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58 results for “water storage”
Data from: Recent decreases in snow water storage in western North America
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Stand-level variation drives canopy water storage by non-vascular epiphytes across a temperate-boreal ecotone
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Compilation of experimental data on water storage capacity in olivine, wadsleyite and ringwoodite (061721 updated)
<p>We compiled high-pressure mineral physics data on water storage capacities in olivine, wadsleyite, and ringwoodite from the literature (published as Tables S1–S3 in Dong et al., 2021). The references are listed in the second tab of each xlsx file.</p> <p>This version was published on Oct. 21, 2020 (version 1.0) and includes "data-olivine-table-s1.xlsx", "data-wadsleyite-table-s2.xlsx", and "data-ringwoodite-table-s3.xlsx"</p> <p>Please cite "Dong, J., Fischer, R. A., Stixrude, L. P., & Lithgow‐Bertelloni, C. R. (2021). Constraining the Volume of Earth's Early Oceans With a Temperature‐Dependent Mantle Water Storage Capacity Model. <em>AGU Advances</em>, <em>2</em>(1), e2020AV000323." for the dataset version 1.0.</p> <p><strong>An update to the data compilation of Dong et al. (2021) (version 1.0) is now published as version 2.0 on Jun. 17, 2021, and includes additional literature data on the water storage capacity in olivine, wadsleyite, and ringwoodite ("additional-data-for-mars-061721.xlsx")</strong></p>
Terrestrial water storage data based on the generalized three-cornered hat fusion method
<ul> <li>The dataset combines three GRACE Mascon products (CSR, JPL and GSFC) using the generalized three-cornered hat method (GTCH) to generate, referred to as TWS_GTCH. The dataset spans from 2003 to 2022, with a spatial resolution of 0.5 degrees and a temporal resolution of monthly.</li> <li>We deduct 2004-2009 means from the three GRACE terrestrial water storage (TWS) Mascon products to ensure that they have the same reference period. To enhance the reliability of the results and eliminate differences between datasets, we employ the GTCH method to calculate the root mean square error (rmse) of the GRACE TWS data to assess uncertainty. Finally, we utilize the least squares method to fuse the data based on their uncertainties.</li> </ul>
Conceptualizing relationships among hyporheic exchange, storage, and water age: data represented in published figures
<p>Hyporheic exchange is a key driver of ecosystem processes in streams, yet stream ecologists often fail to leverage detailed conceptual models developed by engineers and hydrologists describing the relationship between water storage, water balance, and water age (time elapsed since a conceptual parcel of water entered the hyporheic zone) in hyporheic zones. In a companion paper (G.C. Poole et al. <em>Hyporheic Hydr</em><em>au</em><em>lic Geometry: Conceptualizing relationships among hyporheic exchange, storage, and water age</em>, published in PLoS ONE; doi:<a href="https://doi.org/10.1371/journal.pone.0262080">10.1371/journal.pone.0262080</a>), we provide visualizations of these relationships in an effort to allow non-hydrologists to grasp four primary concepts along with associated research and management implications: 1) the rate of hyporheic exchange, size of the hyporheic zone, and hyporheic water age are inexorably linked; 2) such linkages can be leveraged to build understanding of hyporheic processes; 3) the age distribution of hyporheic water and hyporheic discharge is heavily skewed toward young water ages -- at any temporal scale of observation (minutes, hours, days, or months) older hyporheic water is rare relative to younger water; 4) the age distribution of water discharged from any hyporheic zone is not the same as the age distribution of water stored within that hyporheic zone. The data set presented here represents the numerical values represented by the figures published in the companion paper.</p>
Flood Detection Using GRACE Terrestrial Water Storage and Extreme Precipitation
