Resources

1.Drought in Central‐Northern Europe (EDO, 2018); https://edo.jrc.ec.europa.eu/documents/news/EDODroughtNews201809_Central_North_Europe.pdf2.Drought in Europe (EDO, 2019); https://edo.jrc.ec.europa.eu/documents/news/EDODroughtNews201908_Europe.pdf3.Kovats, R. S. et al. in Climate Change 2014: Impacts, Adaptation, and Vulnerability (eds Fields, C. B. et al.) 1267–1326 (Cambridge Univ. Press, 2014).4.Spinoni, J., Naumann, G. & Vogt, J. V. Pan-European seasonal trends and recent changes of drought frequency and severity. Glob. Planet. Change 148, 113–130 (2017).Article 

Google Scholar 
5.Vörösmarty, C. J., Green, P., Salisbury, J. & Lammers, R. B. Global water resources: vulnerability from climate change and population growth. Science 289, 284–288 (2000).Article 

Google Scholar 
6.Döll, P., Fiedler, K. & Zhang, J. Global-scale analysis of river flow alterations due to water withdrawals and reservoirs. Hydrol. Earth Syst. Sci. 13, 2413 (2009).Article 

Google Scholar 
7.Wada, Y., Van Beek, L. P., Wanders, N. & Bierkens, M. F. Human water consumption intensifies hydrological drought worldwide. Environ. Res. Lett. 8, 034036 (2013).Article 

Google Scholar 
8.Tijdeman, E., Hannaford, J. & Stahl, K. Human influences on streamflow drought characteristics in England and Wales. Hydrol. Earth Syst. Sci. 22, 1051–1064 (2018).Article 

Google Scholar 
9.Beniston, M. et al. Future extreme events in European climate: an exploration of regional climate model projections. Climatic Change 81, 71–95 (2007).Article 

Google Scholar 
10.Nikulin, G., Kjellstrom, E., Hansson, U. L. F., Strandberg, G. & Ullerstig, A. Evaluation and future projections of temperature, precipitation and wind extremes over Europe in an ensemble of regional climate simulations. Tellus A 63, 41–55 (2011).Article 

Google Scholar 
11.Forzieri, G. et al. Ensemble projections of future streamflow droughts in Europe. Hydrol. Earth Syst. Sci. 18, 85–108 (2014).Article 

Google Scholar 
12.Samaniego, L. et al. Anthropogenic warming exacerbates European soil moisture droughts. Nat. Clim. Change 8, 421 (2018).Article 

Google Scholar 
13.Marx, A. et al. Climate change alters low flows in Europe under global warming of 1.5, 2, and 3 °C. Hydrol. Earth Syst. Sci. 22, 1017–1032 (2018).Article 

Google Scholar 
14.Stahl, K. et al. Impacts of European drought events: insights from an international database of text-based reports. Nat. Hazards Earth Syst. Sci. 16, 801–819 (2016).Article 

Google Scholar 
15.Mapping the Impacts of Natural hazards and Technological Accidents in Europe: An Overview of the Last Decade Technical Report No. 13/2010 (EEA, 2011); http://op.europa.eu/en/publication-detail/-/publication/4f5878ba-0947-4fb6-964b-8818cfda3de7/language-en16.Schär, C. et al. The role of increasing temperature variability in European summer heatwaves. Nature 427, 332–336 (2004).Article 
CAS 

Google Scholar 
17.Gil, M., Garrido, A. & Hernández-Mora, N. Direct and indirect economic impacts of drought in the agri-food sector in the Ebro River basin (Spain). Nat. Hazards Earth Syst. Sci. 13, 2679–2694 (2013).Article 

Google Scholar 
18.García-León, D., Standardi, G. & Staccione, A. An integrated approach for the estimation of agricultural drought costs. Land Use Policy 100, 104923 (2021).Article 

Google Scholar 
19.Byers, E. A., Coxon, G., Freer, J. & Hall, J. W. Drought and climate change impacts on cooling water shortages and electricity prices in Great Britain. Nat. Commun. 11, 2239 (2020).CAS 
Article 

Google Scholar 
20.Salmoral, G., Rey, D., Rudd, A., Margon, Pde & Holman, I. A probabilistic risk assessment of the national economic impacts of regulatory drought management on irrigated agriculture. Earths Future 7, 178–196 (2019).Article 

Google Scholar 
21.Naumann, G., Spinoni, J., Vogt, J. V. & Barbosa, P. Assessment of drought damages and their uncertainties in Europe. Environ. Res. Lett. 10, 124013 (2015).Article 

Google Scholar 
22.Stagge, J. H., Kohn, I., Tallaksen, L. M. & Stahl, K. Modeling drought impact occurrence based on meteorological drought indices in Europe. J. Hydrol. 530, 37–50 (2015).Article 

Google Scholar 
23.Blauhut, V. et al. Estimating drought risk across Europe from reported drought impacts, drought indices, and vulnerability factors. Hydrol. Earth Syst. Sci. 20, 2779–2800 (2016).Article 

Google Scholar 
24.Freire-González, J., Decker, C. & Hall, J. W. The economic impacts of droughts: a framework for analysis. Ecol. Econ. 132, 196–204 (2017).Article 

Google Scholar 
25.The 2015 Ageing Report: Underlying Assumptions and Projection Methodologies (European Commission, 2014).26.Berg, A. et al. Land–atmosphere feedbacks amplify aridity increase over land under global warming. Nat. Clim. Change 6, 869–874 (2016).Article 

Google Scholar 
27.Dosio, A. & Fischer, E. M. Will half a degree make a difference? Robust projections of indices of mean and extreme climate in Europe under 1.5 °C, 2 °C, and 3 °C global warming. Geophys. Res. Lett. 45, 935–944 (2018).Article 

Google Scholar 
28.Jacob, D. et al. Climate impacts in Europe under +1.5 °C global warming. Earths Future 6, 264–285 (2018).Article 

Google Scholar 
29.Alfieri, L., Dottori, F., Betts, R., Salamon, P. & Feyen, L. Multi-model projections of river flood risk in Europe under global warming. Climate 6, 6 (2018).Article 

Google Scholar 
30.Vousdoukas, M. I. et al. Climatic and socioeconomic controls of future coastal flood risk in Europe. Nat. Clim. Change 8, 776–780 (2018).Article 

Google Scholar 
31.Estrela, T. & Vargas, E. Drought management plans in the European Union. The case of Spain. Water Resour. Manag. 26, 1537–1553 (2012).Article 

Google Scholar 
32.Dellink, R., Chateau, J., Lanzi, E. & Magné, B. Long-term economic growth projections in the shared socioeconomic pathways. Glob. Environ. Change 42, 200–214 (2017).Article 

Google Scholar 
33.Christensen, P., Gillingham, K. & Nordhaus, W. Uncertainty in forecasts of long-run economic growth. Proc. Natl Acad. Sci. USA 115, 5409–5414 (2018).CAS 
Article 

Google Scholar 
34.Global Assessment Report on Disaster Risk Reduction 2019 (United Nations Office for Disaster Risk Reduction, 2019).35.Forzieri, G. et al. Escalating impacts of climate extremes on critical infrastructures in Europe. Glob. Environ. Change 48, 97–107 (2018).Article 

Google Scholar 
36.Erfurt, M., Glaser, R. & Blauhut, V. Changing impacts and societal responses to drought in southwestern Germany since 1800. Reg. Environ. Change 19, 2311–2323 (2019).37.Swiss Re The hidden risks of climate change: an increase in property damage from soil subsidence in Europe. PreventionWeb https://www.preventionweb.net/publications/view/20623 (2011).38.Formetta, G. & Feyen, L. Empirical evidence of declining global vulnerability to climate-related hazards. Glob. Environ. Change 57, 101920 (2019).Article 

Google Scholar 
39.Zhang, H., Li, Y. & Zhu, J.-K. Developing naturally stress-resistant crops for a sustainable agriculture. Nat. Plants 4, 989–996 (2018).Article 

Google Scholar 
40.Lohrmann, A., Farfan, J., Caldera, U., Lohrmann, C. & Breyer, C. Global scenarios for significant water use reduction in thermal power plants based on cooling water demand estimation using satellite imagery. Nat. Energy 4, 1040–1048 (2019).Article 

Google Scholar 
41.Hallegatte, S., Przyluski, V. & Vogt-Schilb, A. Building world narratives for climate change impact, adaptation and vulnerability analyses. Nat. Clim. Change 1, 151–155 (2011).Article 

Google Scholar 
42.Di Baldassarre, G. et al. Water shortages worsened by reservoir effects. Nat. Sustain. 1, 617–622 (2018).Article 

Google Scholar 
43.Guerreiro, S. B., Dawson, R. J., Kilsby, C., Lewis, E. & Ford, A. Future heat-waves, droughts and floods in 571 European cities. Environ. Res. Lett. 13, 034009 (2018).Article 

Google Scholar 
44.Vetter, T. et al. Evaluation of sources of uncertainty in projected hydrological changes under climate change in 12 large-scale river basins. Climatic Change 141, 419–433 (2017).CAS 
Article 

Google Scholar 
45.Hattermann, F. F. et al. Sources of uncertainty in hydrological climate impact assessment: a cross-scale study. Environ. Res. Lett. 13, 015006 (2018).Article 

Google Scholar 
46.Hattermann, F. F. et al. Cross-scale intercomparison of climate change impacts simulated by regional and global hydrological models in eleven large river basins. Climatic Change 141, 561–576 (2017).Article 

Google Scholar 
47.Addressing the Challenge of Water Scarcity and Droughts in the European Union (European Commission, 2007); https://www.eea.europa.eu/policy-documents/addressing-the-challenge-of-water48.Smith, A. B. U.S. Billion-Dollar Weather and Climate Disasters, 1980–Present (NCEI Accession 0209268) (NOAA, 2020); https://doi.org/10.25921/STKW-7W7349.Martin-Ortega, J., González-Eguino, M. & Markandya, A. The costs of drought: the 2007/2008 case of Barcelona. Water Policy 14, 539–560 (2012).Article 

Google Scholar 
50.Zampieri, M. et al. Climate resilience of the top ten wheat producers in the Mediterranean and the Middle East. Reg. Environ. Change 20, 41 (2020).Article 

Google Scholar 
51.Vliet, M. T. H., van, Vögele, S. & Rübbelke, D. Water constraints on European power supply under climate change: impacts on electricity prices. Environ. Res. Lett. 8, 035010 (2013).Article 

Google Scholar 
52.Lehner, B., Czisch, G. & Vassolo, S. The impact of global change on the hydropower potential of Europe: a model-based analysis. Energy Policy 33, 839–855 (2005).Article 

Google Scholar 
53.Jenkins, K. Indirect economic losses of drought under future projections of climate change: a case study for Spain. Nat. Hazards 69, 1967–1986 (2013).Article 

Google Scholar 
54.Gall, M., Borden, K. A. & Cutter, S. L. When do losses count? Six fallacies of natural hazards loss data. Bull. Am. Meteorol. Soc. 90, 799–810 (2009).Article 

Google Scholar 
55.Xu, C. et al. Increasing impacts of extreme droughts on vegetation productivity under climate change. Nat. Clim. Change 9, 948–953 (2019).CAS 
Article 

Google Scholar 
56.Choat, B. et al. Triggers of tree mortality under drought. Nature https://www.nature.com/articles/s41586-018-0240-x (2018).57.Seidl, R. et al. Invasive alien pests threaten the carbon stored in Europe’s forests. Nat. Commun. 9, 1626 (2018).Article 
CAS 

Google Scholar 
58.Sutanto, S. J., Vitolo, C., Di Napoli, C., D’Andrea, M. & Van Lanen, H. A. J. Heatwaves, droughts, and fires: exploring compound and cascading dry hazards at the pan-European scale. Environ. Int. 134, 105276 (2020).Article 

Google Scholar 
59.de Ruiter, M. C. et al. Why we can no longer ignore consecutive disasters. Earths Future 8, e2019EF001425 (2020).Article 

Google Scholar 
60.Ford, T. W. & Labosier, C. F. Meteorological conditions associated with the onset of flash drought in the eastern United States. Agric. For. Meteorol. 247, 414–423 (2017).Article 

