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    Three-dimensional open architecture enabling salt-rejection solar evaporators with boosted water production efficiency

    Evaporation structure design and fabricationFor conventional salt-rejection solar evaporation systems, water evaporation is confined to the solar absorber surface, and the salt backflow is accompanied by an undesired heat dissipation from the solar absorber to bulk water, thus resulting in a low evaporation rate. This limitation can be solved to a considerable extent by our 3D evaporator. As illustrated in Fig. 1a, the top surface of our evaporator is a solar absorber layer used for light-to-heat conversion to generate vapour. Beneath the solar absorber are a number of vertically aligned MTBs connecting the saline water to the solar absorber. MTBs have hydrophilic microchannels that can pump saline water to the solar absorber via a capillary force. Furthermore, excessive salt can flow back into the bulk water through these brine-filled microchannels via diffusion and convection (Fig. 1b-1). The adequate mass transfer via a high density of MTBs ensures a continuous water supply and an efficient salt backflow, thus enabling a unique salt rejection capability. Unlike conventional salt-rejection systems, where the heat conducted from the solar absorber to the bulk water is simply dissipated and considered “wasted,” the MTBs can efficiently recover this conductive heat to generate additional vapour from the brine flowing through their microchannels (Fig. 1b-2). The microchannels within the MTBs and macrochannels between the spaced MTBs together form a highly open structure that allows the generated vapour to be easily released from the MTB surfaces in all directions. We envision that by optimizing the MTB height, conductive heat can be largely confined in them for vapour generation, thereby significantly improving the water evaporation efficiency.Fig. 1: Design and fabrication of the 3D salt-rejection evaporation structure.a Schematic of the 3D salt-rejection solar evaporator. b Working principle includes salt rejection and evaporation enhancement. c UV–Vis–NIR spectra of the GFM, CNT-coated GFM, and standard solar irradiation spectrum of AM 1.5 G. d SEM image of the CNT-coated GFM surface. e SEM image of the GFM. f Image of the water drop hanging above the GFM and the moment it touches the GFM surface. g Anti-gravity transport of water along a GFM. h 3D salt-rejection evaporator prototype. i Schematic illustration of the fabricating process of the evaporator.Full size imageWe achieved the designed structure by fabricating the top solar absorber layer by loading carbon nanotubes (CNTs) with a diameter of about one hundred nanometres on a glass fibre membrane (GFM). The solar absorption of wet CNT-coated GFM can reach ~96% (Fig. 1c) because of the porous fibrous light-trapping structure (Fig. 1d) and the inherent black property of the CNT27. Considering their abundant hydrophilic microchannels formed by intertwined glass fibres (Fig. 1e), the GFMs were also selected for use as MTBs. A GFM can immediately absorb a water droplet upon touching it because of its high affinity to water (Fig. 1f). Moreover, vertically aligned GFMs (i.e., MTBs) can pump water to 25 cm height in 60 min, demonstrating its strong capillary force for water transfer (Fig. 1g). A complete evaporation system was fabricated by assembling a number of MTBs and the solar absorber in a plastic frame (Fig. 1h, 1i and Fig. S1).Salt rejection capabilityTo avoid salt crystallization, excess salt must be efficiently transported back to maintain the top surface salinity below the saturation point. In this system, salt can be rejected via diffusion and convection through brine-filled microchannels under the driving force of the concentration gradient (osmosis) and gravity25. Its mass flow rate ((J)) can be described by the diffusion–convection equation as follows28,29:$$J={J}_{{diff}}+{J}_{{conv}}={nA}varepsilon ({k}_{d}({C}_{{evp}}-{C}_{0})/l+{k}_{c}({rho }_{{evp}}-{rho }_{0}))$$
    (1)
    where ({J}_{{diff}}) and ({J}_{{conv}}) are the mass flow rate caused by diffusion and convection, respectively; (n) is the number of MTBs; (A), (varepsilon), and (l) are the cross-section area, porosity, and height of the MTBs, respectively; ({k}_{d}) and ({k}_{c}) are the diffusion and average convective coefficients of salt, respectively; ({C}_{{evp}}) and ({C}_{0}) are the salt concentrations on the evaporation surface and in the bulk saline water, respectively; and ({rho }_{{evp}}) and ({rho }_{0}) are the salt solution densities on the evaporation surface and in the bulk saline water, respectively.In Eq. (1), the mass transport rate is proportional to the bridge number (n). We validated this relation by fabricating MTB structures with different bridge numbers ranging from 2 to 32 [Fig. 2a, cross-section area ((A)): ~0.135 cm2; height ((l)): 3 cm; porosity ((varepsilon)): ~65%] and evaluating their evaporation performance using high-salinity water (10 wt.% NaCl). The evaluation was performed under 1 sun illumination for 12 h. Figure 2b shows that salt crystals massively accumulated on the 2-bridge evaporator surface because of insufficient mass transfer. This salt accumulation was mitigated with increase in the bridge number. For the evaporator containing 32 MTBs, no salt crystals were observed on the surface after the 12 h operation (Fig. 2b). At an insufficient number of MTBs (≤16), the evaporation rate gradually decreased as the vapour generation progressed because of the increased evaporation surface salinity (Fig. 2c, see the corresponding mass change curves in Fig. S2). In contrast, with sufficient MTBs (e.g., 32 bridges), the excess salt can be efficiently rejected to maintain the evaporation surface at a relatively low salinity. Remarkably, the evaporation rate of the 32-bridge evaporator was ~1.44 kg/m2/h without degradation during the 12 h operation.Fig. 2: Salt rejection performance.a Photograph of evaporators with various bridge numbers (bridg height: 3 cm). b Photographic recordings of the salt accumulation on the 3D evaporators with different MTB numbers. c Evaporation rate variations of evaporators during long-term operations. d Photos of salt redissolving from the surface of a 32-bridge evaporator.Full size imageSubsequently, we performed a complementary experiment to more intuitively demonstrate the salt backflow introduced by the 32-bridge evaporator. In this experiment, the evaporator was placed in a high-concentration saline water (10 wt.% NaCl solution) and exposed to 1 sun illumination, and 1 g of NaCl salt was added on its surface (upper panel, Fig. 2d). It was seen that during vapour generation, the added salt was gradually dissolved and completely removed in 11 h (lower panel, Fig. 2d; more details in Fig. S3). This experiment demonstrated that the salt backflow rate of the 32-bridge evaporator in the 10 wt.% NaCl solution was higher than the salt generation rate, thus confirming the salt rejection feature of the proposed MTB architecture. We further increased the brine salinity to test the maximum applicable salt concentration of this evaporator. Because the effects of diffusion and convection backflow decreased as the salinity (i.e., ({C}_{0}) and ({rho }_{0})) increased, salt started to crystallize at the edges of the solar absorber after 12 h operation when 14 wt.% NaCl solution was used for the test (Fig. S4). Based on the corresponding evaporation rate, the salt backflow along the MTBs was calculated as ~1.1 g/cm2/h. Interestingly, this unique mass transport feature is intertwined with its heat transport feature, as demonstrated in the subsequent section.Heat managementWe fabricated 32-bridge evaporators with different bridge heights (Fig. 3a) and evaluated their evaporation performance. Under dark conditions, the evaporator without MTBs (i.e., bridge height: 0 cm) exhibited a natural evaporation rate of 0.15 kg/m2/h, which became more pronounced with the incorporation of MTBs due to the increased surface area (Fig. S5). Specifically, it linearly increased by ~0.04 kg/m2/h for every 1 cm increase in the MTB height. Under 1 sun illumination, the evaporation rate of the evaporator without MTBs was only 0.99 kg/m2/h because of the massive conductive heat dissipation to the bulk water (Fig. 3b, see the mass change curves in Fig. S6). The MTB usage considerably promoted solar evaporation. The evaporation rate increased to 1.58–1.73 kg/m2/h when the bridge height reached 2–5 cm (Fig. 3b). These values are even higher than the theoretical upper limit for solar evaporation (~1.44 kg/m2/h, Supplementary Note 1 and Fig. S7), which can be attributed to the natural evaporation contribution (Fig. S8). When the MTB height exceeded 3 cm, the evaporation rate increased by ~0.04 kg/m2/h for every 1 cm increase in MTB height (Fig. 3b), which was consistent with the result obtained under dark conditions. This consistency suggests that the 3 cm height is sufficient for the MTB structure to maximize solar evaporation (note that additional increase in MTB height only increases natural evaporation). To reveal the mechanism of this observation, we analyzed the heat transport in this unique architecture.Fig. 3: Evaporation performance, heat management, and stability evaluation.a Photograph of evaporators with various bridge heights (bridge number: 32). b Evaporation rate of evaporators with different MTB heights under 1 sun illumination (error bar type: standard deviation). c Internal temperature variation at different distances from the solar absorber. d Demonstration of the bulk water temperature after 3 h operation with different evaporators. e Photograph of the enclosed evaporator after 3 h evaporation. f Mass change curves and evaporation rate during the cycling experiment.Full size imageThe energy loss channels for this evaporation system primarily include conductive heat loss into the bulk water (({P}_{{cond}.})), radiative heat loss (({P}_{r{ad}.})), and convective heat loss to the environment (({P}_{{convec}.})). Therefore, the power flux available for evaporation (({P}_{{evp}})) can be described as follows16:$${P}_{{evp}}={P}_{{solar}}-{P}_{{cond}.}-{P}_{{rad}.}-{P}_{{convec}.}$$
    (2)
    where the solar energy input ({P}_{{solar}}={{{{{rm{alpha }}}}}}{C}_{{opt}}{q}_{i}); ({{{{{rm{alpha }}}}}}) is the light absorption coefficient; ({C}_{{opt}}) is the optical concentration; and ({q}_{i}) is the direct solar illumination. The conductive heat flux ({P}_{{cond}.}=k({T}_{{sa}}-{T}_{{bw}})/l), where (k) is the thermal conductivity; ({T}_{{sa}}) and ({T}_{{bw}}) are the temperatures of the solar absorber and the bulk water, respectively; and (l) is the heat conduction path referring to the MTB height in our model. The radiative heat flux ({P}_{{rad}.}=varepsilon {{{{{rm{sigma }}}}}}({{T}_{1}}^{4}-{{T}_{2}}^{4})), while the convective heat flux ({P}_{{convec}.}=hleft({T}_{1}-{T}_{2}right),) (varepsilon) is the optical emission, ({{{{{rm{sigma }}}}}}) is the Stefan–Boltzmann constant, (h) is the convection heat transfer coefficient, and ({T}_{1}) and ({T}_{2}) are the temperatures of the evaporator and environment, respectively.The energy loss caused by the heat transfer from the top surface to the bulk water (i.e., ({P}_{{cond}.})) can be minimized by increasing the MTB height (i.e., (l)) to confine the conductive heat within the MTB structure. This effect was visualized using infrared imaging to display the temperature gradients along MTBs with different heights. The results showed that the temperature at the bottom of the evaporator was similar to the ambient temperature when the MTB height reached or exceeded 3 cm (Fig. S9). This temperature distribution agreed well with the simulation modelled by COMSOL (Fig. S10). To obtain more insights into the heat transport in the architecture, we carefully recorded the internal temperature variation at different distances to the solar absorber under solar illumination. The results showed that the temperature stabilized after 60 min, when the internal temperature at 3 cm to the solar absorber was similar to that of the surrounding environment (Fig. 3c), indicating that the conductive heat was completely confined in the top 3 cm of the MTB structure. This confinement effect was also demonstrated by the temperature change of the bulk water (Fig. 3d): for the evaporator without MTBs, the bulk water temperature increased from ~21 to ~26.2 °C after a 3 h operation due to the continuous heat input (left panel); for the evaporator with 3-cm MTBs, however, the bulk water temperature was maintained at room temperature (~21.3 °C) (right panel), thus confirming the suppression of heat dissipation into the bulk water.Importantly, the confined heat energy can be exploited to generate additional vapour from the MTB surfaces, which can be efficiently released via the highly open interbridge spaces. To reveal this additional vapour generation from the vertical surfaces of MTBs, we used an evaporator having 32 MTBs (3 cm high) to perform a control experiment. In this experiment, the evaporator body was enclosed with an airtight polypropylene film, thus leaving only the upper surface exposed to the open space for vapour release (Fig. S11). After a 3 h operation, many water droplets condensed on the inner film surface, thus confirming that the MTBs released vapour (Fig. 3e). Compared to the completely open evaporator, the evaporation rate of the partially enclosed system decreased by ~31% (Fig. S12), demonstrating the importance of the open-channel design for enhanced interfacial evaporation.Furthermore, we performed a cycling experiment to evaluate the evaporator stability. In each cycle, the evaporator ran for 12 h under 1 sun illumination and in a dark environment for another 12 h to simulate day and night alternation. Figure 3f shows that during this long-term test (with 10 wt.% NaCl solution), the mass change of the NaCl solution in each cycle linearly evolved and the evaporation rate stabilized at ~1.44 kg/m2/h. No performance degradation was observed after a seven-day cycling experiment.Compared with the previously reported salt-rejection evaporators (evaporation rate: from 1.24 to 1.28 kg/m2/h for 10 wt.% NaCl solution)9,26,30, our evaporator demonstrated a higher evaporation rate under similar conditions due to the heat confinement effect and the natural evaporation contribution. However, high evaporation efficiency alone is not sufficient for water production applications. If the evaporated moisture is not collected, it can only be considered as a pollutant to the environment considering that it has the greatest greenhouse effect among various components in the atmosphere31. Water collection that is equally important as vapour generation has been largely ignored in many previous studies on salt-rejection evaporators.Therefore, we enclosed the evaporator with a transparent cover made of polymethyl methacrylate (PMMA) plates, creating a system that can produce water by condensing the evaporated moisture, and investigated the effects of bridge number and bridge height on the water production capacity of this system (Fig. S13a). When the bridge height was fixed at 3 cm, the amount of collected water increased with the number of bridges (Fig. S13b), which is consistent with the observation in the open system, confirming that the enhanced salt backflow facilitates water evaporation. When the bridge number was fixed at 32, the amount of collected water increased with the bridge height and reached the maximum at 3 cm, while further increasing the bridge height did not produce more water (Fig. S13c). This result is consistent with the conclusion above that 3 cm is sufficient to confine the conductive heat while further increasing bridge height only increases natural evaporation that does not contribute to water production. According to the three-hour test results, the water production rate of the enclosed evaporator in the optimal configuration (32 bridges; 3 cm high) is calculated to ∼0.68 kg/m2/h (Fig. S13).We also investigated the water generation performance of the enclosed system under different salinity conditions using NaCl solutions (3.5−20 wt.%). The results showed that the water production efficiency monotonically decreased from ~0.73 kg/m2/h for 3.5 wt.% NaCl solution to ~0.63 kg/m2/h for 20 wt.% NaCl solution (Fig. S14a). The relatively low water production efficiency associated with the high-salinity brines is mainly due to their low saturated vapour pressure, partly due to the decreased photothermic conversion efficiency caused by salt precipitation. For instance, when using brine containing 20 wt.% NaCl, salt precipitation emerged at the periphery of the evaporator after three hours of testing (Fig. S14b).Field testsAs per the recently announced “best practice for solar water production”32, the daily water yield is an important evaluation criterion that deserves additional consideration in practical implementations. Therefore, we prepared closed system based on the MTB structure and measured their water generation capacity under practical outdoor conditions.Rooftop experimentThe fabricated solar-driven water generation system has a 15 × 26 cm2 evaporator area (see Fig. S15). We first tested the system on the rooftop in KAUST, Thuwal, Saudi Arabia (Fig. S16). In this experiment, we employed the discharged water from an RO system of the KAUST Seawater Desalination Plant as the source water (salinity: ~8.7%). Our daily evaluation started at 8:00 and ended at 17:00. As shown in Fig. 4a, the evaporator surface was heated by solar light to a temperature 4–15 °C higher than the environment. However, the temperature at the bridge bottom was almost the same as the environment temperature, indicating that the conductive heat was confined, with only a small amount transferred to the bulk water. Consequently, saline water can be efficiently evaporated and condensed at the cover surface for the water collection. Figure 4b and Supplementary Movie 1 illustrate the relevant details. The total collected water was ~175 ml, of which ~110 ml flowed in the graduated cylinder, and ~65 ml was retained in the PMMA cover. Based on the evaporator area (390 cm2), the daily water productivity was calculated as ~5.0 L/m2. We measured the ion contentions of our water samples to evaluate the water quality. Compared with the discharged water from the RO plant, the ion concentration of condensed water was reduced by at least four orders of magnitude, thus fully meeting the WHO drinking water requirements (Fig. 4c).Fig. 4: Field tests.a Real-time temperature variation of the solar absorber, environment, bottom of bridges and bulk water, and solar flux from 8:00 to 17:00 on Apr. 11, 2022. b Timelapse photos of the collected water in the graduated cylinder from 8:00 to 17:00. c Ion concentration in the effluent water collected from the RO facility and collected freshwater from our system. d Daily water generation, solar insolation, and solar–water collection efficiency from Apr. 7 to 11, 2022. e Photograph of the evaporator after five-day operation. f Photograph of the floating system for the ocean test. g Schematic illustration of the structure of the floating system. h Daily water collection, solar insolation, and solar–water efficiency during the ocean test from Apr. 17 to 21, 2022.Full size imageWe calculated its practical solar–water collection efficiency of the system, ({eta }_{{prac}}), using Eq. (3):$${eta }_{{prac}}={m}_{{cond}}{h}_{{lv}}/left({A}_{{evp}}int {q}_{{solar}}left(tright){dt}right)$$
    (3)
    where ({m}_{{cond}}) is the daily water collection amount; ({h}_{{lv}}) is the total enthalpy of the liquid–vapour phase transition; ({A}_{{evp}}) is the evaporator area; and ({q}_{{solar}}) is the time-dependent solar flux. Benefiting from the highly efficient vapour generation, the overall solar–water collection efficiency of our system reached ~41.6%, representing a considerable improvement compared to the previously reported salt-rejection solar evaporation systems (e.g., maximum efficiency of a rooftop system: ~24%25). We performed a continuous test from Apr. 7 to Apr. 11, 2022 to evaluate the performance stability (Fig. 4d). The daily water collection rate fluctuated in the range of 4.7–5.2 L/m2 depending on the specific solar insolation of the day. The corresponding solar–water collection efficiency was 39%–42%. Remarkably, no salt accumulation was observed during this five-day outdoor operation (Fig. 4e). These results demonstrate the potential of the fabricated evaporator to extract freshwater from the wastewater discharged by RO plants.Floating testAfter the 5-day rooftop experiment, the same MTB-based evaporation system was tested in a floating configuration in the Red Sea (salt content: ~4.3%) to demonstrate its potential for practical seawater desalination (Fig. 4f, g). The test started and ended at 8:00 and 17:00, respectively, each day and lasted for five days from Apr. 17 to Apr. 21, 2022. As shown in Fig. 4h, the daily freshwater productivity ranged from 5.0 to 5.8 L/m2 with a stable solar–water collection efficiency of 42%–45%, which was consistent with the rooftop test. This freshwater productivity was approximately two times higher than the previous record of the salt-rejection solar evaporator (~2.5 L/m2 per day)25. The field test demonstrated a high-performance solar evaporator that will help in disaster relief or strengthen the resilience of individuals living on boats and coastal areas. More

