GLOBAL MODELLING OF DOMESTIC AND MANUFACTURING WATER USES

Transcription

1 GLOBAL MODELLING OF DOMESTIC AND MANUFACTURING WATER USES Minor M.Sc. Thesis September 2016 M.Sc. International Land and Water Management Chairgroup Water Systems and Global Change Juliane Schillinger Supervisor: Dr. Michelle van Vliet Water Systems and Global Change Wageningen University Examiner: Dr. Fulco Ludwig Water Systems and Global Change Wageningen University

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3 Contents Acknowledgements... II List of Abbreviations...III List of Figures... V List of Tables... VII Summary... VIII 1. Introduction Global Water Use Models Terminology Water use sectors WFaS initiative models WFaS input data Methodology and Model Description Model development Driving forces Model routines Model parameters Model Application Input data Scale Model validation Results Domestic water withdrawal Domestic water consumption Manufacturing water withdrawal Manufacturing water consumption Discussion Availability of input data Uncertainties of modelled water use Uncertainties introduced in downscaling Conclusions and Recommendations References...40 Appendix...43 I

4 Acknowledgements I would like to thank my supervisor Dr. Michelle van Vliet for the opportunity to get involved with her work and her support, advice and feedback throughout the thesis work, be it about the access to input data or the latest model peculiarity. I am also thankful to Dr. Fulco Ludwig, who acted as co-supervisor and examiner, for his input in several discussions that made me critically assess and improve my own work. This thesis research is based on the work of many colleagues who have been involved with the modelling of global water use for years. I am grateful to them for not only sharing their results, but also giving deeper insights in the technical and mathematical details of model routines. It is thanks to their groundwork on water use modelling that I was able to conduct the research presented in this thesis. I would like to particularly thank Martina Flörke at the University of Kassel for her direct cooperation and provision of additional information on the WaterGAP model that were vital to the development of a new model as outlined in this thesis. II

5 List of Abbreviations Organisations: FAO ISI-MIP NUTS OECD UN DESA UNEP WFaS WHO Food and Agriculture Organisation of the United Nations Inter-Sectoral Impact Model Intercomparison Project Nomenclature of Territorial Units for Statistics Organisation for Economic Co-operation and Development United Nations Department of Economic and Social Affairs United Nations Environmental Programme Water Futures and Solutions initiative World Health Organisation Models: H08 PCR-GLOBWB WaterGAP SSPs global hydrological model global hydrological model global hydrological model shared socioeconomic pathways Model parameters and routines: DSWI domestic structural water use intensity DWC domestic water consumption DWI domestic water use intensity DWW domestic water withdrawal EFR environmental flow requirements GDP gross domestic product GVA gross value added MSWI manufacturing structural water use intensity MWC manufacturing water consumption MWI manufacturing water use intensity MWW manufacturing water withdrawal RR recycling ratio TC technological change WU water use Analytical measures: MSoS mean sum of squares r Pearson's correlation coefficient RMSE root mean square error SoS sum of squares Global regions: AFR CPA EEU FSU LAM Africa Centrally planned Asia Eastern Europe Former Soviet Union Latin America III

6 MEA NAM PAO PAS SAS WEU Middle East North America OECD countries in the Pacific Ocean Other Pacific Asia South Asia Western Europe IV

7 List of Figures Figure 1. Proportions of water withdrawal for human use in the sectors agriculture (green), industry (yellow) and domestic (orange) on the global level (top) and the main global regions (bottom) for the year Underlying data obtained from FAO AQUASTAT Figure 2. Number of reference points per country in the domestic water withdrawal input dataset Figure 3. The eleven regions used to fit regional DSWI curves. Taken from Grahn (2006)..17 Figure 4. Water withdrawal in the domestic sector as simulated for the whole world (a) and China (b) by the developed model (black line), WaterGAP (steel blue line) and PCR-GLOBWB (orange line, three scenarios). Records (until 1995) and predictions (2000 onwards) from Shiklomanov (2000) and Chinese national records are given as reference values (blue crosses). Comparison of global and Chinese domestic water withdrawal in panel c) Figure 5. Chinese GDP over the simulation period in billion US$ Figure 6. Domestic water withdrawal on country level (a) and on grid cell level (b). Shown are values for the beginning of the simulation period (1960) and the end (2010) Figure 7. Index of agreement calculated for the fitting of a sigmoid curve for each country (a) and global region (b) during the domestic water withdrawal routine Figure 8. Difference between simulated and recorded domestic water withdrawal on countrylevel for 2000 and Values in percent of recorded value. Negative values indicate simulated values below the observed records, positive values indicate simulated values above the observed record Figure 9. Domestic water withdrawal per NUTS2 region in France for the year 2008, based on downscaled model results (left) and Eurostat records (right) Figure 10. Global water consumption in the domestic sector as simulated by the developed model (black line) and WaterGAP (steel blue line). Records (until 1995) and predictions (2000 onwards) from Shiklomanov (2000) are given as reference values (blue crosses) Figure 11. Simulations of domestic water consumption (solid lines) on country-level for Benin, Japan and Macedonia. The developed model s simulation in black, WaterGAP in steel blue. Modelled water withdrawal (dashed line) given for reference Figure 12. Domestic water consumption on country level (a) and on grid cell level (b). Shown are values for the beginning of the simulation period (1960) and the end (2010) Figure 13. European water withdrawal in the manufacturing sector as simulated by the developed model (black line) and WaterGAP (steel blue line) Figure 14. Simulations of manufacturing water withdrawal on country-level for Belgium, Latvia and Poland. The developed model s simulation in black, WaterGAP in steel blue Figure 15. Manufacturing water withdrawal for Europe on country level (a) and on grid cell level (b). Shown are values for the beginning of the simulation period (1960) and the end (2010) Figure 16. Difference between simulated and recorded manufacturing water withdrawal on country-level for 2001 and 2010 in Europe. Values in percent of recorded value. Negative V

8 values indicate simulated values below the observed records, positive values indicate simulated values above the observed record Figure 17. Global water withdrawal in the industrial sector as simulated by the developed model with GDP and electricity consumption as driver (black line, solid and dashed respectively), WaterGAP (steel blue line) and the three scenarios with PCR-GLOBWB (orange line). Records (until 1995) and predictions (2000 onwards) from Shiklomanov (2000) are given as reference values (blue crosses) Figure 18. Industrial water withdrawal on country level for the GDP-driven simulation (a) and the electricity-driven simulation (b). Shown are values early into the simulation period (1971) and at the end (2010) Figure 19. Difference between simulated and recorded industrial water withdrawal on countrylevel for 2000 in the GDP-driven simulation (a) and the electricity-driven simulation (b). Values in percent of recorded value. Negative values indicate simulated values below the observed records, positive values indicate simulated values above the observed record Figure 20. European water consumption in the manufacturing sector as simulated by the developed model (black line) and WaterGAP (steel blue line) Figure 21. Manufacturing water consumption for Europe on country level (a) and on grid cell level (b). Shown are values for the beginning of the simulation period (1960) and the end (2010) Figure 22. Simulations of domestic water withdrawal on country-level for France and the United Kingdom. The developed model s simulation in black, an earlier model version with technological change being calculated previously to 1980 in grey. Reference values from water use records as blue crosses Figure 23. Simulations of domestic water withdrawal for different model reference years for Australia (developed country), Kazakhstan (country in transition) and Mozambique (developing country). Model results with reference year 2005 as black solid line, simulations with reference years 1980, 1990 and 2000 in grey and solid, dashed and dotted, respectively. National records given for reference (blue crosses) Figure A1. Input data required to calculate industrial and domestic water withdrawal with the three WFaS models. Solid lines indicate data on country level, dashed lines indicate data on grid cell level. Sources: H08: Hanasaki et al. (2013); PCR-GLOBWB: Wada et al. (2011a), Wada et al. (2014); WaterGAP: Flörke et al. (2013), Wada et al. (2016)...43 Figure A2. Input data required to calculate industrial and domestic water consumption with the three WFaS models. Solid lines indicate data on country level, dashed lines indicate data on grid cell level. Blue shading indicates results from the water withdrawal routine. Sources: H08: Hanasaki et al. (2008); PCR-GLOBWB: (Wada et al., 2011a); WaterGAP: Flörke et al. (2013), Wada et al. (2016) Figure A3. National development states according to UN classification Figure A4. Countries with OECD membership before Figure A5. European manufacturing water withdrawal simulations with GDP (black solid line), GVA (grey solid line) and national electricity production (black dashed line) as drivers VI

9 Figure A6. Histograms of recycling ratio values based on the WaterGAP simulations for the domestic sector (a) and the manufacturing sector (b). Numbers are based on all countries included in the simulations and the respective GDP classes defined by Wada et al. (2011b) List of Tables Table 1. Overview of the three WFaS models. After Wada et al. (2016) Table 2. Overview of input data sources of the three WFaS models Table 3. Technological change in the domestic sector from 1980 onwards. Data from UN DESA (2014) and Flörke et al. (2013) Table 4. Recycling ratio, values taken from Wada et al. (2011b) Table 5. Technological change in the manufacturing sector. Data from Flörke et al. (2013). 12 Table 6. Model parameters Table 7. Data sources for input data used to run the regular model Table 8. Data sources for input data used to in model alterations Table 9. Availability of socio-economic drivers for the simulation of the manufacturing water withdrawal in Europe Table A1. Index of agreement calculated for the fitting of a sigmoid curve for each global region during the domestic water withdrawal routine Table A2. Spatial correlation (Pearson s correlation coefficient, p-value) between downscaled and recorded domestic water withdrawal within each country, based on the NUTS2 regions Table A3. Spatial correlation (Pearson s correlation coefficient, p-value) between downscaled and recorded manufacturing water withdrawal within each country, based on the NUTS2 regions VII

10 Summary The simulation of global water use is an important asset for sustainable water management all over the world, but is often hindered by the low availability of reference data, particularly water use records. This study therefore develops a new global water use model for the domestic and manufacturing sectors that requires a minimum of input data. A major part of the model is based on previous research in the Water Futures and Soluntions (WFaS) initiative. The model is tested on both country and grid cell (0.5 x0.5 ) level for the period , using historical records of water use and socio-economic indicators. The simulated domestic water withdrawal is similar to records and simulations done by other models on the global scale, with the exception of a steep increase in Chinese water withdrawal in the early 21 st century that heavily influences the global withdrawal. Manufacturing water withdrawal is simulated lower than by the global water use model WaterGAP and fits better to the reference data used for validation. Consumption based on the recycling ratio approach is overestimated compared to other models and records in both sectors. Four important sources of uncertainty in the simulated water use are found: (1) the implementation of technological change, (2) the applied model reference year, (3) the empirical assumptions on which the recycling ratio approach is based, and (4) the reference data used for the simulation of water use in the manufacturing sector. Downscaling of the model results on country level to the global grid is based on total population. This works well for the domestic sector, but does not achieve satisfactory results for the manufacturing sector. Keywords: global water use; modeling; water withdrawal; water consumption; domestic; manufacturing; recycling ratio. VIII

