Markus Andrews

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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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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

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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

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Devastating floods that struck Iran in 2017 were caused by a ‘sky river’ that ferried in water from hundreds or thousands of kilometres away — and that brought benefits, as well as destruction1.

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doi: https://doi.org/10.1038/d41586-021-03646-5

References1.Dezfuli, A., Bosilovich, M. G. & Barahona, D. Geophys. Res. Lett. https://doi.org/10.1029/2021GL095441 (2021).Article 

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2.Dezfuli, A. Bull. Am. Meteorol. Soc. 101, E394–E400 (2020).Article 

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