Google Scholar 
61.Yuan, X., Wang, L. & Wood, E. F. Anthropogenic intensification of southern African flash droughts as exemplified by the 2015/16 season. Bull. Am. Meteorol. Soc. 99, S86–S90 (2018).Article 

Google Scholar 
62.Yuan, X., Ma, Z., Pan, M. & Shi, C. Microwave remote sensing of short-term droughts during crop growing seasons. Geophys. Res. Lett. 42, 4394–4401 (2015).Article 

Google Scholar 
63.Nguyen, H. et al. Using the evaporative stress index to monitor flash drought in Australia. Environ. Res. Lett. 14, 064016 (2019).Article 

Google Scholar 
64.Pendergrass, A. G. et al. Flash droughts present a new challenge for subseasonal-to-seasonal prediction. Nat. Clim. Change 10, 191–199 (2020).Article 

Google Scholar 
65.Hagenlocher, M. et al. Drought vulnerability and risk assessments: state of the art, persistent gaps, and research agenda. Environ. Res. Lett. 14, 083002 (2019).Article 

Google Scholar 
66.Jacobs-Crisioni, C. et al. The LUISA Territorial Reference Scenario 2017: A Technical Description (Publications Office of the European Union, 2017); https://ec.europa.eu/jrc/en/publication/luisa-territorial-reference-scenario-201767.Capros, P. et al. GEM-E3 Model Documentation (Publications Office of the European Union, 2013); https://publications.jrc.ec.europa.eu/repository/handle/111111111/3236668.Keramidas, K., Kitous, A., Després, J. & Schmitz, A. POLES-JRC Model Documentation (Publications Office of the European Union, 2017); https://publications.jrc.ec.europa.eu/repository/handle/JRC11375769.Feyen, L. & Dankers, R. Impact of global warming on streamflow drought in Europe. J. Geophys. Res. Atmos. 114, D17116 (2009).Article 

Google Scholar 
70.Tallaksen, L. M. & Van Lanen, H. A. Hydrological Drought: Processes and Estimation Methods for Streamflow and Groundwater Vol. 48 (Elsevier, 2004).71.Lehner, B., Döll, P., Alcamo, J., Henrichs, T. & Kaspar, F. Estimating the impact of global change on flood and drought risks in Europe: a continental, integrated analysis. Climatic Change 75, 273–299 (2006).Article 

Google Scholar 
72.Roudier, P. et al. Projections of future floods and hydrological droughts in Europe under a +2 °C global warming. Climatic Change 135, 341–355 (2016).Article 

Google Scholar 
73.Knijff, J. M. V. D., Younis, J. & Roo, A. P. J. D. LISFLOOD: a GIS‐based distributed model for river basin scale water balance and flood simulation. Int. J. Geogr. Inf. Sci. 24, 189–212 (2010).Article 

Google Scholar 
74.Salamon, P. et al. EFAS Upgrade for the Extended Model Domain (Publications Office of the European Union, 2019); https://publications.jrc.ec.europa.eu/repository/handle/111111111/5558775.Jacob, D. et al. EURO-CORDEX: new high-resolution climate change projections for European impact research. Reg. Environ. Change 14, 563–578 (2014).Article 

Google Scholar 
76.Knutti, R. et al. Meeting Report. In IPCC Expert Meeting on Assessing and Combining Multi Model Climate Projections (eds Stocker, T. F. et al.) (IPCC, 2010).77.Shepherd, T. G. Storyline approach to the construction of regional climate change information. Proc. R. Soc. Lond. A 475, 20190013 (2019).
Google Scholar 
78.Mentaschi, L. et al. Independence of future changes of river runoff in Europe from the pathway to global warming. Climate 8, 22 (2020).Article 

Google Scholar 
79.Mentaschi, L. et al. The transformed-stationary approach: a generic and simplified methodology for non-stationary extreme value analysis. Hydrol. Earth Syst. Sci. 20, 3527–3547 (2016).Article 

Google Scholar 
80.Forzieri, G. et al. Resilience of Large Investments and Critical Infrastructures in Europe to Climate Change (Publications Office of the European Union, 2015); https://publications.jrc.ec.europa.eu/repository/handle/111111111/3889481.Batista e Silva, F. et al. HARCI-EU, a harmonized gridded dataset of critical infrastructures in Europe for large-scale risk assessments. Sci. Data 6, 126 (2019).Article 

Google Scholar 
82.Doornkamp, J. C. Clay shrinkage induced subsidence. Geogr. J. 159, 196–202 (1993).Article 

Google Scholar 
83.Boivin, P., Garnier, P. & Tessier, D. Relationship between clay content, clay type, and shrinkage properties of soil samples. Soil Sci. Soc. Am. J. 68, 1145–1153 (2004).CAS 
Article 

Google Scholar 
84.Hiederer, R. Mapping Soil Properties for Europe—Spatial Representation of Soil Database Attributes (Publications Office of the European Union, 2013); https://publications.jrc.ec.europa.eu/repository/handle/111111111/2917085.Crilly, M. Analysis of a database of subsidence damage. Struct. Surv. 19, 7–15 (2001).Article 

Google Scholar 
86.Corti, T., Wüest, M., Bresch, D. & Seneviratne, S. I. Drought-induced building damages from simulations at regional scale. Nat. Hazards Earth Syst. Sci. 11, 3335–3342 (2011).Article 

Google Scholar 
87.Batista e Silva, F., Lavalle, C. & Koomen, E. A procedure to obtain a refined European land use/cover map. J. Land Use Sci. 8, 255–283 (2013).Article 

Google Scholar 
88.Florczyk, A. et al. GHSL Data Package 2019 (Publications Office of the European Union, 2019); https://publications.jrc.ec.europa.eu/repository/handle/111111111/5655289.Kron, W., Steuer, M., Löw, P. & Wirtz, A. How to deal properly with a natural catastrophe database—analysis of flood losses. Nat. Hazards Earth Syst. Sci. 12, 535–550 (2012).90.Felbermayr, G. & Gröschl, J. Naturally negative: the growth effects of natural disasters. J. Dev. Econ. 111, 92–106 (2014).Article 

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Quantitative variablesThe intervention described here relies on the IT platform “SmartH2O” for the collection and visualization of smart meter data, the provision of consumption feedback to the user, the delivery of water-saving recommendations, and the engagement of the consumer through a gamification program20,21,34,35. We embedded a gamification mechanism in the digital platform to maximize user retention and stimulate the exploration and sharing of content and the setting and achievement of personal saving goals. Via the gamification mechanisms, users could collect reward points for different actions performed in the digital platform or the achievement of water-saving targets. Reward points consisted of virtual points that the users could redeem for physical rewards. The design of the SmartH2O digital platform and the behavioral change stimuli that have been introduced in the Valencia case-control study (e.g., web and mobile app, different reward schemes), along with their individual elements and the corresponding illustrative screenshots of the platform are provided in the Supplementary Information, consistently with the information published in a previous study21. Other platforms similar to SmartH2O or approaches for water conservation based on digital technologies are reported in the literature, including, e.g., real-time water consumption feedback on in-home displays, interactive dashboards, and games36,37. Yet, to the author’s knowledge, SmartH2O is the first platform of its kind whose effect is rigorously assessed in the medium term and long term.Household-scale water consumption data and smart meter sampling frequencyWater consumption readings measured at the household scale constitute the main quantitative variable of interest used in this observational study to identify behavior changes. The SmartH2O digital platform relies on water consumption information stored in a central database and enables data communication from the water utility to the water consumers (see Supplementary Fig. 1 for its software architecture). Water consumption data are collected by smart meters installed at the household premises, according to a schedule that considers the maximum available frequency of data sampling at each installation (hourly or daily). The consumption data are anonymized by the utility company, filtered, and transferred to the central database of the SmartH2O platform. The content of the central database is published to the user via a web portal and a mobile application, which are the entry points of all users’ interactions with the platform.Besides the time series of water consumption, we also stored the sampling frequency allowed by each household-scale smart meter. Two types of sampling frequencies were available in the considered population, depending on the installed smart meter hardware: hourly or daily.Digital user engagement variablesThe central database of the SmartH2O platform comprises content for improving user awareness, such as water-saving recommendations, and for implementing the gamification program, such as the description of virtual and physical rewards. The interaction of the users with the platform and the overall user experience features several functionalities, including user login, water consumption and smart meter-based feedback visualization, conservation goal settings, and different gamified water conservation awareness actions (see also Supplementary Notes1). We monitored the activity of each user in the SmartH2O platform for the entire duration of the treatment period and gathered quantitative data on these four digital user engagement variables:

(i)

Login count, defined as the total number of logins executed by each user.

(ii)

Non-rewarded action count, defined as the total number of actions performed by each user, with no reward points associated.

(iii)

Rewarded action count, defined as the total number of actions performed by each user, with associated reward points upon their completion.

(iv)

Cumulative reward importance, defined as the total amount of points achieved by each user by completing the rewarded actions. It accounts for the total amount of points, badges, and rewards achieved by an individual user in the SmartH2O platform.

Each user profile in the SmartH2O platform was associated with a unique smart meter ID, which allowed linking the user activity in the platform with the household water consumption data. User confidentiality was maintained throughout the full study as data were anonymized by the water utility managing the water meters and the central database.Population and study sizeOur observational study was conducted in the city of Valencia, Spain. With a population of 794,288 inhabitants, as reported in 2019 by the Spanish National Institute of Statistics (Institudo Nacional de Estadística)38, Valencia is the third-largest city in Spain. The water utility of Valencia (Global Omnium–EMIVASA) has installed more than 425,000 smart water meters since the early developments in 2006 to monitor the water consumption of nearly all the population39 (the last official census data, recorded in 2011, report 419,994 households in total in Valencia40). The total population considered in this study after application of the exclusion criteria described in the next section included 334 individual households, each equipped with a water meter.The architecture of the smart metering infrastructure deployed in Valencia has been designed in order to be vendor-independent, so it allows for different smart metering solutions to be integrated39. While this is clearly an advantage for procurement, the diversity of hardware has an impact on data sampling and only one of the available technologies supports hourly data collection, which is a preferred requirement for water consumption data quality assessment and provision of sub-daily water consumption information to households in our case-control study. The number of hourly reading meters in Valencia amounts to 168,172 as of July 12th, 2020. EMIVASA also offered its customers access to a web platform where bills and invoices could be managed and also information about the current (daily and monthly) water consumption data was made available.During our observational study, we integrated the digital SmartH2O platform20,21 in the EMIVASA portal. We invited users who already had an account in the platform and a compatible meter reading frequency to voluntarily join our observational study and sign up to the SmartH2O platform. The recruitment campaign was performed using different media channels, namely, newspaper articles on consumer magazines, radio programs, banners on the digital and printed invoices sent to EMIVASA customers, and also a Facebook campaign targeting the Valencia area. At the end of the recruitment campaign, we received 525 applications out of which we obtained a treatment group composed of 223 households after application of the inclusion/exclusion criteria. Out of the households who did not apply to join the case-control study during the recruitment phase, 111 households agreed to be monitored as part of the self-selected control group to be considered as a benchmark group not subject to treatment, after active recruitment via phone by the EMIVASA call center (client service management). Households in the control group had only access to their water consumption data through the already existing platform, which did not offer any type of smart meter-based consumption feedback, behavioral stimuli, and/or gamification elements.Informed consent was obtained from the households monitored in this study. Moreover, the water utility (Global Omnium–EMIVASA) supervised and approved the collection, usage, and processing of the anonymized quantitative variables above described in compliance with the EU General Data Protection Regulation 2016/679 and the pre-existing Spanish law 15/1999 LOPD of 1999 (the SmartH2O study started before the adoption of the GDPR in 2016).Baseline and observation periodsThe treatment period of the case-control study lasted 8.5 months, from June 2016 to February 2017. We also continuously collected anonymized water consumption data for the study population from June 2016 to February 2019 both to conduct the longitudinal study presented in this paper and evaluate water consumption changes over time in comparison with a pre-treatment baseline (June 1st, 2015– April 30th, 2016), as well as to compare water consumption changes in the treatment and control groups. Consistently with the months included in the treatment period (short-term behavior change), we identify the observation period June 1st, 2017–February 2nd, 2018 for medium-term behavior change assessment, and the observation period June 1st, 2018–February 2nd, 2019 for long-term behavior change.Exclusion criteriaThe population considered for analysis of water consumption changes in this observational study was obtained by sequential application of the following exclusion criteria.

