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1,103 results for “moisture”
Fig. 3 in Changes in soil moisture and riparian forest structure after a dam construction
Fig. 3. Major changes that drives the community changes. Before river diversion, the sectors near the river had greater basal areas because they had many thick trees while distant sectors had thin trees (the density was statistically similar). After four years of river diversion, there were many trunks of still alive trees and dead trees in the sector closer to the river. Even with high growth, the basal area in this sector was severely reduced and became similar to the distant sector (which already has small basal area).
Fig. 2 in Changes in soil moisture and riparian forest structure after a dam construction
Fig. 2. Soil moisture changes that occurred due to construction of the dams. A and C represent soil moisture in dry forests before damming, and B and D represent soil moisture after damming construction. The continuous line represents soil surface; vertical black bars represent soil sampling sites; blue bars represent soil moisture and their thickness illustrates soil moisture; and thicker bars represent more moisture. After dam influence, soil moisture increased mainly in the dry season and mainly near the lakeshore.
(U)SAXS data (ID02 beamline, ESRF): Effects of pH on the fibrous structure formation of plant proteins during high-moisture extrusion
<p>Due to health and environmental factors, the food industry is looking for ways to introduce meat replacers made from plant-based proteins to consumer markets. The presence of structural anisotropy in the form of fibre is a prerequisite for meat analogues. Structure formation ability depends on the protein ingredients used, which leads to plant protein products with varying texture hardness and extent of fibre alignment. In the current study, we will test if it is possible to tune these properties based on the hypothesis that plant proteins have different structure formation abilities under varying pH conditions.</p>
Arctic Moisture Intrusion Dataset v1 1/2 (1979-1999)
<p>Moisture intrusion tracking algorithm output (1/2) from 1979-1999. Output contains binary files with associated ID numbers of moisture intrusion events and assoicated ERA5 total column water vapor and northward water vapor flux. </p> <p>Dataset 2/2 - 10.5281/zenodo.13984122</p>
CoUDlabs_TA_OTHU-SuDS-NBS-RECON: Correlating plant health with soil moisture in NBS
<p>This repository contains the dataset ‘CoUDlabs_TA_OTHU-SuDS-NBS-RECON: Correlating plant health with soil moisture in NBS’ which is a result from the Transnational Access, within Co-UDlabs project, funded under the European Union’s Horizon 2020 research and innovation programme under grant agreement No 101008626. </p> <p>These experiments introduce a new approach which uses low-cost multi-spectral camera (Plant-o-Meter) to estimate plant health (through various parameters) to link with soil moisture of the bioretention systems (biofilters and green roof) as a common element of nature-based solutions (NBS). Soil moisture is used as a proxy for polluted water retention and pollutant removal within the system. The aim of this work is to provide a proof of concept for indirect measurement of urban polluted water treatment through different types of NBS. The experiments have been done on real stormwater biofilters, under controlled conditions (artificial irrigation). Data from the experimental campaign presented here will help to understand fundamental correlations between plant health parameters and the change in soil moisture, giving us a tool for quick assessment of the need for maintenance of various NBS as a crucial parts of UD systems.</p> <p>The authors acknowledge financial support from the European Union under the Horizon 2020 program within a contract for Integrating Activities for Starting Communities (Ref. 101008626)</p> <p>>>>For detailed description of the dataset, please see <a href="https://zenodo.org/api/records/14191602/draft/files/README_NBS-RECON_dataset.txt/content" target="_blank" rel="noopener noreferrer">README_NBS-RECON_dataset.txt</a> and <a href="https://zenodo.org/api/records/14191602/draft/files/CoUDlabsDataStorageReport_NBS-RECON.docx/content" target="_blank" rel="noopener noreferrer">CoUDlabsDataStorageReport_NBS-RECON.docx</a></p>
Derived daily timeseries of weather, soil moisture and temperature, flow and nitrogen species (nitrate and nitrite, ammonium) concentrations data for the North Wyke Farm Platform National Biosciences Research Infrastructure, England
