scholarly journals Influence of soil texture on hydraulic properties and water relations of a dominant warm-desert phreatophyte

2006 ◽  
Vol 26 (3) ◽  
pp. 313-323 ◽  
Author(s):  
K. R. Hultine ◽  
D. F. Koepke ◽  
W. T. Pockman ◽  
A. Fravolini ◽  
J. S. Sperry ◽  
...  
Keyword(s):  
Water Relations ◽  
Soil Texture ◽  
Hydraulic Properties ◽  
Warm Desert

scholarly journals Geophysical mapping of soil texture variability in the root zone to improve modelling of the water and nutrient flow.

2020 ◽  
Author(s):  
Kim Schwartz Madsen ◽  
Bo Vangsø Iversen ◽  
Christen Duus Børgesen
Keyword(s):  
Soil Texture ◽  
Hydraulic Properties ◽  
Root Zone ◽  
Clay Fraction ◽  
Clay Content ◽  
Auxiliary Variable ◽  
Pedotransfer Functions ◽  
Soil Hydraulic Properties ◽  
Sampling Density ◽  
Soil Hydraulic

<p>Modelling is often used to acquire information on water and nutrient fluxes within and out of the root zone. The models require detailed information on the spatial variability of soil hydraulic properties derived from soil texture and other soil characteristics using pedotransfer functions (PTFs). Soil texture can vary considerably within a field and is cumbersome and expensive to map in details using traditionally measurements in the laboratory. The electrical conductivity (EC) of the soil have shown to correlate with its textural composition.</p><p>This study investigates the ability of electromagnetic induction (EMI) methods to predict clay content in three soil layers of the root zone. As the clay fraction often is a main predictor in PTFs predicting soil hydraulic properties this parameter is of high interest. EMI and soil textural surveys on four Danish agricultural fields with varying textural composition were used. Sampling density varied between 0.5 and 38 points per hectare. The EMI data was gathered with a Dualem21 instrument with a sampling density 200-3000 points per hectare. The EC values were used together with the measured values of the clay content creating a statistical relationship between the two variables. Co-kriging of the clay content from the textural sampling points with the EC as auxiliary variable produces clay content maps of the fields. Unused (80%) texture points were used for validation. EMI-predicted clay content maps and clay content maps based on the survey were compared. The two sets of soil texture maps are used as predictors for PTF models to predict soil hydraulic properties as input in field-scale root zone modelling.</p><p>The comparisons between EC and clay content show some degree of correlation with an R<sup>2</sup> in the range of 0.55 to 0.80 for the four fields. The field with the highest average clay content showed the best relationship between the two parameters. Co-kriging with EC decreased mean error by 0.016 to 0.52 and RMSE by 0.04 to 1.80 between observed and predicted clay maps.</p>

scholarly journals Spatial Variability of Soil Physical and Hydraulic Properties in a Durum Wheat Field: An Assessment by the BEST-Procedure

Water ◽  
2019 ◽  
Vol 11 (7) ◽  
pp. 1434 ◽  
Author(s):  
Mirko Castellini ◽  
Anna Maria Stellacci ◽  
Matteo Tomaiuolo ◽  
Emanuele Barca
Keyword(s):  
Spatial Variability ◽  
Crop Yield ◽  
Soil Texture ◽  
Hydraulic Properties ◽  
Wheat Yield ◽  
Structure Stability ◽  
Soil Hydraulic Properties ◽  
Field Scale ◽  
Cross Correlation Analysis ◽  
Soil Hydraulic

Spatial variability of soil properties at the field scale can determine the extent of agricultural yields and specific research in this area is needed. The general objective of this study was to investigate the relationships between soil physical and hydraulic properties and wheat yield at the field scale and test the BEST-procedure for the spatialization of soil hydraulic properties. A simplified version of the BEST-procedure, to estimate some capacitive indicators from the soil water retention curve (air capacity, ACe, relative field capacity, RFCe, plant available water capacity, PAWCe), was applied and coupled to estimates of structure stability index (SSI), determinations of soil texture and measurements of bulk density (BD), soil organic carbon (TOC) and saturated hydraulic conductivity (Ks). Variables under study were spatialized to investigate correlations with observed medium-high levels of wheat yields. Soil physical quality assessment and correlations analysis highlighted some inconsistencies (i.e., a negative correlation between PAWCe and crop yield), and only five variables (i.e., clay + silt fraction, BD, TOC, SSI and PAWCe) were spatially structured. Therefore, for the soil–crop system studied, application of the simplified BEST-procedure did not return completely reliable results. Results highlighted that (i) BD was the only variable selected by stepwise analysis as a function of crop yield, (ii) BD showed a spatial distribution in agreement with that detected for crop yield, and (iii) the cross-correlation analysis showed a significant positive relationship between BD and wheat yield up to a distance of approximately 25 m. Such results have implications for Mediterranean agro-environments management. In any case, the reliability of simplified measurement methods for estimating soil hydraulic properties needs to be further verified by adopting denser measurements grids in order to better capture the soil spatial variability. In addition, the temporal stability of observed spatial relationships, i.e., between BD or soil texture and crop yields, needs to be investigated along a larger time interval in order to properly use this information for improving agronomic management.