11 1. Introduction The global water withdrawal has multiplied by six times over the period from 1900 to 2000, from approximately 500 km³ per year to approximately 3000 km³ per year (Wada et al., 2014). This increase accompanies the global population growth from 1.7 to 7 billion people over the same period of time (Wada et al., 2014). With a further increase in population and the prospect of aggravating water scarcity problems in semi-arid and arid regions of the world due to climate change, the assessment of the future water scarcity gains in importance (Vassolo and Döll, 2005). As water resources on both the global and the regional scale are ultimately limited, an indefinite development of new resources to match the increase in water demand is impossible (Rockström et al., 2009). Therefore, water managers in many regions of the world are shifting their focus towards human water use and the introduction of more efficient technologies (Gleick, 2003). Being able to project future water use as accurately as possible is an important prerequisite for the planning of sustainable water management all over the world. There is thus an increasing demand for simulations of future human water use and its relation to socioeconomic and technological development, particularly in developing countries with limited resources available and a relatively high vulnerability towards water scarcity (Wada et al., 2014). A number of global water use models have been developed and refined over the course of the past years (such as Flörke et al., 2013; Hanasaki et al., 2013a; Wada et al., 2014). These models use a variety of different approaches and include varying sets of water use sectors. They also vary in their level of detail with regards to spatial and temporal resolution. Therefore, simulations of water withdrawal quantities for the upcoming decades still differ greatly from study to study (Wada et al., 2016). In addition, the objective to model water use worldwide means that model routines have to be applicable to the global scale and be able to make projections based on input data that is available from all regions around the globe. The low availability of suitable data and consistent observation records in parts of the world, most notably in many developing countries, presents a central impediment to this worldwide application (Gleick, 2003). It is hence one of the main challenges of global water use models to meet these requirements of applicability while at the same time ensure a sufficient accuracy of the results (Flörke et al., 2013). This research will particularly focus on water use in two areas: the domestic sector and the manufacturing sector. The decision to focus on these particular sectors is motivated by the rapid increase of the global population that will have an impact on the amount of water used in the domestic sector (Wada et al., 2014) and by the notion that manufacturing can be expected to produce a lot more wastewater than electricity production and thus plays an important role for future treatment capacities and possible pollution problems (Vassolo and Döll, 2005). The main objective of this research is thus to develop a new global water use model in order to simulate water withdrawal and water consumption in both the domestic and the manufacturing sectors that requires a minimum of input data and variables. While it is the aim to keep the model as simple as possible with regards to input data in order to allow global coverage, it should also be possible to model water use as accurately as possible. Finding an appropriate balance between model simplicity and accuracy is thus an additional objective. From these objectives, the following three explicit research questions emerge: 1

12 1. Which approaches are useful to model domestic water use and manufacturing water use on a global scale? 2. Which is the most suitable way to make a distinction between water withdrawal and water consumption for the domestic and manufacturing sectors in a global model with regards to minimising the required input data? 3. How can individual modelling routines used in global water use modelling be simplified without losing simulation accuracy? This thesis report elaborates on how the aforementioned objectives were approached. It first gives an overview of global water use models in general and the three models used in the Water Futures and Solutions initiative in particular (Section 2). It then introduces the methodology applied in this research, explains the model that was developed over its course (Section 3), and elaborates on how this model was applied to real data in order to test its performance (Section 4). The results of this model application are then explained for each model routine (Section 5) and subsequently discussed with regards to issues with input data and uncertainties in the model (Section 6). Some concluding remarks and recommendations for further research round off the report (Section 7). 2. Global Water Use Models 2.1 Terminology There is a number of water-related terms that need to be distinguished from each other. Water use is the general term to refer to a certain volume of water that is utilised in a specific way (Flörke et al., 2013). Water withdrawal refers to the volume of water that is abstracted from water resources (surface water or groundwater), usually for human needs. It does not disclose any information on what happens to the water after it is used (Gleick, 2003). The Return Flow is the share of the water that is returned to the water resources after use, for instance as treated wastewater. Water consumption in contrast refers to the portion of the water withdrawal that is not directly returned to surface or groundwater. Instead it is consumed by humans, incorporated into products or taken up by plants and released as evapotranspiration (Flörke et al., 2013). 2.2 Water use sectors Human water use is subsequently separated into five different sectors that may or may not be included in water use models, depending on the model s focus. Irrigation is the biggest water user worldwide, with approximately 70% of the global water withdrawal being utilised in irrigation systems. Livestock, the other agricultural water use sector, is a significantly smaller water user than irrigation, adding up to as little as 0.6% of the global water withdrawal (Steinfeld et al., 2006). However, livestock related water use is currently growing in many countries (Wada et al., 2016). Industrial water use is often treated as one sector, although some models like WaterGAP (Flörke et al., 2013) make a distinction between electricity production and manufacturing. Electricity production only refers to thermoelectric (e.g. fossil-, biomass-fuelled or nuclear) power plants where water is used for cooling purposes, accounting for a great part of water withdrawals in many developed countries. It does, however, not include hydropower. The manufacturing water use comprises of all other industrial withdrawals that are not energy- 2

13 related. The domestic sector finally includes household water use as well as small businesses and municipal uses (Alcamo et al., 2003; Wada et al., 2016). The proportions of water use per sector differ widely from region to region. Figure 1 shows an overview of the percentage of the agriculture, industrial and domestic sectors in the total water withdrawal on the global level and for the main global regions in Note that agricultural withdrawal includes both livestock and irrigation, and that industrial withdrawal includes both thermoelectric power production and manufacturing. While most regions, as well as the global average, show a great bias towards agricultural water use, Europe and North America are characterised by high industrial water use, surpassing and tying with agricultural use, respectively. Wada et al. (2016) point out that this is particularly due to the high water use in electricity production. In addition to human water use, most models also account for the so-called environmental flow requirements (EFR). EFRs describe the amount of water that is required to maintain the World Domestic 12% Agriculture 69% Industrial 19% Domestic Industrial Agriculture Europe Africa Asia Oceania* 22% 22% 13% 5% 9% 10% 26% 56% 82% 81% 59% 15% Northern America Central America Southern America 43% 14% 43% 63% 28% 9% 71% 17% 12% Figure 1. Proportions of water withdrawal for human use in the sectors agriculture (green), industry (yellow) and domestic (orange) on the global level (top) and the main global regions (bottom) for the year Underlying data obtained from FAO AQUASTAT. * Oceania including Australia. 3

14 aquatic ecosystem of the respective river and should therefore not be withdrawn (Wada et al., 2016). There is a number of definitions and approaches to calculate the EFR, with some of the most common ones being based on different fixed percentiles of the annual or monthly river runoff (Gerten et al., 2013). Pastor et al. (2014) also find that when EFRs are calculated on the global scale, the best fit with local assessments is reached for approaches that calculate the EFR on a monthly basis and thus take variable flow regimes into account. 2.3 WFaS initiative models The Water Futures and Solutions (WFaS) initiative is a joint effort of different research institutes in order to identify water-related problems and solutions and to develop policies and management strategies to improve water security (Cosgrove et al., 2015). One part of this initiative is the simulation of future water use on the global level. A first fast-track assessment (Wada et al., 2016) used three global water use models to gain an overview of future developments and to assess the different modelling approaches, namely H08 (Hanasaki et al., 2008), PCR-GLOBWB (van Beek et al., 2011; Wada et al., 2011a) and WaterGAP (Döll et al., 2001; Müller Schmied et al., 2014). The short summary of each of the three models is given below. Table 1 additionally presents an overview of the water demand routines in each model. Of particular interest for this research are the approaches to calculate the water use in the domestic and the industrial sectors. During the fast-track assessment, all three models were forced with the same bias-corrected climate model output and projections of socio-economic development in order to model global Table 1. Overview of the three WFaS models. After Wada et al. (2016). Model H08 PCR-GLOBWB WaterGAP Spatial resolution 0.5 x 0.5 grid 0.5 x 0.5 grid 0.5 x 0.5 grid Water use sectors: Agriculture Industry Domestic Environment Flow Requirement (EFR) Sources Irrigation based on simulated crop calendar using climate data Aggregated water use based on energy production Domestic water use based on population Livestock Irrigation based on prescribed crop calendar Aggregated water use based on energy production and consumption Domestic water use based on population Livestock Irrigation based on prescribed crop calendar Thermal electric water use based on power plant characteristics Manufacturing based on GVA Domestic water use based on population and GDP Empirical EFR EFR based on Q90* EFR based on Q90 Hanasaki et al. (2013a; 2013b) Wada et al. (2011a); Wada et al. (2014) Alcamo et al. (2003); Flörke et al. (2013) * Q90 is equivalent to the river discharge that is exceeded 90 % of the time. 4

15 future water use in the domestic and industrial sectors. While all models showed the same trend towards an increase in global water use, there are great differences between the different models on the country scale that are mostly due to the differences in modelling approaches and the use of different reference data sets as starting point of the simulation (Wada et al., 2016) H08 H08 is an integrated global water resources model that comprises of six different modules that cover land surface hydrology, river routing, crop growth, reservoir operation, environmental flow requirement estimation, and anthropogenic water withdrawal (Hanasaki et al., 2008). The simulation of human water use is subdivided into the three sectors irrigation, industry and domestic, with the focus on the irrigation sector where a relatively detailed approach is used based on a simulated crop calendar that is forced with climate data (Wada et al., 2016). All water quantities are simulated in daily time intervals and on a grid of the resolution 0.5 x 0.5 (Hanasaki et al., 2013a). This enables the model to simulate both availability and demand on a sub-annual time scale which allows for scarcity assessment on a seasonal level or below Hanasaki et al. (2013b) PCR-GLOBWB PCR-GLOBWB is a global hydrological model with a resolution of 0.5 x 0.5 that was recently amended by an integrated water demand model by Wada et al. (2014). This water demand model simulates water use for livestock and irrigation as well as for the industrial and the domestic sectors (Wada et al., 2011a). Water use was first simulated in monthly time steps (Wada et al., 2011a), later one on a daily basis (Wada et al., 2014). This allowed for the inclusion of a routine that accounts for a higher domestic water use during summer months by using several temperature parameters in the calculation (Wada et al., 2011b) WaterGAP WaterGAP combines a global hydrological model with a global water use model, where the latter distinguishes five different sectors that are simulated separately: livestock, irrigation, thermal electricity production, manufacturing and domestic (Döll et al., 2012). It simulates water availability and demand on an annual basis for a global grid of 0.5 x 0.5 (Wada et al., 2016). Next to separating the manufacturing water use from the electricity production, WaterGAP also pays close attention to the return flow from the manufacturing sector and its split into treated and untreated wastewater as well as cooling water (Flörke et al., 2013). 2.4 WFaS input data Wada et al. (2016) find that the agreement between the WFaS models in the fast-track assessment is higher for countries were long time series of the required input data are available. In many parts of the world, however, consistent observation records are rare, especially in developing countries. Additionally, there is great variability in the type of data that is collected. While population series and economic parameters are regularly recorded, data on sectoral water use is limited (Flörke et al., 2013). 5

16 Depending on the modelling approaches, the different models require a variety of input data of matching temporal and spatial resolution. A visual overview of the input data needed to calculate industrial and domestic water withdrawal and consumption with each of the three WFaS models is given in Figure A1 and Figure A2 in the appendix. Details on the agricultural sector are subsequently left out as agriculture only plays a minor role in this research. H08 and PCR-GLOBWB show some similarity as they both calculate an aggregated industrial water use that includes both thermoelectric power production and manufacturing. The main driver in both cases is the national electricity production as a proxy for industrial activity (Hanasaki et al., 2013a; Wada et al., 2011b). PCR-GLOBWB, however, also uses additional information on energy consumption to account for technological development (Wada et al., 2014). As WaterGAP separates between electricity production and manufacturing, it aims to calculate the water use in both sectors with higher accuracy and thus requires more detailed input data (Voß and Flörke, 2010). The required input data for the domestic sector is relatively similar across all models, mainly using population data as a driver. PCR-GLOBWB and WaterGAP both also account for a nation s technological advancement, which requires economic data such as the GDP (Wada et al., 2016). On top of the data used to calculate water withdrawal, additional input data that is required in order to calculate the industrial and the domestic water consumption based on the respective water withdrawal. While WaterGAP relies on actual records of the return flow to simulate the water consumption as accurately as possible (Flörke et al., 2013), H08 and PCR-GLOBWB use ratios derived from literature and case studies, respectively, to account for return flow and consumption (Hanasaki et al., 2008; Wada et al., 2011b). Table 2 provides an overview of the different data sources used to obtain the required input data in order to force the three WFaS models in different studies. As most of the input data is only recorded on the national level, all three models start by calculating water use indicators for entire countries. Gridded population data is then used to downscale the results to a 0.5 grid (Flörke et al., 2013; Hanasaki et al., 2013a). In addition to socio-economic indicators, all models also require some sort of historical records of water use or withdrawal data as a point of reference. The researchers applying the different models went about obtaining this water use data in different ways, using either global datasets like the FAO AQUASTAT or collecting data from individual national agencies (Flörke et al., 2013; Wada et al., 2016). 3. Methodology and Model Description 3.1 Model development In order to address the research objective and questions (Section 1), I developed a simple water use model in R that includes routines to model the domestic water use and the manufacturing water use. Both water withdrawal and water consumption are modelled in those two sectors, with routines of different WFaS models used as a starting point. In a first step, the model simulates water use on the national level. As a part of the application and validation of 6

