# Additional surface-water deficit to meet global universal water accessibility by 2030

Yan Bo, Feng Zhou, Jianshi Zhao, Junguo Liu, Jiahong Liu, Philippe Ciais,

Jinfeng Chang, Lei Chen

► To cite this version:

Yan Bo, Feng Zhou, Jianshi Zhao, Junguo Liu, Jiahong Liu, et al.. Additional surface-water deficit to meet global universal water accessibility by 2030. Journal of Cleaner Production, 2021, 320, pp.128829. 10.1016/j.jclepro.2021.128829. hal-03330166

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Distributed under a Creative Commons Attribution 4.0 International License
1 **Additional surface-water deficit to meet global universal water accessibility by 2030**
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3 Yan Bo¹, Feng Zhou¹,*, Jianshi Zhao², Junguo Liu³, Jiahong Liu⁴, Philippe Ciais⁵, Jinfeng Chang⁶, Lei Chen⁷

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6 ¹Sino-France Institute of Earth Systems Science, Laboratory for Earth Surface Processes, College of Urban and Environmental Sciences, Peking University, Beijing, 100871, China.

9 ²State Key Laboratory of Hydroscience and Engineering, Department of Hydraulic Engineering, Tsinghua University, Beijing 100084, China.

10 ³School of Environmental Science and Engineering, Southern University of Science and Technology, Shenzhen 518055, China.

13 ⁴State Key Laboratory of Simulation and Regulation of Water Cycle in River Basin, China Institute of Water Resources and Hydropower Research, Beijing, 100038, China.

16 ⁵Laboratoire des Sciences du Climat et de l'Environnement, LSCE, Gif sur Yvette, France.

18 ⁶College of Environmental and Resource Sciences, Zhejiang University, 310058 Hangzhou, China.

20 ⁷State Key Joint Laboratory of Environmental Simulation and Pollution Control, School of Environment, Beijing Normal University, Beijing 100875, China

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23 * Corresponding author: Address: Room 3355, 2nd Yifu Bld, Peking University, Beijing, 100871, China; Phone / fax: +86 10 62756511; Email: zhouf@pku.edu.cn
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Character Count: 6248

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# Additional surface-water deficit to meet global universal water accessibility by 2030

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**Abstract:** Universal access to safe drinking water for all is a key target of the United Nations Sustainable Development Goal 6. However, how water accessibility improvement impacts domestic water demand and surface-water deficit (the quantity of water demand that exceeds surface-water availability) are poorly known due to uncertainty and variability in domestic water use intensities (WUI<sub>dom</sub>). Here, we developed a data-driven model to create global gridded maps of historical and future WUI<sub>dom</sub>, constrained by 1,758 survey-based records spanning 220 administrative units.

The results show that water accessibility improvement dominated the growth in WUI<sub>dom</sub> over the period 1990-2015, particularly in developing and least-developed economies. Achieving universal water accessibility by 2030 may lead to an additional growth in global mean WUI<sub>dom</sub> by 21.7 L capita<sup>-1</sup> day<sup>-1</sup>. Additional 88.3 million people, from 14 developing countries and 9 least-developed countries, may move into domestic surface-water deficit. Another 16% of global irrigated areas would be at risk of irrigation surface-water deficit mainly in South Asia, Central Asia, and North Africa. These findings imply the need of spatially flexible set of actions to achieve global universal water accessibility by 2030 without worsening surface-water deficit.

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**Keywords:** water accessibility, water use, water availability, water deficit, sustainable development goal

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# 1. Introduction

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Universal access to safe drinking water for all by 2030, a key target of the United Nations Sustainable Development Goal 6 (SDG6.1) (United Nations, 2015), is fundamental human rights and essential for water-borne disease reduction and poverty eradication (Banerjee et al., 2019; Bayu et al., 2020; Fukuda et al., 2019; Saravanan, 2013). Achieving universal water accessibility will stimulate the growth in domestic water use at least through changing water supply pattern (e.g., continuous or intermittent), geographical proximity, and time spent for fetching water (Arouna and Dabbert, 2010; Fan et al., 2013; Haziq and Panezai, 2017). This positive feedback implies higher requirements of water supply reliability which potentially increases the dependence on external water resources and expectedly induces the competition between domestic and other water-use sectors particularly in water-stressed regions (Flörke et al., 2018; Munia et al., 2020). Although water accessibility improvement is more of a local issue, water deficit resulting from additional water demand would be exacerbated by global processes (e.g. climate change, population and economic growth, food production and trade) and thus needs global resolution (Wada et al., 2014). It's therefore necessary to know global-scale influence of achieving universal water accessibility to help guiding policies from both local and international aspects. Nevertheless, the magnitude and spatial distribution of additional domestic water demands due to universal water accessibility achieved have not been quantified. This hinders identifying hotspots of water supply challenges and thus precludes governments from developing spatial targeted water management policies to ensure
45 achievement of SDG6.1.

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47 In order to project additional domestic water demands and assess associated
48 challenges from universal water accessibility achieved by 2030, it is necessary to
49 understand how water accessibility improvements may impact domestic water
demand under the context of future socio-economic development and climate change.

