LAUSR

Drought, due to climate change, has in recent years become more severe. Capability to monitor drought conditions and to assess drought risk is essential to the development of an effective drought adaptation plan, especially for an agricultural country like Thailand. Current drought monitoring is provided by separate indices such as Standardized Precipitation Index (SPI), Soil Moisture Index (SMI) and Moisture Available Index (MAI), calculated from weather station datasets which are not easily comprehensible to users. This research develops a countrywide integrated satellite-based drought model consisting of three parameters: accumulated estimated rainfall generated from FY-2E satellite data, the difference in Land Surface Temperature (LST) and Normalized Difference Vegetation Index (NDVI) generated from MODIS. A simple drought hazard is introduced as a multiple linear regression model (R2=0.795) of these satellite products, calibrated with daily soil moisture measurements in 2015. Consequently, drought conditions are represented by the Drought Hazard Index (DHI) whose assigned integer values are from –3 (extremely dry) to +3 (extremely wet), according to the defined thresholds (presently at 0.05, 0.15, 0.30, 0.70, 0.80, and 0.95) of the cumulative distribution function (CDF) of drought hazard values. The model is validated with 426 countrywide drought situations announced by the Department of Disaster Prevention and Mitigation (DDPM), during the drought season of 2016, yielding a 0.96 probability of detection. Subsequently, the model outputs are processed with relevant GIS data, which are agricultural and irrigation areas to represent drought exposure and vulnerability respectively, to generate a drought risk map for further analysis and planning. This platform can benefit not only policymakers but also the farmers themselves.