Markus Andrews

1.Wilkinson, B. H. Humans as geologic agents: a deep-time perspective. Geology 33, 161–164 (2005).
Google Scholar 
2.Syvitski, J. P. M., Vörösmarty, C., Kettner, A. J. & Green, P. Impact of humans on the flux of terrestrial sediment to the global coastal ocean. Science 308, 376–380 (2005).
Google Scholar 
3.Meade, R. H. Movement and storage of sediment in river systems. In Physical and Chemical Weathering in Geochemical Cycles (eds Lerman, A. & Meybeck, M.) 165–179, (Springer, 1988).4.Nicholas, A. P., Ashworth, P. J., Kirkby, M. J., Macklin, M. G. & Murray, T. Sediment slugs: large-scale fluctuations in fluvial sediment transport rates and storage volumes. Prog. Phys. Geography Earth Environ. 19, 500–519 (1995).
Google Scholar 
5.Trimble, S. W. The fallacy of stream equilibrium in contemporary denudation studies. Am. J. Sci. 277, 876–887 (1977).
Google Scholar 
6.Vörösmarty, C. J., Fekete, B. M., Meybeck, M. & Lammers, R. Global system of rivers: its role in organizing continental land mass and defining land-to-ocean linkages. Glob. Biogeochem. Cycles 14, 599–621 (2000).
Google Scholar 
7.Syvitski, J. et al. Extraordinary human energy consumption and resultant geological impacts beginning around 1950 CE initiated the proposed Anthropocene Epoch. Commun. Earth Environ. 1, 32 (2020).
Google Scholar 
8.Zalasiewicz, J. et al. When did the Anthropocene begin? A mid-twentieth century boundary level is stratigraphically optimal. Quat. Int. 383, 196–203 (2015).
Google Scholar 
9.Waters, C. N. et al. The Anthropocene is functionally and stratigraphically distinct from the Holocene. Science 351, aad2622 (2016).
Google Scholar 
10.Hay, W. H. Pleistocene–Holocene fluxes are not the Earth’s norm. In Global Sedimentary Geofluxes (ed. Hay, W. H.) 15–27 (National Academy of Sciences Press, 1994).11.Walling, D. E. & Webb, B. W. Erosion and sediment yield: a global overview. In Erosion and Sediment Yield: Global and Regional Perspectives 3–19 (IAHS, 1996).12.Syvitski, J. P. M. Sediment fluxes and rates of sedimentation. In Encyclopedia of Sediments and Sedimentary Rocks (ed. Middleton, G. V.) 600–606 (Kluwer Academic, Netherlands, 2003).13.Hallet, B., Hunter, L. & Bogen, J. Rates of erosion and sediment evacuation by glaciers: a review of field data and their implications. Glob. Planet. Change 12, 213–235 (1996).
Google Scholar 
14.Métivier, F., Gaudemer, Y., Tapponnier, P. & Klein, M. Mass accumulation rates in Asia during the Cenozoic. Geophys. J. Int. 137, 280–318 (1999).
Google Scholar 
15.Latrubesse, E. M. & Restrepo, J. D. Sediment yield along the Andes: continental budget, regional variations, and comparisons with other basins from orogenic mountain belts. Geomorphology 216, 225–233 (2014).
Google Scholar 
16.Syvitski, J. P. M. & Milliman, J. D. Geology, geography, and humans battle for dominance over the delivery of sediment to the coastal ocean. J. Geol. 115, 1–19 (2007).
Google Scholar 
17.Syvitski, J. P. M. & Kettner, A. J. Sediment flux and the Anthropocene. Phil. Trans. R. Soc. A 369, 957–975 (2011).
Google Scholar 
18.Milliman, J. D. & Syvitski, J. P. M. Geomorphic/tectonic control of sediment discharge to the ocean: the importance of small mountainous rivers. J. Geol. 100, 525–544 (1992).
Google Scholar 
19.Holmes, R. M. et al. A circumpolar perspective on fluvial sediment flux to the Arctic ocean. Glob. Biogeochem. Cycles 16, 45-1–45-14 (2002).
Google Scholar 
20.Gordeev, V. V. Fluvial sediment flux to the Arctic Ocean. Geomorphology 80, 94–104 (2006).
Google Scholar 
21.Syvitski, J. P., Kettner, A. J., Overeem, I., Brakenridge, G. R. & Cohen, S. Latitudinal controls on siliciclastic sediment production and transport. In Latitudinal Controls on Stratigraphic Models and Sedimentary Concepts 14–28 (eds Fraticelli, C. M., Martinius, A. W., Markwick, P. & Suter, J. R.) (Geological Society Special Publication, 2019).22.Dadson, S. J. et al. Earthquake triggered increase in sediment delivery from an active mountain belt. Geology 32, 733–736 (2004).
Google Scholar 
23.Hovius, N. et al. Prolonged seismically induced erosion and the mass balance of a large earthquake. Earth Planet. Sci. Lett. 3–4, 347–355 (2011).
Google Scholar 
24.Overeem, I. et al. Substantial export of suspended sediment to the global oceans from glacial erosion in Greenland. Nat. Geosci. 10, 859–863 (2017).
Google Scholar 
25.Steinberger, B. Topography caused by mantle density variations: observation-based estimates and models derived from tomography and lithosphere thickness. Geophys. J. Int. 205, 604–621 (2016).
Google Scholar 
26.Stallard, R. Terrestrial sedimentation and the carbon cycle: coupling weathering and erosion to carbon burial. Glob. Biogeochem. Cycles 12, 231–257 (1998).
Google Scholar 
27.Syvitski, J. P. M., Overeem, I., Brakenridge, G. R. & Hannon, M. D. Floods, floodplains, delta plains — a satellite imaging approach. Sediment. Geol. 267/268, 1–14 (2012).
Google Scholar 
28.Kettner, A. J., Restrepo, J. D. & Syvitski, J. P. M. A spatial simulation experiment to replicate the fluvial sediment fluxes within the Magdalena River Basin, Colombia. J. Geol. 118, 363–379 (2010).
Google Scholar 
29.Chen, Z., Wang, Z., Finlayson, B., Chen, J. & Yin, D. Implications of flow control by the Three Gorges Dam on sediment and channel dynamics of the middle Yangtze (Changjiang) River, China. Geology 38, 1043–1046 (2010).
Google Scholar 
30.Wilkinson, B. H. & McElroy, B. J. The impact of humans on continental erosion and sedimentation. Geol. Soc. Am. Bull. 119, 140–156 (2007).
Google Scholar 
31.Beach, T. The fate of eroded soil: sediment sinks and sediment budgets of agrarian landscapes in southern Minnesota, 1851–1988. Ann. Assoc. Am. Geogr. 84, 5–28 (1994).
Google Scholar 
32.Restrepo, J. D., Kettner, A. J. & Syvitski, J. P. Recent deforestation causes rapid increase in river sediment load in the Colombian Andes. Anthropocene 10, 13–28 (2015).
Google Scholar 
33.Smith, S. V., Renwick, W. H., Buddemeier, R. W. & Crossland, C. J. Budgets of soil erosion and deposition for sediments and sedimentary organic carbon across the conterminous United States. Glob. Biogeochem. Cycles 15, 697 (2001).
Google Scholar 
34.Ibáñez, C. et al. Basin-scale land use impacts on world deltas: human vs natural forcings. Glob. Planet. Change 173, 24–32 (2019).
Google Scholar 
35.Curtis, W. F., Culbertson, K. & Chase, E. B. Fluvial-sediment discharge to the oceans from the conterminous United States. US Geol. Surv. Circ. 670, 1–17 (1973).
Google Scholar 
36.Chen, Z., Syvitski, J. P. M., Gao, S., Overeem, I. & Kettner, A. J. Socio-economic impacts on flooding: a 4000-year history of the Yellow River, China. Ambio 41, 682–698 (2012).
Google Scholar 
37.Zhou, L. Y. et al. Coastal erosion as a major sediment supplier to continental shelves: example from the abandoned Old Huanghe (Yellow River) delta. Continental Shelf Res. 82, 43–59 (2014).
Google Scholar 
38.Zhou, L. et al. Sediment budget of the Yellow River delta during 1959–2012, estimated from morphological changes and accumulation rates. Mar. Geol. 430, 106363 (2020).
Google Scholar 
39.Ericson, J. P., Vörösmarty, C. J., Dingman, S. L., Ward, L. G. & Meybeck, M. Effective sea-level rise in deltas: sources of change and human-dimension implications. Glob. Planet. Change 50, 63–82 (2006).
Google Scholar 
40.Walsh, J. P. & Nittrouer, C. A. Understanding fine-grained river-sediment dispersal on continental margins. Mar. Geol. 263, 34–45 (2009).
Google Scholar 
41.Bernhardt, A. & Schwanghart, W. Where and why do submarine canyons remain connected to the shore during sealevel rise? — Insights from global topographic analysis and Bayesian regression. Geophys. Res. Lett. 48, e2020GL092234 (2021).
Google Scholar 
42.Morehead, M. D., Syvitski, J. P. M., Hutton, E. W. H. & Peckham, S. D. Modelling the temporal variability in the flux of sediment in ungauged river basins. Glob. Planet. Change 39, 95–110 (2003).
Google Scholar 
43.Meybeck, M., Laroche, L., Darr, H. H. & Syvitski, J. P. M. Global variability of daily total suspended solids and their fluxes in rivers. Glob. Planet. Change 39, 65–93 (2003).
Google Scholar 
44.Paszkowski, A., Goodbred, S., Borgomeo, E., Shah Alam Khan, M. & Hall, J. W. Geomorphic change in the Ganges–Brahmaputra–Meghna delta. Nat. Rev. Earth Environ. 2, 763–780 https://www.nature.com/articles/s43017-021-00213-4 (2021).
Google Scholar 
45.Overeem, I. & Syvitski, J. P. M. Shifting discharge peaks in Arctic rivers 1977–2007. Geogr. Ann. A 92, 285–296 (2010).
Google Scholar 
46.Rawlins, M. A. et al. Analysis of the Arctic system for freshwater cycle intensification: observations and expectations. J. Clim. 23, 5715–5737 (2010).
Google Scholar 
47.Syvitski, J. P. M. et al. Dynamics of the coastal zone. In Coastal Fluxes in the Anthropocene 39–94 (eds Crossland, C. J., Kremer, H. H., Lindeboom, H. J., Marshall Crossland, J. I. & Le Tissier, M. D. A.) (Springer, 2005).48.Syvitski, J. P. M., Morehead, M. D., Bahr, D. & Mulder, T. Estimating fluvial sediment transport: the rating parameters. Wat. Resour. Res. 36, 2747–2760 (2000).
Google Scholar 
49.N’kaya, G. D. M., Orange, D., Bayonne Padou, S. M., Datok, P. & Laraque, A. Temporal variability of sediments, dissolved solids and dissolved organic matter fluxes in the Congo river at Brazzaville/Kinshasa. Geosciences 10, 341 (2020).
Google Scholar 
50.Huang, T.-H., Fu, Y.-H., Pan, P.-Y. & Chen, C.-T. A. Fluvial carbon fluxes in tropical rivers. Curr. Opin. Environ. Sustain. 4, 162–169 (2012).
Google Scholar 
51.Lyons, W. B., Nezat, C. A., Carey, A. E. & Hicks, D. M. Organic carbon fluxes to the ocean from high-standing islands. Geology 30, 443–446 (2002).
Google Scholar 
52.Beusen, A. H. W., Dekkers, A. L. M., Bouwman, A. F., Ludwig, W. & Harrison, J. Estimation of global river transport of sediments and associated particulate C, N, and P. Glob. Biogeochem. Cycles 19, GB4S05 (2005).
Google Scholar 
53.Bianchi, T. S. et al. Centers of organic carbon burial and oxidation at the land–ocean interface. Org. Geochem. 115, 138–155 (2018).
Google Scholar 
54.Burdige, D. J. Burial of terrestrial organic matter in marine sediments: a reassessment. Glob. Biogeochem.Cycles 19, GB4011 (2005).
Google Scholar 
55.Qiao, J. et al. Runoff-driven export of terrigenous particulate organic matter from a small mountainous river: sources, fluxes and comparisons among different rivers. Biogeochemistry 147, 71–86 (2020).
Google Scholar 
56.Usman, M. O. et al. Reconciling drainage and receiving basin signatures of the Godavari River system. Biogeosciences 15, 3357–3375 (2018).
Google Scholar 
57.Pradhan, U. K. et al. Multi-proxy evidence for compositional change of organic matter in the largest tropical (peninsula) river basin of India. J. Hydrol. 519, 999–1009 (2014).
Google Scholar 
58.Gruca-Rokosz, R. Quantitative fluxes of the greenhouse gases CH4 and CO2 from the surfaces of selected Polish reservoirs. Atmosphere 11, 286 (2020).
Google Scholar 
59.Zalasiewicz, J. et al. The geological cycle of plastics and their use as a stratigraphic indicator of the Anthropocene. Anthropocene 13, 4–17 (2016).
Google Scholar 
60.Geyer, R., Jambeck, J. R. & Law, K. L. Production, use, and fate of all plastics ever made. Sci. Adv. 3, e1700782 (2017).
Google Scholar 
61.Jambeck, J. R. et al. Plastic waste inputs from land into the ocean. Science 347, 768–771 (2015).
Google Scholar 
62.Bergmann, M. et al. White and wonderful? Microplastics prevail in snow from the Alps to the Arctic. Sci. Adv. 5, eaax1157 (2019).
Google Scholar 
63.Probst, J. L. & Tardy, Y. Global runoff fluctuations during the last 80 years in relation to world temperature change. Am. J. Sci. 289, 267–285 (1989).
Google Scholar 
64.Knox, J. C. Large increase in flood magnitude in response to modest changes in climate. Nature 361, 430–432 (1993).
Google Scholar 
65.Milliman, J. D. & Kao, S. J. Hyperpycnal discharge of fluvial sediment to the ocean: impact of super-typhoon Herb (1996) on Taiwanese rivers. J. Geol. 113, 503–516 (2005).
Google Scholar 
66.Wang, H. et al. Recent changes of sediment flux to the western Pacific Ocean from major rivers in east and southeast Asia. Earth Sci. Rev. 108, 80–100 (2011).
Google Scholar 
67.Depetris, P. J., Kempe, S., Latif, M. & Mook, W. G. ENSO controlled flooding in the Parana River (1904-1991). Naturwissenschaften 83, 127–129 (1996).
Google Scholar 
68.Vörösmarty, C. J. et al. Analyzing the discharge regime of a large tropical river through remote sensing, ground-based climatic data, and modelling. Water Resour. Res. 32, 3137–3150 (1996).
Google Scholar 
69.Restrepo, J. D. & Kjerfve, B. Magdalena river: interannual variability (1975–1995) and revised water discharge and sediment load estimates. J. Hydrol. 235, 137–149 (2000).
Google Scholar 
70.Mulder, T. & Syvitski, J. P. M. Climatic and morphologic relationships of rivers. Implications of sea level fluctuations on river loads. J. Geol. 104, 509–523 (1996).
Google Scholar 
71.Poag, C. W. U.S. middle Atlantic continental rise: provenance, dispersal, and deposition of Jurassic to Quaternary sediments. In Geologic Evolution of Atlantic Continental Rises 100–156 (eds Poag, C. W. & Graciansky, P. C.) (Van Nostrand Reinhold, 1992).72.Elverhøi, A., Hooke, R. L. & Solheim, A. Late Cenozoic erosion and sediment yield from the Svalbard-Barents Sea region; implications for understanding erosion of glacierized basins. Quat. Sci. Rev. 17, 209–241 (1998).
Google Scholar 
73.O’Grady, D. B. & Syvitski, J. P. M. Large-scale morphology of Arctic continental slopes: the influence of sediment delivery on slope form. In Glacier-Influenced Sedimentation on High-Latitude Continental Margins (eds Dowdeswell, J. A. & O’Cofaigh, C.) 11–31 (Geological Society, 2002).74.Goodbred, S. L. & Kuehl, S. A. Holocene and modern sediment budgets for the Ganges–Brahmaputra river system: evidence for highstand dispersal to floodplain, shelf and deep-sea depocenters. Geology 27, 559–562 (1999).
Google Scholar 
75.Kettner, A. J. & Syvitski, J. P. M. Predicting discharge and sediment flux of the Po River, Italy since the Late Glacial Maximum. In Analogue and Numerical Forward Modelling of Sedimentary Systems: from Understanding to Prediction (eds De Boer, P. L., Postma, G., van der Zwan, C. J., Burgess, P. M. & Kukla, P.) 171–189 (International Association of Sedimentologists, 2008).76.Goodbred, S. L. & Kuehl, S. A. Enormous Ganges–Brahmaputra sediment discharge during strengthened early Holocene monsoon. Geology 28, 1083–1086 (2000).
Google Scholar 
77.Wang, Z. et al. Three-dimensional evolution of the Yangtze River mouth, China during the Holocene: impacts of sea level, climate and human activity. Earth Sci. Rev. 185, 938–955 (2018).
Google Scholar 
78.Jenny, J. P. et al. Human and climate global-scale imprint on sediment transfer during the Holocene. Proc. Natl Acad. Sci. USA 116, 22972–22976 (2019).
Google Scholar 
79.Sima, R. J. A dirty truth: humans began accelerating soil erosion 4,000 years ago. Eos https://doi.org/10.1029/2019EO137634 (2019).80.Wu, Z. et al. Anthropogenic impacts on the decreasing sediment loads of nine major rivers in China, 1954–2015. Sci. Total. Environ. 739, 139653 (2020).
Google Scholar 
81.Wu, X. et al. Climate and humans battle for dominance over the Yellow River’s sediment discharge: from the Mid-Holocene to the Anthropocene. Mar. Geol. 425, 106188 (2020).
Google Scholar 
82.Kong, R. et al. Increasing carbon storage in subtropical forests over the Yangtze River basin and its relations to the major ecological projects. Sci. Total. Environ. 709, 136163 (2020).
Google Scholar 
83.Kettner, A. J., Gomez, B. & Syvitski, J. P. M. Modeling suspended sediment discharge from the Waipaoa River system, New Zealand: the last 3000 years. Water Resour. Res. 43, W07411 (2007).
Google Scholar 
84.Giosan et al. Massive erosion in monsoonal central India linked to late Holocene land cover degradation. Earth Surf. Dynam. 5, 781–789 (2017).
Google Scholar 
85.Meybeck, M. & Vörösmarty, C. J. Fluvial filtering of land to ocean fluxes: from natural Holocene variations to Anthropocene. Comptes Rendus 337, 107–123 (2005).
Google Scholar 
86.Tréguer, P. J. & De La Rocha, C. L. The world ocean silica cycle. Annu. Rev. Mar. Sci. 5, 477–501 (2013).
Google Scholar 
87.Isson, T. T. & Planavsky, N. J. Reverse weathering as a long-term stabilizer of marine pH and planetary climate. Nature 560, 471–475 (2018).
Google Scholar 
88.Tréguer, P. et al. Reviews and syntheses: the biogeochemical cycle of silicon in the modern ocean. Biogeosciences 18, 1269–1289 (2021).
Google Scholar 
89.Leithold, E. L. & Blair, N. E. Watershed control on the carbon loading of marine sedimentary particles. Geochim. Cosmochim. Acta 65, 2231–2240 (2001).
Google Scholar 
90.Nittrouer, C. A. et al. Writing a Rosetta stone: insights into continental-margin sedimentary processes and strata. In Continental-Margin Sedimentation: From Sediment Transport to Sequence Stratigraphy (eds Nittrouer, C. A. et al.) 1–48 (IAS, 2007).91.Milliman, J. D. & Farnsworth, K. L. River Discharge to the Coastal Ocean 384 (Cambridge Univ. Press, 2011).92.Tanaka, T. Y. & Chiba, M. A numerical study of the contributions of dust source regions to the global dust budget. Glob. Planet. Change 52, 88–104 (2006).
Google Scholar 
93.Maher, B. A. et al. Global connections between aeolian dust, climate and ocean biogeochemistry at the present day and at the Last Glacial Maximum. Earth Sci. Rev. 99, 61–97 (2010).
Google Scholar 
94.Saito, Y., Chaimanee, N., Jarupongsakul, T. & Syvitski, J. P. M. Shrinking megadeltas in Asia: sea-level rise and sediment reduction impacts from case study of the Chao Phraya delta. Inprint Newsletter of the IGBP/IHDP Land Ocean Interaction in the Coastal Zone 2007/2, 3–9 (2007).
Google Scholar 
95.Latrubesse, E. M., Amsler, M. L., de Morais, R. P. & Aquino, S. The geomorphologic response of a large pristine alluvial river to tremendous deforestation in the South American tropics: the case of the Araguaia River. Geomorphology 113, 239–252 (2009).
Google Scholar 
96.Restrepo, J. D. & Syvitski, J. P. M. Assessing the effect of natural controls and land use change on sediment yield in a major Andean river: the Magdalena drainage basin, Colombia. Ambio 35, 65–74 (2006).
Google Scholar 
97.Wang, H., Yang, Z., Saito, Y., Liu, J. P. & Sun, X. Stepwise decreases of the Huanghe (Yellow River) sediment load (1950–2004): impacts from climate changes and human activities. Glob. Planet. Change 57, 331–354 (2007).
Google Scholar 
98.Chen, Y., Overeem, I., Gao, S., Syvitski, J. P. M. & Kettner, A. J. Quantifying sediment storage on the floodplains outside levees along the lower Yellow River during the years 1580–1849. Earth Surf. Process. Landf. 44, 581–594 (2018).
Google Scholar 
99.Syvitski, J. P. M., Kettner, A., Peckham, S. D. & Kao, S. J. Predicting the flux of sediment to the coastal zone: application to the Lanyang watershed, northern Taiwan. J. Coast. Res. 21, 580–587 (2005).
Google Scholar 
100.Walling, D. E. & Fang, D. Recent trends in the suspended sediment loads of the world’s rivers. Glob. Planet. Change 39, 111–126 (2003).
Google Scholar 
101.Wu, X. et al. Can reservoir regulation along the Yellow River be a sustainable way to save a sinking delta? Earths Future 8, e2020EF001587 (2020).
Google Scholar 
102.Syvitski, J. P. M. Deltas at risk. Sustain. Sci. 3, 23–32 (2008).
Google Scholar 
103.Kondolf, G. M. et al. Changing sediment budget of the Mekong: cumulative threats and management strategies for a large river basin. Sci. Total. Environ. 625, 114–134 (2018).
Google Scholar 
104.Syvitski, J. P. M. & Brakenridge, G. R. Causation and avoidance of catastrophic flooding along the Indus River, Pakistan. GSA Today 23, 4–10 (2013).
Google Scholar 
105.Cooper, A. H., Brown, T. J., Price, S. J., Ford, J. R. & Waters, C. N. Humans are the most significant global geomorphological driving force of the 21st century. Anthropocene Rev. 5, 222–229 (2018).
Google Scholar 
106.Global Aggregates Information Network (GAIN). GAIN newsletter #4, January 2019. GAIN https://www.gain.ie/s/GAIN_Newsletter_Jan19.pdf (2019).107.Waters, C. N. & Zalasiewicz, J. Concrete: the most abundant novel rock type of the Anthropocene. In The Encyclopedia of the Anthropocene Vol. 1 75–85 (eds Sala, D. A. D. & Goldstein, M. I.) (Elsevier, 2018).108.Kemp, D. B., Sadler, P. M. & Vanacker, V. The human impact on North American erosion, sediment transfer, and storage in a geologic context. Nat. Commun. 11, 6012 (2020).
Google Scholar 
109.Montgomery, D. R. Soil erosion and agricultural sustainability. Proc. Natl Acad. Sci. USA 104, 133268–133272 (2007).
Google Scholar 
110.Reusser, L., Bierman, P. & Rood, D. Quantifying human impacts on rates of erosion and sediment transport at a landscape scale. Geology 43, 171–174 (2014).
Google Scholar 
111.Bonachea, J. et al. Natural and human forcing in recent geomorphic change; case studies in the Rio de la Plata basin. Sci. Total. Env. 408, 2674–2695 (2010).
Google Scholar 
112.Liu et al. Global vegetation biomass change (1988–2008) and attribution to environmental and human drivers. Glob. Ecol. Biogeogr. 22, 692–705 (2013).
Google Scholar 
113.Faour, G. et al. Global trends analysis of the main vegetation types throughout the past four decades. Appl. Geogr. 97, 184–195 (2018).
Google Scholar 
114.Hooke, R. L. On the history of humans as geomorphic agents. Geology 28, 843–846 (2000).
Google Scholar 
115.Borrelli, P. et al. An assessment of the global impact of 21st century land use change on soil erosion. Nat. Commun. 8, 2013 (2017).
Google Scholar 
116.Harden, D. R. A comparison of flood-producing storms and their impacts in northwestern California. In Geomorphic Processes and Aquatic Habitat in the Redwood Creek Basin, Northwestern California. US Geological Survey Professional Paper 1454, D1–D9 (USGS, 1995).117.Hewawasam, T., von Blanckenburg, F., Schaller, M. & Kubik, P. Increase of human over natural erosion rates in tropical highlands constrained by cosmogenic nuclides. Geology 31, 597–600 (2003).