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51 Most approaches to date have focused on two main aspects. First, the simulations of
52 global domestic water demand through explicitly accounting for per-capita income
53 change and technological improvements over time but implicitly for other
54 socio-economic and climatic drivers over space through model stratification by
55 regions or countries (e.g. Global Hydrological Models) (Flörke et al., 2013; Hanasaki
56 et al., 2013a; Wada et al., 2016). However, it is difficult to reproduce the spatial
57 variation in domestic water-use intensity (defined as domestic water use per capita per
58 day, WUI<sub>dom</sub>) spanning more than two order of magnitude across countries surveyed
59 by the Food and Agriculture Organization (FAO) in AQUASTAT database (FAO,
60 2016). Second, the analyses of impacts of intensive socio-economic and climatic
61 factors on domestic water use based on economic equations (Ward et al., 2010;
62 Hughes et al., 2010), system dynamics, agent-based models and field surveys under
63 the sociohydrological framework (Konar et al., 2019; Wu et al., 2019). However, to
64 date, no model framework has integrated a set of those drivers of WUI<sub>dom</sub> to identify
65 the most important ones and to isolate the impacts of universal water accessibility on
66 future domestic water demand.
Here, we address these limitations by developing a data-driven model to connect WUI<sub>dom</sub> variations to climate change, income growth, population structure shift, and water accessibility improvement, based on an extensive compilation of 1,758 survey-based records spanning 50 states in the USA, 31 provinces in China, and other 139 countries. With this data-driven model, we can begin to address three key questions: how strongly does water accessibility improvement influence variations in WUI<sub>dom</sub> at the global and regional scales? What are the magnitude and spatial distribution of domestic water demands when achieving universal water accessibility by 2030? And whether, and to what extent does additional growth in domestic water demands reshuffle global hotspots of surface-water deficit?

To do so, we first use model-based scenario simulations to disentangle the relative contributions of climatic, socioeconomic, and technology-related variables to temporal change in WUI<sub>dom</sub>. We then combine spatially-explicit information of simulated WUI<sub>dom</sub> with the latest population maps to project domestic water demand in 2030 at 30-arc-minute spatial resolution. The additional growth in domestic water demands due to the achievement of universal water accessibility is isolated from scenario simulations. By combining this with the multi-model ensemble assessment of surface-water availability (Hanasaki et al., 2018; Pokhrel et al., 2012; Rost et al., 2008), we further quantify additional surface-water deficits of domestic and irrigation sectors. In the end, we discuss about the advantages and limitations of our data-driven model, as well as the implications for designing spatially flexible set of actions.
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## 2. Methods and data

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### 2.1. Domestic water use model

Domestic water use is defined as the annual quantity of water withdrawal for the direct use of urban and rural residents, which is in line with that from the FAO AQUASTAT (FAO, 2016). This includes water that is lost in treatment and in water supply networks. Generally, domestic water use consists of indoor use for drinking, cooking, toilet flushing, laundry, and bathing, and outdoor uses for watering lawns, maintaining pools, and municipal activities. We developed a data-driven model by quantifying each of the indoor and outdoor water use behaviors as the sum of a baseline and an elastic change in response to different drivers. The drivers considered in the model included mean daily maximum temperature (T), annual precipitation (P), per-capita income (G, measured as gross domestic product per capita), population structure (U, measured as percentage of urban population), and water accessibility (A, defined as percentage of the total population using improved water sources that are located on premises, available when needed or free from contamination). These drivers were selected as they were reported to have direct effects on domestic water use and widely used in domestic water use estimation (Flörke et al., 2013; Bigelow et al., 2017; Fan et al., 2013; Opalinski et al., 2020) (See details in *Supplementary Method*). Besides, selected drivers should be accessible at administrative scale in line with the surveyed WUI<sub>dom</sub> for historical period to support model calibration and also for future periods to support demand projection. However, the proposed model did not
consider the impacts of the other technical improvements, demographic features, or
consumption habits due to lack of data availability. After simplification, the model is
expressed below, with the detailed description in *Supplementary Method*.

$$WUI_{dom} = C_1 + [C_2 + f^{CLM}(T, P) \cdot f^G(G)] \cdot f^A(A) \cdot f^U(U), \quad [1a]$$

where

$$\left\{
\begin{array}{l}
f^{CLM}(T, P) = T \cdot (a_1 \cdot P + a_2) \\
f^G(G) = (b + e^{-c \cdot G})^{-1} \\
f^A(A) = (A - p)^3 \\
f^U(U) = U^m
\end{array}
\right.
, \quad [1b]$$

and parameters ($C_1$, $C_2$, $a_1$, $a_2$, $b$, $c$, $p$ and $m$) were calibrated using a least-squares
error technique based on historical records (Table S1). Domestic water use (DOM)
and related population (POP) were collected to calculate $WUI_{dom}$ from local statistical
agencies (see details in Table 1). $WUI_{dom}$ records were weighted by reciprocals of the
number of occurrences at each administrative unit to compensate unequal
probabilities of administrative unit being surveyed (United Nations, 2005,
*Supplementary Method*). Population weighting was also evaluated and found to be
less relevant for evaluating change in access to water (*Supplementary Method*). For
each record, data of five predictive factors were obtained from publicly available data
repositories (Table 1), and were aggregated for the administrative units in line with
the surveyed $WUI_{dom}$ if applicable. Note that climatic drivers were upscaled into
administrative scales using population in each region in each grid cell to weight the
grid cell means, reflecting the average exposure for the population of each region
(Hughes et al., 2010). We finally excluded the outliers of $WUI_{dom}$ based on the
132 Z-Score outlier test and created a global database of 1,758 records.

