# Rainfall Standard of Disaster Prediction for Agricultural Droughs in S. Korea
## 1 Introduction
Unlike previous studies that intended to develop some methods of predicting droughs and validate their accuracy, this study regarded droughs as a natural disaster and established some reference points for disaster management. Others proposed a drought-prediction system, which analyzed and adjusted the uncertainties of probability distributions, using the Bayesian Network in relation to drought persistence [37; 38]. An array of studies used actual rainfall data to propose highly accurate drought analysis methods and mostly focused on assessing their drought prediction results, without analyzing the end of droughs or setting up applicable reference points. The parameters for drought predictions, diverse observational data including rainfall, evapotranspiration and underground water were collected and monitored to predict disasters. To manage hydrologic data, many governments run drought information and prediction/warning systems. Additionally, the reference points for drought-related prediction and decision making were established to help in developing drought indices for disaster managers and decision-makers [39]. Droughs are difficult to predict, and this warrants for the need to develop diverse prediction models and apply appropriate prediction methods in line with a range of reference points [40]. Despite previous studies on various droughs, the importance of disaster management for drought in Korea has been recognized since 2010. In Korea, drought is mostly short-term drought that occurs for three to four months before monsoon season, and large-scale damage has not occurred since 1994. Recently, the need for disaster management has emerged due to the continuous drought caused by climate change. However, the data of drought damage were only published in the year of the large-scale drought damage, but only brief damage contents were recorded. Thus, in this study, we intended to set up some reference points for rainfall in view of disaster prediction for drought relief based on South Korea's agricultural drought damage. The final goal of the study is to set the criteria for disaster management, not to analyze the damage range of extreme drought or drought, in calculating the disaster prediction rainfall for agricultural drought. The research method is to analyze annual rainfall from 1965 to 2018 for 68 rainfall observation stations that have more than 30 years of rainfall observation data nationwide. In addition, in order to analyze the rainfall characteristics of the occurrence of damage to agricultural drought, the annual average rainfall based on the beginning of the drought damage and the monthly average rainfall in the months before and after the end of the drought damage were analyzed. Various methods of analysis for drought were proposed to analyze the drought with high accuracy or to evaluate the results of drought prediction. However, this study applied linear regression analysis to establish rainfall standard for disaster reduction of agricultural drought considering linear variation characteristics by year. This study aims to establish a rainfall standard that can predict the occurrence and termination of agricultural drought in Korea.