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Electronic supplement material to the article
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Modelling the effects of cross-sectoral water
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allocation schemes in Europe
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by
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Florian Wimmer, Eric Audsley, Marcus Malsy, Cristina Savin, Robert Dunford,
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Paula A. Harrison, Rüdiger Schaldach, and Martina Flörke
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Climatic Change 2014 (doi:10.1007/s10584-014-
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1161-9)
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
corresponding author F. Wimmer
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Center for Environmental Systems Research, University of Kassel, Kassel,
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Germany
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email: wimmer@cesr.de
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The WaterGAP metamodel
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The WaterGAP metamodel (WGMM) is used in the CLIMSAVE IAP to assess both the
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impact of climate change on water resources and the change in water demand for human
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use due to socio-economic development. WGMM is designed to be an emulator of the
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global hydrological model WaterGAP3 (Water – Global Assessment and Prognosis)
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(Verzano 2009), which is an advancement of WaterGAP2 (Alcamo et al. 2003; Döll et al.
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2003) with increased spatial and temporal resolution and improvements to the
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implementation of hydrological processes. Originally, WaterGAP3 operates on a five arc
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minute grid in daily internal time steps. The resulting runtimes (>30 min) are too long and
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the input data requirements are too demanding for application within an interactive
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platform like the CLIMSAVE IAP. To shorten the runtime considerably, the spatial detail
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of WGMM is reduced from more than 180,000 grid cells for Europe in WaterGAP3 to 92
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spatial units with an area larger than 10,000 km². Those spatial units, hereafter referred
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to as river basins, are made up of either single large river basins (split into three sub-
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basins for the Danube) or clusters of smaller, neighbouring river basins with similar hydro-
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geographic properties. Moreover, the input data requirements are largely reduced as
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long-term statistics over a 30-year period are computed instead of daily or monthly time
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series.
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For each river basin, the metamodel computes the change in long-term (30 years)
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average water availability (WA), resulting from changes in mean annual precipitation and
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air temperature compared to a baseline value. The metamodel relies on look-up tables
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populated with simulated WA from pre-run WaterGAP3 simulations driven by monthly
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CRU climate input for the baseline period 1971-2000 (Mitchell & Jones, 2005) with
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simultaneously modified mean temperature and precipitation. A set of constant offsets dT
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were added to all values in the input time series of temperature leading to a shift in mean
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annual temperature, while the spatial patterns and temporal dynamics are preserved. The
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manipulation of precipitation was achieved in a similar manner by multiplying the values
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in the precipitation time series by a set of factors fP. The variation of dT applied ranges
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from 0.0 to +6.0 K in steps of 0.5 K, while fP ranges from 0.5 (-50%) to 1.5 (+50%) in
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steps of 0.05. In this way, a three-dimensional response surface with 273 grid points for
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the combinations of all dT, and fP is constructed for each river basin relating WA to
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changes in temperature and precipitation.
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When WGMM analyses any scenario input data of gridded mean annual air temperature
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and precipitation, it first computes the actual change in temperature dT’ and precipitation
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fP’ in each river basin compared to the baseline. In a second step, WA for the given
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scenario is interpolated from the values for WA at the four neighbouring grid points.
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WGMM also provides estimates of annual water withdrawals (WW) and water
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consumption (WC) in the water use sectors of domestic, manufacturing, and electricity
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(cooling in thermal electricity production). Water withdrawals are defined as the volume of
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water withdrawn from surface water or groundwater, while WC is the share in WW that is
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evaporated, transpired, incorporated in products, or consumed by humans. The modelling
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approach is based on gridded results of WaterGAP3 for the base year 2005 (EU FP6
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project SCENES), which were aggregated at the river basin level. A detailed description
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of the approaches to calculate sector WW and WC used in WaterGAP3 is given in Flörke
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et al. (2013).
