Spatial variability of saturated hydraulic conductivity at the hillslope scale: Understanding the role of land management and erosional effect

Papanicolaou, A. N.; Elhakeem, Mohamed; Wilson, Christopher G.; Burras, C. Lee; West, L. T.; Lin, H.; Clark, B.; O'Neal, Brad

doi:10.1016/j.geoderma.2014.12.010

Cited by 77 publications

(45 citation statements)

References 60 publications

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“…This approach has been commonly used in plot experiments and provides a good estimate of the spatially averaged infiltration rates (e.g., Mohamoud et al, 1990;Wainwright et al, 2000). Averaged saturated hydraulic conductivity values ranged from 3.20 to 4.56 mm h −1 , which are in agreement with the averaged saturated hydraulic conductivity value of 4.3 mm h −1 measured by Papanicolaou et al (2015a) using semiautomated double-ring infiltrometers at the field where the study was performed. Although the average saturated hydraulic conductivity values were low with respect to the applied rainfall rates, minimal ponding was observed on the experimental plot, owing to the smooth bare conditions and the high plot gradient of 9 %, which led to low depressional storage.…”

Section: Experimental Conditionssupporting

confidence: 67%

Quantifying the changes of soil surface microroughness due to rainfall impact on a smooth surface

Abban

Papanicolaou

Giannopoulos

et al. 2017

Nonlin. Processes Geophys.

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Abstract. This study examines the rainfall-induced change in soil microroughness of a bare smooth soil surface in an agricultural field. The majority of soil microroughness studies have focused on surface roughness on the order of ∼ 5-50 mm and have reported a decay of soil surface roughness with rainfall. However, there is quantitative evidence from a few studies suggesting that surfaces with microroughness less than 5 mm may undergo an increase in roughness when subject to rainfall action. The focus herein is on initial microroughness length scales on the order of 2 mm, a low roughness condition observed seasonally in some landscapes under bare conditions and chosen to systematically examine the increasing roughness phenomenon. Three rainfall intensities of 30, 60, and 75 mm h −1 are applied to a smoothened bed surface in a field plot via a rainfall simulator. Soil surface microroughness is recorded via a surface-profile laser scanner. Several indices are utilized to quantify the soil surface microroughness, namely the random roughness (RR) index, the crossover length, the variance scale from the MarkovGaussian model, and the limiting difference. Findings show a consistent increase in roughness under the action of rainfall, with an overall agreement between all indices in terms of trend and magnitude. Although this study is limited to a narrow range of rainfall and soil conditions, the results suggest that the outcome of the interaction between rainfall and a soil surface can be different for smooth and rough surfaces and thus warrant the need for a better understanding of this interaction.

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Section: Experimental Conditionssupporting

confidence: 67%

Quantifying the changes of soil surface microroughness due to rainfall impact on a smooth surface

Abban

Papanicolaou

Giannopoulos

et al. 2017

Nonlin. Processes Geophys.

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“…In many studies, K is linked to soil texture, bulk density, soil structure and organic matter content (e.g. Jabro, ; Schaap et al ., ; Li et al ., ; Jarvis et al ., ; Papanicolaou et al ., ). The diversity of properties affecting K can make interpretation of the variation in data and of interrelations difficult.…”

Section: Introductionmentioning

confidence: 97%

Experimental and statistical study of saturated hydraulic conductivity and relations with other soil properties of a desert soil