<p>The supplementary figures contain 2380 precipitation-type flood events recorded by the DFO compared with results derived based on GRACE and MODIS. "DFO-GRACE-MODIS" folder means comparison among flood derived from DFO, GRACE and MODIS while "DFO-GRACE" folder means comparison between DFO and GRACE due to lack of MODIS-derived flood inundation. As for detailed information, please refer to the corresponding paper. </p>
Monthly water storage anomalies, precipitation, and groundwater recharge in two karstic basins, southwest China (2003-2014)
<p>This dataset includes the regionally-averaged monthly hydrological data in two karstic basins, southwest China which are processed or estimated for the manuscript entitled "A novel approach for assessing groundwater recharge by combining GRACE and baseflow with case studies in karst areas of southwest China" by Huang et al., 2022 (submitted to Water Resources Research, Major Revision).</p> <p><strong>Basin description:</strong></p> <p>The Wujiang River Basin (WRB, ~87,900 km<sup>2</sup>, ~70% karstification) and Xijiang River Basin (XRB, ~360,000 km<sup>2</sup>, ~44% karstification) are two typical karstic basins in southwest China. The Wujiang River is the largest tributary in the southern part of the upper Yangtze River. It is originated from the Wumeng Mountain in the Yunnan-Guizhou Plateau and flows from Guizhou to Chongqing. The Xijiang River is the largest tributary of the Pearl River (the largest river in southern China). It is originated from the mountains in eastern Yunnan and flows to Guangxi, Guangdong, and finally into the South China Sea.</p> <p><strong>Data description:</strong></p> <p>1. TWSA (unit: mm in equivalent water thickness) is the average terrestrial water storage anomaly data obtained from three release 6 GRACE (Gravity Recovery and Climate Experiment) mascon solutions, i.e., the Center for Space Research (CSR) at the University of Texas (<a href="http://www2.csr.utexas.edu/grace/RL06_mascons.html">http://www2.csr.utexas.edu/grace/RL06_mascons.html</a>), Jet Propulsion Laboratory (JPL, <a href="https://podaac.jpl.nasa.gov/dataset/TELLUS_GRAC-GRFO_MASCON_CRI_GRID_RL06_V2">https://podaac.jpl.nasa.gov/dataset/TELLUS_GRAC-GRFO_MASCON_CRI_GRID_RL06_V2</a>), and Goddard Space Flight Center (GSFC, <a href="https://earth.gsfc.nasa.gov/geo/data/grace-mascons">https://earth.gsfc.nasa.gov/geo/data/grace-mascons</a>). The anomalies were estimated by removing a mean background value during 2006-2012.</p> <p>2. SMSA and SWSA (unit: mm in equivalent water thickness) are the soil moisture storage anomaly and surface water storage anomaly data based on the model simulations by WGHM (v2.2d) provided by Dr. Hannes Müller Schmied (email: hannes.mueller.schmied@em.uni-frankfurt.de) at Institute of Physical Geography, Goethe-University Frankfurt. The anomalies were estimated by removing a mean background value during 2006-2012.</p> <p>3. Precipitation data (unit: mm/month) were based on the monthly gridded (0.5×0.5 degree) data product obtained from China Meteorological Administration (CMA, https://data.cma.cn/) which was interpolated from ground-based data measured by meteorological stations.</p> <p>4. Groundwater recharge (unit: mm/month) was estimated based on the groundwater budget method, i.e., the summation of groundwater storage change (GWSC) and baseflow. GRACE-based recharge was estimated using the GWSC derived from GRACE TWSA, WGHM-simulated SMSA and SWSA and in situ-based reservoir storage data. Observation-based recharge was estimated using the GWSC based on in situ groundwater-level data and specific yield (or storage coefficient). Both GRACE- and observation-based recharge were estimated using the baseflow separated by a multiple linear regression using the in situ streamflow as predictand, and precipitation and water table depth data as predictors.</p> <p>Time span: 2003-2014. The time lable like "200301" means January, 2003. "200312" means December, 2003.</p>
Nitrogen and water availability control plant carbon storage with warming
