[논문]증발스트레스지수를 활용한 국내 돌발가뭄 감지

Lee, Hee-Jin; Nam, Won-Ho; Yoon, Dong-Hyun; Mark, D. Svoboda; Brian, D. Wardlow

doi:10.3741/jkwra.2021.54.8.577

증발스트레스지수를 활용한 국내 돌발가뭄 감지
Detection of flash drought using evaporative stress index in South Korea 원문보기

Journal of Korea Water Resources Association = 한국수자원학회논문집, v.54 no.8, 2021년, pp.577 - 587

Lee, Hee-Jin (한경대학교 국가농업용수연구센터) , Nam, Won-Ho (한경대학교 사회안전시스템공학부) , Yoon, Dong-Hyun (한경대학교 융합시스템공학과) , Mark, D. Svoboda (네브라스카-링컨대학교 국립가뭄경감센터) , Brian, D. Wardlow (네브라스카-링컨대학교 CALMIT)

초록
AI-Helper

가뭄은 수개월, 수년 이상에 걸쳐 서서히 발생 및 지속되며, 식생에 대한 피해가 발생할 때까지 확실한 인식이 어렵다. 최근에는 기후변화에 따른 기상이변 및 기온상승 등으로 인하여 가뭄의 발생빈도가 증가하고 있으며, 기상 이상으로 몇 주 또는 몇 달 이내 빠르게 발전하는 가뭄을 확인할 수 있다. '돌발가뭄(Flash Drought)은 일반적인 가뭄과 달리 비교적 짧은 기간 동안 표면온도의 상승과 비정상적으로 낮고 빠르게 감소하는 토양수분으로 인하여 식생에 대한 극심한 스트레스를 유발하면서 광범위한 작물 손실 및 용수공급 감소 등에 대한 피해를 야기하는 가뭄으로 정의된다. 짧은 기간의 급속하게 발생하는 (rapid-onset) 돌발가뭄은 발생원인인 토양수분함량의 감소와 강수의 부족, 낮은 습도, 고온 및 강풍 등으로 인한 증발 수요의 증가 등과 유사한 관계가 있기 때문에 농업 및 자연 생태계에 미치는 영향이 크며, 발생원인 또한 농업가뭄의 범주에 속하기 때문에 이에 대한 모니터링이 필수적이다. 본 연구에서는 증발산량을 활용한 위성영상 기반 가뭄지수인 증발스트레스지수(Evaporative Stress Index, ESI)를 활용하여 국내의 돌발가뭄 감지 조건을 제시하였으며, 표준강수지수, 토양수분, 최고기온, 상대습도, 풍속, 강수량 등과 비교를 통하여 돌발가뭄사상의 수문기상학적 특성에 대하여 분석하였다. 돌발가뭄 발생 이전 8주간을 기준으로 상관분석하였으며, ESI와 표준강수지수, 토양수분, 최고기온에 대하여 0.8(-0.8) 이상의 높은 상관관계를 보였다. 본 연구를 통하여 아직 명확하게 정의되지 않은 돌발가뭄에 대한 유형별 분석 및 국내 돌발가뭄의 수문기상학적 특성을 파악하였으며, 위성영상 기반 가뭄지수인 증발스트레스지수는 돌발가뭄사상의 모니터링에 활용될 수 있을 것으로 판단된다.

Abstract ▼ AI-Helper

Drought is generally considered to be a natural disaster caused by accumulated water shortages over a long period of time, taking months or years and slowly occurring. However, climate change has led to rapid changes in weather and environmental factors that directly affect agriculture, and extreme weather conditions have led to an increase in the frequency of rapidly developing droughts within weeks to months. This phenomenon is defined as 'Flash Drought', which is caused by an increase in surface temperature over a relatively short period of time and abnormally low and rapidly decreasing soil moisture. The detection and analysis of flash drought is essential because it has a significant impact on agriculture and natural ecosystems, and its impacts are associated with agricultural drought impacts. In South Korea, there is no clear definition of flash drought, so the purpose of this study is to identify and analyze its characteristics. In this study, flash drought detection condition was presented based on the satellite-derived drought index Evaporative Stress Index (ESI) from 2014 to 2018. ESI is used as an early warning indicator for rapidly-occurring flash drought a short period of time due to its similar relationship with reduced soil moisture content, lack of precipitation, increased evaporative demand due to low humidity, high temperature, and strong winds. The flash droughts were analyzed using hydrometeorological characteristics by comparing Standardized Precipitation Index (SPI), soil moisture, maximum temperature, relative humidity, wind speed, and precipitation. The correlation was analyzed based on the 8 weeks prior to the occurrence of the flash drought, and in most cases, a high correlation of 0.8(-0.8) or higher(lower) was expressed for ESI and SPI, soil moisture, and maximum temperature.

주제어

표/그림 (12)

그림 Fig. 1. Location of meteorological stations for the acquisition of data in South Korea
그림 Fig. 2. Example of flash drought detection condition using ESI
표 Table 1. Drought categories for USDM based on percentile method
그림 Fig. 3. Flash drought detection through time series changes of ESI from 2014 to 2018
표 Table 2. Drought severity threshold based on each index
그림 Fig. 4. Comparison of each indicator during flash drought using heat map
그림 Fig. 5. Scatter plot for flash drought period beween ESI-4W and each index in 2015
표 Table 3. Correlation coefficient between ESI-4W and each index for flash drought period in 2015
그림 Fig. 6. Scatter plot for flash drought period beween ESI-4W and each index in 2016
그림 Fig. 7. Scatter plot for flash drought period beween ESI-4W and each index in 2018
표 Table 4. Correlation coefficient between ESI-4W and each index for flash drought period in 2016
표 Table 5. Correlation coefficient between ESI-4W and each index for flash drought period in 2018

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