Google Scholar 
118.Vörösmarty, C. et al. Anthropogenic sediment retention: major global-scale impact from the population of registered impoundments. Glob. Planet. Change 39, 169–190 (2003).
Google Scholar 
119.Best, J. Anthropogenic stresses on the world’s big rivers. Nat. Geosci. 12, 7–21 (2019).
Google Scholar 
120.Grill, G. et al. Mapping the world’s free-flowing rivers. Nature 569, 215–221 (2019).
Google Scholar 
121.Zarfl, C., Lumsdon, A. E., Berlekamp, J., Tydecks, L. & Tockner, K. Global boom in hydropower dam construction. Aquat. Sci. 77, 161–170 (2015).
Google Scholar 
122.International Commission on Large Dams. World Register of Dams: general synthesis. WRD http://www.icold-cigb.org/GB/world_register/general_synthesis.asp (2017).123.Syvitski, J. P., Zalasiewicz, J. & Summerhayes, C. Changes to Holocene/Anthropocene patterns of sedimentation from terrestrial to marine. In The Anthropocene as a Geological Time Unit: A Guide to the Scientific Evidence and Current Debate (eds Zalasiewicz, J., Waters, C., Williams, M. & Summerhayes, C.) 90–108 (Cambridge Univ. Press, 2019).124.Syvitski, J. P. M., Kettner, A. J., Correggiari, A. & Nelson, B. W. Distributary channels and their impact on sediment dispersal. Mar. Geol. 222/223, 75–94 (2005).
Google Scholar 
125.Syvitski, J. P. M. & Kettner, A. J. On the flux of water and sediment into the Northern Adriatic. Continental Shelf Res. 27, 296–308 (2007).
Google Scholar 
126.Syvitski, J. P. M. et al. Anthropocene metamorphosis of the Indus delta and lower floodplain. Anthropocene 3, 24–35 (2013).
Google Scholar 
127.Laruelle, G. G. et al. Anthopogenic perturbations of the silicon cycle at the global scale: key role of the land-ocean transition. Glob. Biogeochem. Cycles 23, GB4031 (2009).
Google Scholar 
128.Harmer, O. P. & Clifford, N. J. Geomorphological explanation of the long profile of the Lower Mississippi River. Geomorphology 84, 222–240 (2007).
Google Scholar 
129.Galat, D. L. A. O. Flooding to restore connectivity of regulated, large-river wetlands: natural and controlled flooding as complementary processes along the lower Missouri River. Bioscience 48, 721–733 (1998).
Google Scholar 
130.Day, J. W., Lane, R. R., D’Elia, C. & Kemp, G. P. Large infrequently operated river diversions for Mississippi delta restoration. Estuar. Coast. Shelf Sci. 183, 1–12 (2016).
Google Scholar 
131.Higgins, S. A., Overeem, I., Rogers, K. G. & Kalina, E. A. River linking in India: downstream impacts on water discharge and suspended sediment transport to deltas. Elem. Sci. Anthrop. 6, 20 (2018).
Google Scholar 
132.Monteiro, P. J. M. et al. Towards sustainable concrete. Nat. Mater. 16, 698–699 (2017).
Google Scholar 
133.Filho, W. L. et al. The unsustainable use of sand: reporting on a global problem. Sustainability 13, 3356 (2021).
Google Scholar 
134.Kamboj, V., Kamboj, N. & Sharma, S. Environmental impact of riverbed mining — a review. Intl. J. Sci. Res. Rev. 7, 504–520 (2017).
Google Scholar 
135.Mechi, A. & Sanches, D. L. The environmental impact of mining in the state of São Paulo. Estudos Avançados 24, 209–220 (2010).
Google Scholar 
136.Bisht, A. & Gerber, J. F. Ecological distribution conflicts (EDCs) over mineral extractivism in India: an overview. Extract. Indust. Soc. 4, 548–563 (2017).
Google Scholar 
137.Bisht, A. Discontent, conflict, social resistance and violence at non-metallic mining frontiers in India. Ecol. Econ. Soc. 2, 31–42 (2019).
Google Scholar 
138.Bisht, A. Conceptualizing sand extractivism: deconstructing an emerging resource frontier. Extract. Indust. Soc. 8, 100904 (2021).
Google Scholar 
139.Bravard, J.-P. et al. Geography of sand and gravel mining in the Lower Mekong River. EchoGeo http://echogeo.revues.org/13659 (2013).140.Hackney, C. R. et al. Sand mining far outpaces natural supply in a large alluvial river. Earth Surf. Dyn. 9, 1323–1334 (2021).
Google Scholar 
141.Hackney, C. R. et al. Riverbank instability from unsustainable sand mining in the lower Mekong River. Nat. Sustain. 3, 217–225 (2020).
Google Scholar 
142.United Nations. Import of natural sand except sand for mineral extraction as reported. United Nations Commodity Trade Statistics Database http://comtrade.un.org (2014).143.Torres, A., Brandt, J., Lear, K. & Liu, J. A looming tragedy of the sand commons. Science 357, 970–971 (2017).
Google Scholar 
144.James, J. et al. The effective development of offshore aggregates in south-east Asia. Technical Report WC/99/9 (British Geological Survey, 1999).145.Jia, L. et al. Impacts of the large amount of sand mining on riverbed morphology and tidal dynamics in lower reaches and delta of the Dongjiang River. J. Geogr. Sci. 17, 197–211 (2007).
Google Scholar 
146.Velegrakis, A. F. et al. European marine aggregates resources: origins, usage, prospecting and dredging techniques. J. Coast. Res. 51, 1–14 (2010).
Google Scholar 
147.Amoroso, R. O. et al. Bottom trawl fishing footprints on the world’s continental shelves. Proc. Natl Acad. Sci. USA 115, E10275–E10282 (2018).
Google Scholar 
148.Martín, J., Puig, P., Masqué, P., Palanques, A. & Sánchez-Gómez, A. Impact of bottom trawling on deep-sea sediment properties along the flanks of a submarine canyon. PLoS ONE 9, e104536 (2014).
Google Scholar 
149.Paradis, S. et al. Bottom trawling along submarine canyons impacts deep sedimentary regimes. Sci. Rep. 7, 43332 (2017).
Google Scholar 
150.Oberle, F. K. J., Storlazzi, C. D. & Hanebuth, T. J. J. What a drag: quantifying the global impact of chronic bottom trawling on continental shelf sediment. J. Mar. Syst. 159, 109–119 (2016).
Google Scholar 
151.GISTEMP Team. GISS surface temperature analysis (GISTEMP), version 4. NASA Goddard Institute for Space Studies http://data.giss.nasa.gov/gistemp/ (2021).152.Lenssen, N. J. L. et al. Improvements in the GISTEMP uncertainty model. J. Geophys. Res. Atmos. 124, 6307–6326 (2019).
Google Scholar 
153.Schmidt, G. A., Ruedy, R. A., Miller, R. L. & Lacis, A. A. Attribution of the present-day total greenhouse effect. J. Geophys. Res. 115, D20106 (2010).
Google Scholar 
154.Zanna, L., Khatlwala, S., Gregory, J. M., Ison, J. & Helmbach, P. Global reconstruction of historical ocean heat storage and transport. Proc. Natl Acad. Sci. USA 116, 1126–1131 (2019).
Google Scholar 
155.Rempel, A. W., Marshall, J. A. & Roering, J. J. Modeling relative frost weathering rates at geomorphic scales. Earth Planet. Sci. Lett. 453, 87–95 (2016).
Google Scholar 
156.Syvitski, J. P. M., Peckham, S. D., Hilberman, R. D. & Mulder, T. Predicting the terrestrial flux of sediment to the global ocean: a planetary perspective. Sediment. Geol. 162, 5–24 (2003).
Google Scholar 
157.Farquharson, L. M. et al. Climate change drives widespread and rapid thermokarst development in very cold permafrost in the Canadian high Arctic. Geophys. Res. Lett. 46, 6681–6689 (2019).
Google Scholar 
158.Kokelj, S. V. et al. Thawing of massive ground ice in mega slumps drives increases in stream sediment and solute flux across a range of watershed scales. J. Geophys. Res. Earth Surf. 118, 681–692 (2013).
Google Scholar 
159.Syvitski, J. P. M. Sediment discharge variability in Arctic rivers: implications for a warmer future. Polar Res. 21, 323–330 (2002).
Google Scholar 
160.Li, D. et al. Exceptional increases in fluvial sediment fluxes in a warmer and wetter high mountain Asia. Science 374, 599–603 (2021).
Google Scholar 
161.Syvitski, J. P. M. & Andrews, J. T. Climate change: numerical modelling of sedimentation and coastal processes, Eastern Canadian Arctic. Arctic Alpine Res. 26, 199–212 (1994).
Google Scholar 
162.Van der Broeke, M. et al. On the recent contribution of the Greenland ice sheet to sea level change. Cryosphere 10, 1933–1946 (2016).
Google Scholar 
163.Bendixen, M. et al. Delta progradation in Greenland driven by increasing glacial mass loss. Nature 550, 101–104 (2017).
Google Scholar 
164.Bamber, J. L., Westaway, R. M., Marzeion, B. & Wouters, B. The land ice contribution to sea level during the satellite era. Environ. Res. Lett. 13, 063008 (2018).
Google Scholar 
165.Mankoff, K. D. et al. Greenland Ice Sheet solid ice discharge from 1986 through March 2020. Earth Syst. Sci. Data 12, 1367–1383 (2020).
Google Scholar 
166.Hugelius, G. et al. Estimated stocks of circumpolar permafrost carbon with quantified uncertainty ranges and identified data gaps. Biogeosciences 11, 6573–6593 (2014).
Google Scholar 
167.Zimov, S. A. et al. Permafrost carbon: stock and decomposability of a globally significant carbon pool. Geophys. Res. Lett. 33, L20502 (2006).
Google Scholar 
168.Turetsky, M. R. et al. Carbon release through abrupt permafrost thaw. Nat. Geosci. 13, 138–143 (2020).
Google Scholar 
169.Schaefer, K., Lantuit, H., Romanovsky, V. E., Schuur, E. A. G. & Witt, R. The impact of the permafrost carbon feedback on global climate. Environ. Res. Lett. 9, 085003 (2014).
Google Scholar 
170.Olefeldt, D. et al. Circumpolar distribution and carbon storage of thermokarst landscapes. Nat. Commun. 7, 13043 (2016).
Google Scholar 
171.Schweiger, A., Zhang, J., Lindsay, R., Steele, M. & Stern, H. PIOMAS arctic sea ice volume reanalysis. Polar Science Centre http://psc.apl.uw.edu/research/projects/arctic-sea-ice-volume-anomaly/ (2019).172.Overeem, I. et al. Sea ice loss enhances wave action at the Arctic coast. Geophys. Res. Lett. 38, L17503 (2011).
Google Scholar 
173.Crawford, A. D. & Serreze, M. C. Projected changes in the Arctic frontal zone and summer Arctic cyclone activity in the CESM large ensemble. J. Clim. 30, 9847–9869 (2017).
Google Scholar 
174.Jones, B. M. et al. Increase in the rate and uniformity of coastline erosion in Arctic Alaska. Geophys. Res. Lett. 36, L03503 (2009).
Google Scholar 
175.Gibbs, A. E., Ohman, K. A., Coppersmith, R. & Richmond, B. M. National Assessment of Shoreline Change: A GIS Compilation of Updated Vector Shorelines and Associated Shoreline Change Data for the North Coast of Alaska, U.S. Canadian border to Icy Cape (US Geological Survey, 2017).176.Lantuit, H. et al. The Arctic coastal dynamics database: a new classification scheme and statistics on arctic permafrost coastlines. Estuaries Coasts 35, 383–400 (2012).
Google Scholar 
177.Barnhart, K. R. et al. Modelling erosion of ice-rich permafrost bluffs along the Alaskan Beaufort Sea coast. J. Geophys. Res. Earth 119, 1155–1179 (2014).
Google Scholar 
178.Barnhart, K. R., Overeem, I. & Anderson, R. S. The effect of changing sea ice on the physical vulnerability of Arctic coasts. Cryosphere 8, 1777–1799 (2014).
Google Scholar 
179.Syvitski, J., Cohen, S., Miara, A. & Best, J. River temperature and the thermal-dynamic transport of sediment. Glob. Planet. Change 178, 168–183 (2019).
Google Scholar 
180.Scott, K. M. Effects of Permafrost on Stream Channel Behavior in Arctic Alaska USGS Professional Paper 1068, 1–19 (US Geological Survey, 1978).181.Madakumbura, G. D. et al. Event-to-event intensification of the hydrologic cycle from 1.5 °C to a 2 °C warmer world. Sci. Rep. 9, 3483 (2019).
Google Scholar 
182.Wiman, C., Hamilton, B., Dee, S. G. & Muñoz, S. E. Reduced lower Mississippi River discharge during the Medieval era. Geophys. Res. Lett. 48, e2020GL091182 (2021).
Google Scholar 
183.Kettner, A. et al. Estimating change in flooding for the 21st Century under a conservative RCP forcing: a global hydrological modelling assessment. In Global Flood Hazard: Applications in Modeling, Mapping, and Forecasting AGU Geophysical Monograph Vol. 233 157–167 (Wiley-Blackwell, 2018).184.McDonald, K. C., Kimball, J. S., Njoku, E., Zimmerman, R. & Zhao, M. Variability in springtime thaw in the terrestrial high latitudes: monitoring a major control on the biospheric assimilation of atmospheric CO2 with spaceborne microwave remote sensing. Earth Interact. 8, 1–23 (2004).
Google Scholar 
185.Cohen, S., Kettner, A. J. & Syvitski, J. P. M. Global suspended sediment and water discharge dynamics between 1960 and 2010: continental trends and intra-basin sensitivity. Glob. Planet. Change 115, 44–58 (2014).
Google Scholar 
186.Bywater-Reyes, S., Segura, C. & Bladon, K. D. Geology and geomorphology control suspended sediment yield and modulate increases following timber harvest in temperate headwater streams. J. Hydrol. 548, 754–769 (2017).
Google Scholar 
187.Tenorio, G. E. et al. Tracking spatial variation in river load from Andean highlands to inter-Andean valleys. Geomorphology 308, 175–189 (2018).
Google Scholar 
188.Zeng, Z. et al. A reversal in global terrestrial stilling and its implications for wind energy production. Nat. Clim. Change 9, 979–985 (2019).
Google Scholar 
189.Mirzabaev, A. et al. Desertification. In Climate Change and Land: an IPCC Special Report on Climate Change, Desertification, Land Degradation, Sustainable Land Management, Food Security, and Greenhouse Gas Fluxes in Terrestrial Ecosystems (eds Shukla, P. R. et al.) Ch. 3, 249–343 (IPCC, 2019).190.Runesson, U. Forest fires — an overview. Boreal Forests http://www.borealforest.o.rg/world/innova/forest_fire.htm (2020).191.Dennison, P. E., Brewer, S. C., Arnold, J. D. & Moritz, M. A. Large wildfire trends in the western United States, 1984–2011. Geophys. Res. Lett. 41, 2928–2933 (2014).
Google Scholar 
192.Warrick, J. A. et al. The effects of wildfire on the sediment yield of a coastal California watershed. GSA Bull. 124, 1130–1146 (2012).
Google Scholar 
193.DeBano, L. F. The role of fire and soil heating on water repellency in wildland environments: a review. J. Hydrol. 231–232, 195–206 (2000).
Google Scholar 
194.Cannon, S. H. et al. Predicting the probability and volume of post-wildfire debris flows in the intermountain western United States. Geol. Soc. Am. Bull. 122, 127–144 (2010).
Google Scholar 
195.Santi, P. M. & Ringers, F. K. Wildfire and landscape change. In Treatise on Geomorphology 262–287 (Elsevier, 2020).196.DiBiase, R. A. & Lamb, M. P. Vegetation and wildfire controls on sediment yield in bedrock landscapes. Geophys. Res. Lett. 40, 1093–1097 (2013).
Google Scholar 
197.DiBiase, R. A. & Lamb, M. P. Dry sediment loading of headwater channels fuels post-wildfire debris flows in bedrock landscapes. Geology 48, 189–193 (2019).
Google Scholar 
198.Moody, J. A. & Martin, D. A. Synthesis of sediment yields after wildland fire in different rainfall regimes in the western United States. Int. J. Wildland Fire 18, 96–115 (2009).
Google Scholar 
199.Neukom, R. et al. No evidence for globally coherent warm and cold periods over the preindustrial Common Era. Nature 571, 550–554 (2019).
Google Scholar 
200.Hooijer, H. & Vernimmen, R. Global LiDAR land elevation data reveal greatest sea-level rise vulnerability in the tropics. Nature. Nat. Commun. 12, 3592 (2021).
Google Scholar 
201.Syvitski, J. P. M. & Saito, Y. Morphodynamics of deltas under the influence of humans. Glob. Planet. Changes 57, 261–282 (2007).
Google Scholar 
202.Syvitski, J. P. M. et al. Sinking deltas due to human activities. Nat. Geosci. 2, 681–689 (2009).
Google Scholar 
203.Tessler, Z. et al. Profiling risk and sustainability in coastal deltas of the world. Science 349, 638–643 (2015).
Google Scholar 
204.Giosan, L., Syvitski, J., Constantinescu, S. & Day, J. Climate change: protect the world’s deltas. Nature 516, 31–33 (2014).
Google Scholar 
205.Tessler, Z. D., Vörösmarty, C. J., Overeem, I. & Syvitski, J. P. M. A model of water and sediment balance as determinants of relative sea level rise in contemporary and future deltas. Geomorphology 305, 209–220 (2018).
Google Scholar 
206.Minderhoud, P. S. J., Coumou, L., Erkens, G., Middelkoop, H. & Stouthamer, E. Mekong deltas much lower than previously assumed in sea-level rise impact assessments. Nat. Commun. 10, 3847 (2019).
Google Scholar 
207.Kulp, S. A. & Strauss, B. H. New elevation data triple estimates of global vulnerability to sea-level rise and coastal flooding. Nat. Commun. 10, 4844 (2019).
Google Scholar 
208.Ishitsuka, K. et al. Natural surface rebound of the Bangkok plain and aquifer characterization by persistent scatterer interferometry. Geochem. Geophys. Geosyst. 15, 965–974 (2014).
Google Scholar 
209.Turner, R. E., Swenson, E. M., Milan, C. S. & Lee, J. M. Hurricane signals in salt marsh sediments: Inorganic sources and soil volume. Limnol. Oceanogr. 52, 1231–1238 (2007).
Google Scholar 
210.Turner, R. E., Baustian, J. J., Swenson, E. M. & Spicer, J. S. Wetland sedimentation from hurricanes Katrina and Rita. Science 314, 449–452 (2006).
Google Scholar 
211.Rogers, K. G., Syvitski, J. P. M., Overeem, I., Higgins, S. & Gilligan, J. Farming practices and anthropogenic delta dynamics. In Proceedings IAHS-IAPSO-IASPEI Joint 37th Scientific Assembly, Gothenburg, Sweden Vol. 358 133–142 (IAHS, 2013).212.Wang, H. J. et al. Impacts of the dam-orientated water-sediment regulation scheme on the lower reaches and delta of the Yellow River (Huanghe): a review. Glob. Planet. Change 157, 93–113 (2017).
Google Scholar 
213.Vousdoukas, M. I. et al. Economic motivation for raising coastal flood defenses in Europe. Nat. Commun. 11, 2119 (2020).
Google Scholar 
214.Luijendijk, A. et al. The state of the world’s beaches. Sci. Rep. 8, 6641 (2018).
Google Scholar 
215.Mentaschi, L., Vousdoukas, M. I., Pekel, J.-F., Voukouvalas, E. & Feyen, L. Global long-term observations of coastal erosion and accretion. Sci. Rep. 8, 12876 (2018).
Google Scholar 
216.Peduzzi, P. Sand, rarer than one thinks. Environ. Dev. 11, 208–218 (2014).
Google Scholar 
217.Webb, A. Technical Report — an assessment of coastal processes, impacts, erosion mitigation options and beach mining (Bairiki/Nanikai causeway, Tungaru Central Hospital coastline and Bonriki runway — South Tarawa, Kiribati). EU-SOPAC Project Report Vol.46 (EU-SOPAC, 2005).218.McKenzie, E., Woodruff, A. & McClennen, C. Economic assessment of the true costs of aggregate mining in Majuro atoll, Republic Of The Marshall Islands (South Pacific Applied Geoscience Commission (SOPAC), 2006).219.De Schipper, M. A., Ludka, B. C., Raubenheimer, B., Luijendijk, A. P. & Schlaucher, T. A. Beach nourishment has complex implications for the future of sandy shores. Nat. Rev. Earth Environ. 2, 70–84 (2021).
Google Scholar 
220.Syvitski, J. P. M. Supply and flux of sediment along hydrological pathways: research for the 21st century. Glob. Planet. Change 39, 1–11 (2003).
Google Scholar 
221.Warrick, J. A. & Milliman, J. D. Hyperpycnal sediment discharge from semiarid southern California rivers: implications for coastal sediment budgets. Geology 31, 781–784 (2003).
Google Scholar 
222.Milliman, J. D., Farnsworth, K. L., Jones, P. D., Xu, K. H. & Smith, L. C. Climatic and anthropogenic factors affecting river discharge to the global ocean, 1951–2000. Glob. Planet. Change 62, 187–194 (2008).
Google Scholar 
223.National Oceanic and Atmospheric Administration (NOAA). NOAA delivers new U.S. climate normals: decadal update from NCEI gives forecasters and public latest averages for 1991–2020. NOAA https://www.ncei.noaa.gov/news/noaa-delivers-new-us-climate-normals (2021).224.Brakenridge, G. R. et al. Calibration of satellite measurements of river discharge using a global hydrology model. J. Hydrol. 475, 123–136 (2013).
Google Scholar 
225.Hudson, B. et al. MODIS observed increase in duration and spatial extent of sediment plumes in Greenland fjords. Cryosphere 8, 1161–1176 (2014).
Google Scholar 
226.Dethier, E. N., Renshaw, C. E. & Magilligan, F. J. Toward improved accuracy of remote sensing approaches for quantifying suspended sediment: Implications for suspended-sediment monitoring. J. Geophys. Res. Earth Surf. 125, e2019JF005033 (2020).
Google Scholar 
227.Brakenridge, G. R. et al. Design with nature: causation and avoidance of catastrophic flooding, Myanmar. Earth Sci. Rev. 165, 81–109 (2017).
Google Scholar 
228.Verburg, P. H. et al. Methods and approaches to modelling the Anthropocene. Glob. Environ. Change 39, 328–340 (2016).
Google Scholar 
229.Moragoda, N. & Cohen, S. Climate-induced trends in global riverine water discharge and suspended sediment dynamics in the 21st century. Glob. Planet. Change 191, 103199 (2020).
Google Scholar 
230.Dunn, F. E. et al. Projections of declining fluvial sediment delivery to major deltas worldwide in response to climate change and anthropogenic stress. Environ. Res. Lett. 14, 084034 (2019).
Google Scholar 
231.VEMAP Members. Vegetation/ecosystem modeling and analysis project: comparing biogeography and biogeochemistry models in a continental-scale study of terrestrial ecosystem responses to climate change and CO2 doubling. Glob. Biogeochem. Cycles 9, 407–437 (1995).
Google Scholar 
232.Warszawski, L. et al. The Inter-Sectoral Impact Model Intercomparison Project (ISI–MIP): project framework. Proc. Natl Acad. Sci. 111, 3228–3232 (2014).
Google Scholar 
233.Tucker, G. E. et al. CSDMS: A community platform for numerical modeling of Earth-surface processes. Geosci. Model Dev. Discuss. https://doi.org/10.5194/gmd-2021-223 (2021).234.Rousseau, Y., Watson, R. A., Blanchard, J. L. & Fulton, E. A. Evolution of global marine fishing fleets and the response of fished resources. Proc. Natl Acad. Sci. USA 116, 12238–12243 (2019).
Google Scholar 
235.Nageswara Rao, K. et al. Palaeogeography and evolution of the Godavari delta, east coast of India during the Holocene: An example of wave-dominated and fan-delta settings. Palaeogeogr. Palaeoclimatol. Palaeoecol. 440, 213–233 (2015).
Google Scholar 
236.Tanabe, S., Saito, Y., Vu, Q. L., Hanebuth, T. J. J. & Ngo, Q. L. Holocene evolution of the Song Hong (Red River) delta system, northern Vietnam. Sediment. Geol. 187, 29–61 (2006).
Google Scholar 
237.Pithan, F. & Mauritsen, T. Arctic amplification dominated by temperature feedbacks in contemporary climate models. Nat. Geosci. 7, 181–184 (2014).
Google Scholar  More

