Markus Andrews

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MeteorologyWe reconstruct the meteorology at the South Col using observations from the automatic weather station (AWS) there (at 7945 m a.s.l)5 to downscale ERA5 reanalysis via a parsimonious blend of bias correction and machine learning. Initial screening indicates strong correlations between hourly ERA5 pressure level data bilinearly interpolated to South Col and air temperatures (r = 0.98), wind speed (r = 0.94), and relative humidity (r = 0.80) observed by the AWS. We therefore apply a simple empirical quantile mapping correction29 to remove systematic bias for these variables.Incident shortwave (SW) and longwave (LW) radiation are not available on ERA5 pressure levels, so we reconstruct them by downscaling the transmissivity (τ) and emissivity (α) of the atmosphere, defined:$$tau = {{{mathrm{SW/}}}}{Psi}$$
(1)
$${{{mathrm{and}}}};alpha = {{{mathrm{LW/}}}}sigma T_{{{mathrm{a}}}}^4$$
(2)
Where Ψ is the theoretical top of atmosphere solar radiation, σ is the Stefan Boltzmann constant (5.67 × 10−8 W m−2 K−4), and Ta is the 2 m air temperature (Kelvin). Observed values for τ and α are evaluated using AWS measurements of incident radiation and air temperature (using calculations of solar geometry to compute Ψ). We then train a Random Forest model (with 100 trees and a minimum leaf size of three) using Python’s Scikit Learn (version 0.20.1), modeling τ and α as a function of the ERA5 predictors in Methods Table 1. SW and LW could then be computed from:$${{{mathrm{SW}}}} = tau {Psi}$$
(3)
$${{{mathrm{LW}}}} = varepsilon T^4$$
(4)
Where T is the estimate from the bias-corrected ERA5 data.Table 1 Predictor variables used in the machine learning downscaling.Full size tableWe calibrate the bias correction and RF models using between 5012 (wind speed) and 12,810 (air temperature) overlapping hours of AWS observations and ERA5 data (May 2019 to December 2020). We evaluate the performance using a fivefold cross-validation, with results indicating very strong agreement between the observed and downscaled meteorology: hourly Pearson correlations range from 0.83 (relative humidity) to 0.98 (air temperature), translating respectively to root mean square errors between ~31 and 8% of the observed means (Supplementary Fig. 7). We also detect no sign of a seasonal dependence in the performance of bias correction and RF models (Supplementary Fig. 8). The resulting downscaled ERA5 data provide a complete annual series of hourly values for 1950–2019.We estimate precipitation at the South Col also using ERA5. First, we linearly interpolate the reanalysis data to the location of the Phortse AWS5 and then compute the ratio of the total observed precipitation (Po) and ERA5 precipitation (PE) during the overlapping period (April 2019-November 2020). We then multiply all reanalysis precipitation by this scalar to produce a corrected precipitation series (PE’) for Phortse 1950–2019:$$P_{{{mathrm{E}}}}^prime = frac{{P_{{{mathrm{o}}}}}}{{P_{{{mathrm{E}}}}}}P_{{{mathrm{E}}}}$$
(5)
To extrapolate to the South Col, we assume that precipitation decays exponentially with increasing elevation30. However, we recalibrate the regression using the Phortse and Basecamp AWSs because these new sites have weighing precipitation gauges protected by double alter shields5, and hence are less prone to under-catch error (Supplementary Fig. 9). Note that, as described below, the precipitation estimate is adjusted to an “effective” flux before being used to simulate glacier mass balance changes.Mass balanceWe use the precipitation, along with the other downscaled