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26 results for “water towers”
Canopy-Atmosphere Exchange of Carbon, Water and Energy at Harvard Forest EMS Tower since 1991
The tower-based CO2 measurements and key meteorological drivers are intended to examine how regional and ecosystem level processes in a mid-latitude forest contribute to global carbon cycling. Specifically, we endeavor to understand quantitatively how and why forested ecosystems take up or release carbon, on time scales from hours to decades, and to elucidate responses to climate changes and management interventions. The tower was installed 1989 and the resulting eddy-flux measurements constitute the longest running record of the net-ecosystem carbon exchange in a North American Forest. The resulting long-term record of Net Ecosystem Exchange (NEE) has shown the effects of climate anomalies on carbon fluxes for seasonal and annual time scales. For example, reduced soil frost allows greater respiration in the winter leading to lower C sequestration. Cumulative gross photosynthesis depends on when the canopy emerges in the spring. Warmer springtime temperatures lead to greater uptake of C. As the NEE record is extended and augmented by supporting ecological measurements, we can further identify longer-term effects of climate perturbations on carbon fluxes and further define the relationship between stand history and carbon sequestration. Climatic anomalies in one season or year may have a longer-term effect on the sequestration of carbon in subsequent seasons or years. The flux and ecological measurements are coordinated with studies at other sites through the AmeriFlux network. By examining the relationships between carbon fluxes and the driving physical and biological variables across a range of sites we are enhancing understanding of the processes that control NEE.
Soil Water Content at Harvard Forest HEM and LPH Towers 1998-2007
Water content of the top 20 cm of soil was measured using 1.5 inch diameter soil cores. Soil water content is of particular interest and importance in explaining patterns of soil respiration (including root respiration) and ecosystem respiration, 60 to 75% of which occurs below the ground surface at Harvard Forest. Soil water content in deciduous forest near the Little Prospect Hill Forest was in general found to be more variable than near the Hemlock tower. One influence contributing to this is the presence of a water table within 1 m of the soil surface near the hemlock tower, compared to a water table at unknown depth in the deeply drained soils in most parts of Little Prospect Hill. Relatively low evapotranspiration at the Hemlock tower site during the period when deciduous trees are foliated also contributes to higher water content in soil there, and a relatively thick surface organic layer. These influences tend to maintain soil respiration at higher levels in the hemlock forest during dry summers, but excessive moisture in the soil at the Hemlock site during very wet summers appears to suppress soil respiration.
Long-term Atmospheric, Soil and Water Sensor Data from the GCE-LTER Eddy Covariance Flux Tower on Sapelo Island, Georgia
Long-term measurements of various atmospheric, soil and water properties were made using electronic sensors attached to the GCE-LTER eddy covariance flux tower deployed in a Spartina alterniflora salt marsh on Sapelo Island, Georgia. Variables measured include air and water temperature, relative humidity, precipitation, wind speed and direction, soil temperature, water pressure and solar radiation components (i.e. incident and reflected photosynthetically available, total, long-wave and shortwave radiation). Measurements were logged at 5 minute intervals using multiple Campbell Scientific Instruments CR3000 data loggers, and then combined into a single monotonic time series data set. Quality control analyses were performed to remove values deemed invalid due to sensor failure or miscalibration and to assign Q/C qualifiers to values outside expected ranges or failing various sanity and quality checks of the data. Note that some measurements were spatially replicated with multiple sensors deployed in different micro-habitats (e.g. at the tower and in a nearby marsh platform or creek). Sensors were also added to the tower at various times after the initial installation, therefore some variables do not span the entire period of record. Measurements at this site are ongoing, and the data set will be updated annually to include additional observations.