<p>For a selection of catchments from the North Wyke Farm Platform in southwest England, where land use conversions have been introduced, daily time series data covering weather conditions (minimum temperature, maximum temperature, total rainfall, wind speed and solar radiation), near-surface soil status (moisture content and temperature), flow and concentrations of key nitrogen species (nitrate and nitrite, ammonium) have been filtered based on attached data quality tags . The datasets run between 2013 and March 2024. For the main climate variables, data gaps were infilled with preceding- and following-on daily data, observations from a nearby weather station or existing national datasets to generate a continuous data series for modelling. For the other data series, annual and seasonal summary statistics on data coverage are provided. Information on significant field events, such as ploughing, drilling and harvest, fertiliser applications and manure spreading were also tabulated.</p>
Country-ocean-moisture-flows-reconciled-with-ERA5-reanalysis obtained processing Lagrangian moisture connections
<p>The dataset "Reconciled global atmospheric moisture flows between countries/oceans and subcontinents" presents tracked volumes of precipitation and evaporation reconciled with reanalysis data, closing the annual hydrological balance, and provides robust estimates of terrestrial moisture recycling and net moisture flows to support global water governance analysis.</p> <p>This repository is supplement to a study by De Petrillo & Fahrländer et al. (2025), which describes the development of the reconciliation framework, includes a perfromance analysis of the method and shows an exemplary case study on the published data. </p> <p>The atmospheric moisture flows are sourced from the UTrack atmospheric moisture flow dataset by Tuinenburg et al. (2020a) (dataset access: Tuinenburg et al., 2020b) and reconciled with ERA5 precipitation and evaporation data (Hersbach et al., 2020) on the mean annual basis in the period 2008-2017, by means of a post-processing framework, based on the Iterative Proportional Fitting (IPF) algorithm.</p> <p>NOTE: The final dataset is available in form of bilateral matrices (country/ocean and subcontinent/ocean) and in form of direct flows (flow edges). Supporting material to read the dataset is in the folder "List" . Processed ERA5 data (where the precipitation-evaporation annual balance is met) and input data to generate the figures are also available.</p> <p>References:</p> <p>De Petrillo, E., Fahrländer, S., Tuninetti, M., Andersen, L.S., Monaco, L., Ridolfi, L., Laio, F. (2025). Reconciling tracked atmospheric moisture flows to close the global freshwater cycle.<em> </em><em>Commun Earth Environ <strong>6</strong>, 347 (2025). </em><a href="https://doi.org/10.1038/s43247-025-02289-y">https://doi.org/10.1038/s43247-025-02289-y</a></p> <p>Tuinenburg, O. A., Theeuwen, J. J. E., & Staal, A. (2020a). High-resolution global atmospheric moisture connections from evaporation to precipitation. <em>Earth System Science Data</em>, <em>12</em>(4), 3177–3188. <a href="https://doi.org/10.5194/essd-12-3177-2020">https://doi.org/10.5194/essd-12-3177-2020</a></p> <p>Tuinenburg, O. A., Theeuwen, J. J. E., Staal, A. (2020b): Global evaporation to precipitation flows obtained with Lagrangian atmospheric moisture tracking. PANGAEA, <a href="https://github.com/ObbeTuinenburg/UTrack_global_database">https://doi.pangaea.de/10.1594/PANGAEA.912710</a></p> <p>Hersbach, H., Bell, B., Berrisford, P., Hirahara, S., Horányi, A., Muñoz‐Sabater, J., et al. (2020). The ERA5 global reanalysis. <em>Quarterly Journal of the Royal Meteorological Society</em>, <em>146</em>(730), 1999–2049. <a href="https://doi.org/10.1002/qj.3803">https://doi.org/10.1002/qj.3803</a></p> <p> </p> <p> </p>
Data for: "High-resolution Soil Moisture Evolution in Hyper-arid Regions: A Comparison of InSAR, SAR, Microwave, Optical, and Data Assimilation Systems in the southern Arabian Peninsula"
<p>Data accompanying the publication: High-resolution Soil Moisture Evolution in Hyper-arid Regions: A Comparison of InSAR, SAR, Microwave, Optical, and Data Assimilation Systems in the southern Arabian Peninsula. For filenames starting with T: Exponential fit parameters time0 and mag0 for InSAR coherence data. they are binary files, where fit = a*exp(-b*x); a = -log(mag0); b = 1/time0. timeerr contains the uncertainty of the time0 parameter, and maghigh/maglow contain the high and low uncertainty for the mag0 parameter, respectively. For for each frame or overlap region (T101, T28, T130, T28_T101, T130_T28), there is a vrt file (T..._20180524.time0.vrt), which is the metadata file applicable to all files of the same frame. Files starting with mags_times: Exponential fit parameters for ASCAT/SMAP/GLDAS data. the same parameters (time0, timeerr, mag0, maghigh, maglow) can be found in these matlab structure files. In addition, the .mat files contain the offset parameter and related uncertainty, as well as lat/lon information. </p>
