scholarly journals A Field-Scale Sensor Network Data Set for Monitoring and Modeling the Spatial and Temporal Variation of Soil Water Content in a Dryland Agricultural Field

2017 ◽  
Vol 53 (12) ◽  
pp. 10878-10887 ◽  
Author(s):  
C. K. Gasch ◽  
D. J. Brown ◽  
C. S. Campbell ◽  
D. R. Cobos ◽  
E. S. Brooks ◽  
...  
Keyword(s):  
Temporal Variation ◽  
Water Content ◽  
Soil Water ◽  
Soil Water Content ◽  
Sensor Network ◽  
Spatial And Temporal Variation ◽  
Network Data ◽  
Agricultural Field ◽  
Field Scale ◽  
Data Set

scholarly journals Using an inverse modelling approach to evaluate the water retention in a simple water harvesting technique

2009 ◽  
Vol 6 (3) ◽  
pp. 4265-4306 ◽  
Author(s):  
K. Verbist ◽  
W. M. Cornelis ◽  
D. Gabriels ◽  
K. Alaerts ◽  
G. Soto
Keyword(s):  
Water Content ◽  
Soil Water ◽  
Soil Water Content ◽  
Water Retention ◽  
Measurement Techniques ◽  
Inverse Modelling ◽  
Model Parameters ◽  
Arid Zones ◽  
Water Harvesting ◽  
Data Set

Abstract. In arid and semi-arid zones runoff harvesting techniques are often applied to increase the water retention and infiltration on steep slopes. Additionally, they act as an erosion control measure to reduce land degradation hazards. Nevertheless, few efforts were observed to quantify the water harvesting processes of these techniques and to evaluate their efficiency. In this study a combination of detailed field measurements and modelling with the HYDRUS-2D software package was used to visualize the effect of an infiltration trench on the soil water content of a bare slope in Northern Chile. Rainfall simulations were combined with high spatial and temporal resolution water content monitoring in order to construct a useful dataset for inverse modelling purposes. Initial estimates of model parameters were provided by detailed infiltration and soil water retention measurements. Four different measurement techniques were used to determine the saturated hydraulic conductivity (Ksat) independently. Tension infiltrometer measurements proved a good estimator of the Ksat value and a proxy for those measured under simulated rainfall, whereas the pressure and constant head well infiltrometer measurements showed larger variability. Six different parameter optimization functions were tested as a combination of soil-water content, water retention and cumulative infiltration data. Infiltration data alone proved insufficient to obtain high model accuracy, due to large scatter on the data set, and water content data were needed to obtain optimized effective parameter sets with small confidence intervals. Correlation between observed soil water content and simulated values was as high as R2=0.93 for ten selected observation points used in the model calibration phase, with overall correlation for the 22 observation points equal to 0.85. Model results indicate that the infiltration trench has a significant effect on soil water storage, especially at the base of the trench.

Continuous and simultaneous measurement of reflector depth and average soil-water content with multichannel ground-penetrating radar

Geophysics ◽  
2008 ◽  
Vol 73 (4) ◽  
pp. J15-J23 ◽  
Author(s):  
Holger Gerhards ◽  
Ute Wollschläger ◽  
Qihao Yu ◽  
Philip Schiwek ◽  
Xicai Pan ◽  
...  
Keyword(s):  
Water Content ◽  
Soil Water ◽  
Soil Water Content ◽  
Ground Penetrating Radar ◽  
Synthetic Data ◽  
Evaluation Procedure ◽  
Noninvasive Technique ◽  
Data Set ◽  
Large Variability ◽  
Ground Penetrating

Ground-penetrating radar is a fast noninvasive technique that can monitor subsurface structure and water-content distribution. To interpret traveltime information from single common-offset measurements, additional assumptions, such as constant permittivity, usually are required. We present a fast ground-penetrating-radar measurement technique using a multiple transmitter-and-receiver setup to measure simultaneously the reflector depth and average soil-water content. It can be considered a moving minicommon-midpoint measurement. For a simple analysis, we use a straightforward evaluation procedure that includes two traveltimes to the same reflector, obtained from different antenna separations. For a more accurate approach, an inverse evaluation procedure is added, using traveltimes obtained from all antenna separations at one position and its neighboring measurement locations. The evaluation of a synthetic data set with a lateral variability in reflector depth and an experimental example with a large variability in soil-water content are introduced to demonstrate the applicability for field-scale measurements. The crucial point for this application is the access to absolute traveltimes, which are difficult to determine accurately from common-offset measurements.

