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12 results for “root depth”
Global rooting zone water storage capacity and rooting depth estimates
<p>Global rooting zone water storage capacity (<em>S</em><sub>CWDX80</sub>, mm) and rooting depth (<em>z</em><sub>CWDX80</sub>, mm) estimates from Stocker et al., (2023). </p> <p>Additional global maps for rooting zone water storage capacity and rooting depth are provided and may be used as vegetation model forcing. These are created using the code from <code>whc_forcing_map.Rmd</code> , available <a href="https://github.com/geco-bern/mct/blob/master/whc_forcing_map.Rmd">here</a> (Zenodo entry: https://doi.org/10.5281/zenodo.7429129). The following steps were taken for creating these maps:</p> <ol> <li>The relationship between vegetation height and rooting depth was fitted using quantile regression (lower 10%) and data from Tumber-Davila et al. (2023). This yields a lower-bound rooting depth.</li> <li>A global map of vegetation height (Simard et al., 2011) was used for predicting the lower-bound rooting depth distribution globally.</li> <li>The lower-bound rooting depth was converted into a lower-bound root zone water storage capacity following methods as described in Stocker et al. (2023).</li> <li>The maximum of the lower-bound rooting depth and the inferred rooting depth (<em>z</em><sub>CWDX80</sub>) from Stocker et al., (2023) was determined for each grid cell. This is what's in the file <code>zroot_cwdx80_forcing.nc</code>. Anaologusly for <code>cwdx80_forcing.nc</code>.</li> </ol> <p>Please cite published paper:</p> <div> <div>Stocker, B. D., Tumber-Dávila, S. J., Konings, A. G., Anderson, M. C., Hain, C., and Jackson, R. B.: Global patterns of water storage in the rooting zones of vegetation, Nat. Geosci., 1–7, <a href="https://doi.org/10.1038/s41561-023-01125-2">https://doi.org/10.1038/s41561-023-01125-2</a>, 2023.</div> </div> <p> </p>
Below ground root biomass, carbon and nitrogen concentrations by depth increments from the Anaktuvuk River Fire site in 2011
Below ground root biomass was measured by depth increments at three sites at and around the Anaktuvuk River Burn: severely burned, moderately burned and unburned. Roots were also analyzed for carbon and nitrogen concentrations.
Vascular Root Biomass and N Concentrations at Two Depths in an Alberta Peatland Subjected to Increasing Nitrogen Deposition, 2014-2015
Development of the oil sands has led to increasing atmospheric N deposition, with values as high as 17 kg N ha-1 yr-1; regional background levels <2 kg N ha-1 yr-1. Bogs, being ombrotrophic, may be especially susceptible to increasing N deposition. To examine responses to N deposition, over five years, we experimentally applied N (as NH4NO3) to a bog near Mariana Lakes, Alberta, at rates of 0, 5, 10, 15, 20, and 25 kg N ha-1 yr-1, plus controls (no water or N addition). From 2014-2015, we examined the effects of N addition on root production and nitrogen assimilation in those roots by measuring root biomass at two depths and root production over one and two years. Root biomass in the 0-15 cm and 15-30 cm depth increments in peat increased with increasing N input; the response was similar in the two depth increments. Root production integrated over the top 30 cm of peat increased with increasing N input at a rate of 5.3 g m-2 yr-1 with an increase in N input of 1 kg N ha-1 yr-1. Water addition alone had no significant effect on root biomass (p > 0.72) or root production. Given the rather consistent finding increasing N deposition stimulates aboveground vascular plant biomass and production, and our results that root biomass and production at Mariana Lakes Bog are stimulated as well, further work on belowground responses seems warranted.
Vascular Root Biomass and Production at Two Depths in an Alberta Poor Fen Subjected to Increasing Nitrogen Deposition, 2014-2015
Development of the oil sands has led to increasing atmospheric N deposition, with values as high as 17 kg N ha-1 yr-1; regional background levels <2 kg N ha-1 yr-1. To examine responses to N deposition, over five years, we experimentally applied N (as NH4NO3) to a poor fen near Mariana Lake, Alberta, at rates of 0, 5, 10, 15, 20, and 25 kg N ha-1 yr-1, plus controls (no water or N addition). From 2014-2015, we examined the effects of N addition on root production by measuring root biomass at two depths and root production over one and two years. Root biomass, measured in 2014, increased with increasing N addition in the 0-15 and 15-30 cm depth increments. Root production increased with increasing N addition in the 0-15 cm, but not the 15-30 cm depth increment; annual root production in the 0-15 cm depth increment was higher when ingrowth bags remained in the peat for two growing seasons, compared to first-year root production. As a result, over the top 30 cm, annual root production was greater when ingrowth bags were in the peat for two growing seasons. We expected a threshold N addition level associated with stimulation of root production but found no evidence of such a threshold at Mariana Lake Poor Fen. Given the rather consistent finding increasing N deposition stimulates aboveground vascular plant biomass and production, and our results that root biomass and production at Mariana Lakes Bog are stimulated as well, further work on belowground responses seems warranted.
