Rainfall Standard of Disaster Prediction for Agricultural Droughts in S. Korea

Youngseok Song,Moojong Park

doi:10.3390/app10217423

Abstract

With the climate change adding to the frequency and intensity of natural disasters, drought has devastated large areas of lands in South Korea. Still, the exact beginning and end of the drought is difficult to identify, and this impedes the development and implementation of disaster predictions. Although the drought phenomenon has been well-documented, predictions thereof are limited due to the non-linear and complex temporal fluctuations of the hydrologic factors. Hence, this study set up some reference points for disaster-prediction rainfall based on South Korea’s agricultural drought damage data, to help in drought relief. To set up the proposed reference points for disaster-prediction rainfall, we analyzed rainfall in light of the disaster-prevention relevance to agricultural droughts and the disaster reduction. As an analysis method, rainfall of municipality was calculated through Thiessen’s polygonal method, to apply rainfall weighting value for each rainfall observatory. In addition, the linear regression analysis was applied to suggest the calculation formula for setting the annual disaster reduction rainfall. The results of this study, standard of judgment point for disaster prevention of agricultural drought at the time of disaster management, were analyzed for rainfall for local governments and the whole country. Rather than using various drought indices that are currently developed, policy makers or public servant made suggestions based on rainfall that is most accessible and convenient for judging the timing of agricultural drought. As the disaster-prevention rainfall with agricultural droughts is expected to occur, we established the average annual rainfall of ≤1200 or 100 mm below the preceding year’s average annual rainfall. Moreover, as the disaster-reduction rainfall for agricultural droughts to end, we determined the average monthly rainfall of ≥150 mm.

Highlights

Unlike other natural disasters, such as floods, typhoons, earthquakes and tsunamis, droughts occur slowly, expansively and persistently for months or years
Based on the days when regression model assumes that the relationship between the dependent variable yi and the p of agricultural drought damage occurred from 1965 to 2018 in South Korea, we calculated the past average repressors xi is linear
In Korea, the drought prediction using the drought index is based on meteorological drought prediction using SPI6 only in the Korea Meteorological Administration among the various ministries

Summary

Introduction

Unlike other natural disasters, such as floods, typhoons, earthquakes and tsunamis, droughts occur slowly, expansively and persistently for months or years. Their spatial scale or intensity is difficult to identify [1]. As part of the impact of global climate change, the rising temperature is projected to cause more frequent droughts, which will presumably last for years, exerting an enormous impact on agriculture and water resources. Many researchers have developed methods of assessing and predicting droughts and analyzed their applicability, in view of diverse hydrologic factors, with the intent to minimize the damage from droughts. Utilizing parameters such as rainfall data, drought indices, statistical methods and climate models, drought researchers have assessed and predicted droughts, as well as developed some quantitative

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Conclusion