<p>Plants may slow global warming through enhanced growth, because increased levels of photosynthesis stimulate the land carbon (C) sink. However, how climate warming affects plant C storage globally and key drivers that determining the response of plant C storage to climate warming remains unclear, causing uncertainty in climate projections. We performed a comprehensive meta-analysis, compiling 393 observations from 99 warming studies to examine the global patterns of plant C storage responses to climate warming and explore the key drivers. Warming significantly increased total biomass (+8.4%), aboveground biomass (+12.6%) and belowground biomass (+10.1%). The effect of experimental warming on plant biomass was best explained by the availability of soil nitrogen (N) and water. Across the entire dataset, warming-induced changes in total, aboveground and belowground biomass all positively correlated with soil C:N ratio, an indicator of soil N availability. In addition, warming stimulated plant biomass more strongly in humid than in dry ecosystems, and warming tended to decrease root:shoot ratios at high soil C:N ratios. Together, these results suggest dual controls of warming effects on plant C storage; warming increases plant growth in ecosystems where N is limiting plant growth, but it reduces plant growth where water availability is limiting plant growth. Together, these findings suggested that warming effects on plant C storage largely depend on soil N and water status, which should be considered into Earth system models to improve the future prediction of C-climate change feedbacks. </p>
Dataset used in Kittel et al., 2017 (https://doi.org/10.5194/hess-2017-549), including model files and processed remote sensing observations (CryoSat-2 radar altimetry and GRACE total water storage)
<p>Dataset used in</p> <p>Kittel, C. M. M., Nielsen, K., Tøttrup, C., Bauer-Gottwein, P., 2017.Informing a hydrological model of the Ogooué with multi-mission remote sensing data. Hydrol. Earth Syst. Sci. Discuss., https://doi.org/10.5194/hess-2017-549</p> <p>The dataset contains</p> <p>Model files:</p> <ul> <li>River delineation of the Ogooué river based on the SRTM 3 arc-second DEM </li> <li>Climate input data for the Ogooué model subbasins (TRMM and FEWS-RFE precipitation and ECMWF temperature)</li> <li>Parameter files</li> </ul> <p>Processed remote sensing data:</p> <ul> <li>CryoSat-2 satellite altimetry data over the Ogooué River from July 2010 to February 2015</li> <li><strong> </strong>Water mask derived from Sentinel-1 SAR, used to filter CryoSat-2 data</li> <li>GRACE TWS time series for the Ogooué basin</li> </ul> <p>The data is provided in a .zip file with a README.txt file providing additional information and details on the data, including where to obtain similar/original datasets.</p> <p>(c) Author(s) and Technical University of Denmark (DTU) 2018</p>
Minimum number of experimental data for the thermal characterization of a hot water storage tank. Videos of temperature evolution
<p>Videos that show the evolution of the instantaneous temperature profile measured and determined with the models and in the cases developed in the paper "Minimum number of experimental data for the thermal characterization of a hot water storage tank" by the same authors. Thermocline thickness evolution is also shown.</p> <p>Video 1. Evolution of the instantaneous temperature profile estimated with the 5 MEL models during the charging period. Evolution of thermocline thickness during the same period is displayed in a separate frame also for all MEL models.<br> Video 2. Evolution of the instantaneous temperature profile estimated with the 5 MDH models during the charging period. Evolution of thermocline thickness during the same period is displayed in a separate frame also for all MDH models.</p>
Data from: Climatic water availability mainly drives context-dependency of tree functional diversity effects on soil organic carbon storage in European forests
