WATER RETENTION EQUATIONS AND THEIR RELATIONSHIP TO SOIL ORGANIC MATTER AND PARTICLE SIZE DISTRIBUTION FOR DISTURBED SAMPLES

Functional relationships between soil water content and water suction were examined and related to textural and organic carbon content data. Soil water retention curves between 5 and 10 000 kPa were determined on disturbed samples of 18 soils representing various soil Great Groups in the Canadian prairies. The best fit was obtained with a two-straight-line regression model. Correlation and regression analysis showed that texture was the main soil property influencing the shape and position of the water retention curve. Organic matter influenced primarily the water content at which a break in the curve occurred. Soil zone and cultivation history had little effect on water retention. Key words: Water retention, texture, organic matter, two-straight-line regression

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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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Relating soil structure and hydrophysical characteristics to aerobic and anaerobic soil respiration

10.5194/egusphere-egu2020-21234 ◽

2020 ◽

Author(s):

Teamrat Ghezzehei ◽

Jennifer Alvarez ◽

Yocelyn Villa ◽

Rebecca Ryals

Keyword(s):

Organic Matter ◽

Soil Moisture ◽

Soil Water ◽

Soil Structure ◽

Water Retention ◽

Water Retention Curve ◽

Soil Water Retention Curve ◽

Soil Water Retention ◽

Retention Curve ◽

Aerobic And Anaerobic

The dynamics of soil organic matter is strongly controlled by the hydrophysical environmental factors, including motility, aqueous diffusivity of substrates, gaseous diffusivity, and energetic constraints on microbial physiology. The relationships among these physical factors depend on soil moisture and the architecture of the soil pores. In this regard, the soil water retention curve can serve as a macroscopic signature of pore-size distribution. Therefore, the sensitivity of aerobic and anaerobic microbial activity must be closely associated with the shape of the soil water retention curve. The soil water retention curve is, in turn, strongly dependent on soil texture and structure. Here, we present a physically-based model of aerobic and anaerobic microbial respiration rates. We also present a novel experimental technique for the characterization of the soil-moisture sensitivity of soil microbial activity. The proposed experimental and modeling approaches allow direct coupling of the fate soil organic matter with the nature of soil structure.

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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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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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