Gamie

Smedt

2017

European J Soil Science

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Agricultural expansion in the Kharga Oasis, in the western desert of Egypt, depends strongly on irrigation. Soil hydraulic conductivity is therefore a key property for reclaiming desert land and planning irrigation schemes. Soil samples collected at 10‐m intervals in a 120 m by 120 m plot were analysed for hydraulic conductivity together with 12 other basic physical and chemical soil properties. The resulting data were analysed statistically using Pearson correlation, principal component analysis and linear regression. The hydraulic conductivity values varied over four orders of magnitude and, because of the saline–sodic nature of the soil, were about two orders of magnitude smaller than what is generally reported in the literature for similar soil textures. Results showed that the hydraulic conductivity was correlated significantly with soil variables that relate to soil structure, such as wilting point, field capacity and SAR, and less to variables that relate to soil texture, such as silt and clay fractions. Pedotransfer functions for hydraulic conductivity were derived by stepwise multiple linear regression and fitted by residual maximum likelihood. The first model was based directly on soil properties, whereas the second model was based on principal components. Both models showed that part of the variation in hydraulic conductivity encountered in the field could be explained, but with large uncertainty probably resulting from sampling and measurement errors, randomness and soil heterogeneity, or other soil properties that were not observed. The most significant predictors for the first model were wilting point, SAR and silt fraction. The second model used the first two principal components of the soil variables as predictors, the first one related to soil structure and the second to soil texture. Highlights We analysed relations between saturated hydraulic conductivity and other basic soil properties. Principal component analysis showed that the variation in hydraulic conductivity is related to soil structure. A pedotransfer function predicted hydraulic conductivity from wilting point, sodium adsorption ratio and silt fraction. A pedotransfer function was derived from principal components one and two related to structure and texture, respectively.

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“…Working under initially more or less dry soil conditions has a particular interest since equilibration times are expected to be particularly long in this case but the run duration cannot exceed limits that are dictated by practical and environmental constraints. Indeed, only a few investigations making use of very long field runs can be found in the literature (e.g., Papanicolaou et al, 2015; Alagna et al, 2016).…”

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confidence: 99%

Accuracy of Saturated Soil Hydraulic Conductivity Estimated from Numerically Simulated Single‐Ring Infiltrations

Bagarello

Iovino

Lai

2019

Vadose Zone Journal

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Core Ideas The accuracy of the PI method in estimating soil Ks was tested by numerical simulations. Estimated Ks using two‐ponding‐depth and multiple‐ponding‐depth infiltration were compared. Transient and steady‐state infiltration data for six soils were used to estimate Ks. The PI should yield more accurate Ks estimates in coarse‐ than fine‐textured soils. The transient method does not solve the Ks inaccuracy problems in fine‐textured soils. The single‐ring pressure infiltrometer (PI) method is widely used to determine saturated soil hydraulic conductivity, Ks, directly in the field. The original and still most common way to analyze the data makes use of the steady‐state model developed by the Canadian School in the 90s and two (two‐ponding‐depth, TPD, approach) or more (multiple‐ponding‐depth, MPD, approach) depths of ponding. The so‐called Wu method based on a generalized infiltration equation allows analysis of the transient infiltration data collected by establishing a single ponding depth of water on the infiltration surface. This investigation, making use of simulated infiltration runs for initially unsaturated sand to silty clay loam soils, showed that, with a run duration of practical interest (e.g., 2 h), the PI can be expected to yield more accurate estimates of Ks in coarse‐textured soils than in fine‐textured soils even if the transient method is used instead of the steady‐state method. Performing a three‐level experiment and analyzing the estimated steady‐state infiltration rates with both the TPD and MPD approaches is a way to predict the reliability of the estimated Ks value. The Ks accuracy should be acceptable if the two approaches yield similar results. Otherwise, the MPD approach should be expected to yield more accurate Ks estimates than the TPD approach. The transient method does not solve the Ks inaccuracy problems in fine‐textured soils because obtaining accurate Ks data requires that the portion of total infiltration varying linearly with time represent a high percentage of total infiltration, but this percentage is small in fine‐textured soils when the run does not exceed a few hours. This investigation opens some new perspective on the use of infiltration data to make predictions on the expected reliability of the Ks calculations with reference to both steady‐state and transient data analysis procedures.

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Spatial variability of saturated hydraulic conductivity at the hillslope scale: Understanding the role of land management and erosional effect

Cited by 77 publications

References 60 publications

Quantifying the changes of soil surface microroughness due to rainfall impact on a smooth surface

Quantifying the changes of soil surface microroughness due to rainfall impact on a smooth surface

Experimental and statistical study of saturated hydraulic conductivity and relations with other soil properties of a desert soil

Accuracy of Saturated Soil Hydraulic Conductivity Estimated from Numerically Simulated Single‐Ring Infiltrations

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