<p>The interplay of forest stand and environmental factors shape soil organic C (SOC) storage in forest ecosystems but little is known about their relative impacts in different soil layers. Moreover, how environmental factors modulate the impact of stand factors, particularly species mixing, on SOC storage, is largely unexplored. In this study conducted in 21 forest triplets (two-species mixed stand and respective monocultures nearby) distributed in Europe, we tested the hypothesis that stand factors (functional identity and diversity) have stronger effects on topsoil (FF+0-10 cm) C storage than environmental factors (climatic water availability, clay+silt content, oxalate-extractable Al - Al<sub>ox</sub>) but that the opposite occurs in the subsoil (10-40 cm). We also tested the hypothesis that functional diversity improves SOC storage under high climatic water availability, clay+silt contents, Al<sub>ox</sub>. We characterized functional identity as the proportion of broadleaved species (beech and/or oak), and functional diversity as the product of broadleaved and conifer (pine) proportions. The results show that functional identity was the main driver of topsoil C storage while climatic water availability had the largest control on subsoil C storage. Contrary to expectations, functional diversity decreased topsoil C storage under increasing climatic water availability but the opposite was observed in the subsoil. Functional diversity effects on topsoil C increased with increasing clay+silt content, while its effects on subsoil C was negative at increasing Al<sub>ox</sub> content. This suggests that functional diversity effect on SOC storage along environmental gradients depends on the specific environmental factor and the soil depth under consideration.</p>
Data from: Climatic water availability mainly drives context-dependency of tree functional diversity effects on soil organic carbon storage in European forests
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Conceptualizing relationships among hyporheic exchange, storage, and water age: data represented in published figures
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Nitrogen and water availability control plant carbon storage with warming
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Data from: Seasonal and spatial dynamics of gas ebullition in a temperate water-storage reservoir
Gas ebullition of river impoundments plays an increasingly significant role, particularly in transporting methane CH4 from their sediments to the atmosphere, and contributing to the global carbon budget and global warming. Quantifying stochastic and episodic nature of gas ebullition is complicated especially when conventionally conducted by using coverage-limited gas traps. Current knowledge of seasonality in a reservoir's gas ebullition is lacking in the literature. For this reason, advanced acoustic surveying was intensively applied to determine spatiotemporal distributions of gas ebullition in a European water-storage reservoir for two years. Additionally, the sampling was accompanied with gas collecting for analyzing gas composition. The gas released from the reservoir was primarily composed of CH4 (on average 52%, up to 94%). The longitudinal distribution of gas ebullition was mainly determined by a proximity to the river inflow as a source of organic matter. A magnitude of ebullitive fluxes within the reservoir varied up to 1,300 mL m-2 d-1(30 mmol CH4 m-2 d-1). The most significant period of ebullition has turned out to be in fall, on average reaching a sevenfold ebullitive flux (70 mL m-2 d-1, 1.6 mmol CH4 m-2 d-1) higher than in the rest of the season. A substantial contribution to the fall peak was induced by an expansion of gas ebullition into greater depths, covering two thirds of the reservoir in late fall. The study demonstrates that the ebullitive fluxes of the temperate water storage reservoir were correlated to season, depth, and inflow proximity.
RRR input and output files corresponding to "Global patterns in river water storage dependent on residence time"
