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1,103 results for “moisture”
Soil temperature, moisture, and ground heat flux measurements at LPTEG-TREES-1 site, 2019/07/01-2019/09/09
<p>This dataset includes the original measurements of soil temperature, moisture, and surface ground heat flux reconstructed from heat flux plate measurements at the LPTEG-TREES-1 site (N66°53’55’’, E66°45’27’’). Soil temperature (T_soil, °C) was measured at 2 cm below the peat layer surface. Soil liquid water content (theta_liq, m<sup>3</sup>/m<sup>3</sup>) was measured 2 cm below the mineral soil layer surface. Observation for ground heat flux at the soil surface (G_obs, W/m<sup>2</sup>) was reconstructed from the heat flux plate (buried 6 cm below the mineral soil surface) measurement plus the energy storage above the heat flux plate calculated based on soil temperature and soil heat capacity.</p>
A 1 km daily soil moisture dataset over the Qinghai-Tibet Plateau (2001-2010)
<p>Soil moisture is the key variable of water and energy cycle, but the long-term, high-resolution soil moisture data with high accuracy is still relatively lacking on the Qinghai-Tibet Plateau. Therefore, we provide the 1 km seamless daily soil moisture data over the Qinghai-Tibet Plateau during 2001-2010 (named as BTCH). Firstly, several predictors including the vegetation index (NDVI and EVI), land surface temperature (LST), evapotranspiration, precipitation, topography (DEM, aspect, slope, TWI), soil properties and three soil moisture related indices (SWCI SIWSI VSDI) were utilized. Five machine/deep learning methods including the artificial neural network (ANN), convolutional neural network (CNN), residual neural networks (ResNet), the long short-term memory network (LSTM) and XGBoost were trained for each year taking the ESA CCI soil moisture data as target. Then the Bayesian three-cornered hat method was adopted for model integration and the final dataset was generated. Evaluaion against four in-situ measurement networks shows that the BTCH dataset has relativey high accuracy both as station and network scales with mean unbiased RMSE values of 0.048 m3/m3 and 0.034 m3/m3. The dataset can be used for various regional hydrological, meteorological, ecological analysis and modeling.</p>
Amazon and Congo forest moisture recycling 1979-2014
<p>Evaporation and precipitation recycling of the rainforests in South America and Africa 1979-2014. Do not use the spin-up years 1979 and 2014. </p>
Datasets for Quantifying the impact of land use and land cover change on moisture recycling with convection-permitting WRF-tagging modeling in the agro-pastoral ecotone of northern China
<p>These datasets are the processed and refined data that support and lead to the described results and allow other readers to assess the conclusions in the paper, entitled “<strong>Quantifying the impact of land use and land cover change on moisture recycling with convection-permitting WRF-tagging modeling in the agro-pastoral ecotone of northern China </strong> ”.</p>
IODP Expedition 385 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 396 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 354 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>
NAIADES Soil moisture sensors raw data
<p>Raw data from initial soil moisture sensors (LSE-01) tests performed during the initial phases of the NAIADES project. Sensors were installed in flower boxes and flowerbeds across the city of Carouge, Switzerland, data was transmitted each 20 minutes via LoRaWAN.</p> <p>This data was exported from influxdb, unrelevant fields were omitted, device addresses were partially obfuscated.</p> <p>Units: (field:unit):</p> <p>water_SOIL: V/V%</p> <p>temp_SOIL:°C</p> <p>conduct_SOIL:uS/cm</p> <p> </p>
Average monthly backward moisture footprints for 40 Ramsar wetland basins under potential and current vegetation scenarios (2008 - 2017)
<p>The dataset contains the backward moisture footprints of the basins of 40 selected Ramsar wetlands for a base run with ERA5 reanalysis evaporation and precipitation, and two additional runs based on evaporation and precipitation from a potential vegetation and a current land use scenario. The dataset can be used to study the upwind moisture sources of the 40 included wetland basins under 'normal' conditions (ERA5 reanalysis), and under a potential vegetation scenario and a current land used scenario.<br>The data was generated to study the impact of upwind land use changes and hydroclimatic changes on selected wetland basins (Fahrländer et al. (2024) using the UTrack atmospheric moisture tracking database by Tuinenburg et al. (2020) and data inputs from the ERA5 reanalysis dataset (Hersbach et al. 2020) and from Wang-Erlandsson et al. (2018) (see References section).</p> <p>The moisture footprints are stored in individual NetCDF format files for each wetland basin and in separate folders for each run. The files are marked with the according Ramsar Convention ID for each respective wetland. The footprints are saved in a spatial resolution of 0.5° and contain monthly average evaporation flows for the period 2008 - 2017. The backward footprints contain the moisture sources for the precipitation in the wetland basins, whereas the forward footprint contain the locations where the evaporation from the basins rains down again.</p> <p>In addition, the dataset contains the delineated basins of the 40 Ramsar wetlands, which are provided in shapefile format and marked with the individual wetland ID of the Ramsar Convention.</p> <p> </p> <p>References:</p> <p>Fahrländer, S. F., Wang‐Erlandsson, L., Pranindita, A., & Jaramillo, F. (2024). Hydroclimatic Vulnerability of Wetlands to Upwind Land Use Changes. <em>Earth’s Future</em>, <em>12</em>(3). <a href="https://doi.org/10.1029/2023EF003837">https://doi.org/10.1029/2023EF003837</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>Tuinenburg, O. A., Theeuwen, J. J. E., & Staal, A. (2020). 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>Wang-Erlandsson, L., Fetzer, I., Keys, P., van der Ent, R. J., Savenije, H. H. G., & Gordon, L. J. (2018). Remote land use impacts on river flows through atmospheric teleconnections. <em>Hydrology and Earth System Sciences</em>, <em>22</em>(8), 4311–4328. <a href="https://doi.org/10.5194/hess-22-4311-2018">https://doi.org/10.5194/hess-22-4311-2018</a></p>
