Future global urban water scarcity and potential solutions

Description of scenarios used in this study

To assess future urban water scarcity, we used the scenario framework from the Scenario Model Intercomparison Project (ScenarioMIP), part of the International Coupled Model Intercomparison Project Phase 6 (CMIP6)³⁸. The scenarios have been developed to better link the Shared Socioeconomic Pathways (SSPs) and Representative Concentration Pathways (RCPs) to support comprehensive research in different fields to better understand global climatic and socioeconomic interactions^38,39. We selected the four ScenarioMIP Tier 1 scenarios (i.e., SSP1&RCP2.6, SSP2&RCP4.5, SSP3&RCP7.0, and SSP5&RCP8.5) to evaluate future urban water scarcity. SSP1&RCP2.6 represents the sustainable development pathway of low radiative forcing level, low climate change mitigation challenges, and low social vulnerability. SSP2&RCP4.5 represents the business-as-usual pathway of moderate radiative forcing and social vulnerability. SSP3&RCP7.0 represents a higher level of radiative forcing and high social vulnerability. SSP5&RCP8.5 represents a rapid development pathway and very high radiative forcing³⁸.

Estimation of urban water scarcity

To estimate urban water scarcity, we quantified the total urban population living in water-scarce areas^2,3,7,19. Specifically, we first corrected the spatial distribution of the global urban population, then identified water-scarce areas around the world, and finally quantified the urban population in water-scarce areas at different scales (Supplementary Fig. 1).

Correcting the spatial distribution of global urban population

The existing global urban population data from the History Database of the Global Environment (HYDE) provided consistent information on historical and future population, but it has a coarse spatial resolution of 10 km (Supplementary Table 1)^40,41. In addition, it was estimated using total population, urbanization levels, and urban population density, and does not align well with the actual distribution of urban land⁴². Hence, we allocated the HYDE global urban population data to high-resolution urban land data. We first obtained global urban land in 2016 from He et al.⁴². Since the scenarios used in existing urban land forecasts are now dated^43,44, we simulated the spatial distribution of global urban land in 2050 under each SSP at a grid-cell resolution of 1km² using the zoned Land Use Scenario Dynamics-urban (LUSD-urban) model^45,46,47 (Supplementary Methods 1). The simulated urban expansion area in this study was significantly correlated with that in existing datasets (Supplementary Table 6). We then converted the global urban land raster layers for 2016 and 2050 into vector format to characterize the spatial extent of each city. The total population within each city was then summed and the remaining HYDE urban population cells located outside urban areas were allocated to the nearest city. Assuming that the population density within an urban area was homogeneous, we calculated the total population per square kilometer for all urban areas and converted this back to raster format at a spatial resolution of 1 km². The new urban population data had much lower error than the original HYDE data (Supplementary Table 7).

Identification of global water-scarce areas

Annual and monthly WSI values were calculated at the catchment level in 2014 and 2050 as the ratio of water withdrawals (TWW) to availability (AWR)³³. Due to limited data availability, we combined water-scarce areas in 2014 and the urban population in 2016 to estimate current urban water scarcity. WSI for catchment i for time t as:

For each catchment defined by Masutomi et al.⁴⁸, the total water withdrawal (TWW_t,i) equalled the sum of water withdrawals (WW_t,n,i) for each sector n (irrigation, livestock, industrial, or domestic), while the water availability equalled the sum of available water resources for catchment i (R_t,i), inflows/outflows of water resources due to interbasin water transfer ((varDelta {{{{mathrm{W{R}}}}}}_{t,i})), and water resources from each upstream catchment j (WR_t,i,j):

The changes of water resources due to interbasin water transfer were calculated based on City Water Map produced by McDonald et al.³. The number of water resources from upstream catchment j was calculated based on its water availability (AWR_t,_i,_j) and water consumption for each sector n (WC_t,n,i,j)⁴⁹:

For areas without upstream catchments, the number of available water resources was equal to the runoff. Following Mekonnen and Hoekstra³⁶, and Hofste et al.³³, we did not consider environmental flow requirements in calculating water availability.

