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473 results for “Soil temperatures”
Soil Moisture and Temperature following experimental drought in the LEF
We used throughfall exclusion shelters to determine effects of short-term (3 month) drought on trace gas fluxes and nutrient availability in humid tropical forests in Puerto Rico. Exclusion and control plots were replicated within and across three topographic zones (ridge, slope, valley) to account for spatial heterogeneity typical of these ecosystems. Support for this work was provided by grants BSR-8811902, DEB-9411973, DEB-9705814 , DEB-0080538, DEB-0218039 , DEB-0620910 , DEB-1239764, DEB-1546686, and DEB-1831952 from the National Science Foundation to the University of Puerto Rico as part of the Luquillo Long-Term Ecological Research Program. Additional support provided by the University of Puerto Rico and the International Institute of Tropical Forestry, USDA Forest Service.
Elevational Transects (ET): Soil temperature depth profiles from Garwood, Miers, and Taylor Valley, McMurdo Dry Valleys, Antarctica (2019-2024, ongoing)
As part of the McMurdo Dry Valleys Long Term Ecological Research (LTER) project, the Elevational Transects (ET) study was established in 1993 to investigate how elevation and topographic variation influence soil biotic communities and associated soil properties in the McMurdo Dry Valleys of Antarctica. Transects consisting of low-, mid-, and high-elevation sites were initially established in the Bonney, Hoare, and Fryxell basins of Taylor Valley, with additional transects added in Garwood and Miers Valleys during the 2012-2013 austral summer. This data package contains year-round soil temperature measurements recorded at four depths (0, 5, 10, and 20 cm) by HOBO dataloggers installed at the mid-elevation sites of each valley during the 2018–2019 austral summer.
North Temperate Lakes LTER Soil Temperature - Woodruff Airport 2006 - current
Soil temperature data are being gathered at a site at the Noble F. Lee municipal airport located at Woodruff, WI. Soil temperature is measured at depths of 0.05m, 0.1m and 0.5m at 1-minute intervals. High resolution data are collected (typically at 10 minute intervals) along with 1-hour and 24-hour averages. Daily minimum and maximum soil temperatures and the times these occur are reported for these same depths. Data are automatically updated into the database every six hours. Prior to August 2006, only hourly averaged data are available. Starting in 2008, soil temperatures are only available from 0.5m depth. Sampling frequency: varies for instantaneous samples; averaged to hourly and daily values from one minute samples. Number of sites: 1. Data collection failure caused data loss for the first half of 2024.
Supplemental soil moisture and temperature data from the saddle catchment sensor network, 2019 - 2021.
Hand-held soil moisture measurements were taken at 8 of 16 soil moisture sensors within the sensor network at Niwot Ridge to supplement the continuous measurement system at these locations. The hand-held measurements occur much less frequently than the 10 min sensor data, but they are important in determining the spatial variability of soil moisture in the alpine.
Monsoon Rainfall Manipulation Experiment (MRME): Soil Temperature Data from the Sevilleta National Wildlife Refuge, NM
The Monsoon Rainfall Manipulation Experiment (MRME) is to understand changes in ecosystem structure and function of a semiarid grassland caused by increased precipitation variability, which alters the pulses of soil moisture that drive primary productivity, community composition, and ecosystem functioning. The overarching hypothesis being tested is that changes in event size and variability will alter grassland productivity, ecosystem processes, and plant community dynamics. These data are soil temperature data collected at two depths.
Monsoon Rainfall Manipulation Experiment (MRME) Soil Temperature, Moisture and Carbon Dioxide Data from the Sevilleta National Wildlife Refuge, New Mexico
The Monsoon Rainfall Manipulation Experiment (MRME) is designed to understand changes in ecosystem structure and function of a semiarid grassland caused by increased precipitation variability, by altering rainfall pulses, and thus soil moisture, that drive primary productivity, community composition, and ecosystem functioning. The overarching hypothesis being tested is that changes in event size and frequency will alter grassland productivity, ecosystem processes, and plant community dynamics. Treatments include (1) a monthly addition of 20 mm of rain in addition to ambient, and a weekly addition of 5 mm of rain in addition to ambient during the months of July, August and September. It is predicted that changes in event size and variability will alter grassland productivity, ecosystem processes, and plant community dynamics. In particular, we predict that many small events will increase soil CO2 effluxes by stimulating microbial processes but not plant growth, whereas a small number of large events will increase aboveground NPP and soil respiration by providing sufficient deep soil moisture to sustain plant growth for longer periods of time during the summer monsoon.
