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

Authors :
Maliko Tanguy
Michael Eastman
Eugene Magee
Lucy J. Barker
Thomas Chitson
Chaiwat Ekkawatpanit
Daniel Goodwin
Jamie Hannaford
Ian Holman
Liwa Pardthaisong
Simon Parry
Dolores Rey Vicario
Supattra Visessri
Source :
eISSN
Publication Year :
2023

Abstract

Droughts in Thailand are becoming more severe due to climate change. Developing a reliable Drought Monitoring and Early Warning System (DMEWS) is essential to strengthen a country’s resilience to droughts. However, for a DMEWS to be valuable, the drought indicators it provides stakeholders must have relevance to tangible impacts on the ground. Here, we analyse drought indicator-to-impact relationships in Thailand, using a combination of correlation analysis and machine learning techniques (random forest). In the correlation analysis, we study the link between 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-/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-/region-specific indicator-to-impact links, which can form the basis of targeted mitigation actions in an improved DMEWS in Thailand, and could be applied in other parts of Southeast Asia and beyond.

Details

Language :
English
ISSN :
16849981
Database :
OpenAIRE
Journal :
eISSN
Accession number :
edsair.doi.dedup.....7209d9b2d2dc9a4ca491e57c9c99efaf