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    Large and inequitable flood risks in Los Angeles, California

    Smith, A. B. U.S. Billion-dollar Weather and Climate Disasters, 1980–Present (NCEI, 2020); https://doi.org/10.25921/stkw-7w73National Academies of Sciences, Engineering, and Medicine Framing the Challenge of Urban Flooding in the United States (National Academies Press, 2019).Rainey, J. L., Brody, S. D., Galloway, G. E. & Highfield, W. E. Assessment of the growing threat of urban flooding: a case study of a national survey. Urban Water J. 18, 375–381 (2021).Article 

    Google Scholar 
    Gall, M., Borden, K. A., Emrich, C. T. & Cutter, S. L. The unsustainable trend of natural hazard losses in the United States. Sustainability 3, 2157–2181 (2011).Article 

    Google Scholar 
    Zhang, W., Villarini, G., Vecchi, G. A. & Smith, J. A. Urbanization exacerbated the rainfall and flooding caused by hurricane Harvey in Houston. Nature 563, 384–388 (2018).Article 
    CAS 

    Google Scholar 
    Davenport, F. V., Burke, M. & Diffenbaugh, N. S. Contribution of historical precipitation change to US flood damages. Proc. Natl Acad. Sci. USA 118, e2017524118 (2021).Article 
    CAS 

    Google Scholar 
    Hino, M. & Nance, E. Five ways to ensure flood-risk research helps the most vulnerable. Nature 595, 27–29 (2021).Article 
    CAS 

    Google Scholar 
    Bullard, R. D. & Wright, B. The Wrong Complexion for Protection: How the Government Response to Disaster Endangers African American Communities (New York Univ. Press, 2012).Chambliss, S. E. et al. Local- and regional-scale racial and ethnic disparities in air pollution determined by long-term mobile monitoring. Proc. Natl Acad. Sci. USA 118, e2109249118 (2021).Article 
    CAS 

