. D. Svoboda, 2017. Assessment of the extreme 2014-2015 drought events in North Korea using weekly Standardized Precipitation Evapotranspiration index (SPEI). Journal of the Korean Society of Agricultural Engineers 59(4): 65-74. doi:10.5989/KSAE.2017.59.4.065. https://doi.org/10.5389/KSAE.2017.59.4.065
  24. Norman, J. M., W. P. Kustas, and K. S. Humes, 1995. Source approach for estimating soil and vegetation energy fluxes in observations of directional radiometric surface temperature. Agricultural and Forest Meteorology 77(3-4):263-292. doi:110.1016/0168-1923(95)02265-Y. https://doi.org/10.1016/0168-1923(95)02265-Y
  25. Otkin, J. A., M. C. Anderson, C. Hain, I. E. Mladenova, J. B. Basara, and M. Svoboda, 2013. Examining rapid onset drought development using thermal infrared-based evaporative stress index. Journal of Hydrometeorology 14: 1057-1074. doi:10.1175/JHM-D-12-0144.1. https://doi.org/10.1175/JHM-D-12-0144.1
  26. Otkin, J. A., M. C. Anderson, C. Hain, and M. Svoboda, 2014. Examining the relationship between drought development and rapid changes in the evaporative stress index. Journal of Hydrometeorology 15: 938-956. doi:10.1175/JHM-D-13-0110.1. https://doi.org/10.1175/JHM-D-13-0110.1
  27. Palmer, W. C., 1965. Meteorological drought. Research Paper 45. US Department of Commerce Weather Bureau, Washington DC.
  28. Smith, A. D., and R. W. Katz, 2013. US billion-dollar weather and climate disasters: Data sources, trends, accuracy and biases. Natural Hazards 67: 387-410. doi:10.1007/s11069-013-0566-5. https://doi.org/10.1007/s11069-013-0566-5
  29. Sohn, K. H., D. H. Bae, and J. H. Ahn, 2014. Projection and analysis of drought according to future climate and hydrological information in Korea. Journal of the Korea Water Resources Association 47(1): 71-82. doi:10.3741/JKWRA.2014.47.1.71. https://doi.org/10.3741/JKWRA.2014.47.1.71
  30. Steinemann, A., 2003. Drought triggers: A stochastic approach to evaluation. Journal of the American Water Resources Association 39(5): 1217-1234. doi:10.1111/j.1752-1688.2003.tb03704.x. https://doi.org/10.1111/j.1752-1688.2003.tb03704.x
  31. Sternberg, T., 2011. Regional drought has a global impact. Nature 472: 169. doi:10.1038/472169d.
  32. Sur, C. Y., K. J. Kim, W. J. Choi, J. H. Sim, and M. H. Choi, 2014. Drought assessments using satellite-based drought index in Korea; Southern region case in 2013. Journal of Korean Society of Hazard Mitigation 14(3):127-131. doi:10.9798/KOSHAM.2014.14.3.127. https://doi.org/10.9798/KOSHAM.2014.14.3.127
  33. Svoboda, M., D. LeComte, M. Hayes, R. Heim, K. Gleason, J. Angel, B. Rippey, R. Tinker, M. Palecki, D. Stooksbury, D. Miskus, and S. Stephens, 2002. The drought monitor. Bulletin of the American Meteorological Society 83(8):1181-1190. doi:10.1175/1520-0477-83.8.1181. https://doi.org/10.1175/1520-0477-83.8.1181
  34. Wilhite, D. A., and M. H. Glantz, 1985. Understanding the drought phenomenon: The role of definitions. Water International 10(3): 111-120. doi:10.1080/02508068508686328. https://doi.org/10.1080/02508068508686328
  35. Wilhite, D. A., M. J. Hayes, C. Knutson, and K. H. Smith, 2000. Planning for drought: Moving from crisis to risk management. Journal of the American Water Resources Association 36(4): 697-710. doi:10.1111/j.1752-1688.2000.tb04299.x. https://doi.org/10.1111/j.1752-1688.2000.tb04299.x
  36. Xia, Y., M. B. Ek, C. D. Peters-Lidard, D. Mocko, M. Svoboda, J. Sheffield, and E. F. Wood, 2014. Application of USDM statistics in NLDAS-2: Optimal blended NLDAS drought index over the continen-tal United States. Journal of Geophysical Research: Atmosphere 119: 2947-2965. doi:10.1002/2013JD020994. https://doi.org/10.1002/2013JD020994
  37. Yoon, D. H., W. H. Nam, H. J. Lee, E. M. Hong, T. G. Kim, A. K. Shin, and M. D. Svoboda, 2018. Application of evaporative stress index (ESI) for satellite-based agricultural drought monitoring in South Korea. Journal of the Korean Society of Agricultural Engineers 60(6): 121-131. doi:10.5389/KSAE.2018.60.6.121. https://doi.org/10.5389/KSAE.2018.60.6.121