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<table><caption>135 Table 1 Data source of domestic water use and associated drivers</caption><thead><tr><th>Name*</th><th>Region</th><th>Resolution</th><th>Period</th><th>Source</th></tr></thead><tbody><tr><td rowspan="3">DOM, POP</td><td>China</td><td>Province</td><td>1979-2015</td><td>(Zhou et al., 2020)</td></tr><tr><td>USA</td><td>State</td><td>1980-2015</td><td>USGS<br>(http://water.usgs.gov/watuse/)</td></tr><tr><td>Others</td><td>Country</td><td>1990-2015</td><td>DOM: FAO's AQUASTAT<br>(http://www.fao.org/faostat/)<br>POP: World Bank</td></tr><tr><td rowspan="3">A, G, U</td><td>China</td><td>Province</td><td>1979-2015</td><td>(Zhou et al., 2020)</td></tr><tr><td>USA</td><td>State</td><td>1980-2015</td><td>G: Bureau of Economic Analysis<br>U: U.S. Census Bureau<br>A: U.S. Environmental Protection Agency</td></tr><tr><td>Others</td><td>Country</td><td>1990-2015</td><td>G and U: World bank<br>A: FAO's AQUASTAT database<br>(http://www.fao.org/faostat/)</td></tr><tr><td>T, P</td><td>Globe</td><td>0.5°×0.5°</td><td>1990-2015</td><td>NOAA (https://psl.noaa.gov/)</td></tr></tbody></table>

136 * DOM: domestic water use, POP: total population, A: water accessibility, G: per-capita income, U: population structure, T: mean daily maximum temperature, P: annual precipitation

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140 We conducted additional analyses to evaluate the robustness and predictability of the data-driven model. First, five-fold cross validation was performed by dividing the surveyed dataset into 5 nearly-equal random subsets (Phillips et al., 2019). To avoid the bias due to subsets randomly divided, we repeated the above steps by 50 times for possible subdivisions. The R² and RMSE were calculated for all predicted data. Second, the method from van den Hoogen et al. (2019) was used to investigate the
interpolation capability for global prediction. Interpolation percentage is defined to
estimate how much our newly-compiled dataset adequately captured the multivariate
covariate space of the global layers. For example, if all global layers within a pixel
were within the convex hull derived from the dataset, the interpolation percentage
would be 100%, while it would be 50% if only half of the layers were the convex hull.

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2.2. Driver of historical change in WUI<sub>dom</sub>

Detection of historical change in domestic water use intensities (ΔWUI<sub>dom</sub>) requires at least two surveyed years covering a certain time period of specific region. Thus, administrative units were first selected from our historical dataset for which (i) at least two records exist, and (ii) surveyed years cover a period of more than five years within 1990-2015. We then defined the earliest and latest surveyed year as the starting (P1) and ending year (P2) for each region for attribution analysis (see details in Fig. S1).

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Five scenario simulations were then conducted with the validated model to isolate the
effects of each driver (i.e. *G*, *T*, *P*, *U*, *A*) on Δ*WUI*<sub>dom</sub> (see details in Table 2). For
each simulation, *WUI*<sub>dom</sub> in P1 and P2 were first predicted at 30-arc-minute spatial
resolution forced by the input data of 5 predictive factors (Table 1), and then
aggregated into 220 administrative units. In addition, the difference between surveyed
and simulated Δ*WUI*<sub>dom</sub> represented the effect from other factors (*OF*) that are not
incorporated in the model. We finally identified the dominant driver that contributes
the most to ΔWUI<sub>dom</sub> for each of administrative units.

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<table><caption>Table 2 Simulation protocol for attributing historical change in domestic water use intensities to different drivers</caption><thead><tr><th rowspan="2">Driver*</th><th colspan="5">Scenario simulation</th><th rowspan="2">Contribution</th></tr><tr><th>S1</th><th>S2</th><th>S3</th><th>S4</th><th>S5</th></tr></thead><tbody><tr><td>A</td><td>P1-P2<sup>#</sup></td><td>P1</td><td>P1</td><td>P1</td><td>P1</td><td>ΔWUI<sub>dom</sub>(S1)-ΔWUI<sub>dom</sub> (S2)</td></tr><tr><td>G</td><td>P1-P2</td><td>P1-P2</td><td>P1</td><td>P1</td><td>P1</td><td>ΔWUI<sub>dom</sub> (S2)-ΔWUI<sub>dom</sub> (S3)</td></tr><tr><td>T</td><td>P1-P2</td><td>P1-P2</td><td>P1-P2</td><td>P1</td><td>P1</td><td>ΔWUI<sub>dom</sub> (S3)-ΔWUI<sub>dom</sub> (S4)</td></tr><tr><td>P</td><td>P1-P2</td><td>P1-P2</td><td>P1-P2</td><td>P1-P2</td><td>P1</td><td>ΔWUI<sub>dom</sub> (S4)-ΔWUI<sub>dom</sub> (S5)</td></tr><tr><td>U</td><td>P1-P2</td><td>P1-P2</td><td>P1-P2</td><td>P1-P2</td><td>P1-P2</td><td>ΔWUI<sub>dom</sub> (S5)</td></tr></tbody></table>

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* A: water accessibility, G: per-capita income, T: mean daily maximum temperature, P: annual precipitation, U: population structure

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# P1 indicates that the driver was held as in P1 (the starting year of historical records), P1-P2 indicates that the driver varied from P1 to P2 (ending year of historical records).

