Uncertainty analysis for large-scale prediction of the van Genuchten soil-water retention parameters with pedotransfer functions
K. Liao A , S. Xu B D , J. Wu C and Q. Zhu A D
+ Author Affiliations
- Author Affiliations
A Key Laboratory of Watershed Geographic Sciences, Nanjing Institute of Geography and Limnology, Chinese Academy of Sciences, Nanjing 210008, China.
B Department of Environmental Science, Qingdao University, Qingdao 266071, China.
C Department of Hydrosciences, Nanjing University, Nanjing 210093, China.
D Corresponding authors. Email: shhxu@qdu.edu.cn; qzhu@niglas.ac.cn
Soil Research 52(5) 431-442 https://doi.org/10.1071/SR13230
Submitted: 3 January 2013 Accepted: 23 March 2014 Published: 16 June 2014
Abstract
Hydrological, environmental and ecological modellers require van Genuchten soil-water retention parameters that are difficult to measure. Pedotransfer functions (PTFs) are thus routinely applied to predict hydraulic parameters (θs, ln(α) and n) from basic soil properties (e.g. bulk density, soil texture and organic matter content). This study investigated the spatial variations of van Genuchten parameters via geostatistical methods (e.g. kriging and co-kriging with remote-sensing data) and multiple-stepwise-regression-based PTFs with a limited number of samples (58) collected in Pingdu City, Shandong Province, China. The uncertainties in the spatial estimation of van Genuchten parameters were evaluated using bootstrap and Latin hypercube sampling methods. Results show that PTF-estimated parameters are less varied than observed parameters. The uncertainty in the parameter estimation is mainly due to the limited number of samples used for deriving PTFs (intrinsic uncertainty) and spatial interpolations of basic soil properties by (co)kriging (input uncertainty). When considering the intrinsic uncertainty, 36%, 29% and 47% of measurements are within the corresponding error bars (95% confidence intervals of the predictions) for the θs, ln(α) and n, respectively. When considering both intrinsic and input uncertainties, 86%, 66% and 88% of observations are within the corresponding error bars for the θs, ln(α) and n, respectively. Therefore, the input uncertainty is more important in the spatial estimation of van Genuchten parameters than the intrinsic uncertainty. Measurement of basic soil properties at high resolution and properly use of powerful spatial interpolation approach are both critical in the accurate spatial estimation of van Genuchten parameters.
Additional keywords: Bootstrap, Latin hypercube sampling, pedotransfer functions, uncertainty analysis, van Genuchten parameters.
References
Chirico GB, Medina H, Romano N (2007) Uncertainty in predicting soil hydraulic properties at the hillslope scale with indirect methods. Journal of Hydrology 334, 405–422.| Uncertainty in predicting soil hydraulic properties at the hillslope scale with indirect methods.Crossref | GoogleScholarGoogle Scholar |
Chirico GB, Medina H, Romano N (2010) Functional evaluation of PTF prediction uncertainty: An application at hillslope scale. Geoderma 155, 193–202.
| Functional evaluation of PTF prediction uncertainty: An application at hillslope scale.Crossref | GoogleScholarGoogle Scholar | 1:CAS:528:DC%2BC3cXitlWgtb8%3D&md5=9e2338c0f67f07f2a8a172b613cfe64aCAS |
Cressie N (1991) ‘Statistics of spatial data.’ (John Wiley: New York)
Deng HL, Ye M, Schaap MG, Khaleel R (2009) Quantification of uncertainty in pedotransfer function-based parameter estimation for unsaturated flow modeling. Water Resources Research 45, W04409
| Quantification of uncertainty in pedotransfer function-based parameter estimation for unsaturated flow modeling.Crossref | GoogleScholarGoogle Scholar |
Deutsch CV, Journel AG (1998) ‘GSLIB-geostatistical software library and user’s guide.’ (Oxford University Press: Oxford, UK)
Efron B, Tibshirani RJ (1993) ‘An introduction to the bootstrap.’ Monographs on Statistics and Applied Probability, Vol. 57. (Chapman and Hall: London)
FAO/ISRIC/ISSS (1998) ‘World Reference Base for Soil Resources.’ World Soil Resources Reports. (FAO: Rome)
Guber AK, Pachepsky YaA, van Genuchten MTh, Rawls WJ, Simunek J, Jacques D, Nicholson TJ, Cady RE (2006) Field-scale water flow simulations using ensembles of pedotransfer functions for soil water retention. Vadose Zone Journal 5, 234–247.
