Hazard Indicators

The CO-MICC multi-model ensemble (MME) is based on a selection of multiple Representative Concentration Pathways (RCPs), General Circulation Models (GCMs), Global Hydrological Models (GHMs) and variants of the latter (hereafter referred to as “GHM variants”). This selection comprises 4 RCPs, 4 GCMs, 3 GHMs and 2 GHM variants. Each ensemble member consists of a combination of 1 RCP, 1 GCM, 1 GHM and 1 GHM variant. There are 96 ensemble members in total (4 RCPs x 4 GCMs x 3 GHMs x 2 GHM variants).

Each ensemble member was run to compute a series of climate and hydrological variables (see Table 1). These variables are climate-dependent and thus relevant when assessing climate change impacts. They describe the state and changes of the hydrosphere on land. Different variables describe different freshwater compartments (e.g., soil, groundwater, snow).

The Data Portal gives access to MME data of hydrological climate impact indicators (HCIIs). The risk of climate-related impacts results from the interaction of climate-related physical hazards with the vulnerability and exposure of human and natural systems (Figure 1). In other words, the risk of a climate-related impact is the probability of an event or a trend, multiplied by damage. The indicators considered here are specifically dedicated to quantifying the probability of climate-related physical hazards that have an effect on freshwater resources. In the following, we refer to them as hazard indicators.

Figure 1: The IPCC AR5 conceptual framework of the risk of climate-related impacts. This figure’s original source is: https://www.ipcc.ch/report/ar5/wg2/summary-for-policymakers/.
Figure 1: The IPCC AR5 conceptual framework of the risk of climate-related impacts. This figure’s original source is: https://www.ipcc.ch/report/ar5/wg2/summary-for-policymakers/.

The calculation of hazard indicators based on the variables computed by the MME is done on-the-fly in the Data Portal. Multiple indicators can be derived from each variable. To derive a specific indicator, first, the variable in question is spatially and temporally aggregated and, secondly, a statistic based on the aggregated variable is calculated (Figure 2).

Figure 2: Generation of hydrological hazard indicators based on a hydrological variable. As an example, this scheme shows the hazard indicators related to the water scarcity hydrological variable. By default, the indicators are calculated at the scale of individual grid cells in the CO-MICC Data Portal. However, there is also the option of calculating them over larger areas, namely the cell’s upstream area, over pre-defined large basins (e.g., Amazon, Rhine) or over countries.
Figure 2: Generation of hydrological hazard indicators based on a hydrological variable. As an example, this scheme shows the hazard indicators related to the water scarcity hydrological variable. By default, the indicators are calculated at the scale of individual grid cells in the CO-MICC Data Portal. However, there is also the option of calculating them over larger areas, namely the cell’s upstream area, over pre-defined large basins (e.g., Amazon, Rhine) or over countries.

The values computed for a certain indicator correspond to either absolute or relative changes as compared to a reference value. This reference value is equal to the MME median value over the reference period 1981-2010. The projected absolute/relatives changes show how the indicator in question is expected to vary in the future according to different scenarios of climate change. By combining the simulated relative/absolute changes with the reference value, it is possible to obtain a rough estimate of the indicator in future time periods. Changes are averaged over future time periods of 30 years each. Fourteen future time periods are included in the Data Portal in total. In the time series chart that can be visualized in the latter, each time period is defined by its centre year (Figure 3). For example, the year 2030 actually refers to the period 2015-2044. The future periods are defined by a 30-year rolling window that shifts forward at a 5-year time step, resulting in overlapping periods (Figure 3).

Figure 3: Illustration of the temporal dimension of the time series of absolute/relative changes provided in the CO-MICC Data Portal for each hazard indicator. The changes are relative to the reference period 1981-2010 and are calculated for consecutive overlapping future time periods defined by a 30-year rolling window. In the x axis, the centre year of each 30-year period is given. There is a data point every 5 years. Note that the example graph here contains dummy data.
Figure 3: Illustration of the temporal dimension of the time series of absolute/relative changes provided in the CO-MICC Data Portal for each hazard indicator. The changes are relative to the reference period 1981-2010 and are calculated for consecutive overlapping future time periods defined by a 30-year rolling window. In the x axis, the centre year of each 30-year period is given. There is a data point every 5 years. Note that the example graph here contains dummy data.

In general, relative changes can be computed more reliably by the models than absolute changes. Relative changes should thus be favoured over absolute ones for climate change risk assessments. Only if relative changes are not available (e.g., in the case of water scarcity and water stress indicators), absolute changes should be considered. Relative changes are not provided in a certain grid cell if the MME reference value in that cell is equal to zero or smaller than a pre-defined threshold. Pre-defined thresholds were implemented to avoid very small reference values, which can lead to unreasonably large relative changes.

