A comparison of measured and estimated saturated hydraulic conductivity of various soils in the Czech Republic

https://doi.org/10.17221/123/2022-PSECitation:

Báťková K., Matula S., Hrúzová E., Miháliková M., Kara R.S., Almaz C. (2022): A comparison of measured and estimated saturated hydraulic conductivity of various soils in the Czech Republic. Plant Soil Environ., 68: 338–346.

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The study aims to indirectly determine the saturated hydraulic conductivity (Ks). The applicability of recently-published pedotransfer functions (PTFs) based on a machine learning approach has been tested, and their performance has been compared with well-known hierarchical PTFs (computer software Rosetta) for 126 soil data sets in the Czech Republic. The quality of estimates has been statistically evaluated in comparison with the measured Ks values; the root mean squared error (RMSE), the mean error (ME) and the coefficient of determination (R2) were considered. The eight tested models of PTFs were ranked according to the RMSE values. The measured results reflected high Ks variability between and within the study areas, especially for those areas where preferential flow occurred. In most cases, the tested PTFs overestimated the measured Ks values, which is documented by positive ME values. The RMSE values of the Ks estimate ranged on average from 0.5 (coarse-textured soils) to 1.3 (medium to fine-textured soils) for log-transformed Ks in cm/day. Generally, the models based on Random Forest performed better than those based on Boosted Regression Trees. However, the best estimates were obtained by Neural Network analysis PTFs in Rosetta, which scored for four best rankings out of five.

References:
Araya S.N., Ghezzehei T.A. (2019): Using machine learning for prediction of saturated hydraulic conductivity and its sensitivity to soil structural perturbations. Water Resources Research, 55: 5715–5737. https://doi.org/10.1029/2018WR024357
 
Arshad R.R., Sayyad G., Mosaddeghi M., Gharabaghi B. (2013): Predicting saturated hydraulic conductivity by artificial intelligence and regression models. ISRN Soil Science, 2013: 308159. https://doi.org/10.1155/2013/308159
 
Bouma J. (1989): Using soil survey data for quantitative land evaluation. Advances in Soil Sciences, 9: 177–213.
 
Friedman J.H. (2002): Stochastic gradient boosting. Computational Statistics and Data Analysis, 38: 367–378. https://doi.org/10.1016/S0167-9473(01)00065-2
 
Gamie R., De Smedt F. (2018): Experimental and statistical study of saturated hydraulic conductivity and relations with other soil properties of a desert soil. European Journal of Soil Science, 69: 256–264. https://doi.org/10.1111/ejss.12519
 
Gunarathna M.H.J.P., Sakai K., Nakandakari T., Momii K., Kumari M.K.N. (2019): Machine learning approaches to develop pedotransfer functions for tropical Sri Lankan soils. Water, 11: 1940. https://doi.org/10.3390/w11091940
 
Klute A.E. (1986): Methods of Soil Analysis, Part 1. Physical and Mineralogical Methods. Monograph 9. Madison, ASA and SSSA.
 
Lilly A., Nemes A., Rawls W.J., Pachepsky Y.A. (2008): Probabilistic approach to the identification of input variables to estimate hydraulic conductivity. Soil Science Society of America Journal, 72: 16–24. https://doi.org/10.2136/sssaj2006.0391
 
Matula S., Kozáková H. (1997): A simple pressure infiltrometer for determination of soil hydraulic properties by in situ infiltration measurements. Rostlinná výroba, 43: 405–413.
 
