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1,103 results for “moisture”
IODP Expedition 398 Moisture and Density
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.
Soil moisture in Teutoburg forest/Bielefeld dataset
<p>Daten von Bodenfeuchte-Sensoren aus der Region des Teutoburger Waldes in Ostwestfalen-Lippe (Deutschland), die im Rahmen eines ehrenamtlichen Citizen Science Projektes von Code for Bielefeld e.V. gesammelt werden. Visualisierung auf <a href="https://bodenfeuchte.org" target="_blank" rel="noopener">bodenfeuchte.org</a></p> <p>Version 1 enthält Daten von August 2023 bis September 2024.</p> <h1><strong>Spaltenbeschreibung</strong></h1> <h2>Geräte-ID (device)</h2> <p>Diese Spalte enthält eine eindeutige Kennung für jedes Gerät. Die Geräte-ID ist ein alphanumerischer Code, der verwendet wird, um die Daten eines bestimmten Sensors eindeutig zu bestimmen</p> <h2>Gerätemarke (device_brand)</h2> <p>Marke des Bodenfeuchtesensors (z.B. dragino)</p> <h2>Gerätemodell (device_model)</h2> <p>Modell des Bodenfeuchtesensors (z.B. lse01)</p> <h2>Breitengrad (latitude)</h2> <p>Der Breitengrad des Standorts, an dem das Gerät installiert ist.</p> <h2>Längengrad (longitude)</h2> <p>Der Längengrad des Standorts, an dem das Gerät installiert ist.</p> <h2>Bodenfeuchtigkeit (soil_moisture)</h2> <p>Diese Spalte enthält den aktuellen Wert der Bodenfeuchtigkeit, gemessen in Prozent. Sie gibt an, wie viel Wasser im Boden enthalten ist, wobei höhere Werte auf eine höhere Bodenfeuchtigkeit hinweisen.</p> <h2>Bodenleitfähigkeit (soil_conductivity)</h2> <p>Dieser Wert gibt die elektrische Leitfähigkeit des Bodens in Mikrosiemens pro Zentimeter (µS/cm) an, was ein Indikator für die Menge an gelösten Salzen oder Nährstoffen im Boden ist.</p> <h2>Bodentemperatur (soil_temperature)</h2> <p>Die aktuelle Temperatur des Bodens in Grad Celsius gemessen.</p> <h2>Batteriestatus (battery)</h2> <p>Diese Spalte gibt die aktuelle Spannung der Batterie des Geräts in Volt an. Eine ausreichende Batteriespannung ist notwendig, um sicherzustellen, dass das Gerät weiterhin ordnungsgemäß funktioniert. </p> <h2>Letztes Update (last_update)</h2> <p>Das Datum und die Uhrzeit des letzten Datenupdates für den jeweiligen Sensor. Der Zeitstempel folgt dem ISO 8601-Format und enthält Informationen über das Datum, die Uhrzeit und die Zeitzone.</p> <h2>Tiefe der Messung (depth_cm)</h2> <p>Diese Spalte gibt an, in welcher Tiefe unter der Erdoberfläche der Sensor installiert wurde. Die Tiefe wird in Zentimetern angegeben.</p> <h2>Beschattung (shaded)</h2> <p>Ein boolescher Wert, der angibt, ob der Sensor in einem beschatteten Bereich installiert ist (`true` für ja, `false` für nein). Die Beschattung kann die gemessenen Temperaturwerte beeinflussen.</p> <h2>Versiegelter Boden (sealed_ground)</h2> <p>Diese Spalte gibt an, ob der Sensor in einem versiegelten Bodenbereich installiert ist (`true` für ja, `false` für nein). Versiegelter Boden beeinflusst Wasserinfiltration und Temperatur.</p> <h2>Notizen (note)</h2> <p>Freitextfeld für zusätzliche Informationen über den Standort oder die Umstände der Sensorinstallation. Hier können beispielsweise Hinweise auf besondere Bedingungen oder Standorte vermerkt sein.</p> <h2>Bodennotizen (soil_note)</h2> <p>Freitextfeld für zusätzliche Informationen über die Bodenbeschaffenheit.</p>
IODP Expedition 355 Moisture and Density
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.
IODP Expedition 356 Moisture and Density
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.
IODP Expedition 353 Moisture and Density
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.
IODP Expedition 359 Moisture and Density
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.