1.

Exclusion of empty households. First, we excluded the households with no data in the baseline and treatment period. We classified in this category also the households with a cumulative water consumption lower than 1.5 m3 over the whole baseline and treatment period (which together last nearly 20 months). This threshold value was identified as a conservative choice after consultation with the local water utility and comparison with the average values of water consumption in the entire population (slightly above 0.21 m3/day) and the European average water consumption, which amounts to 128 liters per inhabitant per day (0.128 m3/day)41. A household in the considered population would use ~1.5 m3 in one week (0.21 m3/day × 7 days). While lower values than the average consumption are observed in those days in which the inhabitants spend little time at home, a cumulative consumption of 1.5 m3 over the course of more than 1 year can indicate that the house is generally empty (and possibly the observed water consumption is due to leaks).

2.

Exclusion of households with insufficient data length. We removed the households with water consumption readings for less than 1000 h (approximately 6 weeks). This step guarantees a minimum representation of weekend/weekday water demand variation for more than 1 month (please note that the total duration of the treatment period is 8.5 months).

3.

Exclusion of partially empty households. We excluded the households with more than 90% water consumption readings equal to zero in the baseline or observation period or completely lacking data for one of these two periods. We considered these households to be empty or equipped with faulty meters at least during one of the two short-term periods of interest. The above value threshold of 90% was identified with a trial-and-error procedure and expert-based data analysis that balance the rate of exclusion with the size of the remaining dataset.

4.

Exclusion of households lacking day-of-week representation. We excluded the households with available observations for less than 7 unique day types, to guarantee a minimum representation of water consumption routines that depend on the day of the week. For those households with smart meters recording water consumption with hourly sampling frequency, we removed days with more than 4 h of gaps from the smart meter time series (anomalous meter data logging).

5.

Exclusion of households with anomalous high water consumption. We considered hourly water consumption readings larger than 1 m3 as outliers (we thus removed these hourly readings) and we removed the households with a daily average water consumption larger than 1 m3 in at least one phase of the longitudinal study. High values of water consumption can be observed for specific days (e.g., when customers use water for outdoor irrigation or filling up a pool), yet average daily water consumption values over the selected threshold are more than three times higher than the European average (equivalent to approximately 0.3 m3/day per household). We did not apply more restrictive thresholds, in order not to bias our analysis and avoid unjustified exclusion of high water consumers.

6.

Exclusion of households with unrealistic short-term consumption change levels. We excluded the households with extreme values of short-term consumption change during the treatment period, which were identified as outliers by Tukey’s fences42. According to Tukey’s fences, a data point xi is considered an outlier if:$$x_i notin [Q_1 – kleft( {Q_3 – Q_1} right),Q_3 + k(Q_3 – Q_1)]$$
(1)

where Q1 is the 25th empirical quartile (i.e., 25% of the data is lower than this point) and Q3 is the 75th empirical quartile (i.e., 75% of the data is lower than this point), and k = 1.5. Tukey’s fences with k = 1.5 approximate the 99.7% confidence interval defined for normal distributions by a distance of three standard deviations from the mean.

7.

Exclusion of households with anomalous conditions in medium-term and long-term. We excluded 51 households that met the above exclusion criteria 1–6 during either the medium-term or long-term observation periods. Water consumption change patterns would be incomplete/anomalous for these households, with at least one missing/anomalous period out of the four periods of interest (i.e., baseline, treatment period, or following observation periods in 2018 and 2019).

With the above exclusion criteria, we obtained the 334 households considered for behavior change analysis in this observational study. More details on the population size after application of each exclusion criteria are reported with a flow diagram in Supplementary Fig. 1043. It is worth noting that only less than 2% of high consumption households have been excluded, while most of the other excluded households had insufficient data or unrealistically low consumption levels. Also, the number of households in the sample considered here differ from those considered in the evaluation of the SmartH2O project44, due to the different temporal length of the two studies and the application of the exclusion criteria on data recorded in different periods (the SmartH2O project only included the baseline and treatment periods).Adopting the same criteria to exclude households from the behavior change analysis only during the summer period (Fig. 1d) resulted in a reduced population of 179 households (101 households in the treatment group and 78 households in the control group), due to limited data availability for the summer period. Similarly, a subset of 198 households in the treatment group was considered for the correlation analysis by logistic regression (Fig. 4), as the excluded 25 households presented incomplete smart meter data or incomplete information on their usage of the digital SmartH2O application.Data analysis and statistical methodsWe performed customer segmentation to analyze heterogeneous long-term behavior change patterns (Fig. 3 and Supplementary Fig. 9). We applied agglomerative hierarchical clustering45 to the patterns of average daily household water consumption during the entire duration of the longitudinal study. Here, a water consumption pattern of a household is a vector that contains four values of average daily water consumption, i.e., one for each period of the observational study, including the baseline (see “Methods” section–Baseline and observation periods). The only variable given as input to the hierarchical clustering algorithm consists of household-scale average water consumption per day for each phase of our observational study, which spans the baseline and the three observation periods in 2017, 2018, and 2019. Complete linkage and correlation distance were considered for hierarchical clustering. Complete linkage calculates the distance between two household clusters as the distance between the farthest pair of household water consumption patterns in the two household clusters, i.e., the maximum distance formulated as follows:46$$dleft( {u,v} right) = {mathrm{max}}left( {{mathrm{dist}}left( {uleft( {x_i} right),vleft( {z_i} right)} right)} right.$$
(2)
where d(u,v) is the distance between clusters u and v, xi are the points belonging to cluster u and zi those belonging to cluster v. Given two vectors of observations xi and xj, which in our study correspond to the water consumption patterns of two households (each with N elements, with N = 4, where each element is the household-scale average water consumption per day for the baseline and three observation periods) and their mean values ((bar x_i) and (bar x_j)) the correlation distance used by hierarchical clustering is calculated as follows:47$${mathrm{dist}}left( {x_i,x_j} right) = 1 – frac{{(x_i – bar x_i) cdot (x_j – bar x_j)}}{{left| {x_i – bar x_i} right|_2left| {x_j – bar x_j} right|_2}}$$
(3)
We considered hierarchical clustering as an appropriate choice because the analysis of the different hierarchical levels allowed the discovery of heterogeneous water consumption behaviors that would be potentially hidden if algorithms requiring a predefined number of clusters were used. We adopted complete linkage clustering to avoid that individual, mutually close households would force pairs of clusters representing different behaviors to merge. Also, we adopted correlation distance as we wanted to identify similarities in water consumption patterns over time, rather than in water consumption volumes.After clustering the households in the treatment group with the above hierarchical clustering, similarly to a previous study18, we analyzed the coefficients of a logistic regression classifier cross-validated with binary tests to identify which candidate factors correlate with the main behavior change patterns that characterize the households in the treatment group (Fig. 4 and Supplementary Table 3). In this study, the input candidate factors consist of five independent variables that comprise the availability of smart meter hourly data frequency and the four digital user engagement variables, i.e., login count, non-rewarded action count, rewarded action count, and cumulative reward importance. First, we balanced the distribution of the households in the treatment group across the behavior change segments considered in the binary tests by Synthetic Minority Over-sampling Technique (SMOTE)48. SMOTE oversamples the minority class to balance the sample distribution of a labeled dataset over the different classes. As we consider binary test where only two behavior change segments (or two groups of behavior change segments) are compared, the majority class represents the behavior change segment (or group of behavior change segments) with the highest number of samples and vice versa for the minority class. According to the SMOTE formulation48, starting from a sample ci,initial, which in this study is the vector of input candidate factors for a household i in the minority class, a new sample ci,new is generated on the line between ci,initial, and one of its k nearest-neighbors cj,initial, with the following formula:$$c_{i,{mathrm{new}}} = c_{i,{mathrm{initial}}} + lambda (c_{j,{mathrm{initial}}} – c_{i,{mathrm{initial}}})$$
(4)
where λ is a random number between 0 and 1, and k = 5 nearest neighbors computed based on Euclidean distance are considered by default48. Among the possible options to perform class balancing, here we adopted a “not majority” strategy to over-sample the minority classes, i.e., we resample all classes but the majority class (which, in our binary problem, is equivalent to resampling the minority class).Second, we trained a logistic regression classifier49 with k-fold cross-validation (k = 5) and evaluated its performance via weighted F1 score. In our binary problem, the logistic regression classifier models the class membership probability P(yi,p = 1) for household i, where yi,p = 1 indicates that the household belongs to behavior change pattern p (else yi,p = 0, according to the following logistic function:$$Pleft( {y_{i,p} = 1} right) = frac{1}{{1 + exp^{ – f(c_i)}}}$$
(5)
where f(ci) is a linear function where the input variables ci are weighted by corresponding coefficients α:$$fleft( {c_i} right) = alpha _0 + alpha _1c_{i,1} + alpha _2c_{i,2} + ldots + alpha _Mc_{i,M} + varepsilon _i$$
(6)
In this study, M = 5, ci,1 is a binary variable representing the availability of smart meter with hourly data frequency, ci,{2,3,4,5} are the four digital user engagement variables defined above, α0 is the intercept of the logistic regression, and εi is random noise. We normalized the variables before logistic regression classification by subtracting the mean and dividing by the standard deviation to rescale them to comparable value ranges. The analysis of their corresponding logistic regression coefficients reveals how these variables discriminate among different clusters of water consumers and, thus, how they are potential determinants of defined water consumption behaviors. The F1 score (FS) is first calculated for each behavior change pattern (or group of patterns) p as the harmonic mean of the precision and recall achieved by the logistic regression classifier, formulated as follows:$${mathrm{FS}}_p = 2 times frac{{({mathrm{precision}}_p times {mathrm{recall}}_p)}}{{({mathrm{precision}}_p + {mathrm{recall}}_p)}}$$
(7)
$${mathrm{Precision}}_p = frac{{{mathrm{TP}}_p}}{{{mathrm{TP}}_p + {mathrm{FP}}_p}}$$
(8)
$${mathrm{Recall}}_p = frac{{{mathrm{TP}}_p}}{{{mathrm{TP}}_p + {mathrm{FN}}_p}}$$
(9)
where, given positive and negative classes, TPp, FPp, and FNp are the number of true positive elements (the classifier correctly predicts the positive class for them), false-positive elements (the classifier incorrectly predicts the positive class), and false negative elements (the classifier incorrectly predicts the negative class). A weighted average of the FSp is then computed to account for class imbalance:$${mathrm{FS}}_{{mathrm{average}}} = frac{1}{H}mathop {sum }limits_{p in P} |p| times {mathrm{FS}}_p$$
(10)
where P is the total number of classes p and H is the total number of elements aggregated across all classes.Software implementationWe coded the exclusion criteria in Matlab and used the “prctile” function for the calculation of the quantiles in Tukey’s fences (last Matlab version tested: R2020b)50. We implemented the customer segmentation analysis and logistic regression classifier in Python (version 3.7.1): the customer segmentation analysis relies on the hierarchical clustering included in the SciPy library51; the logistic regression classifier, along with its k-fold cross-validation and performance evaluation, were implemented using the machine learning library Scikit-learn52; SMOTE oversampling was implemented using the Imbalanced-learn toolbox53. A notebook with the Python code used to generate the results reported in this article is available in a public GitHub repository54. More

1.Prest, E. I., Hammes, F., van Loosdrecht, M. C. M. & Vrouwenvelder, J. S. Biological stability of drinking water: controlling factors, methods, and challenges. Front. Microbiol. 7, 45 (2016).Article 