Initial Sample of HYPERNETS Hyperspectral Water Reflectance Measurements for Satellite Validation at the measurement tower MOW1, M1BE site (Belgium)
<p>The HYPERNETS project (www.hypernets.eu) has the overall aim to ensure that high quality in situ measurements are available to support the (VNIR/SWIR) optical Copernicus products. Therefore, it established a new autonomous hyperspectral spectroradiometer (HYPSTAR® - www.hypstar.eu) dedicated to land and water surface reflectance validation with instrument pointing capabilities. In the prototype phase, the instrument is being deployed at 24 sites covering a range of water and land types and a range of climatic and logistic conditions. This dataset provides the first published data for the HYPERNETS site at the measurement pole near the <em>Zeebrugge</em> harbour 3.65km from land, often called MOW1, in Belgium (M1BE). It is a subset of the complete data record which consists of the best quality M1BE measurements which could be used for satellite validation. </p> <p>The provided NetCDF files are the L2A hypernets products with water leaving radiance and reflectances, with and without NIR Similarity Correction (see Ruddick et al., 2006, DOI:<a href="http://dx.doi.org/10.2307/3841124">10.2307/3841124</a>). The reflectance in the L2A products is the Water Reflectance without NIR Similarity Correction (referred to as reflectance_nosc in the file) defined as:</p> <p><span class="math-tex">\(\rho_wnosc=\pi (Lu-\rho_FLd)/E_d\)</span></p> <p>where Lu is the upwelling radiance (at 40° zenith angle, and, 90° or 135° azimuth angle relative to the sun), Ld is the downwelling radiance (at 140° zenith angle, and, 90° or 135° azimuth angle relative to the sun). Ed is the (hemispherical) downwelling irradiance (i.e. including both direct solar and diffuse sky irradiance).</p> <p>For the M1BE site, the reflectance corrected for the NIR Similarity correction (epsilon, see Ruddick et al., 2006) is also provided:</p> <p><span class="math-tex">\(\rho_w=\pi (Lu-\rho_FLd)/E_d-\epsilon\)</span></p> <p>These reflectances have dimensions of wavelength and series, where each series is a set of measurements for the computation of a water reflectance measurement. In addition to variables for wavelength and bandwidth, the files also contain variables that provide for each series the acquisition time, viewing and solar angles, and quality flags (typically no flags are set in the data provided in this dataset). These NetCDF files also contain further relevant metadata as attributes. See https://hypernets-processor.readthedocs.io/ for further info.</p> <p>The HYPSTAR®-SR (Standard Range) instruments deployed at each land HYPERNETS site consist of a VNIR sensor and autonomously collect data between 380-1000 nm at various viewing geometries and send it to a central server for quality control and processing. The VNIR sensor spans 1330 channels between 380 and 1000 nm with a FWHM of 3 nm. The hypernets_processor (Goyens et al. 2021, DOI: <a href="https://doi.org/10.1109/IGARSS47720.2021.9553738">10.1109/IGARSS47720.2021.9553738</a>; De Vis et al. in prep.) automatically processes all this data into various products, including the L2A surface reflectance product provided here. The current dataset is limited to the 400-900 nm range. Uncertainties are not yet included.</p> <p>To obtain this dataset, we start from the full M1BE data record and omit all the data that do not pass all of the quality checks performed as part of the hypernets_processor. In addition, an additional screening procedure was developed to supply the best quality data suitable for satellite validation:</p> <p>1. The coefficient of variation in water reflectance is below 10% in the 500-600 nm range</p> <p>2. The water reflectance at 500 nm is below 0.1</p> <p>The data consists of 73 spectra ranging from 20230226T1431 till 20230429T1502.</p> <p>Coordinates of the site are the following:</p> <p>site_latitude = 51.360548<br> site_longitude = 3.118246</p> <p>The site is owned by Afdeling Kust (https://www.agentschapmdk.be/nl).</p> <p> </p>
Water Tower Index for Saddle Catchment, 2000 - 2018.