Infiltration and soil moisture Saugraben and Weiherbach experimental sites
<p><em>Infiltration experiments at Saugraben infiltration experimental site and measured and modelled soil moisture at Weiherbach hydrological experimental catchment</em></p>
Satellite-driven 10km global root-zone soil moisture analysis for drought monitoring
<p>Root-zone soil moisture condition is an important component of water cycle at all spatial scales, as it controls various hydrological, biological and meteorological processes such as plant transpiration and hydraulic redistribution. Passive microwave remote sensing offers the possibility to access to near-surface (0 - 5cm) soil moisture measurements over large areas, providing valuable information for agricultural and water resource management. However, the spatial resolution of satellite soil moisture estimates from passive microwave sensor is relatively coarse (25 to 50km) and infrequent in time. Data assimilation algorithms are widely used to obtain spatially complete and daily continuous soil moisture estimates from intermittent remotely sensed soil moisture data and numerical models. </p> <p>The dataset contains the most recent global surface and root-zone soil moisture conditions at 10km generated from the Satellite-Guided Root-zone moisture Analysis and Forecasting System (S-GRAFS) from 2015 to 2022. S-GRAFS is a near-real time data assimilation system that combines complementary information from model simulations and satellite observations to provide soil moisture estimates at near surface and root-zone. In S-GRAFS, satellite soil moisture observations from Soil Moisture Active Passive (SMAP) are assimilated into a simple first-order autoregressive model that captures the soil moisture conditions in response to precipitation. Satellite precipitation from Global Precipitation Measurement (GPM) is used to drive the model to simulate near surface soil moisture at 5cm. The assimilation of SMAP data relies on the four-dimensional variational (4DVAR) method to adjust the modelled surface soil moisture towards observations within a 4-day assimilation window. Soil Water Index (SWI) is then derived from the analysed surface soil moisture using an exponential filter and represents the root-zone soil wetness at approximate 1m depth. The surface and root-zone wetness from S-GRAFS can be converted into absolute soil moisture content using soil physical properties to provide essential support for a wide variety of hydrological and agricultural applications.</p>
African Savanna grasses outperform trees across the full spectrum of soil moisture availability
<p>Summary</p> <ul> <li>Models of tree-grass coexistence in savannas make different assumptions about the relative performance of trees and grasses under wet vs. dry conditions. We quantified transpiration and drought tolerance traits in 26 tree and 19 grass species from the African savanna biome across a gradient of soil water potentials to test for a tradeoff between water use under wet conditions and drought tolerance.</li> <li>We measured whole-plant hourly transpiration in a growth chamber and quantified drought tolerance using leaf osmotic potential (Ψ<sub>osm</sub>). We also quantified whole-plant water use efficiency (WUE) and relative growth rate (RGR) under well-watered conditions.</li> <li>Grasses transpired twice as much as trees on a leaf-mass basis across all soil water potentials. Grasses also had a lower Ψ<sub>osm</sub> than trees, indicating higher drought tolerance in the former. Higher grass transpiration and WUE combined to largely explain the threefold RGR advantage in grasses.</li> <li>Our results suggest that grasses outperform trees under a wide range of conditions, and that there is no evidence for a trade-off in water use patterns in wet vs. dry soils. This work will help inform mechanistic models of water use in savanna ecosystems, providing much-needed whole-plant parameter estimates for African species.</li> </ul>
40 years of continental moisture tracking using WAM-2layers
<p>The WAM-2layers is an Eulerian moisture tracking numerical model. We used the Python version available on the GitHub repository (https://github.com/ruudvdent/WAM2layersPython). For a detailed description of the WAM-2layers, we refer the reader to van der Ent et al. (2013, 2014). Moisture tracking was conducted forward in time, focusing on the fate of evapotranspiration contributing to Terrestrial Moisture Recycling to calculate the amount of precipitation with continental origin. Our tracking experiment spans from 1979 to 2018. The model domain covers a global grid from 79.5ºN to 79.5ºS latitude. Calculations use data of specific humidity, zonal and meridional wind speeds at 24 pressure levels, surface pressure at 6-h intervals, and 3-h accumulated precipitation and evaporation, from the ERA-Interim reanalysis (Dee et al., 2011) on a 1.5º grid with 25,680 cells total (nx=240, ny=107). This set-up is based on previous studies indicating that it is adequate to track moisture spatially, both forward and backward in time and at regional and global scales.</p>
SM2RAIN CHINA: 1 km rainfall estimation for CHINA region derived from SMCI Soil Moisture (2000-2020)
