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수문기상가뭄지수 (HCDI) 개발 및 가뭄 예측 효율성 평가

Development of Hydroclimate Drought Index (HCDI) and Evaluation of Drought Prediction in South Korea

김정진|이경도|류재현
한국농공학회 (The Korean Society Of Agricultural Engineers)|2019.01|한국농공학회논문집 (Journal of the Korean Society of Agricultural Engineers)|제61권 제1호|pp. 31-44|DOI https://doi.org/10.5389/KSAE.2019.61.1.031

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키워드

droughtHCDIdrought indicesskill score
주제분류농학

초록

The main objective of this research is to develop a hydroclimate drought index (HCDI) using the gridded climate data inputs in a Variable InfiltrationCapacity (VIC) modeling platform. Typical drought indices, including, Standardized Precipitation Index (SPI), Standardized PrecipitationEvapotranspiration Index (SPEI), and Self-calibrated Palmer Drought Severity Index (SC-PDSI) in South Korea are also used and compared. InverseDistance Weighting (IDW) method is applied to create the gridded climate data from 56 ground weather stations using topographic information betweenweather stations and the respective grid cell (12 km x 12 km). 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 weatherstations. 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 potentialdrought 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.

Abstract

The main objective of this research is to develop a hydroclimate drought index (HCDI) using the gridded climate data inputs in a Variable InfiltrationCapacity (VIC) modeling platform. Typical drought indices, including, Standardized Precipitation Index (SPI), Standardized PrecipitationEvapotranspiration Index (SPEI), and Self-calibrated Palmer Drought Severity Index (SC-PDSI) in South Korea are also used and compared. InverseDistance Weighting (IDW) method is applied to create the gridded climate data from 56 ground weather stations using topographic information betweenweather stations and the respective grid cell (12 km x 12 km). 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 weatherstations. 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 potentialdrought 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.

참고문헌 (42)

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