Watertable fluctuation-induced variability in the water retention curve: Sand column experiments

The soil water retention curve (WRC), describing the relation between the soil water content and its corresponding capillary pressure, relies not only on whether drying or wetting occurs but also on the pore scale water flow velocity. Here, we investigated the effects of the watertable fluctuations on the WRC through 28 laboratory experiments covering a wide range of fluctuation amplitudes and periods. Results show that both the response of the capillary pressure and soil water content lag behind the watertable fluctuation, and the vertical capillary pressure distribution in the unsaturated zone is non-hydrostatic, especially for the fluctuations with shorter period. As a consequence of watertable fluctuation, the measured WRC deviates from that under static conditions, depending on both the fluctuation amplitude and period. Moreover, the air-entry pressure under dynamic conditions is considerably larger than that under static conditions, and it first increases and then decreases as the fluctuation period decreases. The effects of the watertable fluctuations on the dynamic capillary coefficient was further examined. It is found that the relation between the dynamic capillary coefficient and saturation is nonunique even for the drying and wetting of a given sand and watertable fluctuation, suggesting a hysteretic dynamic capillary coefficient, and the dynamic capillary coefficient is rate-dependent, decreasing with an increase of fluctuation rate.

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Retrieving soil-water retention curve at the wet part by remote sensing

10.5194/egusphere-egu2020-21677 ◽

2020 ◽

Author(s):

Zampela Pittaki-Chrysodonta ◽

Per Moldrup ◽

Bo V. Iversen ◽

Maria Knadel ◽

Lis W. de Jonge

Keyword(s):

Remote Sensing ◽

Water Content ◽

Soil Water ◽

Soil Water Content ◽

Spectral Data ◽

Water Retention ◽

Water Retention Curve ◽

Soil Water Retention Curve ◽

Soil Water Retention ◽

Retention Curve

The soil water retention curve (SWRC) at the wet part is important for understanding and modeling the water flow and solute transport in the vadose zone. However, direct measurements of SWRC is often laborious and time consuming processes. The Campbell function is a simple method to fit the measured data. The parameters of the Campbell function have been recently proven that can be predicted using visible-near-infrared spectroscopy. However, predicting the SWRC using image spectral data could be an inexpensive and fast method. In this study, 100-cm3 soil samples from Denmark were included and the soil water content was measured at a soil-water matric potential from pF 1 [log(10)= pF 1] up to pF 3. The anchored Campbell soil-water retention function was selected instead of the original. Specifically, in this function the equation is anchored at the soil-water content at pF 3 (&#952;pF3) instead at the saturated water content. The image spectral data were correlated with the Campbell parameters [&#952;pF3, and the pore size distribution index (Campbell b). The results showed the potential of remote sensing to be used as a fast and alternative method for predicting the SWRC in a large-scale.

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Effect of forest and grassland vegetation on soil hydrology in Mátra Mountains (Hungary)

Biologia ◽

10.2478/s11756-006-0169-7 ◽

2006 ◽

Vol 61 (19) ◽

Cited By ~ 3

Author(s):

Andrea Hagyó ◽

Kálmán Rajkai ◽

Zoltán Nagy

Keyword(s):

Water Content ◽

Soil Water ◽

Soil Water Content ◽

Water Retention ◽

Soil Layer ◽

Water Retention Curve ◽

Soil Water Retention Curve ◽

Soil Water Retention ◽

Mountain Area ◽

The Difference

AbstractWater retention characteristics, rainfall, throughfall and soil water content dynamics were investigated in a low mountain area to compare a forest and a grassland. The soil water retention curve of the topsoil has similar shape in both studied areas, however that of the deeper soil layer shows more difference. We determined the precipitation depth, duration and intensity values of rainfall events. The relationship between rainfall and throughfall depth was described in linear regressions. Interception was calculated as the difference between rainfall and throughfall plus stemflow, assuming stemflow to be 3% of rainfall. Soil water content dynamics show a similar trend in the two vegetation types but the drying is more intensive in the forest in the soil layers deeper than 20 cm during the growing-season.

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Evaluating the Splintex model for estimating the soil water retention curve for a wide range of soils

Soil and Tillage Research ◽

10.1016/j.still.2021.104974 ◽

2021 ◽

Vol 209 ◽

pp. 104974

Author(s):

Alessandra Calegari da Silva ◽

Robson André Armindo ◽

Budiman Minasny ◽

Celso Luiz Prevedello

Keyword(s):

Soil Water ◽

Water Retention ◽

Water Retention Curve ◽

Soil Water Retention Curve ◽

Soil Water Retention ◽

Retention Curve ◽

Wide Range

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Predicting the soil water retention curve from the particle size distribution based on a pore space geometry containing slit-shaped spaces

Hydrology and Earth System Sciences ◽

10.5194/hess-22-4621-2018 ◽

2018 ◽

Vol 22 (9) ◽

pp. 4621-4632

Author(s):

Chen-Chao Chang ◽

Dong-Hui Cheng

Keyword(s):

Particle Size ◽

Particle Size Distribution ◽

Water Content ◽

Size Distribution ◽

Soil Water ◽

Water Retention ◽

Water Retention Curve ◽

Soil Water Retention Curve ◽

Soil Water Retention ◽

Retention Curve

Abstract. Traditional models employed to predict the soil water retention curve (SWRC) from the particle size distribution (PSD) always underestimate the water content in the dry range of the SWRC. Using the measured physical parameters of 48 soil samples from the UNSODA unsaturated soil hydraulic property database, these errors were proven to originate from an inaccurate estimation of the pore size distribution. A method was therefore proposed to improve the estimation of the water content at high suction heads using a pore model comprising a circle-shaped central pore connected to slit-shaped spaces. In this model, the pore volume fraction of the minimum pore diameter range and the corresponding water content were accordingly increased. The predicted SWRCs using the improved method reasonably approximated the measured SWRCs, which were more accurate than those obtained using the traditional method and the scaling approach in the dry range of the SWRC.

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Effects of various input levels and different soil water retention curve models on water content estimation using different statistical methods

Hydrology Research ◽

10.2166/nh.2015.219 ◽

2015 ◽

Vol 47 (2) ◽

pp. 312-332 ◽

Cited By ~ 1

Author(s):

Hossein Bayat ◽

Eisa Ebrahimi

Keyword(s):

Water Content ◽

Soil Water ◽

Water Retention ◽

Water Retention Curve ◽

Model Parameters ◽

Soil Water Retention Curve ◽

Soil Water Retention ◽

Retention Curve ◽

Input Variables ◽

Gardner Model

This study investigated the impact of different input variables on the predictability of the water content using soil water retention curve (SWRC) models. The particle and aggregate size distribution model parameters were calculated by fitting the Perrier model to the related distributions for 75 soil samples. Nine SWRC models were fitted to the experimental data and their coefficients were obtained. The regression method was used to estimate the coefficients for nine SWRC models at three input levels. Cluster analysis classified the SWRC models into more homogeneous groups according to the accuracy of their predictions. The SWRC estimated using the Gardner model had the highest accuracy, but it was not an appropriate model for the soils because of its low fitting accuracy. Boltzman, Campbell, and Fermi models obtained the highest accuracy after the Gardner model. The Durner model yielded the lowest prediction accuracy due to the lack of correlation between the input variables and coefficients in this model. Thus, the water content predictions obtained using different SWRC models varied because different input variables were employed.

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