<p><strong>SM2RAIN-SMCI provides rainfall estimates for the whole CHINA at 1 km/daily spatial resolution </strong>by applying SM2RAIN algorithm (<em>Brocca et al., 2014; 2019) </em>to Soil Moisture data from SMCI Soil moisture (<a href="https://doi.org/10.5194/essd-14-5267-2022"><em>https://doi.org/10.5194/essd-14-5267-2022</em></a>) for the period from January 2000 to December 2020 (21 years). The SM2RAIN-SMCI rainfall dataset (in mm/day) is provided over a regular grid at 0.01-degree sampling. The product represents the accumulated rainfall between the 00:00 and the 23:59 UTC of the indicated day. The uncalibrated SM2RAIN version (<a href="https://doi.org/10.5194/hess-26-2481-2022">https://doi.org/10.5194/hess-26-2481-2022</a>) was adopted to obtain rainfall. Precipitation data from HRLT (<a href="https://doi.org/10.5194/essd-14-4793-2022">https://doi.org/10.5194/essd-14-4793-2022</a>) were adopted to obtain mean climatology.</p> <p>The rainfall dataset is provided in NetCDF format. To simplify data access, the CHINA region is divided in 101 subregion containing 540 (longitude) * 360 (latitude) pixels each.</p>
South Portugal Live Fuel Moisture Content (LFMC) dataset
<p>This dataset contains a collection of biweekly LFMC field samples collected between April 2022 and July 2023 over the Alentejo region, South Portugal.<br><br>Metadata:</p> <p>Coordinate Reference System: ETRS_1989_Portugal_TM06 - EPSG 3763<br>File Format: ESRI shapefile<br>Column Fields:</p> <ul> <li>FID - Internal ID</li> <li>Ponto - control point for backup purposes.</li> <li>LFMC - Live fuel moisture content (LFMC) in percentage.</li> <li>DATE - Sample date. AREA - in-situ field name.</li> <li>POINT_X - Longitude.</li> <li>POINT_Y - Latitude.</li> <li>POINT_Z - Altitude.</li> </ul> <p>Fundings:</p> <p>Filippe Santos was supported by the Portuguese Foundation for Science and Technology, I.P (Grant 2022.11960.BD).<br>This research was funded by national funds through FCT-Foundation for Science and Technology, I.P. under the PyroC.pt project (Refs. PCIF/MPG/0175/2019), ICT project (Refs. UIDB/04683/2020 and UIDP/04683/2020).</p>
Relationship between decay resistance and moisture properties in wood modified with phenol formaldehyde and sorbitol-citric acid
<p>This dataset contains measurement data from the following publication: Belt, T.; Kyyrö, S.; Kilpinen, A. T. (2023) Relationship between decay resistance and moisture properties in wood modified with phenol formaldehyde and sorbitol-citric acid. Journal of Materials Science, 10.1007/s10853-023-08874-w. Small samples of Scots pine sapwood were modified using different concentrations of phenol formaldehyde (2.5, 5, 10, 20 and 30% resin solids content) and sorbitol-citric acid (5, 10, 20, 30 and 40% resin solids content) and then exposed to brown rot decay by <em>Coniophora puteana</em> and <em>Rhodonia placenta</em>. Sample masses and dimensions were measured at different points to determine their weight gain, anti-swelling efficiency and moisture exclusion efficiency due to modification, their mass loss due to decay and their moisture content at the end of the decay test. Fluorescence images were collected from decayed and control samples after the decay test. Further details on the experimental procedures can be found in the publication. </p> <p>The "Sample IDs and measurement data.csv" -file contains the sample IDs and all measured dimensions and mass data for every sample. Areas A<sub>dry0</sub>, Ad<sub>ry1</sub>, A<sub>wet</sub>, and A<sub>dry2</sub> are the cross-sectional areas of the samples in the dry state before modification, in the dry state after modification and before leaching, in the wet state during leaching, and in the dry state after leaching, respectively. Masses m<sub>dry0</sub>, m<sub>dry1</sub>, m<sub>dry2</sub>, m<sub>RH85</sub>, m<sub>wet</sub>, and m<sub>dry3</sub> are the masses of the samples in the dry state before modification, in the dry state after modification and before leaching, in the dry state after leaching, in the conditioned state at RH 85%, in the wet state at the end of the decay test, and in the dry state after the decay test, respectively.</p> <p>The "Fluorescence images" -folder contains fluorescence images collected from the samples. The image files are named according to the ID of the imaged sample, followed by additional tags. The samples modified using phenol formaldehyde were imaged using both green and UV excitation, and the file names contain the tag "green" or "UV" to denote the used excitation wavelengths. For all samples, the sample ID (and the excitation tag) are followed by a number to differentiate replicate images collected from the sample. </p>
Soil moisture data across a Florida scrub and sandhill landscape collected from 1998-2018 at Archbold Biological Station
This project was initiated 1998 to examine the variation in percent soil moisture in relation to rainfall, vegetation type, gaps, and time-since-fire in upland habitats at Archbold Biological Station, in south-central Florida. Data were collected from 78 sampling points across four vegetation types (rosemary scrub, scrubby flatwoods, oak-hickory scrub and sandy roadsides) with different time-since-fires (2-3 years or >20 years post-fire). In January 2006, 30 additional sampling points were added to include a fourth vegetation type (southern ridge sandhill). Data were collected at three depths below the soil surface (10, 50 and 90 cm) weekly (1 October 1998 – 9 June 1999), then bi-weekly (23 June 1999 – 3 October 2001), monthly (17 October 2001 – 15 June 2011), and every other month thereafter until the project ended on 16 July 2018.