Google Scholar 
2.de Moel, P. J., Verberk, J. Q. J. C. & van Dijk, J. C. Drinking water: Principles and practices. Drinking water: Principles and practices. (World Scientific Publishing Co., 2006). https://doi.org/10.1142/6135.3.Nescerecka, A., Rubulis, J., Vital, M., Juhna, T. & Hammes, F. Biological instability in a chlorinated drinking water distribution network. PLoS ONE 9, e96354 (2014).Article 
CAS 

Google Scholar 
4.Skjevrak, I., Lund, V., Ormerod, K., Due, A. & Herikstad, H. Biofilm in water pipelines; a potential source for off-flavours in the drinking water. Water Sci. Technol. 49, 211–217 (2004).CAS 
Article 

Google Scholar 
5.Zhang, Y., Love, N. & Edwards, M. Nitrification in drinking water systems. Crit. Rev. Envi Sci. Tech. 39, 153–208 (2009).CAS 
Article 

Google Scholar 
6.Liu, S. et al. Understanding, monitoring, and controlling biofilm growth in drinking water distribution systems. Environ. Sci. Technol. 50, 8954–8976 (2016).CAS 
Article 

Google Scholar 
7.Litke, D. W. Review of phosphorus control measures in the United States and their effects on water quality. U.S. Geological Survey Water-Resources Investigations Report 99–4007. http://pubs.usgs.gov/wri/wri994007/pdf/wri99-4007.pdf (1999).8.EC. Directive 2000/60/EC of the European Parliament and of the Council of 23 October 2000 establishing a framework for Community action in the field of water policy. OJEC https://doi.org/10.1039/ap9842100196 (2000).9.European Environmental Agency. European waters Assessment of status and pressures 2018. https://www.eea.europa.eu/publications/state-of-water (2018).10.Konapala, G., Mishra, A. K., Wada, Y. & Mann, M. E. Climate Change will affect global water availability through compounding changes in seasonal precipitation and evaporation. Nat. Commun. 11, 3044 (2020).CAS 
Article 

Google Scholar 
11.Grillakis, M. G. Increase in severe and extreme soil moisture droughts for Europe under climate change. Sci. Total Environ. 660, 1245–1255 (2019).CAS 
Article 

Google Scholar 
12.Myhre, G. et al. Frequency of extreme precipitation increases extensively with event rareness under global warming. Sci. Rep. 9, 012131 (2019).Article 
CAS 

Google Scholar 
13.Richey, A. S. et al. Quantifying renewable groundwater stress with GRACE. Water Resour. Res. 51, 5217–5237 (2015).Article 

Google Scholar 
14.Lace, I., Krauklis, K., Spalviņš, A. & Laicans, J. Implementations of Riga city water supply system founded on groundwater sources. IOP Conf. Ser. Mater. Sci. Eng. 251, 012131 (2017).Article 

Google Scholar 
15.Oron, G. et al. Greywater use in Israel and worldwide: standards and prospects. Water Res. 58, 92–101 (2014).CAS 
Article 

Google Scholar 
16.Lahnsteiner, J. & Lempert, G. Water management in Windhoek, Namibia. Water Sci. Technol. 55, 441–448 (2007).CAS 
Article 

Google Scholar 
17.Vandenbohede, A., Houtte, E. Van & Lebbe, L. Water quality changes in the dunes of the western Belgian coastal plain due to artificial recharge of tertiary treated wastewater. Appl. Geochem. 24, 370–382 (2009).CAS 
Article 

Google Scholar 
18.Fish, K. E., Osborn, A. M. & Boxall, J. Characterising and understanding the impact of microbial biofilms and the extracellular polymeric substance (EPS) matrix in drinking water distribution systems. Environ. Sci. Water Res. Technol. 2, 614–630 (2016).Article 

Google Scholar 
19.Flemming, H. C. & Wingender, J. The biofilm matrix. Nat. Rev. Microbiol. 8, 623–633 (2010).CAS 
Article 

Google Scholar 
20.Tsao, H. F. et al. The cooling tower water microbiota: Seasonal dynamics and co-occurrence of bacterial and protist phylotypes. Water Res. 159, 464–479 (2019).CAS 
Article 

Google Scholar 
21.van der Wielen, P. W. J. J. & van der Kooij, D. Nontuberculous mycobacteria, fungi, and opportunistic pathogens in unchlorinated drinking water in the Netherlands. Appl. Environ. Microbiol. 79, 825–834 (2013).CAS 
Article 

Google Scholar 
22.Cann, K. F., Thomas, D. R., Salmon, R. L., Wyn-Jones, A. P. & Kay, D. Extreme water-related weather events and waterborne disease. Epidemiol. Infect. 141, 671–686 (2013).CAS 
Article 

Google Scholar 
23.Sedlak, D. L. & Von Gunten, U. The chlorine dilemma. Science 331, 42–43 (2011).CAS 
Article 

Google Scholar 
24.Nystrom, A., Grimvall, A., Krantz-Rulcker, C., Savenhed, R. & Akerstrand, K. Drinking water off-flavour caused by 2,4,6-trichloroanisole. Water Sci. Technol. 25, 241–249 (1992).Article 

Google Scholar 
25.van der Kooij, D. Assimilable organic carbon as an indicator of bacterial regrowth. J. Am. Water Work. Assoc. 84, 57–65 (1992).Article 

Google Scholar 
26.Rosario-Ortiz, F., Rose, J., Speight, V., Gunten, U. V. & Schnoor, J. How do you like your tap water? Science 351, 912–914 (2016).CAS 
Article 

Google Scholar 
27.Hammes, F., Berger, C., Köster, O. & Egli, T. Assessing biological stability of drinking water without disinfectant residuals in a full-scale water supply system. J. Water Supply Res. T 59, 31–40 (2010).CAS 
Article 

Google Scholar 
28.Baghoth, S. A., Dignum, M., Grefte, A., Kroesbergen, J. & Amy, G. L. Characterization of NOM in a drinking water treatment process train with no disinfectant residual. Water Sci. Tech.-W. Sup 9, 379–386 (2009).CAS 
Article 

Google Scholar 
29.Hambsch, B. Distributing groundwater without a disinfectant residual. J. Am. Water Work. Assoc. 91, 81–85 (1999).CAS 
Article 

Google Scholar 
30.Sousi, M. et al. Measuring Bacterial Growth Potential of Ultra-Low Nutrient Drinking Water Produced by Reverse Osmosis: Effect of Sample Pre-treatment and Bacterial Inoculum. Front. Microbiol. 11, 791 (2020).Article 

Google Scholar 
31.Lechevallier, M. W., Welch, N. J. & Smith, D. B. Full-scale studies of factors related to coliform regrowth in drinking water. Appl. Environ. Microbiol. 62, 2201–2211 (1996).CAS 
Article 

Google Scholar 
32.Servais, P., Barillier, A. & Garnier, J. Determination of the biodegradable fraction of dissolved and particulate organic carbon in waters. Ann. Limnol. – Int. J. Lim 31, 75–80 (1995).Article 

Google Scholar 
33.Van Nevel, S., De Roy, K. & Boon, N. Bacterial invasion potential in water is determined by nutrient availability and the indigenous community. FEMS Microbiol. Ecol. 85, 593–603 (2013).Article 
CAS 

Google Scholar 
34.Vital, M., Stucki, D., Egli, T. & Hammes, F. Evaluating the growth potential of pathogenic bacteria in water. Appl. Environ. Microbiol. 76, 6477–6484 (2010).CAS 
Article 

Google Scholar 
35.Lehtola, M. J., Miettinen, I. T., Vartiainen, T., Myllykangas, T. & Martikainen, P. J. Microbially available organic carbon, phosphorus, and microbial growth in ozonated drinking water. Water Res. 35, 1635–1640 (2001).CAS 
Article 

Google Scholar 
36.WHO. Guidelines for drinking-water quality, 4th edition, incorporating the 1st addendum. (World Health Organization, 2017).37.Sathasivan, A., Fisher, I. & Tam, T. Onset of severe nitrification in mildly nitrifying chloraminated bulk waters and its relation to biostability. Water Res. 42, 3623–3632 (2008).CAS 
Article 

Google Scholar 
38.Rittmann, B. E. & Snoeyink, V. L. Achieving biologically stable drinking water. J. Am. Water Work. Assoc. 76, 106–110 (1984).CAS 
Article 

Google Scholar 
39.Favere, J., Buysschaert, B., Boon, N. & De Gusseme, B. Online microbial fingerprinting for quality management of drinking water: Full-scale event detection. Water Res. 170, 115353 (2020).CAS 
Article 

Google Scholar 
40.Prest, E. I., Hammes, F., Kötzsch, S., van Loosdrecht, M. C. M. & Vrouwenvelder, J. S. Monitoring microbiological changes in drinking water systems using a fast and reproducible flow cytometric method. Water Res. 47, 7131–7142 (2013).CAS 
Article 

Google Scholar 
41.Prest, E. I. et al. Combining flow cytometry and 16S rRNA gene pyrosequencing: a promising approach for drinking water monitoring and characterization. Water Res. 63, 179–189 (2014).CAS 
Article 

Google Scholar 
42.Chatzigiannidou, I., Props, R. & Boon, N. Drinking water bacterial communities exhibit specific and selective necrotrophic growth. npj Clean Water 1, 22 (2018).Article 

Google Scholar 
43.MacArthur, R. H. & Wilson, E. O. The Theory of Island biogeography (MPB-1). (Princeton University Press, 2015).44.De Schryver, P. & Vadstein, O. Ecological theory as a foundation to control pathogenic invasion in aquaculture. ISME J. 8, 2360–2368 (2014).Article 

Google Scholar 
45.Brzeszcz, J., Steliga, T., Kapusta, P., Turkiewicz, A. & Kaszycki, P. r-strategist versus K-strategist for the application in bioremediation of hydrocarbon-contaminated soils. T Biodeterior. Biodegrad. Int. Biodeter. 106, 41–52 (2016).CAS 
Article 

Google Scholar 
46.De Vrieze, J., Christiaens, M. E. R. & Verstraete, W. The microbiome as engineering tool: manufacturing and trading between microorganisms. N. Biotechnol. 39, 206–214 (2017).Article 
CAS 

Google Scholar 
47.Tilman, D. Resources: a Graphical-Mechanistic Approach to Competition and Predation. Am. Nat. 116, 362–393 (1980).Article 

Google Scholar 
48.Jia, M., Winkler, M. K. H. & Volcke, E. I. P. Elucidating the Competition between Heterotrophic Denitrification and DNRA Using the Resource-Ratio Theory. Environ. Sci. Technol. 54, 13953–13962 (2020).Article 
CAS 

Google Scholar 
49.Ho, A. et al. Conceptualizing functional traits and ecological characteristics of methane-oxidizing bacteria as life strategies. Environ. Microbiol. Rep. 5, 335–345 (2013).CAS 
Article 

Google Scholar 
50.Vadstein, O., Attramadal, K. J. K., Bakke, I. & Olsen, Y. K-selection as microbial community management strategy: a method for improved viability of larvae in aquaculture. Front. Microbiol. 9, 1–17 (2018).Article 

Google Scholar 
51.Liu, G., Verberk, J. Q. J. C. & Van Dijk, J. C. Bacteriology of drinking water distribution systems: an integral and multidimensional review. Appl. Microbiol. Biotechnol. 97, 9265–9276 (2013).CAS 
Article 

Google Scholar 
52.Lehtola, M. J., Miettinen, I. T. & Martikainen, P. J. Biofilm formation in drinking water affected by low concentrations of phosphorus. Can. J. Microbiol. 48, 494–499 (2002).CAS 
Article 

Google Scholar 
53.Odum, E. P. & Barrett, G. W. Fundamentals of Ecology. Third Edition. Thomson, Brooks/Cole (W.B. Saunders Co., 1971).54.Andrews, J. H. & Harris, R. F. r- and K-Selection and Microbial Ecology. Advances in Microbial Ecology (Springer, Boston, MA, 1986). https://doi.org/10.1007/978-1-4757-0611-6_3.55.O’Neil, J. M., Davis, T. W., Burford, M. A. & Gobler, C. J. The rise of harmful cyanobacteria blooms: the potential roles of eutrophication and climate change. Harmful Algae 14, 313–334 (2012).CAS 
Article 