| Field-scale water flow simulations using ensembles of pedotransfer functions for soil water retention.Crossref | GoogleScholarGoogle Scholar |
Guber AK, Pachepsky YaA, van Genuchten MTh, Simunek J, Jacques D, Nemes A, Nicholson TJ, Cady RE (2009) Multimodel simulation of water flow in a field soil using pedotransfer functions. Vadose Zone Journal 8, 1–10.
| Multimodel simulation of water flow in a field soil using pedotransfer functions.Crossref | GoogleScholarGoogle Scholar |
Iman RL, Davenport JM, Zeigler DK (1980) ‘Latin hypercube sampling. Program user’s guide.’ SAND79-1473. (Sandia National Laboratories: Albuquerque, NM, USA)
Isaaks EH, Srivastava RM (1989) ‘Applied geostatistics.’ (Oxford University Press: New York)
Istok JD, Smyth JD, Flint AL (1993) Multivariate geostatistical analysis of ground-water contaminant: A case history. Ground Water 31, 63–74.
| Multivariate geostatistical analysis of ground-water contaminant: A case history.Crossref | GoogleScholarGoogle Scholar | 1:CAS:528:DyaK3sXhvFOmt7Y%3D&md5=d0d20b65820b39af908c400fc63c01cfCAS |
Janssen PHM, Heuberger PSC, Sanders R (1992) UNCSAM 1.1: a software package for sensitivity and uncertainty analysis. Report No. 959101004. National Institute of Public Health and Environmental Protection, Bilthoven, The Netherlands.
Klier C (2012) Use of an uncertainty analysis for genome-scale models as a prediction tool for microbial growth processes in subsurface environments. Environmental Science & Technology 46, 2790–2798.
| Use of an uncertainty analysis for genome-scale models as a prediction tool for microbial growth processes in subsurface environments.Crossref | GoogleScholarGoogle Scholar | 1:CAS:528:DC%2BC38Xit1Ogsr8%3D&md5=f899ac949259f66145363f3c8aa1aea6CAS |
Klute A (1986) Water retention: laboratory methods. In ‘Agronomy monograph. No. 9’. (Ed. VT Holliday) pp. 635–662. (American Society of Agronomy and Soil Science Society of America: Madison, WI, USA)
Lagacherie P, Baret F, Feret JB, Madeira Netto J, Robbez-Masson JM (2008) Estimation of soil clay and calcium carbonate using laboratory, field, and airborne hyperspectral measurements. Remote Sensing of Environment 112, 825–835.
| Estimation of soil clay and calcium carbonate using laboratory, field, and airborne hyperspectral measurements.Crossref | GoogleScholarGoogle Scholar |
Liao K, Xu S, Wu J, Ji S, Lin Q (2011) Assessing soil water retention characteristics and their spatial variability using pedotransfer functions. Pedosphere 21, 413–422.
| Assessing soil water retention characteristics and their spatial variability using pedotransfer functions.Crossref | GoogleScholarGoogle Scholar |
Lima A, Plant JA, De Vivo B, Tarvainen T, Albanese S, Cicchella D (2008) Interpolation methods for geochemical maps: a comparative study using arsenic data from European stream waters. Geochemistry: Exploration, Environment, Analysis 8, 41–48.
| Interpolation methods for geochemical maps: a comparative study using arsenic data from European stream waters.Crossref | GoogleScholarGoogle Scholar | 1:CAS:528:DC%2BD1cXjt1Wntbk%3D&md5=74dc619386baa11f046c0b075f97a916CAS |
Matula S, Spongrova K (2007) Pedotransfer function application for estimation of soil hydrophysical properties using parametric methods. Plant, Soil and Environment 53, 149–157.
McKay MD, Conover WJ, Beckman RJ (1979) A comparison of three methods for selecting values of input variables in the analysis of output from a computer code. Technometrics 21, 239–245.