SEV-LTER Mean Variance Experiment Desert Shrubland Soil Moisture and Temperature
We designed novel field experimental infrastructure to resolve the relative importance of changes in the climate mean and variance in regulating the structure and function of dryland populations, communities, and ecosystem processes. The Mean x Variance Experiment (MVE) adds three novel elements to prior designs (Gherardi & Sala 2013) that have manipulated interannual variance in climate in the field by (i) determining interactive effects of mean and variance with a factorial design that crosses a drier mean with increased (more) variance, (ii) studying multiple dryland ecosystem types to compare their susceptibility to transition under interactive climate drivers, and (iii) adding stochasticity to our treatments to permit the antecedent effects that occur under natural climate variability. This new infrastructure enables direct experimental tests of the hypothesis that interactions between the mean and variance of precipitation will have larger ecological impacts than either the mean or variance in precipitation alone. A subset of plots have soil moisture and temperature sensors to evaluate treatment effectiveness by addressing, How do MVE manipulations alter the mean and variance in soil moisture and temperature? And, how does micro-environmental variation among plots influence how much MVE treatments alter soil moisture profiles over three soil depths? This data package includes soil moisture and temperature sensor data from the Mean x Variance Climate experiment in the Desert Shrubland ecosystem at the Sevilleta National Wildlife Refuge, Socorro, NM.
SEV-LTER Mean Variance Experiment Juniper Savanna Soil Moisture and Temperature
We designed novel field experimental infrastructure to resolve the relative importance of changes in the climate mean and variance in regulating the structure and function of dryland populations, communities, and ecosystem processes. The Mean x Variance Experiment (MVE) adds three novel elements to prior designs (Gherardi & Sala 2013) that have manipulated interannual variance in climate in the field by (i) determining interactive effects of mean and variance with a factorial design that crosses a drier mean with increased (more) variance, (ii) studying multiple dryland ecosystem types to compare their susceptibility to transition under interactive climate drivers, and (iii) adding stochasticity to our treatments to permit the antecedent effects that occur under natural climate variability. This new infrastructure enables direct experimental tests of the hypothesis that interactions between the mean and variance of precipitation will have larger ecological impacts than either the mean or variance in precipitation alone. A subset of plots have soil moisture and temperature sensors to evaluate treatment effectiveness by addressing, How do MVE manipulations alter the mean and variance in soil moisture and temperature? And, how does micro-environmental variation among plots influence how much MVE treatments alter soil moisture profiles over three soil depths? This data package includes soil moisture and temperature sensor data from the Mean x Variance Climate experiment in the Juniper Savanna ecosystem at the Sevilleta National Wildlife Refuge, Socorro, NM.
SEV-LTER Mean x Variance Experiment Pinon Juniper Soil Moisture and Temperature
We designed novel field experimental infrastructure to resolve the relative importance of changes in the climate mean and variance in regulating the structure and function of dryland populations, communities, and ecosystem processes. The Mean x Variance Experiment (MVE) adds three novel elements to prior designs (Gherardi & Sala 2013) that have manipulated interannual variance in climate in the field by (i) determining interactive effects of mean and variance with a factorial design that crosses a drier mean with increased (more) variance, (ii) studying multiple dryland ecosystem types to compare their susceptibility to transition under interactive climate drivers, and (iii) adding stochasticity to our treatments to permit the antecedent effects that occur under natural climate variability. This new infrastructure enables direct experimental tests of the hypothesis that interactions between the mean and variance of precipitation will have larger ecological impacts than either the mean or variance in precipitation alone. A subset of plots have soil moisture and temperature sensors to evaluate treatment effectiveness by addressing, How do MVE manipulations alter the mean and variance in soil moisture and temperature? And, how does micro-environmental variation among plots influence how much MVE treatments alter soil moisture profiles over three soil depths? This data package includes soil moisture and temperature sensor data from the Mean x Variance Climate experiment in the Pinon Juniper ecosystem at the Sevilleta National Wildlife Refuge, Socorro, NM.
Code and data for Mire microclimate: groundwater buffers temperature in waterlogged versus dry soils.