    Google Scholar 
    Chakraborty, J., Collins, T. W. & Grineski, S. E. Exploring the environmental justice implications of Hurricane Harvey flooding in Greater Houston, Texas. Am. J. Public Health 109, 244–250 (2019).Article 

    Google Scholar 
    Siders, A. R. & Keenan, J. M. Variables shaping coastal adaptation decisions to armor, nourish, and retreat in North Carolina. Ocean Coast. Manag. 183, 105023 (2020).Article 

    Google Scholar 
    Wing, O. E. J. et al. Inequitable patterns of US flood risk in the Anthropocene. Nat. Clim. Change 12, 156–162 (2022).Article 

    Google Scholar 
    Finch, C., Emrich, C. T. & Cutter, S. L. Disaster disparities and differential recovery in New Orleans. Popul. Environ. 31, 179–202 (2010).Article 

    Google Scholar 
    WMO Atlas of Mortality and Economic Losses from Weather, Climate and Water Extremes (1970–2019) (World Meteorological Organization, 2021).Brakenridge, R. Global Active Archive of Large Flood Events, 1985–Present (Dartmouth Flood Observatory, 2021); https://floodobservatory.colorado.edu/Archives/index.htmlTate, E., Rahman, M. A., Emrich, C. T. & Sampson, C. C. Flood exposure and social vulnerability in the United States. Nat. Hazards 106, 435–457 (2021).Article 

    Google Scholar 
    Porter, K. et al. Overview of the ARkStorm Scenario (USGS, 2011); https://pubs.usgs.gov/of/2010/1312/Ralph, F. M., Dettinger, M. D., Cairns, M. M., Galarneau, T. J. & Eylander, J. Defining “atmospheric river”: how the glossary of meteorology helped resolve a debate. Bull. Am. Meteorol. Soc. 99, 837–839 (2018).Article 

    Google Scholar 
    Jones, L. M. The Big Ones: How Natural Disasters have Shaped Us (and What we can do About Them) (Anchor Books, 2019).Population Estimates for Los Angeles County for July 1, 2021 (U.S. Census Bureau, accessed 1 February 2022); https://www.census.gov/quickfacts/losangelescountycaliforniaRegional Data, GDP and Personal Income for Los Angeles, CA (U.S. Bureau of Economic Analysis, accessed 1 February 2022); https://apps.bea.gov/itable/iTable.cfm?ReqID=70&step=1&acrdn=5Orsi, J. Hazardous Metropolis: Flooding and Urban Ecology in Los Angeles (Univ. of California Press, 2004).Wing, O. E. J. et al. Validation of a 30 m resolution flood hazard model of the conterminous United States. Water Resour. Res. 53, 7968–7986 (2017).Article 

    Google Scholar 
    Bates, P. D. et al. Combined modeling of US fluvial, pluvial, and coastal flood hazard under current and future climates. Water Res. 57, e2020WR028673 (2021).Sheng, J. & Wilson, J. P. Watershed urbanization and changing flood behavior across the Los Angeles metropolitan region. Nat. Hazards 48, 41–57 (2009).Article 

    Google Scholar 
    Hydraulics Report. Floodplain Analysis, Los Angeles River: Barham Boulevard to First Street. Flood Plain Management Services Special Study. Los Angeles, California (U.S. Army Corps of Engineers, 2016); https://eng2.lacity.org/projects/LARIVER_Glendale_Narrows/docs/LAR_FPMS_Hydraulic_Report_FINAL_October2016_CompleteDocument.pdfLevee Certification Program (Los Angeles County Department of Public Works, accessed 1 February 2022); https://dpw.lacounty.gov/wmd/nfip/dsp_LeveeCertificationFAQs.aspxLevee Safety Program. Inspection Summaries for the Los Angeles River, San Gabriel River, Rio Hondo Channel, and Compton Creek (US Army Corps of Engineers, 2022); https://www.spl.usace.army.mil/Missions/Civil-Works/Levee-Safety-Program/Engineering and Design, Safety of Dams—Policy and Procedures (US Army Corps of Engineers, 2011).Kahl, D. T., Schubert, J. E., Jong-Levinger, A. & Sanders, B. F. Grid edge classification method to enhance levee resolution in dual-grid flood inundation models. Adv. Water Res. 168, 104287 (2022).Article 

    Google Scholar 
    County of Los Angeles Open Data (County of Los Angeles, accessed 1 February 2022); https://data.lacounty.gov/American Community Survey 5-Year Data (2009–2019): Detailed Tables (U.S. Census Bureau, 2020); https://www.census.gov/data/developers/data-sets/acs-5year.htmlMessager, M. L., Ettinger, A. K., Murphy-Williams, M. & Levin, P. S. Fine-scale assessment of inequities in inland flood vulnerability. Appl. Geogr. 133, 102492 (2021).Article 

    Google Scholar 
    Dorfman, R. A formula for the Gini coefficient. Rev. Econ. Stat. 61, 146 (1979).Article 

    Google Scholar 
    Mach, K. J. et al. Managed retreat through voluntary buyouts of flood-prone properties. Sci. Adv. 5, eaax8995 (2019).Article 

    Google Scholar 
    Lehmann, M., Major, D. C., Fitton, J. M., Doust, K. & O’Donoghue, S. Towards a typology for coastal towns and small cities for climate change adaptation planning. Ocean Coast. Manag. 212, 105784 (2021).Article 

    Google Scholar 
    Sanders, B. F. & Grant, S. B. Re‐envisioning stormwater infrastructure for ultrahazardous flooding. WIREs Water 7, e1414 (2020).Markhvida, M., Walsh, B., Hallegatte, S. & Baker, J. Quantification of disaster impacts through household well-being losses. Nat. Sustain. 3, 538–547 (2020).Article 

    Google Scholar 
    Shi, L. From Progressive cities to resilient cities: lessons from history for new debates in equitable adaptation to climate change. Urban Aff. Rev. 57, 1442–1479 (2021).Article 

    Google Scholar 
    Domingue, S. J. & Emrich, C. T. Social vulnerability and procedural equity: exploring the distribution of disaster aid across counties in the United States. Am. Rev. Public Admin. 49, 897–913 (2019).Article 

    Google Scholar 
    Hornbeck, R. & Naidu, S. When the levee breaks: black migration and economic development in the American South. Am. Econ. Rev. 104, 963–990 (2014).Article 

    Google Scholar 
    Smiley, K. T. Social inequalities in flooding inside and outside of floodplains during Hurricane Harvey. Environ. Res. Lett. 15, 0940b3 (2020).Article 

    Google Scholar 
    Winsemius, H. C., Van Beek, L. P. H., Jongman, B., Ward, P. J. & Bouwman, A. A framework for global river flood risk assessments. Hydrol. Earth Syst. Sci. 17, 1871–1892 (2013).Article 

    Google Scholar 
    Bakkensen, L. & Barrage, L. Flood Risk Belief Heterogeneity and Coastal Home Price Dynamics: Going Under Water? (NBER, 2017); http://www.nber.org/papers/w23854.pdf; https://doi.org/10.3386/w238542015–2016 LARIAC Lidar: Los Angeles Region, CA. (OCM Partners, 2022); https://www.fisheries.noaa.gov/inport/item/55233Galloway, G. E. Flood risk management in the United States and the impact of Hurricane Katrina. Int. J. River Basin Manag. 6, 301–306 (2008).Article 

    Google Scholar 
    Sanders, B. F. et al. Collaborative modeling with fine‐resolution data enhances flood awareness, minimizes differences in flood perception, and produces actionable flood maps. Earth’s Future 8, 2019 (2020).Article 

    Google Scholar 
    Goodrich, K. A. et al. Addressing pluvial flash flooding through community-based collaborative research in Tijuana, Mexico. Water 12, 1257 (2020).Article 

    Google Scholar 
    Glossary (U.S. Census Bureau, 2022); https://www.census.gov/programs-surveys/geography/about/glossary.htmlCarpiano, R. M. Neighborhood social capital and adult health: an empirical test of a Bourdieu-based model. Health Place 13, 639–655 (2007).Article 

    Google Scholar 
    Sampson, R. J., Raudenbush, S. W. & Earls, F. Neighborhoods and violent crime: a multilevel study of collective efficacy. Science 277, 918–924 (1997).Article 
    CAS 

    Google Scholar 
    Wodtke, G. T., Elwert, F. & Harding, D. J. Neighborhood effect heterogeneity by family income and developmental period. Am. J. Sociol. 121, 1168–1222 (2016).Article 

    Google Scholar 
    Stata: Release 17 Multivariate Statistics Reference Manual (StataCorp, 2021).The CDC/ATSDR Social Vulnerability Index (CDC/ATSDR SVI) (The Center for Disease Control and Agency for Toxic Substances and Disease Registry, accessed 1 February 2022); https://www.atsdr.cdc.gov/placeandhealth/svi/index.htmlThe Social Vulnerability Index (SoVI) 2010–2014 (The University of South Carolina Hazards and Vulnerability Research Institute, accessed 1 February 2022); https://www.sc.edu/study/colleges_schools/artsandsciences/centers_and_institutes/hvri/data_and_resources/sovi/index.phpZuzak, C. et al. The national risk index: establishing a nationwide baseline for natural hazard risk in the US. Nat. Hazards https://doi.org/10.1007/s11069-022-05474-w (2022).Sanders, B. F. & Schubert, J. E. PRIMo: parallel raster inundation model. Adv. Water Resour. 126, 79–95 (2019).Article 

    Google Scholar 
    Los Angeles County Storm Drain (Los Angeles County Public Works, accessed 1 February 2022); https://pw.lacounty.gov/fcd/StormDrain/index.cfmPerica, S. et al. Precipitation-Frequency Atlas of the United States, California NOAA Atlas 14 Vol. 6 v.2.3 (NOAA, 2014).Ragno, E., AghaKouchak, A., Cheng, L. & Sadegh, M. A generalized framework for process-informed nonstationary extreme value analysis. Adv. Water Res. 130, 270–282 (2019).Article 

    Google Scholar 
    Moftakhari, H., Schubert, J. E., AghaKouchak, A., Matthew, R. A. & Sanders, B. F. Linking statistical and hydrodynamic modeling for compound flood hazard assessment in tidal channels and estuaries. Adv. Water Resour. 128, 28–38 (2019).Article 

    Google Scholar 
    Sayers, P. et al. Believe it or not? The challenge of validating large scale probabilistic risk models. E3S Web Conf. 7, 11004 (2016).Article 

    Google Scholar 
    World Terrain Base (ESRI, 2022); https://www.arcgis.com/home/item.html?id=33064a20de0c48d2bb61efa8faca93a8 More

  • in

    Opportunities to curb hydrological alterations via dam re-operation in the Mekong

