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1,103 results for “moisture”
McMurdo Dry Valleys Soilwetting Effects on Soil Moisture
Investigation of the effect of short-term variation in soil moisture and soil temperature on nematode anhydrobiosis as part of the McMurdo Dry Valleys Long Term Ecological Research (LTER) project. The percent of anhydrobiotic (coiled) nematodes with relation to soil moisture, temperature, and salinity was determined. The study began at 1030 on 10 December 1997 and ended on 11 December 1997. The samples were taken at 0, 6, 12, 18, and 24 hrs. Samples were collected in the south side of Lake Hoare
McMurdo Dry Valleys Habitat Suitability - Soil Moisture
Investigation of the variation in soil biota and soil properties across the McMurdo Dry Valleys as part of the McMurdo Dry Valleys Long Term Ecological Research (LTER) project. The moisture content of soil samples which are collected for organism extraction and identification is determined. This study was carried out in the austral summer 1993/1994
Con-mod soil moisture data in the Black Sand experiment plots for East Knoll, Audubon, Lefty, Soddie and Trough, 2019 - ongoing.
In the alpine tundra, shrubs modify wind distribution of snow, increasing snowpack on the leeward side of shrubs, and they provide shading, which modifies temperatures. In order to mimic these abiotic effect of shrubs, structures called connectivity modifiers, hereafter referred to as con-mods, were deployed in Black Band experiment plots in September 2018. Briefly, the Black Sand experiment was initiated in May 2018 to measure the effects of an extended growing season, by initiating earlier snow melt through the application of black sand, on plants and biogeochemistry. Together, this experimental framework allows us to ask how biotic effects influence climate exposure and ecological responsiveness.
Soil methane flux, temperature, moisture, C, and N data for South of saddle, 1991.
Methane emissions were measured from dry and wet meadow plots established by W. Bowman in the Boulder watershed south of the Saddle. The effects of N and P fertilization were evaluated.
Soil moisture and nutrient data for Saddle grid, 1990.
Soil samples from a 10-cm depth were collected at each vegetated grid stake on the Saddle. These samples were analyzed for carbon and organic matter content. Texture, pH, moisture, and bulk density were also determined. The samples were collected in 1990 and were analyzed at the Soil Testing Laboratory at Colorado State University and at the INSTAAR sedimentology laboratory.
Time domain reflectometry soil moisture data for Niwot Ridge, 1992 - 2002.
Soil moisture was estimated at several locations on Niwot Ridge using a portable PC in conjunction with time-domain reflectometry (TDR) technology. The system used consisted of a Tektronics 1502B reflectometer, a Tektronics SP232 interface module, a Compaq LTE/232 PC, coaxial cable, and a probe that was left semi-permanently in the soil. The reflectometer generated an electrical step voltage pulse which was propagated down the cable to the probe. The reflected voltage waveform was measured, stored, and displayed by the reflectometer and the Tektronics SP232 interface module. The waveform is represented as a 2-dimensional data array, with the x-axis representing time (which is converted to distance using the dielectric constant of the cable) and the y-axis representing voltage for the given time: in this system this value is represented in millirho units (Tektronics 1991). The length of the waveform is determined by measuring the difference between the soil air interface (SAI) and the end of the voltage step reflection (RE). The SAI value is equivalent to the apex of the first peak in the wave and the RE value is equivalent to the intersection of tangents "drawn" at (a) the bottom of the wave and (b) the point of the steepest rise (see Taylor and Seastedt 1992). The waveform length is then used to estimate the volumetric and gravimetric moisture of the surrounding soil.
Soil moisture data for Saddle nodal plots, 1982 - 1989.
Soil moisture sampling in the Saddle area was performed at the ends (outside of) 12 permanent plots representing 6 plant communities or noda (two from each nodum) in the Saddle (May 1973). The design and location of the nodal plots was described in Pollak et al. (1988). Soil moisture data were collected from the 12 nodal plots weekly during the growing season from 1 June (Julian day 152) 1982 through mid-August 1988. Less-intensive sampling was done during the 1989 growing season. Sampling began when the plots became snow-free. The last sampling date varied among years and depended upon when plants had dehisced, weather, and available personnel. Soil moisture percentages were determined gravimetrically using soil samples collected outside of but adjacent to the ends of the plots (within 0.5 m of plot end). A #6 cork borer (64 mm^2) was used to remove a soil sample from each plot end (two per plot). Samples were taken from the rooting zone to a depth of 5 to 10 cm. A length of 5/8-inch diameter pipe (198 mm^2) was used instead of the cork borer in 1988. If rocks prevented an adequate sample depth, collection of a new sample was attempted nearby. Prior to the 1986 season a metal probe was used to detect below-ground rocks. Extracted cores were pushed into film canisters which were then sealed and labeled. Samples were collected before noon and were weighed within 10 hr. The wet soil samples were placed on aluminum pans, weighed, and oven dried at 100 degrees C for at least 24 hr. Dried soil was removed from the oven and immediately weighed. Prior to 1986 dried soil was placed in a dessicator for 15 min before weighing. Dried soil cores were saved and returned to the core holes. Wet+pan weights, dry+pan weights, and pan weights were recorded. Soil moisture was calculated as (((wet + pan weight) - (dry + pan weight)/((dry + pan weight) - pan weight)) * 100.