Data from: Contrasting depth-related fine root plastic responses to soil warming in a subtropical Chinese fir plantation
<p>Warming-induced soil drought especially in topsoil may enlarge the spatial mismatch between nutrients and water along the soil profile, which impedes the uptake of not only water but also nutrients by trees. Therefore, coordinating the acquisition of soil water and nutrients along the soil profile is an important strategy for trees to cope with global warming.</p> <p>This study examined soil depth-related changes in nutrient concentrations, biomass, and morphology of fine roots in a Chinese fir plantation after 3 years of large-scale manipulative soil warming.</p> <p>Soil warming (ambient + 4°C) increased fine root nitrogen (N) concentrations but decreased fine root phosphorus (P) concentrations across soil depths. Warming did not affect total fine root biomass and its vertical distribution. At the 0–10 cm depth, warming increased specific root length (SRL), specific root area (SRA), fine root diameter (RD), and root length density (RLD) but reduced root tissue density (RTD). In the 40–60 cm layer, warming reduced RD, SRL, and RLD while increasing RTD mainly for roots of the 1–2 mm diameter class.</p> <p><em>Synthesis</em>: We concluded that roots of Chinese fir plantations could adapt to warming-induced moderate water stress through contrasting depth-related root morphological adjustments, probably to optimize the acquisition of both soil water and nutrients. The results of this study are crucial for understanding the adaptation strategies of subtropical forests under future climate conditions.</p>
Data from: Rooting depth and specific leaf area modify the impact of experimental drought duration on temperate grassland species
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Data from: Allometric scaling laws linking biomass and rooting depth vary across ontogeny and functional groups in tropical dry forest lianas and trees
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Data from: Contrasting depth-related fine root plastic responses to soil warming in a subtropical Chinese fir plantation
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The Relationship Between Root Coverage Procedures and Buccal Vestibular Depth
ClinicalTrials.gov study NCT05777811. IPD Sharing: Not stated. Countries: 1. Publications: 1.
ISLSCP II Ecosystem Rooting Depths
The goal of this study was to predict the global distribution of plant rooting depths based on data about global aboveground vegetation structure and climate. Vertical root distributions influence the fluxes of water, carbon, and soil nutrients and the distribution and activities of soil fauna. Roots transport nutrients and water upwards, but they are also pathways for carbon and nutrient transport into deeper soil layers and for deep water infiltration. Roots also affect the weathering rates of soil minerals. For calculating such processes on a global scale, data on vertical root distributions are needed as inputs to global biogeochemistry and vegetation models. In the Project for Intercomparison of Land Surface Parameterization Schemes (PILPS), rooting depth and vertical soil characteristics were the most important factors explaining scatter for simulated transpiration among 14 land-surface models. Recently, the Terrestrial Observation Panel for Climate of the Global Climate Observation System (GCOS) identified the 95% rooting depth as a key variable needed to quantify the interactions between the climate, soil, and plants, stating that the main challenge was to find the correlation between rooting depth and soil and climate features (GCOS/GTOS Terrestrial Observation Panel for Climate 1997). In response to this challenge, a data set of vertical rooting depths was collected from the literature in order to construct maps of global ecosystem rooting depths.The parameters included in these data sets are estimates for the soil depths containing 50% and 95% of all roots, termed 50% and 95% rooting depths (D50 and D95, respectively). Together, these variables can be used to calculate estimates for vertical root distributions, using a logistic equation provided in this documentation. The data represent mean ecosystem rooting depths for 1 by 1 degree grid cells. Related data sets:Â The ORNL DAAC offers related data sets by Jackson et al. (2003), Gordon and Jackson (2003), Schenk and Jackson (2003), and Gill and Jackson (2003).This data set is one of the products of the International Satellite Land-Surface Climatology Project, Initiative II (ISLSCP II) data collection which contains 50 global time series data sets for the ten-year period 1986 to 1995. Selected data sets span even longer periods. ISLSCP II is a consistent collection of data sets that were compiled from existing data sources and algorithms, and were designed to satisfy the needs of modelers and investigators of the global carbon, water and energy cycle. The data were acquired from a number of U.S. and international agencies, universities, and institutions. The global data sets were mapped at consistent spatial (1, 0.5 and 0.25 degrees) and temporal (monthly, with meteorological data at finer (e.g., 3-hour)) resolutions and reformatted into a common ASCII format. The data and documentation have undergone two peer reviews.ISLSCP is one of several projects of Global Energy and Water Cycle Experiment (GEWEX) [http://www.gewex.org/] and has the lead role in addressing land-atmosphere interactions -- process modeling, data retrieval algorithms, field experiment design and execution, and the development of global data sets.
