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 provided to 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- 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 form the basis of targeted mitigation actions in an improved DMEWS in Thailand and could be applied to other parts of Southeast Asia and beyond.

Due to a continuous increase in global temperature, the climate has been changing without sign of alleviation. An increase in the air temperature has caused changes in the hydrologic cycle, which have been followed by several emergencies of natural extreme events around the world. Thailand is one of the countries that has incurred a huge loss in assets and lives from the extreme flood and drought events, especially in the northern part. Therefore, the purpose of this study was to assess the hydrological regime in the Yom and Nan River basins, affected by climate change as well as the possibility of extreme floods and droughts. The hydrological processes of the study areas were generated via the physically-based hydrological model, namely the Soil and Water Assessment Tool (SWAT) model. The projected climate conditions were dependent on the outputs of the Global Climate Models (GCMs) as the Representative Concentration Pathways (RCPs) 2.6 and 8.5 between 2021 and 2095. Results show that the average air temperature, annual rainfall, and annual runoff will be significantly increased in the intermediate future (2046–2070) onwards, especially under RCP 8.5. According to the Flow Duration Curve and return period of peak discharge, there are fluctuating trends in the occurrence of extreme floods and drought events under RCP 2.6 from the future (2021–2045) to the far future (2071–2095). However, under RCP 8.5, the extreme flood and drought events seem to be more severe. The probability of extreme flood remains constant from the reference period to the near future, then rises dramatically in the intermediate and the far future. The intensity of extreme droughts will be increased in the near future and decreased in the intermediate future due to high annual rainfall, then tending to have an upward trend in the far future.

This study evaluated the impacts of climate change on hydro-meteorological droughts in the Chao Phraya River Basin (CPRB), Thailand under two Representative Concentration Pathway (RCP) scenarios (RCP4.5 and RCP8.5). We used three Reginal Climate Models (RCMs) of the Southeast Asia Regional Climate Downscaling/Coordinated Regional Climate Downscaling Experiment—Southeast Asia (SEACLID/CORDEX-SEA), which are bias corrected. The Soil and Water Assessment Tool (SWAT) was used to simulate streamflow for future periods. The Standardized Precipitation Index (SPI) and Standardized Streamflow Index (SSI) were estimated and used for drought characterization at three time scales (3, 6, and 12 months). The lag time between meteorological and hydrological droughts is approximately 1–3 months. The results suggest that the CPRB is likely to experience less frequent hydro-meteorological drought events in the future. The meteorological drought is projected to be longer, more severe, and intense. The severity of hydrological drought tends to decrease, but the intensity could increase. Climate change has been discovered to alter drought behaviors in the CPRB, posing a threat to drought monitoring and warning because droughts will be less predictable in future climate scenarios. The characterization of historical and future droughts over the CPRB is therefore valuable in developing an improved understanding of the risks of drought.

Drought communications constitute an important source of learning about climate risks and responses that can assist adaptation decision-making amongst those whose livelihoods are threatened by drought. This paper applies Protection Motivation Theory to explore associations between drought communications and attitudes towards drought risk and adaptation amongst farmers in Northern Thailand. The analysis reveals links between drought communications, farmers’ adaptation appraisal, and their adaptation decisions, whilst links with risk appraisal are minimal. The results highlight positive feedbacks between adaptation experience and appraisal and reveal a weak negative relationship between risk appraisal and adaptation appraisal. The findings imply benefits to framing drought communications in terms of the efficacy and attainability of suitable adaptations, rather than simply highlighting drought risks or providing drought warnings, to best enable farmers to build drought resilience.

Drought is a natural hazard that stresses ecosystems, agricultural production, food security, and local economies. Given ongoing hydropower dam development in the Sesan and Srepok Basins, the two most dammed tributaries in the Lower Mekong Basin, characterizing baseline drought events and understanding how dams modify downstream flow is needed to manage water resources and mitigate drought effects. We used the Soil & Water Assessment Tool (SWAT) to estimate streamflow data from 2001 to 2019. For both rivers, we found that runoff varied, but contributed about 75% of streamflow, followed by shallow and deep groundwater, which contributed up to 25%. We used the Standardized Runoff Index to characterize drought and detected frequent, severe, and prolonged drought events in the two basins. Severe and prolonged droughts in the 2009–2011 and 2015–2016 periods corresponded to the occurrence of Typhoon Ketsana and the El Niño-Southern Oscillation. Streamflow alteration can be caused by climatic conditions and anthropogenic activities such as hydropower dam development and operations (e.g., the timing and magnitude of water releases). Results from this study can be used as a baseline to gauge potential future droughts and design appropriate drought management plans to preserve ecosystems and food security in the Lower Mekong Basin and its tributaries.

Ground heat flux may play an important role in surface energy balance. In this study we evaluate the performance of the objective hysteresis model (OHM) for estimating ground heat flux from net radiation and compare it with the linear regression model. The experimental sites include residential roofs (concrete), campus grassland, agricultural grassland, and peat bog. Our field measurements show that the mean partition coefficient from net radiation to ground heat flux varied from 0.47 (concrete roof) to 0.079 (agricultural grassland). The mean hysteresis (lag) factors for residential roof, campus grassland, and peat bog were 0.55, 0.26, and −0.11 h, respectively; and the hysteresis factor at the agricultural site was only 0.032 h. However, the partition and hysteresis coefficients in the OHM were found to vary with time for the same surface. Our measurements and analysis show that when the hysteresis factor is larger than 0.11 h, ground heat flux estimates from net radiation can be improved (17–37% reduction in the root mean square error) by using OHM instead of a simple linear regression model.