The Saddle Catchment of the Niwot Ridge LTER experiences significant spatial variation regarding snow distribution and, in turn, the timing and amount and surface water input generation (i.e., rainfall and snowmelt). In winter months and in areas that receive a high amount of redistributed snow (via wind), snow accumulation is large and the snowpack persists until the snowmelt season, creating a lag between the timing of precipitation (as snow) and the timing of surface water inputs (as snowmelt). Alternatively, areas that are scoured of snow, retain little snow. In these areas, rain is the primary source of surface water inputs, and thus there is little/no lag in the timing of precipitation and surface water inputs (as rain). The lag in the timing of precipitation and surface water inputs in the wind deposition zones, however, is critical to providing water to the surrounding and downstream environments later in the year. The snow in these areas thus act as a natural water tower to retain water (as snow) until the snowmelt season. To capture the timing and magnitude of the delay between precipitation and surface water inputs, a Water Tower Index (WTI) was generated. The WTI uses equations (see Methods) to fit a sine curve to annual precipitation (P) and annual surface water inputs (SWI). A third equation then compares to the phase and amplitude of the two sine curves, generating a metric between -1 and 1. Positive WTI values signify P and SWI out of temporal alignment (where WTI = 1 indicates strong (i.e., high amplitude) temporal misalignment between P and SWI). Negative WTI values signify P and SWI in temporal alignment (where WTI = -1 indicates strong (i.e., high amplitude) temporal alignment between P and SWI). P and SWI data were taken from DHSVM output run by Nels Bjarke, which included 19 years of information (WY2000-WY2018). The methodology was applied, spatially, across the Saddle Catchment of the Niwot Ridge LTER. In turn, this dataset reveals, importantl
Supplementary data to: Importance and vulnerability of the world's water towers
<p>This archive contains data produced for a study assessing the importance and vulnerability of the world’s water towers. Code (R-scripts) used to process these files is available on the <a href="https://github.com/mountainhydrology/pub_ngs-watertowers">MountainHydrology Github page</a></p> <p>The archive is organized in directories with specific topics. Each directory contains input files (optional) and output/processed files. The input files can be used in combination with the R-scripts published on <a href="https://github.com/mountainhydrology/pub_ngs-watertowers">Github</a> to generate the processed files included in this archive. In many cases external published data is used as input data for the calculations. In that case the data is not included in this archive but literature references and links to the specific files are provided in the description below. Files which have been preprocessed before use in the R-scripts are included in this archive. For calculation details please see the publication, in particular Extended Data Tables 3 and 4.</p> <p><strong>Archive contents</strong></p> <p>The archives contents are organized in eight separate directories, which are listed here, along with their contents:</p> <ul> <li><strong>ERA5</strong></li> </ul> <p>Precipitation and evaporation data are extracted from ERA5 reanalysis available online in the Copernicus Climate Data Store at https://cds.climate.copernicus.eu</p> <p>This directory includes:</p> <p><em>Input</em></p> <pre><code>ERA5_evaporation_avgannual_2001_2017.nc - Average annual evaporation (mm) for 2001-2017 ERA5_evaporation_ymonmean_2001_2017.nc - Multi-year mean monthly evaporation (mm) for 2001-2017 era5_total-precipitation_ymonmean_2001-2017_global.tif - Multi-year mean monthly precipitation (mm) for 2001-2017 era5_total-precipitation_yearsum_2001-2017.tif - Average annual precipitation (mm) for 2001-2017</code></pre> <p><em>Output</em></p> <pre><code>P_avg_annual_basin_mm.tif - Average annual precipitation 2001-2017 (mm) aggregated to basins P_avg_annual_DS_mm.tif - Average annual