A Global Dataset of Standardized Moisture Anomaly Index Incorporating Snow Dynamics (SZIsnow) from 1948 to 2010
<p>The SZI<sub>snow</sub> dataset was calculated based on systematic physical fields from the Global Land Data Assimilation System Version 2 (GLDAS-2) with the Noah land surface model. This SZI<sub>snow</sub> dataset considers different physical water-energy processes, especially snow processes. The evaluation shows the dataset is capable of investigating different types of droughts across different timescales. The assessment also indicates that the dataset has an adequate performance to capture droughts across different spatial scales. The consideration of snow processes improved the capability of SZI<sub>snow</sub>, and the improvement is evident over snow-covered areas (e.g., Arctic region) and high-altitude areas (e.g., Tibet Plateau). Moreover, the analysis also implies that SZI<sub>snow</sub> dataset is able to well capture large-scale drought events across the world. This drought dataset has high application potential for monitoring, assessing, and supplying information on drought, and also can serve as a valuable resource for drought studies.</p>
Long-term reconstruction of satellite-based precipitation, soil moisture, and snow water equivalent in China
<p>A daily 0.1<sup>°</sup> dataset of precipitation (<em>P</em>), soil moisture (SM), and snow water equivalent (SWE) in 1981-2017 across China.</p>
Global Soil Moisture-Air Temperature Interactions from Linear and Nonlinear Granger Causalities
<p>These datasets were generated to assess linear and nonlinear Granger causalities in the submitted manuscript, Global Soil Moisture-Air Temperature Interactions from Linear and Nonlinear Granger Causalities by Bhatti et al. submitted to AGU-GRL. Nonlinear GC here is achieved with the Kernel Granger causality by Marinazzo et al. (2008). The data was used to develop theoretical experiments that help validate the strengths and limitations of both the linear Granger causality and the Kernel Granger causality before applying to real world datasets</p>
Model output for "Numerically consistent budgets of potential temperature, momentum, and moisture in Cartesian coordinates: application to the WRF model"
<p>These data were produced with WRFlux v1.2.1 (https://github.com/matzegoebel/WRFlux/) from a numerical simulation with the community model WRF. Simulations represent the evolution of a convective boundary layer in the atmosphere over an idealized 2D mountain ridge. The data are published in connection with the article "Numerically consistent budgets of potential temperature, momentum and moisture in Cartesian coordinates: Application to the WRF model" in "Geoscientific Model Development" (https://doi.org/10.5194/gmd-15-669-2022).</p> <p>Three-dimensional (x, z, t) fields of five prognostic variables are provided: Potential temperature (T), water vapor mixing ratio (Q), cross-mountain (U), along-mountain (V), and vertical windspeed (W). All fields are averaged in time (30 min averaging interval) and in the along-mountain direction y.</p> <p>The repository contains the following files:</p> <p>grid.nc : variables related to the WRF numerical grid, air density<br> [U,W,T,Q]_flux.nc : resolved and subgrid-scale fluxes<br> [U,W,T,Q]_tendency.nc : resolved and subgrid-scale tendency components<br> UVWT_MEAN.nc : averaged values of the variables themselves<br> plotting.py : python script to approximately reproduce the figures of the paper. Requires the python packages matplotlib, xarray, and netcdf4.</p> <p>Figure 6 in the paper cannot be accurately reproduced with these data since the original figure uses 4D (x, y, z, t) output.</p> <p>For details on the simulation, refer to the article.</p>
PrISM satellite rainfall product (2010-2021) based on SMOS soil moisture measurements in Africa (3h, 0.1°)
<p>The PrISM product is a satellite precipitation product available for Africa over a regular grid at 0.1° (about 10x10 km²) and every 3 hours. It is obtained from the synergy of SMOS satellite soil moisture measurements and IMERG-Early Run precipitation product through the PrIMS algorithm (<em>Pellarin et al., 2009, 2013, 2020, 2022, Louvet et al., 2015, </em>Román-Cascón et al. 2017). </p>
Data: Spatio-temporal prediction of soil moisture using soil maps, topographic indices and SMAP retrievals
<p><strong>Data used in:</strong></p> <p>Schönauer, M., Prinz, R., Väätäinen, K., Astrup, R., Pszenny, D., Lindeman, H., et al. (2022). Spatiotemporal prediction of soil moisture using soil maps, topographic indices and SMAP retrievals. <em>International Journal of Applied Earth Observation and Geoinformation</em>, 102730. doi: 10.1016/j.jag.2022.102730</p>