17 Table 2. Overview of input data sources of the three WFaS models. Input data Model Data source Domestic water withdrawal Industrial water withdrawal Manufacturing water withdrawal Wastewater and return flow Gross Domestic Product (GDP) Gross Value Added (GVA) Electricity production Energy consumption Power plant characteristics PCR-GLOBWB (Wada et al., 2011a) H08 (Hanasaki et al., 2013), WaterGAP (Flörke et al., 2013) WaterGAP* (Flörke and Alcamo, 2004) PCR-GLOBWB (Wada et al., 2011a) The World s Water Datasets 1, FAO AQUASTAT 2 FAO AQUASTAT 2 EUROSTAT 3, national statistical agencies H08 (Hanasaki et al., 2013) FAO AQUASTAT 2 WaterGAP (Flörke et al., 2013) WaterGAP (Flörke et al., 2013) PCR-GLOBWB (Wada et al., 2011a), H08 (Hanasaki et al., 2013) World Water Development Report II 4 National statistical agencies National statistical agencies, Shiklomanov (2000) World Bank Development Indicators 5 WaterGAP (Flörke et al., 2013) World Bank Development Indicators 5, CIA World Factbook 6 WaterGAP (Flörke et al., 2013) World Bank Development Indicators 5 PCR-GLOBWB (Wada et al., 2011a) UNEP 7 H08 (Hanasaki et al., 2013) World Bank Development Indicators 5 PCR-GLOBWB (Wada et al., 2011a) UNEP 7 WaterGAP (Flörke et al., 2013) Utility Data Institute 8 * Flörke and Alcamo (2004) used WaterGAP for Europe only the model to historical data from 1960 to 2014, it is downscaled to a global grid of the resolution 0.5 x 0.5. The modelling approach for the manufacturing sector is based on the WaterGAP model as it is the only global water use model that differentiates between water use for manufacturing and thermoelectricity production (Wada et al., 2016). The domestic sector routine is also based on the approach used in WaterGAP (outlined for instance by Voß et al. (2009)). In both sectors, routines for the simulation of water consumption are based on the work of Wada et al. (2011b) for the PCR-GLOBWB model as it does not require any return flow or consumption reference data. 7

18 As it is an objective of this research to find a balance between simulation accuracy and required input data, different alterations were made to the initial model routines based on the WFaS models and tested for their performance. These alterations mainly aim at reducing the amount of input data that is necessary to run the model and at improving the model s accuracy with the data given. Details on different alterations to model routines are introduced in the respective subchapters in Section Driving forces Driving forces are parameters and processes that have a great influence on the dynamic of water use over time. Different water use sectors are affected by different drivers, and models vary in their choice of drivers that are included in the modelling routines (Voß et al., 2009). The following paragraphs give an overview of the most important drivers within the newly developed model and the reasoning behind their usage. Following the WaterGAP approach, the main driving force of water use in the manufacturing sector is the country s annual Gross Value Added (GVA). The GVA is an economic indicator that describes the manufacturing output of a certain industry and is closely linked to the Gross Domestic Product (GDP). GVA records for almost all countries worldwide can be obtained from the World Bank s World Development Indicators database (Flörke and Alcamo, 2004; Voß and Flörke, 2010). When simulating the aggregated industrial water use, H08 and PCR-GLOBWB use annual electricity production and GDP as drivers (Wada et al., 2016). Both of these will be tested for their suitability as drivers for the manufacturing sector as well. The main driving force of domestic water use is the population number of the country or area in question. Next to the population, WaterGAP also uses the country s GDP to account for structural development (Flörke and Alcamo, 2004; Voß et al., 2009). This structural development refers to the observation that with increasing GDP and, thus, wealth, a country s water use first increases as people can afford additional water-using appliances and an overall more water-intensive lifestyle. Eventually, however, the water use levels off or even declines as investments in more water efficient technologies are made. This effect is modelled for each country separately, using past GDP and water use records to fit an individual curve future simulations are based on (Flörke et al., 2013). Additional drivers of domestic water use on a smaller temporal resolution such as temperature as used by Wada et al. (2011b) are left out of the developed model in order to reduce the amount of required input data. A last factor to be taken into account for both manufacturing and domestic water use is the socalled technological change, accounting for the technological advancement of a country that results in more efficient technologies and therefore a lower water use intensity. Flörke et al. (2013) derived values to use in WaterGAP for both sectors from literature review. They make a distinction between OECD and non-oecd countries for technological change in the manufacturing sector and between different development classes defined by the United Nations in the domestic sector. Wada et al. (2011a) derive the technological development of a country from its electricity production and consumption values. As information on the OECD membership and the overall development state of a country are readily available at the respective international organisations, the WaterGAP approach is used to account for technological change later on. 8

19 3.3 Model routines Domestic Water Withdrawal Following the WaterGAP approach as outlined by Flörke and Alcamo (2004) and Flörke et al. (2013), domestic water withdrawal is calculated in three steps. Step 1: Domestic Structural Water use Intensity (DSWI, m 3 capita -1 year -1 ) The first step accounts for the structural development component of domestic water withdrawal, namely the connection between domestic water use and GDP per capita. This structural change is represented by a sigmoid curve this is fitted to historical records of domestic water withdrawal and GDP per capita following (1. DSWI = DSWI min + α(1 e γgdp2 ) (1) where DSWI is the domestic structural water use intensity (m 3 capita -1 year -1 ), taken from reference data on the domestic water withdrawal, GDP is the national GDP per capita (US$ capita -1 year -1 ), and α and γ are the parameters to be fitted to the input data. Following, the WHO s recommendation of a minimum water use intensity of 50 L capita -1 day -1, DSWI min is set at 18 m 3 capita -1 year -1 (Gleick, 1996). When optimising the sigmoid curve fit, the index of agreement applied by Flörke et al. (2013) is used as it is suitable for the evaluation of sigmoid curves. The index is calculated by the following three equations: d = 1 n RMSE2 PE (2) RMSE = 1 n (P i O i ) 2 n i=1 n PE = ( P i O + O i O ) 2 i=1 (3) (4) where d is the index of agreement (-), n is the number of reference points, RMSE is the root mean square error, PE is the potential error variance, P i are model-simulated values, O i are observed values and Ō is the observed mean. The index of agreement d varies between 0 and 1, where 1 corresponds to perfect agreement between modelled and observed values and thus a perfect curve fit. Based on the sigmoid curve, the domestic structural water use intensity for each year within the simulation period is calculated from the GDP per capita input data. For countries that lack sufficient reference data on national domestic water withdrawal, the curve is fitted on a regional basis. The individual regions can be assigned with the model s input data. Each regional curve uses all national reference data available within the region in order to represent the average structural development. Step 2: Domestic Water use Intensity (DWI, m 3 capita -1 year -1 ) The next step accounts for the technological change, i.e. an improvement in water use efficiency over time. 9

20 DWI = DSWI TC (5) where DWI is the domestic water use intensity (m 3 capita -1 year -1 ), DSWI is the previously calculated domestic structural water use intensity (m 3 capita -1 year -1 ), and TC is the technological change (-). Flörke et al. (2013) define technological change in the domestic sector as dependent on the development status of a country, using the United Nations classification system (shown in Figure A3 in the appendix), and effective from 1980 onwards. The technological change rates per development class are given in Table 3. Technological change is calculated relative to the model s reference year as outlined by Alcamo et al. (2003): TC = (1 ε nat ) t t 0 (6) Where TC is the technological change (-), ε nat is the national technological change rate as given in Table 3, t is the year in question and t 0 is the model s global reference year. Note that for all years before 1980, the technological change is equal to TC in Step 3: Domestic Water Withdrawal (DWW, m 3 year -1 ) Finally, the national domestic water withdrawal is calculated by multiplying the domestic water use intensity times the national population. DWW = DWI POP (7) where DWW is the domestic water withdrawal (m 3 year -1 ), DWI is the domestic water use intensity (m 3 capita -1 year -1 ) and POP is the population (capita). Modified approach for sigmoid curve fitting As the sigmoid curve fitting process in step 1 is the basis of the simulation, additional attention was paid on how to improve the fit for countries where little reference data is available. Previously, the DSWI was calculated on country-level by equation 1 unless there were less than three reference data points available or the calculated γ was below 0, in which cases the regional curve would be used. This approach was to be improved. Countries were therefore subdivided into three categories, based on the number of reference data points and the performance of the curve fitting process using national reference data: (1) three or more reference data points and γ>0, (2) three or more reference data points and γ<0, and (3) two or less reference data points. For case 1 countries, the domestic water withdrawal is simulated as described above. For case 2 countries, γ takes a negative value due to the existence of reference data points with a domestic structural water use intensity below the DSWI min set at 18 m 3 capita -1 year -1. This occurs in countries where the water availability per capita does not meet the minimum recommended by the WHO, such as Benin or Burundi. In this case, DSWI min in (1 is set to 0 m 3 capita -1 year -1 to account for the country s chronical low water availability and thus use. Table 3. Technological change in the domestic sector from 1980 onwards. Data from UN DESA (2014) and Flörke et al. (2013). Country classification Developed countries Economies in transition Developing economies Technological change rate 2 % per year 1 % per year 0.5 % per year 10

21 For case 3 countries, the available reference data is not deemed sufficient to fit an appropriate curve based on national data. Instead, reference data from all countries in the respective region is used. In order to account for the potential heterogeneity of the region and make the simulation more sensitive to the conditions in the individual countries, the domestic structural water use intensity that results from the curve fitting process is scaled up or down depending on the water use in the country at question relative to the rest of the region. This scaling is done using the average of the historical domestic water withdrawal values available for the country and comparing it to the average of all domestic water withdrawal values in the region for the same years. The national domestic structural water use intensity is thus calculated following Eq. 1: DSWI nat = DSWI nat,ref (DSWI DSWI min + α reg (1 e γ 2 reggdp nat )) (8) reg,ref where the subscripts nat and reg indicate values on national and regional level, respectively, and the subscript ref indicates the years for which national reference data is available. Note that while α and γ are taken from the sigmoid curve fitted to regional data, DSWI is ultimately calculated on the national level. For this reason, national GDP is used in equation 8 rather than regional GDP. DSWI min is set to 18 m 3 capita -1 year -1 as usual Domestic Water Consumption The calculation of the domestic water consumption out of the domestic water withdrawal follows the recycling ratio approach by Wada et al. (2011b) that is used in the PCR-GLOBWB model. Being an empirical measure based solely on GDP and, in case of the domestic sector, the country s urban population, the recycling ratio approach requires little input data which does not include wastewater or consumption reference data that might be difficult to obtain for some countries. The recycling ratio (RR) represents the ratio of wastewater to withdrawal, i.e. the portion of water withdrawal that is re-used. Wada et al. (2011b) distinguished three different levels of economic development based on GDP where the recycling ratio increases with the economic advancement. The three categories are based on historical records of Japan and summarised in Table 4Error! Reference source not found.. Based on a country s recycling ratio, the domestic water consumption is calculated with the following equation: DWC = DWW (1 RR F urban ) (9) where DWC is the domestic water consumption (m 3 year -1 ), DWW is the domestic water withdrawal (m 3 year -1 ), RR is the recycling ratio (-) and F urban is the fraction of urban population in the country s total population (-). Table 4. Recycling ratio, values taken from Wada et al. (2011b). Annual GDP Recycling ratio < 755 US$ capita US$ capita US$ capita > 9265 US$ capita