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## 2.3. Future projection of domestic water demand

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In order to represent the impact of achieving universal water accessibility, we designed two scenario simulations: water accessibility was held as in 2015 for the 1st scenario (S1) but set to be 100% by 2030 for the 2nd scenario (S2), while the climatic and socio-economic factors varied to 2030 for both scenarios. We then projected WUI<sub>dom</sub> and domestic water demand at 30-arc-minute spatial resolution for simulations S1 and S2. The difference in domestic water demand between S1 and S2 represented the effect from universal water accessibility achieved.
For each simulation, the latest set of global change scenarios on climate and socio-economic developments were considered for climatic and socio-economic factors, i.e., RCP2.6-SSP2 and RCP6.0-SSP2 scenarios. Bias-corrected climate data was from an ensemble of four general circulation models (30-arc-minute) from the Inter-Sectoral Impact Model Intercomparison Project (ISIMIP 2b), including IPSL-CM5A-LR, GFDL-ESM2M, MIROC5 and HadGEM2-ES under RCP2.6 and 6.0. Global gridded data of GDP and population (30-arc-minute) were available from Jing et al. (2020) under SSP2, from which per-capita income was calculated and harmonized to match the level for the initial year 2015. However, future urbanization rates was only available at the national scale from the IIASA SSP database (Jiang and O'Neill, 2017). We then projected the urbanization rate at 30-arc-minute spatial resolution up to 2030 by extending global gridded data in 2015 (Table 1) based on the future temporal change of the IIASA SSP database.

## 2.4. Domestic and irrigation surface-water deficits

We used surface-water deficit as an indicator to quantify how much water demand exceeds surface-water availability. The difference in surface-water deficits between simulations S1 and S2 represents the additional surface-water deficit when achieving universal water accessibility by 2030. This indicator is calculated separately for domestic and irrigation sectors. First, domestic surface-water deficit (SWD<sub>dom</sub>) in 2030 was calculated as the difference between domestic water demand and total
surface-water availability, given the highest priority for this sector within water supply system (Lei et al., 2018). Second, irrigation surface-water deficit (SWD<sub>irr</sub>) in 2030 was calculated as the difference between irrigation water demand and the surface-water availability after meeting domestic and industrial sectors:

$$SWD_{i,y,dom} = \begin{cases} DOM_{i,y} - Q_{i,y}, & DOM_{i,y} > Q_{i,y}, \\ 0, & DOM_{i,y} \le Q_{i,y} \end{cases}, \qquad [2a]$$

$$SWD_{i,y,irr} = \begin{cases} IRR_{i,y}, & Q_{i,y} < DOM_{i,y} + IND_{i,y} \\ IRR_{i,y} - Q_{i,y}^*, & DOM_{i,y} + IND_{i,y} < Q_{i,y} \le WU_{i,y} \\ 0, & Q_{i,y} \ge WU_{i,y} \end{cases}, \qquad [2b]$$

where DOM<sub>i,y</sub>, IND<sub>i,y</sub>, and IRR<sub>i,y</sub> are water demands for domestic, industrial, and irrigation sectors for cell i and scenario y, respectively. WU<sub>i,y</sub> is total water demands for cell i and scenario y. Q<sub>i,y</sub> is total surface-water availability for cell i and scenario y. Q<sub>i,y</sub><sup>*</sup> is surface-water availability for irrigation for cell i and scenario y, calculated as total surface-water availability (Q<sub>i,y</sub>) minus water demands for domestic and industrial sectors. It should be noted that we underestimate the total amount of water resources that may be available from groundwater, and thus overestimate surface-water deficit where groundwater is used for water supply.

DOM<sub>i,y</sub> was predicted by our model, while IND<sub>i,y</sub> and IRR<sub>i,y</sub> were available from the estimates of three impact models (i.e. H08 (Hanasaki et al., 2018), LPJmL (Rost et al., 2008) and MATSIRO (Pokhrel et al., 2012)) participating the ISIMIP2b. Three impact models were only selected because they provided IND<sub>i,y</sub>, IRR<sub>i,y</sub>, and the outputs for quantifying Q<sub>i,y</sub> under both scenarios RCP2.6-SSP2 and RCP6.0-SSP2. Each impact model was forced by four meteorological datasets (IPSL-CM5A-LR, GFDL-ESM2M,
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233 Following the approach in previous studies (Mekonnen and Hoekstra, 2016;

234 Veldkamp et al., 2017), $Q_{i,y}$ was defined the sum of local runoff and incoming

235 discharge from the upstream cells minus environmental flow requirements:

$$Q_{i,y} = Q_{loc,i,y} + \sum_{j=i+1}^{n} (Q_{j,y} - WC_{j,y}) - EFR_{i,y}, \quad [3]$$

237 where $Q_{loc,i}$ is the surface and subsurface runoff generated for cell $i$, $Q_j$ is natural

238 discharge entering cell $i$ from all upstream cells $j$. Both of $Q_{loc,i}$ and $Q_j$ were available

239 from an ensemble of three impact models forced by four meteorological datasets.

240 WC$_{j,y}$ is total upstream water consumptions for cell $i$ and scenario $y$, encompassing

241 domestic, industrial and irrigation sectors. Water consumption was calculated based

242 on the water consumption to water withdrawal ratio derived from Abu-Zeid and

243 Shiklomanov (2003). EFR$_i$ is the environmental flow requirements in cell $i$. Monthly

244 EFR was first calculated based on the variable monthly flow method (Pastor et al.,

245 2019) by using monthly natural flow that was obtained from the ISIMIP. Then

246 monthly EFR was aggregated to obtain annual values and annual average of EFR for

247 1991-2010 was used to calculate annual surface-water availability. In addition, $Q_{i,y}$

248 was different between scenario S1 and S2, as achieving universal water accessibility

249 means an increase in $WU_{i,y}$ and $WC_{j,y}$ that will decrease water resources available for

250 downstream cells. The “flow accumulation” function of ArcGIS was used to route

251 water consumption from upstream to downstream grid cells for two scenarios. The

252 flow direction raster at 0.5°×0.5° was obtained from the Simulated Topological
253 Network STN-30 (Ceola et al., 2019).