Fractal Description of the Spatial and Temporal Variability of Soil Water Content Across an Agricultural Field

Soil Science ◽  
2012 ◽  
Vol 177 (2) ◽  
pp. 131-138 ◽  
Author(s):  
Eva Vidal-Vázquez ◽  
Jorge Paz-Ferreiro ◽  
Sidney Vieira ◽  
George Topp ◽  
José Miranda ◽  
...  
Keyword(s):  
Water Content ◽  
Soil Water ◽  
Soil Water Content ◽  
Temporal Variability ◽  
Spatial And Temporal Variability ◽  
Agricultural Field ◽  
Fractal Description

scholarly journals Kalman filters for assimilating near-surface observations into the Richards equation – Part 3: Retrieving states and parameters from laboratory evaporation experiments

2014 ◽  
Vol 18 (7) ◽  
pp. 2543-2557 ◽  
Author(s):  
H. Medina ◽  
N. Romano ◽  
G. B. Chirico
Keyword(s):  
Kalman Filter ◽  
Water Content ◽  
Soil Water ◽  
Soil Water Content ◽  
Initial Conditions ◽  
Estimation Method ◽  
Richards Equation ◽  
Porous System ◽  
Data Set ◽  
Near Surface

Abstract. The purpose of this work is to evaluate the performance of a dual Kalman filter procedure in retrieving states and parameters of a one-dimensional soil water budget model based on the Richards equation, by assimilating near-surface soil water content values during evaporation experiments carried out under laboratory conditions. The experimental data set consists of simultaneously measured evaporation rates, soil water content and matric potential profiles. The parameters identified by assimilating the data measured at 1 and 2 cm soil depths are in very good agreement with those obtained by exploiting the observations carried out in the entire soil profiles. A reasonably good correspondence has been found between the parameter values obtained from the proposed assimilation technique and those identified by applying a non-sequential parameter estimation method. The dual Kalman filter also performs well in retrieving the water state in the porous system. Bias and accuracy of the predicted state profiles are affected by observation depth changes, particularly for the experiments involving low state vertical gradients. The assimilation procedure proved flexible and very stable in both experimental cases, independently from the selected initial conditions and the involved uncertainty.

Field and laboratory calibration and test of TDR and capacitance techniques for indirect measurement of soil water content

Soil Research ◽  
2001 ◽  
Vol 39 (6) ◽  
pp. 1371 ◽  
Author(s):  
P. N. J. Lane ◽  
D. H. Mackenzie
Keyword(s):  
Water Content ◽  
Soil Water ◽  
Soil Water Content ◽  
Calibration Procedure ◽  
Indirect Measurement ◽  
Time Domain Reflectometry ◽  
Large Core ◽  
Data Set ◽  
Laboratory Calibration ◽  
Capacitance Probe

Time domain reflectometry (TDR) and a frequency domain sensor, the Didcot Capacitance Probe, were tested in the field and laboratory. The results from an undisturbed large core TDR laboratory test found the Topp equation returned a close correspondence to thermogravimetrically derived water content, although there was a slight underestimation. Coefficients of determination and efficiency were >0.98 and 0.92, respectively, for individual cores, and 0.98 and 0.97 for the whole data set. The field exercise revealed the Topp equation to be superior to the laboratory derived equation and other published empirical equations, suggesting the Topp equation to be adequate. A field test of the capacitance probe found poor correspondence between measured and predicted observations of profile point soil water content. Although 81% of the variance was explained by the calibration regression, there was a poor fit to the 1:1 line (E = 0.34), and a non-significant relationship between measured and predicted soil water content for the A horizon. The instrument design proved problematic for use as a determiner of point profile soil water content, and the recommended calibration procedure was impossible in the study site soils.

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