Mbonimpa M., Aubertin M., Chapuis R.P., Bussière B. (2002): Practical pedotransfer functions for estimating the saturated hydraulic conductivity. Geotechnical and Geological Engineering, 20: 235–259. https://doi.org/10.1023/A:1016046214724
 
Miháliková M., Matula S., Doležal F. (2013): HYPRESCZ – database of soil hydrophysical properties in the Czech Republic. Soil and Water Research, 8: 34–41. https://doi.org/10.17221/58/2012-SWR
 
Minasny B., McBratney A.B., Bristow K.Y. (1999): Comparison of different approaches to the development of pedotransfer functions for water retention curves. Geoderma, 93: 225–253. https://doi.org/10.1016/S0016-7061(99)00061-0
 
Němeček J., Macků J., Vokoun J., Vavříček D., Novák P. (2001): The Taxonmic Classification System of Soils in the Czech Republic. Prague, Czech University of Life Sciences Prague, Research Institute for Soil and Water Conservation. ISBN 80-238-8061-6 (In Czech)
 
Pachepsky Y.A., Rawls W.J. (2004): Development of Pedotransfer Functions in Soil Hydrology. Developments in Soil Science. Amsterodam, Elsevier.
 
Parasuraman K., Elshorbagy A., Si B. (2006): Estimating saturated hydraulic conductivity in spatially variable fields using neural network ensembles. Soil Science Society of America Journal, 70: 1851–1859. https://doi.org/10.2136/sssaj2006.0045
 
Schaap M.G., Leij F.J. (2000): Improved prediction of unsaturated hydraulic conductivity with the Mualem-van Genuchten model. Soil Science Society of America Journal, 64: 843–851. https://doi.org/10.2136/sssaj2000.643843x
 
Schaap M.G., Leij F.J., van Genuchten M.T. (1998): Neural network analysis for hierarchical prediction of soil hydraulic properties. Soil Science Society of America Journal, 62: 847–855. https://doi.org/10.2136/sssaj1998.03615995006200040001x
 
Schaap M.G., Leij F.J., van Genuchten M.T. (2001): Rosetta: a computer program for estimating soil hydraulic parameters with hierarchical pedotransfer functions. Journal of Hydrology, 251: 163–176. https://doi.org/10.1016/S0022-1694(01)00466-8
 
Tóth B., Weynants M., Nemes A., Makó A., Bilas G., Tóth G. (2015): New generation of hydraulic pedotransfer functions for Europe. European Journal of Soil Science, 66: 226–238. https://doi.org/10.1111/ejss.12192
 
Tuffour H., Abubakari A., Agbeshie A., Khalid A., Tetteh E., Keshavarzi A., Bonsu M., Quansah C., Oppong J., Danso L. (2019): Pedotransfer functions for estimating saturated hydraulic conductivity of selected benchmark soils in Ghana. Asian Soil Research Journal, 2: 1–11. https://doi.org/10.9734/asrj/2019/v2i230046
 
United States Department of Agriculture, Natural Resources Conservation Service. National soil survey handbook, title 430-VI. http://www.nrcs.usda.gov/wps/portal/nrcs/detail/soils/ref/?cid=nrcs142p2_054242 (accessed 4 March 2022).
 
Vereecken H., Weynants M., Javaux M., Pachepsky Y., Schaap M.G. ,van Genuchten M.T. (2010): Using pedotransfer functions to estimate the van Genuchten-Mualem soil hydraulic properties: a review. Vadose Zone Journal, 9: 795–820. https://doi.org/10.2136/vzj2010.0045
 
Wösten J.H.M., Finke P.A., Jansen M.J.W. (1995): Comparison of class and continuous pedotransfer functions to generate soil hydraulic characteristics. Geoderma, 66: 227–237. https://doi.org/10.1016/0016-7061(94)00079-P
 
Wösten J.H.M., Pachepsky Y., Rawls W.J. (2001): Pedotransfer functions: bridging the gap between available basic soil data and missing soil hydraulic characteristics. Journal of Hydrology, 251: 123–150. https://doi.org/10.1016/S0022-1694(01)00464-4
 
Zhang Y., Schaap M.G. (2019): Estimation of saturated hydraulic conductivity with pedotransfer functions: a review. Journal of Hydrology, 575: 1011–1030. https://doi.org/10.1016/j.jhydrol.2019.05.058
 
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