22 3.3.3 Manufacturing Water Withdrawal The manufacturing water withdrawal routine is based on the approach in WaterGAP, as it is the only model that makes a distinction between thermoelectric power production and manufacturing within the industrial sector. Following Flörke et al. (2013) and Voß and Flörke (2010), the water withdrawal is calculated in two steps. Step 1: Manufacturing Water use Intensity (MWI, m 3 US$ -1 year -1 ) The simulation of the manufacturing water withdrawal is based on the reference manufacturing structural water use intensity. This is a single reference value used to account for the status of structural development in the country, obtained from manufacturing water withdrawal reference data. For this purpose, the available manufacturing withdrawal record for each country is checked for an annual value as close to the model s global reference year as possible. Ideally thus, the resulting manufacturing reference year coincides with the global reference year, however, based on the data availability in each country, the two years might differ. The manufacturing water use intensity is then calculated from the manufacturing structural water use intensity and the technological change rate as follows: MWI = MSWI ref TC (10) where MWI is the manufacturing water use intensity (m 3 US$ -1 year -1 ), MSWI ref is the manufacturing structural water use intensity in the reference year (m 3 US$ -1 year -1 ), and TC is the technological change (-). For the manufacturing sector, the technological change rate depends on the time period in question and on whether the respective country is member of the OECD, as a membership is assumed to speed up technological advancement through information exchange (Flörke et al., 2013). Table 5 gives an overview of the different change rates. Note that since the technological change rates for OECD members and other countries are identical after 1980, only countries with an OECD membership prior to 1980 are relevant here. Figure A4 in the appendix gives an overview of which countries this applies to. Based on these technological change rates, TC is calculated as given in (6, however, using the manufacturing reference year rather than the global reference year in case of a discrepancy between both years. Step 2: Manufacturing Water Withdrawal (MWW, m 3 year -1 ) The manufacturing water use intensity is translated into the manufacturing water withdrawal by multiplication with the socioeconomic driver. WaterGAP uses the GVA as driver, leading to the following equation: MWW = MWI GVA (11) where MWW is the manufacturing water withdrawal (m 3 year -1 ), MWI is the manufacturing water use intensity (m 3 year -1 ) and GVA is the gross value added (US$). Table 5. Technological change in the manufacturing sector. Data from Flörke et al. (2013). Time period OECD member state onwards No OECD member state Until onwards Technological change rate 2.4 % per year 1 % per year 0 % per year 2.4 % per year 1 % per year 12

23 Modified approach with various manufacturing water use drivers WaterGAP is the only one of the three WFaS models using GVA as a driver in the manufacturing or industrial sector. As PCR-GLOBWB and H08 both aggregate the water use from manufacturing and thermoelectric power production into one value, the national electricity production is more important as driver in the industrial sector. PCR-GLOBWB also used GDP as an additional driver (Wada et al., 2016). Records of all three indicators are relatively easy to obtain on a global scale as they are all collected by the World Bank, however, the completeness of timelines varies from country to country. In order to assess which of the three indicators GVA, GDP and electricity consumption is the most suitable driver for the manufacturing water use with regards to accuracy and data availability, all of them are used in separate simulations and the results are compared Manufacturing Water Consumption Similarly to the water consumption in the domestic sector, the manufacturing water consumption is calculated using the recycling ratio approach by Wada et al. (2011b). As the PCR-GLOBWB model does not include a separate routine for manufacturing water use, it is the approach used to simulate water consumption in the entire industrial sector. It uses the same categories of economic development as mentioned in Error! Reference source not found., however, in contrast to the domestic sector ((9), the recycling ratio is not multiplied by the fraction of urban population: MWC = MWW (1 RR) (12) where MWC is the manufacturing water consumption (m 3 year -1 ), MWW is the manufacturing water withdrawal (m 3 year -1 ) and RR is the recycling ratio (-). 3.4 Model parameters An overview of the input parameters used to force the developed model is given in Table 6. There are three basic types of parameter formats: (1) a timeline of input data on a national level as a dataset containing countries as rows and years as columns, (2) data on grid cell level as a dataset containing a row for each grid cell accompanied by information on the cell s longitude and latitude in the first two columns in order to allow for timelines to be represented in one dataset, and (3) country information that is a single value per country, such as the development state, as datasets with a column Country containing all countries and a column for the associated value. The model output, i.e. water use simulations on the national level, has the same unit as the reference data submitted for domestic and manufacturing water withdrawal. While the unit of socio-economic parameters is not relevant for the output, all units need to be consistent with regards to currency, volumes and time steps of the different time lines. 13

24 Table 6. Model parameters. Model parameter Description Format Pop_nat Total population per country Dataset covering all countries (columns) and years (rows). First column contains years. Urban_nat Urban population per country as fraction of the total population See Pop_nat GDP Total GDP per country See Pop_nat GVA Total GVA per country See Pop_nat DWWref Records of domestic water withdrawal per country MWWref DevState OECD Regions tref Records of manufacturing water withdrawal per country Development state of each country according to UN classification, coded as: 1 - Developed countries 2 - Economies in transition 3 - Developing economies OECD membership status of each country in 1979, as TRUE or FALSE List of all countries and their respective global regions to be used for regional estimates Global reference year for the calculation See Pop_nat See Pop_nat Dataset of all countries (first column, Country ) and their development state (second column, DevState ) Dataset of all countries (first column, Country ) and their membership state (second column, OECD ) Dataset of all countries (first column, Country ) and their regions (second column, Region ) Numeric value 14

25 4. Model Application 4.1 Input data For the model runs in this study, input data was gathered from several sources outlined in Table 7. Input data that is not required for the regular model as described in Section 4.3, but for the alterations mentioned at the same place, is given in Table 8. Following Wada et al. (2016), the global reference year for the model was set to 2005, as historical records for this year are available for almost all socio-economic indicators and for water use data worldwide. As the availability of national records on socio-economic indicators still varies from country to country, gaps in the national data were filled up with approximations based on regional values taken from The World Bank Development Indicators database. Next to records on national level, this database also contains records for different global regions. The World Bank database fundamentally distinguishes seven global regions: East Asia and Pacific, Europe and Central Asia, Latin America and the Caribbean, Middle East and North America, North America, South Asia, and Sub-Saharan Africa. Similarly to the approach used to approximate the national domestic structural water use intensity from a regional curve in (8, regional values were multiplied by a performance factor that accounts for the economic strength of the country in question relative to the rest of the region. Table 7. Data sources for input data used to run the regular model. Input data (scale) Routine Source National population (country) DWW World Bank Development Indicators 1 Gross Domestic Product (country) Domestic Water Withdrawal (country) National development state (country) DWW, DWC, MWC DWW DWW UN DESA 5 World Bank Development Indicators FAO AQUASTAT 2, EUROSTAT 3, The World s Water Datasets 4 Urban population (country) DWC World Bank Development Indicators Gross Value Added (country) MWW World Bank Development Indicators Manufacturing Water Withdrawal (country) OECD membership state (country) MWW EUROSTAT MWW OECD UN DESA (2014) 6 OECD (2016) Table 8. Data sources for input data used to in model alterations. Input data (scale) Routine Source Gross Domestic Product (country) MWW World Bank Development Indicators Electricity consumption (country) MWW World Bank Development Indicators Industrial Water Withdrawal (country) MWW/IWW FAO AQUASTAT 15

26 As mentioned earlier, the availability of water use records differs greatly from country to country and, even more so, from global region to global region (Gleick, 2003). While AQUASTAT has at least some reference data for the domestic water use from each country worldwide, there is a high diversity in the total number of reference data points per country. After combining several sources to increase the amount of reference data for the domestic sector (see Table 7), most European countries are covered fairly well thanks to the availability of additional data from the Eurostat database. Figure 2 shows an overview of the number of reference data points used to fit the curves of the domestic water withdrawal per country, indicating a very low data availability in South America, sub-saharan Africa and the Middle East. The regions used to estimate a regional domestic structural water use intensity for countries with a low number of reference data points are based on a division of the world into eleven regions as shown in Figure 3, taking into account economic and socio-political similarities. Note that these regions are different from the ones obtained from the World Development Indicators database described with regards to socio-economic input data above. The set of eleven regions used here is preferred over the seven regions used by the World Bank as it allows a more detailed depiction of differences between various regions based on socio-economic indicators. For instance, the World Bank region Europe and Central Asia is now made up of the regions Western Europe, Eastern Europe and Former Soviet Union. The term global regions in the remainder of this report refers to this set of eleven global regions only. The main problem regarding the manufacturing sector and corresponding reference data is the lack of a global database collecting data that goes beyond the level of industrial water use in total. The only supra-national organisation keeping track of manufacturing water withdrawal is Eurostat, providing data on most European countries. National records beyond the European boundaries have to be obtained from individual national agencies, as done by the working group around WaterGAP (Flörke et al., 2013). A data collection of such extent is, however, beyond the scope of this research. Analyses based on reference values of manufacturing water withdrawal are therefore limited on European countries. Figure 2. Number of reference points per country in the domestic water withdrawal input dataset. 16

27 NAM: North America LAM: Latin America WEU: Western Europe EEU: Eastern Europe FSU: Former Soviet Union PAO: OECD countries in the Pacific Ocean MEA: Middle East AFR: Africa CPA: Centrally planned Asia SAS: South Asia PAS: Other Pacific Asia Figure 3. The eleven regions used to fit regional DSWI curves. Taken from Grahn (2006). As mentioned in Sections and on the calculation of water consumption, wastewater or return flow data is difficult to obtain for both the domestic and the manufacturing sector. While a number of wastewater datasets are included in global databases like AQUASTAT and Eurostat, the different datasets are seldom consistent across databases and only distinguish between administrative levels or service providers, but not between water use sectors. It is therefore not possible to derive the sources of different wastewater quantities as is necessary in order to use them as reference values for either the domestic or the manufacturing sector. 4.2 Scale Water withdrawal and consumption are primarily modelled per country, based on the availability of required input data such as GVA and water use records on the national level only. However, in order to reach the same spatial resolution as all three WFaS models, a global grid of 0.5 x 0.5 (Wada et al., 2016), the different simulations are also downscaled to the grid cell level afterwards in order to facilitate further usage. National water use quantities are downscaled to the 0.5 x 0.5 grid level by using the total grid cell population which is analogous to the approach used by WaterGAP (Voß et al., 2009; Voß and Flörke, 2010). For both the domestic 1 and the manufacturing sector, the water use quantities on the national level are multiplied by the fraction of the national population living in the respective grid cell: 1 Note that if downscaling was included in the model itself, the domestic water withdrawal could also be downscaled using the grid cell population instead of the national population in (7. 17

28 WU cell = WU nat POP cell POP nat (13) where WU is the respective water use parameter (m 3 year -1 ) and POP is the population. The subscripts cell and nat indicate values on grid cell level and national level, respectively. Water use in all sectors is simulated in yearly time steps. This decision is based on the availability of most input data on a yearly basis, such as GDP and population. While other models like H08 and PCR-GLOBWB use a higher temporal resolution in order to account for climatic conditions and their influence on irrigation and domestic water use (Hanasaki et al., 2013a; Wada et al., 2011b), the omission of climatic drivers means that a higher resolution than yearly time steps is not necessary. It should be noted, however, that while this research uses yearly time steps, the model itself does not include absolute information on temporal resolution. This information is fully dependent on the input data submitted to the model. It would thus be possible to simulate water use on a higher resolution if the input data is available on the respective scale. 4.3 Model validation The model is tested for the period In order to evaluate the model s performance, results are compared to national observation records of domestic and manufacturing water use obtained from the FAO AQUASTAT database. The model results are also compared to simulations on country level from the PCR-GLOBWB model in the context of the WFaS initiative and to two different WaterGAP simulations. The PCR-GLOBWB simulations originally cover the period and were done based on three different scenarios of shared socioeconomic pathways (SSPs). Model results from this research are therefore compared to the first ten years of each simulation ( ). The simulations were done for domestic and industrial water withdrawal. One WaterGAP simulation, done in the context of the WATCH project with WaterGAP 2.1 (Döll et al., 2001), covers the period , the other one, from the WFaS initiative using WaterGAP 3 (Wada et al., 2016), covers for different SSPs, alongside a baseline simulation based on historical data for the period A dataset to match the simulation period in this research was thus created by combining the different simulations, covering with the WATCH simulation, and with the WFaS simulation. The simulations include results on water withdrawal and consumption on both the domestic and the manufacturing sector. Additional data on the water use in the electricity production sector was available, too. Additionally, the aforementioned downscaling mechanism is evaluated by comparing gridded results for domestic and manufacturing water withdrawal to the corresponding records for the European NUTS2 regions. The NUTS 2 classification subdivides European countries into smaller regions on three hierarchical levels. The NUTS2 regions, the second level, are small enough to depict spatial heterogeneity within a country, but also large enough to allow for a sensible aggregation of grid cells within the region. These regions usually range between 800,000 and 3 million inhabitants (Eurostat, 2015). 2 Nomenclature of Territorial Units for Statistics 18