Water relations of riparian plants from warm desert regions

Wetlands ◽  
1998 ◽  
Vol 18 (4) ◽  
pp. 687-696 ◽  
Author(s):  
Stanley D. Smith ◽  
Dale A. Devitt ◽  
Anna Sala ◽  
James R. Cleverly ◽  
David E. Busch
Keyword(s):  
Water Relations ◽  
Riparian Plants ◽  
Desert Regions ◽  
Warm Desert

scholarly journals A global data set of soil hydraulic properties and sub-grid variability of soil water retention and hydraulic conductivity curves

2017 ◽  
Vol 9 (2) ◽  
pp. 529-543 ◽  
Author(s):  
Carsten Montzka ◽  
Michael Herbst ◽  
Lutz Weihermüller ◽  
Anne Verhoef ◽  
Harry Vereecken
Keyword(s):  
Hydraulic Conductivity ◽  
Soil Texture ◽  
Hydraulic Properties ◽  
Water Retention ◽  
Climate Models ◽  
Hydraulic Parameters ◽  
Soil Hydraulic Properties ◽  
Soil Water Retention ◽  
Data Set ◽  
Global Data

Abstract. Agroecosystem models, regional and global climate models, and numerical weather prediction models require adequate parameterization of soil hydraulic properties. These properties are fundamental for describing and predicting water and energy exchange processes at the transition zone between solid earth and atmosphere, and regulate evapotranspiration, infiltration and runoff generation. Hydraulic parameters describing the soil water retention (WRC) and hydraulic conductivity (HCC) curves are typically derived from soil texture via pedotransfer functions (PTFs). Resampling of those parameters for specific model grids is typically performed by different aggregation approaches such a spatial averaging and the use of dominant textural properties or soil classes. These aggregation approaches introduce uncertainty, bias and parameter inconsistencies throughout spatial scales due to nonlinear relationships between hydraulic parameters and soil texture. Therefore, we present a method to scale hydraulic parameters to individual model grids and provide a global data set that overcomes the mentioned problems. The approach is based on Miller–Miller scaling in the relaxed form by Warrick, that fits the parameters of the WRC through all sub-grid WRCs to provide an effective parameterization for the grid cell at model resolution; at the same time it preserves the information of sub-grid variability of the water retention curve by deriving local scaling parameters. Based on the Mualem–van Genuchten approach we also derive the unsaturated hydraulic conductivity from the water retention functions, thereby assuming that the local parameters are also valid for this function. In addition, via the Warrick scaling parameter λ, information on global sub-grid scaling variance is given that enables modellers to improve dynamical downscaling of (regional) climate models or to perturb hydraulic parameters for model ensemble output generation. The present analysis is based on the ROSETTA PTF of Schaap et al. (2001) applied to the SoilGrids1km data set of Hengl et al. (2014). The example data set is provided at a global resolution of 0.25° at https://doi.org/10.1594/PANGAEA.870605.

scholarly journals Analysis of the suitability of Polish soil texture classification for estimating soil water retention and hydraulic properties

2017 ◽  
Vol 68 (4) ◽  
pp. 197-204 ◽  
Author(s):  
Michał Kozłowski ◽  
Jolanta Komisarek
Keyword(s):  
Cluster Analysis ◽  
Hydraulic Conductivity ◽  
Soil Water ◽  
Soil Texture ◽  
Hydraulic Properties ◽  
Water Retention ◽  
Soil Water Retention ◽  
Saturated Water ◽  
Saturated Water Content ◽  
Retention Parameters

Abstract The objective of this study was to examine whether the Polish soil textural classification is useful for evaluation of soil water retention and hydraulic properties and, furthermore, for determining which textural classes are characterized by the highest diversity of soil water retention and hydraulic properties. The texture triangle was divided into a 1% grid of particle-size classes resulting in 5151 different data points. For each data point, soil water retention parameters and saturated hydraulic conductivity were obtained using the ROSETTA program. The silt classes showed the highest uncertainty in the estimation of the saturated water content based on the soil texture. These classes are characterized by high variations of saturated water content within the class. Estimations of field capacity and permanent wilting point on the basis of textural classes are encumbered with highest errors for gp, pg, pl and pyg soils, which are characterized by the highest values of coefficient of variation. Saturated soil hydraulic conductivity is better classified into homogeneous classes by the Polish texture classes than by the clusters obtained by the k-means cluster analysis based on the soil hydraulic and retention properties. Soil water retention parameters are better classified into homogeneous groups by the k-means cluster analysis than by the traditional textural classes. Cluster analysis using the k-means can be helpful for grouping similar soils from the point of view of their retention properties.

scholarly journals Using remotely-sensed estimates of soil moisture to infer soil texture and hydraulic properties across a semi-arid watershed