Merdun H, Çınar Ö, Meral R, Apan M (2006) Comparison of artificial neural network and regression pedotransfer functions for prediction of soil water retention and saturated hydraulic conductivity. Soil & Tillage Research 90, 108–116.
| Comparison of artificial neural network and regression pedotransfer functions for prediction of soil water retention and saturated hydraulic conductivity.Crossref | GoogleScholarGoogle Scholar |
Minasny B, McBratney AB (2002) Uncertainty analysis for pedotransfer functions. European Journal of Soil Science 53, 417–429.
| Uncertainty analysis for pedotransfer functions.Crossref | GoogleScholarGoogle Scholar |
Minasny B, McBratney AB, Bristow KL (1999) Comparison of different approaches to the development of pedotransfer functions for water retention curves. Geoderma 93, 225–253.
| Comparison of different approaches to the development of pedotransfer functions for water retention curves.Crossref | GoogleScholarGoogle Scholar |
Moreira CS, Brunet D, Verneyre L, Sá SMO, Galdos MV, Cerri CC, Bernoux M (2009) Near infrared spectroscopy for soil bulk density assessment. European Journal of Soil Science 60, 785–791.
| Near infrared spectroscopy for soil bulk density assessment.Crossref | GoogleScholarGoogle Scholar | 1:CAS:528:DC%2BD1MXht1ylsLfK&md5=9ccfa9ddb263b88c13e91eb1291fab3eCAS |
Mortimore JL, Marshall LJR, Almond MJ, Hollins P, Matthews W (2004) Analysis of red and yellow ochre samples from Clearwell Caves and Catalhoyuk by vibrational spectroscopy and other techniques. Spectrochimica Acta. Part A: Molecular and Biomolecular Spectroscopy 60, 1179–1188.
| Analysis of red and yellow ochre samples from Clearwell Caves and Catalhoyuk by vibrational spectroscopy and other techniques.Crossref | GoogleScholarGoogle Scholar |
Nelson DW, Sommers LE, Sparks D, Page A, Helmke P (1996) Total carbon, organic carbon, and organic matter. In ‘Methods of soil analysis. Part 3. Chemical methods’. pp. 961–1010. (Soil Science Society of America: Madison, WI, USA)
Norusis JM (1994) ‘SPSS professional statistics 6.1.’ (SPSS Inc.: Chicago, IL, USA)
Pachepsky YA, Rawls WJ (2004) ‘Development of pedotransfer functions in soil hydrology.’ (Elsevier: Amsterdam)
Rawls WJ, Gish TJ, Brakensiek DL (1991) Estimating soil water retention from soil physical properties and characteristics. Advances in Soil Science 16, 213–234.
| Estimating soil water retention from soil physical properties and characteristics.Crossref | GoogleScholarGoogle Scholar |
Sallaberry CJ, Helton JC, Hora SC (2008) Extension of Latin hypercube samples with correlated variables. Reliability Engineering & System Safety 93, 1047–1059.
| Extension of Latin hypercube samples with correlated variables.Crossref | GoogleScholarGoogle Scholar |
Santra P, Sahoo RN, Das BS (2009) Estimation of soil hydraulic properties using proximal spectral reflectance in visible, near-infrared, and shortwave-infrared (VIS–NIR–SWIR) region. Geoderma 152, 338–349.
| Estimation of soil hydraulic properties using proximal spectral reflectance in visible, near-infrared, and shortwave-infrared (VIS–NIR–SWIR) region.Crossref | GoogleScholarGoogle Scholar |
Schaap MG, Leij FJ, van Genuchten MTh (1998) Neural network analysis for hierarchical prediction of soil water retention and saturated hydraulic conductivity. Soil Science Society of America Journal 62, 847–855.
| Neural network analysis for hierarchical prediction of soil water retention and saturated hydraulic conductivity.Crossref | GoogleScholarGoogle Scholar | 1:CAS:528:DyaK1cXls1Cmurs%3D&md5=ece8cb7fbdec01cddb70c448f6c432e8CAS |
Scharf PC, Schmidt JP, Kitchen NR, Sudduth KA, Hong SY, Lory JA, Davis JG (2002) Remote sensing for nitrogen management. Journal of Soil and Water Conservation 57, 518–524.