29 5. Results 5.1 Domestic water withdrawal Global domestic water withdrawal The modelled global domestic water withdrawal shows an increase from approximately 170 km³ to 1030 km³ over the course of the simulation period from 1960 to Figure 4a shows that a great portion of this increase is happening in the new millennium, with global domestic withdrawal in the year 2000 at 310 km³. It also shows a rather good agreement with the WaterGAP simulations and the global records taken from Shiklomanov (2000), although there is a steep increase in withdrawal in the new millennium which is not visible in the WaterGAP results or the observation data. A closer look at the domestic water withdrawal simulation for China (Figure 4b) shows a similar pattern to the global withdrawal, with a steep increase towards the end of the simulation period from 38 km³ in 2000 to almost 700 km³ in Neither WaterGAP nor PCR-GLOBWB simulate a comparable increase. With a national withdrawal that accounts for roughly two thirds of the global domestic water withdrawal in 2014, China s influence on the simulated global sum is evident (Figure 4c). A similar pattern of a steep increase in the 21 st century can also be found a) global: b) China: c) Figure 4. Water withdrawal in the domestic sector as simulated for the whole world (a) and China (b) by the developed model (black line), WaterGAP (steel blue line) and PCR-GLOBWB (orange line, three scenarios). Records (until 1995) and predictions (2000 onwards) from Shiklomanov (2000) and Chinese national records are given as reference values (blue crosses). Comparison of global and Chinese domestic water withdrawal in panel c). 19

30 for Bolivia, Nigeria and the United Arab Emirates, although of smaller magnitude. In all four cases, the increase is caused by an equally steep increase of GDP (Figure 5 for China). Figure 6 shows the distribution of the global domestic water withdrawal over the different countries and on the 0.5 x 0.5 grid for the beginning and the end of the simulation period. Note that the year 2010 is used to illustrate the water use at the end of the simulation period, as the downscaling routine is limited to the period due to the lack of more recent population data on grid cell level. It shows a rather constant domestic water withdrawal for many countries in Africa and North America, a decrease in Europe and a relatively pronounced increase in South America and Southeast Asia, dominated by China. A central step of the simulation of domestic water withdrawal is the fitting of the sigmoid curve to the reference data in step 1 of the domestic water withdrawal simulation. The goodness of the fit is evaluated with the index of agreement d calculated by equations 2-4 as suggested by Flörke et al. (2013). The index of agreement can take any value between 0 and 1 where 1 is a perfect agreement between the curve and the reference data from domestic water use records. Overall, there are 8 countries where no index of agreement could be calculated as there was only one reference data point, 4 countries with d < 0.3, 118 countries with d between 0.3 and Figure 5. Chinese GDP over the simulation period in billion US$. a) 1960: 2010: b) 1960: 2010: Figure 6. Domestic water withdrawal on country level (a) and on grid cell level (b). Shown are values for the beginning of the simulation period (1960) and the end (2010). 20

31 0.5, and 48 countries above 0.5, which Flörke et al. (2013) describe as good agreement. 34 out of these 48 countries even score above 0.8. All values are country-based, although some of the countries will later use regional curves due to the lack of reference data. Figure 7 shows the index of agreement calculated during the model run for each country (Figure 7a) and region (Figure 7b). On the regional level, the index of agreement ranged between 0.21 for Western Europe 3 and 0.86 for Africa. An overview of the different indices of agreement per region is given in Table A1 in the appendix. For the three regional curves used most often due to the lack of reference data in the respective countries 4, the index of agreement is 0.78 (Latin America), 0.86 (Africa) and 0.84 (Middle East and North Africa). The overall goodness of the simulated domestic water withdrawal differs across the globe. Figure 8 shows the difference between the simulated water withdrawal and the recorded water withdrawal from the domestic sector on the country-level for the years 2000 and The years were selected as the FAO AQUASTAT database has the most records for these two. The figure indicates that the domestic water withdrawal in the early 21 st century is understated rather than overstated for most countries included in the figures. It also shows a heterogeneous a) b) Figure 7. Index of agreement calculated for the fitting of a sigmoid curve for each country (a) and global region (b) during the domestic water withdrawal routine. 2000: 2005: Figure 8. Difference between simulated and recorded domestic water withdrawal on country-level for 2000 and Values in percent of recorded value. Negative values indicate simulated values below the observed records, positive values indicate simulated values above the observed record. 3 Note that the Western European value is highly influenced by outlying reference values from Iceland. A calculation of the index of agreement that omits Iceland results in d = 0.43, which is comparable to the other global regions with a rather low index of agreement. 4 More than 75 % of the countries with less than three reference data points fall within the regions Latin America, Africa and Middle East and North Africa. 21

32 bias, i.e. both simulations higher and lower than the records on country level, within most of the different global regions. The modified approach for the curve fitting routine (Section 3.3.1) regarding the use of reference data on country or regional level helped to improve the model s overall accuracy. Based on the difference between the sum of squares (SoS) on country-level for the original approach and for the altered approach, the simulation accuracy was improved for 35 countries and decreased for 16 countries. The change did not affect the 127 countries that fall into class 1 in Section Regional domestic water withdrawal The performance of the downscaling routine based on the total population as described in Section 4.2 was tested for Europe, using the NUTS2 regional classification of sub-national regions. Domestic water withdrawal records for the NUTS2 regions are available from the Eurostat database for the period As the availability of data for each year varies from country to country and there are no records for most regions prior to 2004, the subsequent analysis is limited to the period and includes ten countries 5. In order to analyse the goodness of the routine s distribution of the national domestic water withdrawal over the grid cells within each country, the focus lies on the comparison of the spatial pattern produced by the downscaling routine with the spatial pattern visible in the Eurostat water use records from the different regions within each country. An example for France is given in Figure 9. Instead of comparing the absolute withdrawn quantities in each region, attention is therefore paid to the spatial correlation between modelled (and downscaled) water withdrawal and recorded water withdrawal over all regions per country and year. A high spatial correlation (r) means that subnational regions with a higher real water use than other regions were also allocated a higher water use in the downscaling and is thus an indicator for a good downscaling routine and the use of a suitable driver within the routine. Six out of the ten countries 6 showed a strong correlation with r > 0.9 for all years, and two additional countries 7 with r > 0.7. For two countries 8 no correlation between modelled and Model results: Eurostat: Figure 9. Domestic water withdrawal per NUTS2 region in France for the year 2008, based on downscaled model results (left) and Eurostat records (right). 5 Austria, Bulgaria, Czech Republic, Germany, France, Hungary, Poland, Portugal, Turkey, Sweden 6 Austria, Bulgaria, France, Hungary, Poland, Sweden 7 Germany, Turkey 8 Czech Republic, Portugal 22

33 recorded regional withdrawal was found (r < 0.3). A complete table of the correlation coefficients for each country and year is added in the appendix (Table A2). This makes the total population an overall suitable driver of the downscaling routine in the domestic sector. 5.2 Domestic water consumption There are unfortunately no consistent records of domestic water consumption or return flow on a global scale, only numerous different databases containing various measures of wastewater on wastewater on different administrative levels. While most of them distinguish quantities handled by different service providers such as municipal or private water authorities, there is no clear distinction between water use sectors. It is therefore only possible to compare model results to other simulations and to Shiklomanov s (2000) records on the global scale, but not to assess the performance of either model on the country level. The modelled global domestic water consumption follows roughly the pattern of the domestic water withdrawal, staying almost constant in the 20 th century before sharply increasing from around 190 km³ in 2000 to 670 km³ in 2014 (Figure 10). Compared to the WaterGAP simulation results which closely align with the global records from Shiklomanov (2000), the model developed in this research systematically overstates the domestic consumption by more than 100 km³ on the global level. Possible reasons for this result such as the high sensitivity for the empirical recycling ratio values used by the approach are discussed in Section 6.2. A similar pattern emerges in most cases on the country level, where the WaterGAP simulation is usually a lot lower. Some examples are given in Figure 11. Note that abrupt changes in the national water consumption that do not mirror a similar change in water withdrawal (e.g. for Benin and Japan) originate from the country transitioning from one recycling ratio class to another one, based on a change in GDP. This transition mainly happens in countries that go through economic development over the course of the simulation period. The distribution of the domestic water consumption over the globe at the beginning and the end of the simulation period (Figure 12), shows a very similar pattern as found for domestic water withdrawal (Figure 6). This is due to the rather simple modelling approach used to calculate domestic consumption where for most countries the water withdrawal is multiplied by Figure 10. Global water consumption in the domestic sector as simulated by the developed model (black line) and WaterGAP (steel blue line). Records (until 1995) and predictions (2000 onwards) from Shiklomanov (2000) are given as reference values (blue crosses). 23

34 a recycling factor that hardly changes over the course of the simulation period for many countries. Benin: Japan: Macedonia: Figure 11. Simulations of domestic water consumption (solid lines) on country-level for Benin, Japan and Macedonia. The developed model s simulation in black, WaterGAP in steel blue. Modelled water withdrawal (dashed line) given for reference. a) 1960: 2010: b) 1960: 2010: Figure 12. Domestic water consumption on country level (a) and on grid cell level (b). Shown are values for the beginning of the simulation period (1960) and the end (2010). 24

35 5.3 Manufacturing water withdrawal European manufacturing water withdrawal As mentioned in Section 4.1, the availability of water use records for the manufacturing sector is limited. This analysis is therefore restricted to 27 European countries 9 for which reference data could be obtained. Section introduced the goal to test the manufacturing water use routine for three different drivers: GVA, GDP and electricity consumption. As the time periods covered by records for each driver differ, their performances are compared by the use of the mean sum of squares (MSoS) in order to avoid a bias in the results. Five countries were excluded from the analysis as there was only one reference data point available that was used as reference year by the routine, leading to the sum of squares being 0 for that particular year (Finland, Italy, Luxembourg, Switzerland, United Kingdom). Four additional countries had records on GVA and GDP available, but not on electricity consumption (Estonia, France, Malta, Spain). Of the remaining 18 countries, using electricity consumption as driver performed best in nine countries, GDP in five and GVA in four. The four countries without electricity consumption data were split with GDP and GVA each performing best in two of them. The choice of the most suitable driver to use for the remainder of this section depends on two factors: accuracy and availability (Table 9). Accuracy in this context is understood as the simulation goodness for each driver. While the electricity consumption as driver of manufacturing water use outperforms the other two drivers with regards to the number of countries where it reached the best fit, GDP shows the lowest root mean square error (RSME) per country. Availability refers to both the spatial availability of records in as many countries as possible and the temporal availability of records covering as many years as possible. In both regards, GDP is the most widely available driver. Combining these two factors, GDP is thus used as driver subsequently. This decision also decreases the amount of input data required to run the model, as GDP is already needed in other routines such as the domestic water withdrawal, while GVA and electricity consumption would solely be used in this context. The overall manufacturing water withdrawal simulated for the 27 European countries is steadily rising over the course of the whole simulation period from below 1 km³ in 1960 to 21 km³ in Table 9. Availability of socio-economic drivers for the simulation of the manufacturing water withdrawal in Europe. GVA GDP Electricity consumption Availability (of 27 countries) 27 countries 27 countries 23 countries Average length of record (of 55 years) Number of countries with best fit (of 22 countries) 30.6 years 54.7 years 42.5 years 6 countries 7 countries 9 countries Average RMSE per country km³ km³ km³ 9 Belgium, Bulgaria, Croatia, Cyprus, Czech Republic, Estonia, Finland, France, Germany, Hungary, Iceland, Italy, Latvia, Lithuania, Luxembourg, Malta, Netherlands, Norway, Poland, Portugal, Slovenia, Spain, Sweden, Switzerland, TFYR Macedonia, Turkey, United Kingdom 25