2007 ◽  
Vol 110 (1) ◽  
pp. 79-97 ◽  
Author(s):  
Joseph A. Santanello ◽  
Christa D. Peters-Lidard ◽  
Matthew E. Garcia ◽  
David M. Mocko ◽  
Michael A. Tischler ◽  
...  
Keyword(s):  
Soil Moisture ◽  
Soil Texture ◽  
Hydraulic Properties ◽  
Remotely Sensed ◽  
Semi Arid

The Role of Soil Properties on Regional Climate Simulations

2020 ◽  
Author(s):  
E. Hugo Berbery ◽  
Eli Dennis
Keyword(s):  
Heat Flux ◽  
Latent Heat ◽  
Great Plains ◽  
Soil Texture ◽  
Land Surface ◽  
Hydraulic Properties ◽  
Regional Climate ◽  
Sensible Heat Flux ◽  
Heat Fluxes ◽  
Sensible Heat

<p>The land surface is inextricably linked to the atmospheric circulation as it dictates the location and strength of land surface-atmosphere (LA) coupling mechanisms. In this context, soil hydraulic properties are critical to estimate sub-surface processes and fluxes at the surface.  In most numerical weather and climate models, those properties are assigned through maps of soil texture complemented with look-up tables.  Then, the hydraulic properties are used in a large variety of process parameterizations within the models.  In this study, we investigate the sensitivity of the simulated regional climate to changes in the prescribed soil maps in the WRF/CLM4 modeling suite.  Comparison of two widely used soil texture databases, the USGS State Soil Geographic Database (STATSGO) and Beijing Normal University’s soil texture database (GSDE), over the United States and Central America reveals that only 32% of soil texture classifications are in common. Further, the differences are not random but tend to depict small-to-large spatial patterns with a preponderance of either finer or coarser grains. Over North America, the US Great Plains have finer grains in GSDE than in STATSGO, while the opposite is true over Central Mexico.</p><p> </p><p>Seasonal simulations were carried out to assess the changes in the soil-water system that result from changing the soil types (GSDE vs. STATSGO) and their corresponding hydraulic properties. Wherever GSDE has finer grains than STATSGO (e.g., over the US Great Plains), the soil will retain water more strongly as evidenced by smaller latent heat fluxes and larger sensible heat flux. On the other hand, areas of coarser grains in GSDE (e.g., over central Mexico) exhibit an increase in latent heat fluxes and a corresponding decrease in sensible heat flux. Regions with an increase/decrease in latent heat flux have a corresponding increase/decrease in the 2-m moisture content. Similar relations are obtained between sensible heat flux and 2-m temperature. These changes also affect the atmospheric column, which responds with an increase/decrease of temperature and height of the planetary boundary layer. Changes in the vertical structure induce changes in the vertical instability and winds. Interestingly, the chain of modifications resulting from soil texture changes impact the moisture fluxes, and more generally, the atmospheric water budget.</p>

scholarly journals The Role of Soil Texture in Local Land Surface–Atmosphere Coupling and Regional Climate

2020 ◽  
Author(s):  
Eli J. Dennis ◽  
Ernesto Hugo Berbery
Keyword(s):  
Heat Flux ◽  
Great Plains ◽  
Soil Texture ◽  
Hydraulic Properties ◽  
Regional Climate ◽  
Surface Fluxes ◽  
Central Mexico ◽  
Soil Hydraulic Properties ◽  
The Us ◽  
Us Great Plains

AbstractSoil hydraulic properties are critical in estimating surface and sub-surface processes, including surface fluxes, the distribution of soil moisture, and the extraction of water by root systems. In most numerical weather and climate models, those properties are assigned using maps of soil texture complemented by look-up tables. Comparison of two widely-used soil texture databases, the USDA State Soil Geographic database (STATSGO) and Beijing Normal University’s soil texture database (GSDE), reveals that differences are widespread and can be spatially coherent over large areas that can eventually lead to regional climate differences. For instance, over the US Great Plains, GSDE stipulates finer soil grains than STATSGO, while the opposite is true over Central Mexico.In this study, we employ the WRF/CLM4 modeling suite to investigate the sensitivity of the simulated regional climate to changes in the prescribed soil maps. Wherever GSDE has finer grains than STATSGO (e.g., over the US Great Plains), the soil retainswater more strongly as evidenced by smaller latent heat flux (–20 W m−2), larger sensible heat flux (+20 W m−2), and correspondingly, a decrease in the 2-m humidity (–1 g kg−1) and an increase in 2-m temperature (+1.5 K). The opposite behavior is found over areas of coarser grains in GSDE (e.g., over Central Mexico). Further, the changes in surface fluxes via soil texture lead to differences in the thermodynamic structure of the PBL. Results suggest that neither soil hydraulic properties nor soil moisture solely dictate the strength of surface fluxes, but in combination they alter the land–atmosphere coupling in non-trivial ways.

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