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767 results for “Soil Moisture”
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.
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>
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>
Dataset for: Neutrons on Rails -- trans-regional monitoring of soil moisture and snow water equivalent
<p>Using the railway system for regular environmental monitoring could extend the measurement capability to trans-regional and nationwide scales. Cosmic-ray neutron detectors in trains respond to spatial patterns of water content in their environment. Three distinct real world experiments support a proof of concept for soil and snow water monitoring using trains on short and long-range tracks across Germany:</p> <ul> <li>Supplement S4: Data (raw and processed) for the train journey from Leipzig to Berlin.</li> <li>Supplement S5: Data (raw and processed) for the train journey from Dessau to Zerbst, the subsequent car-borne Rover measurements, and the TDR measurements.</li> <li>Supplement S6: Data (raw and processed) for the train journey from Garmisch-Partenkirchen to Munich to Leipzig.</li> </ul> <p>This is the dataset supplementing the corresponding GRL publication "Neutrons on Rails -- trans-regional monitoring of soil moisture and snow water equivalent", preprint available from: https://doi.org/10.1002/essoar.10507363.1</p>
Coupling Novel Soil Moisture-Suction Sensors and UAV Photogrammetry Technology to the Performance of Highway Embankments
<p>The movement of water plays a critical role in the mechanical performance and service life of transportation infrastructure, especially for pavement subgrades and highway embankments consisting of high-plasticity, expansive soils that saturate and ultimately lead to infrastructure distress. Shallow slides along highway embankments are ubiquitous across Region 6 because long-term wetting and drying cycles considerably weaken these compacted soils. In the aftermath of heavy rains, pore-water pressures increase to a critical threshold such that a failure occurs. The implications of embankment failures range from repeated maintenance repairs to long-term road closures. A comprehensive approach to model highway embankments comprising of laboratory testing, setup, and field data collection using unmanned aerial vehicles has been proposed in this study.</p>
An in situ observation dataset of soil hydraulic properties and soil moisture in a high and cold mountainous area on the northeastern Qinghai-Tibet Plateau
<p>Based on soil profile data at depths of 5 cm and 25 cm from 238 sampling sites, and on soil data from 32 soil moisture monitoring stations at depths of 5 cm, 15 cm, 25 cm, 40 cm, and 60 cm, we have compiled a soil hydraulic properties and soil moisture dataset for a high and cold mountainous area, Northeastern Qinghai-Tibet Plateau. Specifically, the soil hydraulic properties include clay, silt, sand, soil organic carbon, soil saturated hydraulic conductivity, soil water retention curve parameters (Van Genuchten model) and soil dry bulk density.</p>
ChinaCropSM1km: a fine 1km daily Soil Moisture dataset for dryland wheat and maize across China during 1993–2018
<p>ChinaCropSM1km provides 1km gridded daily Soil Moisture for Crop drylands across China over 1993–2018, which contains four daily datasets separately by crop type and soil depth (crop-type <sub>soil-depth</sub>). This dataset is maize<sub>0-10.</sub></p> <p>*** The data file is in “.tif" format</p> <p>*** Spatial extent: maize drylands across China</p> <p>*** Soil depth: 0-10cm</p> <p>*** Temporal Resolution: Daily</p> <p>*** Pixel size: 1000 m</p> <p>*** Projection information: GCS_WGS_1984</p> <p>*** Filename convention: Year_DOY.tif</p> <p>*** Year: Values from 1993 to 2018</p>
ChinaCropSM1km: a fine 1km daily Soil Moisture dataset for dryland wheat and maize across China during 1993–2018
<p>ChinaCropSM1km provides 1km gridded daily Soil Moisture for Crop drylands across China over 1993–2018, which contains four daily datasets separately by crop type and soil depth (crop-type <sub>soil-depth</sub>). This dataset is maize<sub>10-20.</sub></p> <p>*** The data file is in “.tif" format</p> <p>*** Spatial extent: maize drylands across China</p> <p>*** Soil depth: 10-20cm</p> <p>*** Temporal Resolution: Daily</p> <p>*** Pixel size: 1000 m</p> <p>*** Projection information: GCS_WGS_1984</p> <p>*** Filename convention: Year_DOY.tif</p> <p>*** Year: Values from 1993 to 2018</p>
ChinaCropSM1km: a fine 1km daily Soil Moisture dataset for dryland wheat and maize across China during 1993–2018