precipitation 2001-2017 (mm) aggregated to downstream basins P_avg_annual_mm.tif - Average annual precipitation 2001-2017 (mm) P_avg_annual_WT_mm.tif - Average annual precipitation 2001-2017 (mm) aggregated to Water Tower Units P_var_interannual.tif - Interannual variablity in precipitation 2001-2017 P_var_interannual_basin.tif - Interannual variablity in precipitation 2001-2017 aggregated to basins P_var_interannual_DS.tif - Interannual variablity in precipitation 2001-2017 aggregated to downstream basins P_var_interannual_WT.tif - Interannual variablity in precipitation 2001-2017 aggregated to Water Tower Units P_var_intraannual.tif - Intra-annual variablity in precipitation 2001-2017 P_var_intraannual_basin.tif - Intra-annual variablity in precipitation 2001-2017 aggregated to basins P_var_intraannual_DS.tif - Intra-annual variablity in precipitation 2001-2017 aggregated to downstream basins P_var_intraannual_WT.tif - Intra-annual variablity in precipitation 2001-2017 aggregated to Water Tower Units WTU_P_indicators.csv - Table listing all calculated precipition indicators per Water Tower Unit</code></pre> <ul> <li><strong>Glaciers</strong></li> </ul> <p>Glacier volume and mass balance are derived from published datasets. This directory includes:</p> <p><em>Output</em></p> <pre><code>Glac_area_WT_km2.tif - Glacier area (km2) aggregated for Water Tower Units Glac_volume_WT_km3.tif - Glacier volume (km3) aggregated for Water Tower Units WTU_Glacier_indicators.csv - Table listing all derived glacier indicators per Water Tower Unit WTU_MB.shp - shapefile of Water Tower Units including the glacier mass balance per Water Tower Units as attribute</code></pre> <p><em>External data</em></p> <p>Glacier volume data published in<em> Farinotti et al., 2019, Nature Geoscience</em>, were used.<br> Reference: Farinotti, D. et al. A consensus estimate for the ice thickness distribution of all glaciers on Earth. Nat. Geosci. 12, 168–173 (2019).<br> Glacier volume (km3) and glacier area (km2) at 0.05 degrees spatial resolution were used, which are available <a href="https://www.research-collection.ethz.ch/bitstream/handle/20.500.11850/315707/global_fraction-of-degree_grids.zip?sequence=60&isAllowed=y">here</a>.<br> The used files are <em>p05_degree_glacier_area_km2.tif</em> and <em>p05_degree_glacier_volume_km3.tif</em></p> <p>Glacier mass balance data published by the World Glacier Monitoring Service were used to derive an average glacier mass balance per Water Tower Unit.<br> References:<br> Zemp, M. et al. Global glacier mass changes and their contributions to sea-level rise from 1961 to 2016. Nature 568, 382–386 (2019).<br> World Glacier Monitoring Service. Fluctuations of Glaciers (FoG) Database. (2018). doi:10.5904/wgms-fog-2018-06</p> <ul> <li><strong>HydroLAKES</strong></li> </ul> <p>Surface lake and water storage per Water Tower Unit was calculated. This directory includes:</p> <p><em>Output</em></p> <pre><code>WTU_lake_storage_volume.csv - Table listing lake and reservoir volume (km3) per Water Tower Unit WTU_surface_water_storage_km3.tif - Lake and reservoir storage volume (km3) aggregated to Water Tower Units</code></pre> <p><em>External data</em></p> <p>For surface water lakes and reservoirs the HydroLAKES dataset is used. The shapefile <em>HydroLAKES_polys_v10.shp</em> can be downloaded from <a href="http://https://97dc600d3ccc765f840c-d5a4231de41cd7a15e06ac00b0bcc552.ssl.cf5.rackcdn.com/HydroLAKES_polys_v10_shp.zip">HydroSheds</a></p> <p>Reference: Messager, M. L., Lehner, B., Grill, G., Nedeva, I. & Schmitt, O. Estimating the volume and age of water stored in global lakes using a geo-statistical approach. Nat. Commun. 7, 1–11 (2016).</p> <ul> <li><strong>Indicators</strong></li> </ul> <p>All indicators and subindicators calculated for the Water Tower Index calculation are stored per Water Tower Unit.</p> <p>This directory includes:</p> <pre><code>indicators.csv - Table with all indicators and subindicators per Water Tower Unit</code></pre> <ul> <li><strong>Snow</strong></li> </ul> <p>The MODIS MOD10CM006 snow cover product was used to derive snow persistence.