    Junk, W. J. Long-term environmental trends and the future of tropical wetlands. Environ. Conserv. 29, 414–435 (2002).
    Google Scholar 
    Tockner, K. & Stanford, J. A. Riverine flood plains: present state and future trends. Environ. Conserv. 29, 308–330 (2002).
    Google Scholar 
    Grill, G. et al. Mapping the world’s free-flowing rivers. Nature 569, 215–221 (2019).CAS 

    Google Scholar 
    Ziv, G., Baran, E., Nam, S., Rodríguez-Iturbe, I. & Levin, S. A. Trading-off fish biodiversity, food security, and hydropower in the Mekong River Basin. Proc. Natl Acad. Sci. USA 109, 5609–5614 (2012).CAS 

    Google Scholar 
    Poff, N. L., Olden, J. D., Merritt, D. M. & Pepin, D. M. Homogenization of regional river dynamics by dams and global biodiversity implications. Proc. Natl Acad. Sci. USA 104, 5732–5737 (2007).CAS 

    Google Scholar 
    Junk, W. J., Bayley, P. B. & Sparks, R. E. et al. The flood pulse concept in river–floodplain systems. Can. J. Fish. Aquat. Sci. 106, 110–127 (1989).
    Google Scholar 
    Winemiller, K. O. et al. Balancing hydropower and biodiversity in the Amazon, Congo, and Mekong. Science 351, 128–129 (2016).CAS 

    Google Scholar 
    McIntyre, P. B., Liermann, C. A. R. & Revenga, C. Linking freshwater fishery management to global food security and biodiversity conservation. Proc. Natl Acad. Sci. USA 113, 12880–12885 (2016).CAS 

    Google Scholar 
    O’Connor, J. E., Duda, J. J. & Grant, G. E. 1,000 dams down and counting. Science 348, 496–497 (2015).
    Google Scholar 
    Baumann, P. & Stevanella, G. Fish passage principles to be considered for medium and large dams: the case study of a fish passage concept for a hydroelectric power project on the Mekong mainstem in Laos. Ecol. Eng. 48, 79–85 (2012).
    Google Scholar 
    Moran, E. F., Lopez, M. C., Moore, N., Müller, N. & Hyndman, D. W. Sustainable hydropower in the 21st century. Proc. Natl Acad. Sci. USA 115, 11891–11898 (2018).CAS 

    Google Scholar 
    Poff, N. L. & Schmidt, J. C. How dams can go with the flow. Science 353, 1099–1100 (2016).CAS 

    Google Scholar 
    Poff, N. L. et al. The ecological limits of hydrologic alteration (ELOHA): a new framework for developing regional environmental flow standards. Freshwater Biol. 55, 147–170 (2010).
    Google Scholar 
    Acreman, M. et al. Environmental flows for natural, hybrid, and novel riverine ecosystems in a changing world. Front. Ecol. Environ. 12, 466–473 (2014).
    Google Scholar 
    Palmer, M. & Ruhi, A. Linkages between flow regime, biota, and ecosystem processes: implications for river restoration. Science 365, eaaw2087 (2019).CAS 

    Google Scholar 
    Poff, N. L. et al. The natural flow regime. BioScience 47, 769–784 (1997).
    Google Scholar 
    Jumani, S. et al. River fragmentation and flow alteration metrics: a review of methods and directions for future research. Environ. Res. Lett. 15, 123009 (2020).
    Google Scholar 
    Olden, J. D. et al. Are large-scale flow experiments informing the science and management of freshwater ecosystems? Front. Ecol. Environ. 12, 176–185 (2014).
    Google Scholar 
    Opperman, J. J., Kendy, E. & Barrios, E. Securing environmental flows through system reoperation and management: lessons from case studies of implementation. Front. Environ. Sci. 7, 104 (2019).
    Google Scholar 
    Sabo, J. L. et al. Designing river flows to improve food security futures in the Lower Mekong basin. Science 358, eaao1053 (2017).
    Google Scholar 
    Chen, W. & Olden, J. D. Designing flows to resolve human and environmental water needs in a dam-regulated river. Nat. Commun. 8, 2158 (2017).
    Google Scholar 
    Merme, V., Ahlers, R. & Gupta, J. Private equity, public affair: hydropower financing in the Mekong basin. Glob. Environ. Change 24, 20–29 (2014).
    Google Scholar 
    Owusu, A., Mul, M., Van Der Zaag, P. & Slinger, J. May the odds be in your favor: why many attempts to reoperate dams for the environment stall. J. Water Resour. Plann. Manage. 148, 04022009 (2022).
    Google Scholar 
    Hetch, J. S., Lacombe, G., Arias, M. E., Dang, T. D. & Piman, T. Hydropower dams of the Mekong River basin: a review of their hydrological impacts. J. Hydrol. 568, 285–300 (2019).
    Google Scholar 
    Dang, H. et al. Hydrologic balance and inundation dynamics of Southeast Asia’s largest inland lake altered by hydropower dams in the Mekong River basin. Sci. Total Environ. 831, 154833 (2022).CAS 

    Google Scholar 
    Latrubesse, E. M. et al. Dam failure and a catastrophic flood in the Mekong basin (Bolaven Plateau), southern Laos, 2018. Geomorphology 362, 107221 (2020).
    Google Scholar 
    Chowdhury, A. K., Dang, T. D., Nguyen, H. T., Koh, R. & Galelli, S. The greater Mekong’s climate–water–energy nexus: how ENSO-triggered regional droughts affect power supply and CO2 emissions. Earth’s Future 9, e2020EF001814 (2021).
    Google Scholar 
    Schmitt, R. J., Bizzi, S., Castelletti, A., Opperman, J. & Kondolf, G. M. Planning dam portfolios for low sediment trapping shows limits for sustainable hydropower in the Mekong. Sci. Adv. 5, eaaw2175 (2019).CAS 

    Google Scholar 
    Cochrane, T. A., Arias, M. E. & Piman, T. Historical impact of water infrastructure on water levels of the Mekong River and the Tonle Sap system. Hydrol. Earth Syst. Sci. 18, 4529–4541 (2014).
    Google Scholar 
    Dang, T. D., Cochrane, T. A., Arias, M. E., Van, P. D. T. & de Vries, T. T. Hydrological alterations from water infrastructure development in the Mekong floodplains. Hydrol. Processes 30, 3824–3838 (2016).
    Google Scholar 
    Räsänen, T. A. et al. Observed river discharge changes due to hydropower operations in the Upper Mekong basin. J. Hydrol. 545, 28–41 (2017).
    Google Scholar 
    Halls, A. S. & Hortle, K. G. Flooding is a key driver of the Tonle Sap dai fishery in Cambodia. Sci. Rep. 11, 3806 (2021).CAS 

    Google Scholar 
    Richter, B. D., Baumgartner, J. V., Powell, J. & Braun, D. P. A method for assessing hydrologic alteration within ecosystems. Conserv. Biol. 10, 1163–1174 (1996).
    Google Scholar 
    Arias, M. E., Piman, T., Lauri, H., Cochrane, T. A. & Kummu, M. Dams on Mekong tributaries as significant contributors of hydrological alterations to the Tonle Sap floodplain in Cambodia. Hydrol. Earth Syst. Sci. 18, 5303–5315 (2014).
    Google Scholar 
    Williams, J. M. Is three a crowd? River basin institutions and the governance of the Mekong River. Int. J. Water Resour. Dev. 37, 720–740 (2021).
    Google Scholar 
    Tiezzi, S. Facing Mekong drought, China to release water from Yunnan Dam. The Diplomat https://thediplomat.com/2016/03/facing-mekong-drought-china-to-release-water-from-yunnan-dam/ (2016).Johnson, K. China commits to share year-round water data with Mekong River Commission. Reuters https://www.reuters.com/article/us-mekong-river/china-commits-to-share-year-round-water-data-with-mekong-river-commission-idINKBN277135 (2020).Ulibarri, N. Tracing process to performance of collaborative governance: a comparative case study of federal hydropower licensing. Policy Stud. J. 43, 283–308 (2015).
    Google Scholar 
    Pool, T. et al. Fish assemblage composition within the floodplain habitat mosaic of a tropical lake (Tonle Sap, Cambodia). Freshwater Biol. 64, 2026–2036 (2019).
    Google Scholar 
    Arthington, A. H., Bunn, S. E., Poff, N. L. & Naiman, R. J. The challenge of providing environmental flow rules to sustain river ecosystems. Ecol. Appl. 16, 1311–1318 (2006).
    Google Scholar 
    Halls, A. S. & Welcomme, R. L. Dynamics of river fish populations in response to hydrological conditions: a simulation study. River Res. Appl. 20, 985–1000 (2004).
    Google Scholar 
    Ngor, P. B. et al. Evidence of indiscriminate fishing effects in one of the world’s largest inland fisheries. Sci. Rep. 8, 8947 (2018).
    Google Scholar 
    Bonnema, M., Hossain, F., Nijssen, B. & Holtgrieve, G. Hydropower’s hidden transformation of rivers in the Mekong. Environ. Res. Lett. 15, 044017 (2020).
    Google Scholar 
    Siala, K., Chowdhury, A. K., Dang, T. & Galelli, S. Solar energy and regional coordination as a feasible alternative to large hydropower in Southeast Asia. Nat. Commun. 12, 4159 (2021).CAS 

    Google Scholar 
    Hauer, C., Siviglia, A. & Zolezzi, G. Hydropeaking in regulated rivers—from process understanding to design of mitigation measures. Sci. Total Environ. 579, 22–26 (2017).CAS 

    Google Scholar 
    Ahmed, T. et al. ASEAN power grid: a secure transmission infrastructure for clean and sustainable energy for South-East Asia. Renew. Sust. Energy Rev. 67, 1420–1435 (2017).
    Google Scholar 
    Mohammed, I. N., Bolten, J. D., Souter, N. J., Shaad, K. & Vollmer, D. Diagnosing challenges and setting priorities for sustainable water resource management under climate change. Sci. Rep. 12, 796 (2022).CAS 

    Google Scholar 
    Chowdhury, A. K. et al. Enabling a low-carbon electricity system for southern Africa. Joule 6, 1826–1844 (2022).
    Google Scholar 
    Giuliani, M., Lamontagne, J., Reed, P. & Castelletti, A. A state-of-the-art review of optimal reservoir control for managing conflicting demands in a changing world. Water Resour. Res. 57, e2021WR029927 (2021).
    Google Scholar 
    Turner, S. W., Ng, J. Y. & Galelli, S. Examining global electricity supply vulnerability to climate change using a high-fidelity hydropower dam model. Sci. Total Environ. 590-591, 663–675 (2017).CAS 