Annual and monthly WSI for 2014 were calculated directly based on water withdrawal, water consumption, and runoff data from AQUEDUCT3.0 (Supplementary Table 1). The data from AQUEDUCT3.0 were selected because they are publicly available and the PCRaster Global Water Balance (PCRGLOBWB 2) model used in the AQUADUCT 3.0 can better represent groundwater flow and available water resources in comparison with other global hydrologic models (e.g., the Water Global Assessment and Prognosis (WaterGAP) model)³³. The annual and monthly WSI for 2050 were calculated by combining the global water withdrawal data from 2000 to 2050 provided by the National Institute of Environmental Research of Japan (NIER)³⁴ and global runoff data from 2005 to 2050 from CMIP6 (Supplementary Table 1). Water withdrawal ({{{{{mathrm{W{W}}}}}}}_{s,m,n,i}^{2050}) in 2050 for each sector n (irrigation, industrial, or domestic), catchment i, and month m under scenario s was calculated based on water withdrawal in 2014 (({{{{{mathrm{W{W}}}}}}}_{m,n,i}^{2014})):

adjusted by the mean annual change in water withdrawal from 2000 to 2050 (WWR_{s, m, n, i}), calculated using the global water withdrawal for 2000 (({{{{{mathrm{W{W}}}}}}}_{{{{{mathrm{NIER}}}}},m,n,i}^{2000})) and 2050 (({{{{{mathrm{W{W}}}}}}}_{{{{{mathrm{NIER}}}}},s,m,n,i}^{2050})) provided by the NIER³⁴:

Based on the assumption of a constant ratio of water consumption to water withdrawal in each catchment, water consumption in 2050 (({{{{{mathrm{W{C}}}}}}}_{s,m,n,i}^{2050})) was calculated as:

where ({{{{{mathrm{W{C}}}}}}}_{m,n,i}^{2014}) denotes water consumption in 2014. Due to a lack of data, we specified that water withdrawal for livestock remained constant between 2014 and 2050, and used water withdrawal simulation under SSP3&RCP6.0 provided by the National Institute of Environmental Research in Japan to approximate SSP3&RCP7.0.

To estimate water availability, we calculated available water resources (({R}_{s,m,i}^{2041-2050})) for each catchment i and month m under scenario s for the period of 2041–2050 as:

based on the amount of available water resources with 10-year ordinary least square regression from 2005 to 2014 (({R}_{m,i}^{{{{{mathrm{ols}}}}},,2005-2014})) from AQUEDUCT3.0 (Supplementary Table 1). ({overline{R}}_{m,i}^{2005-2014}) and ({overline{R}}_{s,m,i}^{2041-2050}) denote the multi-year average of runoff (i.e., surface and subsurface) from 2005 to 2014, and from 2041 to 2050, respectively, calculated using the average values of simulation results from 10 global climate models (GCMs) (Supplementary Table 2).

We then identified water-scarce catchments based on the WSI. Two thresholds of 0.4 and 1.0 have been used to identify water-scarce areas from WSI (Supplementary Table 4). While the 0.4 threshold indicates high water stress⁴⁹, the threshold of 1.0 has a clearer physical meaning, i.e., that water demand is equal to the available water supply and environmental flow requirements are not met^36,37. We adopted the value of 1.0 as a threshold representing extreme water stress to identify water-scarce areas. The catchments with annual WSI >1.0 were identified as perennial water-scarce catchments; the catchments with annual WSI equal to or <1.0 and WSI for at least one month >1.0 were identified as seasonal water-scarce catchments.

Estimation of global urban water scarcity

Based on the corrected global urban population data and the identified water-scarce areas, we evaluated urban water scarcity at the global and national scales via a spatial overlay analysis. The urban population exposed to water scarcity in a region (e.g., the whole world or a single country) is equal to the sum of the urban population in perennial water-scarce areas and that in seasonal water-scarce areas. Limited by data availability, we used water-scarce areas in 2014 and the urban population in 2016 to estimate current urban water scarcity. Projected water-scarce areas and urban population in 2050 under four scenarios were then used to estimate future urban water scarcity. In addition, we obtained the location information of large cities (with population >1 million in 2016) from the United Nations’ World Urbanization Prospects¹ (Supplementary Table 1) and identified those in perennial and seasonal water-scarce areas.