<br> Reference: Hall, D. K. & Riggs, G. A. MODIS/Terra Snow Cover Monthly L3 Global 0.05Deg CMG, Version 6. (2015). doi:10.5067/MODIS/MOD10CM.006</p> <p>This archive includes:<br> <em>Input</em></p> <pre><code>MOD10CM006_yearmean_2001-2017.tif - Annual mean snow cover 2001-2017 MOD10CM006_ymonmean_2001-2017.tif - Multi-year mean monthly snow cover 2001-2017</code></pre> <p><em>Output</em></p> <pre><code>Snow_persistence_avg_annual.tif - Average annual snow persistence 2001-2017 Snow_persistence_avg_annual_WT.tif - Average annual snow persistence 2001-2017 aggregated to Water Tower Units Snow_persistence_var_interannual.tif - Interannaul variability in snow persistence 2001-2017 Snow_persistence_var_interannual_WT.tif - Interannaul variability in snow persistence 2001-2017 aggregated to Water Tower Units Snow_persistence_var_intraannual.tif - Intra-annaul variability in snow persistence 2001-2017 Snow_persistence_var_intraannual_WT.tif - Intra-annaul variability in snow persistence 2001-2017 aggregated to Water Tower Units WTU_Snow_indicators.csv - Table listing all derived snow indicators per Water Tower Unit</code></pre> <ul> <li><strong>Uncertainty</strong></li> </ul> <p>The directory contains the uncertainty ranges used in the uncertainty analysis<br> The directory includes:</p> <pre><code>ET_uncertainty_per_downstream.csv - Table listing SD in evaporation per downstream basin ET_uncertainty_per_WTU.csv - Table listing SD in evaporation per Water Tower Unit P_uncertainty_per_downstream.csv - Table listing SD in precipitation per downstream basin P_uncertainty_per_WTU.csv - Table listing SD in precipitation per Water Tower Unit WTU_IceVol_uncertainty.csv - Table listing uncertainty in ice volume per Water Tower Unit</code></pre> <ul> <li><strong>Water demands</strong></li> </ul> <p>Net water demands for irrigation, industrial and domestic water use, as well as the environmental flow requirement are extracted from PCR-GLOBWB hydrological model output.<br> Reference: Wada, Y., De Graaf, I. E. M. & van Beek, L. P. H. High-resolution modeling of human and climate impacts on global water resources. J. Adv. Model. Earth Syst. 8, 735–763 (2016).</p> <p>The directory includes:<br> <em>Input</em></p> <pre><code>Dom_use_ymonmean_2001_2014_005.tif - Multi-year mean monthly net domestic water demand 2001-2014 at 0.05 degrees resolution (km3) Ind_use_ymonmean_2001_2014_005.tif - Multi-year mean monthly net industrial water demand 2001-2014 at 0.05 degrees resolution (km3) Irr_use_ymonmean_2001_2014_005.tif - Multi-year mean monthly net irrigation water demand 2001-2014 at 0.05 degrees resolution (km3) Tot_use_ymonmean_2001_2014_005.tif - Sum of the three above global_historical_riverdischarge_ymonmean_m3second_5min_2001_2014.nc4 - Multi-year mean monthly natural discharge (m3/s) 2001-2014</code></pre> <p><em>Output</em></p> <pre><code>Domestic_use_avg_annual_basin_km3.tif - Average annual net domestic water demand 2001-2014 aggregated to basins Domestic_use_avg_annual_km3.tif - Average annual net domestic water demand 2001-2014 Industrial_use_avg_annual_basin_km3.tif - Average annual net industrial water demand 2001-2014 aggregated to basins Industrial_use_avg_annual_km3.tif - Average annual net industrial water demand 2001-2014 Irrigation_use_avg_annual_basin_km3.tif - Average annual net irrigation water demand 2001-2014 aggregated to basins Irrigation_use_avg_annual_km3.tif - Average annual net irrigation water demand 2001-2014 Natural_demand_avg_annual_basin_km3.tif - Average annual natural water demand 2001-2014 aggregated to basins Total_human_demand_avg_annual_basin_km3.tif - Average annual net human (sum of domestic, industrial and irrigation) water demand 2001-2014 aggregated to basins Water_gap_average_annual_basin.tif - Average annual water gap 2001-2014 aggregated to basins WTU_Demand_DS_P_available.csv - Table listing dowstream water availability per sector per basin WTU_Demand_indicators.csv - Table listing demand per sector per basin WTU_Domestic_Water_Gap_monthly.csv - Table listing multi-year average monthly domestic water gap per basin WTU_Industrial_Water_Gap_monthly.csv - Table listing multi-year average monthly