    Google Scholar 
    De Stefano, L., Petersen-Perlman, J. D., Sproles, E. A., Eynard, J. & Wolf, A. T. Assessment of transboundary river basins for potential hydro-political tensions. Glob. Environ. Change 45, 35–46 (2017).
    Google Scholar 
    Liang, X., Lettenmaier, D. P., Wood, E. F. & Burges, S. J. A simple hydrologically based model of land surface water and energy fluxes for general circulation models. J. Geophys. Res. 99, 14415–14428 (1994).
    Google Scholar 
    Dang, T. D., Vu, D. T., Chowdhury, A. K. & Galelli, S. A software package for the representation and optimization of water reservoir operations in the VIC hydrologic model. Environ. Modell. Software 126, 104673 (2020).
    Google Scholar 
    Chowdhury, A. K., Dang, T. D., Bagchi, A. & Galelli, S. Expected benefits of Laos’ hydropower development curbed by hydro-climatic variability and limited transmission capacity: opportunities to reform. J. Water Resour. Plann. Manage. 146, 05020019 (2020).
    Google Scholar 
    Sheffield, J., Goteti, G. & Wood, E. F. Development of a 50-year high-resolution global dataset of meteorological forcings for land surface modeling. J. Clim. 19, 3088–3111 (2006).
    Google Scholar 
    Reed, P. M., Hadka, D., Herman, J. D., Kasprzyk, J. R. & Kollat, J. B. Evolutionary multiobjective optimization in water resources: the past, present, and future. Adv. Water Resour. 51, 438–456 (2013).
    Google Scholar 
    Shin, S. et al. High resolution modeling of river–floodplain–reservoir inundation dynamics in the Mekong River basin. Water Resour. Res. 56, e2019wr026449 (2020).
    Google Scholar 
    Kabir, T., Pokhrel, Y. & Felfelani, F. On the precipitation-induced uncertainties in process-based hydrological modeling in the Mekong River basin. Water Resour. Res. 58, e2021WR030828 (2022).
    Google Scholar 
    Piman, T., Cochrane, T., Arias, M., Green, A. & Dat, N. Assessment of flow changes from hydropower development and operations in Sekong, Sesan, and Srepok Rivers of the Mekong basin. J. Water Resour. Plann. Manage. 139, 723–732 (2013).
    Google Scholar 
    Chowdhury, A. K., Kern, J., Dang, T. D. & Galelli, S. PowNet: a network-constrained unit commitment/economic dispatch model for large-scale power systems analysis. J. Open Res. Software 8, 5 (2020).
    Google Scholar  More

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    Uncertain water increase

    Joachim Ayiiwe Abungba from the University of Science and Technology in Ghana and colleagues from India used a water evaluation and planning model to estimate runoff, streamflow and future water demand under different climate scenarios. They reveal that between 1990 and 2019, human settlements, open savannah woodland, croplands and waterbodies increased, while closed savannah woodlands, wetlands and grasslands decreased. The model shows increased water availability from river discharge compared with the current scenario. However, the uncertainties in future changes exceeded the predicted increases. There is an urgency to further improve model certainty and to develop integrated water management in order to ensure sustainable livelihoods for local people. More

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    Toxicity analysis supports reuse

    Dolan, F. et al. Nat. Commun. 12, 1915 (2021).Article 

    Google Scholar 
    Gerrity, D., Pecson, B., Trussell, R. S. & Trussel, R. R. J. Water Supply Res. Technol. AQUA 62, 321–338 (2013).Article 
    CAS 

    Google Scholar 
    Giammar, D. E. et al. ACS EST Eng. 2, 489–507 (2022).Article 
    CAS 

    Google Scholar 
    Keller, A. A., Su, Y. & Jassby, D. ACS EST Eng. 2, 273–291 (2022).Article 
    CAS 

    Google Scholar 
    Nkhoma, P. R., Alsharif, K., Ananga, E., Eduful, M. & Acheampong, M. Environ. Conserv. 48, 278–286 (2021).Article 

    Google Scholar 
    Lau, S. S. et al. Nat. Sustain. https://doi.org/10.1038/s41893-022-00985-7 (2022).Article 

    Google Scholar 
    Wagner, E. D. & Plewa, M. J. J. Environ. Sci. 58, 64–76 (2017).Article 
    CAS 

    Google Scholar 
    McKenna, E., Thompson, K. A., Taylor-Edmonds, L., McCurry, D. L. & Hanigan, D. Environ. Sci. Processes Impacts 22, 708–718 (2020).Article 
    CAS 

    Google Scholar 
    Lau, S. S. et al. Environ. Sci. Technol. 54, 5729–5736 (2020).Article 
    CAS 

    Google Scholar 
    Holmer, M., Steinle-Darling, E. & Pecson, B. Removing Barriers to Direct Potable Reuse (WateReuse Association, 2019). More

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    High-resolution bathymetries and shorelines for the Great Lakes of the White Nile basin

    The survey of Lake Albert was conducted in February 2020. The Lake Edward and Lake George surveys were conducted in August of 2020. The surveys of Lake Victoria were conducted between September and November of 2017, 2018, 2019, and 2020. We assume no significant morphological change occurred in Lake Victoria across these 4-years. All collection periods correspond to the end of a traditional dry season and the transition period into the beginning of a traditional wet season. Water levels were monitored during the period of each Lakes’ survey. Benchmarks were installed during each Lakes survey aside from Lake Victoria, where an existing benchmark nail existed. Unmanned aerial systems (UAS) were flown during the Lake Albert survey to assess our shoreline delineation methodology.Lake elevation levelsLake Victoria utilizes spaceborne altimetry to ascertain its lake elevation. Lakes Albert, Edward, and George have no systematic high accuracy spaceborne altimeter measure of lake elevations. Therefore, Lake Albert, Edward, and George’s lake elevations are derived from statistical analyses of observed water levels.Lake sounding datumsFor Lake Albert, Lake Edward, and Lake George, visual water-level (WL) observations taken throughout the survey are averaged to obtain the lake elevation (LE), also known as the project sounding datum (SDp). The method for determining SDp is to observe the WL on a graduated board, often called a tide board or a staff gauge (G), securely attached to a piling or other solid vertical structure extending below the lake surface. The graduations are then marked relative to the gauge zero (G0). The WL is read as the distance above or below G0 where the water surface intersects the gauge.A fixed, tamper-resistant benchmark (Bm) was installed or in operation at each Lake within the optical leveling distance of each gauge to achieve the conversion from local water levels to ellipsoidal heights and EGM 2008 elevations. First, each Bm’s horizontal and vertical position was measured using theGlobal Navigation Satellite System (GNSS). Then, the vertical distance between the benchmark elevation (BmE) and G0 is measured using standard optical or laser-based survey methods. This distance is the vertical gauge offset (VGO).The Lakes’ elevation methodology is summarized in Fig. 2 and is defined in Eq. 1. At this point, SDp for Lakes Albert, Edward, and George is merely an ellipsoidal height; the ellipsoidal height is converted to EGM:2008 using Harmonic Synthesis at the horizontal coordinate location of each Bm15,16.Fig. 2Lake Elevation (SDp). This diagram represents the relationship between the various Lake Elevation parameters directly measured (in black text), obtained from instruments (in blue text), or calculated (red text).Full size imageEq. 1 – Lake Albert, Lake Edward, and Lake George Sounding Datums$${rm{SDp}}=left({rm{Bm}}+{rm{VGO}}+WLright)$$SDp is the lake elevation or project sounding datum, Bm is the benchmark elevation from RTK GPS, VGO is the vertical gauge offset derived by using an optical level, and WL is the water level obtained from the gauge reading.Unlike Lake Albert, Lake Edward, and Lake George, due to Lake Victoria’s size, with a maximum diameter exceeding 375 km, hydrodynamic effects could readily negate the hydrostatic assumption that the lake surface is uniformly level. On Lake Victoria, wind setup, seiching, and the significant outflow into the Victoria Nile would result in hydraulic gradients that would make any single, nearshore water level gauge unrepresentative of lake levels at points distant from the gauge. To establish a meaningful SDp for Lake Victoria using nearshore water level gauges, at least three stations distributed equilaterally around the Lake’s perimeter would need to be established and operated simultaneously for long periods. However, this approach was deemed unfeasible primarily due to cost and logistical constraints. For example, creating a multi-country concurrent network of gauges would require at least three times the equipment, three times the labor, and three times the training.The alternate approach utilizes Jason-3 spaceborne altimeter data. This method has been used in Lake Victoria and is supported by the USDA G-REALM program17. Jason-3 is a radar altimeter launched in January 2017. The primary goal of Jason-3 is to provide sea-level variations with accuracies under 2.5 cm at a repetition cycle of 10-days18. As Jason-3 passes over Lake Victoria, it can establish EGM 2008 elevations for the Lake from numerous measures towards the middle of the Lake. Jason-3 passes over 150 km of Lake Victoria. The collection path runs from approximately Nyabansari in Tanzania to Bugaia in Uganda. As the instrument is radar-based, climatic conditions rarely limit the data collection. The raw altimeter data collected by Jason-3 undergoes numerous corrections before a lake surface elevation is determined, including a dry tropospheric correction, a wet tropospheric correction, an ionosphere correction, and an instrument-specific bias adjustment19. Lake elevation observations were obtained from Jason-3 during Lake Victoria’s surveys across 2017, 2018, 2019, and 2020. The average of the Jason-3 readings from 2020, which itself is an average of many hundreds of observations, defines the SDp for the Lake Victoria surveys.A Lake Victoria benchmark is still surveyed at a water level gauge to allow for past and future data integration, and the benchmarks are tied to the altimeter measures used. At this point, SDp for Lake Victoria is already in EGM:2008 as Jason-3 uses EGM:2008 as opposed to ellipsoidal elevations, so harmonic synthesis is not required as it is for the other Lakes.Lake benchmarks
    Benchmarks for Lake Albert (BmA), Lake Edward (BmEd), and Lake George (BmG) were installed along each of the three Lakes’ shorelines. Each benchmark is situated within a few meters and line-of-sight of a water level staff gauge. A preexisting benchmark nail (BmV) located above the gauge was utilized for Lake Victoria. Aside from Lake Victoria, each installed benchmark is an 8 cm diameter brass disc stamped with LEAF II. Each installed benchmark was anchored approximately 15 cm into a larger concrete pad using a twisted steel reinforcement bar. Each benchmark’s location was obtained using long-term GNSS averaging, captured by a Hemisphere GNSS receiver with Atlas satellite-based augmentation system wide-area corrections applied. Observations without a corrective signal were discarded. Ellipsoidal elevation, recorded to the millimeter level, was also captured by the GPS receiver. Conversion of benchmark ellipsoid elevations to EGM 2008 WGS 1984 Version used the harmonic synthesis coefficients provided by the U.S. National Geospatial-Intelligence Agency (NGA) EGM Development Team15,16.
    BmA was installed on 1/31/2020 within the UPDF Marine compound at Mbegu, approximately 6.5 km east-northeast of Kaiso, Uganda, on the eastern side of Lake Albert. Across seven days between 2/1/2020 and 2/20/2020, the horizontal location of the benchmark was recorded by a GNSS receiver with built-in averaging. The GPS unit averaged horizontal locations at the benchmark until it reached 95 percent confidence. In addition, the ellipsoidal height was collected on the surface of Lake Albert across the survey period and adjusted to the benchmark elevation using the vertical gauge offset and the water level readings. The total number of vertical observations is 35,550.
    BmEd was installed on 2/13/2020 at the fish landing site in Katwe Village, Uganda, at the northern end of Lake Edward. Across portions of 8/5/2020, 8/10/2020, 8/13/2020, and 8/15/2020, one X, Y, and Z GPS location were recorded every 5-seconds, totaling 11,242 observations.
    BmG was installed on 8/11/2020 at the landing site in Kahendero, Uganda, on the western side of Lake George. On 8/13/2020, one X, Y, and Z GPS location were recorded every 5-seconds, totaling 2,663 observations. Unfortunately, BmG does not have a full unobstructed 360° view of the sky and may require further refinement.
    A preexisting benchmark nail (BmV) at the railroad dock in Jinja, Uganda, is used for Lake Victoria. The nail is located directly above the water level gauge and marked with a white paint X. Across portions of 3/22/2021 and 3/23/2021, one X, Y, and Z GPS location was recorded every 5-seconds, totaling 6,842 observations. Still, as noted earlier, altimetry data was used for the actual SDp.