Monsoon Rainfall Manipulation Experiment (MRME) Soil Moisture Data from the Sevilleta National Wildlife Refuge, New Mexico (7/2007 - 8/2009)
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. 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.
Adjacency matrices of global atmospheric moisture networks during 2007-2016
<p>This dataset provides the adjacency matrices used to run Infomap to conduct community detection of global atmospheric moisture networks. A detailed description of file naming convention, dimension information and how to read the files can be found in 'Readme.txt'.</p>
Maximum moisture content of contemporary birch bark.
<p>Data accompanying the article published on the Icom-CC proceedings 2017 Copenhagen:</p> <p> https://www.icom-cc-publications-online.org/PublicationDetail.aspx?cid=92b3e81e-ba73-46d2-875a-0fe7b3b4ffb6</p>
Supplementary Data for Remote soil moisture measurement from drone-borne reflectance spectroscopy: Applications to hydroperiod measurement in desert playas
<p>This supporting dataset includes:</p> <p>• Ground-based reflectance measurements for the two plume-crossing transects (AHT3 and AHT4). Data show reflectance by wavelength at each scan position.</p> <p>• Reduced data records (RDR) showing GPS position data for the drone and the associated reflectance measurement, by wavelength, averaged over 1 second intervals for the three mapping sorties, AHS4, 5, and 7.</p> <p>• The sample datasheet showing ground-based and lab-base measurements of the sampling transects.</p>
Forest reorganisation effects on fuel moisture content can exceed changes due to climate warming in wet temperate forests
<p>48-year modelled dataset of below canopy fuel moisture content (FMC) for field sites established in wet temperate eucalypt forests in south-eastern Australia (allsites.csv). Raw hourly data for input to the model (hrlyallsitesgit.csv). </p>
Data archive for: Exploring the use of machine learning to improve vertical profiles of temperature and moisture
<p>Vertical profiles of temperature and dewpoint are useful in predicting deep convection that leads to severe weather that threatens property and lives. Currently, forecasters rely on observations from radiosonde launches and numerical weather prediction (NWP) models. Radiosonde observations are, however, temporally and spatially sparse, and NWP models contain inherent errors that influence short-term predictions of high-impact events. This work explores using machine learning (ML) to postprocess NWP model forecasts, combining them with satellite data to improve vertical profiles of temperature and dewpoint. We focus on different ML architectures, loss functions, and input features to optimize predictions. Because we are predicting vertical profiles at 256 levels in the atmosphere, this work provides a unique perspective at using ML for 1-D tasks. Compared to baseline profiles from the Rapid Refresh (RAP), ML predictions offer the largest improvement for dewpoint, particularly in the mid- and upper-atmosphere. emperature improvements are modest, but CAPE values are improved by up to 40%. Feature importance analyses indicate that the ML models are primarily improving incoming RAP biases. While additional model and satellite data offer some improvement to the predictions, architecture choice is more important than feature selection in fine-tuning the results. Our proposed deep residual UNet performs the best by leveraging spatial context from the input RAP profiles; however, the results are remarkably robust across model architecture. Further, uncertainty estimates for every level are well-calibrated and can provide useful information to forecasters.</p>
Strong plastic responses in aerenchyma formation in F1 hybrids of Imperata cylindrica under different soil moisture conditions
<p>There is insufficient evidence demonstrating how phenotypic plasticity in specific traits mediates hybrid performance.<strong> </strong>Two ecotypes of <em>Imperata cylindrica</em> produce F1 hybrids. The early flowering type (E-type) in wet habitats has larger internal gas spaces (aerenchyma) than the common type (C-type) in dry habitats. This study evaluated the relationships between the habitat utilisation, aerenchyma plasticity, and growth of <em>I. cylindrica</em> accessions. We hypothesize that plasticity in expressing parental traits explains hybrid establishment in habitats with various soil moisture conditions.</p> <p>Aerenchyma formation was examined in the leaf midribs, rhizomes, and roots of two parental ecotypes and their F1 hybrids in their natural habitats. In common garden experiments, we examined plastic aerenchyma formation in the leaf midribs, rhizomes and roots of natural and artificial F1 hybrids and parental ecotypes. Their vegetative growth performance was also quantified.</p> <p>In the natural habitats where soil moisture content varied widely, the F1 hybrids showed larger variation of aerenchyma formation in the rhizomes than their parental ecotypes. In the common garden experiments, the F1 hybrids showed high plasticity of aerenchyma formation in the rhizomes, and their growth was similar to that of C-type and E-type under drained and flooded conditions, respectively.</p> <p>The results demonstrate that the F1 hybrids of <em>I. cylindrica</em> exhibit plasticity in aerenchyma development in response to varying local soil moisture content. This characteristic allows the hybrids to thrive in diverse soil moisture conditions.</p>