industrial water gap per basin WTU_Irrigation_Water_Gap_monthly.csv - Table listing multi-year average monthly irrigation water gap per basin WTU_Natural_Water_Gap_monthly.csv - Table listing multi-year average monthly natural water gap per basin WTU_Total_Water_Gap_monthly.csv - Table listing multi-year average monthly water gap per basin</code></pre> <ul> <li><strong>WTU units</strong></li> </ul> <p>The spatial units for all calculations are the Water Tower Units, their downstream basins, and the entire basins (Water Tower Unit + downstream basin). They are extracted using definitions of basins and mountain ranges. This directory includes:</p> <p><em>Output</em></p> <pre><code>basins.tif - Definition of basins with Water Tower Units at 0.05 degrees spatial resolution basins_downstream.tif - Definition of downstream basins at 0.05 degrees spatial resolution basins_vector.shp - Definition of basins with Water Tower Units as vector data downstream_vector.shp - Definition of downstream basins as vector data gmba_all.shp - All GMBA mountain ranges including glacier volume and snow persistence gmba_ss.shp - GMBA mountain ranges included in Water Tower Units WTU.tif - Definition of Water Tower Units as 0.05 degrees spatial resolution WTU_specs.csv - Table with set of specifications of Water Tower Units WTU_vector.shp - Definition of Water Tower Units as vector data</code></pre> <p><em>External data</em></p> <p>FAO's classification of major hydrological basins and FAO's classification of subbasins per continent are used. These are based on HydroSheds and are available as shapefiles at <a href="http://www.fao.org/nr/water/aquamaps/">FAO Aquamaps</a></p> <p>The specific shapefiles used are:</p> <p><a href="http://www.fao.org/geonetwork/srv/en/resources.get?id=38047&fname=Major_hydrological_basins.zip&access=private">major_hydrobasins.shp</a><br> <a href="http://www.fao.org/geonetwork/srv/en/resources.get?id=37039&fname=hydrobasins_asia.zip&access=private">hydrobasins_asia.shp</a><br> <a href="http://www.fao.org/geonetwork/srv/en/resources.get?id=37174&fname=hydrobasins_southam.zip&access=private">hydrobasins_southam.shp</a><br> <a href="http://www.fao.org/geonetwork/srv/en/resources.get?id=38044&fname=hydrobasins_northam.zip&access=private">hydrobasins_northam.shp</a><br> <a href="http://www.fao.org/geonetwork/srv/en/resources.get?id=37299&fname=hydrobasins_neareast.zip&access=private">hydrobasins_neareast.shp</a><br> <a href="http://www.fao.org/geonetwork/srv/en/resources.get?id=37250&fname=hydrobasins_europe.zip&access=private">hydrobasins_europe.shp</a><br> <a href="http://www.fao.org/geonetwork/srv/en/resources.get?id=37173&fname=hydrobasins_centralam.zip&access=private">hydrobasins_centralam.shp</a><br> <a href="http://www.fao.org/geonetwork/srv/en/resources.get?id=37251&fname=hydrobasins_austpacific.zip&access=private">hydrobasins_austpacific.shp</a>:</p>
UAV Surveying - Water Tower inside a school
<p>The data provided in this dataset is from a surveying flight in a water infrastructure inside a school.</p> <p>The dataset contains information of a 3D LiDAR, a camera, 3 IMUs, drone GPS position and velocity, and RTK position and velocity data, in rosbag format.</p> <p>The LiDAR is an Ouster OS1-128 Rev7, the IMUs are: drone IMU (unknown model), Xsens MTi 630 AHRS and LiDAR internal IMU.</p> <p>The camera has 1280x960p and 145º FOV.</p> <p>The sensor intrinsic and extrinsic are available in the dataset.</p>
MODIS Daily Cloud-gap-filled Fractional Snow Cover Dataset of the Asian Water Tower Region (2000-2022)
<p>The Asia Water Tower region, with the Qinghai-Tibet Plateau at its core, is the most widespread region of snow cover on Earth, except for the North and South Poles. The topographic heterogeneity of the Asian Water Tower region is so great that the snow cover is thin and patchy, resulting in a highly time-varying snow cover in the region, and therefore daily-scale fractional snow cover data are urgently needed. This dataset is based on the MODIS surface reflectance product MO/YD09GA product, and the MODIS daily cloud-free fractional snow cover dataset for the Asian Water Tower region from 2000 to 2022 was produced using the MESMA-AGE algorithm and the MSTI algorithm. The high spatial resolution Landsat-8 image was taken as the "ground truth", the RMSE was 0.16, and the MAE was 0.10. This dataset has a time series from 26 February 2000 to 31 December 2022 with a spatial resolution of 0.005°, which can provide quantitative snow cover information on the spatial distribution of snow for mountain hydrological models, land surface models, numerical weather forecasts, etc.</p>