    Lake gauges
    Within a few meters of each benchmark, a water level staff gauge was either installed or already existed. For Lake Victoria (GV), Lake Albert (GA), and Lake Edward (GE), preexisting gauges were used. At Lake George (GG), a temporary gauge was established for the duration of field operations.
    GA is a staff gauge of unknown origin. The staff is a simple iron square tube painted decimeter intervals subdivided into 5 cm steps. The 100 cm subdivision at the top of the gauge was surveyed relative to the BmA (Fig. 2, YBG) using an optical level on 1/31/2020. Between 2/1/2020 and 2/20/2020, twelve lake level observations were collected. The water level only varied by 6 cm across the entire survey. The average of the 12-daily readings was used to help define the SDp for the Lake Albert bathymetric survey.
    GE is a long-term gauge installed by the Ugandan Ministry of Water. The gauge is a stepped gauge consisting of three separate concrete pillars of increasing height with graduated measurement strips attached at the centimeter level. The water level on the gauge, relative to the BmEd, was surveyed using an optical level on 8/10/2020. Twice-daily Lake level observations continued throughout the 11-day survey operation between 8/5/2020 to 8/22/2020. The water level only varied by 3 cm across the entire survey. The average of the 11-daily readings was used to help define the SDp for the Lake Edward bathymetric survey.
    GG is a temporary gauge installed for the duration of field operations. The gauge is a simple wooden gauge with painted centimeter intervals anchored to a galvanized steel pipe driven between 1 m and 2 m into the substrate. The water level on the gauge, relative to the BmG, was surveyed using an optical level on 8/12/2020. Once-daily Lake level observations were collected across the two days of the hydrographic survey and the day before and after the survey. The water was stable across the entire survey. The two average daily readings were used to define the SDp for the Lake George bathymetric survey.
    GV is a long-term gauge installed by the Ugandan Ministry of Water. The gauge has graduated measurement markers at the two-centimeter level. The zero level on the gauge, relative to the BmV, was surveyed on 3/22/2021 and 3/23/2021. As BmV and GV are at the same horizontal coordinates, leveling is not required. Water level observations were not utilized from this gauge during the survey, as the Jason-3 altimeter was used to establish the Lake elevation level for Lake Victoria. Instead, the closest four Jason-3 measures across the survey dates are used to calculate the water level. The water level varied by 4 cm across the 2017 bathymetric survey, 9 cm across the 2018 survey, 5 cm across the 2019 survey, and 13 cm across the 2020 survey. The 2020 water level is used as the SDp to allow for as close as possible temporal consistency across all Lakes in the database.

    Lake elevation data
    Table 1 provides each lake’s SDp in the most common gravitational models and all input parameters to the lake elevation models. The SDp for Lake Edward is 915.77 m (EGM08), the E/SDp for Lake George is 915.74 m (EGM08), and the SDp for Lake Albert is 622.18 m (EGM08), and the SDp for Lake Victoria is 1136.92 m (EGM08). Measures of uncertainty are provided in the technical validation.Table 1 Lake Level Parameters for each Lake.Full size table
    Lake bathymetriesThe Lake Albert hydroacoustic survey was conducted across 14-days between February 1st, 2020, and February 20th, 2020. The Lake Edward hydroacoustic survey was conducted across 10-days between August 4th, 2020 and August 22nd, 2020. On August 13th, 2020 and August 14th, 2020, the Lake George hydroacoustic survey occurred during a Lake Edward Survey break. The Lake Victoria hydroacoustic survey occurred daily between September 8th, 2017 and October 7th, 2017, September 10th, 2018 and October 9th, 2018, September 15th, 2019 and October 13th, 2019, and finally between October 20th, 2020 and November 25th, 2020. The Lake Victoria soundings from 2017, 2018, and 2019 were vertically corrected to align to the 2020 water levels. The earlier year were adjusted by 1.28 m (0.03 m, 95CI), 0.975 m (0.06, 95CI), and 1.025 m (0.05 m, 95 CI), respectively.The hydroacoustic survey transect designs were based on local topography, available bathymetry, and cost considerations. Both Lake Albert and Lake Edward had dominant relief patterns running from the Congolese highlands in the west to the Ugandan Plateau in the east, forming a deep U shape perpendicular to the Albertine Rift. The survey transects were designed to follow this axis of high relief across the Albertine Rift. Lake George and Lake Victoria have no discernable relief patterns, both being relatively shallow bowls situated across flat planes. Therefore, the survey designs were optimized to capture an adequate portion of these two Lakes while minimizing cost.Lake soundingsAcross Lake Albert, Lake Edward, and Lake George, a 9 m, V-bottomed, shallow draft research vessel was deployed with a Ugandan crew out of Jinja, Uganda. The echosounder used to collect the soundings was a dual-frequency sounder with a built-in data logger, external GNSS receiver, and a combined low-frequency (33 kHz) high-frequency (200 kHz) transducer. Both frequencies were operational and recorded during the survey, but only the high-frequency signal was processed to produce Lake Albert and Lake George’s soundings. Greater than 90 percent of Lake Edward also used the high-frequency sounder, but the instrument was switched to low-frequency in areas over 90 m deep. A speed of sound adjustment was made based on the water sampling that occurred on average twice each transect. Calibration was performed before the initial deployment.For Lake Albert, Lake Edward, and Lake George, Hydromagic 9.1 software was used to record and process the acoustic soundings into tabular X, Y, and Z formats. The echosounder’s echogram was output in real-time to a laptop. A dedicated 12-volt battery, maintained by a 60-watt solar panel mounted on the cabin top, powered all equipment. Positions were obtained by a multi-frequency GNSS antenna connected to the echosounder. The transducer was mounted on an aluminum extension pole that supported the GNSS antenna directly above the transducer. The antenna received Atlas L-band satellite-based augmentation system (SBAS) correction signals that allow precise positioning.Lake Victoria soundings were collected by the stern trawler RV Lake Victoria Explorer by members of the Hydroacoustics Regional Working Group of the Lake Victoria Fisheries Organization. This group is based out of Jinja, Uganda, Kisumu in Kenya, and Mwanza in Tanzania. This group has conducted twenty-three acoustic surveys of Lake Victoria since 1999 under an established protocol20. The RV Explorer is a 17 m research vessel and a V-shaped hull with a draft of 1.8 m. The echosounder used on the RV Explorer is a dual-frequency system operating at 70 kHz and 120 kHz, respectively. The transducers are mounted on a protruding instrument keel under the boat and powered by the vessel’s electrical system. Calibration was performed immediately before each daily survey. The GPS logger used on this system is not differentially corrected.For Lake Victoria, Echoview 8.0 software was used to record and process the soundings into tabular X, Y, and Z formats. After noise was removed from the raw signal and adjustments were made to correct the beam angle, the initial lakebed soundings were obtained using the best bottom candidate algorithm21. A CTD probe was used at each calibration site to determine the local environmental conditions. The average water temperature at the calibration site was input into the system to predict the sound speed. Lake Victoria’s survey’s calibration protocol is detailed in the Standard Operating Procedures for Hydroacoustics surveys on Lake Victoria20.Across all Lakes, either a certified coastal engineer or an individual with relevant expertise processed the echograms from the echosounder. The process essentially involves detecting the average bottom in the echogram and digitizing through small peaks and pits caused by the boat’s motion. A narrow interpretation is needed on calm days, and the automated extraction of the lake bottoms often suffices. On days with rough water, manual digitization of the trace is required. Sometimes, the signal may reflect off anything in its path to the bottom, including suspended sediment, debris, animals, subaquatic vegetation, silt, mud, or a harder compacted layer beneath a softer surface layer. The digitization process removes such anomalies as well as smoothing over dropouts and other noise. Finally, the digitized trace is exported to tabular soundings for use in GIS and other software. Figure 3 represents the soundings across all Lakes.Fig. 3Project Soundings. All soundings across all Lakes.Full size imageLake bathymetries dataFor Lake Albert, Lake Edward, and Lake George, the output spatial and tabular data contains; the date of the sounding, the horizontal position of the sounding, and corrected depth using a local-verified speed of sound adjustment for both high-frequency and low-frequency soundings when applicable, the vessel speed at the time of the sounding, the vessel heading at the time of the sounding, and a field indicating if the GNSS was operating in uncorrected or corrected mode for each sounding. For Lake Victoria, the output spatial and tabular data contains the date of the sounding, the time of the sounding, the horizontal position of the sounding, corrected depth using a local-verified speed of sound adjustment, and a field indicating if the GNSS was operating in uncorrected or corrected mode for each sounding. Depth zero corresponds to the LE /SDp for each Lake as already defined.Across Lake Albert, 290,018 soundings were collected (Table 2), resulting in 53 soundings per square kilometer. Across Lake Edward, 225,528 soundings were collected (Table 2), resulting in 101 soundings per square kilometer. Across Lake George, 59,281 soundings were collected (Table 2), resulting in a density of 211 soundings per square kilometer. Finally, across Lake Victoria, 17,958,859 soundings were collected (Table 2), resulting in a density of 269 soundings per square kilometer. The water volume and mean depth are calculated using constrained Delaney Triangulation, whereas the maximum depth is the deepest collect sounding. The summary information for each Lakes’ bathymetry is shown in Table 2 and is compared against values from the (WLD) World Lakes Database22 unless otherwise noted.Table 2 Bathymetry Characteristics.Full size tableLake shorelinesFor each of the Lakes, we constructed high-resolution shorelines from spaceborne imagery at a combination of 15 m, 10 m, 5 m, 3 m, 50 cm, and 30 cm. Accuracy statistics were generated using UAS-derived imagery at 10 cm.Sentinel-2 imagerySentinel-2 is designed to map and monitor water cover, inland waterways, and coastal areas24. The baseline spaceborne imagery used to delineate the shorelines across Lake Albert, Lake Edward, and Lake George is Sentinel-2. Sentinel-2 is a European Space Agency (ESA) wide-swath, high-resolution (HR), a multi-spectral imaging system that consists of two satellites flying in the same orbit but phased at 180°23. The system carries an optical instrument payload that samples thirteen spectral bands: four bands at 10 m resolution, six bands at 20 m resolution, and three bands at 60 m resolution25. The four bands at 10 m resolution are centered on the wavelengths 0.490 µm, 0.56 µm, 0.665 µm, and 0.842 µm, respectively. These wavelengths correspond to the blue, green, red, and near-infrared portions of the electromagnetic spectrum. These spectral properties of Sentinel-2 allow for color composites and false color composites of each of the Lakes at 10 m resolution. Furthermore, as the radiometric signal in the near-infrared band is almost entirely absorbed by open water, it can assist in delineating a water-terrestrial edge boundary.The Sentinel-2 data granules used to delineate the Lake Albert shoreline are:

    S2B_MSIL1C_20190403T080609_N0207_R078_T36NUH_20190403T110906, S2B_MSIL1C_20190503T080619_N0207_R078_T36NTG_20190503T112849, S2B_MSIL1C_20190503T080619_N0207_R078_T36NTH_20190503T112849, S2B_MSIL1C_20190503T080619_N0207_R078_T36NUG_20190503T112849
    The Sentinel-2 data granules used to delineate the Lake Edward shoreline are:

    MSIL1C_20170702T081009_N0205_R078_T35MRV_20170702T082404, MSIL1C_20170821T080959_N0205_R078_T35MQV_20170821T082855
    The Sentinel-2 data granule used to delineate the Lake George shoreline is:

    S2B_MSIL1C_20191229T081239_N0208_R078_T35NRA_20191229T100818

    Landsat imageryThe baseline spaceborne imagery used to delineate the Lake Victoria shoreline is Landsat-8. Landsat-8 is a USGS/NASA, high-resolution (HR), multi-spectral imaging system. Landsat-8 uses a push-broom Operational Land Imager and Thermal Infrared Sensor to collect data with a spatial resolution of 30 meters in the visible and near-infrared regions of the electromagnetic spectrum. The relevant bands at 30 m resolution are the blue band located between 0.45 µm to 0.51 µm, the green band located between 0.53 µm to 0.58 µm, the red band located between 0.64 µm to 0.67 µm, and the near-infrared band located between 0.85 µm to 0.88 µm. As the infrared band is almost entirely absorbed by open water, it can assist in delineating a water-terrestrial edge boundary. In addition, a 15 m panchromatic band is located between 0.64 µm to 0.67 µm and is used to pansharpen the 30 m bands to allow for feature digitizing at 15 m resolution. These spectral properties of Landsat-8 allow for color composites and color-infrared composites of Lake Victoria at 15 m resolution when pan-sharpened.The Landsat data are listed below.LC81700602020049LGN00, LC81700602021003LGN00, LC81700612020001LGN00, LC81700612020049LGN00, LC81700622020017LGN00, LC81700622020049LGN00. LC81710602020040LGN00, LC81710602021026LGN00, LC81710612020040LGN00, LC81710622020040LGN00, LC81720602020047LGN00Very high-resolution planetscope eye imageryIn highly dynamic vegetative areas where Sentinel-2 or Landsat-8 cannot delineate a clear shoreline, very high resolution (VHR) imagery was obtained and used (Table 3). For example, the southern wetland of Lake Albert across both the DRC and Uganda uses 50 cm Worldview 2 (WV2) and 30 cm Worldview 3 (WV3) imagery as opposed to Sentinel-2 (Table 3), as this region has ephemeral floating grasses, sub-aquatic vegetation, and therefore shows a reflected signal response in the near-infrared bands of the satellite imagery. Thus, the wetland areas of Lake Albert are of substantially higher resolution than the rest of the Lake Albert shoreline.Table 3 Shoreline Remote Sensing Instrument.Full size tableSub-meter resolution UASFinally, sub-meter resolution (SMR) UAS was flown over Lake Albert to ascertain the shorelines’ positional accuracies. Once the accuracy statistics were calculated, the UAS data was incorporated back into the shorelines for these areas. These UAS-derived shorelines are the regions around Kaiso, Butiaba, and Ntoroko on Lake Albert in Uganda.Shoreline digitizationThe initial step of the shoreline delineation was selecting the required satellite scenes—the selected scenes needed to meet the following criteria, be mostly cloud-free over the Lakes, and have suitable flags indicating high-quality data. The ESA Copernicus Hub and USGS GLOVIS sites were searched until the images met the above criteria. The selected granules were then subset only the Blue, Green, Red, and near-infrared bands, and the Landsat-8 imagery was pan-sharpened. Once composited, each 4-band raster is represented as a color-IR composite and a visible color composite. Before digitizing began, the resolution was set to 1:20,000 for all Lakes aside from Lake Victoria, which was set to 1:30,000.Fishnets were constructed that covered the entirety of each Lake. The shoreline in each cell of the fishnet is manually digitized in a heads-up manner. The first pass of each cell digitizes the exterior shoreline of the Lake. The second pass of each cell digitizes all islands in the cell, and the third pass digitizes potential nearshore obstructions. Once each cell is complete, a second cartographer verifies the digitization and sends all questions back to the original digitizer, making the required updates. The final stage is to combine all the individual shoreline cells of the fishnet into a singular whole for each Lake and then verify the constructed shoreline feature’s topology.Resolution and scaleUsing Tobler’s rule of scale and resolution26, it is possible to create a shoreline that approximates 1:20,000 scale from the 10 m Sentinel-2 images and 1:30,000 from the Landsat-8 imagery using appropriate error monitoring and control. The Planet Scope imagery at 3 m resolution would equate to 1:6,000, the WV2 imagery at 50 cm resolution would equate to 1:1000, the WV3 imagery at 30 cm resolution would equate to 1:600, the UAS imagery at 10 cm resolution would equate to 1:200. For these reasons, the Lakes Albert, Edward, and George shorelines can be considered at a minimum 10 m resolution or a 1:20,000 scale product. The Lake Victoria shoreline can be regarded as a minimum 15 m resolution or a 1:30,000 scale product. We report the coarsest resolution as the shoreline’s resolution from the coarsest instrument, but large portions of the shorelines are higher resolution from less coarse instruments.Lake shorelines dataWe find the surface area of Lake Edward, Lake Albert, Lake George, and Lake Victoria to be 2,241,119,039 m2, 5,423,949,967 m2, 281,121,696 m2, and 66,792,882,259 m2, respectively. We find the shoreline lengths of Lake Edward, Lake Albert, Lake George, and Lake Victoria to be 241,395 m, 484,454 m, 89,204 m, and 3,063,755 m, respectively. The summary information for each Lakes’ shoreline is shown in Table 4, and the data are compared to the Global Self-Consistent, Hierarchical, High-Resolution Geography Database (GSHHG)27, considered the current best available consistent across these Lakes27.Table 4 Shoreline Characteristics.Full size tableHardware and SoftwareSoundings were collected and processed using Eye4Software Hydromagic or Echoview Software Pty Ltd, Echoview software. The sounding collection system used for Lake Albert, Lake Edward, and Lake George was the CEESystems CEESCOPE. High-frequency soundings for Lake Albert, Lake Edward, and Lake George were collected using a 200 Khz transducer from CEE Systems. The low-frequency soundings for the deep-water portion of Lake Edward were collected using a 33 kHz transducer from CEE Systems. The sounding collection system used on Lake Victoria before 2020 was a Simrad EK 60 dual frequency echo sounder with a 7° beam angle connected to 70 kHz and 120 kHz general-purpose dual transducer produced by Kongsberg Maritime AS. For 2020, the sounding collection system was changed to a Simrad EK80 dual frequency echo sounder, which operated at the same frequencies. The GNSS system used on Lake Albert, Lake Edward, and Lake George was a Novatel Hemisphere GPS. The Hemisphere Atlas system provided the SBAS L-Band GPS real-time correction. The Hemisphere Atlas system provided the SBAS L-Band GPS real-time correction. GNSS system used on Lake Victoria was a Globalsat Technology Corporation GPS.ESRI ArcGIS ArcPro28, GDAL/OGR29, and QGIS30 were used to perform all horizontal coordinate transfers, conduct geostatistical analysis, produce cartographic outputs, digitize shorelines, post-process the soundings, and analyze the soundings. Microsoft Excel was used to process and transform the SDp GPS data. Harmonic synthesis transformation for data conversion to EGM 2008 was conducted in the Harmonic Synth WGS 84 Fortran code provided by the NGA15.Sentinel-2 and PlanetScope were the primary data sources for the satellite imagery The SenseFly EBee + UAS31, with the SODA survey camera32, was used to fly the data and then assess the accuracy of the shoreline delineation. SenseFly Emotion33 software was used to plan and fly all UAV missions. Pix4D34 was used to process all UAV imagery.Tinfour 2.7.135 to triangulate mass bathymetric soundings and calculate each Lakes’ mean depths and volume. More

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    A data set of distributed global population and water withdrawal from 1960 to 2020