<p>ChinaCropSM1km provides 1km gridded daily Soil Moisture for Crop drylands across China over 1993–2018, which contains four daily datasets separately by crop type and soil depth (crop-type <sub>soil-depth</sub>). This dataset is wheat<sub>10-20.</sub></p> <p>*** The data file is in “.tif" format</p> <p>*** Spatial extent: wheat drylands across China</p> <p>*** Soil depth: 10-20cm</p> <p>*** Temporal Resolution: Daily</p> <p>*** Pixel size: 1000 m</p> <p>*** Projection information: GCS_WGS_1984</p> <p>*** Filename convention: Year_DOY.tif</p> <p>*** Year: Values from 1993 to 2018</p>
Combined_ST_SM_Changes_Impacts Soil moisture observations
<p>In situ observations of soil moisture used in the paper.</p>
Global soil moisture–atmosphere feedback and N2O emission dataset
<p><span>Soil moisture is essential to microbial nitrogen (N)-cycling networks in terrestrial ecosystems. Studies have found that soil moisture–atmosphere feedbacks dominate the changes in land carbon fluxes. </span><span>However, the influence of soil moisture–atmosphere feedbacks on the N fluxes changes, and the underlying mechanisms remain highly unsure, leading to uncertainties in climate projections. </span><span>To fill this gap, we utilized in situ observation coupled with gridded and remote sensing data to analyze N<sub>2</sub>O fluxes emissions globally. Here, we investigated the synergistic effects of temperature, hydroclimate on global N<sub>2</sub>O fluxes, as the result of soil moisture–atmosphere feedback impact on N fluxes. We found that soil moisture–temperature feedback dominates land N<sub>2</sub>O emissions by controlling the balance between nitrifier and denitrifier genes. The mechanism is that atmospheric water demand increases with temperature and thereby reduces soil moisture, which increases the dominant N<sub>2</sub>O production nitrifier (containing <em>amoA </em>AOB gene) and decreases the N<sub>2</sub>O consumption denitrifier (containing the<em> nosZ</em> gene), consequently will potential increasing N<sub>2</sub>O emissions. However, we find that the spatial variations of soil–water availability as a result of the nonlinear response of soil moisture to vapor pressure deficit caused by temperature are some of the greatest challenges in predicting future N<sub>2</sub>O emissions. Our data-driven assessment deepens the understanding of the impact of soil moisture-atmosphere interactions on the soil N cycle, which remains uncertain in earth system models. We suggest that the model needs to account for feedback between soil moisture and atmospheric temperature when estimating the response of the N<sub>2</sub>O emissions to climatic change globally, as well as when conducting field-scale investigations of the response of the ecosystem to warming.</span></p>
CONUS VPD and soil moisture thresholds for suboptimal vegetation function
<p class="MsoNormal"><span>The dataset contains minimum and maximum vapor pressure deficit (VPD) and soil moisture parameters that define the range of suboptimal vegetation function (i.e., between uninhibited photosynthesis and complete shutdown). </span>The ranges were estimated using the Dynamic Canopy Biophysical Properties model (DCBP; Lowman & Barros 2018), which consists of a phenology forecasting model based on the growing season index (GSI), which provides a unitless measure of the potential phenologic state of vegetation based on the concurrent meteorological conditions (Jolly et al. 2005).</p>
Traits of soil bacteria predict plant responses to soil moisture
<p>Microbes can promote beneficial plant and animal responses to abiotic environments, but the ecological drivers of this benefit remain elusive. Here we investigated byproduct benefits, which occur when traits that increase the fitness of one species provide incidental benefits to another species with no direct cost to the provider species. In experimental mesocosms, microbial traits predicted plant responses to soil moisture such that bacteria with self-beneficial traits in drought increased plant early growth, size at reproduction, and chlorophyll concentration under drought, while bacteria with self-beneficial traits in well-watered environments increased these same plant traits in well-watered environments. Thus, microbial traits that promote microbial success in different soil moisture environments also promote plant success in these same environments. Our results show that the concept of byproduct benefits, originally conceived to explain the evolution of cooperation in pairwise mutualisms, also applies to interactions between plants and non-symbiotic soil microbes.</p>
Effect of Soil Moisture on Future Heatwaves over Eastern China: Convection-Permitting Regional Climate Simulations
<p>Data used in the manuscript "Effect of Soil Moisture on Future Heatwaves Over Eastern China: Convection-Permitting Regional Climate Simulations" which will be submitted to Journal of Geophysical Research: Atmospheres.</p>
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