<p><strong>Corresponding peer-reviewed publication</strong></p> <p>This dataset corresponds to all the RRR input and output files that were used in the study reported in:</p> <ul> <li>Collins, E. L., C. H. David, R. Riggs, G. H. Allen, T. M. Pavelsky, P. Lin, M. Pan, D. Yamazaki, R. K. Meentemeyer, and G. M. Sanchez (2024), Global patterns in river water storage dependent on residence time.</li> </ul> <p>When making use of any of the files in this dataset, please cite both the aforementioned article and the dataset herein.</p> <p><strong>Input datasets</strong></p> <p>The dataset herein benefited from and was built upon other datasets:</p> <ul> <li>MERIT-Basins (version 1.0) derived from MERIT-Hydro (version 0.7) available under a CC BY-NC-SA 4.0 license. <a href="https://www.reachhydro.org/home/params/merit-basins">https://www.reachhydro.org/home/params/merit-basins</a></li> <li>GLDAS VIC Land Surface Model L4 monthly 1.0 x 1.0 degree (V2.0) available under a CC0 license. <a href="https://science.nasa.gov/spd-41/">https://science.nasa.gov/spd-41/</a>. DOI: <a href="https://doi.org/10.5067/ZRIHVF29X43C">10.5067/ZRIHVF29X43C</a></li> <li>GLDAS Noah Land Surface Model L4 monthly 1.0 x 1.0 degree (V2.0) available under a CC0 license. <a href="https://science.nasa.gov/spd-41/">https://science.nasa.gov/spd-41/</a>. DOI: <a href="https://doi.org/10.5067/QN80TO7ZHFJZ">10.5067/QN80TO7ZHFJZ</a></li> <li>GLDAS Catchment Land Surface Model L4 monthly 1.0 x 1.0 degree (V2.0) available under a CC0 license. <a href="https://science.nasa.gov/spd-41/">https://science.nasa.gov/spd-41/</a>. DOI: <a href="https://doi.org/10.5067/SGSL3LNKGJWW">10.5067/SGSL3LNKGJWW</a></li> </ul> <p><strong>Known bugs and limitations in this dataset or the associated manuscript</strong></p> <p>In the original files for MERIT-Basins (version 1.0) derived from MERIT-Hydro (version 0.7), as obtained from the pfaf_level_02 zip files, two river reaches do not have a corresponding catchment: COMID=31000001 and COMID=61000003. These two river reaches were removed from our analysis, hence bringing the total number of river reaches used from 2,938,143 to 2,938,141, the same value as the total number of catchments.</p> <p>In the original files for MERIT-Basins (version 1.0) derived from MERIT-Hydro (version 0.7), as obtained from the pfaf_level_02 zip files, the attribute table for the river reach with COMID=67072432 contains only four upstream reaches (COMID=64072433, COMID=64072735, COMID=64073772, and COMID=64073786), while the reach actually has five upstream reaches on the map (those above and COMID=64073793). This was modified herein because all connectivity in this dataset was created by associating upstream and downstream nodes rather than using the attribute table.</p> <div> <div> <div> <p>Our monthly dataset of river gauges with 95% daily data availability for 1980–2009 and with average discharge greater than or equal to 100 m3/s contains 1,148 stations. After mapping gauges to the MERIT Hydro river network using a joint criteria of 1) within a distance of 0.05 degrees and 2) uncorrected 30-yr average simulated discharge of the river reach within one order of magnitude relative to the observed average discharge of the gauge; the number of gauges reduces to 1129. In cases of multiple gauges per reach, we selected the gauge that had the closest observed average discharge to the uncorrected 30-yr averages simulated, resulting in 1,001 gauges. The dataset was further split in calibration gauges (702) and validation gauges (299). Three specific gauges are located in places with 0 accumulation of runoff in their specific sub-basin, they are located at river reaches with ID 41003992, 41001545, and 24005996. These three gauges were removed from the overall analysis at all gauges, resulting in 998 total gauges. All three gauges are part of the 299 validation gauges, not the 702 calibration gauges. They were retained in the analysis of 299 validation gauges. </p> <p> </p> </div> </div> </div> <p><strong>Files included in this version</strong></p> <p>All monthly discharge observation files for 1148 gauges, in shapefile and CSV formats:</p> <ul> <li>sites_1980-01_2009-12_100cms_095p.zip</li> <li>timeseries_obs_1980-01_2009-12_100cms_095p_monthly.zip</li> </ul> <p>All CSV files corresponding to the hydrography of all pfaf_level_02 regions of MERIT-Hydro v0.7 Basins v1.0 (i.e. the 61 values of <em>ii</em>):</p> <ul> <li>coords_pfaf_<em>ii</em>.zip</li> <li>kfac_pfaf_<em>ii</em>_1km_hour.zip</li> <li>k_pfaf_<em>ii</em>_low.zip</li> <li>k_pfaf_<em>ii</em>_nrm.zip</li> <li>k_pfaf_<em>ii</em>_hig.zip</li> <li>rapid_catchment_pfaf_<em>ii</em>.zip</li> <li>rapid_connect_pfaf_<em>ii</em>.zip</li> <li>rapid_coupling_pfaf_<em>ii</em>_GLDAS.zip</li> <li>riv_bas_id_pfaf_<em>ii</em>_topo.zip</li> <li>sort_pfaf_<em>ii</em>_topo.zip</li> <li>xfac_pfaf_<em>ii</em>_0.1.zip</li> <li>x_pfaf_<em>ii</em>_low.zip</li> <li>x_pfaf_<em>ii</em>_nrm.zip</li> <li>x_pfaf_<em>ii</em>_hig.zip</li> </ul> <p>All netCDF4 files with monthly surface and subsurface runoff that were combined from GLDAS 2.0 and corresponding to the following land surface models for the years 1980 to 2009 (both included):</p> <ul> <li>GLDAS_CLSM_M_1980-01_2009-12_utc.zip</li> <li>GLDAS_NOAH_M_1980-01_2009-12_utc.zip</li> <li>GLDAS_VIC_M_1980-01_2009-12_utc.zip</li> <li>GLDAS_ENS_M_1980-01_2009-12_utc.zip (the 3-model ensemble average based on the three above models).</li> </ul> <p>All netCDF4 files with monthly lateral inflows (based on the GLDAS data above), corresponding to each one of the 61 values of <em>ii</em>, the pfaf_level_02 code:</p> <ul> <li>m3_riv_pfaf_<em>ii</em>_GLDAS_CLSM_M_1980-01_2009-12_utc.zip</li> <li>m3_riv_pfaf_<em>ii</em>_GLDAS_NOAH_M_1980-01_2009-12_utc.zip</li> <li>m3_riv_pfaf_<em>ii</em>_GLDAS_VIC_M_1980-01_2009-12_utc.zip</li> <li>m3_riv_pfaf_<em>ii</em>_GLDAS_ENS_M_1980-01_2009-12_utc.zip</li> </ul> <p>All netCDF4 files with monthly discharge (based on the m3_riv data above), corresponding to each one of the 61 values of <em>ii</em>, the pfaf_level_02 code: </p> <ul> <li>Qout_pfaf_<em>ii</em>_GLDAS_CLSM_M_1980-01_2009-12_utc.zip</li> <li>Qout_pfaf_<em>ii</em>_GLDAS_NOAH_M_1980-01_2009-12_utc.zip</li> <li>Qout_pfaf_<em>ii</em>_GLDAS_VIC_M_1980-01_2009-12_utc.zip</li> <li>Qout_pfaf_<em>ii</em>_GLDAS_ENS_M_1980-01_2009-12_utc.zip</li> </ul> <p>All netCDF4 files with monthly storage (based on the Qout_pfaf_<em>ii</em>_GLDAS_ENS data above), corresponding to each one of the 61 values of <em>ii</em>, the pfaf_level_02 code, and to each characteristic value of residence time (low, nrm, hig):</p> <ul> <li>V_pfaf_<em>ii</em>_GLDAS_ENS_M_1980-01_2009-12_utc_low.zip</li> <li>V_pfaf_<em>ii</em>_GLDAS_ENS_M_1980-01_2009-12_utc_nrm.zip</li> <li>V_pfaf_<em>ii</em>_GLDAS_ENS_M_1980-01_2009-12_utc_hig.zip</li> </ul> <p>All shapefiles with MERIT Hydro (v0.7) Basins (v1.0) river network on which the 30-year mean of lumped discharge was appended (based on the Qout_pfaf_<em>ii</em>_GLDAS_ENS_M_1980-01_2009-12_utc.zip data above):</p> <ul> <li>riv_pfaf_<em>ii</em>_MERIT_Hydro_v07_Basins_v01_GLDAS_ENS.zip</li> </ul> <p>All monthly discharge observation files for 1148 gauges, in shapefile format, with observed monthly mean appended:</p> <ul> <li>sites_1980-01_2009-12_100cms_095p_meanQ.zip</li> </ul> <p>All monthly discharge observation files for 1001 gauges snapped on the MERIT Hydro (v0.7) Basins (v1.0) river network, in shapefile and CSV formats, corresponding to each one of the 61 values of <em>ii</em>, the pfaf_level_02 code: </p> <ul> <li>sites_1980-01_2009-12_100cms_095p_meanQ_COR_pfaf_<em>ii</em>.zip</li> <li>sites_1980-01_2009-12_100cms_095p_meanQ_COR.zip</li> <li>obs_tot_id_1980-01_2009-12_100cms_095p_meanQ_COR_pfaf_<em>ii</em>.zip</li> <li>Qobs_1980-01_2009-12_100cms_095p_meanQ_COR_pfaf_<em>ii</em>.zip</li> </ul> <p>All monthly discharge observation files for 1001 gauges snapped on the MERIT Hydro (v0.7) Basins (v1.0) river network, separated into calibration (CAL) and validation (VAL) gauges, in shapefile and CSV formats, corresponding to each one of the 61 values of <em>ii</em>, the pfaf_level_02 code: </p> <ul> <li>sites_1980-01_2009-12_100cms_095p_meanQ_CAL.zip</li> <li>obs_bas_id_1980-01_2009-12_100cms_095p_meanQ_CAL_pfaf_<em>ii</em>.zip</li> <li>sites_1980-01_2009-12_100cms_095p_meanQ_VAL.zip</li> </ul> <p>All netCDF4 files with monthly lateral inflows (based on the m3_riv_pfaf_<em>ii</em>_GLDAS_ENS_M_1980-01_2009-12_utc.zip data above) corrected using Long-Term Inverse Routing for calibration (CAL) gauges only and associated monthly discharge data, corresponding