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255 On the basis of the considerations described above, we quantified affected population
256 and croplands at the grid scale that are exposed to additional surface-water deficit due
257 to achievement of universal water accessibility by 2030. The number of people who
258 move into domestic surface-water deficit from scenario S1 to S2 was determined
259 based on the global gridded population data (Jing et al., 2020). The irrigated area at
260 risk of suffering from additional irrigation surface-water deficit was calculated based
261 on the FAO's AQUASTAT (FAO, 2016).

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263 **3. Results**

264 **3.1. Historical change in domestic water use intensity and its drivers**

265 WUI<sub>dom</sub> of 220 administrative units increased by 29.8 L capita<sup>-1</sup> day<sup>-1</sup> from 155.9 L
capita<sup>-1</sup> day<sup>-1</sup> in P1 to 185.7 L capita<sup>-1</sup> day<sup>-1</sup> in P2 (Fig. 1A). To exclude the
266 possibility that the growth in WUI<sub>dom</sub> was caused by large increase in a few regions,
267 we repeated the analysis 300 times by randomly sampling 90% of the regions each
268 time and found consistent increase (29.9 &plusmn; 2.5 L capita<sup>-1</sup> day<sup>-1</sup>, P < 0.001). Spatial
269 analyses further confirmed that the growth in WUI<sub>dom</sub> was widespread affecting 68%
270 of global population (Fig. 1B). Change in domestic water-use intensities between P1
271 and P2 (&Delta;WUI<sub>dom</sub>) differed largely among economies (Figs 1A and Fig. S2). &Delta;WUI<sub>dom</sub>
272 was the largest in developing economies (58.0 L capita<sup>-1</sup> day<sup>-1</sup> [interquartile range: 6.9
273 &ndash; 94.8]), while &Delta;WUI<sub>dom</sub> was quite small in the least-developed economies (8.7 [-2.1
– 12.9] L capita⁻¹ day⁻¹). By contrast, WUI dom decreased in 75% of developed economies (-48.8 [-91.8 – -1.9] L capita⁻¹ day⁻¹), specifically in Canada, western USA, Australia, and Europe. ΔWUI dom showed a large spread between the economies in transition (from -71.2 to 136.7 L capita⁻¹ day⁻¹) with a decrease occurred primarily in eastern Europe and an increase in Central Asia.

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<figure><img src="image_2.png" /></figure>

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<caption>**Figure 1.** Historical changes in domestic water-use intensity (ΔWUI<sub>dom</sub>) and their drivers. A. ΔWUI<sub>dom</sub> surveyed from P1 to P2 for the globe and four economies. Four economies are classified according to United Nations development categories (United Nations, 2019), i.e., developed economies (D1), developing economies (D2), least-developed economies (D3), and economies in transition (D4) (see Fig. S2). Error bars present interquartile range across administrative units. Note that the first year (P1) and the last year (P2) may differ between administrative units depending on the data availability (see Fig. S1). B. Spatial pattern of ΔWUI<sub>dom</sub> surveyed from P1 to P2. C. Contribution of ΔWUI<sub>dom</sub> drivers for the globe and four economies. Bars represent contribution from changes in water accessibility (A), per-capita income (G), mean daily maximum temperature (T), annual precipitation (P), population structure (U), and unexplained factors (OF). D. Dominant factor of ΔWUI<sub>dom</sub>, defined as the driving</caption>

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factor that contributed the most to ΔWUI<sub>dom</sub>. See contribution of each driver to ΔWUI<sub>dom</sub> in Fig.S3.

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<figure><img src="image_3.png" /></figure>

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<caption>**Figure 2. Evaluation of the data-driven model.** A. Pattern of *WUI<sub>dom</sub>* surveyed from FAO's AQUASTAT, USGS, and (Zhou et al., 2020). B. Pattern of *WUI<sub>dom</sub>* simulated from our data-driven model. C. Pattern of *WUI<sub>dom</sub>* simulated from the income-based model. The model only considered the effect of per-capita income growth on *WUI<sub>dom</sub>*, and calibrated based on our survey data. D. Pattern of *WUI<sub>dom</sub>* simulated from an</caption>

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ensemble mean of three GHMs (H08, PCR-GLOBWB, and MATSIRO participating the ISIMIP2a). The value of WUI<sub>dom</sub> for each administrative unit is averaged over the period 1990 to 2015.

We attempted to quantify the relative contributions of climatic and socioeconomic drivers to ΔWUI<sub>dom</sub>. Prior to this analysis, we evaluated model performance of WUI<sub>dom</sub>.

First, the model explained 63% of the variations of surveyed WUI<sub>dom</sub> globally and avoided over-fitting as evidenced by the five-fold cross-validation (R<sup>2</sup> = 0.6-0.7 and RMSE = 62-78 L capita<sup>-1</sup> day<sup>-1</sup> due to 50-times resampling of possible subdivisions, Figs S4A and B). Second, the model performed better in reproducing the spatial variation of WUI<sub>dom</sub> averaged over 1990-2015 than income-based model and an ensemble of GHMs, particularly for the regions with high WUI<sub>dom</sub> and DOM (Fig. 2, Figs S4C and D). Third, our model required limited extrapolation to the global predictor layers, with covariate values in our compiled observations spanning most of the full multivariate environmental covariate space at the global scale, which suggests a high confidence of the data-driven model (Fig. S5). Last, our model performed well in predicting domestic water use especially for regions with higher quantities (Fig. S4E), and presented a lower RMSE of 0.83 km<sup>3</sup> yr<sup>-1</sup> than 2.2 km<sup>3</sup> yr<sup>-1</sup> presented by Hejazi et al. (2013). The global total over 1990-2015 was also in general consistent with previous studies (Fig. S4F).

