Field-scale Spatial Variability of Electrical Conductivity of the Inland, Salt-affected Soils of Northeast Thailand

Salt-affected soil maps for Northeast Thailand focus on the percentage of salt crusts. Investigation was done to find the field-scale spatial variability of the electrical conductivity of saturation extract (ECe) in salt-affected areas (percentage salt crusts: very severely = class 1; severely = class 2, and moderately = class 3). Two study sites were selected for each class (n = 6). Soil samples (n = 100) were collected at each site using stratified, systematic, unaligned sampling, and analyzed for ECe. Variations in ECe were assessed using basic statistics and geostatistics. At the field-scale, in every class, the best-fit semivariogram model generated was satisfactory (R2 > 0.8). Interpretation from the relevant model parameters (i.e., nugget, sill, and effective range), together with the interpolated (kriged) maps, demonstrated that the characteristics of spatial variability of soil ECe were inconsistent, even between different sites of the same salt-affected soil class. In general, various degrees of small-scale variation were observed, very high variation of ECe was common, spatial dependence was strong to moderate, while the spatial distribution pattern was in distinctive patches. The size of patches depended on the effective range at each site. This study also revealed that the class 1 areas were entirely, very strongly saline (ECes range, 56.70 and 433.00 dS·m-1), whereas the areas of class 3 were non-saline to moderately saline (range, 0.11 - 5.26 dS·m-1). Class 2 areas were much more complex; the soils varied from non-saline to very strongly saline (range, 0.16 - 49.00 dS·m-1). Information on the nature and characteristics in the spatial variability of soil ECe is useful for developing strategies for management of salt-affected soils in precision agriculture in this region.

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Temporal stability of electrical conductivity in a sandy soil

International Agrophysics ◽

10.1515/intag-2016-0005 ◽

2016 ◽

Vol 30 (3) ◽

pp. 349-357 ◽

Cited By ~ 12

Author(s):

Aura Pedrera-Parrilla ◽

Eric C. Brevik ◽

Juan V. Giráldez ◽

Karl Vanderlinden

Keyword(s):

Electrical Conductivity ◽

Spatial Variability ◽

Water Content ◽

Soil Water ◽

Soil Water Content ◽

Precision Agriculture ◽

Temporal Stability ◽

Principal Component ◽

Model Parameters ◽

Soil Spatial Variability

Abstract Understanding of soil spatial variability is needed to delimit areas for precision agriculture. Electromagnetic induction sensors which measure the soil apparent electrical conductivity reflect soil spatial variability. The objectives of this work were to see if a temporally stable component could be found in electrical conductivity, and to see if temporal stability information acquired from several electrical conductivity surveys could be used to better interpret the results of concurrent surveys of electrical conductivity and soil water content. The experimental work was performed in a commercial rainfed olive grove of 6.7 ha in the ‘La Manga’ catchment in SW Spain. Several soil surveys provided gravimetric soil water content and electrical conductivity data. Soil electrical conductivity values were used to spatially delimit three areas in the grove, based on the first principal component, which represented the time-stable dominant spatial electrical conductivity pattern and explained 86% of the total electrical conductivity variance. Significant differences in clay, stone and soil water contents were detected between the three areas. Relationships between electrical conductivity and soil water content were modelled with an exponential model. Parameters from the model showed a strong effect of the first principal component on the relationship between soil water content and electrical conductivity. Overall temporal stability of electrical conductivity reflects soil properties and manifests itself in spatial patterns of soil water content.

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Spatial Variability of Sodium Adsorption Ratio and Sodicity in Salt-Affected Soils of Northeast Thailand

Advanced Materials Research ◽

10.4028/www.scientific.net/amr.931-932.709 ◽

2014 ◽

Vol 931-932 ◽

pp. 709-715 ◽

Cited By ~ 1

Author(s):

Porntip Phontusang ◽

Roengsak Katawatin ◽

Krirk Pannangpetch ◽

Sununtha Kingpaiboon ◽

Rattana Lerdsuwansri

Keyword(s):