36 2014 (Figure 13). Compared to the simulations for the same European countries done with WaterGAP, the modelled manufacturing withdrawal is, however, a lot lower than in the WaterGAP results. Instead of a steady increase over time, WaterGAP simulates a rather strong increase up till the 1980s, with a slow decrease in water withdrawal afterwards. It is worth noting here that the simulation done with GDP as the driver for the manufacturing sector resulted in the highest water withdrawal (Figure A5). Where WaterGAP based its simulations on manufacturing water withdrawal reference data from individual national agencies, this research uses the records from the European database Eurostat. These Eurostat records are also used as reference values to assess the goodness of each model s simulation and Figure 13. European water withdrawal in the manufacturing sector as simulated by the developed model (black line) and WaterGAP (steel blue line). Belgium: Latvia: Poland: Figure 14. Simulations of manufacturing water withdrawal on country-level for Belgium, Latvia and Poland. The developed model s simulation in black, WaterGAP in steel blue. 26

37 compare the performances on a country level. The comparison leaves out the same five countries with only one reference data point as mentioned above. Once again using the mean sum of squares (MSoS) to account for difference in the time period covered by each model per country, this research s results perform better in 16 out of the remaining 22 countries, the WaterGAP results perform better in six countries. For most countries, there are distinct differences between the two models mirroring the results on the European level where WaterGAP simulates a higher water withdrawal, especially up till Figure 14 shows some example countries where this effect can be seen in different ways. Spatial patterns with the shows the distribution of the manufacturing water withdrawal over Europe at the beginning (1960) and the end (2010) of the simulation period are presented on country (Figure 15a) and grid-based level (Figure 15b). As the downscaling routine is based on total population, the grid cells in metropolitan areas (e.g. Paris, Madrid, Ankara and Oslo) are allocated a greater portion of the national manufacturing water withdrawal. Figure 16 additionally shows how this research s manufacturing water withdrawal simulation performs relative to the Eurostat records in the years 2001 and The two years were chosen based on the availability of reference data in as many countries as possible. While the modelled values for majority of countries included for 2001 are more than 25 % below the manufacturing withdrawal record, 2010 shows both positive and negative biases in simulated manufacturing water withdrawal Regional manufacturing water withdrawal The goodness of the downscaling routine for the manufacturing sector with the total population as driver is assessed in the same way as described for the domestic sector in Section on the basis of the NUTS2 regions. The analysis included six countries 10 for which sub-national manufacturing water withdrawal records are available from Eurostat. a) 1960: 2010: b) 1960: 2010: Figure 15. Manufacturing water withdrawal for Europe on country level (a) and on grid cell level (b). Shown are values for the beginning of the simulation period (1960) and the end (2010). 10 Bulgaria, Czech Republic, Germany, Poland, Sweden, Turkey 27

38 2001: 2010: Figure 16. Difference between simulated and recorded manufacturing water withdrawal on country-level for 2001 and 2010 in Europe. Values in percent of recorded value. Negative values indicate simulated values below the observed records, positive values indicate simulated values above the observed record. The complete table with spatial correlation coefficients per country and year is included in the appendix (Table A3). The highest spatial correlation found between the regional records and the downscaled model results is for Bulgaria in the year 2009, most other values are below 0.2 or even negative. Possible reasons for and implications of this poor performance as well as other possible drivers for downscaling in the manufacturing sector are discussed further below in Section Global industrial water withdrawal In addition to the simulation of European manufacturing water withdrawal, the same model routine was also used in an attempt to simulate global industrial water withdrawal based on records of industrial water use available from the AQUASTAT database. As the industrial sector is a combination of the manufacturing sector and the thermal electricity consumption sector, it can be expected that the annual electricity consumption is a suitable driver for the simulation of industrial water withdrawal. An analysis similar to the one conducted for the manufacturing sector indeed showed it indeed reached the lowest mean sum of squares for 67 out of 159 countries that were included in the analysis. GVA and GDP reached the best results in 47 and 45 countries, respectively. Both GDP and electricity consumption are used as drivers for the industrial water withdrawal in the subsequent section. GVA is left out of the analysis notwithstanding its slightly higher accuracy than GDP as records are usually only available for a significantly shorter time period (see also Table 9 for Europe). An additional simulation based on both GDP and electricity consumption as drivers was tested, but did not result in an improvement of the simulation accuracy. Figure 17 shows the industrial water withdrawal on a global level, highlighting the difference between the GDP-driven and the electricity-driven simulation in the beginning 21 st century. It also indicates that the electricity-driven simulation follows model results from WaterGAP 11 and PCR-GLOBWB as well as the records taken from Shiklomanov (2000) more closely. It should be noted, however, that a number of countries (e.g. Russia) are not included in the electricitydriven simulation as there was no input data on electricity consumption available. A 11 WaterGAP distinguishes between the manufacturing sector and the electricity consumption sector. Modelled quantities from both sectors were added up in order to obtain values for the industrial sector. 28

39 Figure 17. Global water withdrawal in the industrial sector as simulated by the developed model with GDP and electricity consumption as driver (black line, solid and dashed respectively), WaterGAP (steel blue line) and the three scenarios with PCR-GLOBWB (orange line). Records (until 1995) and predictions (2000 onwards) from Shiklomanov (2000) are given as reference values (blue crosses). a) 1971: 2010: b) 1971: 2010: Figure 18. Industrial water withdrawal on country level for the GDP-driven simulation (a) and the electricitydriven simulation (b). Shown are values early into the simulation period (1971) and at the end (2010). comparable timeline of global withdrawal based on the simulation with both GDP and electricity consumption as drivers was not possible as no national values could be simulated for 40 countries due to the lack of reference data points 12. The steeper increase in industrial water withdrawal simulated by the GDP-driven routine is also visible on the country level. Figure 18 gives an overview of the development of the industrial withdrawal in both simulations from 1971 till Note that 1971 was chosen as earlier year instead of 1960 as there are only very few countries with records of electricity consumption for In addition, Figure 19 shows the difference between the two simulations and national records of industrial water withdrawal in the year 2000, chosen for the highest number of reference 12 Note that data for both drivers and industrial water withdrawal records need to be available for the same year in order to calculate a reference value. 29

40 a) b) Figure 19. Difference between simulated and recorded industrial water withdrawal on country-level for 2000 in the GDP-driven simulation (a) and the electricity-driven simulation (b). Values in percent of recorded value. Negative values indicate simulated values below the observed records, positive values indicate simulated values above the observed record. Figure 20. European water consumption in the manufacturing sector as simulated by the developed model (black line) and WaterGAP (steel blue line). values available. It indicates that for most countries, both simulations are either similar in their error or the electricity-driven simulation performs better. 5.4 Manufacturing water consumption Replicating the manufacturing water withdrawal results discussed above, the manufacturing consumption simulated in this research for the same 27 European countries lies far below the consumption simulated by WaterGAP, particularly for the 20 th century (Figure 20). The sharp decrease in the WaterGAP simulation from 2000 to 2001 coincides with the switch from the simulation based on WaterGAP 2.1 to the simulation based on WaterGAP 3. As for the aggregated European water consumption, the manufacturing consumption stays relatively constant over the 20 th century for most countries, and only increases slightly with the onset of the 21 st century alongside a stronger increase in the manufacturing water withdrawal. The distribution over Europe, especially on the grid level (Figure 21b), shows great similarity to the manufacturing water withdrawal as well (Figure 15), highlighting the same metropolitan areas as hotspots with high water consumption. 30

41 a) 1960: 2010: b) 1960: 2010: Figure 21. Manufacturing water consumption for Europe on country level (a) and on grid cell level (b). Shown are values for the beginning of the simulation period (1960) and the end (2010). 31

42 6. Discussion 6.1 Availability of input data The quality of the water use simulations presented in this report strongly depend on the availability and quality of suitable input data. This is an issue very common to models in general (Hughes et al., 2010) and particularly pronounced for those operating on a global scale where the availability of data on different indicators varies greatly between countries and global regions (Wada et al., 2016). Data on water use in the manufacturing sector to use as reference data in this study was particularly hard to obtain. Additionally, the low availability of suitable wastewater or return flow data from any water use sector led to the use of the rather simple, empirical recycling ratio approach in the place of a more sophisticated approach. While organisations like the World Bank maintain global databases of socio-economic parameters, coherent reference data of water use on national or global level in general is harder to find. Gleick (2003) distinguishes four overall problems relating to the usage and analysis of water use records: (1) data is rarely collected in a systematic way that facilitates comparison of water use between countries, (2) some water uses are hard to record accurately, such as the water demand for hydropower production, and there is generally more information on water withdrawal than water consumption, (3) there is high regional variability in the quantity and quality of records, and (4) inaccurate records are still rather common. Additionally, there are no consistent working definitions of water uses and sectors worldwide, making it more difficult to merge information and data sets from different sources (Vassolo and Döll, 2005). For the manufacturing sector, these problems are particularly pronounced, as distinctions between overall industrial use and specific manufacturing use are not always done (Wada et al., 2016). The issues particularly linked to sparse reference data for the manufacturing sector encountered in this research are discussed further in Section Uncertainties of modelled water use General uncertainties The global water use model developed in this study simulates human water use only, independently of water availability. It thereby omits feedback processes that originate from a certain limit to the available water resources, such as an increase in water efficiency instead of in water demand as soon as the development of new resources becomes too expensive. It is therefore likely to overestimate the human water use in general (Kim et al., 2016). Other models solve this problem by coupling the water use component with a global hydrology model (GHM) as is for instance done for the three WFaS models (Hanasaki et al., 2008; Döll et al., 2012; Wada et al., 2014). Even in the combination with a GHM, however, several sources of uncertainty remain. A very influential factor on the water withdrawal in both the domestic and the manufacturing sector is the technological change (Kim et al., 2016). Based on the approach included in WaterGAP (Flörke et al., 2013), technological advancements are accounted for according to a linear change rate. The implications of this linear rate became particularly visible for domestic sector in developed countries. As the domestic water use intensity and the population stay 32

43 rather constant for most developed countries over the course of the simulation period, the technological change is the main driver of increase or decrease in these countries. While the final model only includes technological change in the domestic sector from 1980 onwards, in an earlier stage of model development the domestic water withdrawal routine was tested for an approach that accounts for technological change over the whole simulation period, also based on 2005 as reference year. This approach led to a clearly visible pattern for developed countries that were simulated with a technological change rate of 2% per year: the water withdrawal in the first few decades of the simulation period was significantly higher compared to the final version of the model, the WaterGAP simulations and to national records where available. Figure 22 shows this effect against the normal model results for France and the United Kingdom as examples. An overestimation of the domestic water withdrawal in the relatively distant past is avoided by assuming that technological advancements only matter from 1980 onwards (Flörke et al., 2013). However, when attempting to simulate water use for future time periods, a similar effect as depicted in Figure 22 is very likely to occur: instead of overestimating the past domestic water use based on the linear change rate, water use will be underestimated towards the more distant future, e.g. towards the end of the 21 st century (Hejazi et al., 2013). In order avoid this source of simulation errors, Parkinson et al. (2016) recommend the technology frontier approach (p. 11). Instead of using a linear change rate, they calculate the technological change per year and country as a function of GDP based on a sigmoid curve where eventually, a state of saturation is reached in which the most efficient technologies are already implemented and a further increase in efficiency is unlikely. PCR-GLOBWB follows a different approach, using the ratio of energy consumption to energy production as a proxy for technological change in order to account for industrial and technical advancement in both the domestic and the industrial sector. The technological change for a certain year is then reached by comparing this ratio for the year at hand with the ratio on the model s reference year (Wada et al., 2016). As this approach requires additional input data, it was not used in this research. It can, however, be another starting point when updating the linear technological change rate currently used. France: United Kingdom: Figure 22. Simulations of domestic water withdrawal on country-level for France and the United Kingdom. The developed model s simulation in black, an earlier model version with technological change being calculated previously to 1980 in grey. Reference values from water use records as blue crosses. 33