meteorological variables, to force the COSIPY model20 at hourly resolution for 1950–2019. First, we compute the effective precipitation (which implicitly includes the net effects of avalanching and wind transport, as well as correcting for any systematic bias in the downscaling/extrapolation method described above) required for the glacier to be in equilibrium for the period 1950–1959, iterating until the surface mass balance is zero. This is achieved when the precipitation is decreased by 65%. We then run two simulations with COSIPY. The first (referred to as the “snow” simulation) assumes a starting snowpack that is arbitrarily deep (20 m) to ensure that it remains present throughout the entire (70-year) simulation. We set the initial surface density of the snowpack to 350 kg m−3, the bottom density to 800 kg m−3, and linearly interpolate between. The second simulation (hereafter the “ice” simulation) uses the same effective precipitation but assumes no initial snowpack. However, snow is free to accumulate in the model in response to meteorological forcing. The algorithms and parameter values used in our application of COSIPY are outlined in Methods Table 2.Table 2 Parameterizations and parameter values used in the COSIPY model runs.Full size tableMeltThe surface melt rate depends on the surface energy balance (SEB):$$Q_{{{mathrm{h}}}} + Q_{{{mathrm{l}}}} + Q_{{{{mathrm{lw}}}}} + Q_{{{{mathrm{sw}}}}} + Q_{{{mathrm{g}}}} + Q_{{{mathrm{r}}}} – Q_{{{mathrm{m}}}} = 0$$
(6)
where Q denotes energy flux (W m−2) and the subscripts h, l, lw, sw, g, and r refer to the sensible, latent, net longwave radiative, net shortwave radiative, ground, and precipitation heat fluxes, respectively. The fluxes are defined as positive when directed towards the surface. The energy consumed in melting (Qm) is also defined as positive, meaning the melt rate (M; mm w.e. s−1 or kg s−1) can be calculated:$$M = frac{1}{{L_{{{mathrm{f}}}}}}mathop {sum}Hleft( {Q_{{{mathrm{m}}}},T_{{{mathrm{s}}}}} right)Q_i$$
(7)
in which H(Q,Ts) is a Heaviside function that returns a value of one unless both the sum of the first six terms in Eq. (6) (Qm = ∑Qi, with i indexing terms Qh to Qr) is positive and the surface temperature is also at the melting point; otherwise, it returns zero. The melt total over a period of ∆t seconds can then be expressed:$$M = Pfrac{{{Delta}t}}{{L_{{{mathrm{f}}}}}}{sum} {overline {Q_i} }$$
(8)
where P is the fraction of ∆t during which melting conditions occurred, and the overbar for energy component Qi indicates the mean value calculated during melting conditions. In terms of energy components Eq. (8) is the major driver of the amplification in melt totals between the snow and ice COSIPY simulations, increasing by a factor of 4.4; the energy sinks (sum of the remaining terms) amplify by a factor of 3.6 (Methods Fig. 3). The resulting amplification in mean melt rate (left( {acute{A} = frac{{left( {{sum} {overline {Q_i} } } right)_{{{{mathrm{ice}}}}}}}{{left( {{sum} {overline {Q_i} } } right)_{{{{mathrm{snow}}}}}}}} right)), though, is by almost a factor of 500. To understand this result, note that the proportional increase in melt rate can be written:$$acute{A} = frac{{koverline {Q_{{{{mathrm{sw}}}}}} – joverline {Q_{{{{mathrm{sinks}}}}}} }}{{overline {Q_{{{{mathrm{sw}}}}}} – overline {Q_{{{{mathrm{sinks}}}}}} }}$$
(9)