Seasonal relationships between soil respiration and water-extractable carbon as influenced by soil temperature and moisture in forest soils of the Andrews Experimental Forest, 1992-1993
The overall objective of this study is to model trace gas emissions from forest soils of the H. J. Andrews Experimental Forest. This is to be accomplished by studying trace gas emissions and related variable at a set of 20 permanent plots at the HJA.
Baltimore Ecosystem Study: Soil moisture in long-term study plots, 1999-2011
The Baltimore Ecosystem Study (BES) has established a network of long-term permanent biogeochemical study plots. These plots will provide long-term data on vegetation, soil and hydrologic processes in the key ecosystem types within the urban ecosystem. The current network of study plots includes eight forest plots, chosen to represent the range of forest conditions in the area, and four grass plots. These plots are complemented by a network of 200 less intensive study plots located across the Baltimore metropolitan area. Plots are currently instrumented with lysimeters (drainage and tension) to sample soil solution chemistry, time domain reflectometry probes to measure soil moisture, dataloggers to measure and record soil temperature and trace gas flux chambers to measure the flux of carbon dioxide, nitrous oxide and methane from soil to the atmosphere. Measurements of in situ nitrogen mineralization, nitrification and denitrification were made at approximately monthly intervals from Fall 1998 - Fall 2000. Detailed vegetation characterization (all layers) was done in summer 1998. This data record contains near-monthly water content measurements, and the record continues with hourly data found in: Baltimore Ecosystem Study: Soil moisture and temperature along an urban to rural gradient, 2011- present https://portal.edirepository.org/nis/mapbrowse?scope=knb-lter-bes&identifier=3400 Data from these plots has been published in the following papers: Groffman PM, Pouyat RV, Cadenasso ML, Zipperer WC, Szlavecz K, Yesilonis IC,. Band LE and Brush GS. 2006. Land use context and natural soil controls on plant community composition and soil nitrogen and carbon dynamics in urban and rural forests. Forest Ecology and Management 236:177-192. Groffman, P.M., C.O. Williams, R.V. Pouyat, L.E. Band and I.C. Yesilonis. 2009. Nitrate leaching and nitrous oxide flux in urban forests and grasslands. Journal of Environmental Quality 38:1848-1860. Groffman, P.M. and R.V. Pouyat. 2009. Meth
Soil Moisture taken with a neutron probe 1969-1974 from 11 sites: Weekly
These original basic fertilization experiments were started to give preliminary information for designing future experiments and to study basic long term fertilization responses in a range of age classes for the dominant forest types of interior Alaska. In addition, the instrumentation of the control plots provide long term information on various environmental parameters. No fertilization or growth measurements were done in the black spruce stands.
Bonanza Creek moisture gradient physical data at BZBS: hourly temperature, moisture and photosynthetically active radiation.
This dataset contains the hourly output from temperature, moisture and PAR sensors at five unique vegetative sites at the Bonanza Creek moisture gradient. This data can be sorted and viewed by site, year, hour and depth of probe below surface. Data from each site within this transect will be update yearly. Start dates for the probes at each site vary depending on when they were installed.
Bonanza Creek moisture gradient physical data at BZWB: hourly temperature, moisture and photosynthetically active radiation.
This dataset contains the hourly output from temperature, moisture and PAR sensors at five unique vegetative sites at the Bonanza Creek moisture gradient. This data can be sorted and viewed by site, year, hour and depth of probe below surface. Data from each site within this transect will be update yearly. Start dates for the probes at each site vary depending on when they were installed.
Bonanza Creek moisture gradient physical data at BZTG: hourly temperature, moisture and photosynthetically active radiation.
This dataset contains the hourly output from temperature, moisture and PAR sensors at five unique vegetative sites at the Bonanza Creek moisture gradient. This data can be sorted and viewed by site, year, hour and depth of probe below surface. Data from each site within this transect will be update yearly. Start dates for the probes at each site vary depending on when they were installed.
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