Methodology

How we measure drought

The GlobMaps Drought Severity Index (MDI v2.2) combines four complementary data sources to produce a single, reliable severity score calibrated for Southeast Asian conditions.

Index components

ComponentThailandVietnamMalaysiaDescription
SPEI-350%45%50%Rainfall deficit vs. 30-year climatology
VCI20%25%20%Vegetation health (MODIS NDVI satellite)
ESI15%15%15%Evapotranspiration stress (SSEBop)
SMI15%15%15%Topsoil moisture (ERA5 reanalysis)

Vietnam uses higher VCI weight (25%) to better represent Central Highlands coffee/rubber agriculture. Highlighted weights differ from THA/MYS defaults.

Severity scale

NoneMDI score within normal rangeSPEI-3 > −0.3
MildD0 Abnormally Dry−0.5 to −0.3
ModerateD1–D2 Moderate to Severe−1.3 to −0.5
SevereD3 Extreme Drought−2.0 to −1.3
ExtremeD4 Exceptional Drought≤ −2.0

What this index does NOT measure

✗Reservoir or dam water levels
✗River flow or streamflow
✗Municipal water supply availability
✗Groundwater levels
✗Flood risk

MDI v2.2 reflects atmospheric and soil moisture deficits, plus vegetation stress. For water supply planning, consult national hydrology agencies (TMD, NCHMF, JPS).

ERA5 data quality

🇹🇭 Thailand0.996×Validated vs TMD station network
🇻🇳 Vietnam1.284×ERA5 overestimates rainfall ~28%; severity may be understated. Cross-reference NCHMF.
🇲🇾 Malaysia1.002×Validated vs MetMalaysia station network

References

Sepulcre-Canto et al. (2012) — EU Combined Drought Indicator (CDI)

Anderson et al. (2011) — Evaporative Stress Index (ESI)

Vicente-Serrano et al. (2010) — SPEI: Multiscalar Drought Index

Liu et al. (2015) — Vegetation Condition Index (VCI) for drought monitoring

Muñoz-Sabater et al. (2021) — ERA5-Land: global reanalysis dataset

Cite This Work

Drought Risk Patterns in Southeast Asia 2000-2025: Province-Level Analysis Using ERA5 Reanalysis and a Multi-Index Composite Methodology

Yodsri, W. (2026). GlobMaps Climate Intelligence. CC BY 4.0.

https://doi.org/10.5281/zenodo.20774311

Peer-review preprint describing the open data backbone (ERA5, MODIS, ALEXI ESI) and the descriptive, province-level regional analysis underlying the GlobMaps drought methodology. Proprietary composite parameters are not disclosed in the preprint.

The component names, relative weights, and data sources shown above are disclosed for transparency and regulatory compliance purposes. The underlying algorithms, calibration coefficients, interpolation procedures, ensemble downscaling methods, validation datasets, and implementation parameters are proprietary trade secrets of GlobMaps. No inference about these proprietary elements should be drawn from the information disclosed herein.

MDI v2.2 · Updated monthly · GlobMaps Climate Intelligence Platform