where (overline {Q_{{{{mathrm{sw}}}}}}) and (overline {Q_{{{{mathrm{sinks}}}}}}) are the mean energy gains and losses, respectively, during melt conditions in the snow simulation, and k and j are the proportional increases in these terms when transitioning to an ice surface (4.3 and 3.6, respectively). Critically, Eq. (9) reveals that (acute{A}) is inversely proportional to the baseline melt rate in the snow simulation (left( {overline {Q_{{{{mathrm{sw}}}}}} – overline {Q_{{{{mathrm{sinks}}}}}} } right)). The very low melt rate in the snow scenario (3.3 mm w.e. a−1), therefore acts to amplify the numerator of in Eq. (9).Sublimation and melt sensitivity to climate forcingTotal sublimation (S, mm w.e. or kg) can be written:$$S = rho ;U;C_{{{mathrm{e}}}}(e_{{{mathrm{s}}}} – e_{{{mathrm{a}}}}{Upsilon})frac{varepsilon }{{P_{{{mathrm{a}}}}}}{Delta}t$$
(10)
where ρ is the air density (kg m−3), U is the wind speed (m s−1), Ce is the turbulent exchange coefficient for moisture (dimensionless), ε is the ratio of gas constants for water vapor and air (0.622), Pa is air pressure (Pa), and Υ is relative humidity (fraction). The saturation vapor pressure for the surface (es) and the near-surface atmosphere (ea) are functions of the surface (Ts) and air temperature (Ta), respectively. If we assume that Ts = Ta (which is a reasonable simplification at the South Col where air temperature does not rise above 0 °C), and use the Clausius Clapeyron equation:$$e_{{{mathrm{s}}}} = e_0e^{frac{L}{{R_{{{mathrm{v}}}}}}left( { – frac{1}{{T_{{{mathrm{a}}}}}} + frac{1}{{273.15}}} right)}$$
(11)
in which e0 is the saturation vapor pressure at the melting point, L is the latent heat of sublimation (2.83 × 106 J kg−1), and Rv is the gas constant for moist air (461 J K−1); Eq. (10) becomes:$$S = rho ;U;C_{{{mathrm{e}}}}(1 – {Upsilon})e_0e^{frac{L}{{R_{{{mathrm{v}}}}}}left( { – frac{1}{{T_{{{mathrm{a}}}}}} + frac{1}{{273.15}}} right)}frac{varepsilon }{{P_{{{mathrm{a}}}}}}{Delta}t$$
(12)
which can be differentiated with respect to U, Ta, and Υ to explore the sensitivity of sublimation to changes in these meteorological parameters. In turn, the contribution of temporal trends (left( {frac{{{mathrm{d}}x}}{{{mathrm{d}}t}}} right)) in these variables to the trend sublimation can be evaluated via the chain rule:$$frac{{{mathrm{d}}S}}{{{mathrm{d}}t}} = frac{{partial S}}{{partial U}}frac{{{mathrm{d}}U}}{{{mathrm{d}}t}} + frac{{partial S}}{{partial Y}}frac{{{mathrm{d}}Y}}{{{mathrm{d}}t}} + frac{{partial S}}{{partial T_{{{mathrm{a}}}}}}frac{{{mathrm{d}}T_{{{mathrm{a}}}}}}{{{mathrm{d}}t}}$$
(13)
With:$$frac{{partial S}}{{partial U}} = rho ;C_{{{mathrm{e}}}};e_0left( {1 – {Upsilon}} right)e^{frac{L}{{R_{{{mathrm{v}}}}}}left( { – frac{1}{{T_{{{mathrm{a}}}}}} + frac{1}{{273.15}}} right)}frac{varepsilon }{{P_{{{mathrm{a}}}}}}{Delta}t$$
(14)
$$frac{{partial S}}{{partial T_{{{mathrm{a}}}}}} = L;C_{{{mathrm{e}}}};e_0;rho ;Uleft( {1 – {Upsilon}} right)frac{{e^{frac{L}{{R_{{{mathrm{v}}}}}}left( { – frac{1}{{T_{{{mathrm{a}}}}}} + frac{1}{{273.15}}} right)}}}{{R_{{{mathrm{v}}}}P_{{{mathrm{a}}}}T_{{{mathrm{a}}}}^2}}varepsilon {Delta}t$$
(15)
$$frac{{partial S}}{{partial {Upsilon}}} = – rho ;C_{{{mathrm{e}}}};e_0;Ue^{frac{L}{{R_{{{mathrm{v}}}}}}left( { – frac{1}{{T_{{{mathrm{a}}}}}} + frac{1}{{273.15}}} right)}frac{varepsilon }{{P_{{{mathrm{a}}}}}}{Delta}t$$
(16)
To evaluate Eq. (13) we compute the derivatives (Eqs. (14)–(16)) using the mean meteorology at the South Col during the ERA5 reconstruction (1950–2019), and ∆t to the number of seconds in 1 year (3.2 × 107 s). We prescribe the turbulent exchange coefficient (Ce) using the output from the COSIPY snow simulation, dividing the simulated sublimation by (rho ;U(e_{{{mathrm{s}}}} – e_{{{mathrm{a}}}}Upsilon )frac{varepsilon }{{P_{{{mathrm{a}}}}}}Delta t) (see Eq. (12)).Inserting these values into Eqs. (14) (16) yields:$$frac{{partial S}}{{dU}} = 6.0;{{{mathrm{mm}}}};{{{mathrm{w}}}}{{{mathrm{.e}}}}{{{mathrm{.