# OneMap Drought Monitoring Analysis Based on Statistical Models
###### Abstract  
As the effects of droughts on agriculture and industrial water availability intensify with climate change, developing suitable drought prevention and mitigation measures has become increasingly important. However, measuring drought conditions using different indices leads to disjointed drought management responses by ministries and agencies. Additionally, indices based on only one variable are insufficient to accurately assess drought conditions. Therefore, creating and adopting a OneMap drought index would be beneficial in the assessment of drought conditions and the implementation of appropriate measures. In this study, we used multivariate statistical modeling using Bayesian principal component analysis to develop a OneMap drought index that unifies existing measures of drought conditions, including meteorological, agricultural, and hydrological drought indices. After evaluating the accuracy of the corrected OneMap drought index based on the self-organizing migrating algorithm optimization technique, it was found that the applicability of the OneMap drought index and its ability to regenerate drought were excellent for ground and satellite data. Therefore, the authors recommend implementing step-by-step drought management action plans using the integrated index to generate drought forecasts and warnings, thus promoting concerted and effective responses of local governments and authorities. reflecting soil moisture conditions; the aforementioned SPI; and the Palmer drought severity index (PDSI). For the severe drought scenarios that occurred in South Korea after 2010, ESI and EWDI showed high applicability, confirming high degrees of correlation between the drought indices. Drought phenomena are related to several other variables; hence, drought analysis based on a single index may not suffice to monitor complex drought phenomena. Therefore, research is needed on the development of a OneMap drought index for accurate drought monitoring using information from various drought indices (meteorological, agricultural, hydrological) [15; 16; 17]. [PERSON] and [PERSON] [16] proposed a multivariate multi-drought monitoring framework using a non-parametric method. [PERSON] [15] presented a global-based integrated drought monitoring and prediction system, integrated meteorological drought index, and soil moisture information using the multivariate standardized drought index (MSDI) for monitoring and measurement prediction. In previous studies, monthly drought conditions were analyzed using two drought indices (SPI and the standardized streamflow index, SSI), the MSDI index was calculated by applying an empirical or Copula-based joint distribution, seasonal predictions were made using univariate and multivariate drought indices, providing globally integrated drought monitoring information. While most of the drought studies deal with meteorological drought, a few recent studies [18; 19] have been conducted for all major drought types (meteorological, hydrological, and agricultural droughts) using multivariate techniques to provide a comprehensive assessment of drought events. In July 2004, South Korea introduced the \"Basic Guidelines for National Crisis Management\", a crisis alert system for national crisis management. This system accurately determines the level of crisis and issues an alarm at the corresponding level (attention, caution, alert, or severe) when organizations in charge and related organizations detect signs of crisis. This system enables systematic crisis management by helping organizations to prevent and prepare for crises in advance. Analysis of the crisis response processes of the ministries and agencies concerned determines the crisis alert level and associated countermeasures. In addition, the Ministry of Public Administration Security issues an integrated forecast and warning for administrative districts through the information that can intuitively understand drought conditions.