Spatial Variability ◽

Spatial Correlation ◽

Sodium Adsorption Ratio ◽

Descriptive Statistics ◽

Sodic Soils ◽

Soil Sodicity ◽

Class 1 ◽

Salt Affected Soils ◽

Study Sites ◽

Very High

Information on spatial variability of Sodium Adsorption Ratio (SAR) is useful for implementation of appropriate control measures for the salt-affected soils. The major objective of this study was to use geostatistics to describe the spatial variability of (i) the SAR and consequently (ii) the soil sodicity, in areas of different classes of salt-affected soils. Attention was on areas of very severely salt-affected soils (class 1), severely salt-affected soils (class 2), and moderately salt-affected soils (class 3). For each class, 2 study sites were chosen, totally 6 sites were taken into consideration. In each site, 100 soil samples were collected at 0-30 cm depth according to the stratified systematic unaligned sampling method in the dry season of 2012, and analyzed for the SAR in the laboratory. Descriptive statistics and Geostatistics were applied to describe the variability and spatial variability of SAR and soil sodicity, respectively. The result revealed very high variability of SAR. Descriptive statistics showed the CV values of ≥ 35% for every site of every class. When using semivariogram to describe the spatial correlation of SAR, it was found that in 3 study sites, the semivariogram models fitted well with the corresponding semivariogram samples indicating spatial correlation of SAR in the areas. In these cases, the Ordinary Kriging was applied to generate soil sodicity map. The relatively short range values especially for class 1 indicated very high variation of SAR. However, for the other 3 study sites, the linear models were fitted indicating no spatial correlation. Consequently, Trend Surface Analysis was applied instead. According to the soil sodicity maps generated in this study, the areas of class 1 were entirely occupied by strongly sodic soils. For classes 2 and 3, the soils in all study sites belonging to these classes included normal and slightly sodic soils of different proportions. Furthermore, inconsistency of the spatial variability patterns of SAR was found even in areas within the same class of salt-affected soils. As a result, prior to the intensive management of this problem soil in a particular area, investigation on the spatial variability pattern should be performed

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Small-scale Spatial Variability of Hydro-physical Properties of Differently Degraded Peat

10.5194/egusphere-egu2020-18534 ◽

2020 ◽

Author(s):

Miaorun Wang ◽

Haojie Liu ◽

Bernd Lennartz

Keyword(s):

Spatial Variability ◽

Physical Properties ◽

Bulk Density ◽

Peat Soil ◽

Soil Physical Properties ◽

Small Scale ◽

Model Parameters ◽

Strong Positive Correlation ◽

Undisturbed Soil ◽

Van Genuchten Model

<p>Spatial variability of soil properties is important for hydrological studies. However, little information is available on the spatial variability of hydro-physical properties of peat soils. Three study sites: natural, degraded and extremely degraded peatland were selected for this study. At each site, 72 undisturbed soil cores were collected from 5m by 5m grid cells in an area of 40m by 45m. The saturated hydraulic conductivity (Ks), soil water retention curves, total porosity, macroporosity, bulk density and soil organic matter (OM) content were determined for all sampling locations. The van Genuchten model parameters (&#952;s, &#945;, n) were optimized using the RETC software package. A strong positive correlation between macroporosity and Ks was observed irrespective of the degradation stage of the peat. However, the relationships between macroporosity and Ks differed for the different sites. The soil physical properties (e.g. OM content and bulk density) exhibited different levels of spatial autocorrelation depending on the soil degradation stage. The cross-semivariograms showed a strong or moderate spatial dependency between soil physical properties and van Genuchten model parameters. The more a peat soil is degraded, the more likely it is that soil physical properties are spatially dependent. In conclusion, degradation stage plays an important role and should be considered more often in spatial analysis. The obtained cross-semivariogram may serve as a basis for 2D and 3D hydrological modelling.&#160;</p>

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Scale-dependency and spatial variability of soil hydraulic properties

10.32747/2004.7587220.bard ◽

2004 ◽

Author(s):

Renduo Zhang ◽

David Russo

Keyword(s):