}}}};{{{mathrm{a}}}}^{ – 1}{{{mathrm{m}}}}^{ – 1};{{{mathrm{s}}}}^1$$$$frac{{partial S}}{{d{Upsilon}}} = – 1.8;{{{mathrm{mm}}}};{{{mathrm{w}}}}{{{mathrm{.e}}}}{{{mathrm{.}}}};{{{mathrm{a}}}}^{ – 1}% ^{ – 1}$$$$frac{{partial S}}{{dT_{{{mathrm{a}}}}}} = 6.7,{{{mathrm{mm}}}},{{{mathrm{w}}}}{{{mathrm{.e}}}}{{{mathrm{.}}}},{{{mathrm{a}}}}^{ – 1},^circ {{{mathrm{C}}}}^{ – 1}$$We then estimate the time derivatives for Eq. (13) using the Theil-Sen slope estimation, yielding:$$frac{{{{d}}{U}}}{{{{d}}t}} = – 0.08;{mathrm{m}};{mathrm{s}}^{-1} ;{{{mathrm{decade}}}}^{ – 1};{{{mathrm{and}}}};frac{{partial S}}{{partial {U}}}frac{{{{d}}{U}}}{{{{d}}t}}; = ;0.05;{{{mathrm{mm}}}};{{{mathrm{w}}}}{{{mathrm{.e}}}}.;{{{mathrm{a}}}}^{ – 2}$$$$frac{{{{d}}{Upsilon}}}{{{{d}}t}} = – 0.6% ;{{{mathrm{decade}}}}^{ – 1};{{{mathrm{and}}}};frac{{partial S}}{{partial {Upsilon}}}frac{{{{d}}{Upsilon}}}{{{{d}}t}}; = ;0.11;{{{mathrm{mm}}}};{{{mathrm{w}}}}{{{mathrm{.e}}}}.;{{{mathrm{a}}}}^{ – 2}$$$$frac{{{{d}}T_a}}{{{{d}}t}} = 0.16;^circ {{{mathrm{C}}}};{{{mathrm{decade}}}}^{ – 1}{{{mathrm{and}}}};frac{{partial S}}{{partial T_{{{mathrm{a}}}}}}frac{{{{d}}T_{{{mathrm{a}}}}}}{{{{d}}t}}; = ;0.11;{{{mathrm{mm}}}};{{{mathrm{w}}}}{{{mathrm{.e}}}}{{{mathrm{.}}}};{{{mathrm{a}}}}^{ – 2}$$Summing these terms indicates a theoretical trend (left( {frac{{{mathrm{d}}S}}{{{mathrm{d}}t}}} right)) of 0.27 mm w.e. a−2, in reasonably close agreement with the 0.22 mm w.e. a−2 derived from the COSIPY snow simulation and reported in the main text. This theoretical analysis also indicates that 82% of the trend can be attributed to increasing air temperature (41%) and declining relative humidity (41%), with strengthening winds explaining the remaining 18%.An alternative (empirical) method to estimate the sensitivity of sublimation in the COSIPY snow simulation is to use multiple linear regression:$$S = alpha + beta _{{{mathrm{U}}}}U + beta _{{{mathrm{{Upsilon}}}}}{Upsilon} + beta _{T_{{{mathrm{a}}}}}T_{{{mathrm{a}}}}$$
(17)
Where the slope coefficients (βx) are linear approximations of the derivatives, relating the changes in the annual mean of the meteorological variables to the total annual sublimation. Performing the regression (Supplementary Fig. 11) lends support to the interpretation from the theoretical analysis above, attributing 49, 26, and 25% of the sublimation increase to the trends in air temperature relative humidity, and wind speed, respectively.In the main text, we highlight that sublimation and melt rates may differ in their response to climate forcing. To support this assertion, we repeat the sensitivity assessment above, evaluating the derivatives of the melt rate with respect to air temperature, wind speed, and relative humidity.We simplify the analysis by assuming that the proportion of time that the surface is melting (P) is constant (see Eq. (8)). Although physically incomplete, we note that there is no temporal trend in P for the COSIPY snow simulation (p  > 0.05 according to Seil-Then slope estimation). With this simplification, the sensitivity of the melt rate to changes in meteorological component x can then be written:$$frac{{{mathrm{d}}M}}{{{mathrm{d}}x}} = Pfrac{{{Delta}t}}{{L_{{{mathrm{f}}}}}}{sum} {frac{{partial underline {Q_i} }}{{partial x}}}$$
(18)
Wind speed, air temperature, and relative humidity appear in the expressions for the sensible, latent, and longwave heat fluxes (Qh, Ql, and Qlw, respectively):$$Q_h = rho ;U;c_{{{mathrm{p}}}};C_{{{mathrm{h}}}}(T_{{{mathrm{a}}}} – 273.15)$$
(19)
$$Q_l = rho ;U;C_{{{mathrm{e}}}};L_{{{mathrm{v}}}}({Upsilon}e_{{{mathrm{a}}}} – 611.3)frac{varepsilon }{{P_{{{mathrm{a}}}}}}$$