LEADER (Leaf Element Accumulation from Deep Roots): a nondestructive phenotyping platform to estimate rooting depth in the field
<p>Data and code for figure generation for <strong>LEADER (Leaf Element Accumulation from Deep Roots): a nondestructive phenotyping platform to estimate rooting depth in the field</strong></p>
Depth to top of root or water soil restrictive layer (resdept) soil maps of the Upper Colorado River Basin
<p>The data here were originally posted to facilitate timely and transparent peer review. The final public data release with formal metadata is now available from at the following location:</p> <p>Nauman, T.W., and Duniway, M.C., 2020, Predictive soil property maps with prediction uncertainty at 30 meter resolution for the Colorado River Basin above Lake Mead: U.S. Geological Survey data release,<a href="http://https://doi.org/10.5066/P9SK0DO2"> https://doi.org/10.5066/P9SK0DO2</a>.</p> <p>Associated publication:</p> <p>Nauman, T. W., and Duniway, M. C., 2020, A hybrid approach for predictive soil property mapping using conventional soil survey data: Soil Science Society of America Journal, v. 84, no. 4, p. 1170-1194. <a href="https://doi.org/10.1002/saj2.20080">https://doi.org/10.1002/saj2.20080</a>.</p> <p>This version includes updated training data that accounts for making sure that if multiple restrictions are in a soil, the first is chosen. It also incorporates the updates in version 2 that included very deep soils with no restriction not included in version 1.</p> <p>Repository includes maps describing the depth (cm) to the top of any water or root soil restrictive layer (resdept) as defined by United States soil survey program.</p> <p>These data are preliminary or provisional and are subject to revision. They are being provided to meet the need for timely best science. The data have not received final approval by the U.S. Geological Survey (USGS) and are provided on the condition that neither the USGS nor the U.S. Government shall be held liable for any damages resulting from the authorized or unauthorized use of the data.</p> <p>The creation and interpretation of this data is documented in the following article.</p> <p>Nauman, T. W., and Duniway, M. C., 2020, A hybrid approach for predictive soil property mapping using conventional soil survey data: Soil Science Society of America Journal, v. 84, no. 4, p. 1170-1194.</p> <p>File Name Details:</p> <p>ACCURACY!! Please see manuscript and Github repository (https://github.com/usgs/Predictive-Soil-Mapping/tree/master/SoilSurvReconstrProperties) for full details on accuracy. We do provide 10-fold cross validation (CV) accuracy plots in this repository for the training sample (file ending _CV_plots.tif). These plots compare CV predictions with observed values relative to a 1:1 line. Values plotted near the 1:1 line are more accurate. Note that values are plotted in hex-bin density scatter plots because of the large number of observations (most are >3000).</p> <p>Elements are separated by underscore (_) in the following sequence:</p> <p>property_r_depth_cm_geometry_model_additional_elements.extension</p> <p>Example: resdept_r_cm_2D_QRF.tif</p> <p>Indicates depth to top of restriction (resdept; in cm) using a 2D model (separate model for each depth) employing a quantile regression forest. This file is the raster prediction map for this model. There may be additional GIS files associated with this file (e.g. pyramids) that have the same file name, but different extensions. </p> <p>The following elements may also exist on the end of filenames indicating other spatial files that characterize a given model's uncertainty (see below).</p> <p>_95PI_h: Indicates the layer is the upper 95% prediction interval value.</p> <p>_95PI_l: Indicates the layer is the lower 95% prediction interval value.</p> <p>_95PI_relwidth: Indicates the layer is the 95% relative prediction interval (RPI). The RPI is a standardization of the prediction interval that indicates that model is constraining uncertainty relative to the original sample. RPI values less than one represent uncertainty is being improved by the model relative to the original sample, and values less than 0.5 indicate low uncertainty in predictions. See paper listed above and also Nauman and Duniway (2019) for more details on RPI.</p> <p>References</p> <p> Nauman, T. W., and Duniway, M. C., 2019, Relative prediction intervals reveal larger uncertainty in 3D approaches to predictive digital soil mapping of soil properties with legacy data: Geoderma, Vol 347, pp 170-184.</p>
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