At the global scale, water accessibility improvement was identified as the most important driver of ΔWUI<sub>dom</sub> accounting for 20.9 L capita<sup>-1</sup> day<sup>-1</sup> (Fig. 1C), followed by a shift in population structure (14.5 L capita<sup>-1</sup> day<sup>-1</sup>) and per-capita income growth
(9.8 L capita⁻¹ day⁻¹), while climate change was less important. Similarly, water accessibility improvement dominated the growth in WUIₗd in both developing and least-developed economies (Fig. 1C). This finding is consistent with the observational evidences in China (Fan et al., 2013), Nigeria (Akoteyon, 2019; Nyong and Kanaroglou, 2001), and Benin (Arouna and Dabbert, 2010). Spatial analyses further confirmed the dominant role of water accessibility improvement primarily in southern China, India, Mexico, and Sub-Saharan Africa, where lives 39% of the global population together (Fig. 1D). ΔWUIₗd was also positively associated with the shift in population structure in regions accounting for 7% of global population, mainly in eastern USA, northern and northwest China, Angola, Botswana, Mozambique and Rwanda (Fig. 1D). We further found a positive correlation between ΔWUIₗd and per-capita income growth in regions accounting for 8% of global population, mainly in Brazil, Columbia, and Argentina. In addition, our analyses suggest that other factors unexplained by our model might have non-negligible effects (colored in grey in Fig. 1). These factors dominated the increased WUIₗd in regions accounting for 17% of global population mainly in Central Asia, Southeast Asia, and North Africa. And these factors dominated the decreased WUIₗd in regions accounting for 23% of global population primarily in North America and Europe (Fig. 1D).

## 3.2. Additional growth in domestic water demand by 2030

For the RCP2.6-SSP2, our results indicate that global mean WUIₗd will increase to 233.4 L capita⁻¹ day⁻¹ in 2030 in S1 (i.e., water accessibility held as in 2015) and
further to 255.0 L capita⁻¹ day⁻¹ in S2 (i.e., water accessibility set to be 100%).

Namely, achieving universal water accessibility by 2030 may lead to an additional growth of WUIdom by 21.7 L capita⁻¹ day⁻¹ at the global scale (Fig. 3A), as quantified by the difference between scenarios S2 and S1. Such additional growth differs between four economies. Overall, additional growth in WUIdom would be 48.1 [32.4 – 66.0] L capita⁻¹ day⁻¹ in least-developed countries, higher than that in developing countries (19.6 [3.6 – 30.3] L capita⁻¹ day⁻¹), economies in transition (15.2 [1.3 – 28.4] L capita⁻¹ day⁻¹), and developed countries (6.2 [0–23.3] L capita⁻¹ day⁻¹) (Fig. 3A). The largest additional growth in WUIdom will occur in eastern USA, Mongolia, southwestern China, Sub-Saharan Africa, and Turkmenistan (Fig. 3B), as a result of high potential in water accessibility improvements (Fig. S6). For states in eastern USA (e.g. South Carolina, North Carolina, Indiana and Maine), possible improvements in access to water happen because percentage of total population served by self-supply are relatively high, ranging from 24-50% in 2015 (see *Supplementary Method* for the details on how water access is determined). The large additional growth in similar developed regions implies that although with overall high level of water accessibility, water supply challenges may occur from local perspective and needs finer investigations for further detailed research and management.
<figure><img src="image_4.png" /></figure>

<figure><img src="image_5.png" /></figure>

<figure><img src="image_6.png" /></figure>

<figure><img src="image_7.png" /><figcaption>**Figure 3.** Additional growth in domestic water-use intensity (ΔWUI<sub>dom</sub>) and domestic water demand (ΔDOM) due to achieving universal water accessibility. A. ΔWUI<sub>dom</sub> for the globe and four economies. Error bars present their interquartile range across administrative units. B. Spatial pattern of ΔWUI<sub>dom</sub>. C and D present similar contents to A and B, respectively, but for DOM. Additional growth was calculated as the difference between S1 and S2.</figcaption></figure>

Achieving universal water accessibility by 2030 may lead to the additional growth in domestic water demand by 65.2 km³ for the RCP2.6-SSP (Fig. 3C), which is ~30% of its total increase from 2015 to 2030 in S2. Developing countries would be the largest contributor to the additional growth in domestic water demand (62%), mostly because of the increase in population, followed by least-developed economies with a contribution of 31%, both from additional WUI<sub>dom</sub> and population increase, while the rest (7%) from developed and economies in transition. Additional growth in domestic water demand tend to concentrate in eastern USA, Sub-Saharan Africa, eastern China, and India as a result of densely distributed population (Fig. 3D and Fig. S7). It is worth to be noted that industrial and irrigation water demands in these regions would
be simultaneously large (Fig. S8), possibly intensifying the competition among sectors for surface-water resources. In addition, the results of additional growth in domestic water demand for RCP6.0-SSP2 could be found in Table S2 and Fig. S9.