1.Lamontagne, J., Reed, P., Marangoni, G., Keller, K. & Garner, G. Robust abatement pathways to tolerable climate futures require immediate global action. Nat. Clim. Change 9, 290–294 (2019).
Google Scholar 
2.Luderer, G. et al. Residual fossil CO2 emissions in 1.5–2 °C pathways. Nat. Clim. Change 8, 626–633 (2018).CAS 

Google Scholar 
3.van Vuuren, D., Hof, A., van Sluisveld, M. & Riahi, K. Open discussion of negative emissions is urgently needed. Nat. Energy 2, 902–904 (2017).
Google Scholar 
4.Santos Da Silva, S. et al. The Paris pledges and the energy–water–land nexus in Latin America: exploring implications of greenhouse gas emission reductions. PLoS ONE 14, e0215013 (2019).5.Fujimori, S. et al. A multi-model assessment of food security implications of climate change mitigation. Nat. Sustain. 2, 386–396 (2019).
Google Scholar 
6.Rogelj, J., McCollum, D., O’Neill, B. & Riahi, K. 2020 emissions levels required to limit warming to below 2 °C. Nat. Clim. Change 3, 405–412 (2013).CAS 

Google Scholar 
7.Tavoni, M. et al. Post-2020 climate agreements in the major economies assessed in the light of global models. Nat. Clim. Change 5, 119–126 (2015).
Google Scholar 
8.Garner, G., Reed, P. & Keller, K. Climate risk management requires explicit representation of societal trade-offs. Climatic Change 134, 713–723 (2016).
Google Scholar 
9.Dearing, J. et al. Safe and just operating spaces for regional social–ecological systems. Glob. Environ. Change 28, 227–238 (2014).
Google Scholar 
10.Kling, H., Stanzel, P. & Preishuber, M. Impact modelling of water resources development and climate scenarios on Zambezi River discharge. J. Hydrol. Reg. Stud. 1, 17–43 (2014).
Google Scholar 
11.Payet-Burin, R., Kromann, M., Pereira-Cardenal, S., Strzepek, K. & Bauer-Gottwein, P. WHAT-IF: an open-source decision support tool for water infrastructure investment planning within the water–energy–food–climate nexus. Hydrol. Earth Syst. Sci. 23, 4129–4152 (2019).
Google Scholar 
12.Fant, C., Gebretsadik, Y., McCluskey, A. & Strzepek, K. An uncertainty approach to assessment of climate change impacts on the Zambezi River basin. Clim. Change 130, 35–48 (2015).CAS 

Google Scholar 
13.Spalding-Fechera, R., Joyceb, B. & Winklerc, H. Climate change and hydropower in the Southern African Power Pool and Zambezi River basin: system-wide impacts and policy implications. Energy Policy 103, 84–97 (2017).
Google Scholar 
14.GCAM v4.3 Documentation: Global Change Assessment Model (GCAM) (JGCRI, 2017).15.Thomson, A. et al. RCP 4.5: a pathway for stabilization of radiative forcing by 2100. Clim. Change 109, 77–94 (2011).CAS 

Google Scholar 
16.Clarke, L. et al. in Climate Change 2014: Mitigation of Climate Change (eds Edenhofer, O. et al.) Ch. 6 (Cambridge Univ. Press, 2014).17.Calvin, K. et al. The SSP4: a world of deepening inequality. Glob. Environ. Change 42, 284–296 (2017).
Google Scholar 
18.van Vuuren, D. et al. The shared socio-economic pathways: trajectories for human development and global environmental change. Glob. Environ. Change 42, 148–152 (2017).
Google Scholar 
19.Kriegler, E., Edmonds, J. & Hallegatte, S. A new scenario framework for climate change research: the concept of shared climate policy assumptions. Climatic Change 122, 401–414 (2014).
Google Scholar 
20.Riahi, K. et al. The shared socioeconomic pathways and their energy, land use, and greenhouse gas emissions implications: An overview. Glob. Environ. Change 42, 153–168 (2017).
Google Scholar 
21.Lamontagne, J. et al. Large ensemble analytic framework for consequence-driven discovery of climate change scenarios. Earth’s Future 6, 488–504, (2018).22.Li, X. et al. Tethys–a Python package for spatial and temporal downscaling of global water withdrawals. J. Open Res. Softw. 6, 9 (2018).23.Huang, Z. et al. Global agricultural green and blue water consumption under future climate and land use changes. J. Hydrol. 574, 242–256 (2019).
Google Scholar 
24.van Vuuren, D. et al. The representative concentration pathways: an overview. Climatic Change 109, 5–31 (2011).25.Sadoff, C., Whittington, D. & Grey, D. Africa’s International Rivers: An Economic Perspective (World Bank, 2003).26.Beilfuss, R. in The Wetland Book (ed. Finlayson, C.) 1–9 (Springer, 2016).27.The Zambezi River Basin. A Multi-Sector Investment Opportunities Analysis (World Bank, 2010).28.Jeuland, M. & Whittington, D. Water resources planning under climate change: assessing the robustness of real options for the Blue Nile. Water Resour. Res. 50, 2086–2107 (2014).
Google Scholar 
29.Warner, J. J. S., Jones, E., Ansari, M. & De Vries, L. The fantasy of the Grand Inga hydroelectric project on the River Congo. Water 11, 407 (2019).
Google Scholar 
30.Winemiller, K. et al. Balancing hydropower and biodiversity in the Amazon, Congo, and Mekong. Science 351, 128–129 (2016).CAS 

Google Scholar 
31.Conway, D. et al. Climate and southern Africa’s water–energy–food nexus. Nat. Clim. Change 5, 837–846 (2015).
Google Scholar 
32.Strategic Plan for the Zambezi Watercourse 2018–2040 (ZAMCOM, 2019).33.Cervigni, R., Liden, R., Neumann, J. & Strzepek, K. Enhancing the Climate Resilience of Africa’s Infrastructure: The Power and Water Sectors (World Bank, 2015).34.World Database of Key Biodiversity Areas (BirdLife International, 2018); www.keybiodiversityareas.org35.Beilfuss, R. & dos Santos, D. Program for the Sustainable Management of Cahora Bassa Dam and the Lower Zambezi Valley. Working Paper http://www.xitizap.com/zambeze-hydrochanges.pdf (2001).36.Coello Coello, C., Lamont, G. & Veldhuizen, D. V. Evolutionary Algorithms for Solving Multi-Objective Problems (Springer, 2007).37.Tilmant, A., Beevers, L. & Muyunda, B. Restoring a flow regime through the coordinated operation of a multireservoir system: the case of the Zambezi River basin. Water Resour. Res. 46, W07533 (2010).38.Rulli, M., Saviori, A. & D’Odorico, P. Global land and water grabbing. Proc. Natl Acad. Sci. USA 110, 892–897 (2013).CAS 

Google Scholar 
39.Zarfl, C., Lumsdon, A., Berlekamp, J., Tydecks, L. & Tockner, K. A global boom in hydropower dam construction. Aquat. Sci. 77, 161–170 (2015).
Google Scholar 
40.Graham, N. et al. Humans drive future water scarcity changes across all shared socioeconomic pathways. Environ. Res. Lett. 15, 014007 (2020).41.Liu, L., Hejazi, M., Iyer, G. & Forman, B. Implications of water constraints on electricity capacity expansion in the United States. Nat. Sustain. 2, 206–213 (2019).
Google Scholar 
42.McCollum, D., Gambhir, A., Rogelj, J. & Wilson, C. Energy modellers should explore extremes more systematically in scenarios. Nat. Energy 5, 104–107 (2020).
Google Scholar 
43.Schlosberg, D. & Collins, L. From environmental to climate justice: climate change and the discourse of environmental justice. Wiley Interdiscip. Rev. Clim. Change 5, 359–374 (2014).
Google Scholar 
44.Taconet, N., Méjean, A. & Guivarch, C. Influence of climate change impacts and mitigation costs on inequality between countries. Climatic Change 160, 15–34 (2020).45.Lindström, G., Johansson, B., Persson, M., Gardelin, M. & Bergström, S. Development and test of the distributed HBV-96 hydrological model. J. Hydrol. 201, 272–288 (1997).
Google Scholar 
46.Akhtar, M., Ahmad, N. & Booij, M. Use of regional climate model simulations as input for hydrological models for the Hindukush–Karakorum–Himalaya region. Hydrol. Earth Syst. Sci. 13, 1075–1089 (2009).
Google Scholar 
47.Bergström, S. et al. in Climate Change and Energy Systems Impacts, Risks and Adaptation in the Nordic and Baltic Countries (eds Thorsteinsson, T. & Björnsson, H.) 13–146 (Nordic Council of Ministers, 2012).48.Vrochidou, A., Tsanis, I., Grillakis, M. & Koutroulis, A. The impact of climate change on hydrometeorological droughts at a basin scale. J. Hydrol. 476, 290–301 (2013).
Google Scholar 
49.Hamududu, B. & Killingtveit, A. Hydropower production in future climate scenarios; the case for the Zambezi River. Energies 9, 502 (2016).50.Funk, C., Peterson, P. & Landsfeld, M. The climate hazards infrared precipitation with stations—a new environmental record for monitoring extremes Sci. Data 2, 150066 (2015).51.Chaney, N., Sheffield, J., Villarini, G. & Wood, E. Development of a high-resolution gridded daily meteorological dataset over sub-Saharan Africa: spatial analysis of trends in climate extremes. J. Clim. 27, 5815–5835 (2014).
Google Scholar 
52.Soncini-Sessa, R., Castelletti, A. & Weber, E. Integrated and Participatory Water Resources Management: Theory (Elsevier, 2007).53.Celeste, A. & Billib, M. Evaluation of stochastic reservoir operation optimization models. Adv. Water Res. 32, 1429–1443 (2009).
Google Scholar 
54.AQUASTAT – FAO’s Global Information System on Water and Agriculture. FAO https://www.fao.org/aquastat/en/geospatial-information/global-maps-irrigated-areas/map-quality55.Castelletti, A., Pianosi, F. & Soncini-Sessa, R. Water reservoir control under economic, social and environmental constraints. Automatica 44, 1595–1607 (2008).
Google Scholar 
56.Bertoni, F., Castelletti, A., Giuliani, M. & Reed, P. Discovering dependencies, trade-offs, and robustness in joint dam design and operation: an ex-post assessment of the Kariba dam. Earth’s Future 7, 1367–1390 (2019).
Google Scholar 
57.Giuliani, M., Castelletti, A., Pianosi, F., Mason, E. & Reed, P. Curses, tradeoffs, and scalable management: advancing evolutionary multi-objective direct policy search to improve water reservoir operations. J. Water Resour. Plan. Manage. 142, 04015050 (2016).58.Busoniu, L., Ernst, D., De Schutter, B. & Babuska, R. Cross-entropy optimization of control policies with adaptive basis functions. IEEE Trans. Syst. Man Cybern. B 41, 196–209 (2011).
Google Scholar 
59.Hadka, D. & Reed, P. Borg: an auto-adaptive many-objective evolutionary computing framework. Evol. Comput. 21, 231–259 (2013).
Google Scholar 
60.Giuliani, M., Quinn, J., Herman, J., Castelletti, A. & Reed, P. Scalable multiobjective control for large-scale water resources systems under uncertainty. IEEE Trans. Control Syst. Technol. 26, 1492–1499 (2018).
Google Scholar 
61.Blöschl, G. et al. Twenty-three unsolved problems in hydrology (UPH)—a community perspective. Hydrol. Sci. J. 64, 1141–1158 (2019).
Google Scholar 
62.Elsawah, S. et al. Eight grand challenges in socio-environmental systems modeling. Socioenviron. Syst. Model. 2, 16226–16226 (2020).
Google Scholar 
63.Giorgi, F., Jones, C. & Asrar, G. R. Addressing climate information needs at the regional level: the CORDEX framework. World Meteorol. Org. Bull. 58, 175–183 (2009).64.Dosio, A. et al. What can we know about future precipitation in Africa? Robustness, significance and added value of projections from a large ensemble of regional climate models. Clim. Dyn. 53, 5833–5858 (2019).
Google Scholar 
65.Dolan, F. et al. Evaluating the economic impact of water scarcity in a changing world. Nat. Commun. 12, 1915 (2021).66.Zhang, X. et al. Indices for monitoring changes in extremes based on daily temperature and precipitation data. Wiley Interdiscip. Rev. Clim. Change 2, 851–870 (2011).
Google Scholar 
67.Mosnier, A. et al. Modeling impact of development trajectories and a global agreement on reducing emissions from deforestation on Congo basin forests by 2030. Environ. Resour. Econ. 57, 505–525 (2014).
Google Scholar 
68.Hattermann, F. et al. Sources of uncertainty in hydrological climate impact assessment: a cross-scale study. Environ. Res. Lett. 13, 015006 (2018).
Google Scholar 
69.Giuliani, M. & Lamontagne, J. R. First release of ZambeziWatercourse_GCAM code (v1.0-alpha). Zenodo https://doi.org/10.5281/zenodo.5726941 (2021). More

