Journal of The Korean Society of Agricultural Engineers (한국농공학회논문집)

The Korean Society of Agricultural Engineers (한국농공학회)
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Development of Hydroclimate Drought Index (HCDI) and Evaluation of Drought Prediction in South Korea

수문기상가뭄지수 (HCDI) 개발 및 가뭄 예측 효율성 평가

  • Ryu, JaeHyun (Department of Soil and Water Systems, College of Agricultural and Life Sciences, University of Idaho) ;
  • Kim, JungJin (Texas A&M AgriLife Research, Texas A&M University) ;
  • Lee, KyungDo (National Institute of Agricultural Science, Rural Development Administration)
  • Received : 2017.12.26
  • Accepted : 2018.10.05
  • Published : 2019.01.31
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Abstract

The main objective of this research is to develop a hydroclimate drought index (HCDI) using the gridded climate data inputs in a Variable Infiltration Capacity (VIC) modeling platform. Typical drought indices, including, Standardized Precipitation Index (SPI), Standardized Precipitation Evapotranspiration Index (SPEI), and Self-calibrated Palmer Drought Severity Index (SC-PDSI) in South Korea are also used and compared. Inverse Distance Weighting (IDW) method is applied to create the gridded climate data from 56 ground weather stations using topographic information between weather stations and the respective grid cell ($12km{\times}12km$). R statistical software packages are used to visualize HCDI in Google Earth. Skill score (SS) are computed to evaluate the drought predictability based on water information derived from the observed reservoir storage and the ground weather stations. The study indicates that the proposed HCDI with the gridded climate data input is promising in the sense that it can help us to predict potential drought extents and to mitigate its impacts in a changing climate. The longer term drought prediction (e.g., 9 and 12 month) capability, in particular, shows higher SS so that it can be used for climate-driven future droughts.

Keywords

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Fig. 1 Study area

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Fig. 2 Spatial interpolation method to create the gridded meterological dataset from the selected weather stations

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Fig. 3 Box plot comparisons of total skill scores computed by drought indices (SPI, SEPI, and SC-PDSI) between the gridded dataset (GS) and the observed weather dataset (WS)

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Fig. 4 Skill score comparison between SPI, SPEI, sc-PDSI and HCDI

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Fig. 5 SPI (a), SPEI (b), (c) SC-PDSI, and (d) HCDI drought maps for 1, 3, and 12 month time window in Google Earth

Table 1 The selected multipurpose dam watersheds from the major four river watersheds in Korea

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Table 2 Drought response phase based on the storage rates in the selected multipurpose dams

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Table 3 The classified drought condition of SPI, SPEI, HCDI, and SC-PDSI

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Table 4 The Reclassified drought indices

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Table 5 The matrix table to apply skill scores methods

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Table 6 Average skill score (SS) for each drought index for the observed weather station and the gridded climate data in the selected basins

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Table 7 Average skill score of SPI, SPEI, SC-PDSI, and HCDI (1, 3, 6, 9, 12 month time windows) for KSS and HSS in the selected multipurpose dam watersheds

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