## 3.3. Additional surface-water deficit by 2030

For the RCP2.6-SSP2, domestic water demand is expected to exceed surface-water availability by 73 km³ in 2030 in S1 and by 81 km³ in S2, where their difference (8.1 km³) represents additional domestic surface-water deficit due to achieving universal water accessibility. Such additional deficit will occur mainly in 31 developing countries and 15 least-developed countries, which account for 82% and 14% of the deficit, respectively (Fig. 4A). China and India will experience the largest additional domestic surface-water deficit (1.6 and 1.1 km³, Fig. 4A), as a result of significant additional growth in domestic water demand (8.1 and 6.9 km³) and reduction in surface-water availability (21.5 and 6.8 km³, Fig. S10). More importantly, domestic surface-water deficit will be doubled in 7 countries, i.e., Kenya, Mongolia, Afghanistan, Mali, Senegal, Yemen, and Morocco, when universal water accessibility achieved. An additional 88.3 million people will move into domestic surface-water deficit from 14 developing countries (62.2 million, Fig. 4B) and 9 least-developed countries (26.1 million). In particular, affected population will exceed 15% of the total population in Syria, Zambia, Jordan, Chad, Kenya, Uganda, and Israel (Table S3).
<figure><img src="image_8.png" /><figcaption>**Figure 4.** Domestic and irrigation surface-water deficits due to achieving universal water accessibility. A. Additional domestic surface-water deficit. B. The number of people move into domestic surface-water deficit from scenario S1 to S2. C. Additional irrigation surface-water deficit. D. Proportion of the irrigated area at risk of suffering from additional irrigation surface-water deficit. Inserted bars show values by four economies (developed [D1], developing [D2], least developed economies [D3] and economies in transition [D4]). Hatching and dot mark developing and least-developed economies, respectively.</figcaption></figure>

406

407

408

409

410

411

412

413

414
Achieving universal water accessibility may lead to an additional irrigation surface-water deficit across 51 countries worldwide (Fig. 4C and Table S4), as a result of additional increase in domestic water demand and simultaneous reduction in surface-water availability for irrigation sector. Larger additional irrigation surface-water deficit will concentrate in South Asia, Indonesia and north China (Fig. 4C). Nearly one fifth of global irrigated areas (16%) are expected to be at risk of additional irrigation surface-water deficit, and this proportion is the largest in economies in transition (25%), followed by developing economies (17%), least-developed economies (12%), and developed economies (7%) (Fig. 3D). Larger proportion (≥30%) of affected irrigated areas were found in 5 countries, i.e., Morocco, Uzbekistan, Niger, Yemen, and Tunisia (Table S4). These findings suggest an enhanced pressure on future food security that requires a multi-sectoral water management solution. It should be noted that both domestic and irrigation surface-water deficits are not detected in Central Africa (Fig. 4A and C), but an economic water scarcity was previously reported in these regions (Liu et al., 2017). In addition, the results of additional surface-water deficit for RCP6.0-SSP2 could be found in Table S2 and Fig. S11.

## 4. Discussion

In this study, we develop a data-driven model to project additional growth in domestic water demand due to achieving universal water accessibility by 2030 and to identify
global hotspots of additional surface-water deficits of both domestic and irrigation sectors. The model expands on previous researches in at least three aspects. First, we compiled an extensive dataset of surveyed domestic water use and its key drivers in 220 administrative units covering most of global population. This data provides a broader range of model variables with limited extrapolation requirements for global prediction (Fig. S5). Second, our data-driven model quantified WUI<sub>dom</sub> in response to income and non-income variables explicitly, while previous models were stratified by region to implicitly represent the effects of non-income variables (Flörke et al., 2013; Wada et al., 2016). Compared with an income-based model with four parameters calibrated against all observations, our model performed better for reproducing the spread of WUI<sub>dom</sub> globally (Fig. 2 and Fig. S4) and could support to quantify the influence from water accessibility improvements. The model thus provides a generic framework for more comprehensive factors to be included as it was developed by incorporating different end-uses processes. Third, based on the spatially detailed estimation of future domestic water demands, we identified the global hotspots of additional surface-water deficit. This could serve as an early warning for policymakers to implement flexible measures to ensure water supply reliability and food security.

We do not exclude the possibility that other variables ignored in this study might substantially impact WUI<sub>dom</sub>. First, technological improvements and water pricing policies, which were not incorporated into our model, might contribute to the decrease
of WUI<sub>dom</sub> in developed economies. For example, water efficient fixtures and technologies were deployed in the USA and contributed to the improvement in domestic water efficiency (Donnelly and Cooley, 2015; Schwabe et al., 2020); volume-based water pricing schemes were widely adopted in Canada and helped reduce WUI<sub>dom</sub> by 37% (Environment Canada, 2014). Second, the exclusion of demographic and housing variables (e.g. gender composition, household size, education level, age structure, garden size and type) and religious-related activities (e.g. ablution) might result in the underestimation of the positive ΔWUI<sub>dom</sub> in Central Asia and North Africa (Fan et al., 2013; Haziq and Panezai, 2017; Hussien et al., 2016). Last, diets and consumption habits like food waste can influence not only irrigation water use, but also domestic water use (Liu et al., 2013; Liu et al., 2008).

However, these factors are difficult to quantify over space and time, and their complex effects on domestic water use remain uncertain and should be extensively examined in the future (Fan et al., 2013).

473

Besides, it should be noted that this study focuses on capturing the average behavior of the municipal water use system, and thus be able to reveal difference among regions or economies. In other words, the current study could not reflect influence from varying individual water use habits, because both WUI<sub>dom</sub> and drivers could only reflect overall regional conditions, and data of individual characteristics was not accessible on global coverage. Moreover, we acknowledged that large differences in population size among regions exist, and the calibration using population weights
shown in the supplementary method could be used to ensure equivalent individual coverage. For example, when estimating the economic costs from technological improvements or water engineering projects to avoid such potential surface-water deficit risks, regional varying population weights would be necessary (Hughes et al., 2010). In addition, adoption of the model calibrated at administrative level to gridded level also rises concerns about result sensitivity against aggregation levels, which is difficult to be fully addressed by now due to data limitation. Nevertheless, this study is valuable in casting such a model in the context of an integrated assessment framework, and could serve as a benchmark for further model calibration and accurate assessments at local scales provided necessary data is available.