The global co-occurrence of freshwater stress and freshwater storage trendsWe mapped freshwater stress and trends in freshwater storage at the basin scale and analyzed the co-occurrence of these phenomena (Fig. 1).Fig. 1: Global co-occurrence of freshwater stress and storage trends.a Freshwater stress, derived from freshwater withdrawal and streamflow datasets (see “Methods” section). b Freshwater storage trend per basin. c Combinations of freshwater stress and storage trend per basin, which together derive basin freshwater status (shown in Fig. 2b). Values overlaying the legend indicate the number of basins satisfying each set of conditions. For categorical plotting purposes only, ±3 mm year−1 is used as the threshold denoting a clear directional storage trend, based on the error level of the underlying observations25. d–g The exposure of social-ecological activity to freshwater stress and storage trends. Each plot represents storage trends as the x-axis coordinate, and log-transformed freshwater stress as the y-axis coordinate with the size of each circle based on the basin’s value respective to each plotting dimension.Full size imageFreshwater stress represents the state of demand-driven water scarcity15 and is defined as the ratio of freshwater withdrawal to streamflow (Fig. 1a). Trends in freshwater storage, conversely, represent the evolution of total storage, defined as the vertical sum of groundwater, soil moisture, surface water, and snow water equivalent storages (Fig. 1b). Freshwater stress and storage are linked, as freshwater storage becomes a required source of water during periods when demands exceed supply. As climate change intensifies hydrological extremes globally, the strategic importance of the world’s largest store of liquid freshwater, groundwater, will only continue to increase24. Though studies have focussed on global assessments of freshwater stress13,14,15 and trends in freshwater storage9, no study to date has mapped these two variables against one another. Doing so provides important context to differentiate basins of equal freshwater stress, as drying trends are likely to exacerbate challenges derived from freshwater stress, while wetting trends may yield offsetting effects. However, as freshwater stress calculations do not differentiate between withdrawals sourced from streamflow or storage, the two variables are not necessarily independent.We found that 201 (42%) of the 478 currently stressed basins (withdrawal/streamflow > 0.10) are simultaneously losing freshwater storage (Fig. 1c). These basins are located in south and southwestern USA, northeastern Brazil, central Argentina, Algeria, and concentrate throughout the Middle East, the Caucasus, northern India, and northern China. Predominantly, these regions are agriculturally significant and heavily irrigated9, with the exception of a few basins in South America whose trends are likely the product of natural variability9. Conversely, 98 (21%) of the currently stressed basins are gaining freshwater storage. The storage trends in these basins have largely been attributed to natural variability with the exception of central India, whose trends are partially attributed to groundwater recovery following groundwater policy change9. The remaining 179 stressed basins have freshwater storage trends that are smaller than can be definitively interpreted from the satellites monitoring these trends25. This skew towards negative storage trends (i.e., drying) in the world’s water-stressed basins dissipates and even reverses in the non-stressed basins, where drying and wetting trends are found in 23% and 32% of the 726 non-stressed basins, respectively. While previous work has shown that the world’s dry regions are becoming drier while the wet regions are becoming wetter26, this work reveals that the stressed regions of the world are becoming drier while the non-stressed regions of the world have no clear overall trend in freshwater storage.The encompassed human population, food crop production, gross domestic product (GDP), biodiversity, and wetlands enumerate the potential social-ecological impacts from the current state of global freshwater stress and storage trends. Around 2.2 billion people, 27% of global food crop production, and 28% of global GDP live, grow, and situate in freshwater stressed basins that are drying (Fig. 1d–f). These totals represent an upper limit as not all social and ecological activity within these basins will be affected by freshwater stress and storage loss, which will depend on local levels of adaptive capacity and ecological sensitivity22 (our focus in the subsequent sections). Conversely, 1.2 billion people, 24% of global food crop production, and 19% of global GDP are found in stressed basins that are wetting. We find less taxonomic biodiversity in the freshwater stressed and drying basins, and greater biodiversity in unstressed and wetting basins. Roughly the same number of wetlands of international importance are found in stressed and drying basins as in stressed and wetting basins. While these totals represent the magnitude of potentially affected biodiversity and wetlands, taxonomic biodiversity is only one of many critical facets of biodiversity27, and freshwater stress and storage trends are but two of many variables impacting global biodiversity28. Thus, we urge caution in interpreting the role of freshwater stress and storage in driving differences in these biodiversity distributions.The most vulnerable populations to freshwater stress and storage lossTo better characterize social vulnerability, freshwater stress and storage loss must be placed in the context of social adaptability. We mapped and analyzed the co-occurrence of freshwater stress and storage trends with an existing global dataset of social adaptive capacity23 summarized at the basin scale (Fig. 2). Social adaptive capacity (Fig. 2a), or adaptability, represents “the ability of the system to respond to disturbances”29 and is derived based on input indicators of governance, economic strength, and human development. This consideration of social adaptability enables more representative estimates of social, agricultural, and economic activity that are vulnerable to the co-occurrence of freshwater stress and storage loss. To consider freshwater stress and storage loss together, we developed the basin freshwater status indicator (Box 1) where higher values indicate co-occurring freshwater stress and storage loss (Fig. 2b, see “Methods” section).Fig. 2: The relationship between basin freshwater status and social adaptive capacity.a Social adaptive capacity, or adaptability, per basin. b Basin freshwater status, representing the combination of freshwater stress and storage trend per basin (see “Methods” section). c Combinations of basin freshwater status and social adaptability. Values overlaying the legend indicate the number of basins satisfying each set of conditions. d–g The exposure of social-ecological activity to basin freshwater status (x-axis coordinate) and social adaptive capacity (y-axis coordinate), with the size of each circle scaled based on the basin’s value respective to each plotting dimension. These distributions are summarized below each plot. P notation represents the percentile distribution.Full size imageWe found 73 basins to possess low levels of social adaptability and severe basin freshwater status (Fig. 2c). These basins concentrate in Northern, and Eastern Africa, the Arabian Peninsula, and Western, Central, and Southern Asia; although vulnerable basins are also found in northeast Brazil, Southern Africa, and northern China. These basins encompass approximately 1.2 billion people, 12% of global food crop production, and 6% of global GDP (Fig. 2d–f). Conversely, 119 and 49 basins are found to have similarly severe basin freshwater status yet have moderate or high levels of social adaptability, respectively. These basins are located in southwestern USA and Mexico, Chile and Argentina, the Arabian Peninsula, regions surrounding the Caspian Sea, western Australia, and the North China Plain.These differences in social adaptability across basins with severe freshwater status (i.e., co-occurring freshwater stress and storage loss) raise important economic considerations. First, greater social adaptability likely coincides with greater technological and economic capacity to pursue development. This development may consume greater volumes of freshwater and drive basins towards greater levels of freshwater stress or storage loss, while simultaneously increasing institutional and technical capacity to cope with limited water resources. Furthermore, freshwater stress and storage loss are not certain to induce negative economic impacts on basins, and can lead to positive impacts if a region is able to leverage its comparative advantages (e.g., irrigation efficiency) among other stressed regions30. Second, the divergent economic situation facing basins with severe freshwater status is particularly evident on a per-capita basis. In severe freshwater status, low adaptability basins, there resides 17% of the global population yet only 6% of global GDP. Conversely, in severe freshwater status basins with moderate-and-greater social adaptability, there resides 14% of the global population and an outsized 18% of global GDP (Fig. 2d, f). It is thus paramount that global initiatives prioritize and link economic inequality with freshwater goals. One such example is Sustainable Development Goal (SDG) 6.4 (“reduce the number of people suffering from water scarcity”), which we argue should increasingly be linked to targets of SDG 10 (“reduce inequality within and among countries”).Box 1 Key terminology as used in this paper. See Methods for further informationFreshwater stress: The ratio of annual freshwater withdrawal (W) to annual streamflow (Q). We refer to basins with W/Q ≥ 10% as stressed basins and those with W/Q ≥ 40% as highly stressed basins.Freshwater storage trends: Year-over-year trends in total freshwater storage based on satellite observations over the 2002–2016 time period. Total freshwater storage is a vertically aggregated measure of water storage that includes groundwater, soil water, surface water, canopy water, and ice and snow water equivalents where present. For simplicity, we refer to negative freshwater storage trends as drying trends or storage loss and positive trends as wetting trends or storage gain.Basin freshwater status: An integrated indicator that combines normalized freshwater stress and normalized freshwater storage trends at the basin scale. High indicator scores are assigned to basins with co-occurring freshwater stress and drying trends. We refer to high freshwater status scores through status severity.Vulnerability: The likelihood of society and ecosystems to experience harms due to exposure to freshwater stress and storage loss when considered together as a basin’s freshwater status. This vulnerability definition is an application of Turner et al.’s generic definition29. Vulnerability is quantified using social adaptability, ecological sensitivity, and basin freshwater status indicators. Social adaptability and ecological sensitivity indicators are described in the text and Methods.Hotspot basin: Highlighted basins that possess the greatest vulnerability scores. We identify hotspot basins to support their prioritization in global water resources and integrated management initiatives. Basins are considered hotspots if sorted into “high” and “very high” vulnerability classes following a categorical classification of the numerical vulnerability results.Hotspot basins found on all continentsWe mapped the global gradient in social-ecological vulnerability to freshwater stress and storage loss at the basin scale and, from this, identified those with the greatest vulnerability as hotspot basins (Fig. 3). Hotspot mapping has been a successful endeavor within the field of conservation biogeography31,32, and many global hydrology studies have identified regions of exceptional water scarcity and security challenges e.g.,13,14,15,17,18,19. Here, we seek to combine and apply these concepts in an integrated global social-ecological vulnerability context. As a useful reference, biodiversity hotspots aim to “maximize the number of species “saved” given available resources” by asking “where are places rich in species and under threat?”33. For comparison, the aim of our hotspot mapping is to ‘minimize the social and ecological impacts of freshwater stress and storage loss given available resources’ by asking “what basins with sensitive ecosystems and limited social adaptive capacity are exposed to freshwater stress and storage loss?”Fig. 3: Hotspot basins for social and ecological impacts from freshwater stress and storage loss.a–d Social-ecological vulnerability results. a Hotspot basins of social-ecological vulnerability to freshwater stress and storage loss. b Vulnerability classification, based on the product of basin freshwater status and social-ecological sensitivity to freshwater stress and storage loss (see “Methods” section). c Histograms of the global distribution of vulnerability classes by basin count and surface area. d Summarized social-ecological activity within transitional and hotspot basins. e Ecological vulnerability results, presented as vulnerability classes. f Social vulnerability results, presented as vulnerability classes. Vulnerability classes for e and f are derived using the same methods as shown for social-ecological vulnerability in b.Full size imageWe conceptualize vulnerability as the product of (i) ecological sensitivity, (ii) social adaptive capacity, and (iii) basin freshwater status. To represent ecological sensitivity, we derived an indicator using data products from two global ecohydrological studies that assess broad ecosystem sensitivity to freshwater storage and use (see “Methods” section). To represent social adaptability, we utilized the same adaptive capacity dataset as used in the previous section (Fig. 2a). To classify the derived global vulnerability results into hotspot basins, we implemented a simple classification algorithm developed for heavy-tailed distributions34, which appropriately describes the global vulnerability distribution.The most vulnerable basins are constrained to regions confronting co-occurring freshwater stress and storage loss. When considering social and ecological vulnerability individually (Fig. 3e, f), we find spatial variation between ecological vulnerability (Fig. 3e) and social vulnerability (Fig. 3f). For instance, several basins in affluent nations with sensitive ecosystems reveal high ecological vulnerability but low social vulnerability (southwestern USA; western Australia). Conversely, several basins in Eastern Africa and northeastern India possess high social vulnerability but low to moderate ecological vulnerability. While these differences are notable and could impact regional strategies, it remains essential in most, if not all, regions that social and ecological vulnerabilities be confronted simultaneously4. For this purpose, we combined ecological sensitivity and adaptive capacity indicators into a combined social-ecological sensitivity indicator (see “Methods” section) to map combined social-ecological vulnerability (Fig. 3a).We identify 168 basins, representing 14% of all basins and 11% of the global land area considered in our study, as vulnerability hotspots (Fig. 3a–c). These hotspot basins consist of basins receiving “high” and “very high” vulnerability scores through our classification procedure. Of the 168 basins, 78 (6% of all basins) are classified in the most-severe “very high” vulnerability class, while 90 (7% of all basins) are classified in the “high” vulnerability class. We also identified 232 basins (19% of all basins) as “transitional” basins, which are not classified alongside basins with null vulnerability yet also do not possess extreme values within the global vulnerability distribution. The 78 hotspot basins with “very high” vulnerability represent the multiple epicenters for potential social and ecosystem impacts from freshwater stress and storage loss. These basins are found in Argentina, northeastern Brazil, the American southwest, Mexico, Northern, Eastern, and Southern Africa, the Middle East and Arabian Peninsula, the Caucasus, West Asia, northern India and Pakistan, Southeastern Asia, and northern China.A total of over 1.5 billion people, 17% of global food crop production, and 13% of global GDP are found within hotspot basins (Fig. 3d). Of these, ~300 million people, 4% of global food crop production, and 4% of global GDP situate within the 78 “very high” vulnerability basins. Consistent with the relationship between biodiversity and basin freshwater status, we find the most vulnerable basins to be less taxonomically biodiverse than less vulnerable basins. While it is possible that these lower biodiversity levels may have eroded due to freshwater stress and storage loss, a proper investigation is outside the scope of this study and would require a wider array of pressures to be considered. The hotspot basins encompass 157 wetlands of international importance, which we highlight to prioritize their conservation in these vulnerable environments (Supplementary Table 2).While the degree of social-ecological activity within hotspot basins is substantial, the global proportion of each dimension found in hotspot basins is roughly proportional to the fraction of basins within each vulnerability class. Thus, as the hotspot basins do not contribute disproportionately to global totals of social-ecological activity, we find it important to restate and clarify the motivating purpose of this hotspot mapping. The hotspot basins do not identify the greatest contributors to global social-ecological activity that face severe freshwater challenges. Rather, the hotspot basins are those with sensitive ecosystems and adaptability-limited societies exposed to the co-occurrence of freshwater stress and storage loss, and thus are the basins most likely to suffer social and ecological harms due to these freshwater conditions.The identification of hotspot basins shows high levels of consistency across two uncertainty analyses and a sensitivity analysis focused on the impacts of subjective methodological decisions (Supplementary Section 4). We consider individually the impacts of (i) uniform over-estimation and under-estimation of each data input (spatially uniform uncertainty) and (ii) heterogeneous uncertainty in each data input (spatially variable uncertainty) on our hotspot basin results. Performing 10,000 realizations for each uncertainty analysis, we find that 98% of the identified transitional and hotspot basins are identified as at least transitional basins in over 50% the realizations considering spatially uniform uncertainty, and 96% when considering spatially variable uncertainty (Supplementary Figs. 8 and 9). The subjectivity-focused sensitivity analysis considered 24 alternative methodological configurations, and revealed that our identified transitional and hotspot basins are consistently identified across the majority of configurations (Supplementary Fig. 10).Implementation of integrated water resources management is inconsistent across hotspot basinsWe compared national implementation levels of integrated water resources management (IWRM) with our global vulnerability results (Fig. 4). For IWRM implementation data, we rely on the IWRM Data Portal35 which tracks progress on SDG 6.5.1 (“IWRM implementation at the national scale”).Fig. 4: Integrated water resources management in hotspot basins.a Map of IWRM implementation overlaid by hotspot basin results. b Scatterplot of individual basin values of social-ecological vulnerability (x-axis) and IWRM implementation (y-axis). Transboundary basins are represented by concentric red circles, with the number of circles representing the number of nations present within each basin. See text for interpretation of labels 1, 2, and 3.Full size imageIWRM is defined as “a process which promotes the co-ordinated development and management of water, land and related resources, in order to maximize the resultant economic and social welfare in an equitable manner without compromising the sustainability of vital ecosystems”36, while the SDG framework notes that IWRM implementation “supports all Goals across the 2030 Agenda”37. Thus, as the IWRM paradigm seeks to guide management of water resources to minimize trade-offs between human well-being, ecological health, and water resources sustainability, assessing implementation levels of IWRM against our vulnerability results provides insight regarding the performance of IWRM globally while simultaneously emphasizing the broad sustainability implications within hotspot basins.Globally, we find no direct relationship between vulnerability and IWRM implementation at the basin scale. There is thus a wide range of IWRM implementation across all levels of social-ecological vulnerability to freshwater stress and storage loss, and there is no indication that IWRM implementation levels are greatest where they are most needed. This finding likely derives from variations in proactive versus reactive governance and management approaches to freshwater challenges across the globe. As our analysis is conducted at a snapshot in time (input data align to ~2015), we can only generate hypotheses about the performance of IWRM globally. For example, basins with high levels of IWRM implementation and low vulnerability (label 1 in Fig. 4b) have either proactively implemented IWRM, have effectively reduced their vulnerability through IWRM implementation, or simply benefit from a favorable intersection of regional climate and economy.Alternatively, basins with low levels of IWRM and low vulnerability can be categorized as non-proactive in their IWRM implementation (label 2 in Fig. 4b). We place particular emphasis here on basins with low levels of IWRM where vulnerability is high (label 3 in Fig. 4b), which we argue should be the priority basins and regions of SDG 6.5-focused initiatives. Identified nations with low levels of IWRM implementation and very high vulnerability include Afghanistan, Algeria, Argentina, Egypt, India, Iraq, Kazakhstan, Mexico, Somalia, Ukraine, Uzbekistan, and Yemen. As one-third (36%) of all hotspot basins are transboundary (Fig. 4b), improving basin-level IWRM implementation will require multilateralism and hydro-diplomacy and cannot be left to individual nations acting alone. Furthermore, we observe a lower level of IWRM implementation across hotspot basins that are transboundary versus non-transboundary hotspot basins (mean basin IWRM Data Portal score = 50 vs. 56), suggesting greater multilateralism and cooperation are needed in transboundary basins. More