(20)
$$Q_{lw} = alpha sigma T_{{{mathrm{a}}}}^4 – 312.5$$
(21)
In which we have assumed melting conditions (Ts = 273.15, e0 = 611.3 Pa; Lv is the latent heat of vaporization [2.5 × 105 J kg−1], and the longwave thermal radiation emitted by the snow surface is 312.5 W m−2); cp is the specific heat content of the air (1004.7 J kg−1 K−1). It has been concluded31 that the incident longwave flux Qlw↓ in the Himalaya may be estimated from Υ and Ta:$$Q_{{{{mathrm{lw}}}}} downarrow = c_1 + c_2{Upsilon} + c_3sigma T_{{{mathrm{a}}}}^4 – 312$$
(22)
where the cx terms are empirically determined coefficients, whose value depends on cloudiness. Optimizing this expression for the South Col AWS, we found c1 = −17 (−168) W m−2, c2 = 0.73 (2.12) W m−2 %−1 and c3 = 0.57 (0.84) (dimensionless) for clear (cloudy) conditions.The derivative of these fluxes with respect to air temperature is then:$$frac{{partial Q_{{{mathrm{h}}}}}}{{partial T_{{{mathrm{a}}}}}} = rho ;U;c_{{{mathrm{p}}}};C_{{{mathrm{h}}}}$$
(23)
$$frac{{partial Q_{{{mathrm{l}}}}}}{{partial T_{{{mathrm{a}}}}}} = rho ;U;C_{{{mathrm{e}}}};L_{{{mathrm{v}}}};L;{Upsilon};varepsilon ;e_0frac{{e^{frac{L}{{R_{{{mathrm{v}}}}}}left( { – frac{1}{{T_{{{mathrm{a}}}}}} + frac{1}{{273.15}}} right)}}}{{R_{{{mathrm{v}}}}P_{{{mathrm{a}}}}T_{{{mathrm{a}}}}^2}}$$
(24)
$$frac{{partial Q_{{{{mathrm{lw}}}}}}}{{partial T_{{{mathrm{a}}}}}} = 4sigma c_3T_{{{mathrm{a}}}}^3$$
(25)
Note that all terms in Eqs. (23)–(25) are positive, outlining the physical basis for why melt rates should increase with rising air temperature32.The derivative of these fluxes with respect to relative humidity is:$$frac{{partial Q_{{{mathrm{l}}}}}}{{partial {Upsilon}}} = rho ;U;C_{{{mathrm{e}}}};L_{{{mathrm{v}}}};e_0e^{frac{L}{{R_{{{mathrm{v}}}}}}left( { – frac{1}{{T_{{{mathrm{a}}}}}} + frac{1}{{273.15}}} right)}frac{varepsilon }{{P_{{{mathrm{a}}}}}}$$
(26)
$$frac{{partial Q_{{{{mathrm{lw}}}}}}}{{partial {Upsilon}}} = c_2$$
(27)
Because all terms are positive in Eqs. (26) and (27), increases in relative humidity also drive increases in the melt rate.The derivatives of the sensible and latent heat fluxes with respect to wind speed are then:$$frac{{partial Q_{{{mathrm{h}}}}}}{{partial U}} = rho ;c_{{{mathrm{p}}}};C_{{{mathrm{h}}}}(T_{{{mathrm{a}}}} – 273.15)$$
(28)
$$frac{{partial Q_{{{mathrm{l}}}}}}{{partial U}} = rho ;C_{{{mathrm{e}}}};L_{{{mathrm{v}}}}frac{varepsilon }{{P_{{{mathrm{a}}}}}}(Ye_0e^{frac{L}{{R_{{{mathrm{v}}}}}}left( { – frac{1}{{T_{{{mathrm{a}}}}}} + frac{1}{{273.15}}} right)} – 611.3)$$
(29)
Because Ta is always less than 273.15 K during melt events at the South Col in the COSIPY snow simulation (Supplementary Fig. 12), (e_0;e^{frac{L}{{R_{{{mathrm{v}}}}}}left( { – frac{1}{{T_{{{mathrm{a}}}}}} + frac{1}{{273.15}}} right)}) must be less than 611.3 Pa. Hence, the last terms in Eqs. (28) and (29) are negative, and so increases in wind speed act to reduce the melt rate.In summary, then, theory indicates that rising air temperatures should accelerate both sublimation and melt rates (Eqs. (15) and (24)). However, increases in wind speed and relative humidity will have opposite effects. Due to the persistence of freezing air temperatures during surface melt events, faster winds act to enhance sublimation (Eq. (14)) but reduce melting (Eqs. (28) and (29)), whereas increasing relative humidity amplifies melting (Eqs. (26) and (27)) but dampens sublimation (Eq. (16)). More

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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

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