Spatial Variability ◽

Water Flow ◽

Hydraulic Properties ◽

Small Scale ◽

Scale Dependency ◽

Soil Hydraulic Properties ◽

Field Scale ◽

Essential Information ◽

Lab Experiments ◽

Soil Hydraulic

Water resources assessment and protection requires quantitative descriptions of field-scale water flow and contaminant transport through the subsurface, which, in turn, require reliable information about soil hydraulic properties. However, much is still unknown concerning hydraulic properties and flow behavior in heterogeneous soils. Especially, relationships of hydraulic properties changing with measured scales are poorly understood. Soil hydraulic properties are usually measured at a small scale and used for quantifying flow and transport in large scales, which causes misleading results. Therefore, determination of scale-dependent and spatial variability of soil hydraulic properties provides the essential information for quantifying water flow and chemical transport through the subsurface, which are the key processes for detection of potential agricultural/industrial contaminants, reduction of agricultural chemical movement, improvement of soil and water quality, and increase of agricultural productivity. The original research objectives of this project were: 1. to measure soil hydraulic properties at different locations and different scales at large fields; 2. to develop scale-dependent relationships of soil hydraulic properties; and 3. to determine spatial variability and heterogeneity of soil hydraulic properties as a function of measurement scales. The US investigators conducted field and lab experiments to measure soil hydraulic properties at different locations and different scales. Based on the field and lab experiments, a well-structured database of soil physical and hydraulic properties was developed. The database was used to study scale-dependency, spatial variability, and heterogeneity of soil hydraulic properties. An improved method was developed for calculating hydraulic properties based on infiltration data from the disc infiltrometer. Compared with the other methods, the proposed method provided more accurate and stable estimations of the hydraulic conductivity and macroscopic capillary length, using infiltration data collected atshort experiment periods. We also developed scale-dependent relationships of soil hydraulic properties using the fractal and geostatistical characterization. The research effort of the Israeli research team concentrates on tasks along the second objective. The main accomplishment of this effort is that we succeed to derive first-order, upscaled (block effective) conductivity tensor, K'ᵢⱼ, and time-dependent dispersion tensor, D'ᵢⱼ, i,j=1,2,3, for steady-state flow in three-dimensional, partially saturated, heterogeneous formations, for length-scales comparable with those of the formation heterogeneity. Numerical simulations designed to test the applicability of the upscaling methodology to more general situations involving complex, transient flow regimes originating from periodic rain/irrigation events and water uptake by plant roots suggested that even in this complicated case, the upscaling methodology essentially compensated for the loss of sub-grid-scale variations of the velocity field caused by coarse discretization of the flow domain. These results have significant implications with respect to the development of field-scale solute transport models capable of simulating complex real-world scenarios in the subsurface, and, in turn, are essential for the assessment of the threat posed by contamination from agricultural and/or industrial sources.

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Assessment of the spatial variability of apparent electrical conductivity in a tile drained catchment in Fensholt subcatchment, Jutland, Denmark for improved small-scale prediction of highly reducing areas

Geoderma Regional ◽

10.1016/j.geodrs.2020.e00336 ◽

2020 ◽

Vol 23 ◽

pp. e00336

Author(s):

Maria Isabel S. Senal ◽

Lars Elsgaard ◽

Søren O. Petersen ◽

Triven Koganti ◽

Bo V. Iversen

Keyword(s):

Electrical Conductivity ◽

Spatial Variability ◽

Small Scale ◽

Apparent Electrical Conductivity

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ROLE OF ADDED PHOSPHOGYPSUM AND HUMIC ACIDS IN THE KINETIC RELEASE OF SALT FROM SALT AFFECTED SOIL (SALINE – SODIC)

Anbar Journal of Agricultural Sciences ◽

10.32649/ajas/2020/2 ◽

2020 ◽

pp. 15-27

Keyword(s):

Electrical Conductivity ◽

Humic Acids ◽

Release Rate ◽

Release Kinetics ◽

Sodium Adsorption Ratio ◽

Salt Affected Soil ◽

Two Factors ◽

Dissolved Ions ◽

Kinetic Release

In order to study the effect of phosphogypsum and humic acids in the kinetic release of salt from salt-affected soil, a laboratory experiment was conducted in which columns made from solid polyethylene were 60.0 cm high and 7.1 cm in diameter. The columns were filled with soil so that the depth of the soil was 30 cm inside the column, the experiment included two factors, the first factor was phosphogypsum and was added at levels 0, 5, 10 and 15 tons ha-1 and the second-factor humic acids were added at levels 0, 50, 100 and 150 kg ha-1 by mixing them with the first 5 cm of column soil and one repeater per treatment. The continuous leaching method was used by using an electrolytic well water 2.72 dS m-1. Collect the leachate daily and continue the leaching process until the arrival of the electrical conductivity of the filtration of leaching up to 3-5 dS m-1. The electrical conductivity and the concentration of positive dissolved ions (Ca, Mg, Na) were estimated in leachate and the sodium adsorption ratio (SAR) was calculated. The results showed that the best equation for describing release kinetics of the salts and sodium adsorption ratio in soil over time is the diffusion equation. Increasing the level of addition of phosphogypsum and humic acids increased the constant release velocity (K) of salts and the sodium adsorption ratio. The interaction between phosphogypsum and humic acids was also affected by the constant release velocity of salts and the sodium adsorption ratio. The constant release velocity (K) of the salts and the sodium adsorption ratio at any level of addition of phosphogypsum increased with the addition of humic acids. The highest salts release rate was 216.57 in PG3HA3, while the lowest rate was 149.48 in PG0HA0. The highest release rate of sodium adsorption ratio was 206.09 in PG3HA3, while the lowest rate was 117.23 in PG0HA0.

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Experimental investigation to characterize simple versus multi scaling analysis of hydraulic conductivity at a mesoscale

Stochastic Environmental Research and Risk Assessment ◽

10.1007/s00477-021-02079-w ◽

2021 ◽

Author(s):

Guglielmo Federico Antonio Brunetti ◽

Samuele De Bartolo ◽

Carmine Fallico ◽

Ferdinando Frega ◽

Maria Fernanda Rivera Velásquez ◽

...

