The drought risk estimates how subject an area is to prolonged precipitation deficits, a threat to the asset's water availability. It is measured with the 6-month SPI index (SPI-6), which compares accumulated precipitation with the historical distribution of the reference period: the more negative the index, the scarcer the precipitation relative to the norm and the higher the risk.
Indicator. Standardized Precipitation Index at 6 months (SPI-6), dimensionless.
Data. Variable SPI-6 from the Copernicus Climate Data Store (CDS). Historical reference period: 1950–1970 (20-year window). Scenarios: SSP1-2.6 / SSP2-4.5 / SSP3-7.0 / SSP5-8.5; annual projections 2015–2100.
Calculation. For each (scenario, year) pair the annual mean of the SPI-6 is taken and classified against the thresholds (see below): the more negative the index (more pronounced precipitation deficit relative to the historical norm), the higher the risk level.
Thresholds → level A–F.
• SPI-6 thresholds: -0.5 / -1.0 / -1.5 / -2.0 / -2.5
• A: SPI-6 ≥ -0.5
• B: -1.0 ≤ SPI-6 < -0.5
• C: -1.5 ≤ SPI-6 < -1.0
• D: -2.0 ≤ SPI-6 < -1.5
• E: -2.5 ≤ SPI-6 < -2.0
• F: SPI-6 < -2.5.
The -1, -1.5 and -2 thresholds are the limits of the moderate, severe and extreme drought categories of McKee et al. 1993 and of the WMO guide; -0.5 and -2.5 are a xNatura choice without a published source.
From physical to impact. The physical level (A–F) is combined with the production process's sensitivity through the risk matrix (6 physical levels by 5 sensitivity bands) → impact level. Version 2025 also turned the impact level into a value at risk with a damage factor per class, a xNatura estimate without a published source: it is no longer used.
Economic damage. Version 2026, published source: the output lost in very dry years, with the effect estimated by the World Bank on 82 countries, 1990-2014 (Zaveri, Damania, Engle 2023, Table 3).
• Dry year: annual rain below the site's 1961-2014 mean by at least 1.6 standard deviations; in developing countries the years below 1.3, 1.0 and 0.8 standard deviations count too (the only bins with a significant effect).
• Effect: GDP per capita growth lost in the dry year, 0.39 points in high-income countries; 0.86, 0.58, 0.47 and 0.39 in developing countries; income group from the World Bank classification; counted only in the dry year, with no effect on the following years.
• Share of dry years in the 20 years around the year minus the one of 1995-2014, for each of the ten NASA NEX-GDDP-CMIP6 models, then the mean; fewer dry years count as 0.
• Annual loss = employees × value added per employee × Σ (change of the share of dry years × effect).
Economic damage. There is no Version 2026 estimate for this risk: published drought losses are totals by region and sector, mostly agriculture, energy and water supply, with negligible damage to buildings (Naumann et al. 2021); in high-income countries firm-level studies find no measurable effect of extreme dry days on sales (Gatti et al. 2024), and only extreme droughts lower regional growth (Zaveri et al. 2023). The risk therefore shows the class only, without a euro amount.
Sources.
• [Copernicus Climate Data Store (CDS)](Copernicus Climate Data Store (CDS))
• [The Relationship of Drought Frequency and Duration to Time Scales](The Relationship of Drought Frequency and Duration to Time Scales)
• [Standardized Precipitation Index User Guide](Standardized Precipitation Index User Guide)
• [Naumann et al. 2021, Increased economic drought impacts in Europe with anthropogenic warming](Naumann et al. 2021, Increased economic drought impacts in Europe with anthropogenic warming)
• [Gatti et al. 2024, Thirsty Business: A Global Analysis of Extreme Weather Shocks on Firms](Gatti et al. 2024, Thirsty Business: A Global Analysis of Extreme Weather Shocks on Firms)
• [Zaveri et al. 2023, Droughts and Deficits: The Global Impact of Droughts on Economic Growth](Zaveri et al. 2023, Droughts and Deficits: The Global Impact of Droughts on Economic Growth).