Validation dataset of 30 m resolution Landsat-8 fractional snow cover in the Asian Water Tower region (2013-2022)
<p>This dataset is the validation dataset for the article 'MODIS Daily Cloud-gap-filled Fractional Snow Cover Dataset of the Asian Water Tower Region (2000-2022)', which is a 30m resolution Landsat-8 fractional snow cover dataset for the time period 2013-2022 in the Asian Water Tower Region. This dataset is based on 3046 scene Landsat-8 surface reflectance data and uses the MESMA-AGE algorithm to retrieve the fractional snow cover. Gaofen-2 images with higher resolution were used to evaluate the accuracy, and the results showed that the accuracy was better, with OA of 94.46% and RMSE of 0.094. The cloud cover of each image in this dataset is less than 10% and the snow cover is more than 30%, which can be used to validate medium- or coarse-scale snow products and to study the spatial distribution of snow at high spatial resolution.</p>
Tower Theatre Water Fountain
A restored water fountain on the balcony level of Apple Tower Theatre in Los Angeles, California. Source: Objaverse 1.0 / Sketchfab
City of Rauma Water Tower
A model I made earlier this year, I found in the middle of some folders, is the water tower of the city of rauma in finland, based on a version of a simple game I played as a child called city of rauma, it's like a very simple simcity for those who are curious, the site still exists: https://www.raumagame.com/pt/ultimas-noticias/ Twiter:https://twitter.com/heferumgames Source: Objaverse 1.0 / Sketchfab
Morningside Reservoir Water Tower, 1
One graffiti covered wall at the first water tower of Morningside Reservoir in Syracuse, NY. Created with Trnio Plus Beta. Source: Objaverse 1.0 / Sketchfab
Crystal Palace Water Tower Ruins
The ruins of one of the south water tower at Crystal Palace Park, London. It is located next to the Crystal Palace Museum. The towers were demolished in WW2 so as not to become navigation aids for the enemy. Date: 1855 314 photos taken in February 2020 with a Sony a6000 and processed in Reality Capture. Source: Objaverse 1.0 / Sketchfab
Supplementary data to: Soil Moisture to Runoff (SM2R) A data-driven model for runoff estimation across poorly gauged Asian water towers based on soil moisture dynamics
<p>This data archive includes simulated monthly runoff anomaly using the Soil Moisture to Runoff model during 1981‒2020 over the representative drainage basin in each water tower. Please see Readme for more data information. For calculation details please see the publication.</p>
Wieża wodna WATER TOWER
Wieża wodna – wieża ciśnień zlokalizowana w murze klasztoru brygidek przy ul. Dolnej Panny Marii. W górnej kondygnacji wieży znajdował się zbiornik o pojemności 6m3 służący do retencjonowania wody pitnej (2). Woda tłoczona była do niego z rurmusa metalową rurą zasilającą (1), a następnie prowadzona rurą (3) i rozprowadzana grawitacyjnie rurociągiem po obszarze Krakowskiego Przedmieścia i miasta w murach. Wieża istnieje do dnia dzisiejszego. https://teatrnn.pl/lublin-woda/modele-3-d/ Model wykonano w oparciu o materiały Stanisławy Hoczyk-Siwkowej, Zdzisława Mazurka oraz Dagmary Kociuby. Realizacja 3D: Robert Miedziocha, Wojciech Miedzicha http://poligonstudio.pl/ Opisy i konsultacje merytoryczne: dr Dagmara Kociuba (UMCS Lublin). Koordynacja projektu: Piotr Lasota (Ośrodek "Brama Grodzka – Teatr NN") Projekt dofinansowany przez Muzeum Historii Polski w ramach programu "Patriotyzm Jutra". Source: Objaverse 1.0 / Sketchfab
Understanding the Asian Water Tower requires a new precipitation observation strategy
