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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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    Large and inequitable flood risks in Los Angeles, California

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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

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    Ocean commitment and controversy

    Controversy pervaded the June 2022 UN Ocean Conference, with partisan alliances forming around burgeoning environmental and social issues. Yet, out of the talks, emerged strong aspirations across UN states and other stakeholders to restore and protect the ocean.Sustainable Development Goal (SDG) 14, to conserve and sustainably use the oceans, seas and marine resources, has elicited broad ambition among many sectors of society. The UN Ocean Conference (UNOC) was established to promote global progress towards achieving SDG 14, providing a forum for stakeholders to address disparate but interlinked concerns including ocean pollution, resource extraction and climate. In June 2022, ocean stakeholders spanning politics, science, industry and civil society met in Lisbon for the second high-level UN Ocean Conference, which was chaired by the presidents of Kenya and Portugal. Eight themes were discussed in general sessions — framed as interactive dialogues — although these mainly comprised prepared statements and lacked spontaneous dialogue. Prominent topics included conservation, deep-seabed mining, and the triple nexus of ocean–climate–biodiversity, distributed justice and ocean finance. More

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    Influence of point-of-use dispensers on lead level assessment in drinking water of a lead pipe-free campus

    Lead levels in a lead pipe-free campusFigure 1 (number of dispenser and faucet samples not stacked but overlapped) shows the distribution of Pb levels in the 558 water samples collected from POU dispensers (n = 204) and faucets (n = 354) during the survey. The distribution of total number of samples collected using different protocols is shown in Supplementary Table 1. Table 1 shows the water quality parameters of the sample water. Among the total 558 samples collected, regardless of sampling protocols used, 89 samples (16%) had Pb levels greater than the Taiwan EPA standard value (or the WHO guideline value) of 10 μg/L.Fig. 1: Pb levels in water samples.Distribution of Pb levels in water samples collected from POU dispensers (n = 204) and faucets (n = 354). Dotted line refers to the WHO guideline value of 10 μg/L for Pb in drinking water.Full size imageTable 1 Water quality parameters of samples.Full size tableSamples with Pb levels above 10 μg/L are considered “unsafe” in this study. Since the number of samples collected from dispensers and faucets varied, a percentage was used to represent the proportion of unsafe samples. In this regard, 66 out of 354 (19%) samples from faucets and 23 out of 204 (11%) samples from dispensers were not safe for consumption. Hence, faucet samples were approximately twice as likely to be contaminated as dispenser samples. As expected, the use of POU dispensers could effectively reduce Pb levels, but not always below the regulatory standard of 10 μg/L. Possible reasons include inadequate removal efficiency of dispenser filters and Pb-containing components in the filter system. The extent of Pb reduction (or unlikely addition) through a dispenser was, however, not determined in this study. Although POU dispensers have become necessary in delivering safe drinking water, the occurrence of unsafe samples from such dispensers showed that water from dispensers does not always meet the regulatory standard. The results also indicated that Pb contamination issues could be prevalent even if no aged Pb pipes were present. For faucet samples, the Pb sources are most likely Pb-containing plumbing materials such as brass fittings and Pb solders5,6. Although regulations of Pb in plumbing materials have evolved with time, legacy plumbing materials may still be present in the buildings. Harvey et al.5 collected water samples from kitchen tap fittings in Australia and demonstrated that Pb-containing fittings could significantly contribute to Pb in drinking water. Similarly, Ng and Lin6 concluded that brass fittings were the main source of Pb in drinking water in a simulated copper pipe premise plumbing.All buildings except Building VIII (Supplementary Table 1) had at least one sample from the faucet and dispenser exceeding 10 μg/L Pb. Building VIII is the only building without any unsafe samples from the dispensers. Buildings VII had the highest percentage of samples that were unsafe (24%), followed by buildings VI (23%), IV (17%), and III (16%) (Supplementary Table 2). Although the percentage of unsafe samples from faucets was approximately twice that from dispenser samples (Fig. 1), a higher proportion of faucet samples compared to dispenser samples collected in a building did not always correspond with an increase in the proportion of unsafe samples among the buildings (Supplementary Table 2). For example, Building III had more samples collected from faucets (70%) than Building VII (63%). Still, the proportion of unsafe samples in Building III (16% of samples) was less than in Building VII (24% of samples).Figure 2 shows the median total Pb concentration for dispensers and faucets in the eight buildings surveyed. The median Pb level ranged from 1.3 to 5.7 µg/L and 2.2 to 5.7 µg/L for dispenser and faucet samples, respectively. The median Pb level for dispenser samples was lower than faucet samples in six of the eight buildings. The difference in medians between dispenser (filtered) and faucet (unfiltered) samples were significantly different using t test (p value More