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In the metamodel WGMM, the annual water withdrawals in a river basin are calculated as
𝑊𝑠,𝑟 = ∑n𝑐=1 𝑠,𝑟
𝐷𝑠,𝑐
𝜕𝑠,𝑐
𝑠𝑡 𝑠𝑏 𝐹𝑠,𝑟,𝑐 .
(1)
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In Equation 1,  are the baseline WW in the river basin, D is the main model driver in the
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scenario and  is the main model driver in the base year. The subscripts denote the water
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use sector (s), the river basin (r), and one of n(=28) European countries (c). The factor st
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represents savings in water demand due to technological improvements (negative values
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imply more water-intensive technologies); sb represents water savings due to behavioural
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change to use less water (negative values imply increasing water use due to more water-
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intensive behaviour). Fs,r,c are weighting factors used to translate the country-level relative
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change in the main driver to the water withdrawals at the river basin scale. There is one
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set of weighting factors per water use sector s and river basin r calculated as
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𝐹𝑠,𝑟,𝑐 =
𝑠,𝑟,𝑐
𝑠,𝑟
𝑤𝑖𝑡ℎ ∑𝑛𝑐=1 𝐹𝑠,𝑟,𝑐 = 1 



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In Equation 2, s,r,c are the baseline WW in sector s allocated to the spatial intersection of
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country c and river basin r. In order to compute WC in the various sectors, sectoral WW
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are multiplied by a sector-specific consumption factor, derived separately for each river
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basin as the WC-to-WW ratio in the base year. For each sector, a different main model
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driver (D and ) is used. In addition, a number of calculation steps differ among the
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sectors as described below.
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The main model driver for domestic water use is the product of population and structural
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water use intensity (I). The latter depends on income and specifies the domestic WW per
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person and year. For this purpose, a sigmoidal curve 𝐼 = 𝑎 + 𝑏(1 − 𝑒 𝛼∗𝐺 ) was fitted to
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historic data, where G is income and a, b, and  are curve fitting parameters. The model
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limits WW to a minimum value of annual domestic water withdrawals in a river basin
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calculated as 18.25 m³ capita-1 (50 l capita-1 day-1) times the number of inhabitants.
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Manufacturing water use is modelled taking into account the gross value added (GVA) as
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the main model driver. In order to limit the water savings due to technological
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improvements in this sector, the lower limit for st in Equation 1 is set to st =0.6, i.e., a
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maximum of 40% water savings is possible. Water savings due to behavioural change
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are not taken into account with manufacturing WW, i.e. a value of sb=1 is used.
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The main model driver for cooling water use in the energy sector is thermal electricity
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production (TEP in MWh). The lower limit for the technological change factor is defined
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as st =0.8 in this sector. Water savings due to behavioural change are not taken into
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account in the calculation of cooling WW, i.e. a value of sb=1 is used. In this sector, the
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consumption factor depends on the value of st. Technological improvements of water use
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intensity in the energy sector are mainly achieved by a conversion of the cooling system
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from once-through cooling to tower cooling. In Europe, the average water withdrawals per
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MWh for once-through cooling systems are about 45 times higher than for tower cooling
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systems. At the same time, the average consumption factor with tower cooling (0.53) is
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about 66 times higher than with once-through cooling systems (0.008). From these
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figures, the relationship between the average water consumption factor and the
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percentage share of tower cooling systems can be derived. Further, a given st -value can
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be converted into a fraction of power plants with tower cooling systems, defining s t =1 for
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the European average fraction of tower cooling systems (57%) in for year 2005. In
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combination, this yields a non-linear relationship between the normalized consumption
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factor cnorm=cscenario/c2005 and st, which can be approximated by 𝑐𝑛𝑜𝑟𝑚 (𝑠𝑡 ) ≈ 0.959 𝑠𝑡−1.111 .
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Finally, the consumption factor in the scenario cscenario=c2005*cnorm(st) is used to calculate
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WC in the energy sector.
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