Keyword(s):

Hydraulic Conductivity ◽

Spatial Variability ◽

Hydraulic Properties ◽

Scaling Laws ◽

Scaling Analysis ◽

Field Scale ◽

Porous Formation ◽

Proper Design ◽

First Time

AbstractThe spatial variability of the aquifers' hydraulic properties can be satisfactorily described by means of scaling laws. The latter enable one to relate the small (typically laboratory) scale to the larger (typically formation/regional) ones, therefore leading de facto to an upscaling procedure. In the present study, we are concerned with the spatial variability of the hydraulic conductivity K into a strongly heterogeneous porous formation. A strategy, allowing one to identify correctly the single/multiple scaling of K, is applied for the first time to a large caisson, where the medium was packed. In particular, we show how to identify the various scaling ranges with special emphasis on the determination of the related cut-off limits. Finally, we illustrate how the heterogeneity enhances with the increasing scale of observation, by identifying the proper law accounting for the transition from the laboratory to the field scale. Results of the present study are of paramount utility for the proper design of pumping tests in formations where the degree of spatial variability of the hydraulic conductivity does not allow regarding them as “weakly heterogeneous”, as well as for the study of dispersion mechanisms.

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A Model-Based Tool for Assessing the Impact of Land Use Change Scenarios on Flood Risk in Small-Scale River Systems—Part 1: Pre-Processing of Scenario Based Flood Characteristics for the Current State of Land Use

Hydrology ◽

10.3390/hydrology8030102 ◽

2021 ◽

Vol 8 (3) ◽

pp. 102

Author(s):

Frauke Kachholz ◽

Jens Tränckner

Keyword(s):

Land Use ◽

River Flow ◽

Land Use Changes ◽

Small Rivers ◽

Small Scale ◽

Model Parameters ◽

Ungauged Catchments ◽

Model Based ◽

Current State ◽

The Impact

Land use changes influence the water balance and often increase surface runoff. The resulting impacts on river flow, water level, and flood should be identified beforehand in the phase of spatial planning. In two consecutive papers, we develop a model-based decision support system for quantifying the hydrological and stream hydraulic impacts of land use changes. Part 1 presents the semi-automatic set-up of physically based hydrological and hydraulic models on the basis of geodata analysis for the current state. Appropriate hydrological model parameters for ungauged catchments are derived by a transfer from a calibrated model. In the regarded lowland river basins, parameters of surface and groundwater inflow turned out to be particularly important. While the calibration delivers very good to good model results for flow (Evol =2.4%, R = 0.84, NSE = 0.84), the model performance is good to satisfactory (Evol = −9.6%, R = 0.88, NSE = 0.59) in a different river system parametrized with the transfer procedure. After transferring the concept to a larger area with various small rivers, the current state is analyzed by running simulations based on statistical rainfall scenarios. Results include watercourse section-specific capacities and excess volumes in case of flooding. The developed approach can relatively quickly generate physically reliable and spatially high-resolution results. Part 2 builds on the data generated in part 1 and presents the subsequent approach to assess hydrologic/hydrodynamic impacts of potential land use changes.

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Understanding Spatial Variability of NO2 in Urban Areas Using Spatial Modelling and Data Fusion Approaches

Atmosphere ◽

10.3390/atmos12020179 ◽

2021 ◽

Vol 12 (2) ◽

pp. 179

Author(s):

Said Munir ◽

Martin Mayfield ◽

Daniel Coca

Keyword(s):

Data Fusion ◽

Spatial Variability ◽

Urban Areas ◽

Road Traffic ◽

Dispersion Model ◽

Low Cost ◽

Spatial Modelling ◽

City Centre ◽

Small Scale ◽

The City

Small-scale spatial variability in NO2 concentrations is analysed with the help of pollution maps. Maps of NO2 estimated by the Airviro dispersion model and land use regression (LUR) model are fused with measured NO2 concentrations from low-cost sensors (LCS), reference sensors and diffusion tubes. In this study, geostatistical universal kriging was employed for fusing (integrating) model estimations with measured NO2 concentrations. The results showed that the data fusion approach was capable of estimating realistic NO2 concentration maps that inherited spatial patterns of the pollutant from the model estimations and adjusted the modelled values using the measured concentrations. Maps produced by the fusion of NO2-LCS with NO2-LUR produced better results, with r-value 0.96 and RMSE 9.09. Data fusion adds value to both measured and estimated concentrations: the measured data are improved by predicting spatiotemporal gaps, whereas the modelled data are improved by constraining them with observed data. Hotspots of NO2 were shown in the city centre, eastern parts of the city towards the motorway (M1) and on some major roads. Air quality standards were exceeded at several locations in Sheffield, where annual mean NO2 levels were higher than 40 µg/m3. Road traffic was considered to be the dominant emission source of NO2 in Sheffield.

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