to each one of the 61 values of <em>ii</em>, the pfaf_level_02 code:</p> <ul> <li>m3_riv_pfaf_<em>ii</em>_GLDAS_CAL_M_1980-01_2009-12_utc.zip</li> <li>Qout_pfaf_<em>ii</em>_GLDAS_CAL_M_1980-01_2009-12_utc.zip</li> </ul> <p>All netCDF4 files with monthly lateral inflows (based on the m3_riv_pfaf_<em>ii</em>_GLDAS_ENS_M_1980-01_2009-12_utc.zip data above) corrected using Long-Term Inverse Routing for all correction (COR) gauges only and associated monthly discharge data, corresponding to each one of the 61 values of <em>ii</em>, the pfaf_level_02 code:</p> <ul> <li>m3_riv_pfaf_<em>ii</em>_GLDAS_COR_M_1980-01_2009-12_utc.zip</li> <li>Qout_pfaf_<em>ii</em>_GLDAS_COR_M_1980-01_2009-12_utc.zip</li> </ul> <p>All netCDF4 files with monthly storage (based on the Qout_pfaf_<em>ii</em>_GLDAS_COR_M_1980-01_2009-12_utc.zip data above), corresponding to each one of the 61 values of <em>ii</em>, the pfaf_level_02 code, and to each characteristic value of residence time (low, nrm, hig):</p> <ul> <li>V_pfaf_<em>ii</em>_GLDAS_COR_M_1980-01_2009-12_utc_low.zip</li> <li>V_pfaf_<em>ii</em>_GLDAS_COR_M_1980-01_2009-12_utc_nrm.zip</li> <li>V_pfaf_<em>ii</em>_GLDAS_COR_M_1980-01_2009-12_utc_hig.zip</li> </ul> <p>All shapefiles with MERIT Hydro (v0.7) Basins (v1.0) river network on which the 30-year mean of lumped discharge was appended (based on the Qout_pfaf_<em>ii</em>_GLDAS_COR_M_1980-01_2009-12_utc.zip data above):</p> <ul> <li>riv_pfaf_<em>ii</em>_MERIT_Hydro_v07_Basins_v01_GLDAS_COR.zip</li> </ul> <p>All shapefiles with MERIT Hydro (v0.7) Basins (v1.0) catchments that have been dissolved and of which the perimeter was extracted, corresponding to each one of the 61 values of <em>ii</em>, the pfaf_level_02 code; as well as global combined files:</p> <ul> <li>cat_pfaf_<em>ii</em>_MERIT_Hydro_v07_Basins_v01_disso.zip</li> <li>cat_ MERIT_Hydro_v07_Basins_v01_disso.zip</li> <li>cat_pfaf_<em>ii</em>_MERIT_Hydro_v07_Basins_v01_perim.zip</li> <li>cat_ MERIT_Hydro_v07_Basins_v01_perim.zip</li> </ul> <p>All shapefiles with MERIT Hydro (v0.7) Basins (v1.0) river network retaining only those reaches that flow to the global coast, corresponding to each one of the 61 values of <em>ii</em>, the pfaf_level_02 code:</p> <ul> <li>riv_pfaf_<em>ii</em>_MERIT_Hydro_v07_Basins_v01_coast.zip</li> </ul>
Terrestrial water storage data based on the generalized three-cornered hat fusion method
<p>We deduct 2004-2009 means from the three GRACE terrestrial water storage (TWS) Mascon products (CSR, JPL and GSFC) to ensure that they have the same reference period. To eliminate the discrepancies induced by the different data and improve the reliability of the results, we estimate the relative uncertainties of the GRACE TWS data using the generalized three-cornered hat method (GTCH), and then fuse them according to their uncertainties using a least squares method.</p>
Influences of the South-to-North Water Diversion Eastern Route Project on water storage variations across the water-receiving area over recent decades
<p>This file contains (1) the time series of monthly GRACE data(including CSR,GFZ,JPL, and Weighted average)from April 2002 to December 2020 (11 months of data from July 2017 to May 2018 were missing between GRACE and GRACE-FO) , (2) water consumption of the Huaihe River Basin and Shandong Peninsula from 2002 to 2019, (3) diverted water from Jiangsu to Shandong in 2014-2020, (4) variation trend for the groundwater level data from each groundwater monitoring well from 2005 to 2018 and (5) water volume of the reservoir in the Shandong Province.</p>
Stability of a Mixed-Valence Hydrous Iron-Rich Oxide: Implications for Water Storage and Dynamics in the Deep Lower Mantle
<p>The XRD datasets in Figure 1, Figure2A, Figure2B, Figure4A, Figure4B, Figure6A, and Figure6B.</p>
Data on space-time soil moisture and modelled soil water storage, Ghana
<p>Data on spatial and temporal modelling of soil water storage from in situ soil moisture measurements in the Guinea savannah zone of Northern Ghana, West Africa.</p>
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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.