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Increasing dependence of lowland populations on mountain water resources

Abstract

Mountain areas provide disproportionally high runoff in many parts of the world, but their importance for water resources and food production has not been clarified from the viewpoint of the lowland areas downstream. Here we quantify the extent to which lowland inhabitants potentially depend on runoff contributions from mountain areas (39% of the global land mass). We show that ~1.5 billion people (24% of the world’s lowland population) are projected to depend critically on runoff contributions from mountains by the mid-twenty-first century under a ‘middle of the road’ scenario, compared with ~0.2 billion (7%) in the 1960s. This striking rise is mainly due to increased local water consumption in the lowlands, whereas changes in mountain and lowland runoff play only a minor role. We further show that one-third of the global lowland area equipped for irrigation is currently located in regions that both depend heavily on runoff contributions from mountains and make unsustainable use of local blue water resources, a figure that is likely to rise to well over 50% in the coming decades. Our findings imply that mountain areas should receive particular attention in water resources management and underscore the protection they deserve in efforts towards sustainable development.

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Fig. 1: Analysis framework.
Fig. 2: Dependence on mountain water resources from 1961 to 2050.
Fig. 3: Spatial patterns in dependence on mountain water resources from 1961 to 2050.
Fig. 4: Lowland AEI under non-sustainable blue water use and depending on essential mountain runoff contributions in the 1960s, 2000s and 2040s.

Data availability

The following datasets were used in this study: hydrographic data from http://hydrosheds.org, elevation data from http://topotools.cr.usgs.gov/gmted_viewer/viewer.htm and https://doi.org/10.5066/F77P8WN0, population data from http://themasites.pbl.nl/tridion/en/themasites/hyde/download, hydroclimatic regions from https://doi.org/10.1594/PANGAEA.806957, water extraction data from http://www.fao.org/nr/water/aquastat/main and http://geodata.grid.unep.ch, lake delineations from http://www.worldwildlife.org/pages/global-lakes-and-wetlands-database, delta area delineations from http://www.globaldeltarisk.net/data.html, food production data from http://www.earthstat.org, and irrigated areas from http://mygeohub.org/publications/8. The data from the resulting typology (W) have been deposited in the Dryad Digital Repository85 (https://doi.org/10.5061/dryad.ns1rn8pnt).

Code availability

The R code for the pycnophylactic adjustment has been deposited in the Zenodo Digital Repository71 (https://doi.org/10.5281/zenodo.3563243).

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Acknowledgements

Part of this study was funded by a SNSF Fellowship for advanced researchers to D.V. (grant no. PA00P2_131464). M. Kummu was funded by the Academy of Finland funded projects SCART (grant no. 267463), WASCO (grant no. 305471) and WATVUL (grant no. 317320), the Emil Aaltonen Foundation funded project eat-less-water, the European Research Council under the European Union’s Horizon 2020 research and innovation programme (grant agreement no. 819202), and the Academy of Finland SRC project Winland. M. Kallio received funding from Maa- ja vesitekniikan tuki ry. We thank E. Sutanudjaja and N. Wanders for making the PCR-GLOBWB version 2.0 validation data available. For their roles in producing, coordinating and making available the ISIMIP model output, we thank the modelling groups (see Methods) and the ISIMIP coordination team.

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Contributions

D.V. designed the research together with M. Kummu, M.M. and Y.W. M.M. initiated some of the global-scale river basin typologies used in this paper. D.V. performed the main research. M. Kummu contributed analyses regarding EFRs, food production, irrigation and model efficiency and implemented the delta change method. Y.W. performed the global hydrological model runs and contributed data on the sustainability of blue water use. M. Kallio contributed the pycnophylactic adjustment and supplementary sensitivity analyses. D.V. interpreted the results and wrote the paper, with all coauthors providing comments.

Corresponding author

Correspondence to Daniel Viviroli.

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Extended data

Extended Data Fig. 1 Relief roughness and elevation classes used for distinguishing mountain and lowland areas.

The maps show the global mountain (a) and lowland (b) areas as defined in the Methods section, both excluding Greenland and Antarctica. Large lakes (>50 km²) are drawn here using their actual relief roughness (which is zero) rather than the assumed average roughness used for distinguishing mountain and lowland areas (Methods). The boundaries of catchments with an area of 100,000 km² and more are drawn for orientation.

Extended Data Fig. 2 Share of lowland population depending on mountain water resources 1961–2050.