Indicator. Standardized Precipitation Index at 6 months (SPI-6), dimensionless.
Data. Variable SPI-6 from the Copernicus Climate Data Store (CDS). Historical reference period: 1950–1970 (20-year window). Scenarios: SSP1-2.6 / SSP2-4.5 / SSP3-7.0 / SSP5-8.5; annual projections 2015–2100.
Calculation. For each (scenario, year) pair the annual mean of the SPI-6 is taken and classified against the thresholds (see below): the more negative the index (more pronounced precipitation deficit relative to the historical norm), the higher the risk level.
Thresholds → level A–F.
• SPI-6 thresholds: -0.5 / -1.0 / -1.5 / -2.0 / -2.5
• A: SPI-6 ≥ -0.5
• B: -1.0 ≤ SPI-6 < -0.5
• C: -1.5 ≤ SPI-6 < -1.0
• D: -2.0 ≤ SPI-6 < -1.5
• E: -2.5 ≤ SPI-6 < -2.0
• F: SPI-6 < -2.5.
The -1, -1.5 and -2 thresholds are the limits of the moderate, severe and extreme drought categories of McKee et al. 1993 and of the WMO guide; -0.5 and -2.5 are a xNatura choice without a published source.
From physical to impact. The physical level (A–F) is combined with the production process's sensitivity through the risk matrix (6 physical levels by 5 sensitivity bands) → impact level. Version 2025 also turned the impact level into a value at risk with a damage factor per class, a xNatura estimate without a published source: it is no longer used.
Economic damage. Version 2026, published source: the output lost in very dry years, with the effect estimated by the World Bank on 82 countries, 1990-2014 (Zaveri, Damania, Engle 2023, Table 3).
• Dry year: annual rain below the site's 1961-2014 mean by at least 1.6 standard deviations; in developing countries the years below 1.3, 1.0 and 0.8 standard deviations count too (the only bins with a significant effect).
• Effect: GDP per capita growth lost in the dry year, 0.39 points in high-income countries; 0.86, 0.58, 0.47 and 0.39 in developing countries; income group from the World Bank classification; counted only in the dry year, with no effect on the following years.
• Share of dry years in the 20 years around the year minus the one of 1995-2014, for each of the ten NASA NEX-GDDP-CMIP6 models, then the mean; fewer dry years count as 0.
• Annual loss = employees × value added per employee × Σ (change of the share of dry years × effect).
Economic damage. There is no Version 2026 estimate for this risk: published drought losses are totals by region and sector, mostly agriculture, energy and water supply, with negligible damage to buildings (Naumann et al. 2021); in high-income countries firm-level studies find no measurable effect of extreme dry days on sales (Gatti et al. 2024), and only extreme droughts lower regional growth (Zaveri et al. 2023). The risk therefore shows the class only, without a euro amount.
Sources.
• [Copernicus Climate Data Store (CDS)](Copernicus Climate Data Store (CDS))
• [The Relationship of Drought Frequency and Duration to Time Scales](The Relationship of Drought Frequency and Duration to Time Scales)
• [Standardized Precipitation Index User Guide](Standardized Precipitation Index User Guide)
• [Naumann et al. 2021, Increased economic drought impacts in Europe with anthropogenic warming](Naumann et al. 2021, Increased economic drought impacts in Europe with anthropogenic warming)
• [Gatti et al. 2024, Thirsty Business: A Global Analysis of Extreme Weather Shocks on Firms](Gatti et al. 2024, Thirsty Business: A Global Analysis of Extreme Weather Shocks on Firms)
• [Zaveri et al. 2023, Droughts and Deficits: The Global Impact of Droughts on Economic Growth](Zaveri et al. 2023, Droughts and Deficits: The Global Impact of Droughts on Economic Growth).