Soil Moisture and Sea Surface Temperature Data for Wikle et al. (2022)
<p>Raw data (.nc) in NetCDF4 format, and formatted and rearranged data (.csv) in CSV format. With R Markdown document detailing the steps taken. All data obtained originally from NOAA's NCEP and NCDC data store systems. </p> <p>Data used in developing and demonstrating explainable AI models for <em>An Overview of Model Agnostic Explainability Methods for Machine Learning Applied to Environmental Data</em>, Wikle et al. (2022), for the <em>Special Issue on Environmental Data Science</em> for <em>Environmetrics. </em>See https://zenodo.org/record/6353636 for the corresponding model codebase. </p>
Dataset - Spatially explicit linkages between redox potential cycles and soil moisture fluctuations
<p>This repository holds data collected during three lysimeter experiments where laboratory column scale lysimeters have been subjected to different wetting/drainage cycles. The lysimeters have been filled with forest soil in the Lausanne forest characterised by the SwissMEX project </p> <p>The following dataset contains soil redox potential and soil moisture measurements that have been continuously monitored in time for different depths. Pore water samples of specific dissolved chemicals have been collected at the end of each cycle. </p> <p> </p> <p>Specifically, this dataset is composed by the following files:</p> <ul> <li> "Lysimeter_configuration.png" illustrates the lysimeter used and the sensors scheme adopted.</li> <li>"METADATA.txt" contains specific information about each recorded variable and data point collected throughout the three experiments SM-B, SM-I1 and 2.</li> <li>Pore water analysis data</li> <li>Soil moisture and tension data</li> <li>Soil redox potential data</li> </ul> <p>We thank Pascal Froidevaux for providing the lysimeter used for SM-B experiment. The authors acknowledge key funding provided by the Swiss National Science Foundation through its grant number CRSII5 186422.</p> <p> </p> <p> </p>
A Deep Neural Network Based SMAP Soil Moisture Product
<p>It is demonstrated that while satellite soil moisture (SM) retrievals often have minimum biases, reanalysis data can capture more temporal variability of SM, especially for non-cropland areas -- when validated against in situ measurements. Accordingly, this paper presents a deep neural network (DNN) that utilizes the merits of a suite of existing satellite and reanalysis products to produce a new SM product with minimum (maximum) bias (correlation) -- using NASA’s Soil Moisture Active Passive (SMAP) data and ERA5 reanalysis. The benchmark of the network is a bias-adjusted SM with maximum correlation with in situ data over each land-cover type. The mean of the benchmark data is adjusted to the product that exhibits a minimum bias over each land-cover type. Consistent with the laws of L-band microwave propagation in soil and canopy, the input variables of DNN include polarized SMAP brightness temperatures, incidence angle, vegetation scattering albedo, surface roughness parameter, surface water fraction, effective soil temperatures, bulk density, clay fraction, and vegetation optical depth from the normalized difference vegetation index (NDVI) climatology. The DNN is trained and validated using two years (04/2015--03/2017) of global data and deployed for assessment of its performance from 04/2017 to 03/2021. The testing results against in situ measurements demonstrate that the DNN outputs typically exhibit improved error quality metrics over most land-cover types and climate regimes and can properly capture SM temporal dynamics, beyond each SMAP product across regional to continental scales.</p>
IODP Expedition 372A Moisture and Density
<p>Moisture and density (MAD) data were acquired on ~10 mL sediment or rock samples by measuring three out of four material parameters: wet (saturated) mass, wet volume, dry mass, and/or dry volume after 24 h drying in a convection oven at 105 degrees C. From the moisture and volume measurements, the following phase relationships are calculated: wet and dry water content, wet bulk density, dry bulk density, grain density, porosity, and void ratio. The combination of measurements is defined by the submethod chosen: A, B, C, or D. Wet (A, B, or C) and dry (A, B, C, or D) mass is determined using motion-compensated balances. Wet volume is determined either by helium pycnometry (A) or by the sample's geometric dimensions using calipers (A or D). Dry volume (C or D) is measured by helium pycnometry. Submethods A and B are not recommended by IODP. Submethod C is suitable for saturated materials such as fine-grained sediments. Submethod D is suitable for unsaturated porous material such as certain limestones and basalts.</p>