IODP Expedition 369 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>
The Long-term, High-accuracy and Seamless Soil Moisture (LHS-SM) dataset over the Qinghai-Tibet Plateau: part 1 (2001-2010)
<p>Soil moisture (SM) is a vital variable in the water-energy cycle and characterizing its spatiotemporal dynamics is crucial for understanding the impacts of climate change. Although substantial efforts have been devoted to derive SM data at fine scale, there is still a research gap in obtaining the long-term, high-accuracy and high-resolution SM data over the Qinghai-Tibet Plateau (QTP) due to its complex topography. Therefore, this study generated the long-term, high-accuracy and seamless soil moisture (LHS-SM) dataset over the QTP during 2001-2020 using a two-step downscaling method. First the daily SM data from the Climate Change Initiative program of the European Space Agency (ESA CCI) was downscaled to 1km utilizing five machine learning approaches. Then a dynamic data merging method that considers the spatiotemporal nonstationary error was applied to derive the final LHS-SM data. Results indicated that LHS-SM data exhibited satisfying accuracy (mean R = 0.55, ubRMSE = 0.049 m³/m³) and certain improvement to the ESA CCI SM data both at station and network scales. The dataset can be used for various regional hydrology, meteorology, ecological analysis and modeling.</p>
Figure 6 in Effect of soil class and moisture on the depth of pupation and pupal viability of Bactrocera carambolae Drew & Hancock (1994)
Figure 6 Effect of time and moisture on the emergence of flies from three soil types (sandy, sandy clay loam and clay loam).The lines in the graphs of the relation between the number of flies emerged and time (a-c) represent nonlinear models with a quadratic term [a) y = 4.12***-0.94***x+0.04x^2, b)y = 0.81*+0.5*x-0.098***x^2 and c) y = 2.47***-0.26x-0.02x^2], while the lines in the graphs of the relation between number of flies emerged and moisture (d-f) represent linear models with Poisson distributions [d) y = 0.11+0.0016x, e) y = -0.69***+0.01***x and f) y = -0.37***+0.008***x]. *:P<0.05; *** P<0.001.
Figure 5 in Effect of soil class and moisture on the depth of pupation and pupal viability of Bactrocera carambolae Drew & Hancock (1994)
Figure 5 Effect of soil depth and moisture on the number of pupae in three soil types (sandy, sandy clay loam and clay loam). The lines in the graphs of the relation between the number of pupae and depth (a-c) represent exponential models [a) y=exp(1.58-1.24***x), b) y=exp(1.42-0.21***x) and c) y=exp(1.18-0.16***x)], while the lines in the graphs of the relation between number of pupae and moisture (d-f) represent linear models with Poisson distributions [d) y = 0.56-0.0006x, e) y = 0.55-0.0017x and f) y = 0.55-0.0025x].*** P<0.001.
Figure 4 in Effect of soil class and moisture on the depth of pupation and pupal viability of Bactrocera carambolae Drew & Hancock (1994)
Figure 4 Number of pupae per centimeter for each treatment (T1 to T4 with sandy soil, T5 to T8 with sandy clay loam and T9 to T12 with clay loam) in combination with different moisture levels (0%, 30%, 60% and 90% for T1 to T4, T5 to T8 and T9 to T12, respectively). Treatments: T1 = sandy × 0% moisture, T2 = sandy x 30% moisture, T3 = sandy x 60% moisture, T4 = sandy x 90% moisture, T5 = sandy clay loam x 0% moisture, T6 = sandy clay loam x 30% moisture, T7 = sandy clay loam x 60% moisture, T8 = sandy clay loam x 90% moisture, T9 = clay loam x 0% moisture, T10 = clay loam x 30% moisture, T11 = clay loam x 60% moisture, and T12 = clay loam x 90% moisture.
Figure 3 in Effect of soil class and moisture on the depth of pupation and pupal viability of Bactrocera carambolae Drew & Hancock (1994)
Figure 3 Illustration of the steps of the experiment: A) Larvae on the soil surface; B) Containers used in the experiment; C) Removal of a 1 cm ring; D) Transfer of the soil to a plastic tray; E) Sorting and counting of the pupal cases; and F) Insects that were unable to rupture the soil layer. Photos: Eric Joel Ferreira do Amaral.
Figure 1 in Effect of soil class and moisture on the depth of pupation and pupal viability of Bactrocera carambolae Drew & Hancock (1994)
Figure 1 Representation of the steps for rearing B. carambolae: A) Oviposition container; B) Cage with adults; C) Eggs; D) Feed based on carrots in a plastic tray containing larvae; E) Paper envelope containing the plastic tray with larvae. Photos: Eric Joel Ferreira do Amaral.
Fig. 1 in Effect of temperature and substrate moisture on group survival of Constrictotermes sp. (Isoptera: Termitidae) under laboratory conditions
Fig. 1. Survival of Constrictotermes sp. in different combinations of temperature and substrate moisture (mL of water/7 g of nest substrate). Different letters in each graphic mean significant difference among treatments of humidity.
IODP Expedition 382 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 392 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>
Forecasting live fuel moisture of Adenostema fasciculatum and its relationship to regional wildfire dynamics across southern California shrublands
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