1.Tan, H., Xu, J. & Wong-Parodi, G. The politics of Asian fracking: public risk perceptions towards shale gas development in China. Energy Res. Soc. Sci. 54, 46–55 (2019).
Google Scholar 
2.Mayer, A. Risk and benefits in a fracking boom: Evidence from Colorado. Extr. Ind. Soc. 3, 744–753 (2016).
Google Scholar 
3.Aczel, M. R. & Makuch, K. E. The lay of the land: the public, participation and policy in China’s fracking frenzy. Extr. Ind. Soc. 5, 508–514 (2018).
Google Scholar 
4.Connor, C. D. O. & Fredericks, K. Citizen perceptions of fracking: the risks and opportunities of natural gas development in Canada. Energy Res. Soc. Sci. 42, 61–69 (2018).
Google Scholar 
5.Davies, R. J. et al. Oil and gas wells and their integrity: Implications for shale and unconventional resource exploitation. Mar. Pet. Geol. 56, 239–254 (2014).
Google Scholar 
6.Jackson, R. B. et al. The environmental costs and benefits of fracking. Annu. Rev. Environ. Resour. 39, 327–362 (2014).
Google Scholar 
7.Brantley, S. L. et al. Engaging over data on fracking and water quality: Data alone aren’t the solution, but they bring people together. Science 359, 395–397 (2018).CAS 

Google Scholar 
8.Vengosh, A., Jackson, R. B., Warner, N., Darrah, T. H. & Kondash, A. A critical review of the risks to water resources from unconventional shale gas development and hydraulic fracturing in the United States. Environ. Sci. Technol. 48, 8334–8348 (2014).CAS 

Google Scholar 
9.Kondash, A. J., Lauer, N. E. & Vengosh, A. The intensification of the water footprint of hydraulic fracturing. Sci. Adv. 4, eaar5982 (2018).CAS 

Google Scholar 
10.Rosa, A. L., Rulli, M. C., Davis, K. F. & Odorico, P. D. The water-energy nexus of hydraulic fracturing: a global hydrologic analysis for shale oil and gas extraction. 1–12. https://doi.org/10.1002/2018EF000809 (2018).11.Callies, D. L. & Stone, C. Regulation of Hydraulic Fracturing. J. Int. Comp. Law 1, 1–38 (2014).
Google Scholar 
12.Esterhuyse, S., Vermeulen, D. & Glazewski, J. Regulations to protect groundwater resources during unconventional oil and gas extraction using fracking. Wires Water 6, e1382 (2019).
Google Scholar 
13.Bohlmann, H. R., Horridge, J. M., Inglesi-Lotz, R., Roos, E. L. & Stander, L. Regional economic effects of changes in South Africa’s electricity generation mix. Economic Research Southern Africa (ERSA). ERSA working paper 756, 1–20. (2018).14.Nkosi, N. P. & Dikgang, J. Pricing electricity blackouts among South African households. J. Commod. Mark. 11, 37–47 (2018).
Google Scholar 
15.IPCC. Summary for Policymakers: Climate Change 2021 The Physical Science Basis. https://www.ipcc.ch/report/ar6/wg1/downloads/report/IPCC_AR6_WGI_Full_Report_smaller.pdf (2021).16.IRENA. Global Energy Transformation: A roadmap to 2050. https://www.irena.org/-/media/Files/IRENA/Agency/Publication/2018/Apr/IRENA_Report_GET_2018.pdf (2018).17.EC. Energy roadmap 2050. https://doi.org/10.2833/10759 (2012).18.Olivier, D. W., Xu, Y. & Olivier, D. W. Making effective use of groundwater to avoid another water supply crisis in Cape Town, South Africa. Hydrogeol. J. 27, 823–826 (2019).
Google Scholar 
19.Hobbs, P. et al. Chapter 5 – Water Resources. In Shale Gas Development in the Central Karoo: A Scientific Assessment of the Opportunities and Risks. (eds. Scholes, R., Lochner, P., Schreiner, G., Snyman-Van der Walt, L. & de Jager, M.) 97–111 https://doi.org/10.1016/B978-0-12-799954-8.00005-8 (Council for Scientific and Industrial Research, 2016).20.McGranahan, D. A., Kirkman, K. P. & McGranahan, D. A. Local perceptions of hydraulic fracturing ahead of exploratory drilling in eastern South Africa. Environ. Manag. 63, 338–351 (2019).
Google Scholar 
21.Finkeldey, J. Unconventionally contentious: Frack Free South Africa’s challenge to the oil and gas industry. Extr. Ind. Soc. 5, 461–468 (2018).
Google Scholar 
22.Atkinson, D. Fracking in a fractured environment: Shale gas mining and institutional dynamics in South Africa’s young democracy. Extr. Ind. Soc. 5, 441–452 (2018).
Google Scholar 
23.Schreiner, G. et al. Evidence-based and participatory processes in support of shale gas policy development in South Africa. In Governing Shale Gas: Development, Citizen Participation and Decision Making in the US, Canada, Australia and Europe (eds. Whitton, J., Cotton, M., Charnley-Parry, I. & Brasier, K.) 149–167 (Routledge, 2018).24.Republic of South Africa. Supreme court of appeal judgment—Minister of Mineral Resources v Stern and Others; Treasure the Karoo Action Group and Another v Department of Mineral Resources and Others (1369/2017; 790/2018) [2019] ZASCA 99; [2019] 3 All SA 684 (SCA) (4 July 2019). (2019).25.Gorski, J. & Trenorden, C. The EU and regulation of the shale industry: where do we stand now? Oil gas. law N. 5, 20–25 (2017).
Google Scholar 
26.Gorski, J. & Trenorden, C. Maximizing the EU shale gas potential by minimizing its environmental footprint. https://papers.ssrn.com/sol3/papers.cfm?abstract_id=3070722 (2018).27.Williamson, R. & Esterhuyse, S. Expected wastewater volumes associated with unconventional oil and gas exploitation in South Africa and the management thereof. Bull. Eng. Geol. Environ. 79, 711–728 (2020).28.Webb, R. M. Changing Tides in Water Management: Policy Options to Encourage Greater Recycling of Fracking Wastewater. William Mary Environ. Law Policy Rev. 42, 85–143 (2017).
Google Scholar 
29.Brady, W. J. & Crannell, J. P. Hydraulic Fracturing Regulation in the United States: The Laissez-Faire Approach of the Federal Government and Varying State Regulations. Vt. J. Environ. Law 14, 40–68 (2015).
Google Scholar 
30.Tan, P.-L., George, D. & Comino, M. Cumulative risk management, coal seam gas, sustainable water, and agriculture in Australia. Int. J. Water Resour. Dev. 31, 682–700 (2015).
Google Scholar 
31.Esterhuyse, S. Developing a groundwater vulnerability map for unconventional oil and gas extraction: a case study from South Africa. Environ. Earth Sci. 76, 626 (2017).
Google Scholar 
32.Esterhuyse, S. et al. Development of an interactive vulnerability map and monitoring framework to assess the potential environmental impact of unconventional oil and gas extraction by means of hydraulic fracturing. (Water Research Commission, 2014).33.Buono, R. M., Mayor, B. & López-Gunn, E. A comparative study of water-related issues in the context of hydraulic fracturing in Texas and Spain. Environ. Sci. Policy 0–1 https://doi.org/10.1016/j.envsci.2017.12.006 (2017).34.Fink, E. Dirty little secrets: fracking fluids, dubious trade secrets, confidential contamination, and the public health information vacuum. Fordham Intellect. Prop. Media Entertain. Law J. 29, 971–1023 (2019).
Google Scholar 
35.Becklumb, P., Chong, J. & Williams, T. Shale Gas in Canada: Environmental Risks and Regulation. https://lop.parl.ca/Content/LOP/ResearchPublications/2015-18-e.pdf (2015).36.Ingelson, A. & Hunter, T. A Regulatory Comparison of Hydraulic Fracturing Fluid Disclosure Regimes in the United States, Canada, and Australia. Nat. Resour. J. 54, 217–253 (2014).
Google Scholar 
37.EPA. Hydraulic Fracturing for Oil and Gas: Impacts from the Hydraulic Fracturing. EPA’s Study of Hydraulic Fracturing and Its Potential Impact on Drinking Water Resources (2016).38.National Acadamies Press. Onshore Unconventional Hydrocarbon Development: Legacy Issues and Innovations in Managing Risk Day 1: Proceedings of a Workshop. https://doi.org/10.17226/25083 (2018).39.Centre for Environmental Rights. Minimum requirements for the regulation of the environmental impacts of hydraulic fracturing—A position statement. https://cer.org.za/wp-content/uploads/2014/04/CER-Minimum-Requirements-for-the-Regulation-of-the-Environmental-Impacts-of-Fracking-Web.pdf (2014).40.Jackson, R. B. The integrity of oil and gas wells. PNAS 10–11 https://doi.org/10.1073/pnas.1410786111 (2014).41.Esterhuyse, S., Kemp, M. & Redelinghuys, N. Assessing the existing knowledge base and opinions of decision makers on the regulation and monitoring of unconventional gas mining in South Africa. Water Int. 38, 687–700 (2013).
Google Scholar 
42.Lin, A. China: Replacing coal with shale gas. Could reducing China’s regional air pollution lead to more local pollution in rural China? In The Shale Dilemma: A Global Perspective on Fracking and Shale Development (ed. Gamper-Rabindran, S.) 267–304 (University of Pittsburgh Press, 2018).43.Guo, M. et al. Prospects for shale gas production in China: Implications for water demand. Renew. Sustain. Energy Rev. 66, 742–750 (2016).
Google Scholar 
44.Thomas, M., Partridge, T., Harthorn, B. H. & Pidgeon, N. Deliberating the perceived risks, benefits, and societal implications of shale gas and oil extraction by hydraulic fracturing in the US and UK. Nat. Energy 2, 17054 (2017).
Google Scholar 
45.Kinne, B. Regulating unconventional shale gas in the United States: Diverging priorities, overlapping jurisdictions, and asymmetrical data access. In Governing shale gas: Development, citizen participation and decision-making in the US, Canada, Australia and Europe (eds. Whitton, J., Cotton, M., Charnley-Parry, I. M. & Brasier, K.) 23–36 (Routledge, 2018).46.Mcintosh, J. et al. A critical review of state-of-the-art and emerging approaches to identify fracking-derived gases and associated contaminants in aquifers. Environ. Sci. Technol. 53, 1063–1077 (2019).CAS 

Google Scholar 
47.Worrall, F., Davies, J. & Hart, A. Dynamic baselines for the detection of water quality impacts – the case of shale gas development. Environ. Sci. Process. impacts Qual. impacts 23, 1116–1129 (2021).CAS 

Google Scholar 
48.King, R. We Need a Fracking Baseline. La. Law Rev. 77, 545–584 (2016).
Google Scholar 
49.Daily, T. A. Rules Done Right: How Arkansas Brought Its Oil and Gas Law into a Horizontal World. Ark. Law Rev. 68, 259–294 (2015).
Google Scholar 
50.Brownlow, J., Yelderman, J. C. & James, S. C. Spatial Risk Analysis of Hydraulic Fracturing near Abandoned and Converted Oil and Gas wells. Groundwater 55, 268–280 (2017).CAS 

Google Scholar 
51.Cobbing, J. E., Eales, K., Gibson, J., Lenkoe, K. & Cobbing, B. Operation and maintenance (O&M) and the perceived unreliability of domestic groundwater supplies in South Africa. South Afr. J. Geol. 118, 17–32 (2015).
Google Scholar 
52.Gaye, C. B. & Tindimugaya, C. Review: challenges and opportunities for sustainable groundwater management in Africa. Hydrogeol. J. 27, 1099–1110 (2019).
Google Scholar 
53.Department of Water and Sanitation. National groundwater strategy draft. (Department of Water and Sanitation, 2016).54.Hohne, D., de Lange, F., Esterhuyse, S. & Sherwood-Lollar, B. Case study: methane gas in a groundwater system located in a dolerite ring structure in the Karoo Basin; South Africa. South Afr. J. Geol. 122, 357–368 (2019).CAS 

Google Scholar 
55.Eymold, W. K. et al. Hydrocarbon-Rich Groundwater above Shale-Gas Formations: A Karoo Basin Case Study. Groundwater 56, 1–21 (2018).
Google Scholar 
56.Cramer, B. What the frack? How weak industrial disclosure rules prevent public understanding of chemical practices and toxic politics. South. Calif. Interdiscip. Law J. 25, 67–105 (2016).
Google Scholar 
57.Centner, T. J. & Eberhart, N. S. The use of best management practices to respond to externalities from developing shale gas resources. J. Environ. Plan. Manag. 59, 746–768 (2016).
Google Scholar 
58.Kinchy, A. & Schaffer, G. Disclosure Conflicts: Crude Oil Trains, Fracking Chemicals, and the Politics of Transparency. Sci. Technol. Hum. values 43, 1011–1038 (2018).
Google Scholar 
59.Weible, C. M. et al. An Institutional and Opinion Analysis of Colorado’s Hydraulic Fracturing Disclosure Policy. J. Environ. Policy Plan. 19, 115–134 (2017).
Google Scholar 
60.Rawlins, R. Planning for Fracking on the Barnett Shale: Soul and Water Contamination Concerns, and the Role of Local Government. Envtl. L 44, 135–199 (2014).
Google Scholar 
61.Holding, S., Allen, D. M., Notte, C. & Olewiler, N. Enhancing water security in a rapidly developing shale gas region. J. Hydrol. Reg. Stud. 11, 266–277 (2017).
Google Scholar 
62.Schreurs, M. A. Germany: The German Energiewende and the decision to ban unconventional hydraulic fracturing. In The shale dilemma: A global perspective on fracking and shale development (ed. Gamper-Rabindran, S.) 231–266 (University of Pittsburgh Press, 2018).63.Farah, P. D. & Tremolada, R. A Comparison between Shale Gas in China and Unconventional Fuel Development in the United States: Health, Water and Environmental Risks. Brooklyn J. Int. Law 41, 1–46 (2016).64.Notte, C., Allen, D. M., Gehman, J., Alessi, D. S. & Goss, G. G. Comparative analysis of hydraulic fracturing wastewater practices in unconventional shale developments: Regulatory regimes. Can. Water Resour. J. 42, 122–137 (2017).
Google Scholar 
65.Wiseman, H. J. State Enforcement of Shale Gas Development Regulations, Including Hydraulic Fracturing. Ssrn https://doi.org/10.2139/ssrn.1992064 (2012).66.Eaton, T. T. Science-based decision-making on complex issues: Marcellus shale gas hydrofracking and New York City water supply. Sci. Total Environ. 461–462, 158–169 (2013).
Google Scholar 
67.Gagnon, G. A. et al. Impacts of hydraulic fracturing on water quality: a review of literature, regulatory frameworks and an analysis of information gaps. Environ. Rev. 24, 122–131 (2016).
Google Scholar 
68.Centner, T. J. & Connell, L. K. O. Unfinished business in the regulation of shale gas production in the United States. Sci. Total Environ. 476, 359–367 (2014).
Google Scholar 
69.Lenhard, L. G., Andersen, S. M. & Coimbra-Araújo, C. H. Energy-Environmental Implications Of Shale Gas Exploration In Paraná Hydrological Basin, Brazil. Renew. Sustain. Energy Rev. 90, 56–69 (2018).CAS 

Google Scholar 
70.Saulino, M. F. Argentina: Energy extraction in communities. Can shale development proceed without causing pollution and conflicts? In The shale dilemma: A global perspective on fracking and shale development (ed. Gamper-Rabindran, S.) 305–341 (University of Pittsburgh Press, 2018).71.Wiseman, H. J. The Capacity of States to Govern Shale Gas Development Risks. Environ. Sci. Technol. 48, 8376–8387 (2014).CAS 

Google Scholar 
72.Hull, E. & Evensen, D. Just environmental governance for shale gas? Transitioning towards sustainable local regulation of fracking in Spain. Energy Res. Soc. Sci. 59, 101307 (2020).
Google Scholar 
73.DiGiulio, D. C., Shonkoff, S. B. C. & Jackson, R. B. The need to protect fresh and brackish groundwater resources during unconventional oil and gas development. Curr. Opin. Environ. Sci. Heal. 3, 1–7 (2018).
Google Scholar 
74.Angeles, A. Reforming Natural Gas Fracking Regulations in 2017–2018: How Should States Enforce Regulations? Environ. Claims J. 30, 251–272 (2018).
Google Scholar 
75.Mukherjee, N. et al. Comparison of techniques for eliciting views and judgements in decision-making. Methods Ecol. Evol. 9, 54–63 (2018).
Google Scholar 
76.Baker, E., Bosetti, V., Jenni, K. E. & Ricci, E. C. Facing the experts: Survey mode and expert elicitation. https://doi.org/10.2139/ssrn.2384487 (2014).77.Redelinghuys, N. Effects on communities: The social frabric, local livelihoods and the social psyche. In Hydraulic Fracturing in the Karoo: Critical Legal and Environmental Perspectives (eds. Glazewski, J. & Esterhuyse, S.) 345–365 (JUTA, 2016).78.Young, J. C. et al. A methodological guide to using and reporting on interviews in conservation science research. Methods Ecol. Evol. 9, 10–19 (2018).
Google Scholar 
79.Morgan, M. G. Use (and abuse) of expert elicitation in support of decision making for public policy. PNAS 111, 7177–7184 (2014).
Google Scholar 
80.DWA (Department of Water Affairs). Groundwater strategy 2010. (DWA (Department of Water Affairs), 2010). More