<p>Version of record of the manuscript's code and data, as accepted by the International Journal of Climatology.</p>
BST/NOAA PSL Level 2 UAS Soil Moisture, Digital Elevation, Normalized Difference Vegetative Index, and Surface Temperature for SPLASH
<p>This dataset contains uncrewed aircraft systems (UAS) high-resolution data of soil moisture at the 0-5 cm soil depth, normalized difference vegetation index (NDVI), surface temperature, and digital elevation for the Study of Precipitation, the Lower Atmosphere, and Surface for Hydrology (SPLASH) campaign sponsored by the National Oceanic and Atmospheric Administration (NOAA). These data were collected near Avery Picnic (38.972425 degrees N,106.996855 degrees W) and Kettle Ponds (38.942005 degrees N,106.973006 degrees W) in the East River Watershed in Colorado from a series of flights starting on June 1st, 2022 and ending October 18th, 2023. Soil moisture measurements were retrieved using the Lobe Differencing Correlation Radiometer (LDCR) which is a L-Band (1-2 GHz) microwave radiometer and was flown on the E2 and S2 aerial platforms operated by Black Swift Technologies LLC. </p> <p> </p> <p>Each zip file contains a set of four Level 2 NetCDF files which provides the highest spatial resolution available for each of four products for a given flight location. With the Level 2 data, each flight location and variable can have different spatial resolutions depending on the sensor type, retrieval algorithm, and flight altitude. The file name convention for the zip files is as follows.</p> <p> </p> <p>uas_L2_yyyymmdd_hhmmss_vX.X.zip </p> <p>where</p> <p>L2 = Level 2 data </p> <p>yyyymmdd = year,month,day</p> <p>hhmmss = hour,minute,second</p> <p>vX.X = version number</p> <p>Time is the flight start time in UTC.</p> <p> </p> <p>The NetCDF file format contained in the zip files has a similar format to the zip files with convention</p> <p> </p> <p>uas_<var>_L2_yyyymmdd_hhmmss.nc </p> <p>where</p> <p><var> = vsm, dem, ndvi, or stmp</p> <p>vsm = volumetric soil moisture</p> <p>dem = digital elevation</p> <p>ndvi = normalized difference vegetation index</p> <p>stmp = surface temperature</p> <p> </p> <p>Note that each flight location using the E2 aerial platform required two flights so starting flight times for the soil moisture NetCDF files are different from the other three products.</p> <p><strong>November 2023 update</strong>: Version 2.0 added flight data from 2023. Version 2.0 includes an updated calibration of the soil moisture retrieval that has been applied to 2023 data, and a mask was applied to the soil moisture retrieval over water surfaces for both 2022 and 2023 data.</p> <p><strong>December 2023 update</strong>: Version 2.1 updated soil moisture data with a wet bias in v2.0 for flights #2 (17:40:35 UTC) and #3 (19:24:45 UTC) on July 27, 2022.</p>
High resolution soil moisture and soil temperature data during Hurricane Florence, 2018, over the Carolina region (U.S.)
<p>We set the study domain over the U.S. east coast to cover the Carolinas and the regions that were affected by Hurricane Florence. Therefore, the selected domain covered the area between -86º to -75º longitude and 30º to 40º latitude. We should note that Hurricane Florence made landfall in the Carolinas on September 14, 2018, as a Category 1 storm. Hurricane reports indicate that “Hurricane Florence made landfall near Wrightsville Beach, North Carolina at 7:15 AM EDT (1115 UTC) on September 14 with estimated maximum winds of 90 mph (150 km/h), and a minimum central pressure estimate of 958 millibars. Winds gusts topping 105 mph (169 km/h) were reported in the Outer Banks of North Carolina.” Rainfall from Florence, as per the initial reports, suggest possible new records for North Carolina (breaking the record set by Hurricane Floyd in 1999).</p> <p>we used the latest development of the high-resolution land surface assimilation system (HRLDAS) that was retrieved from the Github repository (<a href="https://github.com/NCAR/hrldas-release">https://github.com/NCAR/hrldas-release</a>). The model was coupled to the Noah land surface modeling system and used the multi-layer soil model, complex canopy resistance with the Penman method for calculating evapotranspiration, and frozen ground physics for the simulations.</p> <p> The atmospheric forcing including 2-meter air temperature, shortwave, and long-wave radiation, 2-meter wind speed, 2-meter specific humidity, and surface pressure data was obtained from the NCEP-DOE Reanalysis 2 (available from <a href="https://psl.noaa.gov/data/gridded/data.ncep.reanalysis2.html">psl.noaa.gov/data/gridded/data.ncep.reanalysis2.html</a>). For the precipitation, we used the high-resolution GCIP/EOP surface precipitation NCEP/EMC gridded data (Stage IV) with 4 km of grid spacing. All the forcing data have been retrieved at an hourly frequency from 2016 to 2019. The model was configured with the initial soil moisture and soil temperature conditions at 4 depths (0-10, 10-40, 40-100, 100-200 cm), retrieved from the NCEP data. Other initialization fields including skin temperature and water equivalent snow depth were retrieved from NCEP. The NCEP reanalysis data, however, does not provide “plant canopy surface water” data which is required as an initialization field. This data was retrieved from the NLDAS dataset. The first two years of the model run (2016 and 2017) were considered as the spin-up, and the outcome during 2018 was used for further analysis and public release.</p>
