Indicator-to-impact links to help improve agricultural drought preparedness in Thailand

Droughts in Thailand are becoming more severe due toclimate change. Developing a reliable drought monitoring and early warningsystem (DMEWS) is essential to strengthen a country's resilience todroughts. However, for a DMEWS to be valuable, the drought indicatorsprovided to stakeholders must have relevance to tangible impacts on theground. Here, we analyse drought indicator-to-impact relationships inThailand, using a combination of correlation analysis and machine learningtechniques (random forest). In the correlation analysis, we study the linkbetween meteorological drought indicators and high-resolution remote sensing vegetation indices used as proxies for crop yield and forest growth impacts. Our analysis shows that this link varies depending on land use, season and region. The random forest models built to estimate regional crop productivity allow a more in-depth analysis of the crop- and region-specific importance of different drought indicators. The results highlight seasonal patterns of drought vulnerability for individual crops, usually linked to their growing season, although the effects are somewhat attenuated in irrigated regions. Integration of the approaches provides new, detailed knowledge of crop- and region-specific indicator-to-impact links, which can formthe basis of targeted mitigation actions in an improved DMEWS in Thailandand could be applied to other parts of Southeast Asia and beyond.