44 Next to the choice of change rate, the reference year used in the model also has a great impact on the simulated water withdrawal (Wada et al., 2016). Model runs with various reference years showed that an earlier reference year leads to a lower domestic withdrawal overall. While the difference for developed countries is the greatest, the same pattern is visible for all three development classes (Figure 23). A similar pattern is visible in the manufacturing sector. This study used 2005 as reference year in order to facilitate the comparison with other models as it was also used by the WaterGAP and PCR-GLOBWB simulations used for model validation (Wada et al., 2016). With regards to water consumption, the difference in modelling approaches between the developed model and WaterGAP became visible in great difference in simulation results where, for the global consumption in the domestic sector, WaterGAP was the more accurate one. Due to the lack of coherent, clear consumption records to use for model validation, it was not possible to assess the performance of both the developed model and WaterGAP relative to historical records on the country level in either water use sector. However, the possibility of WaterGAP being more accurate on national scale for domestic water consumption, especially for those countries exhibiting a similar pattern to the global consumption, has to be considered. The recycling ratio approach is an empirical approach solely based on the socio-economic development of Japan, with the recycling ratio ranging from 0.4 for the economically weakest countries to 0.8 for the strongest (Wada et al., 2011b). An analysis of the recycling ratio for all countries based on the WaterGAP simulation, however, resulted in most values, irrespective of GDP, ranging between 0.8 and 0.9 for the domestic sector, translating into a consumption Australia: Kazakhstan: Mozambique: Figure 23. Simulations of domestic water withdrawal for different model reference years for Australia (developed country), Kazakhstan (country in transition) and Mozambique (developing country). Model results with reference year 2005 as black solid line, simulations with reference years 1980, 1990 and 2000 in grey and solid, dashed and dotted, respectively. National records given for reference (blue crosses). 34

45 of only 10-20% of the domestic water withdrawal. Similar values are mentioned by (Hejazi et al., 2013). For the manufacturing sector, recycling ratios based on the WaterGAP simulations clustered around 0.6 and 0.8, with a high frequency of values around 0.6 compared to 0.8 for low GDP countries (< 755 US$ capita -1 ) and a somewhat higher frequency of values around 0.8 for high GDP countries (> 9265 US$ capita -1 ) 13. It should thus be considered to update the recycling ratio approach with new numbers based on a broader range of countries and global regions. For the domestic sector, an additional factor that lowered the recycling ratio, particularly in developing countries, is the assumption that recycling only takes place in urban areas, implemented by multiplying the national recycling ratio by the fraction of urban population (Wada et al., 2011b). This means that a country like Ethiopia with a GDP of below 600 US$ cap -1 and an urban population that makes up approximately 19% of the total population (World Bank, 2016) is assigned a recycling ratio as low as (instead of 0.4), which translates into the consumption of 92.4% of the domestic water withdrawal. Although rural areas, especially in developing countries, are more likely to lack adequate water infrastructure to allow wastewater treatment or to channel the return flow, numbers in this range are likely to be overstated (Fan et al., 2014). While it is certainly appropriate to make a distinction between the recycling ratio in the domestic sector and in the manufacturing sector, other methods should be tested, including the empirical determination of separate values for both sectors based on historical records. Flörke et al. (2013) also critically assess WaterGAP s consumption simulations, given that wastewater and return flow input data is sparse in developing countries and numbers are therefore mainly based on developed countries which have a lower consumption coefficient (consumption per withdrawal). In combination, the simulations by the model developed in this research and by WaterGAP can thus be seen as likely boundaries to water consumption estimates, with the real value lying in between. Both models also give starting points to the development of more accurate modelling approaches in the future, especially in the light of new and more coherent input data becoming available (Clark et al., 2001). In addition to these general issues, a number of uncertainties are directly linked to either the domestic or the manufacturing sector. These are discussed below Uncertainties related to the domestic sector The simulated global water withdrawal in the domestic sector was highly influenced by the steep increase in domestic withdrawal in China and some smaller countries in the early 21 st century. This increase in the simulation is due to a similarly steep increase in GDP in these countries. While this means that such an increase is based on the socio-economic drivers rather than uncertainties related to the model structure, there are other aspects having an influence on the domestic water withdrawal under real world conditions, such as cultural preferences (Parkinson et al., 2016) and water pricing in connection with cost-effective possibilities to develop new water resources (Hejazi et al., 2013). These aspects were not included in the model as they are difficult to simulate on a global scale due to the lack of a 13 An overview of all recycling rations based on the WaterGAP simulations is given in Figure A6 in the appendix. 35

46 comprehensive research on how these processes function and interact with each other in different parts of the world. Parkinson et al. (2016) approximate cultural preferences and existing water policies such as pricing by introducing path-dependency restricts the simulation to historically observed water use intensities and smooths out sudden steep increases by converging the simulation results towards the historical demand curve. Based on this approach, a simpler solution might be to introduce limits to the change in water withdrawal based on historical data per country, either as total or relative amount Uncertainties related to the manufacturing sector Results and conclusions for the manufacturing sector are limited to Europe due to the lack of manufacturing water withdrawal records from other countries. Additional issues might occur for countries in another global region, especially for developing countries and countries in transition (Flörke et al., 2013). The manufacturing withdrawal model routine is designed in a way that bases the whole simulation on one reference value (Voß and Flörke, 2010). The simulation results are therefore highly dependent on the accuracy and overall quality of input data. This dependence becomes visible in the comparison of the manufacturing withdrawal simulations in this research and by WaterGAP. While both are using the same approach by Voß and Flörke (2010), the simulated water withdrawal for Europe differs vastly, with WaterGAP simulating a higher water withdrawal. This research s simulation was based on input data from the Eurostat database, while Flörke et al. (2013) collected their reference data from national statistical agencies. The latter can be assumed to be more accurate, but was not possible within the scope of this research. The comparison of results of WaterGAP and this research thus highlights the potential for uncertainty in the simulation of manufacturing water use based on different sources of input data. This also affects the reliability of the simulation comparison between this study s model and WaterGAP, where this study s simulation reached a better fit in 72% of the countries included in the study. However, this result was reached by comparing the model simulations to the reference data obtained from Eurostat, but not to the data used by Flörke et al. (2013) as reference data for the WaterGAP simulation. As an alternative, the manufacturing water withdrawal was also calculated based on the routine used for the domestic sector, thereby taking more than one reference data point into account. The results of this modified simulation for different countries varied greatly, with only results for Hungary, Malta and Slovenia getting significantly closer to the WaterGAP simulation, while the fit for many other countries got significantly worse. A routine based on the fitting of a sigmoid curve on manufacturing water withdrawal reference data might nevertheless be an option to reach a better fit to all reference values. However, with the current lack of reference values for the manufacturing sector in most countries worldwide, this approach is not suitable for the global scale at this point. The approach for the manufacturing sector did, however, result in adequate global simulations of industrial water withdrawal when forced with the corresponding input data and with the national electricity production as driver. This result can be a starting point for a model routine calculating the industrial water use worldwide while needing a minimum of input data, namely 36

47 one industrial withdrawal reference value and records (or a scenario in case of future projections) of the electricity production. 6.3 Uncertainties introduced in downscaling National simulations of water use were downscaled using the total grid cell population for domestic and manufacturing sector. This approach assumes that the national values are applicable to all grid cells within the country in the same way. It also omits other potentially important factors like income distribution and differences between predominantly urban and predominantly rural areas (Parkinson et al., 2016). Urban population was planned to be used as a driver of the downscaling routine to account for potential differences between urban and rural areas, however, no appropriate grid of urban population was available to work with. Nevertheless, total population was successfully used to downscale domestic water use within European countries. It should be noted that these results are limited to the European countries for which sub-national records of water use were available, and that the same downscaling routine applied to other countries or global regions might result in different findings. Downscaling based on total population in the manufacturing sector, on the other hand, did not show much promise, although the approach is also used by other researchers (Hanasaki et al., 2013a; Wada et al., 2016). Hejazi et al. (2014) note that more accurate downscaling techniques and drivers for the industrial sector are generally needed. Parkinson et al. (2016) recommend using gridded GDP as driver for downscaling in the industrial sector overall, however, a test for the spatial correlation between GDP, both total and per capita, and manufacturing water withdrawal in the European sub-national regions did not improve the results compared to the total population. One reason for the unsuitability of GDP as downscaling driver for the manufacturing sector most likely lies in the rather low portion of the GDP that originates from the manufacturing sector. For many countries worldwide, the fraction of manufacturing added value in the GDP is below 20%, with the remaining 80% originating for instance from the service sector and agriculture (ITIF, 2012). Some earlier researches used more distinguished drivers for the downscaling of national water use in different sectors, such as nighttime light pollution and the extent of urban areas (Hanasaki et al., 2013a). While these approaches might present an opportunity to improve the downscaling performance, they are also hard to obtain on a global scale. Hanasaki et al. (2013a) particularly point out the difficulties related to global grids of the respective values linked to future scenarios. Additional uncertainty is introduced by the static nature of the population grid used for downscaling. The grid does not account for migration from one grid cell to the other, and thus also does not allow for the population to spread in previously unpopulated areas (Hejazi et al., 2014). 37

48 7. Conclusions and Recommendations The research presented in this thesis report aimed to develop a new global water use model suited to simulate water withdrawal and water consumption in the domestic and the manufacturing sector while requiring a minimum of input data. It also sought to find an appropriate balance between model, and thereby input data, simplicity and simulation accuracy. Over the course of the research, existing global water use models were reviewed, with a certain focus on the models used in the WFaS initiative H08, PCR-GLOBWB and WaterGAP and their different modelling routines compared with regards to simulation approaches and data requirement. Based on these insights, a new model was developed and tested for the simulation period 1960 till Validation of the model results in order to assess the model s performance was based on historical water use records and simulations by other models. Based on these insights, the three main research questions defined in the beginning of the study are successively addressed below. 1. Which approaches are useful to model domestic water use and manufacturing water use on a global scale? A closer analysis of different approaches to model domestic and manufacturing water use on a global scale based on the WFaS models showed that routines are more or less elaborate depending on a model s focus. As WaterGAP is the only model within the WFaS initiative that takes manufacturing water use as a separate sector into account (Wada et al., 2016), the modelling approaches applied to simulate the manufacturing water use in WaterGAP were an important starting point for the newly developed model. For domestic water use, WaterGAP s modelling routine was also chosen as the most suitable approach. While the simulation of domestic water use in PCR-GLOBWB and H08 is rather simplistic, even more so in the latter as its focus lies on irrigation water use (Hanasaki et al., 2008), the WaterGAP approach is quite sophisticated in using a relatively small amount of data to account for technological and structural development in each individual country. 2. Which is the most suitable way to make a distinction between water withdrawal and water consumption for the domestic and manufacturing sectors in a global model with regards to minimising the required input data? The main issue on to the distinction between water withdrawal and water consumption is whether or not a model requires consumption or return flow reference data in order to simulate a timeline of water consumption. The recycling ratio approach by Wada et al. (2011b) used in this study is based on empirical numbers in order to estimate what portion of the water withdrawal is consumed. It thus requires less input data, which presents the approach with advantage over other routines especially when considering that wastewater or return flow data only exists sporadically and differs in its form and definition of sectorial return flow from country to country and agency to agency. At the same time, however, such an empirical approach needs to be based on sufficient reference data in order to make sure that the observed relations hold true on a global scale. 3. How can individual modelling routines used in global water use modelling be simplified without losing simulation accuracy? In an attempt to reduce the amount of required input data, it was shown that the comparison of different drivers in both their accuracy and their availability can lead to balancing simplicity and simulation accuracy. In the case of manufacturing water use, the decision was made to 38