Google Scholar 
56.Temmerman, R., Vervaeren, H., Noseda, B., Boon, N. & Verstraete, W. Necrotrophic growth of Legionella pneumophila. Appl. Environ. Microbiol. 72, 4323–4328 (2006).CAS 
Article 

Google Scholar 
57.Besmer, M. D. et al. Laboratory-scale simulation and real-time tracking of a microbial contamination event and subsequent shock-chlorination in drinking water. Front. Microbiol. 8, 1900 (2017).Article 

Google Scholar 
58.Props, R., Monsieurs, P., Mysara, M., Clement, L. & Boon, N. Measuring the biodiversity of microbial communities by flow cytometry. Methods Ecol. Evol. 7, 1376e1385 (2016).Article 

Google Scholar 
59.Howell, D., Rogier, M., Yzerbyt, V. & Bestgen, Y. Méthodes statistiques en sciences humaines. (De Boeck Université, 1998).60.Marzorati, M., Wittebolle, L., Boon, N., Daffonchio, D. & Verstraete, W. How to get more out of molecular fingerprints: Practical tools for microbial ecology. Environ. Microbiol. 10, 1571–1581 (2008).CAS 
Article 

Google Scholar 
61.Wittebolle, L. et al. Failure of the ammonia oxidation process in two pharmaceutical wastewater treatment plants is linked to shifts in the bacterial communities. J. Appl. Microbiol. 99, 997–1066 (2005).CAS 
Article 

Google Scholar 
62.Boon, N., Pycke, B. F. G., Marzorati, M. & Hammes, F. Nutrient gradients in a granular activated carbon biofilter drives bacterial community organization and dynamics. Water Res. 45, 6355–6361 (2011).CAS 
Article 

Google Scholar 
63.European Union. 98/83/EC on the quality of water intented for human consumption. OJEC 41, 32–54 (1998).
Google Scholar 
64.Liu, G. et al. Potential impacts of changing supply-water quality on drinking water distribution: a review. Water Res. 116, 135–148 (2017).CAS 
Article 

Google Scholar 
65.White, S. A. & Cousins, M. M. Floating treatment wetland aided remediation of nitrogen and phosphorus from simulated stormwater runoff. Ecol. Eng. 127, 468–479 (2013).
Google Scholar 
66.Chang, N. Bin, Islam, K., Marimon, Z. & Wanielista, M. P. Assessing biological and chemical signatures related to nutrient removal by floating islands in stormwater mesocosms. Chemosphere 88, 736–743 (2012).CAS 
Article 

Google Scholar 
67.Wu, Q., Hu, Y., Li, S., Peng, S. & Zhao, H. Microbial mechanisms of using enhanced ecological floating beds for eutrophic water improvement. Bioresour. Technol. 211, 451–456 (2016).CAS 
Article 

Google Scholar 
68.Lin, J. L., Tu, Y. T., Chiang, P. C., Chen, S. H. & Kao, C. M. Using aerated gravel-packed contact bed and constructed wetland system for polluted river water purification: a case study in Taiwan. J. Hydrol. 525, 400–408 (2015).CAS 
Article 

Google Scholar 
69.Benndorf, J. & Pütz, K. Control of eutrophication of lakes and reservoirs by means of pre-dams-I. Mode of operation and calculation of the nutrient elimination capacity. Water Res. 21, 829–838 (1987).CAS 
Article 

Google Scholar 
70.Haghseresht, F., Wang, S. & Do, D. D. A novel lanthanum-modified bentonite, Phoslock, for phosphate removal from wastewaters. Appl. Clay Sci. 46, 369–375 (2009).CAS 
Article 

Google Scholar 
71.Kumar, P., Korving, L., van Loosdrecht, M. C. M. & Witkamp, G. J. Adsorption as a technology to achieve ultra-low concentrations of phosphate: Research gaps and economic analysis. Water Res. X 4, 100029 (2019).CAS 
Article 

Google Scholar 
72.Leistner, L. Basic aspects of food preservation by hurdle technology. Int. J. Food Microbiol. 55, 181–186 (2000).CAS 
Article 

Google Scholar 
73.Barbosa, R. G., Sleutels, T., Verstraete, W. & Boon, N. Hydrogen oxidizing bacteria are capable of removing orthophosphate to ultra-low concentrations in a fed batch reactor configuration. Bioresour. Technol. 311, 123494 (2020).CAS 
Article 

Google Scholar 
74.Jiang, Q., Song, X., Liu, J., Shao, Y. & Feng, Y. Enhanced nutrients enrichment and removal from eutrophic water using a self-sustaining in situ photomicrobial nutrients recovery cell (PNRC). Water Res. 167, 115097 (2019).CAS 
Article 

Google Scholar 
75.Nescerecka, A., Juhna, T. & Hammes, F. Identifying the underlying causes of biological instability in a full-scale drinking water supply system. Water Res. 135, 11–21 (2018).CAS 
Article 

Google Scholar 
76.Pinto, A. J., Schroeder, J., Lunn, M., Sloan, W. & Raskin, L. Spatial-temporal survey and occupancy-abundance modeling to predict bacterial community dynamics in the drinking water microbiomez. mBio 5, e01135–14 (2014).Article 
CAS 

Google Scholar 
77.Fritzmann, C., Löwenberg, J., Wintgens, T. & Melin, T. State-of-the-art of reverse osmosis desalination. Desalination 216, 1–76 (2007).CAS 
Article 

Google Scholar 
78.Grefte, A., Dignum, M., Baghoth, S. A., Cornelissen, E. R. & Rietveld, L. C. Improving the biological stability of drinking water by ion exchange. Water Sci. Tech.-W Sup. 11, 107–112 (2011).CAS 
Article 

Google Scholar 
79.Park, S. K. & Hu, J. Y. Assessment of the extent of bacterial growth in reverse osmosis system for improving drinking water quality. J. Environ. Sci. Health A. 45, 968–977 (2010).CAS 
Article 

Google Scholar 
80.Kirisits, M. J., Emelko, M. B. & Pinto, A. J. Applying biotechnology for drinking water biofiltration: advancing science and practice. Curr. Opin. Biotechnol. 57, 197–204 (2019).CAS 
Article 

Google Scholar 
81.Pinto, A. J., Xi, C. & Raskin, L. Bacterial community structure in the drinking water microbiome is governed by filtration processes. Environ. Sci. Technol. 46, 8851–8859 (2012).CAS 
Article 

Google Scholar 
82.Douterelo, I., Husband, S., Loza, V. & Boxall, J. Dynamics of biofilm regrowth in drinking water distribution systems. Appl. Environ. Microbiol. 82, 4155–4168 (2016).CAS 
Article 

Google Scholar 
83.Van Nevel, S. et al. Flow cytometric bacterial cell counts challenge conventional heterotrophic plate counts for routine microbiological drinking water monitoring. Water Res. 113, 191–206 (2017).Article 
CAS 

Google Scholar  More

1.Renewable Capacity Highlights (International Renewable Energy Agency, 2019); https://www.irena.org/-/media/Files/IRENA/Agency/Publication/2019/Mar/RE_capacity_highlights_2019.pdf2.Renewable Energy Highlights (International Renewable Energy Agency, 2019); https://www.irena.org/-/media/Files/IRENA/Agency/Publication/2019/Jul/IRENA_Renewable_energy_highlights_July_2019.pdf3.Moran, E. F., Lopez, M. C., Moore, N., Müller, N. & Hyndman, D. W. Sustainable hydropower in the 21st century. Proc. Natl Acad. Sci. USA 115, 201809426 (2018).Article 
CAS 

Google Scholar 
4.Gernaat, D. E. H. J., Bogaart, P. W., Vuuren, D. P. V., Biemans, H. & Niessink, R. High-resolution assessment of global technical and economic hydropower potential. Nat. Energy 2, 821–828 (2017).Article 

Google Scholar 
5.Winemiller, K. O. et al. Balancing hydropower and biodiversity in the Amazon, Congo, and Mekong. Science 351, 128–129 (2016).Article 
CAS 

Google Scholar 
6.Pokhrel, Y., Shin, S., Lin, Z., Yamazaki, D. & Qi, J. Potential disruption of flood dynamics in the Lower Mekong River Basin due to upstream flow regulation. Sci. Rep. 8, 17767 (2018).Article 
CAS 

Google Scholar 
7.Pokhrel, Y. et al. A review of the integrated effects of changing climate, land use, and dams on Mekong River Hydrology. Water 10, 266 (2018).Article 

Google Scholar 
8.Stone, R. Dam-building threatens Mekong fisheries. Science 354, 1084–1085 (2016).Article 
CAS 

Google Scholar 
9.Fearnside, P. M. & Pueyo, S. Greenhouse-gas emissions from tropical dams. Nat. Clim. Change 2, 382 (2012).Article 
CAS 

Google Scholar 
10.O’Connor, J. E., Duda, J. J. & Grant, G. E. 1000 dams down and counting. Science 348, 496–497 (2015).Article 

Google Scholar 
11.Timpe, K. & Kaplan, D. The changing hydrology of a dammed Amazon. Sci. Adv. 3, e1700611 (2017).Article 

Google Scholar 
12.Latrubesse, E. M. et al. Damming the rivers of the Amazon basin. Nature 546, 363–369 (2017).Article 
CAS 

Google Scholar 
13.Forsberg, B. R. et al. The potential impact of new Andean dams on Amazon fluvial ecosystems. PLoS ONE 12, e0182254 (2017).Article 
CAS 

Google Scholar 
14.Finer, M. & Jenkins, C. N. Proliferation of hydroelectric dams in the Andean Amazon and implications for Andes-Amazon connectivity. PLoS ONE 7, e35126 (2012).Article 
CAS 

Google Scholar 
15.Anderson, E. P. et al. Fragmentation of Andes-to-Amazon connectivity by hydropower dams. Sci. Adv. 4, eaao1642 (2018).Article 

Google Scholar 
16.Pokhrel, Y. et al. Model estimates of sea-level change due to anthropogenic impacts on terrestrial water storage. Nat. Geosci. 5, 389–392 (2012).Article 
CAS 

Google Scholar 
17.Eiriksdottir, E. S., Oelkers, E. H., Hardardottir, J. & Gislason, S. R. The impact of damming on riverine fluxes to the ocean: a case study from Eastern Iceland. Water Res. 113, 124–138 (2017).Article 
CAS 

Google Scholar 
18.Yang, H. F. et al. Erosion potential of the Yangtze Delta under sediment starvation and climate change. Sci. Rep. 7, 10535 (2017).Article 
CAS 

Google Scholar 
19.Cochrane, S. M. V., Matricardi, E. A. T., Numata, I. & Lefebvre, P. A. Landsat-based analysis of mega dam flooding impacts in the Amazon compared to associated environmental impact assessments: upper Madeira River example 2006–2015. Remote Sens. Appl. Soc. Environ. 7, 1–8 (2017).
Google Scholar 
20.Fearnside, P. M. Impacts of Brazil’s Madeira River dams: unlearned lessons for hydroelectric development in Amazonia. Environ. Sci. Policy 38, 164–172 (2014).Article 

Google Scholar 
21.VanZwieten, J. et al. In-stream hydrokinetic power: review and appraisal. J. Energy Eng. 141, 04014024 (2014).Article 

Google Scholar 
22.Pokhrel, Y. N., Oki, T. & Kanae, S. A grid based assessment of global theoretical hydropower potential. Annu. J. Hydraul. Eng. 52, 7–12 (2008).Article 

Google Scholar 
23.Zhou, Y. et al. A comprehensive view of global potential for hydro-generated electricity. Energy Environ. Sci. 8, 2622–2633 (2015).Article 

Google Scholar 
24.Hoes, O. A. C., Meijer, L. J. J., Van Der Ent, R. J. & Van De Giesen, N. C. Systematic high-resolution assessment of global hydropower potential. PLoS ONE 12, e0171844 (2017).Article 
CAS 

Google Scholar 
25.Bryden, I. G. & Couch, S. J. ME1—marine energy extraction: tidal resource analysis. Renew. Energy 31, 133–139 (2006).Article 