    In this chapter, we describe in detail the method of data set generation, including data collection, data modification and interpolation extension, and grid data generation (Fig. 2).Fig. 2Schematic outline to produce the global population and water withdrawal products.Full size imageFirst, the collection of population and water withdrawal data. Collect as much as possible of the national and sub-national permanent population and water withdrawal data released by governments and institutions on a global scale. Here we provide the source of our data collection.Second, establish a national and sub-national default data interpolation model. Based on the shape of the sample data scatter plot, determine the most appropriate curve model. The simulation modeling is implemented by EXCEL and provided one by one according to the national level.Third, create spatial distribution grids. Spread the population density to the administrative unit and artificial surface, and spread the water intensity to the administrative unit, and artificial surface and cultivated land (Spatial distribution section for details).Fourth, data verification. For population data, we compare the global population of the revised results with data of the World Bank and FAO, and calculate the correlation and deviation between the revised results and the other two sets of data. For the water withdrawal data, we divide the measured data into calibration and verification periods, re-interpolate the data using the data of the calibration period, and then verify the simulation accuracy by using the data of the verification period and the simulation.Data collection and pretreatmentThe data sources include government population data for xx nation and xx sub-nation, government water withdrawal data for xx nations and xx sub-nations, national population and water withdrawal data from the World Bank12, and national population and water withdrawal data from FAO8, water withdrawal data from the United Nation13, national population and water withdrawal data from Eurostat14, and Globeland3015 data for 2000 and 2010. Among them, xx refers to one of many countries in the data set, and only serves as an indicator.Globally, it is believed that the accuracy rate of census results obtained by counting the population of various administrative units in the country is the highest at present when a large amount of manpower and material resources are spent by the country itself16. In addition to the census conducted every certain year, the statistical department gets a high accuracy rate by calculating the overall figures according to the sample survey of population changes and the random sample survey of fertility rate in some areas and some units. To sum up, we believe that the data released by our country on the statistical official website is the most reliable.When national population data are missing, it is generally believed that the data and trends of the World Bank and FAO are authoritative. When the data of the World Bank and FAO are complete, the World Bank data prevails as reference population data. When the length of World Bank data is shorter that of than FAO, the FAO data is used as reference population data17.For water withdrawal data, FAO and UN data are generally considered authoritative when government water withdrawal data is missing. When the FAO and UN data are both complete, the FAO data is used as a reference for water withdrawal data.Interpolation and extrapolation of national and sub-national population dataWhen the lack of data is obvious, the results obtained by the simplest method often have more reference value. The following four basic methods are used for the processing of population data9,10,11,18,19,20.Interpolation method assuming increasing in arithmetic seriesIf discontinuities exist in government data, and the number of data increases in arithmetic series according to the judgement, then the linear interpolation method can be used based on a linear model of arithmetic series growth. This method is suitable for interval data interpolation with a short interruption time and relatively uniform data growth scale. The interpolation model is as follows:$${P}_{N,k}=left[frac{Ileft(jright)-Ileft(iright)}{j-i}cdot left(k-iright)+Ileft(iright)right]cdot {P}_{W,k}$$
    (1)
    Where, PN,k is the government data for the k year, i ≤ k ≤ j; PW,k is the reference data for the k year; I(j) and I(i) are the ratios of government data to reference data for the j year and i year, respectively.Trend extrapolation method based on general trend curve modelIf there are continuous points in the government data, it is better to obtain interpolation results by assisting based on the trend of the ratio of government data to reference data. General trend line functions such as linear, conic, cubic and exponential curves can be used, and the fitting result needs to be comprehensively judged by the linear change of the reference data, and finally a more suitable interpolation result can be obtained. This method is more suitable for interval data interpolation with shorter time and faster data growth.$${P}_{N,k}=F(k)cdot {P}_{W,k}$$
    (2)
    where, PN,k is the government data in the k year, i ≤ k ≤ j; PW,k is the reference data for the k year; F(k) is the trend for the ratio of government data to reference data in the k year.Scale up to the same ratioIf there is only one year of government data, then the reference data will be scaled up to the same ratio according to the ratio of government data to the reference data of the corresponding year.$$I=frac{{P}_{N}}{{P}_{W}},{P}_{N,o}=Icdot {P}_{W,o}$$
    (3)
    Where, PN is the government data; PW is the reference data; I is the ratio of government data to reference data; PN,o is the default government data; PW,o is the reference data corresponding to the default; o is the default year.Based entirely on government data or reference dataIf there is complete government data, the government data is used as the final population result. If there is no government data, the reference data is used as the final result of the population.Interpolation and extrapolation of national and sub-national water withdrawal dataThe total amount of water withdrawal in various countries varies greatly, but the per capita water withdrawal of the country generally remains within a certain range. Therefore, we first calculate the reference data, and then interpolate and extrapolate the missing per capita water withdrawal data. The methods can also be summarized into the following five categories.Interpolation method assuming increasing in arithmetic seriesThe calculation principle is the same as the interpolation method of national population data. This method is more suitable for interval data interpolation with shorter and discrete data, such as the data form before 1990 in Fig. 6(c).Trend extrapolation method based on revised per capita water withdrawal growth rateIf there are continuous points in the data, we assume that the per capita water withdrawal versus time curve is consistent with the S curve, that is, the per capita water withdrawal shows only a slow change in the first years and the last years. We first calculate the growth rate of per capita water withdrawal in the last two years or the first two years, adjust the final growth rate proportionally to reflect the subsequent changes, and adjust the first growth rate proportionally to reflect the previous changes. Equation (4) represents a method of extrapolating the previous missing value data, and Eq. (5) represents a method of extrapolating the subsequent missing value data. This method is more suitable for the situation where continuous government data exists and the change trend of per capita water consumption is clear, such as the form of continuous data after 1990 in Fig. 6(c).$$left{begin{array}{rll}{s}_{i} & = & frac{{w}_{i}-{w}_{i+1}}{{w}_{i+1}}\ {s}_{i-1} & = & {s}_{i}cdot (1-theta )\ {w}_{i-1} & = & {w}_{i}cdot (1+{s}_{i-1})end{array}right.$$
    (4)
    $$left{begin{array}{rll}{s}_{j} & = & frac{{w}_{j}-{w}_{j-1}}{{w}_{j-1}}\ {s}_{j+1} & = & {s}_{j}cdot left(1-theta right)\ {w}_{j+1} & = & {w}_{j}cdot left(1+{s}_{j+1}right)end{array}right.$$
    (5)
    Where wi-1 is the missing per capita water withdrawal value for time step i-1; si-1 is the missing reverse order growth rate value for time step i-1; wi and wi+1 are the first two known per capita water withdrawal values for time step i and i + 1, and si-1 is the known reverse order growth rate value for time step i-1. For Eq. (5), wj+1 is the missing per capita water withdrawal value for time step j + 1; sj+1 is the missing growth rate value for time step j + 1; wj-1 and wj are the last two known per capita water withdrawal values for time step j and j-1, and sj is the known growth rate value for time step j. To ensure that the per capita water withdrawal in the front of the series or in the latter part of the series does not change too fast, the equation introduces θ to represent the correction coefficient for the growth rate, which is generally in the range of 0.1 to 0.2.Scale up to the same ratio or smoothing spline fittingIf there is only one data released, the per capita water withdrawal of that year will be used for all years. For water withdrawal data with long time spans and more data but many intervals, we use smoothing spline to provide smooth interpolation over time, taking into account the equilibrium of per capita water withdrawal fluctuations.Proximity of adjacent regionIf no national water withdrawal data is released, based on the country’s level of development and geographic location, the per capita water withdrawal of adjacent countries with similar development levels is selected as an approximate value for the country’s per capita water withdrawal value.The treatment of sub-national water withdrawal data is similar to sub-national population data. First, the ratio of the sub-national data to the national data of the known year is calculated, and then the interpolation and extrapolation methods are used to calculate the ratio of the missing values, and finally sub-national data is obtained by the national data and the ratio.Spatial distributionThis research further considers the indicative role of specific land use types. Spatial distribution, which means that the data is distributed to a meaningful area. It is assumed that the population and water are only used on an artificial surface and cultivated land. We mainly used the globeland30 data15 of 2000 and 2010 to process the data before and after 2000, respectively (Figs. 3 and 4).Fig. 3The specific regional average population density from 1960 to 2020. (a) The administrative units. (b) The artificial surface grids. Obtain the population of the above-mentioned two groups of specific regions within each 1 km grid in an average manner.Full size imageFig. 4The specific regional average water intensity from 1960 to 2020. (a) The administrative units. (b) The artificial surface and cultivated land grids. Obtain the water withdrawal of the above-mentioned two groups of specific regions within each 1 km grid in an average manner.Full size imageBased on ArcGIS Desktop 10.2, convert the global land use grid into a vector format, and then extract the global artificial surface and cultivated land. The population density and water intensity on the grid are expressed as follows21:$$S{D}_{ad,P}=frac{{P}_{ad}}{{A}_{ad}},S{D}_{lu,P}=frac{{P}_{ad}}{{A}_{lu,a}}$$
    (6)
    $$S{D}_{ad,W}=frac{{W}_{ad}}{{A}_{ad}},{SD}_{lu{rm{,}}W}=frac{{W}_{ad}}{{A}_{lu,ac}}$$
    (7)
    Where, SDad, P and SDad, W are the population density and water intensity of an administrative unit, respectively; SDlu, P is the population density on the artificial surface of an administrative unit; SDlu, W is the water intensity on the artificial surface and cultivated land of an administrative unit; Pad and Wad are the population and water withdrawal of an administrative unit, respectively; Aad, Alu, a and Alu, ac are the area of an administrative unit, the area of the artificial surface of an administrative unit, and the area of artificial surface and cultivated land of an administrative unit. More

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    Author Correction: Split westerlies over Europe in the early Little Ice Age

    High-Precision Mass Spectrometry and Environment Change Laboratory (HISPEC), Department of Geosciences, National Taiwan University, Taipei, 10617, Taiwan, ROCHsun-Ming Hu, Chuan-Chou Shen, Hsien-Chen Tsai, Wei-Yi Chien & Wen-Hui SungResearch Center for Future Earth, National Taiwan University, Taipei, 10617, Taiwan, ROCHsun-Ming Hu, Chuan-Chou Shen, Hsien-Chen Tsai, Wei-Yi Chien & Wen-Hui SungDepartment of Geography, University of California, Berkeley, CA, 94720, USAJohn C. H. ChiangResearch Institute for Environmental Changes, Academia Sinica, Taipei, 11529, Taiwan, ROCJohn C. H. ChiangLaboratory of Tree-Ring Research, University of Arizona, Tucson, AZ, 85721, USAValerie TrouetUniversité Côte d’Azur, CNRS, CEPAM, Nice, 06300, FranceVéronique MichelUniversité Côte d’Azur, CNRS, OCA, IRD, Géoazur, 06560, Valbonne, FranceVéronique MichelHNHP, UMR 7194: CNRS-MNHN-UPVD, Paris, 75013, FrancePatricia ValensiFondation IPH, Laboratoire de Préhistoire Nice-Côte d’Azur, Nice, 06300, FrancePatricia ValensiInstitute of Geology, University of Innsbruck, Innsbruck, 6020, AustriaChristoph SpötlDepartment of Civilizations and Forms of Knowledge, University of Pisa, Pisa, 56126, ItalyElisabetta StarniniArchaeological Superintendency of Liguria, Genova, 16126, ItalyElisabetta StarniniToirano Cave, Piazzale D. Maineri 1, Toirano (SV), 17055, ItalyMarta ZuninoNational Science and Technology Center for Disaster Reduction, New Taipei City, 23143, Taiwan, ROCYu-Tang ChienTexas A&M University, College Station, TX, 77843, USAPing Chang & Robert Korty More