49 use GDP as main driver over the slightly more accurate electricity production, as the former is more readily available on the global scale and for longer time series. In areas where national input data is generally sparse, e.g. developing countries (Gleick, 2003), it is useful to fall back to regional data instead in order to avoid a loss of accuracy. Model routines using regional averages should then be drafted in a way that accounts for heterogeneous conditions within a region by using historical data on country level as scaling factor where available. Overall, this research also found that, at the current point of time, the development of a global model to simulate water withdrawal and consumption in the manufacturing sector is a rather difficult endeavour as there is a lack of homogeneous input or reference data on a global scale, which hinders model validation. More coherent data collection is therefore needed to improve the quality of global water use modelling in this sector. Additional research is also needed with regards to empirical approaches to the simulation of water consumption. The recycling ratio approach by Wada et al. (2011b) is a starting point to reduce the model s dependency on suitable wastewater reference data. It requires, however, an update using a broader set of reference data points from all over the world in order to allow for accurate global simulation. While this study simulated water use over the past 70 years in order to test the developed model, it is ultimately the goal to use different socio-economic scenarios (e.g. the SSPs) to simulate future water use on the global scale. A number of other water use models are already used to make projections of future water demand and availability in different water use sectors, for instance within the WFaS initiative. The combination of these previous studies with the refined work presented in this report and future research will contribute to an improved understanding of human water uses. It will also allow water managers worldwide to gain a better insight into potential hotspots of water scarcity over the next few decades and to make informed water policy decisions. 39

50 8. References Alcamo, J., Döll, P., Henrichs, T., Kaspar, F., Lehner, B., Rösch, T. and Siebert, S. (2003) Development and testing of the WaterGAP 2 global model of water use and availability, Hydrological Science Journal, vol. 48, no. 3, pp Clark, J. S., Carpenter, S. R., Barber, M., Collins, S., Dobson, A., Foley, J. A., Lodge, D. M., Pascual, M., Pielke, R., Pizer, W., Pringle, C., Reid, W. V., Rose, K. A., Sala, O., Schlesinger, W. H., Wall, D. H. and Wear, D. (2001) Ecological Forecasts: An Emerging Imperative, Science, vol. 293, no. 5530, pp Cosgrove, W., Fischer, G., Flörke, M., Hizsniyk, E., Magnuszewski, P., Pahl-Wostl, C., Scherzer, A., Segrave, A., Toth, G., Tramberend, S., van Vliet, M., Wiberg, D., Yillia, P. and Zeller, D. (2015) Towards Innovative Solutions through Integrative Futures Analysis - Preliminary qualitative scenarios [Online]. Available at (Accessed 21 September 2016). Döll, P., Alcamo, J., Henrichs, T., Kaspar, F., Lehner, B., Rösch, T. and Siebert, S. (2001) The global integrated water model WaterGAP 2.1, Center for Environmental Systems Reseach, University of Kassel, Kassel World Water Series 1. Döll, P., Hoffmann-Dobrev, H., Portmann, F. T., Siebert, S., Eicker, A., Rodell, M., Strassberg, G. and Scanlon, B. R. (2012) Impact of water withdrawals from groundwater and surface water on continental water storage variations, Journal of Geodynamics, 59-60, pp Eurostat (2015) Regions in the European Union: Nomenclature of territorial units for statistics NUTS 2013/EU-28, European Union. Fan, L., Liu, G., Wang, F., Ritsema, C. J. and Geissen, V. (2014) Domestic Water Consumption under Intermittent and Continuous Modes of Water Supply, Water Resources Management, vol. 28, no. 3, pp Flörke, M. and Alcamo, J. (2004) European Outlook on Water Use, Center for Environmental Systems Research, University of Kassel. Flörke, M., Kynast, E., Bärlund, I., Eisner, S., Wimmer, F. and Alcamo, J. (2013) Domestic and industrial water uses of the past 60 years as a mirror of socio-economic development: A global simulation study, Global Environmental Change, vol. 23, no. 1, pp Gerten, D., Hoff, H., Rockström, J., Jägermeyr, J., Kummu, M. and Pastor, A. V. (2013) Towards a revised planetary boundary for consumptive freshwater use: Role of environmental flow requirements, Current Opinion in Environmental Sustainability, vol. 5, no. 6, pp Gleick, P. H. (1996) Basic Water Requirements for Human Activities: Meeting Basic Needs, Water International, vol. 21, pp Gleick, P. H. (2003) Water Use, Annual Review of Environment and Resources, vol. 28, no. 1, pp Hanasaki, N., Fujimori, S., Yamamoto, T., Yoshikawa, S., Masaki, Y., Hijioka, Y., Kainuma, M., Kanamori, Y., Masui, T., Takahashi, K. and Kanae, S. (2013a) A global water scarcity assessment under Shared Socio-economic Pathways - Part 1: Water use, Hydrology and Earth System Sciences, vol. 17, no. 7, pp Hanasaki, N., Fujimori, S., Yamamoto, T., Yoshikawa, S., Masaki, Y., Hijioka, Y., Kainuma, M., Kanamori, Y., Masui, T., Takahashi, K. and Kanae, S. (2013b) A global water scarcity 40

51 assessment under Shared Socio-economic Pathways - Part 2: Water availability and scarcity, Hydrology and Earth System Sciences, vol. 17, no. 7, pp Hanasaki, N., Kanae, S., Oki, T., Masuda, K., Motoya, K., Shirakawa, N., Shen, Y. and Tanaka, K. (2008) An integrated model for the assessment of global water resources Part 1: Model description and input meteorological forcing, Hydrology and Earth System Sciences, vol. 12, no. 4, pp Hejazi, M., Edmonds, J., Chaturvedi, V., Davies, E. and Eom, J. (2013) Scenarios of global municipal water-use demand projections over the 21st century, Hydrological Sciences Journal, vol. 58, no. 3, pp Hejazi, M. I., Edmonds, J., Clarke, L., Kyle, P., Davies, E., Chaturvedi, V., Wise, M., Patel, P., Eom, J. and Calvin, K. (2014) Integrated assessment of global water scarcity over the 21st century under multiple climate change mitigation policies, Hydrology and Earth System Sciences, vol. 18, no. 8, pp Hughes, D. A., Kapangaziwiri, E. and Sawunyama, T. (2010) Hydrological model uncertainty assessment in southern Africa, Journal of Hydrology, vol. 387, 3-4, pp ITIF (2012) Worse than the Great Depression: What experts are missing about American manufacturing decline, The Information Technology & Innovation Foundation. Kim, S. H., Hejazi, M., Liu, L., Calvin, K., Clarke, L., Edmonds, J., Kyle, P., Patel, P., Wise, M. and Davies, E. (2016) Balancing global water availability and use at basin scale in an integrated assessment model, Climatic Change, vol. 136, no. 2, pp Müller Schmied, H., Eisner, S., Franz, D., Wattenbach, M., Portmann, F. T., Flörke, M. and Döll, P. (2014) Sensitivity of simulated global-scale freshwater fluxes and storages to input data, hydrological model structure, human water use and calibration, Hydrology and Earth System Sciences, vol. 18, no. 9, pp OECD (2016) List of OECD Member countries - Ratification of the Convention on the OECD [Online]. Available at (Accessed 31 May 2016). Parkinson, S. C., Johnson, N., Rao, N. D., Jones, B., van Vliet, M. T. H., Fricko, O., Djilali, N., Riahi, K. and Flörke, M. (2016) Climate and human development impacts on municipal water demand: A spatially-explicit global modeling framework, Environmental Modelling & Software, vol. 85, pp Pastor, A. V., Ludwig, F., Biemans, H., Hoff, H. and Kabat, P. (2014) Accounting for environmental flow requirements in global water assessments, Hydrology and Earth System Sciences, vol. 18, no. 12, pp Rockström, J., Steffen, W., Noone, K., Persson, Å., Chapin, F. S., Lambin, E., Lenton, T. M., Scheffer, M., Folke, C., Schellnhuber, H. J., Nykvist, B., Wit, C. A. de, Hughes, T., van der Leeuw, S., Rodhe, H., Sörlin, S., Snyder, P. K., Costanza, R., Svedin, U., Falkenmark, M., Karlberg, L., Corell, R. W., Fabry, V. J., Hansen, J., Walker, B., Liverman, D., Richardson, K., Crutzen, P. and Foley, J. (2009) Planetary Boundaries: Exploring the Safe Operating Space for Humanity, Ecology and Society, vol. 14, no. 2 [Online]. Available at Shiklomanov, I. A. (2000) Appraisal and Assessment of World Water Resources, International Water Resources Association, vol. 25, no. 1, pp Steinfeld, H., Gerber, P., Wassenaar, T., Castel, V., Rosales, M. and Haan, C. de (2006) Livestocks long shadow: Environmental issues and options, FAO. UN DESA (2014) World Economic Situation and Prospects

52 van Beek, L. P. H., Wada, Y. and Bierkens, M. F. P. (2011) Global monthly water stress: 1. Water balance and water availability, Water Resources Research, vol. 47, no. 7. Vassolo, S. and Döll, P. (2005) Global-scale gridded estimates of thermoelectric power and manufacturing water use, Water Resources Research, vol. 41, no. 4. Voß, F. and Flörke, M. (2010) Spatially explicit estimates of past and present manufacturing and energy water use, WATCH Technical Report 23. Voß, F., Flörke, M. and Alcamo, J. (2009) Preliminary spatially explicit estimates of past and present domestic water use, WATCH Technical Report 17. Wada, Y., Flörke, M., Hanasaki, N., Eisner, S., Fischer, G., Tramberend, S., Satoh, Y., van Vliet, M. T. H., Yillia, P., Ringler, C., Burek, P. and Wiberg, D. (2016) Modeling global water use for the 21st century: The Water Futures and Solutions (WFaS) initiative and its approaches, Geoscientific Model Development, vol. 9, no. 1, pp Wada, Y., van Beek, L. P. H. and Bierkens, M. F. P. (2011a) Modelling global water stress of the recent past: On the relative importance of trends in water demand and climate variability, Hydrology and Earth System Sciences, vol. 15, no. 12, pp Wada, Y., van Beek, L. P. H., Viviroli, D., Dürr, H. H., Weingartner, R. and Bierkens, M. F. P. (2011b) Global monthly water stress: 2. Water demand and severity of water stress, Water Resources Research, vol. 47, no. 7, pp. n/a-n/a. Wada, Y., Wisser, D. and Bierkens, M. F. P. (2014) Global modeling of withdrawal, allocation and consumptive use of surface water and groundwater resources, Earth System Dynamics, vol. 5, no. 1, pp World Bank (2016) World Development Indicators [Online]. Available at wdi.worldbank.org/tables (Accessed 13 September 2016). 42

53 Appendix H08 PCR-GLOBWB WaterGAP Figure A1. Input data required to calculate industrial and domestic water withdrawal with the three WFaS models. Solid lines indicate data on country level, dashed lines indicate data on grid cell level. Sources: H08: Hanasaki et al. (2013); PCR-GLOBWB: Wada et al. (2011a), Wada et al. (2014); WaterGAP: Flörke et al. (2013), Wada et al. (2016). 43

54 H08 PCR-GLOBWB WaterGAP Figure A2. Input data required to calculate industrial and domestic water consumption with the three WFaS models. Solid lines indicate data on country level, dashed lines indicate data on grid cell level. Blue shading indicates results from the water withdrawal routine. Sources: H08: Hanasaki et al. (2008); PCR-GLOBWB: (Wada et al., 2011a); WaterGAP: Flörke et al. (2013), Wada et al. (2016). 44

55 Figure A3. National development states according to UN classification. Figure A4. Countries with OECD membership before Figure A5. European manufacturing water withdrawal simulations with GDP (black solid line), GVA (grey solid line) and national electricity production (black dashed line) as drivers. 45