1 Electronic supplement material to the article 2 Modelling the effects of cross-sectoral water 3 allocation schemes in Europe 4 by 5 Florian Wimmer, Eric Audsley, Marcus Malsy, Cristina Savin, Robert Dunford, 6 Paula A. Harrison, Rüdiger Schaldach, and Martina Flörke 7 8 Climatic Change 2014 (doi:10.1007/s10584-014- 9 1161-9) 10 11 corresponding author F. Wimmer 12 Center for Environmental Systems Research, University of Kassel, Kassel, 13 Germany 14 email: wimmer@cesr.de 15 16 The WaterGAP metamodel 17 18 The WaterGAP metamodel (WGMM) is used in the CLIMSAVE IAP to assess both the 19 impact of climate change on water resources and the change in water demand for human 20 use due to socio-economic development. WGMM is designed to be an emulator of the 21 global hydrological model WaterGAP3 (Water – Global Assessment and Prognosis) 22 (Verzano 2009), which is an advancement of WaterGAP2 (Alcamo et al. 2003; Döll et al. 23 2003) with increased spatial and temporal resolution and improvements to the 24 implementation of hydrological processes. Originally, WaterGAP3 operates on a five arc 25 minute grid in daily internal time steps. The resulting runtimes (>30 min) are too long and 26 the input data requirements are too demanding for application within an interactive 27 platform like the CLIMSAVE IAP. To shorten the runtime considerably, the spatial detail 28 of WGMM is reduced from more than 180,000 grid cells for Europe in WaterGAP3 to 92 29 spatial units with an area larger than 10,000 km². Those spatial units, hereafter referred 1 30 to as river basins, are made up of either single large river basins (split into three sub- 31 basins for the Danube) or clusters of smaller, neighbouring river basins with similar hydro- 32 geographic properties. Moreover, the input data requirements are largely reduced as 33 long-term statistics over a 30-year period are computed instead of daily or monthly time 34 series. 35 36 For each river basin, the metamodel computes the change in long-term (30 years) 37 average water availability (WA), resulting from changes in mean annual precipitation and 38 air temperature compared to a baseline value. The metamodel relies on look-up tables 39 populated with simulated WA from pre-run WaterGAP3 simulations driven by monthly 40 CRU climate input for the baseline period 1971-2000 (Mitchell & Jones, 2005) with 41 simultaneously modified mean temperature and precipitation. A set of constant offsets dT 42 were added to all values in the input time series of temperature leading to a shift in mean 43 annual temperature, while the spatial patterns and temporal dynamics are preserved. The 44 manipulation of precipitation was achieved in a similar manner by multiplying the values 45 in the precipitation time series by a set of factors fP. The variation of dT applied ranges 46 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 47 steps of 0.05. In this way, a three-dimensional response surface with 273 grid points for 48 the combinations of all dT, and fP is constructed for each river basin relating WA to 49 changes in temperature and precipitation. 50 51 When WGMM analyses any scenario input data of gridded mean annual air temperature 52 and precipitation, it first computes the actual change in temperature dT’ and precipitation 53 fP’ in each river basin compared to the baseline. In a second step, WA for the given 54 scenario is interpolated from the values for WA at the four neighbouring grid points. 55 56 WGMM also provides estimates of annual water withdrawals (WW) and water 57 consumption (WC) in the water use sectors of domestic, manufacturing, and electricity 58 (cooling in thermal electricity production). Water withdrawals are defined as the volume of 59 water withdrawn from surface water or groundwater, while WC is the share in WW that is 60 evaporated, transpired, incorporated in products, or consumed by humans. The modelling 61 approach is based on gridded results of WaterGAP3 for the base year 2005 (EU FP6 62 project SCENES), which were aggregated at the river basin level. A detailed description 63 of the approaches to calculate sector WW and WC used in WaterGAP3 is given in Flörke 64 et al. (2013). 