Catchment locations and input dataTo determine the impact of afforestation on catchment hydrology we select twelve varied catchments from across the British Isles (Supplementary Material-S1 and Fig. 1). These catchments capture a range of hydrological regimes, drainage patterns and catchment soil and land-cover properties to determine how such factors may influence catchment response to afforestation. Being predominantly >1000 km2 in area (ranging from 511 to 9931 km2 in size), they are adequately represented in a hydrological model to integrate processes at a 1 km2 spatial resolution54,55. Two catchments are nested within larger ones, the Ure within the Ouse, and the Severn at Bewdley (Severn-B) within the Severn at Haw Bridge (Severn-HB) (Fig. 1).The period 2000–2010, a flood-rich period for the British Isles36,37, is chosen to assess afforestation influence on streamflow as it allows us to avoid the uncertainty that would be associated with land-cover changes over a longer period when comparing to baseline results. This length of the simulation period also reduces the computational demand with a large ensemble of land-cover scenarios. Accordingly, the CEH land-cover map for the year 200056, in the form of the CHESS-land dataset57, is used to provide configurational datasets specifying soil hydraulic and thermal properties, vegetation characteristics, and orography, for the model at a 1 km2 spatial resolution for the unaltered land-cover scenarios. This dataset has successfully been used in other studies55,58. The 25 m rasterised land-cover map is reclassified into eight different land-cover types (Supplementary Tables S4 and S5) and used to derive afforestation scenarios related to land cover before being converted to a percentage land-cover fraction at a 1 km2 spatial resolution. To provide the required meteorological driving data, we use the CHESS-met dataset59 which includes long-wave and short-wave radiation, air temperature, specific humidity and pressure. The 50 m CEH Integrated Hydrological Digital Terrain Model elevation data is used to derive topographical and catchment attributes as well as catchment boundaries and river networks60. Soil hydraulic information comes from the Harmonised World Soil Database and was made uniform across each grid cell61.The modelThe Joint UK Land Environment Simulator (JULES) is a physically based land-surface model that simulates the fluxes of carbon, water and energy at the land surface when driven by a time series of the atmospheric data23,24. Multiple studies have used JULES before including the investigation into evapotranspiration drivers across Great Britain58,62, atmospheric river formation over Europe63, the impact of solar dimming and carbon dioxide on runoff64 and developing river routing algorithms with a Regional Climate Model65. JULES is routinely used at the Met Office, where it is coupled with several other models to understand future changes globally and across the UK, by bridging the atmosphere, land surface and ocean66. This study is predominantly a theoretical, scenario-based modelling study designed to draw out general principles and to quantify the relation between afforestation and hydrological response, and as such the results are not intended to provide detailed guidance for specific practical actions.The use of a process-based model enables us to investigate physical explanations for the hydrological impacts of changes in land cover and the explicit representation of vegetation that will influence fluxes, partitioning and storages within the realm of epistemic uncertainty for other conceptual and hydrological models where vegetation is not included. JULES models both plant phenology and canopy storages23,24. When changing the plant functional type in JULES, both the properties of the above-ground vegetation (such as canopy height and leaf area index) and the soil infiltration factor and the root depth are altered23. However, there are several caveats that must be considered with this approach. First, the model configuration used in the present study is uncoupled from the atmosphere and so large-scale land-cover changes cannot alter nearby weather67. Second, each grid cell is hydrologically separated from adjacent cells, with streamflow and runoff hydrologically uncoupled from the rest of the system. Soil water also does not flow between grid cells. Third, soil thermal and hydraulic properties are uniform across a grid cell. This reduces the impact of hydrological pathways within a cell and the interaction of vegetation with these varying soil types that could have ramifications at multiple temporal and spatial scales. For example, within the cell there may be vegetation that is water-stressed (e.g. valley sides) compared with vegetation where water is not limited (e.g. floodplain) which would change how much transpiration is possible and thus runoff68.Precipitation in the model is partitioned by vegetation and when it reaches the soil surface it is portioned into either infiltration excess overland flow, at a rate controlled by the hydraulic conductivity of the soil, or saturation-excess overland flow as determined by the Probability Distributed Model (PDM)69,70. Throughfall (TF) through the canopy is dependent on the rainfall and the existing water in the canopy:$${T}_{F}=Pleft(1-frac{C}{{C}_{m}}right)exp left(-frac{{varepsilon }_{r}{C}_{m}}{PvarDelta t}right)+Pfrac{C}{{C}_{m}}$$
(1)
where P is the rainfall rate (kg m−2 s−1), C is the amount of water in the canopy (mm), Cm is the maximum water storage of the canopy (mm) and εr is the fraction of the grid cell occupied by convective precipitation. The maximum amount of canopy water storage is a function of the leaf area index (L):$${C}_{m}={A}_{m}+{B}_{m}L$$
(2)
where Am is the ponding of water on the soil surface and interception by leafless vegetation (mm) and Bm is the rate of change of water holding capacity with leaf area index. At each timestep (n) the canopy storage is updated thus:$${C}^{(n+1)}={C}^{(n)}+(P-{T}_{F})Delta t$$
(3)
Based on the surface energy balance, the fraction of the proportion of water stored in the canopy compared with the maximum canopy capacity of that plant type is used to calculate the effective surface resistance to determine tile evapotranspiration.Surface runoff is generated by two processes in JULES: infiltration excess, where the water flux at the surface is greater than the infiltration rate of the soil, and saturated excess overland flow where the water flux at the surface is converted to runoff when the soil is completely saturated. To calculate the saturation-excess overland flow, the PDM69 is used to determine the fraction of the model grid cell that will be saturated (fsat) which is used as a multiplier to convert any excess water reaching the surface to runoff:$${f}_{{{{{{mathrm{sat}}}}}}}=1-{left[frac{{max }(0,S-{S}_{0})}{{S}_{{max }}-{S}_{0}}right]}^{frac{b}{b-1}}$$
(4)
where S is the fraction of the grid cell soil water storage, S0 is the minimum storage at and below which there is no surface saturation (mm), Smax is the maximum grid cell storage (mm) and b is the Clapp and Hornberger71 soil exponent. We use the topography-derived parameterisation for the b and S0/Smax parameters to reduce individual calibration with the following relationship55:$$left{begin{array}{c}b=2.0hfill\ {S}_{0}/{S}_{{max }}=,{max },(1-frac{s}{{s}_{{max }}},,0.0)end{array}right.$$
(5)
where s is the grid cell slope (°) and smax is the maximum grid cell storage (mm). Once interception and surface runoff have been calculated, the remaining water enters the soil. This water is allocated to the different soil layers within the soil column by using the Darcy–Richards equation:$$W=kleft(frac{{{{{{mathrm{d}}}}}}varphi }{{{{{{mathrm{d}}}}}}z}+1right)$$
(6)
where W is the vertical flux of water through the soil (kg m−2 s−1), k is soil conductivity (kg m−2 s−1), φ is suction (m) and z is the vertical flux of water through the soil (m). To calculate suction and soil conductivity, we use the van Genuchten72 scheme:$$left(frac{theta }{{theta }_{s}}right)=frac{1}{{[1+{(alpha varphi )}^{(frac{1}{1-m})}]}^{m}}$$
(7)
where θ is the average volumetric soil moisture (m3 m−3), θs is the soil moisture at saturation (m3 m−3), α and m are van Genuchten parameters dependent on soil type. The hydraulic conductivity is calculated thus:$${K}_{h}={K}_{hs}{S}^{varepsilon }{left[1-{left(1-{S}^{frac{1}{m}}right)}^{m}right]}^{2}$$
(8)
where Kh is the hydraulic conductivity (m s−1) and Khs is the hydraulic conductivity for saturated soil (m s−1). ε is an empirical value set at 0.5 in JULES and S is found by:$$S=frac{(theta -{theta }_{r})}{({theta }_{s}-{theta }_{r})}$$
(9)
where θr is the residual soil moisture (m3 m−3). Vegetation can access water from each level in the soil column as a function of the root density where the fraction of roots (r) in each soil layer (l) from depth zl-1 to zl is:$${r}_{l}=frac{{e}^{-frac{2{z}_{l-1}}{{d}_{r}}}-{e}^{-frac{2{z}_{l}}{{d}_{r}}}}{1-{e}^{-frac{2{z}_{t}}{{d}_{r}}}}$$
(10)
where zl is the depth of the lth soil layer, dr is the root depth (m) and zt is the total depth of the soil column (m). The water flux extracted from a soil layer is elE where E is transpiration (kg m−2 s−1) and el can be found by:$${e}_{l}=frac{{r}_{l}{beta }_{l}}{{sum }_{l}{r}_{l}{beta }_{l}}$$
(11)
and βl is defined by:$${beta }_{l}=left{begin{array}{cc}1 hfill& {theta }_{l}ge {theta }_{c}hfill\ ({theta }_{l}-{theta }_{w})/({theta }_{c}-{theta }_{w}) & {theta }_{w}, < ,{theta }_{l} , < , {theta }_{c}hfill\ 0 hfill& {theta }_{l}le {theta }_{w}hfillend{array}right.$$ (12) where θc and θw are the volumetric soil moisture critical and wilting points respectively (m3 m−3) and θl is the unfrozen soil moisture at that soil layer (m3 m−3). In this configuration of JULES, when a soil layer becomes saturated, the excess water is routed to lower layers. When the bottom layer becomes fully saturated any excess water is added to the subsurface runoff. Both the surface and subsurface runoff are then passed to the River Flow Model65,73 which routes the flows according to a flow direction grid74.This study uses a combination of calibrated model parameters from the previous work of Robinson et al.59 and Martinez-de la Torre et al.55 (Rose suites u-bi090 and u-au394, respectively, which can be found using the Rose/Cylc suite control system: https://metomi.github.io/rose/doc/html/index.html). We compare observed streamflow from the NRFA database75 with model output for the years 2000–2010 using the base land and CHESS-met datasets. The model is spun-up for the years 1990–2000 to ensure soil moisture content has been equilibrised. To quantify the accuracy of the model, we use a range of standard error metrics. These include the Nash–Sutcliffe efficiency76 measure:$${{{{{mathrm{NSE}}}}}}=1-frac{{sum }_{i=1}^{n}{({Q}_{{{{{{mathrm{sim}}}}}}}-{Q}_{{{{{{mathrm{obs}}}}}}})}^{2}}{{sum }_{i=1}^{n}{({Q}_{{{{{{mathrm{obs}}}}}}}-{bar{Q}}_{{{{{{mathrm{obs}}}}}}})}^{2}}$$ (13) Kling–Gupta efficiency77:$${{{{{mathrm{KGE}}}}}}=1-sqrt{{(r-1)}^{2}+{left(frac{{sigma }_{{{{{{mathrm{sim}}}}}}}}{{sigma }_{{{{{{mathrm{obs}}}}}}}}-1right)}^{2}+{left(frac{{mu }_{{{{{{mathrm{sim}}}}}}}}{{mu }_{{{{{{mathrm{obs}}}}}}}}-1right)}^{2}}$$ (14) Root-mean-squared error:$${{{{{mathrm{RMSE}}}}}}=sqrt{mathop{sum }limits_{i=1}^{n}{({Q}_{{{{{{mathrm{sim}}}}}}}-{Q}_{{{{{{mathrm{obs}}}}}}})}^{2}}$$ (15) Mean absolute error:$${{{{{mathrm{MAE}}}}}}=frac{{sum }_{i=1}^{n}|{Q}_{{{{{{mathrm{sim}}}}}}}-{Q}_{{{{{{mathrm{obs}}}}}}}|}{n}$$ (16) where Qsim is the simulated discharge, Qobs is the observed discharge, r is the linear correlation between observation and simulations, σsim|obs is the standard deviation of discharge, μsim|obs is the mean of discharge and n is the number of observations. We also use NSE(log(Q)) and KGE(1/Q) to understand how well the model can reproduce low flows. Using these measures, we find that JULES performs satisfactorily apart from the Avon Catchment which may be due to fast subsurface flows generated by its geology55 (Supplementary Table S7). With process-based models, it is difficult to both accurately reproduce physical processes and make the output faithful to reality due to epistemic uncertainties78. Even though model performance is not the same as achieved with calibrated conceptual or empirical models, it allows us to determine the effects of vegetation changes on the hydrological cycle.JULES’ ability to faithfully represent hydrological land-surface processes in Great Britain has been evaluated in several studies58,79,80 and the plant functional type parameters it uses at global scales81,82. To validate the ability of our configuration of JULES to represent soil moisture and potential evapotranspiration rates, we compare the model output with observations from twelve COSMOS-UK sites within our catchments covering grasslands, croplands, coniferous and broadleaf woodland83 (Supplementary Fig. S8). We evaluate model performance from the start of the COSMOS-UK station records until January 1, 2018 so that we use the same forcing data as our experiments. Station start dates vary from October 2013 to August 2017. We compare COSMOS-UK observed soil moisture to the first 0.1 m of the soil column in JULES and evaporation to the sum of the soil evaporation and plant transpiration. We find a median KGE score of 0.44 for the topsoil moisture and 0.53 for potential evaporation (Supplementary Tables S9 and S10). Low error metrics observed for topsoil moisture are due to systematic undercalculation by JULES80. At our broadleaf sites, Alice Holt and Wytham Woods, we find both systematic over and underprediction of the topsoil moisture respectively. In line with other studies, we find that there is a slight overestimation of evaporation in JULES58,84. As illustrated by the median coefficients of determination between the COSMOS-UK and JULES data of 0.62 and 0.60 for the topsoil moisture and evaporation respectively, JULES broadly represents changes in these variables over time.Land-cover scenariosModelling the influence of afforestation on catchment hydrology has been attempted before but usually only at the scale of a single catchment for a limited range of scenarios. In this study, we focus on the theoretical effect of widespread planting of broadleaf trees to examine whether planting location is a stronger control on hydrological response than afforestation extent by using a large ensemble of up to 288 land-cover change scenarios. We choose to focus just on broadleaf woodland for several reasons. First, we are trying to replicate a landscape that could be considered a natural climatic climax community that might occur if it had not been for human intervention during the Holocene. Second, broadleaf woodland has the potential to absorb and store carbon in soils for longer time periods. Finally, to reduce computational cost and the issue of potentially expanding the errors induced by potentially spurious parameters of needleleaf woodland in this version of JULES85. Although potential woodland planting locations have been suggested by the Environment Agency and authorities in Scotland and Wales86,87,88, the differences in planting criteria means it is not possible to systematically compare hydrological changes across our chosen catchments. Here we attempt to create afforestation scenarios related to both catchment river network structure and land use that are directly comparable across a range of catchments. Afforestation was in grassland areas to reduce the complexity of the decisions made and enable an understanding behind catchment sensitivity to land-cover changes related to soil and catchment structure.Three metrics were selected to discretise the catchment into distinct areas for afforestation: the Topographic Wetness Index (TWI)28, Strahler27 and Shreve orders26. These metrics capture different parts of the catchment such as propensity for saturation, drainage network location and relative contributing areas. TWI is calculated by:$${{{{{mathrm{TWI}}}}}}=,{{{{mathrm{ln}}}}},frac{a}{tan ,gamma }$$ (17) where a is the upslope area draining through a point, per unit contour length, and γ is the local surface topographic slope in radians. All three metrics were calculated using the 50 m IHDTM60, thresholding stream formation at an accumulation of ten pixels using the D8 flow direction algorithm within ArcGIS 10.6.189. Strahler order ranges from one (headwaters) to seven (lowlands). Due to the continuous nature of TWI (0.05–31.49) and the large ordinal range of Shreve order (1–9523) calculated for the entire British Isles, we group TWI orders into five quantiles and seven quantiles for Shreve. Increasing TWI order in this case indicates increasing propensity for saturation, or potential maximum saturation level, and increasing Shreve order indicates increasing contributing area. Catchments were broken down to watersheds from the downstream point of the Shreve and Strahler orders. Due to the nature of the data, this led to some first order Strahler catchments being incorrectly generated for some catchments (Supplementary Table S7). Using these generated catchment areas, we plant both inside and outside of these watersheds to understand the hydrological difference between opposing planting locations. In each of the catchment areas, two different levels of afforestation were tested of ~25 and 50% of the possible planting area. Planted area was assigned at random in the catchment and was produced by calculating the area available for afforestation and randomly producing points that covered the area required using the Create Random Points tool in ArcGIS 10.6.1.Discussions exist about where to plant woodland in relation to existing land cover, to provide ecosystem services, including around watercourses29,30, urban areas31,32 and woodland4,33. Therefore, in this study we try to understand how these potential planting scenarios will affect hydrology in general. Using the CEH 2000 land-cover map56 buffers of broadleaf land cover were created at 25 and 50 m around these three land uses (Supplementary Fig. S7). These were then discretised according to the catchment areas. As an example, one scenario would be afforesting up to 50 m around existing broadleaf woodland inside the Shreve order one catchment area, whilst another would be randomly afforesting within 25% of the available area outside of TWI order five areas.Afforestation according to different catchment areas and land-cover uses between 234 and 288 scenarios for each catchment and between 0 and c. 40 percentage point increase in broadleaf woodland (Supplementary Fig. S2 and Table S8). Due to the structure and size of the different catchments, and thus differences in Strahler and Shreve orders, not all catchments had a comparable number of higher orders. Produced scenarios were converted to the 1-km2 grid scale by altering the fraction of land-cover types within each grid cell. It should be noted that this work only considers the impact of mature broadleaf woodland and neglects the influence of the initial planting and growing of the woodland that would likely have its own impact on catchment hydrology as frequently reported13,49. Furthermore, it does not include the period when there would be the highest amount of carbon sequestration. This study seeks to understand the theoretical impact of woodland on catchment hydrology when fully developed to understand the long-term implications of management decisions.Hydrological signatures and analysisSeveral hydrologic indices can be used to characterise the influence of afforestation on streamflow regime34,35. To analyse average streamflow and extremes, we look at the top 1% (very high flow), 5% (high flow), 50% (median flow), 90% (low flow) and 95% (very low flow) quantiles of daily streamflow. To quantify flow variability, we use the slope of the flow duration curve38,40 calculated thus:$${{{{{mathrm{FDC}}}}}}=frac{{{{{mathrm{ln}}}}}({Q}_{33 % })-,{{{{mathrm{ln}}}}}({Q}_{66 % })}{(0.66-0.33)}$$ (18) where Q33% is the 33rd flow exceedance quantile and Q66% is the 66th flow exceedance quantile. To ascertain catchment responsiveness to climatic forcing, we use median streamflow elasticity40,41:$${{{{{mathrm{MSE}}}}}}={{{{{mathrm{median}}}}}}left(frac{{{{{{mathrm{d}}}}}}Q}{{{{{{mathrm{d}}}}}}P}frac{P}{Q}right)$$ (19) where dQ and dP are the annual changes in yearly discharge and precipitation, respectively. Finally, we use the runoff ratio to quantify water balance changes related to streamflow and evapotranspiration42:$${{{{{mathrm{RR}}}}}}=frac{{mu }_{Q}}{{mu }_{P}}$$ (20) where µQ and µP are the average yearly discharge and precipitation using daily values, respectively. We also qualitatively assess the largest peak flow daily event in the 10-year record used in this study to determine the impact of afforestation on the highest possible flows in each catchment.To determine how afforestation influences streamflow metrics, percentage changes in flow metrics are plotted as a function of percentage point increases in afforestation (calculated using the difference between original and afforested scenario). Quantile regression is applied to determine the median regression slope of the trend for the entire period43. The benefit of using quantile regression is that it identifies the median response of the input variable (in this case the level of afforestation in both percentage and absolute terms) without being influenced by extreme outliers. In this way, we can estimate the proportional streamflow response to afforestation over the period. We use the regression slope coefficient as a proxy of catchment sensitivity to afforestation for each streamflow metric. The slope coefficient is then correlated to catchment attributes, as stated in the CAMELS-GB dataset44, using Spearman’s rank correlation. This allows us to determine the direction and significance of the catchment property influences on the sensitivity of catchments to afforestation for the different hydrologic signatures. To determine the impact of different planting locations according to catchment and land-cover location a one-way analysis of variance (ANOVA) test is undertaken using R90. More

1.Hinkel, J. et al. Coastal flood damage and adaptation costs under 21st century sea-level rise. PNAS 111, 3292–3297 (2014).CAS 

Google Scholar 
2.Tessler, Z. D. et al. Profiling risk and sustainability in coastal deltas of the world. Science 349, 638–643 (2015).CAS 

Google Scholar 
3.Hinkel, J. et al. The ability of societies to adapt to twenty-first-century sea-level rise. Nat. Clim. Change 8, 570–578 (2018).
Google Scholar 
4.Ericson, J. P., Vörösmarty, C. J., Dingman, S. L., Ward, L. G. & Meybeck, M. Effective sea-level rise and deltas: Causes of change and human dimension implications. Glob. Planet. Change 50, 63–82 (2006).
Google Scholar 
5.Shirzaei, M. et al. Measuring, modelling and projecting coastal land subsidence. Nat. Rev. Earth Environ. 2, 40–58 (2021).
Google Scholar 
6.Syvitski, J. P. M. et al. Sinking deltas due to human activities. Nat. Geosci. 2, 681–686 (2009).CAS 

Google Scholar 
7.Evans, G. Deltas: the fertile dustbins of the continents. Proc. Geol. Assoc. 123, 397–418 (2012).
Google Scholar 
8.Li, X., Liu, J. P., Saito, Y. & Nguyen, V. L. Recent evolution of the Mekong Delta and the impacts of dams. Earth Sci. Rev. 175, 1–17 (2017).
Google Scholar 
9.Tamura, T. et al. Long-term sediment decline causes ongoing shrinkage of the Mekong megadelta, Vietnam. Sci. Rep. 10, 4–10 (2020).
Google Scholar 
10.Nienhuis, J. H. et al. Global-scale human impact on delta morphology has led to net land area gain. Nature 577, 514–518 (2020).CAS 

Google Scholar 
11.Hoitink, A. J. F. et al. Resilience of river deltas in the Anthropocene. J. Geophys. Res. Earth. Surf. 125, e2019JF005201 (2020).
Google Scholar 
12.Nicholls, R. J., Adger, W. N., Hutton, C. W. & Hanson, S. E. Deltas in the Anthropocene p. 282 Springer Nature (2020).13.Minderhoud, P. S. J., Coumou, L., Erkens, G., Middelkoop, H. & Stouthamer, E. Mekong delta much lower than previously assumed in sea-level rise impact assessments. Nat. Commun. 10, 3847 (2019).CAS 

Google Scholar 
14.Anthony, E. J. et al. Linking rapid erosion of the Mekong River delta to human activities. Sci. Rep. 5, 1–12 (2015).
Google Scholar 
15.Schmitt, R. J. P., Rubin, Z. & Kondolf, G. M. Losing ground – scenarios of land loss as consequence of shifting sediment budgets in the Mekong Delta. Geomorphology 294, 58–69 (2017).
Google Scholar 
16.Eslami, S. et al. Tidal amplification and salt intrusion in the Mekong Delta driven by anthropogenic sediment starvation. Sci. Rep. 9, 18746 (2019).CAS 