Google Scholar 
26.Karsten, R., Swan, A. & Culina, J. Assessment of arrays of in-stream tidal turbines in the Bay of Fundy. Philos. Trans. R. Soc. A 371, 20120189 (2013).Article 

Google Scholar 
27.Malki, R., Masters, I., Williams, A. J. & Nick Croft, T. Planning tidal stream turbine array layouts using a coupled blade element momentum—computational fluid dynamics model. Renew. Energy 63, 46–54 (2014).Article 

Google Scholar 
28.Vennell, R., Funke, S. W., Draper, S., Stevens, C. & Divett, T. Designing large arrays of tidal turbines: a synthesis and review. Renew. Sustain. Energy Rev. 41, 454–472 (2015).Article 

Google Scholar 
29.Assessment and Mapping of the Riverine Hydrokinetic Energy Resource in the Continental United States Report No. 1026880 (Electrical Power Research Institute, 2012).30.Ortega-Achury, S., McAnally, W., Davis, T. & Martin, J. Hydrokinetic Power Review (Mississippi State Univ., 2010).31.Garrett, C. & Cummins, P. The efficiency of a turbine in a tidal channel. J. Fluid Mech. 588, 243–251 (2007).Article 

Google Scholar 
32.Garrett, C. & Cummins, P. Limits to tidal current power. Renew. Energy 33, 2485–2490 (2008).Article 

Google Scholar 
33.Miller, G., Franceschi, J., Lese, W. & Rico, J. The Allocation of Kinetic Hydro Energy Conversion Systems (KHECS) in USA Drainage Basins: Regional Resource and Potential Power (USDA,1986).34.Chaudhari, S., Pokhrel, Y., Moran, E. F. & Miguez-Macho, G. Multi-decadal hydrologic change and variability in the Amazon River Basin: understanding terrestrial water storage variations and drought characteristics. Hydrol. Earth Syst. Sci. 23, 2841–2862 (2019).Article 

Google Scholar 
35.Pokhrel, Y. N., Fan, Y., Miguez-Macho, G., Yeh, P. J. F. & Han, S. C. The role of groundwater in the Amazon water cycle: 3. Influence on terrestrial water storage computations and comparison with GRACE. J. Geophys. Res. Atmos. 118, 3233–3244 (2013).Article 

Google Scholar 
36.Ten-Year Energy Expansion Plan 2029 (Ministry of Mines and Energy, 2019).37.Ansar, A., Flyvbjerg, B., Budzier, A. & Lunn, D. Should we build more large dams? The actual costs of hydropower megaproject development. Energy Policy 69, 43–56 (2014).Article 

Google Scholar 
38.Petheram, C. & McMahon, T. A. Dams, dam costs and damnable cost overruns. J. Hydrol. X 3, 100026 (2019).Article 

Google Scholar 
39.Awojobi, O. & Jenkins, G. P. Were the hydro dams financed by the World Bank from 1976 to 2005 worthwhile? Energy Policy 86, 222–232 (2015).Article 

Google Scholar 
40.Previsic, M., Bedard, R. & Polagye, B. System Level Design, Performance, Cost and Economic Assessment—Alaska River In-stream Power Plants (EPRI, 2008).41.Copping, A. E. & Hemery, L. G. OES-Environmental 2020 State of the Science Report: Environmental Effects of Marine Renewable Energy Development Around the World (USDOE, 2020); https://doi.org/10.2172/163287842.Davidson, E. A. et al. The Amazon basin in transition. Nature 481, 321–328 (2012).Article 
CAS 

Google Scholar 
43.Lovejoy, T. E. & Nobre, C. Amazon tipping point. Sci. Adv. 4, eaat2340 (2018).44.Malhi, Y. et al. Climate change, deforestation, and the fate of the Amazon. Science 319, 169–172 (2008).Article 
CAS 

Google Scholar 
45.Miguez-Macho, G. & Fan, Y. The role of groundwater in the Amazon water cycle: 1. Influence on seasonal streamflow, flooding and wetlands. J. Geophys. Res. Atmos. https://doi.org/10.1029/2012JD017539 (2012).46.Shin, S., Pokhrel, Y. & Miguez-Macho, G. High resolution modeling of reservoir release and storage dynamics at the continental scale. Water Resour. Res. 55, 787–810 (2019).Article 

Google Scholar 
47.Pokhrel, Y. et al. Incorporating anthropogenic water regulation modules into a land surface model. J. Hydrometeorol. 13, 255–269 (2012).Article 

Google Scholar 
48.Pokhrel, Y. N., Fan, Y. & Miguez-Macho, G. Potential hydrologic changes in the Amazon by the end of the 21st century and the groundwater buffer. Environ. Res. Lett. 9, 084004 (2014).Article 

Google Scholar 
49.Yamazaki, D., Oki, T. & Kanae, S. Deriving a global river network map and its sub-grid topographic characteristics from a fine-resolution flow direction map. Hydrol. Earth Syst. Sci. 13, 2241–2251 (2009).Article 

Google Scholar 
50.Lehner, B., Verdin, K. & Jarvis, A. New global hydrography derived from spaceborne elevation data. Eos 89, 93–94 (2008).Article 

Google Scholar 
51.Coe, M. T., Costa, M. H. & Howard, E. A. Simulating the surface waters of the Amazon River basin: impacts of new river geomorphic and flow parameterizations. Hydrol. Process. 22, 2542–2553 (2008).Article 

Google Scholar 
52.Mulligan, M., Saenz-Cruz, L., van Soesbergen, A., Smith, V. T. & Zurita, L. Global Dams Database and Geowiki Version 1 (Geodata, 2009); http://geodata.policysupport.org/dams53.Zarfl, C., Lumsdon, A. E., Berlekamp, J., Tydecks, L. & Tockner, K. A global boom in hydropower dam construction. Aquat. Sci. 77, 161–170 (2015).Article 

Google Scholar 
54.Guney, M. S. Evaluation and measures to increase performance coefficient of hydrokinetic turbines. Renew. Sustain. Energy Rev. 15, 3669–3675 (2011).Article 

Google Scholar 
55.Shin, S. et al. High resolution modeling of river–floodplain–reservoir inundation dynamics in the Mekong River Basin. Water Resour. Res. 56, e2019WR026449 (2020).Article 

Google Scholar 
56.Previsic, M. Cost Breakdown Structure for River Current Device (Sandia National Laboratory, 2012).57.Renewable Power Generation Costs in 2019 (International Renewable Energy Agency, 2019); https://www.irena.org/-/media/Files/IRENA/Agency/Publication/2020/Jun/IRENA_Power_Generation_Costs_2019.pdf58.Gridded Population of the World v.4 (CIESIN, 2016); https://doi.org/10.7927/H4SF2T42 More

Characteristics of PES hollow fiber membrane substratesFor a TFC hollow fiber membrane, the mechanical strength is dominated or controlled by the hollow fiber membrane substrate because the mechanical strength of the ultrathin selective layer of  PT30-D. This is because the structural parameters of the three membranes are in the order of PT22-D  More

1.Cañedo-Argüelles, M., Kefford, B. & Schäfer, R. Salt in freshwaters: causes, effects and prospects—introduction to the theme issue. Philos. Trans. R. Soc. Lond. B 374, 20180002 (2018).Article 
CAS 

Google Scholar 
2.Williams, W. D. Anthropogenic salinisation of inland waters. Hydrobiologia 466, 329–337 (2001).Article 

Google Scholar 
3.Dugan, H. A. et al. Salting our freshwater lakes. Proc. Natl Acad. Sci. USA 114, 4453–4458 (2017).CAS 
Article 

Google Scholar 
4.Kaushal, S. S. et al. Increased salinization of fresh water in the northeastern United States. Proc. Natl Acad. Sci. USA 102, 13517–13520 (2005).CAS 
Article 

Google Scholar 
5.Stets, E. G. et al. Landscape drivers of dynamic change in water quality of US rivers. Environ. Sci. Technol. 54, 4336–4343 (2020).CAS 
Article 

Google Scholar 
6.Kaushal, S. S. et al. Freshwater salinization syndrome on a continental scale. Proc. Natl Acad. Sci. USA 115, E574–E583 (2018).CAS 
Article 

Google Scholar 
7.Bird, D. L., Groffman, P. M., Salice, C. J. & Moore, J. Steady-state land cover but non-steady-state major ion chemistry in urban streams. Environ. Sci. Technol. 52, 13015–13026 (2018).CAS 
Article 

Google Scholar 
8.Godwin, K., Hafner, S. & Buff, M. Long-term trends in sodium and chloride in the Mohawk River, New York: the effect of fifty years of road-salt application. Environ. Pollut. 124, 273–281 (2003).CAS 
Article 

Google Scholar 
9.Kelly, V. R. et al. Long-term sodium chloride retention in a rural watershed: legacy effects of road salt on streamwater concentration. Environ. Sci. Technol. 42, 410–415 (2008).CAS 
Article 

Google Scholar 
10.Overbo, A., Heger, S. & Gulliver, J. Evaluation of chloride contributions from major point and nonpoint sources in a northern U.S. state. Sci. Total Environ. 764, 144179 (2021).CAS 
Article 

Google Scholar 
11.Olson, J. R. Predicting combined effects of land use and climate change on river and stream salinity. Philos. Trans. R. Soc. Lond. B 374, 20180005 (2018).Article 
CAS 

Google Scholar 
12.Corsi, S. R., Cicco, L. A. D., Lutz, M. A. & Hirsch, R. M. River chloride trends in snow-affected urban watersheds: increasing concentrations outpace urban growth rate and are common among all seasons. Sci. Total Environ. 508, 488–497 (2015).CAS 
Article 

Google Scholar 
13.Kaushal, S. S. et al. Novel ‘chemical cocktails’ in inland waters are a consequence of the freshwater salinization syndrome. Philos. Trans. R. Soc. Lond. B 374, 20180017 (2018).Article 
CAS 

Google Scholar 
14.Moore, J., Fanelli, R. M. & Sekellick, A. J. High-frequency data reveal deicing salts drive elevated specific conductance and chloride along with pervasive and frequent exceedances of the US Environmental Protection Agency aquatic life criteria for chloride in urban streams. Environ. Sci. Technol. 54, 778–789 (2019).Article 
CAS 

Google Scholar 
15.Löfgren, S. The chemical effects of deicing salt on soil and stream water of five catchments in southeast Sweden. Water Air Soil Pollut. 130, 863–868 (2001).Article 

Google Scholar 
16.Daley, M. L., Potter, J. D. & McDowell, W. H. Salinization of urbanizing New Hampshire streams and groundwater: effects of road salt and hydrologic variability. J. North Am. Benthol Soc. 28, 929–940 (2009).Article 

Google Scholar 
17.Cooper, C. A., Mayer, P. M. & Faulkner, B. R. Effects of road salts on groundwater and surface water dynamics of sodium and chloride in an urban restored stream. Biogeochemistry 121, 149–166 (2014).CAS 
Article 

Google Scholar 
18.Snodgrass, J. W. et al. Influence of modern stormwater management practices on transport of road salt to surface waters. Environ. Sci. Technol. 51, 4165–4172 (2017).CAS 
Article 

Google Scholar 
19.International Stormwater BMP Database: 2020 Summary Statistics Project No. 4968 (The Water Research Foundation, 2020).20.Venkatesan, A. K., Ahmad, S., Johnson, W. & Batista, J. R. Systems dynamic model to forecast salinity load to the Colorado River due to urbanization within the Las Vegas valley. Sci. Total Environ. 409, 2616–2625 (2011).CAS 
Article 

Google Scholar 
21.Steele, M. & Aitkenhead-Peterson, J. Long-term sodium and chloride surface water exports from the Dallas/Fort Worth region. Sci. Total Environ. 409, 3021–3032 (2011).CAS 
Article 

Google Scholar 
22.Davies, P. J., Wright, I. A., Jonasson, O. J. & Findlay, S. J. Impact of concrete and PVC pipes on urban water chemistry. Urban Water J. 7, 233–241 (2010).CAS 
Article 

Google Scholar 
23.Wright, I. A., Davies, P. J., Findlay, S. J. & Jonasson, O. J. A new type of water pollution: concrete drainage infrastructure and geochemical contamination of urban waters. Mar. Freshw. Res. 62, 1355–1361 (2011).CAS 
Article 