65 66 67 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) 68 In Equation 1, are the baseline WW in the river basin, D is the main model driver in the 69 scenario and is the main model driver in the base year. The subscripts denote the water 2 70 use sector (s), the river basin (r), and one of n(=28) European countries (c). The factor st 71 represents savings in water demand due to technological improvements (negative values 72 imply more water-intensive technologies); sb represents water savings due to behavioural 73 change to use less water (negative values imply increasing water use due to more water- 74 intensive behaviour). Fs,r,c are weighting factors used to translate the country-level relative 75 change in the main driver to the water withdrawals at the river basin scale. There is one 76 set of weighting factors per water use sector s and river basin r calculated as 77 78 𝐹𝑠,𝑟,𝑐 = 𝑠,𝑟,𝑐 𝑠,𝑟 𝑤𝑖𝑡ℎ ∑𝑛𝑐=1 𝐹𝑠,𝑟,𝑐 = 1 79 80 In Equation 2, s,r,c are the baseline WW in sector s allocated to the spatial intersection of 81 country c and river basin r. In order to compute WC in the various sectors, sectoral WW 82 are multiplied by a sector-specific consumption factor, derived separately for each river 83 basin as the WC-to-WW ratio in the base year. For each sector, a different main model 84 driver (D and ) is used. In addition, a number of calculation steps differ among the 85 sectors as described below. 86 87 The main model driver for domestic water use is the product of population and structural 88 water use intensity (I). The latter depends on income and specifies the domestic WW per 89 person and year. For this purpose, a sigmoidal curve 𝐼 = 𝑎 + 𝑏(1 − 𝑒 𝛼∗𝐺 ) was fitted to 90 historic data, where G is income and a, b, and are curve fitting parameters. The model 91 limits WW to a minimum value of annual domestic water withdrawals in a river basin 92 calculated as 18.25 m³ capita-1 (50 l capita-1 day-1) times the number of inhabitants. 93 Manufacturing water use is modelled taking into account the gross value added (GVA) as 94 the main model driver. In order to limit the water savings due to technological 95 improvements in this sector, the lower limit for st in Equation 1 is set to st =0.6, i.e., a 96 maximum of 40% water savings is possible. Water savings due to behavioural change 97 are not taken into account with manufacturing WW, i.e. a value of sb=1 is used. 98 The main model driver for cooling water use in the energy sector is thermal electricity 99 production (TEP in MWh). The lower limit for the technological change factor is defined 2 100 as st =0.8 in this sector. Water savings due to behavioural change are not taken into 101 account in the calculation of cooling WW, i.e. a value of sb=1 is used. In this sector, the 102 consumption factor depends on the value of st. Technological improvements of water use 103 intensity in the energy sector are mainly achieved by a conversion of the cooling system 104 from once-through cooling to tower cooling. In Europe, the average water withdrawals per 105 MWh for once-through cooling systems are about 45 times higher than for tower cooling 106 systems. At the same time, the average consumption factor with tower cooling (0.53) is 107 about 66 times higher than with once-through cooling systems (0.008). From these 108 figures, the relationship between the average water consumption factor and the 3 109 percentage share of tower cooling systems can be derived. Further, a given st -value can 110 be converted into a fraction of power plants with tower cooling systems, defining s t =1 for 111 the European average fraction of tower cooling systems (57%) in for year 2005. In 112 combination, this yields a non-linear relationship between the normalized consumption 113 factor cnorm=cscenario/c2005 and st, which can be approximated by 𝑐𝑛𝑜𝑟𝑚 (𝑠𝑡 ) ≈ 0.959 𝑠𝑡−1.111 . 114 Finally, the consumption factor in the scenario cscenario=c2005*cnorm(st) is used to calculate 115 WC in the energy sector. 116 117 References 118 Alcamo J, Döll P, Henrichs T, Kaspar F, Lehner B, Rösch T, Siebert S (2003) 119 Development and testing of the WaterGAP2 global model of water use and availability. 120 Hydrol Sci J 48:317–337 121 122 Alcamo J, Flörke M, Märker M (2007) Future long-term changes in global water resources 123 driven by socio-economic and climatic changes. Hydrol Sci J 52(2):247-275 124 125 Döll P, Kaspar F, Lehner B, (2003) A global hydrological model for deriving water 126 availability indicators: model tuning and validation. J Hydrol 270:105–134 127 128 Flörke M, Kynast E, Bärlund I, Eisner S, Wimmer F, Alcamo J (2013) Domestic and 129 industrial water uses of the past 60 years as a mirror of socio-economic development: A 130 global simulation study. Glob Environ Chang 23:144–156 131 132 Mitchell TD, Jones PD (2005) An improved method of constructing a database of monthly 133 climate observations and associated high-resolution grids. Int J Climatol 25:693-712 4