Google Scholar 
17.Szabo, S. et al. Population dynamics, delta vulnerability and environmental change: Comparison of the Mekong, Ganges-Brahmaputra and Amazon delta regions. Sustain. Sci. 11, 539–554 (2016).
Google Scholar 
18.Kondolf, G. M. et al. Changing sediment budget of the Mekong: cumulative threats and management strategies for a large river basin. Sci. Total Environ. 625, 114–134 (2018).CAS 

Google Scholar 
19.Van Binh, D., Kantoush, S. & Sumi, T. Changes to long-term discharge and sediment loads in the Vietnamese Mekong Delta caused by upstream dams. Geomorphology 353, 107011 (2020).
Google Scholar 
20.Kondolf, G., Rubin, Z. & Minear, J. Dams on the Mekong: cumulative sediment starvation. Water Resour. Res. 50, 5158–5169 (2014).
Google Scholar 
21.Hackney, C. et al. River bank instability is induced by unsustainable sand mining in the lower Mekong River. Nat. Sustain. 3, 217–225 (2020).
Google Scholar 
22.Triet, N. V. K. et al. Has dyke development in the Vietnamese Mekong Delta shifted flood hazard downstream? Hydrol. Earth Syst. Sci. Discuss. 2017, 1–27 (2017).
Google Scholar 
23.Park, E. et al. Dramatic decrease of flood frequency in the Mekong Delta due to river-bed mining and dyke construction. Sci. Total Environ. 723, 138066 (2020).CAS 

Google Scholar 
24.Minderhoud, P. S. J. et al. Impacts of 25 years of groundwater extraction on subsidence in the Mekong delta, Vietnam. Environ. Res. Lett. 12, aa7146 (2017).25.Minderhoud, P. S. J., Middelkoop, H., Erkens, G. & Stouthamer, E. Groundwater extraction may drown mega-delta: projections of extraction-induced subsidence and elevation of the Mekong delta for the 21th century. Environ. Commun. 2, 011005 (2020).
Google Scholar 
26.Zoccarato, C., Minderhoud, P. S. J. & Teatini, P. The role of sedimentation and natural compaction in a prograding delta: insights from the mega Mekong delta, Vietnam. Sci. Rep. 8, 11437 (2018).
Google Scholar 
27.Minderhoud, P. S. J. et al. The relation between land use and subsidence in the Vietnamese Mekong delta. Sci. Total Environ. 634, 715–726 (2018).CAS 

Google Scholar 
28.deWit, K. et al. Identifying Causes of Urban Differential Subsidence in the Vietnamese Mekong Delta by Combining InSAR and Field Observations. Remote Sens. 13, 20189 (2021).29.Eslami, S., et al. Projections of salt intrusion in a mega-delta under climatic and anthropogenic stressors. Nat. Commun. Earth Env. 1–11, 5 (2021).30.Erban, L. E., Gorelick, S. M. & Zebker, H. A. Groundwater extraction, land subsidence, and sea-level rise in the Mekong Delta, Vietnam. Environ. Res. Lett. 9, 084010 (2014).
Google Scholar 
31.Minderhoud, P. S. J., Hlavacova, I., Kolomaznik, J. & Neussner, O. Towards unraveling total subsidence of a mega-delta – the potential of new PS InSAR data for the Mekong delta. Proc. IAHS 382, 327–332 (2020).
Google Scholar 
32.van Staveren, M. F., van Tatenhove, J. P. M. & Warner, J. F. The tenth dragon: controlled seasonal flooding in long-term policy plans for the Vietnamese Mekong delta. Journal of Environmental Policy & Planning 20, 267–281 (2018).
Google Scholar 
33.Government of Viet Nam, Government Resolution 120/NQ-CP on Sustainable and Climate-Resilient Development of the Mekong Delta of Viet Nam (2017).34.MoNRE, “Mekong Delta Plan” (Ministry of Natural Resources and Environment, Hanoi, Vietnam), p. 126 (2013).35.Giosan, L., Syvitski, J., Constantinescu, S. & Day, J. Climate change: Protect the world’s deltas. Nature 516, 31–33 (2014).CAS 

Google Scholar 
36.Islam, M. F. et al. Enhancing effectiveness of tidal river management in southwest Bangladesh polders by improving sedimentation and shortening inundation time. J. Hydrol. 590, 125228 (2020).
Google Scholar 
37.Seijger, C., Hoang, V. T. M., van Halsema, G., Douven, W. & Wyatt, A. Do strategic delta plans get implemented? The case of the Mekong Delta Plan. Reg. Environ. Change 19, 1131–1145 (2019).
Google Scholar 
38.Day, J. W. et al. Approaches to defining deltaic sustainability in the 21st century. Estuar. Coast. Shelf Sci. 183, 275–291 (2016).
Google Scholar 
39.Gain, A. K. et al. Tidal river management in the south west Ganges-Brahmaputra delta in Bangladesh: Moving towards a transdisciplinary approach? Environ. Sci. Policy 75, 111–120 (2017).
Google Scholar 
40.Coastal Protection and Restoration Authority. Coastal Protection and Restoration Authority: Strategic plan fiscal year 2017–2018 through fiscal year 2021–2022 (Strategic Fiscal Plan) https://coastal.la.gov/wp-content/uploads/2021/04/CPRA_FY22-AP_web.pdf (2017).41.Coastal Protection and Restoration Authority. 2017 Coastal Master Plan. 552 https://coastal.la.gov/our-plan/2017-coastal-master-plan/ (2017).42.Meselhe, E. A., Sadid, K. M. & Allison, M. A. Riverside morphological response to pulsed sediment diversions. Geomorphology 270, 184–202 (2016).
Google Scholar 
43.Gaweesh, A. & Meselhe, E. A. Evaluation of Sediment Diversion Design Attributes and Their Impact on the Capture Efficiency. J. Hydraul. Eng. 142, 04016002 (2016).
Google Scholar 
44.Chapman, A. & Darby, S. E. Evaluating sustainable adaptation strategies for vulnerable mega-deltas using system dynamics modelling: rice agriculture in the Mekong Delta’s An Giang Province, Vietnam. Sci. Total Environ. 559, 326–338 (2016).CAS 

Google Scholar 
45.Minderhoud, P. S. J. The sinking mega-delta. Present and future subsidence of the Vietnamese Mekong delta. (PhD dissertation, Utrecht Studies of Earth Sciences 168, Utrecht University, The Netherlands, 2019).
Google Scholar 
46.IPCC Special Report on the Ocean and Cryosphere in a Changing Climate (eds. H. O. Pörtner, et al.), 2019.47.Kuenzer, C. et al. Flood mapping and flood dynamics of the Mekong delta: ENVISAT-ASAR-WSM based time series analyses. Remote Sens. 5, 687–715 (2013).
Google Scholar 
48.Dang, T. D., Cochrane, T. A., Arias, M. E., Van, P. D. T. & de Vries, T. T. Hydrological alterations from water infrastructure development in the Mekong floodplains. Hydrol. Proc. 30, 3824–3838 (2016).
Google Scholar 
49.Wagner, F., Tran, V. B. & Renaud, F. G. Groundwater Resources in the Mekong Delta: Availability, Utilization and Risks. In Renaud F. & Kuenzer C. (eds.) The Mekong Delta System. https://doi.org/10.1007/978-94-007-3962-8_7 (Springer Environmental Science and Engineering, Springer, Dordrecht, 2012).50.Kuchar, J. et al. The influence of sediment isostatic adjustment on sea level change and land motion along the U.S. Gulf Coast. J. Geophys. Res. Solid Earth 123, 780–796 (2018).
Google Scholar 
51.Lu, X., Kummu, M. & Oeurng, C. Reappraisal of sediment dynamics in the Lower Mekong River, Cambodia. Earth Surf. Process. Landf. 39, 1855–1865 (2014).
Google Scholar 
52.Darby, S. E. et al. Fluvial sediment supply to a mega-delta reduced by shifting tropical cyclone activity. Nature 539, 276–279 (2016).
Google Scholar 
53.Brunier, G., Anthony, E. J., Goichot, M., Provansal, M. & Dussouillez, P. Recent morphological changes in the Mekong and Bassac river channels, Mekong delta: The marked impact of river-bed mining and implications for delta destabilisation. Geomorphology 224, 177–191 (2014).
Google Scholar 
54.Loc, H. H. et al. Intensifying saline water intrusion and drought in the Mekong Delta: From physical evidence to policy outlooks. Sci. Total Environ. 757, 143919 (2021).CAS 

Google Scholar 
55.Schmitt, R. J. P., Bizzi, S., Castelletti, A., Opperman, J. J. & Kondolf, G. M. Planning dam portfolios for low sediment trapping shows limits for sustainable hydropower in the Mekong. Sci. Adv. 5, 2175 (2019).
Google Scholar 
56.Nowacki, D. J., Ogston, A. S., Nittrouer, C. A., Fricke, A. T. & Van, P. D. T. Sediment dynamics in the lower Mekong River: transition from tidal river to estuary. J. Geophys. Res. Oceans 120, 6363–6383 (2015).
Google Scholar 
57.Sanks, K. M., Shaw, J. B. & Naithani, K. Field‐based estimate of the sediment deficit in coastal Louisiana. J. Geophys. Res. Earth. Surf. 125, e2019JF005389 (2020).
Google Scholar 
58.Post, W. M. & Kwon, K. C. Soil carbon sequestration and land‐use change: processes and potential. Glob. Change Biol. 6, 317–327 (2000).
Google Scholar 
59.Ha, D. T., Ouillon, S. & Vinh, G. V. Water and Suspended Sediment Budgets in the Lower Mekong from High-Frequency Measurements (2009–2016). Water 10, 846 (2018).
Google Scholar 
60.Dunn, F. E., & Minderhoud, P. S. J. Elevation projections for the Mekong delta (Vietnam) under sedimentation strategies, subsidence, compaction, and sea-level rise [Data set]. Zenodo. https://doi.org/10.5281/zenodo.5645494 (2021).61.Foley, M. M. et al. Dam removal: Listening. In Water Resour. Res. 53, 5229–5246, (2017).62.Schmitt, R. J. P. et al. Strategic basin and delta planning increases the resilience of the Mekong Delta under future uncertainty. Proc. Natl. Acad. Sci. 118, 2026127 (2021).63.Thampanya, U., Vermaat, J. E., Sinsakul, S. & Panapitakkul, N. Coastal erosion and mangrove progradation of Southern Thailand. Estuar. Coast. Shelf Sci. 68, 75–85 (2006).
Google Scholar 
64.Willemsen, P. W. J. M., Horstman, E. M., Borsje, B. W., Friess, D. A. & Dohmen-Janssen, C. M. Sensitivity of the sediment trapping capacity of an estuarine mangrove forest. Geomorphology 273, 189–201 (2016).
Google Scholar 
65.Ibáñez, C., Day, J. W. & Reyes, E. The response of deltas to sea-level rise: Natural mechanisms and management options to adapt to high-end scenarios. Ecol. Eng. 65, 122–130 (2014).
Google Scholar 
66.Cornwall, W. Unleashing big muddy. Science 372, 334–337 (2021).CAS 

Google Scholar 
67.Dunn, F. E. et al. Projections of declining fluvial sediment delivery to major deltas worldwide in response to climate change and anthropogenic stress. Environ. Res. Lett. 14, 084034 (2019).
Google Scholar 
68.Syvitski, J. P. M. & Milliman, J. D. Geology, geography, and humans battle for dominance over the delivery of fluvial sediment to the coastal ocean. J. Geol. 115, 1–19 (2007).
Google Scholar 
69.Lehner, B. et al. High-resolution mapping of the world’s reservoirs and dams for sustainable river-flow management. Frontiers Ecol. Environ. 9, 494–502 (2011).
Google Scholar 
70.Lehner, B. et al. Global Reservoir andDam Database, Version 1 (GRanDv1): Dams, Revision 01. Palisades, NY: NASA Socioeconomic Data and Applications Center (SEDAC), https://doi.org/10.7927/H4N877QK (2011).71.Räsänen, T. A., Varis, O., Scherer, L. & Kummu, M. Greenhouse gas emissions of hydropower in the Mekong River basin. Environ. Res. Lett. 13, 034030 (2018).
Google Scholar 
72.WLE Mekong dam database Greater Mekong. (CGIAR Research Program on Water, Land and Ecosystems (WLE), Vientiane, Lao PDR, 2017).73.MRC Hydropower database (Vientiane, Lao PDR: Mekong River Commission (MRC) Secretariat, 2015).74.Jones, C. D. et al. The HadGEM2-ES implementation of CMIP5 centennial simulations. Geosci. Model Dev. 4, 543–570 (2011).
Google Scholar 
75.Van Manh, N. et al. Future sediment dynamics in the Mekong Delta floodplains: Impacts of hydropower development, climate change and sea level rise. Glob. Planet. Change 127, 22–33 (2015).
Google Scholar 
76.Esposito, C. R., Shen, Z., Törnqvist, T. E., Marshak, J. & White, C. Efficient retention of mud drives land building on the Mississippi Delta plain. Earth Surf. Dynam. 5, 387–397 (2017).
Google Scholar 
77.Kummu, M., Penny, D., Sarkkula, J. & Koponen, J. Sediment: Curse or Blessing for Tonle Sap Lake? Ambio 37, 158–163 (2008).
Google Scholar 
78.Krauss, K. W. et al. How mangrove forests adjust to rising sea level. New Phytologist 202, 19–34 (2014).
Google Scholar 
79.Liu, S. et al. Differential responses of crop yields and soil organic carbon stock to fertilization and rice straw incorporation in three cropping systems in the subtropics. Agric. Ecosyst. Environ. 184, 51–58 (2014).
Google Scholar 
80.Wang, W., Lai, D. Y. F., Wang, C., Pan, T. & Zeng, C. Effects of rice straw incorporation on active soil organic carbon pools in a subtropical paddy field. Soil Till. Res. 152, 8–16 (2015).
Google Scholar 
81.Nisar, S. & Benbi, D. K. Stabilization of organic C in an Indo-Gangetic alluvial soil under long-term manure and compost management in a rice–wheat system. Carbon Manag. 11, 533–547 (2020).CAS 

Google Scholar 
82.Lee, S. B. et al. Changes of soil organic carbon and its fractions in relation to soil physical properties in a long-term fertilized paddy. Soil Till. Res. 104, 227–232 (2009).
Google Scholar 
83.Benbi, D. K. & Yadav, S. K. Decomposition and Carbon Sequestration Potential of Different Rice-Residue-Derived By-products and Farmyard Manure in a Sandy Loam Soil. Commun Soil Sci Plant Anal. 46, 2201–2211 (2015).CAS 

Google Scholar 
84.Sodhi, G. P. S., Beri, V. & Benbi, D. K. Soil aggregation and distribution of carbon and nitrogen in different fractions under long-term application of compost in rice–wheat system. Soil Till. Res. 103, 412–418 (2009).
Google Scholar 
85.Breitenbeck, G. A. & Schellinger, D. Calculating the reduction in material mass and volume during composting. Compost Sci. Util. 12, 365–371 (2004).
Google Scholar 
86.Wakeham, S. G. & Canuel, E. A. The nature of organic carbon in density‐fractionated sediments in the Sacramento‐San Joaquin River Delta (California). Biogeosciences 13, 567–582 (2016).
Google Scholar 
87.Hong Van, N. P. et al. Rice straw management by farmers in a triple rice production system in the Mekong Delta, Vietnam. Trop. Agr. Develop. 58, 155–162 (2014).
Google Scholar 
88.Diep, N. Q., Sakanishi, K., Nakagoshi, N., Fujimoto, S. & Minowa, T. Potential for rice straw ethanol production in the Mekong Delta, Vietnam. Renew. Energy 74, 456–463 (2015).
Google Scholar 
89.Diep, N. Q. & Sakanishi, K. Potential for bio-ethanol production from agriculture residues in the Mekong Delta, Vietnam. Int. Energy J. 12, 145–154 (2011).
Google Scholar 
90.Lovelock, C. E. et al. The vulnerability of Indo-Pacific mangrove forests to sealevel rise. Nature 526, 559–217 (2015).CAS 

Google Scholar 
91.Nguyen, V. K., Le, X. T., Dao, H. H. & Do Van, L. Land surface subsidence in Mekong delta – due to the groundwater extraction? Tap Chi Dia Chat 10–110 (2015).92.Nguyen, V. L., Ta, T. K. O. & Tateishi, M. Late Holocene depositional environments and coastal evolution of the Mekong River Delta, Southern Vietnam. J. Asian Earth Sci. 18, 427–439 (2000).
Google Scholar 
93.Ta, T. K. O. et al. Holocene delta evolution and sediment discharge of the Mekong River, Southern Vietnam. Quat. Sci. Rev. 21, 1807–1819 (2002).
Google Scholar 
94.Tamura, T. et al. Luminescence dating of beach ridges for characterizing multi-decadal to centennial deltaic shoreline changes during Late Holocene, Mekong River delta. Mar. Geol. 326-328, 140–153 (2012).CAS 

Google Scholar 
95.Van Laarhoven, S. Subsidence potential of the Holocene deposits in the Mekong Delta, Vietnam (Masters dissertation supervised by P. S. J. Minderhoud & E. Stouthamer, Utrecht University, 2016).96.Zoccarato, C. & Teatini, P. Numerical simulations of Holocene salt-marsh dynamics under the hypothesis of large soil deformations. Adv. Water Resour. 110, 107–119 (2017).
Google Scholar 
97.Hung, N. N. et al. Sedimentation in the floodplains of the Mekong Delta, Vietnam Part II: deposition and erosion. Hydrol. Process. 28, 3145–3160 (2014).
Google Scholar 
98.Manh, N. V., Dung, N. V., Hung, N. N., Merz, B. & Apel, H. Large-scale suspended sediment transport and sediment deposition in the Mekong delta. Hydrol. Earth Syst. Sci. 18, 3033–3053 (2014).
Google Scholar 
99.Kuenzer, C. et al. Remote sensing of river delta inundation: Exploiting the potential of coarse spatial resolution, temporally-dense MODIS time series. Remote Sens. 7, 8516–8542 (2015).
Google Scholar 
100.Thanh, V. C. et al. Flooding in the Mekong Delta: The impact of dyke systems on downstream hydrodynamics. Hydrol. Earth Syst. Sci. 24, 189–212 (2020).
Google Scholar 
101.Duc Tran, D. et al. Assessing impacts of dike construction on the flood dynamics of the Mekong Delta. Hydrol. Earth Syst. Sci. 22, 1875–1896 (2018).
Google Scholar 
102.Fujihara, Y. et al. Analysis and attribution of trends in water levels in the Vietnamese Mekong Delta. Hydrol. Process. 30, 835–845 (2015).
Google Scholar 
103.Minderhoud, P. S. J., Coumou, L., Erkens, G., Middelkoop, H. & Stouthamer, E. Digital elevation model of the Vietnamese Mekong delta based on elevation points from a national topographical map. PANGAEA. https://doi.org/10.1594/PANGAEA.902136 (2019).104.Lehner, B. & Grill, G. Global river hydrography and network routing: baseline data and new approaches to study the world’s large river systems. Hydrological Processes 27, 2171–2186, http://www.hydrosheds.org (2013).
Google Scholar 
105.Kummu, M., Lu, X. X., Wang, J. J. & Varis, O. Basin-wide sediment trapping efficiency of emerging reservoirs along the Mekong. Geomorphology 119, 181–197 (2010).
Google Scholar 
106.Bussi, G. et al. Impact of dams and climate change on suspended sediment flux to the Mekong delta. Sci. Total Environ. 755, 142468 (2021).CAS 

Google Scholar  More

1.Nat. Sustain. 4, 659 (2021).2.Ertsen, M. Improvising Planned Development on the Gezira Plain, Sudan, 1900–1980 (Palgrave Macmillan, 2016).3.Yates, J. S., Harris, L. & Wilson, N. J. Environ. Plan. D 35, 787–815 (2017).Article 