Google Scholar 
24.Moore, J., Bird, D. L., Dobbis, S. K. & Woodward, G. Nonpoint source contributions drive elevated major ion and dissolved inorganic carbon concentrations in urban watersheds. Environ. Sci. Technol. Lett. 4, 198–204 (2017).CAS 
Article 

Google Scholar 
25.Tippler, C., Wright, I. A., Davies, P. J. & Hanlon, A. The influence of concrete on the geochemical qualities of urban streams. Mar. Freshw. Res. 65, 1009–1017 (2014).CAS 
Article 

Google Scholar 
26.McLennan, S. M. Weathering and global denudation. J. Geol. 101, 295–303 (1993).Article 

Google Scholar 
27.Wilkinson, B. H. Humans as geologic agents: a deep-time perspective. Geology 33, 161–164 (2005).Article 

Google Scholar 
28.Schuler, M. S. et al. Regulations are needed to protect freshwater ecosystems from salinization. Philos. Trans. R. Soc. Lond. B 374, 20180019 (2018).Article 
CAS 

Google Scholar 
29.Haq, S., Kaushal, S. S. & Duan, S. Episodic salinization and freshwater salinization syndrome mobilize base cations, carbon, and nutrients to streams across urban regions. Biogeochemistry 141, 463–486 (2018).CAS 
Article 

Google Scholar 
30.Shanley, J. B. Effects of ion exchange on stream solute fluxes in a basin receiving highway deicing salts. J. Environ. Qual. 23, 977–986 (1994).CAS 
Article 

Google Scholar 
31.Hong, P. K. A. & Macauley, Y. Corrosion and leaching of copper tubing exposed to chlorinated drinking water. Water Air Soil Pollut. 108, 457–471 (1998).CAS 
Article 

Google Scholar 
32.Nguyen, C. K., Stone, K. R. & Edwards, M. A. Chloride-to-sulfate mass ratio: practical studies in galvanic corrosion of lead solder. J. Am. Water Works Assoc. 103, 81–92 (2011).CAS 
Article 

Google Scholar 
33.Stets, E., Lee, C., Lytle, D. & Schock, M. Increasing chloride in rivers of the conterminous US and linkages to potential corrosivity and lead action level exceedances in drinking water. Sci. Total Environ. 613-614, 1498–1509 (2018).CAS 
Article 

Google Scholar 
34.Dietrich, A. M. & Burlingame, G. A. Critical review and rethinking of USEPA secondary standards for maintaining organoleptic quality of drinking water. Environ. Sci. Technol. 49, 708–720 (2015).CAS 
Article 

Google Scholar 
35.Sodium in drinking water. In Guidelines for Drinking-Water Quality 2nd edn, Vol. 2, Health Criteria and Other Supporting Information (World Health Organization, 1996).36.Drinking Water Advisory: Consumer Acceptability Advice and Health Effects Analysis on Sodium EPA 822-R-03-006 (EPA, 2003).37.National Research Council Water Reuse: Potential for Expanding the Nation’s Water Supply Through Reuse of Municipal Wastewater (National Academies Press, 2012).38.Mukherjee, M. & Jensen, O. Making water reuse safe: a comparative analysis of the development of regulation and technology uptake in the US and Australia. Saf. Sci. 121, 5–14 (2020).Article 

Google Scholar 
39.EPA & CDM Smith 2017 Potable Reuse Compendium (EPA, 2017); https://www.epa.gov/sites/production/files/2018-01/documents/potablereusecompendium_3.pdf40.Draft National Water Reuse Action Plan (EPA, 2019); https://www.epa.gov/waterreuse/draft-national-water-reuse-action-plan41.Martin, B. & Via, S. Integrating water reuse into the US water supply portfolio. J. Am. Water Works Assoc. 112, 8–14 (2020).Article 

Google Scholar 
42.Freshwater: Supply Concerns Continue, and Uncertainties Complicate Planning Technical Report GAO-14-43 (GAO, 2014); https://www.gao.gov/assets/670/663343.pdf43.Rice, J. & Westerhoff, P. High levels of endocrine pollutants in US streams during low flow due to insufficient wastewater dilution. Nat. Geosci. 10, 587–591 (2017).CAS 
Article 

Google Scholar 
44.Wiener, M. J., Moreno, S., Jafvert, C. T. & Nies, L. F. Time series analysis of water use and indirect reuse within a HUC-4 basin (Wabash) over a nine year period. Sci. Total Environ. 738, 140221 (2020).CAS 
Article 

Google Scholar 
45.Harris-Lovett, S. & Sedlak, D. Protecting the sewershed. Science 369, 1429–1430 (2020).CAS 
Article 

Google Scholar 
46.Falconer, I. R., Chapman, H. F., Moore, M. R. & Ranmuthugala, G. Endocrine-disrupting compounds: a review of their challenge to sustainable and safe water supply and water reuse. Environ. Toxicol. 21, 181–191 (2006).CAS 
Article 

Google Scholar 
47.Novotny, E. V., Sander, A. R., Mohseni, O. & Stefan, H. G. Chloride ion transport and mass balance in a metropolitan area using road salt. Water Resour. Res. 45, W12410 (2009).Article 

Google Scholar 
48.Potter, J. D., McDowell, W. H., Helton, A. M. & Daley, M. L. Incorporating urban infrastructure into biogeochemical assessment of urban tropical streams in Puerto Rico. Biogeochemistry 121, 271–286 (2013).Article 
CAS 

Google Scholar 
49.Kaushal, S. S. et al. Longitudinal patterns in carbon and nitrogen fluxes and stream metabolism along an urban watershed continuum. Biogeochemistry 121, 23–44 (2014).CAS 
Article 

Google Scholar 
50.Ambient Water Quality Criteria for Chloride Technical Report EPA 440/5-88-001 (EPA, 1998).51.Nelsen, R. B. An Introduction to Copulas (Springer-Verlag, 2007).52.Comprehensive Annual Financial Report (Upper Occoquan Service Authority, 2017); https://www.uosa.org/Documents/0450_012759.pdf53.Tjandraatmadja, G. et al. Sources of Priority Contaminants in Domestic Wastewater: Contaminant Contribution from Household Products (CSIRO, 2008).54.Schwabe, K., Nemati, M., Amin, R., Tran, Q. & Jassby, D. Unintended consequences of water conservation on the use of treated municipal wastewater. Nat. Sustain. 3, 628–635 (2020).Article 

Google Scholar 
55.Cogswell, M. E. et al. Estimated 24-hour urinary sodium and potassium excretion in US adults. JAMA 319, 1209–1220 (2018).CAS 
Article 

Google Scholar 
56.Gleick, P. H. Global freshwater resources: soft-path solutions for the 21st century. Science 302, 1524–1528 (2003).CAS 
Article 

Google Scholar 
57.Grant, S. B. et al. Taking the “waste” out of “wastewater” for human water security and ecosystem sustainability. Science 337, 681–686 (2012).CAS 
Article 

Google Scholar 
58.Liu, C. et al. Robust slippery liquid-infused porous network surfaces for enhanced anti-icing/deicing performance. ACS Appl. Mater. Interfaces 12, 25471–25477 (2020).CAS 
Article 

Google Scholar 
59.Baldassarre, G. D. et al. Sociohydrology: scientific challenges in addressing the sustainable development goals. Water Resour. Res. 55, 6327–6355 (2019).Article 

Google Scholar 
60.Su, J. G. et al. Factors influencing whether children walk to school. Health Place 22, 153–161 (2013).Article 

Google Scholar 
61.Micron Announces Investment in Its Semiconductor Manufacturing Plant in Manassas, Virginia (Micron Technology, 2018); https://investors.micron.com/node/37386/pdf62.Lazarova, V., Savoye, P., Janex, M. L., Blatchley, E. R. & Pommepuy, M. Advanced wastewater disinfection technologies: state of the art and perspectives. Water Sci. Technol. 40, 203–213 (1999).CAS 
Article 

Google Scholar 
63.Davis, M. L. Water and Wastewater Engineering: Design Principles and Practice (McGraw-Hill, 2010).
Google Scholar 
64.Rivera-Utrilla, J., Sánchez-Polo, M., Ferro-García, M. Á., Prados-Joya, G. & Ocampo-Pérez, R. Pharmaceuticals as emerging contaminants and their removal from water: a review. Chemosphere 93, 1268–1287 (2013).CAS 
Article 

Google Scholar 
65.Rauch, W. & Kleidorfer, M. Replace contamination, not the pipes. Science 345, 734–735 (2014).CAS 
Article 

Google Scholar 
66.Potts, J. The innovation deficit in public services: the curious problem of too much efficiency and not enough waste and failure. Innovation 11, 34–43 (2009).Article 

Google Scholar 
67.McKenzie-Mohr, D., Lee, N. R. & Schultz, P. W. Social Marketing to Protect the Environment: What Works (Sage, 2011).68.Calcagno, V. & de Mazancourt, C. glmulti: an R package for easy automated model selection with (generalized) linear models. J. Stat. Softw. 34, 1–29 (2010).Article 

Google Scholar 
69.Schwarz, G. Estimating the dimension of a model. Ann. Stat. 6, 461–464 (1978).Article 

Google Scholar 
70.Appling, A. P., Leon, M. C. & McDowell, W. H. Reducing bias and quantifying uncertainty in watershed flux estimates: the R package loadflex. Ecosphere 6, 269 (2015).Article 

Google Scholar 
71.Madadgar, S., AghaKouchak, A., Farahmand, A. & Davis, S. J. Probabilistic estimates of drought impacts on agricultural production. Geophys. Res. Lett. 44, 7799–7807 (2017).Article 

Google Scholar 
72.Sadegh, M., Ragno, E. & AghaKouchak, A. Multivariate copula analysis toolbox (MvCAT): describing dependence and underlying uncertainty using a Bayesian framework. Water Resour. Res. 53, 5166–5183 (2017).Article 

Google Scholar 
73.Racine, J. & Hyndman, R. Using R to teach econometrics. J. Appl. Econom. 17, 175–189 (2002).Article 

Google Scholar  More

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1.Willett, W. et al. Food in the Anthropocene: the EAT–Lancet commission on healthy diets from sustainable food systems. Lancet 393, 447–492 (2019).PubMed 

Google Scholar 
2.Tilman, D. & Clark, M. Global diets link environmental sustainability and human health. Nature 515, 518–522 (2014).ADS 
CAS 
PubMed 

Google Scholar 
3.Hallstrom, E., Carlsson-Kanyama, A. & Borjesson, P. Environmental impact of dietary change: a systematic review. J. Clean. Prod. 91, 1–11 (2015).
Google Scholar 
4.Kim, B. F. et al. Country-specific dietary shifts to mitigate climate and water crises. Global Environ. Change 62, 101926 (2019).5.Azevedo, L. B., Henderson, A. D., van Zelm, R., Jolliet, O. & Huijbregts, M. A. J. Assessing the importance of spatial variability versus model choices in life cycle impact assessment: the case of freshwater eutrophication in europe. Environ. Sci. Technol. 47, 13565–13570 (2013).ADS 
CAS 
PubMed 

Google Scholar 
6.Transforming Our World: The 2030 Agenda for Sustainable Development (United Nations General Assembly, 2015).7.Foley, J. A. et al. Solutions for a cultivated planet. Nature 478, 337–342 (2011).ADS 
CAS 
PubMed 

Google Scholar 
8.Dieter, C. A. et al. Estimated Use of Water in the United States in 2015. Report No 1441 (US Geological Survey, 2018).9.Whitmee, S. et al. Safeguarding human health in the Anthropocene epoch: report of the Rockefeller Foundation–Lancet commission on planetary health. Lancet 386, 1973–2028 (2015).PubMed 