Table 1: Hazard indicators available in the CO-MICC Data Portal. These indicators correspond to multiple climate and hydrological variables computed by general circulation models and global hydrological models, respectively, over 30-year periods. They are provided at either annual scale, seasonal scale or for each calendar month.
VariableTime scaleStatisticUnit
Blue water production (BWP)AnnualMeanmm/year
Annual high: Q101mm/year
Annual low (1 in 10 years): Q901mm/year
Annual low (1 in 5 years): Q801
mm/year
Year-to-year variability: standard deviationmm/year
Year-to-year variability: coefficient of variation no unit
Streamflow (Q)AnnualMeanm³/s
Annual high: Q101m³/s
Annual low (1 in 10 years): Q901m³/s
Annual low (1 in 5 years): Q801m³/s
Monthly high: Q101m³/s
Monthly low: Q901m³/s
Year-to-year variability: standard deviationm³/s
Year-to-year variability: coefficient of variationno unit
Calendar month with highest mean monthly flowmonth
Calendar month with lowest mean monthly flowmonth
SeasonalMean of

  • March to May

  • June to August

  • September to November

  • December to February


m³/s
Calendar monthMean of

  • January

  • February

  • March

  • April

  • May

  • June

  • July

  • August

  • September

  • October

  • November

  • December

m³/s
Naturalized streamflow (Qnat)AnnualMeanm³/s
Annual high: Q101
m³/s
Annual low (1 in 10 years): Q901
m³/s
Annual low (1 in 5 years): Q801
m³/s
Monthly high: Q101
m³/s
Monthly low: Q901
m³/s
Year-to-year variability: standard deviation
m³/s
Year-to-year variability: coefficient of variation
no unit
Calendar month with highest mean monthly flow
month
Calendar month with lowest mean monthly flowmonth
SeasonalMean of

  • March to May

  • June to August

  • September to November

  • December to February

m³/s
Calendar monthMean of

  • January

  • ...

  • December

m³/s
Potential evapotranspiration (PET)AnnualMeanmm/year
Year-to-year variability: standard deviationmm/year
Year-to-year variability: coefficient of variationno unit
PET/Precipitationno unit
SeasonalMean of

  • March to May

  • June to August

  • September to November

  • December to February


mm/month
Calendar monthMean of

  • January

  • ...

  • December

mm/month
Actual evapotranspiration (AET)AnnualMeanmm/year
Year-to-year variability: standard deviationmm/year
Year-to-year variability: coefficient of variationno unit
AET/Precipitationno unit
SeasonalMean of

  • March to May

  • June to August

  • September to November

  • December to February


mm/month
Calendar monthMean of

  • January

  • ...

  • December

mm/month
Groundwater recharge (GWR)AnnualMeanmm/year
Soil moisture (Ssoil)AnnualMeanno unit
SeasonalMean of

  • March to May

  • June to August

  • September to November

  • December to February


no unit
Calendar monthMean of

  • January

  • ...

  • December

no unit
Snow storage (Ssnow)AnnualMeanmm
Number of months with snowmonth
SeasonalMean of

  • March to May

  • June to August

  • September to November

  • December to February

mm
Calendar monthMean of

  • January

  • ...

  • December

mm
Net irrigation requirement (NIR)AnnualMean
Annual high: NIR102
Annual Low: NIR903mm/year
Year-to-year variability: standard deviationmm/year
Year-to-year variability: coefficient of variationno unit
Temperature (T)AnnualMean°C
SeasonalMean of

  • March to May

  • June to August

  • September to November

  • December to February

°C
Calendar monthMean of

  • January

  • ...

  • December

°C
Precipitation (P)AnnualMeanmm/year
Year-to-year variability: standard deviationmm/year
Year-to-year variability: coefficient of variationno unit
Calendar month with highest mean monthly precipitationmonth
Calendar month with lowest mean monthly precipitationmonth
R95T4no unit
SeasonalMean of

  • March to May

  • June to August

  • September to November

  • December to February

mm/month
Calendar monthMean of

  • January

  • ...

  • December

mm/month
Water scarcity (WSc)AnnualMeanno unit
Year-to-year variability: standard deviationno unit
Year-to-year variability: coefficient of variationno unit
SeasonalMean of

  • March to May

  • June to August

  • September to November

  • December to February

no unit
Calendar monthMean of

  • January

  • ...

  • December

no unit
Water stress (WSt)AnnualMeanno unit
Year-to-year variability: standard deviationno unit
Year-to-year variability: coefficient of variationno unit
SeasonalMean of

  • March to May

  • June to August

  • September to November

  • December to February

no unit
Calendar monthMean of

  • January

  • ...

  • December

no unit
Water availability (WA)AnnualMeanm³/year
Year-to-year variability: standard deviationm³/year
Year-to-year variability: coefficient of variationno unit
SeasonalMean of

  • March to May

  • June to August

  • September to November

  • December to February

m³/month
Calendar monthMean of

  • January

  • ...

  • December

m³/month

1 Q10, Q80, Q90: 10th percentile, 80th percentile, 90th percentile.
2 NIR10: annual net irrigation requirement that is exceeded in only 1 out of 10 years (i.e. net irrigation requirement in a dry year).
3 NIR90: annual net irrigation requirement that is exceeded in 9 out of 10 years (i.e. net irrigation requirement in a wet year).
4 R95T: fraction of annual total precipitation due to events exceeding the 1981-2010 period’s 95th percentile (i.e. fraction of annual total precipitation that occurred via extreme events).