Results are shown as fractions of the respective population totals (decadal averages), summarised for all hydrobelts (a) as well as differentiated by hydrobelt (bi). For absolute values see Fig. 2 in main text.

Extended Data Fig. 3 Causes of missing surplus from mountain areas as well as water balance of lowlands receiving no surplus from mountain areas 1961–2050.

Global results are shown for the time periods 1961–1970 (a, b), 2001–2010 (c, d) and 2041–2050 (e, f), magnifications for selected regions for 2041–2050 only (gj and kn). The boundaries of catchments with an area of 100,000 km² and more are drawn for orientation, and projections refer to the SSP2-RCP6.0 scenario.

Extended Data Fig. 4 Hot spot lowland regions potentially depending on mountain water resources 2041–2050, showing current lowland area equipped for irrigation (a, b) and lowland food production (c, d).

Beige denotes areas where mountains as per the definition used here occupy less than 5% of total catchment area, and an assessment from the viewpoint of lowlands should only be done carefully (shown in Supplementary Fig. 2). The boundaries of catchments with an area of 100,000 km² and more are drawn for orientation, and small catchments with an area of less than 10,000 km² are hatched in white. Results are from the SSP2-RCP6.0 scenario. See also Fig. 3 in main text.

Extended Data Fig. 5 Spatial patterns of disproportionality of mountain runoff 1961–2050.

Global results are shown for 2001–2010 (a), whereas magnifications of selected regions are shown for 1961–1970, 2001–2010 and 2041–2050 (bg). The boundaries of catchments with an area of 100,000 km² and more are drawn for orientation, and projections refer to the SSP2-RCP6.0 scenario. See also Extended Data Fig. 6 and Supplementary Information.

Extended Data Fig. 6. Temporal evolution of disproportionality of mountain runoff 1961–2050.

Results are shown as decadal averages, summarised for all hydrobelts (a) as well as differentiated by hydrobelt (bi). Projections refer to the SSP2-RCP6.0 scenario. See also Extended Data Fig. 5 and Supplementary Information.

Extended Data Fig. 7 Detailed breakdown of drivers causing changes in dependence on mountain water resources as projected for 2041–2050.

As a baseline, the bottom part of each circular plot represents the number of lowland inhabitants per category 2041–2050, assuming that runoff and total water consumption remain unchanged at 1961–1970 level. The corresponding population numbers are noted under the sectors in Panel a (for example, 968 M lowland inhabitants potentially depend on essential and sufficient contributions form mountain areas). The top part of the circular plot in Panel a shows results for 2041–2050 under changed conditions as projected with the SSP2-RCP6.0 scenario, and as shown in the main text. Panels b to e then each show results for changes in only one driver, namely mountain runoff (b), mountain water consumption (c), lowland runoff (d), and lowland water consumption (e), all referring to SSP2-RCP6.0. The changes in population per category are noted above each sector for all panels, giving the numbers reported in Table 2 of the main text.

Extended Data Fig. 8 Population, total water consumption, per-capita water consumption and runoff 1961–2050.

Results are shown individually at decadal scale for mountain and lowland areas (a, d, g, j) as well as differentiated by hydrobelt for mountain (b, e, h, k) and lowland (c, f, i, l) areas.

Extended Data Fig. 9 Lowland area equipped for irrigation (AEI) under non-sustainable blue water use and dependent on essential mountain runoff contributions in the 2000s and 2040s.

For 2041–2050, the SSP2-RCP6.0 scenario was used, and it was assumed that location and extent of AEI are identical to year 2005. All numbers are rounded off to the nearest integer.

Extended Data Fig. 10 Contribution of mountains to total catchment discharge (a) and ratio of mountain share in total catchment discharge to mountain share in total catchment area (b) 2001–2010.

In (a), a figure of 100% means that all discharge in a basin originates in its mountain area, and 0% that all discharge originates in its lowland area. In (b), a value of 1 means that mountains contribute as much to total catchment discharge as they do to total catchment area. Values above 1 denote disproportionally high discharge contributions from mountains, values below 1 disproportionally low discharge contributions from mountains. For both computations, results are shown for the time period 2001–2010 only since changes over the timeframe 1961–2050 are small. The boundaries of catchments with an area of 100,000 km² and more are drawn for orientation.

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Supplementary Figs. 1–10, Tables 1–7, discussion and methods.

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Viviroli, D., Kummu, M., Meybeck, M. et al. Increasing dependence of lowland populations on mountain water resources. Nat Sustain 3, 917–928 (2020). https://doi.org/10.1038/s41893-020-0559-9

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