Improving ERA5-Land soil temperature in permafrost regions
<p>Cao et al. (2020) have previously reported a warm bias in ERA5-Land soil temperature in permafrost regions that was supposedly being caused by an underestimation of the snow density. This study evaluates a new multi-layer snow scheme in the land surface scheme of ERA5-Land, i.e., HTESSEL, with a revised snow densification parametrization. The revised HTESSEL significantly improved the representation of soil temperature in permafrost regions compared to ERA5-Land.</p> <p>The original simulation data, on the Octahedral reduced gaussian grid, were interpolated on a 0.25/0.25 regular lat/lon grid (global) and aggregated in daily mean from 2000-01-01 to 2018-12-31. More information on the dataset is contained in info_dataset.txt</p>
Changes in soil moisture and temperature modify the toxicity of sodium selenite and sodium selenate for Folsomia candida (Collembola) Willem 1902
<p>Effects of sublethal concentrations of selenite and selenate were tested on parameters of mortality, reproduction, growth, and oxidative stress parameters of <em>Folsomia candida</em> (Collembola) in case of different climate scenarios. The standard 20°C and the increased 25°C temperatures were combined with three different soil moisture conditions: drought, standard water content and increased water content.</p>
Temperature moisture interactions soil respiration experiment
<p>These files are from a soil incubation experiment looking at combined effects of temperature and moisture on soil C fluxes. They are prepared for model run and model-data comparison. Description of the data, e.g units, is not in the files themselves.</p> <p><a href="https://zenodo.org/api/files/da1986ad-a035-41ee-b08e-8ed654e9f84f/mtdata_model_input.csv">mtdata_model_input.csv</a></p> <p>Contains model input for simulating the experimental setup.</p> <p><a href="https://zenodo.org/api/files/da1986ad-a035-41ee-b08e-8ed654e9f84f/mtdata_co2.csv">mtdata_co2.csv</a></p> <p>Contains the measured data with averages and standard deviation of three replicates samples for treatment.</p> <p><a href="https://zenodo.org/api/files/da1986ad-a035-41ee-b08e-8ed654e9f84f/site_Closeaux.csv">site_Closeaux.csv </a></p> <p>Containts soil properties required as model input.</p> <p> </p>
New Hampshire Soil Sensor Network: Air Temperature, Soil Temperature, Soil Water Content, and Soil Electrical Conductivity, 2012 - ongoing
The goal of the New Hampshire Soil Sensor Network is to examine spatial and temporal changes in soil properties and processes as the climate changes. Data collected can also calibrate and validate models that examine how ecosystems may respond to changing climate and land use. To determine how soil processes are affected by climate change and land management, this soil sensor network measures snow depth, air temperature, soil temperature, soil volumetric water content, and soil electrical conductivity, as well as soil CO2 fluxes. This data package includes data from the air temperature, soil temperature, soil volumetric water content, and electrical conductivity sensors. Data were collected at the following sites: BRT = Bartlett Experimental Forest, Bartlett, NH; BDF = Burley-Demmerit Farm, Lee, NH; DCF = Dowst Cate Forest, Deerfield, NH; HUB = Hubbard Brook Experimental Forest, Woodstock, NH; SBM = Saddleback Mountain, Deerfield, NH; THF = Thompson Farm, Durham, NH; and Trout Pond Brook, Strafford, NH.
Effects of drying temperature on potential carbon mineralization and water-extractable organic carbon in Iowa cropland and riparian buffer soils
Measuring carbon dioxide (CO2) produced after re-wetting a previously dried soil is an increasingly popular soil health assay, but there is disagreement on the optimal soil drying temperature. We tested whether soil drying temperature impacts water-extractable organic carbon (WEOC) and soil CO2 emissions (potential carbon mineralization) following rewetting of dried soil. Samples were collected at four sites in north-central Iowa, US, and each site had soils planted to corn/soybean or perennial vegetation. The dataset includes measurements of WEOC prior to the incubation experiment, and measurements of CO2 flux and its stable carbon isotope ratio over the course of a 28-day incubation. The manuscript describing these data is under review in Geoderma.
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.
Soil temperatures, lake temperature, lake depth, and evaporation pan depth and pan water temperature data from Toolik Field Station, Toolik Lake, Alaska for 2008.
Weather data file for Arctic Tundra LTER site at Toolik Lake. Only the sensors that are measured every 10 minutes and averaged every three hours are include, i.e. soil temperatures, lake temperature, lake depth, and evaporation pan depth and pan water temperature.
Soil temperatures, lake temperature, lake depth, and evaporation pan depth and pan water temperature data from Toolik Field Station, Toolik Lake, Alaska for 2009.
Weather data file for Arctic Tundra LTER site at Toolik Lake. Only the sensors that are measured every 10 minutes and averaged every three hours are include, i.e. soil temperatures, lake temperature, lake depth, and evaporation pan depth and pan water temperature.
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