IODP Expedition 374 Moisture and Density
<p>Moisture and density (MAD) data were acquired on ~10 mL sediment or rock samples by measuring three out of four material parameters: wet (saturated) mass, wet volume, dry mass, and/or dry volume after 24 h drying in a convection oven at 105 degrees C. From the moisture and volume measurements, the following phase relationships are calculated: wet and dry water content, wet bulk density, dry bulk density, grain density, porosity, and void ratio. The combination of measurements is defined by the submethod chosen: A, B, C, or D. Wet (A, B, or C) and dry (A, B, C, or D) mass is determined using motion-compensated balances. Wet volume is determined either by helium pycnometry (A) or by the sample's geometric dimensions using calipers (A or D). Dry volume (C or D) is measured by helium pycnometry. Submethods A and B are not recommended by IODP. Submethod C is suitable for saturated materials such as fine-grained sediments. Submethod D is suitable for unsaturated porous material such as certain limestones and basalts.</p>
Inputs of the Jupyter Notebook - Cosmos-UK soil moisture
<p>The dataset contains the inputs of the notebook "Cosmos-UK soil moisture" published in The Environmental Data Science Book.</p> <p>The input data refer to a subset of the public 2013-2019 COSMOS-UK dataset, daily and subhourly observations and metadata for four stations: WYTH1, WADDN, SHEEP and CHIMN. These stations represent the first sites to prototype COSMOS sensors in the UK, see further details in Evans et al. (2016) and they are situated in human-intervened areas (grassland and cropland), except for one in a woodland land cover site.</p> <p>Data from COSMOS-UK up to the end of 2019 are available for download from the UKCEH Environmental Information Data Centre (EIDC). The data are accompanied by documentation that describes the site-specific instrumentation, data and processing including quality control. The full dataset is available for <a href="https://doi.org/10.5285/b5c190e4-e35d-40ea-8fbe-598da03a1185">download</a> under the terms of the Open Government License.</p> <p><strong>Contributions</strong></p> <p><em>Notebook</em></p> <ul> <li> <p>Alejandro Coca-Castro (author), The Alan Turing Institute, <a href="https://github.com/acocac">@acocac</a></p> </li> <li> <p>Doran Khamis (reviewer), UK Centre for Ecology & Hydrology, <a href="https://github.com/dorankhamis">@dorankhamis</a></p> </li> <li> <p>Matt Fry (reviewer), UK Centre for Ecology & Hydrology, <a href="https://github.com/mattfry-ceh">@mattfry-ceh</a></p> </li> </ul> <p><em>Dataset originator/creator</em></p> <ul> <li> <p>UK Centre for Ecology & Hydrology (creator)</p> </li> <li> <p>Natural Environment Research Council (support)</p> </li> </ul> <p><em>Dataset reference and documentation</em></p> <ul> <li> <p>S. Stanley, V. Antoniou, A. Askquith-Ellis, L.A. Ball, E.S. Bennett, J.R. Blake, D.B. Boorman, M. Brooks, M. Clarke, H.M. Cooper, N. Cowan, A. Cumming, J.G. Evans, P. Farrand, M. Fry, O.E. Hitt, W.D. Lord, R. Morrison, G.V. Nash, D. Rylett, P.M. Scarlett, O.D. Swain, M. Szczykulska, J.L. Thornton, E.J. Trill, A.C. Warwick, and B. Winterbourn. Daily and sub-daily hydrometeorological and soil data (2013-2019) [cosmos-uk]. 2021. URL: <a href="https://doi.org/10.5285/b5c190e4-e35d-40ea-8fbe-598da03a1185">https://doi.org/10.5285/b5c190e4-e35d-40ea-8fbe-598da03a1185</a>, <a href="https://doi.org/10.5285/b5c190e4-e35d-40ea-8fbe-598da03a1185">doi:10.5285/b5c190e4-e35d-40ea-8fbe-598da03a1185</a>.</p> </li> </ul> <p><strong>Further references</strong></p> <ul> <li> <p>Jonathan G. Evans, H. C. Ward, J. R. Blake, E. J. Hewitt, R. Morrison, M. Fry, L. A. Ball, L. C. Doughty, J. W. Libre, O. E. Hitt, D. Rylett, R. J. Ellis, A. C. Warwick, M. Brooks, M. A. Parkes, G. M.H. Wright, A. C. Singer, D. B. Boorman, and A. Jenkins. Soil water content in southern england derived from a cosmic-ray soil moisture observing system – cosmos-uk. <em>Hydrological Processes</em>, 30:4987–4999, 12 2016. <a href="https://doi.org/10.1002/hyp.10929">doi:10.1002/hyp.10929</a>.</p> </li> <li> <p>M. Zreda, W. J. Shuttleworth, X. Zeng, C. Zweck, D. Desilets, T. Franz, and R. Rosolem. Cosmos: the cosmic-ray soil moisture observing system. <em>Hydrology and Earth System Sciences</em>, 16(11):4079–4099, 2012. URL: <a href="https://hess.copernicus.org/articles/16/4079/2012/">https://hess.copernicus.org/articles/16/4079/2012/</a>, <a href="https://doi.org/10.5194/hess-16-4079-2012">doi:10.5194/hess-16-4079-2012</a>.</p> </li> </ul>
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Allen Brain Atlas
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DANDI Archive for NWB datasets
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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.