491

Despite these limitations, our assessments underscore a need of spatially flexible policies for both supply- and demand-side management to meet universal access to safe drinking water for all without additional surface-water deficits. First, developing economies may experience the largest domestic water use growth and additional surface-water deficits in the world (Fig. 4). Therefore, these countries could focus on technological improvements to accelerate decoupling of water demand from the rapid socioeconomic development, e.g., promotion of water-saving domestic appliance and irrigation technologies (Chu et al., 2009; Fan et al., 2017). In addition to conventional water resources, unconventional water resources, e.g., rainwater harvesting, wastewater recycling, and seawater desalination, could be explored to avoid the additional surface-water deficit. Joint efforts from government, community, school
and public media to raise water conservation awareness and to encourage water use
behavior change could also be taken simultaneously (Fan et al., 2017).

505

Second, the least-developed economies require the highest water accessibility
improvements of >30% (Fig. S6) and associated growth in domestic water demand.

On the one hand, expanding water accessibility through investment into construction
and maintenance of water infrastructures is proposed as the top priority. Water
governance including stakeholder involvement, such as the decentralized and
community-based water governance approach, is also suggested to be helpful in
advancing sustainable water access (Dos Santos et al., 2017). On the other hand,
targeted strategies for farming systems could also be encouraged, such as adopting
water price policies, adjustment of crop varieties into drought-resilient cropping
systems (Pastor et al., 2019) and the deployment of locally appropriate
water-harvesting techniques (Zhou et al., 2020). In addition, international financial
assistance and technology transfer could be inclined to these countries to improve
their water supply reliability.

519

Third, irrigation surface-water deficit may be significant in the economies in
transition, despite a lower additional domestic surface-water deficit compared to other
economies. For example, more than 30% of irrigated areas are threatened in
Uzbekistan and Turkmenistan. Thus it is urgent to curb the irrational water use
behaviors (e.g., fountain and excessive tree-planting) and avoid unintended water
losses in irrigation water supply systems (OECD, 2020; Russell, 2018), given the fact
that the goal of universal water accessibility is not within reach in these countries.

International cooperation between upstream and downstream countries could be
valuable for the Central Asia to improve irrigation water supply and to manage
transboundary water resources more sustainably (Bernauer and Bohmelt, 2020).

In contrast to the abovementioned economies, developed economies are not likely to
be at risk of additional surface-water deficit for domestic or irrigation sectors, largely
owing to their water accessibility almost close to 100%. One exception is the High
Plains in the USA which may suffer from the additional irrigation surface-water
deficit (Fig. S11). Lowering the current high WUI<sub>dom</sub> (&gt;300 L capita<sup>-1</sup> day<sup>-1</sup>, Fig. S12)
would make room for future irrigation water demand. Maintenance of current water
delivery network is proposed as a priority to reduce water loss from supply side, and
stricter water-pricing regulation has also been proposed to control WUI<sub>dom</sub> from
demand side (Maggioni, 2015; Sauri, 2013). Besides, increasing water-intensive food
import and redistributing crops could be an alternative to reduce irrigation water
demand (Chapagain et al., 2006; Davis et al., 2017; Qin et al., 2019).

5. Conclusion

In conclusion, this study reveals that water accessibility improvements dominated the
global growth in domestic water-use intensity over 1990-2015. Combining the
data-driven model of domestic water-use intensity with assessment of surface-water
547 availability from multi-model ensemble, this study suggests that the developing and
548 least-developed countries would likely suffer from additional domestic surface-water
deficit for achieving universal water accessibility by 2030. In addition, South Asia,
549 Central Asia, and North Africa are the hotspots of additional irrigation surface-water
deficits. The findings imply a rising dependence on groundwater resources or external
550 water resources (e.g., transboundary water diversion, virtual water trade) and an
551
552
553 intensive conflict between domestic and irrigation sectors.

554

555 Countries, corporations, and multilateral entities (e.g., the United Nations) have
556 proposed plans to realize universal water accessibility as one of the 17 sustainable
557 development goals (Botswana, 2017; China, 2016; Ireland, 2018; Japan, 2017; South
558 Africa, 2019; Sweden, 2017; Viet Nam, 2018). Although with the possibility of
ignoring some important drivers, our high-resolution assessments could serve as a
559
560 starting point for identifying areas facing surface-water deficit and targeting actions
towards high water supply reliability to meet universal water accessibility. To help
561 designing a spatially flexible set of actions, the data-driven model proposed in this
562 study is valuable, and should be coupled into the global hydrological models (GHMs)
563 (Flörke et al., 2013; Pokhrel et al., 2012; Rost et al., 2008). The empirical linkages
564 between WUI<sub>dom</sub> change and water accessibility identified in this study may be also
565 used for designing more realistic future water demand scenarios, with the ultimate
566 goal to improve GHMs for choosing water sources (e.g., surface water, groundwater,
567 surface-water transfer) to ensure universal water accessibility. Water accessibility
568
569 improvements depends not only on how strong the multi-national collaborations and
570 investments will incentivize the developing and least-developed economies with the
571 highest surface-water deficit, but also on how technical or socio-economic barriers are
572 addressed (Greve et al., 2018; Sadoff et al., 2020; Zhao et al., 2019). With that being
573 said, our study can serve as an important reference for policymakers to make sure that
574 achieving the SDG6.1 is implemented with a dynamic and sustainable water use
575 management framework.

576

577

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