|Koestel, John - Swedish University Of Agricultural Sciences|
|Dathe, Annette - Norwegian Institute Of Bioeconomy Research(NIBIO)|
|Klakegg, Ove - Norwegian Institute Of Bioeconomy Research(NIBIO)|
|Ahmad, Muhammad - Swedish University Of Agricultural Sciences|
|Babko, Maryia - Swedish University Of Agricultural Sciences|
|Gimenez, Daniel - Rutgers University|
|Farkas, Csilla - Norwegian Institute Of Bioeconomy Research(NIBIO)|
|Nemes, Attila - Norwegian Institute Of Bioeconomy Research(NIBIO)|
|Jarvis, Nicholas - Swedish University Of Agricultural Sciences|
Submitted to: Water Resources Research
Publication Type: Peer Reviewed Journal
Publication Acceptance Date: 10/23/2018
Publication Date: 10/30/2018
Citation: Koestel, J., Dathe, A., Skaggs, T.H., Klakegg, O., Ahmad, M.A., Babko, M., Gimenez, D., Farkas, C., Nemes, A., Jarvis, N. 2018. Estimating the permeability of naturally structured soil from percolation theory and pore space characteristics imaged by x-ray. Water Resources Research. 54. https://doi.org/10.1029/2018WR023609.
DOI: https://doi.org/10.1029/2018WR023609 Interpretive Summary: Crop growth and many other ecohydrologic processes are regulated by the amount of water in the soil. A key physical property of soils that affects water content is the soil "permeability". Information about soil permeability permits better planning and management of soil-water resources. In this work, we evaluated a model for estimating soil permeability, using X-ray imaging to independently determine model parameters. The results confirmed that permeability is proportional to a critical pore length parameter that had been identified in previous studies. Future efforts to improve permeability predictions and related soil-water management practices should benefit from a greater focus on the identified critical length parameter. This research will help engineers and researchers working to identify irrigation practices that improve the management of scarce water and soil resources.
Technical Abstract: The saturated hydraulic conductivity of soil, Ks, is a critical parameter in hydrological models that remains notoriously difficult to predict. In this study, we test the capability of a model based on percolation theory and critical path analysis to estimate Ks measured on 95 undisturbed soil cores collected from contrasting soil types. One parameter (the pore geometry factor) was derived by model fitting, while the remaining two parameters (the critical pore diameter, dc, and the effective porosity) were derived from X-ray computed tomography measurements. The model gave a highly significant fit to the Ks measurements (p < 0.0001) although only ~47% of the variation was explained and the fitted pore geometry factor was approximately 1 to 2 orders of magnitude larger than various theoretical values obtained for idealized porous media and pore network models. Apart from assumptions in the model that might not hold in reality, this could also be attributed to experimental error induced by, for example, air entrapment and changes in the soil pore structure occurring during sample presaturation and the measurement of Ks. Variation in the critical pore diameter, dc, was the dominant source of variation in Ks, which suggests that dc is a suitable length scale for predicting soil permeability. Thus, from the point of view of pedotransfer functions, it could be worthwhile to direct future research toward exploring the correlations of dc with basic soil properties and site attributes.