Data for Torppa et al. 2023 'Soil moisture and fertility drive earthworm diversity in north temperate semi-natural grasslands'
<p>The dataset consists of the data that supports the findings of the article 'Soil moisture and fertility drive earthworm diversity in north temperate semi-natural grasslands' written by Torppa et al. and published in Agriculture, Ecosystems & Environment in 2023. The data consists of earthworm community data and environmental data related to soil, vegetation, management and landscape, as well as accession numbers to the earthworm specimens in BOLD and GenBank.</p>
Moisture and temperature effects on the radiocarbon signature of respired carbon dioxide to assess stability of soil carbon in the Tibetan Plateau
<p>Radiocarbon data set and code for the prediction of D14C values in bulk soil and respired CO2.</p> <p>Lab results for TOC, TN, TIC, Dap and physico-chemical properties of grassland and peatland soils</p> <p> </p>
iRON_Soil_Moisture_Uncalibrated_2023
<p><strong>These are minimally processed, near raw data. Potential sensor errors or equipment changes are not flagged. </strong></p> <p> </p> <p>Rain values are from a tipping bucket rain gauge, and values may not accurately represent precipitation in winter months.<br><br>These files have been corrected for data gaps and sensor label errors. V0 Files published prior to Feb 22, 2022 may contain errords.</p>
A seamless global 5 km surface soil moisture product from 1982 to 2021
<p><span>Soil moisture (SM) is an essential climate-sensitive variable that exhibits high spatial and temporal variability. Long-term SM data records (> 30 years) can benefit a range of climate change-related applications. A four-decade global 5-km daily SM product has been generated from 1982 to 2021, as part of the Global Land Surface Satellite (GLASS) products suite. This product (GLASS-AVHRR SM) was derived mainly from the GLASS-AVHRR albedo and LST products, the ERA5-Land reanalysis SM product, and auxiliary datasets, using an attention-based deep learning model. The GLASS-AVHRR SM product has the advantages of long-term coverage, spatial and temporal integrity, reliable accuracy and consistency.</span></p> <p><span>The data values in the GLASS-AVHRR SM product represent the volumetric water content of the uppermost soil layer (0–5 cm). The files are stored in geographic projection and provided in Geo Tiff format, with "No Data" values set to -9999.</span></p>
A Unified Ensemble Soil Moisture Dataset Across the Continental United States
<p>A unified ensemble soil moisture (SM) package has been developed over the Continental United States (CONUS). The data package includes 19 products from land surface models, remote sensing, reanalysis, and machine learning models. All datasets are unified to a 0.25-degree and monthly spatiotemporal resolution, covering various temporal spans and providing a comprehensive view of surface SM dynamics. The statistical analysis of the datasets leverages the Koppen-Geiger Climate Classification to explore surface SM’s spatiotemporal variabilities. The extracted SM characteristics highlight distinct patterns, with the western CONUS showing larger coefficient of variation values and the eastern CONUS exhibiting higher SM values. Remote sensing datasets tend to be drier, while reanalysis products present wetter conditions. In-situ SM observations serve as the basis for wavelet power spectrum analyses to explain discrepancies with respect to temporal scales across the 16 datasets facilitating daily SM records. This study provides a comprehensive soil moisture data package and an analysis framework that can be used for Earth system model evaluations and uncertainty quantification, quantifying drought impacts and land–atmosphere interactions, and making recommendations for drought response planning.</p> <p>Data details: 1. scripts: 1) process data from original spatial resolution to 0.25 degree; 2) process data from original temporal resolution to monthly; 3) process the monthly data to seasonal mean analysis; 4) wavelet analysis. 2. data: 1) monthly 0.25deg data processed from raw datasets; 2) monthly and seasonal climatology data for comparison; 3) site data for wavelet analysis.</p> <p>Reference: The data manuscript is under review now and will add it here later.</p> <p>Contact: Mingjie Shi <mingjie.shi@pnnl.gov>; Lingcheng Li <lingcheng.li@pnnl.gov></p>
Water table depth dynamics and surface soil moisture content from three Scottish peatland areas (2021-2022)
<p>This compilation of datasets from three monitoring sites on peatland in Scotland includes water table depth dynamics and surface soil moisture content and covers the period 2021-2022. Further data will be added on an annual basis. This is version 2 of the dataset, which corrects a small number of data QC issues (see README).</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.