<p>The Asian water tower (AWT) serves as the source of 10 major Asian river systems and supports the lives of ~2 billion people. Obtaining reliable precipitation data over AWT is a prerequisite for understanding the water cycle within this pivotal region. Here, we quantitatively reveal that the “observed” precipitation over the AWT is considerably underestimated in view of observational evidence from three water cycle components, namely, evapotranspiration, runoff and accumulated snow. We found that three paradoxes appear if the so-called “observed” precipitation is corrected, namely, actual evapotranspiration exceeding precipitation, unrealistically high runoff coefficients, and accumulated snow water equivalent exceeding contemporaneous precipitation. We then explained the cause of precipitation underestimation from instrumental error caused by wind-induced gauge undercatch and the representativeness error caused by sparse-uneven gauge density and complexity of local surface conditions. These findings require us to rethink previous results related to the water cycle, and the study then introduced potential solutions.</p> <p>This provides the main dataset used in this study, including the observed precipitation, evapotranspiration, runoff, and snow water equivalent across AWT.</p>
Water Tower
Source: Objaverse 1.0 / Sketchfab
water tower - Wasserturm Essen-Steele
Photogrammetry made thanks to the good work of Klaus Zimmermann, in creative commons : https://www.youtube.com/watch?v=kuJuRCpMNCI&list=FL6qk-kzSZTx_jRRhlNz1OLA&index=5 The Essen-Steele water tower at Laurentiusweg 83 in the Steele district of Essen, Germany, was built in 1898 at the highest geographical point in Steele. Source: Objaverse 1.0 / Sketchfab
Water Tower
A water tower is an elevated structure supporting a water tank constructed at a height sufficient to pressurize a water supply system for the distribution of potable water, and to provide emergency storage in case the town runs out of water. In some places, the term standpipe is used interchangeably to refer to a water tower, especially one with tall and narrow proportions. Source: Objaverse 1.0 / Sketchfab
Roland Water Tower
The Roland Water Tower is a relic of Baltimore's early water supply system. It was designed by William J. Fizone and built in 1905 as a 211,000 gallon water tank to supply the residents in nearby neighborhoods. The structure stands at 148-foot-tall. In 1930, the tower was taken out of service when a new reservoir system was implemented. In 1960, the landmark became a turnaround spot for streetcars. Recently, the structure received funding for restoration and the park space surrounding it. 4210 Roland Ave, Baltimore, MD 21210 **1911 Postcard Image **  Source: [Baltimore Heritage](https://explore.baltimoreheritage.org/items/show/197), [Roland Tower Website](https://rolandwatertower.org/history/) Source: Objaverse 1.0 / Sketchfab
ScienceDex guides
Understand access before you commit
These curated guides explain access requirements, typical timelines, costs, and reuse considerations for widely used research datasets.
Allen Brain Atlas
Allen Brain Atlas is an Allen Institute collection of brain map atlases, datasets, APIs, and analysis tools covering mouse, human, and non-human primate brain resources.
Annotated Behaviour and Observability Dataset (ABODe)
ABODe is a University of Edinburgh DataShare dataset for behavior classification in group-housed mice using home-cage video, identities, bounding boxes, ground-plate positions, and annotator labels.
DANDI Archive for NWB datasets
DANDI is a BRAIN Initiative archive for publishing and sharing neurophysiology data, including electrophysiology, optophysiology, and behavioral data packaged as NWB and related standards.
International Brain Laboratory public data
The International Brain Laboratory public data releases expose standardized mouse decision-making experiments, including Neuropixels recordings, widefield calcium imaging, behavior, and session metadata accessed through the ONE API.
OpenNeuro
OpenNeuro is a free, open platform for sharing neuroimaging datasets, with public search, dataset pages, and download paths for web, S3, DataLad, and the OpenNeuro CLI.