Uncertainty analysis

To evaluate the uncertainty across the 10 GCMs used in this study (Supplementary Table 2), we identified water-scarce areas and estimated urban water scarcity using the simulated runoff from each GCM under four scenarios. To perform the uncertainty analysis, the runoff in 2050 for each GCM was calculated using the following equation:

where ({R}_{s,g,m,i}^{2050}) denotes the runoff of catchment i in month m in 2050 for GCM g under scenario s. ({R}_{g,m,i}^{2005-2014}) and ({R}_{s,g,m,i}^{2041-2050}) denote the multi-year average runoff from 2005 to 2014, and from 2041 to 2050, respectively, calculated using the simulation results from GCM g. Using the runoff for each GCM, the WSI in 2050 for each catchment was recalculated, water-scarce areas were identified, and the urban population exposed to water scarcity was estimated.

Contribution analysis

Based on the approach used by McDonald et al.² and Munia et al.⁵⁰, we quantified the contribution of socioeconomic factors (i.e., water demand and urban population) and climatic factors (i.e., water availability) to the changes in global urban water scarcity from 2016 to 2050. To assess the contribution of socioeconomic factors (({{{{{mathrm{Co{n}}}}}}}_{s,{{{{mathrm{SE}}}}}})), we calculated global urban water scarcity in 2050 while varying demand and population and holding catchment runoff constant (({{{{{mathrm{UW{S}}}}}}}_{s,{{{{mathrm{SE}}}}}}^{2050})). Conversely, to assess the contribution of climate change ((Co{n}_{s,CC})), we calculated scarcity while varying runoff and holding urban population and water demand constant (({{{{{mathrm{UW{S}}}}}}}_{s,{{{{mathrm{CC}}}}}}^{2050})). Socioeconomic and climatic contributions were then calculated as:

Feasibility analysis of potential solutions to urban water scarcity

Potential solutions to urban water scarcity involve two aspects: increasing water availability and reducing water demand². Approaches to increasing water availability include groundwater exploitation, seawater desalination, reservoir construction, and inter-basin water transfer; while approaches to reduce water demand include water-use efficiency measures (e.g., new cultivars for improving agricultural water productivity, sprinkler or drip irrigation for improving water-use efficiency, water-recycling facilities for improving domestic and industrial water-use intensity), limiting population growth, and virtual water trade^2,3,18,32. To find the best ways to address urban water scarcity, we assessed the feasibility of these potential solutions for each large city (Supplementary Fig. 2).

First, we divided these solutions into seven groups according to scenario settings and the scale of implementation of each solution (Supplementary Fig. 2). Among the solutions assessed, water-use efficiency improvement, limiting population growth, and climate change mitigation were included in the simulation of water demand and water availability under the ScenarioMIP SSPs&RCPs simulations³⁴. Here, we considered the measures within SSP1&RCP2.6 which included the lowest growth in population, irrigated area, crop intensity, and greenhouse gas emissions; and the largest improvements in irrigation, industrial, and municipal water-use efficiency³⁴.

We then evaluated the feasibility of the seven groups of solutions according to the characteristics of water-scarce cities (Supplementary Fig. 2). Of the 526 large cities (with population >1 million in 2016 according to the United Nations’ World Urbanization Prospects), we identified those facing perennial or seasonal water scarcity under at least one scenario by 2050. We then selected the cities that no longer faced water scarcity under SSP1&RCP2.6 where the internal scenario assumptions around water-use efficiency, population growth, and climate change were sufficient to mitigate water scarcity. Following McDonald et al.^2,3 and Wada et al.¹⁸, we assumed that desalination can be a potential solution for coastal cities (distance from coastline <100 km) and groundwater exploitation can be feasible for cities where the groundwater table has not significantly declined. For cities in catchments facing seasonal water scarcity and with suitable topography, reservoir construction was identified as a potential solution. Inter-basin water transfer was identified as a potential solution for a city if nearby basins (i.e., in the same country, <1000 km away [the distance of the longest water transfer project in the world]) were not subject to water scarcity and had sufficient water resources to address the water scarcity for the city. Domestic virtual water trade was identified as a potential solution for a city if it was located in a country without national scale water scarcity. International water transfer or virtual water trade was identified as a feasible solution for cities in middle and high-income countries. Based on the above assumptions, we identified potential solutions to water scarcity in each city (see Supplementary Table 1 for the data used).

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