Google Scholar 
4.Linton, J. & Budds, J. Geoforum 57, 170–180 (2014).Article 

Google Scholar 
5.Boelens, R., Hoogesteger, J., Swyngedouw, E., Vos, J. & Wester, P. Water Int. 41, 1–14 (2016).Article 

Google Scholar 
6.Venot, J. P., Kuper, M. & Zwarteveen, M. Drip Irrigation for Agriculture: Untold Stories of Efficiency, Innovation and Development (Routledge, 2017).7.Harrison, E. & Mdee, A. Third World Quart. 39, 2126–2141 (2018).Article 

Google Scholar 
8.Molle, F. Geoforum 40, 484–494 (2009).Article 

Google Scholar 
9.Lankford, B. et al. Glob. Environ. Change 65, 102182 (2020).Article 

Google Scholar 
10.Zeitoun, M. et al. Glob. Environ. Change 39, 143–154 (2016).Article 

Google Scholar 
11.Whaley, K., Cleaver, F. & Mwathuhga, E. World Dev. 138, 105286 (2021).Article 

Google Scholar 
12.Ahlers, R., Cleaver, F., Rusca, M. & Schwartz, K. Water Altern. 7, 1–14 (2014).
Google Scholar 
13.Woodhouse, P. et al. J. Peasant Stud. 44, 213–233 (2017).Article 

Google Scholar 
14.Zwarteveen, M. et al. Curr. Opin. Environ. Sustain. 49, 88–97 (2021).Article 

Google Scholar 
15.Boelens, R., Perreault, T. & Vos, J. Water Justice (Cambridge Univ. Press, 2018). More

HFFO performance evaluationFigure 1 shows the water flux and reverse salt flux (RSF) values of the HFFO element tested at varying (i) operating modes (a and b: FO; c and d: PAO), (ii) flow rates of the FS and DS, and (iii) DS concentrations. The results showed that the flow rate of each side influenced the performance of the element-scale HFFO (Fig. 1a, b). When the DS flow rate was increased from 0.20 to 0.35 L/min with different FS flow rates (0.7, 1.0, and 1.5 L/min), the overall water flux increased (maximum: 35,000 mg/L–1.05 to 1.24 liter per square meter per hour (LMH), minimum: 0.95–1.08 LMH at high DS concentration condition) (maximum: 35,000 mg/L–0.83 to 1.24 LMH at high DS concentration condition, minimum: 0.46–0.60 LMH at low DS concentration condition), although the effect of the FS flow rate was not dominantly than DS flow rate during the HFFO operation. This indicates that the DS flow rate affected the water flux more strongly than the FS flow rate may be due to the flow path diameter and the retention time in the HFFO element. In the HFFO element, the DS flow path was 85 μm (based on inner diameter), and this narrow flow path could significantly enhance the dilution in the channel per unit area (reducing the water flux) (referring Supplementary Tables 1 and 2). However, the FS flow path in the HFFO element did not exist (like a submerged type), and the membranes were packed in a PVC cell with a diameter of 90 mm and a length of 280 mm. Hence, when the DS and FS flow rates were 0.35 and 1.50 L/min, respectively, and a DS concentration of 35,000 mg/L was used, the highest water flux (1.24 L/m2h, LMH) was observed, which was approximately double the flux when the DS concentration was 10,000 mg/L. Interestingly, the overall RSF tendency was more affected by DS flow rates than FS flow rate (e.g., FS 0.7/DS 0.2: 0.0139 gram per square meter per hour (GMH) to FS 0.7/DS 0.35: 0.0266 GMH at 25,000 mg/L DS concentration). The RSF increased when the DS flow rate was increased, like the water flux pattern (refer to Supplementary Tables 1 and 2). However, the RSF tendency does not increase proportionally as well as the water flux tendency, and the fluctuation is relatively high26,32. It is a relatively small amount of salt mass transport phenomenon, which requires clear identification through future lab-scale experiments. In contrast, when the DS flow rate was increased from 0.20 to 0.35 L/min, the RSF value increased, whereas the RSF value decreased as the FS flow rate was increased over the entire range of the DS concentrations. The RSF showed a decreasing pattern with an increase in DS concentrations (from 10,000 to 35,000 mg/L). It should be noted that the HFFO element showed a relatively low water flux and RSF compared with the different types of FO elements. In previous studies, water fluxes of spiral-wound FO (SWFO) and plate-frame FO (PFFO) elements were 26.5 and 17.7 LMH, respectively. In addition, the RSF values were observed as 12.4 and 8.4 g/m2h (GMH), respectively, at a DS concentration of 35,000 mg/L26,28,33,36. However, at 35,000 mg/L, the HFFO showed 0.7–1.3 LMH of water flux (around 20 times less than that of SWFO and PFFO) and 0.005–0.030 GMH of RSF, which is much less than the other elements. Therefore, in the case of the HFFO element, the influence of process operating conditions is not serious, which indirectly shows that RSF consideration is not required for HFFO–RO–sHFFO process operation.Fig. 1: Water flux and RSF variation at various operation conditions without ionic strength in FS.Panels a, b show the water flux and RSF variation at the FO mode and panels c, d show the PAO mode. Concentration and pressure conditions: FO mode (DI water as FS, synthetic seawater (10,000 to 35,000 NaCl mg/L) as DS, and pressure of 0 bar) and PAO mode (DI water as FS, synthetic seawater (10,000 to 35,000 NaCl mg/L) as DS, and pressure of 2 and 3 bar). Flow rates: FO mode (FS: 0.7, 1.0, and 1.5 L/min and DS: 0.20 and 0.35 L/min) and PAO mode (FS: 0.7, 1.0, and 1.5 L/min and DS: 0.35 L/min).Full size imageA lower water flux can be overcome slightly by operating the FO in the PAO mode. As indicated in Fig. 1c, d showing the HFFO operation results in the PAO mode, when the FS and DS flow rates were increased from 0.7 to 1.5 and 0.2 to 0.35 L/min, respectively, with an applied pressure of 3 bar, the water flux was approximately double (from 1.39 to 2.33 LMH) that of the FO mode (without any applied pressure) under the same conditions (referring the black dot circle). With the addition of artificial pressure, the DS dilution rate was observed to be a maximum of 408% (35,000 mg/L, FS 1.5, DS 0.35 L/min, 2 bar) and a minimum of 131% (15,000 mg/L, FS 0.7, DS 0.35 L/min, 3 bar). Interestingly, when the DS concentration was similar to the seawater level (35,000 mg/L), the specific RSF (SRSF = RSF/water flux (g/L)) in the PAO mode was much lower than that in the FO mode (PAO = 0.008 g/L and FO = 0.018 g/L) under the same conditions (FS and DS flow rates = 1.50 and 0.35 L/min, respectively). This indicates that the HFFO operation in the PAO mode can be beneficial for stable water reuse with the pretreatment option for seawater desalination.Detailed water flux, RSF, SRFS values, DS dilution rate, and diluted DS conc. in the FO and PAO modes can be found in Supplementary Tables 1 and 2, respectively.Feasibility of sHFFOFor the characteristics (concept) of FO–RO–sHFFO desalination process, the sHFFO process was simulated under the HFFO operation in the PAO mode depending on the sea level (from the surface of the sea); various natural water pressures can be applied to the membrane by gravity, water density, and depth, and the sHFFO faced an inevitable difference in concentrations between the FS (seawater) and DS (RO brine). Therefore, during this experiment, the FS concentration was changed from 10,000 to 25,000 mg/L, the DS concentration was changed from 35,000 to 80,000 mg/L, and pressures ranging from 2 to 4 bar were applied to the FS side.Figure 2a, b shows the water flux and RSF values, respectively, depending on the concentration differences between the FS and DS (DS–FS) and the applied pressure to the FS. The water flux values increased continuously with increasing FS flow rates, applied pressures, and concentration differences. With the pressure of 4 bar, the highest water flux values obtained were 3.92, 1.04, and 1.21 LMH at the FS flow rates of 1.5, 1.0, and 0.7 L/min, respectively (DS flow rate = 0.35 L/min and concentration difference between FS and DS = 70,000 mg/L). However, the RSF values were relatively stable compared with those in the FO mode. This may be due to the applied pressure of the FS hindering the salt passage from the DS to the FS (RSF) during the HFFO operation. In addition, the applied pressure provided a positive effect on the performance, and as expected, when there was a variation in the FS and DS concentrations, the FS flow rate and applied pressure to the FS positively influenced the FO performance (i.e., water flux and RSF)37.Fig. 2: Water flux and RSF variation at various operation conditions with ionic strength in FS.Panels a, b show the water flux and RSF values of PAO mode HFFO element at the various concentration and pressure conditions. Synthetic seawater (NaCl) as FS, synthetic seawater or brine (NaCl) as DS, FS concentration of 10,000–35,000 mg/L, DS concentrations from 35,000 to 80,000 mg/L, and pressures of 2, 3, and 4 bar. Flow rates: FS: 0.7, 1.0, and 1.5 L/min, and DS: 0.35 L/min.Full size imageDilution effect of HFFO (seawater intake and brine management)Figure 3a, b presents the DS dilution rates and diluted DS concentrations according to the DS and FS flow rates and operation modes (FO and PAO) at the DS concentration of 35,000 mg/L. For the HFFO mode (Fig. 3a), the DS dilution rates were over 150 and 200% when the DS flow rates were 0.20 and 0.35 L/min, respectively. This difference occurred by changing the DS volume and permeation ratio (water flux) as the DS flow rate was changed (Supplementary Tables 1 and 2). Accordingly, the final diluted DS concentrations ranged from 16,000 to 23,000 mg/L, depending on the flow rate. However, when the pressure was applied to the FS side at a constant DS flow rate of 0.35 L/min and varied FS flow rates (0.7 to 1.5 L/min), the diluted DS concentrations decreased further to 11,000 and 9,600 mg/L (at operating pressures of 2 and 3 bar, respectively).Fig. 3: DS dilution rate and concentration at various operation conditions.Panels a, b show the DS dilution rate and concentration at the FO and PAO mode operation. Panels c, d show the DS dilution rate and concentration with varying concentration differences between FS and DS.Full size imageFigure 3c shows the dilution rate and diluted DS concentration depending on the differences between the FS and DS concentrations ranging from 50,000 to 70,000 mg/L, the FS flow rate, and the applied pressure. When the difference between the FS and DS concentrations was 50,000 mg/L with the operating conditions of FS flow rate = 0.70, DS flow rate = 0.35 L/min, and applied pressure = 2 bar, the diluted DS concentration and dilution rate were observed to be 34,000 mg/L and 146%, respectively. If the HFFO element is operated under the suggested conditions (i.e., sHFFO), the DS concentration can be equalized to that of the seawater. Therefore, this condition can be used to optimize (Case 7 in Table 1) the HFFO-based infinity seawater desalination process (FO–RO–sHFFO). With a difference in the concentrations across the membrane and the application of pressure to the FS (in PAO mode), various dilution rates and diluted DS concentrations were observed (Fig. 3c) as to the experiment of the condition where the concentration difference exists (Fig. 3b). This occurs because the external concentration polarization has a significant effect on the FO performance when differential concentrations are presented, and more significant internal concentration polarization occurs with a difference in concentration. With no difference between the FS and DS concentrations, when the FS and DS flow rates were 1.5 and 0.35 L/min, respectively, and a pressure of 3 bar was applied to the FS, a dilution rate of more than 400% dilution rate and a diluted DS concentration of approximately 8500 mg/L could be achieved (Figs. 2 and 3). However, when the difference between the FS and DS concentrations was 70,000 mg/L, approximately 350% of the dilution rate was enabled and the process could dilute the DS concentration to 22,580 mg/L (detailed water flux, RSF, and SRFS values can be found in Supplementary Tables 3). In addition, the expected operating pressures and permeate concentrations with the SWRO process after the HFFO process were simulated under various operating conditions in the cross-flow HFFO process (nine cases including a two-stage SWRO) and two different recovery rates in the RO process (50 and 60%) (Table 1). A total of nine cases, including a control (two-stage RO), were selected based on the HFFO element performance evaluation results under various operating conditions (Sections 1 and 2): four conditions in the FO mode (Cases 1–4) and four conditions in the PAO mode (Cases 5–8). The same operating conditions were applied to the HFFO and sHFFO elements in the HFFO-based infinity desalination process. Depending on the cases, the required pressure and final permeate concentration of the downstream SWRO process were predicted.Table 1 Operating pressure and permeate quality of SWRO for different cases (the cases were selected based on the performance test results, with a total of eight cases: four in FO mode and four in PAO mode, using a two-stage RO as the control) and a total plant recovery rate of 60%.Full size tableHowever, in the FO–RO–sHFFO desalination process, when the downstream two-stage SWRO process is operated at a recovery rate of 60%, the brine concentration is lower than that of the seawater, making the operation of the sHFFO process impossible. Therefore, for the two-stage SWRO process operated at a higher recovery rate (80%), at which the brine concentration discharged is approximately 60,000 mg/L, the operation pressure, permeate concentration, and specific energy consumption (SEC) value were recalculated, as shown in Table 2. In the two-stage SWRO, for Cases 1 and 2, the operating pressures of the SWRO calculated under such conditions were unacceptable. However, in Case 5, it was still possible to operate under lower pressure (37.9 bar) than with the two-stage SWRO process.Table 2 Operating pressure and permeate quality of SWRO for the different cases (selected based on the performance test results, with a total of eight cases: four in FO mode and four in PAO mode, using the two-stage RO as the control), with a total plant recovery rate of 80%.Full size tableThe detailed SEC values, operation pressures of the SWRO process, and the permeate concentrations at various recovery rates can be found in Supplementary Tables 4, 5, and 6.In the following section, an economic evaluation is described in terms of energy, comparing i) two-stage RO versus FO–RO-sHFFO and ii) SWRO with ZLD versus FO–RO-sHFFO.Energy evaluation (two-stage SWRO vs FO–RO–-sHFFO)To evaluate the economic benefits of the FO–RO–sHFFO process, the SEC of both the FO and RO processes were calculated, as shown in Fig. 4a, b. During the calculation, the plant capacity was assumed to be 100,000 m3/day. The pump efficiency and energy consumption were 90% and 0.1 kWh/m3, respectively. Owing to the structural characteristics of the element-scale HFFO process, the energy requirement of the FS pump is higher than that of the DS pump (Fig. 4a). Depending on the HFFO operating conditions (Table 1), the operating energy on the FO side also fluctuates, and the calculated SEC values of the RO process were different (Fig. 4b). Surprisingly, regarding the total SEC values when considering the energy requirement of both the FO and RO sections (Fig. 4c), the lowest energy requirement (1.49 kWh/m3) was observed in Case 5 (FS flow rate = 1.5 L/min, DS flow rate = 0.35 L/min, and applied pressure = 3 bar), and approximately 62% of energy was conserved compared with the two-stage RO process. Consequently, the energy costs based on the SEC value of the FO and RO were calculated (Fig. 4d). The operation period of the desalination plant was assumed to be 20 years. The cost results are similar to those of the SEC, and the FO–RO–HFFO can save approximately 66% of the cost compared with the two-stage RO process (two-stage RO = 280 million USD and FO–RO-HFFO process (Case 5) = 96 million USD). Furthermore, when the recovery rate was increased from 60 to 80%, the SEC value of the two-stage SWRO was increased to 6.02 kWh/m3. However, approximately 170 million USD is saved over the lifetime of the plant compared with the two-stage SWRO at a recovery of 60% (Fig. 4c and Supplementary Fig. S7).Fig. 4: Energy consumption values (SEC) compared with two-stage RO process at the different recovery rates.Panels a, b show the energy consumption values (SEC) compared with two-stage RO process at the different recovery rates. Panel c shows the total energy cost of the FO–RO–HFFO process compared with the RO process at 60 and 80% recovery rate.Full size imageThe amortized CAPEX of the FO–RO hybrid process was calculated based on Case 5 considering the installation/service, legal/professional, intake/outfall, pretreatment, piping/high alloy, civil engineering, pumps, pressure vessels, membranes, equipment/materials, and the design/professional costs. In the case of the HFFO, the incorporated desalination process, the costs of pretreatment, and the intake/outfall were excluded. This exclusion also results in significant CAPEX savings: approximately 15.8% (20 million USD) of the amortized total CAPEXRO and 1.2% (43 million USD) of the amortized total CAPEXFO in the HFFO-incorporated desalination process including the intake/outfall and pretreatment. Consequently, comparing the total cost of the HFFO-incorporated desalination process with the conventional FO–RO hybrid process based on the conditions and performance of Case 5, the FO–RO–sHFFO desalination process can save as much as 63 million USD during a 20-year period. Detailed data on the economic evaluation are presented in Supplementary Fig. 1.Economic and environmental impact evaluation (ZLD vs. brine circulation—no brine discharge)Conventional seawater desalination plants produce clean water, although high-salinity brine is also produced21,24. Depending on the recovery rate, the quality and quantity of the brine vary. In this section, an evaluation of the energy cost was conducted by comparing the HFFO-based infinity seawater desalination process with a two-stage SWRO combined with the ZLD process. The ZLD process can be defined to remove all liquid waste from the desalination process, reduce any harmful environmental effects, and meet the required regulations20. However, the HFFO-based infinity desalination process does not discharge the brine because the brine is recirculated (or diluted) through the HFFOs and then re-fed into the first HFFO process. Therefore, the HFFO-based infinity desalination process presents environmental cost benefits. As shown in Fig. 5, the energy cost of the two-stage SWRO with a brine concentrator and crystallizer was 1191 million USD. The resulting costs were calculated based on a 100,000 m3/day plant capacity and 60% recovery rate. In addition, the brine capacity (brine concentrator feed water) was 400,000 m3/day from the two-stage SWRO process, and the recovery rate of the brine concentrator was 80%. The inlet flow rate of the downstream crystallizer was 8000 m3/day and the recovery rate was assumed to be 100%. The driving force of the brine concentrator and crystallizer is heat energy, and the high energy consumption is required for thermal-based desalination methods (i.e., MED and MSF)). However, as mentioned in the previous section, the HFFO-based infinity desalination process does not require a circulation pump for the FS and DS to recover the brine to the seawater concentrations. Therefore, the HFFO-based infinity desalination process can save more than 1 billion USD in energy costs over a 20-year period.Fig. 5: Energy cost of two-stage SWRO with ZLD (brine concentrator-crystallizer) and FO-based desalination process (brine circulation process, no brine discharge).ZLD plant capacity = 40,000 m3/day (two-stage SWRO process recovery rate = 60%), energy consumption by brine concentrator = 19.8 kWh/m3 (recovery rate = 80%), and crystallizer = 56.8 kWh/m3 (recovery rate = 100%).Full size imageIf the recovery rate is fixed, the concentration and volume of the brine in the FO–RO–sHFFO process differ from those during the production of 100,000 m3/day for the stand-alone two-stage SWRO process. If the recovery rate is 60% in the stand-alone two-stage SWRO process, the concentration and flow rate of the brine can reach 87,500 mg/L and 66,667 m3/day, respectively. For the FO–RO–-sHFFO process, to achieve a final product volume of 100,000 m3/day, the SWRO can be operated at low pressures (25 bar) and a low inlet flow rate (46,519 m3/day) because the DS, which is diluted by the wastewater during the first HFFO process, can be fed into the SWRO process. However, for the second HFFO process (sHFFO) used in the FO–RO-sHFFO process (infinite circulation for zero brine discharge), the concentration of brine from the SWRO must be higher than that of the seawater for a sustainable operation. This means that the recovery rate of the SWRO process must be >60%. Therefore, an additional economic evaluation was conducted with a fixed capacity of the SWRO process, and it was found that reasonable conditions for the SWRO are as follows: recovery rate = 45%, influent = 222,222 m3/day, final product = 100,000 m3/day, operation pressure = 59.2 bar, and brine concentration = 63,636 mg/L. Considering a brine concentration suitable for the sHFFO process, a recovery rate of approximately 85% was recommended to achieve an optimal operation. In this case, the operating pressure is 37.9 bar, and the brine concentration and flow rate are 65,127 mg/L and 33,333 m3/day, respectively. Under modified conditions, the water production of the FO–RO–sHFFO process is approximately twice that of the stand-alone two-stage SWRO process. Detailed economic evaluation results can be found in Supplementary Fig. 1. More