Google Scholar 
10.Gerten, D. et al. Feeding ten billion people is possible within four terrestrial planetary boundaries. Nat. Sustain. 3, 200–208 (2020).
Google Scholar 
11.Boulay, A.-M. et al. The WULCA consensus characterization model for water scarcity footprints: assessing impacts of water consumption based on available water remaining (AWARE). Int. J. Life Cycle Assess. 23, 368–378 (2018).
Google Scholar 
12.Boulay, A.-M. et al. Consensus building on the development of a stress-based indicator for LCA-based impact assessment of water consumption: outcome of the expert workshops. Int. J. Life Cycle Assess. 20, 577–583 (2015).CAS 

Google Scholar 
13.Tom, M. S., Fischbeck, P. S. & Hendrickson, C. T. Energy use, blue water footprint, and greenhouse gas emissions for current food consumption patterns and dietary recommendations in the US. Environ. Syst. Decis. 36, 92–103 (2016).
Google Scholar 
14.Blackstone, N. T., El-Abbadi, N. H., McCabe, M. S., Griffin, T. S. & Nelson, M. E. Linking sustainability to the healthy eating patterns of the Dietary Guidelines for Americans: a modelling study. Lancet Planet. Health 2, e344–e352 (2018).PubMed 

Google Scholar 
15.Birney, C. I., Franklin, K. F., Davidson, F. T. & Webber, M. E. An assessment of individual foodprints attributed to diets and food waste in the United States. Environ. Res. Lett. 12, 105008 (2017).ADS 

Google Scholar 
16.Gephart, J. A. et al. The environmental cost of subsistence: optimizing diets to minimize footprints. Sci. Total Environ. 553, 120–127 (2016).ADS 
CAS 
PubMed 

Google Scholar 
17.Mekonnen, M. M. & Fulton, J. The effect of diet changes and food loss reduction in reducing the water footprint of an average American. Water Int. 43, 860–870 (2018).
Google Scholar 
18.Blas, A., Garrido, A. & Willaarts, B. A. Evaluating the water footprint of the Mediterranean and American diets. Water 8, 448 (2016).19.Rehkamp, S. & Canning, P. Measuring embodied blue water in American diets: an EIO supply chain approach. Ecol. Econ. 147, 179–188 (2018).
Google Scholar 
20.Harris, F. et al. The water footprint of diets: a global systematic review and meta-analysis. Adv. Nutr. 11, 375–386 (2019).PubMed Central 

Google Scholar 
21.Vanham, D., Comero, S., Gawlik, B. M. & Bidoglio, G. The water footprint of different diets within European sub-national geographical entities. Nat. Sustain. 1, 518 (2018).
Google Scholar 
22.Vanham, D., Mekonnen, M. M. & Hoekstra, A. Y. The water footprint of the EU for different diets. Ecol. Indicators 32, 1–8 (2013).
Google Scholar 
23.Environmental Management—Water Footprint—Principles, Requirements and Guidelines ISO 14046:2014 (International Organization for Standardization, 2014).24.Ridoutt, B. G., Hendrie, G. A. & Noakes, M. Dietary strategies to reduce environmental impact: a critical review of the evidence base. Adv. Nutr. 8, 933–946 (2017).PubMed 
PubMed Central 

Google Scholar 
25.Quinteiro, P., Ridoutt, B. G., Arroja, L. & Dias, A. C. Identification of methodological challenges remaining in the assessment of a water scarcity footprint: a review. Int. J. Life Cycle Assess. 23, 164–180 (2018).
Google Scholar 
26.Heller, M. C., Willits-Smith, A., Meyer, R., Keoleian, G. A. & Rose, D. Greenhouse gas emissions and energy use associated with production of individual self-selected US diets. Environ. Res. Lett. 13, 044004 (2018).27.2015–2020 Dietary Guidelines for Americans (US Department of Health and Human Services & US Department of Agriculture, 2015).28.Willits-Smith, A., Aranda, R., Heller, M. C. & Rose, D. Addressing the carbon footprint, healthfulness, and costs of self-selected diets in the USA: a population-based cross-sectional study. Lancet Planet. Health 4, e98–e106 (2020).PubMed 
PubMed Central 

Google Scholar 
29.Hess, T., Andersson, U., Mena, C. & Williams, A. The impact of healthier dietary scenarios on the global blue water scarcity footprint of food consumption in the UK. Food Policy 50, 1–10 (2015).
Google Scholar 
30.Goldstein, B., Hansen, S. F., Gjerris, M., Laurent, A. & Birkved, M. Ethical aspects of life cycle assessments of diets. Food Policy 59, 139–151 (2016).
Google Scholar 
31.Hess, T., Chatterton, J., Daccache, A. & Williams, A. The impact of changing food choices on the blue water scarcity footprint and greenhouse gas emissions of the British diet: the example of potato, pasta and rice. J. Clean. Prod. 112, 4558–4568 (2016).
Google Scholar 
32.Notarnicola, B., Tassielli, G., Renzulli, P. A., Castellani, V. & Sala, S. Environmental impacts of food consumption in Europe. J. Clean. Prod. 140, 753–765 (2017).
Google Scholar 
33.Heller, M. C. et al. Environmental analyses to inform transitions to sustainable diets in developing countries: case studies for Vietnam and Kenya. Int. J. Life Cycle Assess. 25, 1183–1196 (2020).
Google Scholar 
34.Ridoutt, B. G., Baird, D., Anastasiou, K. & Hendrie, G. A. Diet quality and water scarcity: evidence from a large Australian population health survey. Nutrients 11, 1846 (2019).CAS 
PubMed Central 

Google Scholar 
35.Kim, B. F. et al. Country-specific dietary shifts to mitigate climate and water crises. Global Environ. Change 62, 101926 (2020).
Google Scholar 
36.Mekonnen, M. M. & Hoekstra, A. Y. A global assessment of the water footprint of farm animal products. Ecosystems 15, 401–415 (2012).CAS 

Google Scholar 
37.Meier, T. & Christen, O. Environmental impacts of dietary recommendations and dietary styles: Germany as an example. Environ. Sci. Technol. 47, 877–888 (2013).ADS 
CAS 
PubMed 

Google Scholar 
38.Mekonnen, M. M. & Hoekstra, A. Y. The green, blue and grey water footprint of crops and derived crop products. Hydrol. Earth Syst. Sci. 15, 1577–1600 (2011).ADS 

Google Scholar 
39.Zhuo, L., Mekonnen, M. M. & Hoekstra, A. Y. Sensitivity and uncertainty in crop water footprint accounting: a case study for the Yellow River basin. Hydrol. Earth Syst. Sci. 18, 2219–2234 (2014).ADS 

Google Scholar 
40.World Economic Forum Water Initiative Water Security: The Water–Food–Energy–Climate Nexus (Island Press, 2011).41.Bazilian, M. et al. Considering the energy, water and food nexus: towards an integrated modelling approach. Energy Policy 39, 7896–7906 (2011).
Google Scholar 
42.Hoekstra, A. Y., Chapagain, A. K., Aldaya, M. M. & Mekonnen, M. M. The Water Footprint Assessment Manual: Setting the Global Standard (Earthscan, 2011).43.Jefferies, D. et al. Water footprint and life cycle assessment as approaches to assess potential impacts of products on water consumption. Key learning points from pilot studies on tea and margarine. J. Clean. Prod. 33, 155–166 (2012).
Google Scholar 
44.Lovarelli, D., Bacenetti, J. & Fiala, M. Water footprint of crop productions: a review. Sci. Total Environ. 548–549, 236–251 (2016).ADS 
PubMed 

Google Scholar 
45.Chenoweth, J., Hadjikakou, M. & Zoumides, C. Quantifying the human impact on water resources: a critical review of the water footprint concept. Hydrol. Earth Syst. Sci. 18, 2325–2342 (2014).ADS 

Google Scholar 
46.Ridoutt, B. G. & Pfister, S. A revised approach to water footprinting to make transparent the impacts of consumption and production on global freshwater scarcity. Global Environ. Change 20, 113–120 (2010).
Google Scholar 
47.Ridoutt, B. G. & Huang, J. Environmental relevance—the key to understanding water footprints. Proc. Natl Acad. Sci. USA 109, E1424–E1424 (2012).ADS 
CAS 
PubMed 

Google Scholar 
48.Pfister, S. et al. Understanding the LCA and ISO water footprint: a response to Hoekstra (2016) ‘A critique on the water-scarcity weighted water footprint in LCA’. Ecol. Indic. 72, 352–359 (2017).PubMed 
PubMed Central 

Google Scholar 
49.2018 Irrigation and Water Management Survey (USDA, 2019).50.Pfister, S. & Bayer, P. Monthly water stress: spatially and temporally explicit consumptive water footprint of global crop production. J. Clean. Prod. 73, 52–62 (2014).
Google Scholar 
51.Pfister, S. & Bayer, P. Water Consumption of Crop on Watershed Level (Blue and Green Water, Uncertainty, incl. Shapefile) https://doi.org/10.17632/brn4xm47jk.1 (2017).52.Ramankutty, N., Evan, A. T., Monfreda, C. & Foley, J. A. Farming the planet: 1. Geographic distribution of global agricultural lands in the year 2000. Global Biogeochem. Cycles 22, GB1003 (2008).53.Monfreda, C., Ramankutty, N. & Foley, J. A. Farming the planet: 2. Geographic distribution of crop areas, yields, physiological types, and net primary production in the year 2000. Global Biogeochem. Cycles 22, GB1022 (2008).54.Mekonnen, M. M. & Hoekstra, A. Y. The Green, Blue and Grey Water Footprint of Crops and Derived Crop Products (UNESCO-IHE, 2010).
Google Scholar 
55.Hoekstra, A. Y. A critique on the water-scarcity weighted water footprint in LCA. Ecol. Indic. 66, 564–573 (2016).
Google Scholar 
56.Hoekstra, A. Y. Water footprint assessment: evolvement of a new research field. Water Resour. Manage. 31, 3061–3081 (2017).
Google Scholar 
57.Caldeira, C. et al. Water footprint profile of crop-based vegetable oils and waste cooking oil: comparing two water scarcity footprint methods. J. Cleaner Prod. 195, 1190–1202 (2018).
Google Scholar 
58.Boulay, A.-M., Benini, L. & Sala, S. Marginal and non-marginal approaches in characterization: how context and scale affect the selection of an adequate characterization model. The AWARE model example. Int. J. Life Cycle Assess. 25, 2380–2392 (2020).59.Forin, S., Berger, M. & Finkbeiner, M. Comment to ‘Marginal and non-marginal approaches in characterization: how context and scale affect the selection of an adequate characterization factor. The AWARE model example’. Int. J. Life Cycle Assess. 25, 663–666 (2020).
Google Scholar 
60.Boulay, A.-M. & Lenoir, L. Sub-national regionalisation of the AWARE indicator for water scarcity footprint calculations. Ecol. Indic. 111, 106017 (2020).
Google Scholar 
61.Rotz, C. A., Asem-Hiablie, S., Place, S. & Thoma, G. Environmental footprints of beef cattle production in the United States. Agric. Syst. 169, 1–13 (2019).
Google Scholar 
62.Peters, C. J., Picardy, J. A., Darrouzet-Nardi, A. & Griffin, T. S. Feed conversions, ration compositions, and land use efficiencies of major livestock products in US agricultural systems. Agric. Syst. 130, 35–43 (2014).
Google Scholar 
63.Peters, C. J. et al. Carrying capacity of US agricultural land: ten diet scenarios. Elementa 4, 000116 (2016).64.Census of Agriculture Farm and Ranch Irrigation Survey (USDA NASS, 2013).65.Aquaculture Trade Tables (USDA Economic Research Service, 2018).66.Pahlow, M., Van Oel, P., Mekonnen, M. & Hoekstra, A. Y. Increasing pressure on freshwater resources due to terrestrial feed ingredients for aquaculture production. Sci. Total Environ. 536, 847–857 (2015).ADS 
CAS 
PubMed 

Google Scholar 
67.Rose, D., Heller, M. C., Willits-Smith, A. M. & Meyer, R. J. Carbon footprint of self-selected US diets: nutritional, demographic, and behavioral correlates. Am. J. Clin. Nutr. 108, 1–9 (2019).
Google Scholar 
68.NHANES: 2005–2006 Data Documentation, Codebook and Frequencies (National Center for Health Statistics and Centers for Disease Control, 2008). More