Shi W, Liu J, Du Z, Song Y, Chen C, Yue T (2009) Surface modelling of soil pH. Geoderma 150, 113–119.
| Surface modelling of soil pH.Crossref | GoogleScholarGoogle Scholar | 1:CAS:528:DC%2BD1MXjt1Kis70%3D&md5=0958b5a4cad0bafaa3c91de7ccd50aeaCAS |
Simuta-Champo R, Herrera-Zamarrón GS (2010) Convergence analysis for Latin-hypercube lattice-sample selection strategies for 3D correlated random hydraulic conductivity fields. Geofísica Internacional 49, 131–140.
Sivapalan M (2003) IAHS decade on predictions in ungauged basins (PUB). Hydrological Sciences Journal 48, 857–880.
| IAHS decade on predictions in ungauged basins (PUB).Crossref | GoogleScholarGoogle Scholar |
Stenberg B, Viscarra Rossel RA, Mouazen AM, Wetterlind J (2010) Visible and near infrared spectroscopy in soil science. Advances in Agronomy 107, 163–215.
| Visible and near infrared spectroscopy in soil science.Crossref | GoogleScholarGoogle Scholar | 1:CAS:528:DC%2BC3cXht1yht7bE&md5=bf8bdfbc0a7bbc28ebc226f842b31386CAS |
Sullivan DG, Shaw JN, Rickman D (2005) IKONOS imagery to estimate surface soil property variability in two Alabama physiographies. Soil Science Society of America Journal 69, 1789–1798.
| IKONOS imagery to estimate surface soil property variability in two Alabama physiographies.Crossref | GoogleScholarGoogle Scholar | 1:CAS:528:DC%2BD2MXht1WrurjE&md5=9d1759aa726eff1da6adcdd4dbf23c93CAS |
To J, Kay BD (2005) Variation in penetrometer resistance with soil properties: the contribution of effective stress and implications for pedotransfer functions. Geoderma 126, 261–276.
| Variation in penetrometer resistance with soil properties: the contribution of effective stress and implications for pedotransfer functions.Crossref | GoogleScholarGoogle Scholar |
Tomasella J, Pachepsky YA, Crestana S, Rawls WJ (2003) Comparison of two techniques to develop pedotransfer functions for water retention. Soil Science Society of America Journal 67, 1085–1092.
| Comparison of two techniques to develop pedotransfer functions for water retention.Crossref | GoogleScholarGoogle Scholar | 1:CAS:528:DC%2BD3sXlslCqtL0%3D&md5=c36f93505ba1f6eea036d99bc85831d8CAS |
van der Keur P, Iversen BV (2006) Uncertainty in soil physical data at river basin scale—a review. Hydrology and Earth System Sciences 10, 889–902.
| Uncertainty in soil physical data at river basin scale—a review.Crossref | GoogleScholarGoogle Scholar |
van der Meer FD, de Jong SM, Bakker WH (2001) Basic physics of spectrometry. In ‘Imaging spectrometry: basic principles and prospective applications. Remote sensing and digital image processing: Vol. 4’. (Eds FD van der Meer, SM de Jong) pp. 12–13. (Springer: Berlin, Heidelberg)
van Genuchten MTh (1980) A closed-form equation for predicting the hydraulic conductivity of unsaturated soils. Soil Science Society of America Journal 44, 892–898.
| A closed-form equation for predicting the hydraulic conductivity of unsaturated soils.Crossref | GoogleScholarGoogle Scholar |
van Genuchten MTh, Leij FJ, Yates SR (1991) The RETC code for quantifying the hydraulic functions of unsaturated soils, Version 1.0. EPA Report 600/2–91/065, U.S. Salinity Laboratory, USDA, ARS, Riverside, CA, USA.
Vereecken H, Maes J, Darius P (1989) Estimating the soil moisture retention characteristics from texture, bulk density and carbon content. Soil Science 148, 389–403.
| Estimating the soil moisture retention characteristics from texture, bulk density and carbon content.Crossref | GoogleScholarGoogle Scholar |
Webster R, Oliver MA (1992) Sample adequately to estimate variograms of soil properties. Journal of Soil Science 43, 177–192.
| Sample adequately to estimate variograms of soil properties.Crossref | GoogleScholarGoogle Scholar |
Wetterlind J, Stenberg B (2010) Near-infrared spectroscopy for within-field soil characterization: small local calibrations compared with national libraries spiked with local samples. European Journal of Soil Science 61, 823–843.
| Near-infrared spectroscopy for within-field soil characterization: small local calibrations compared with national libraries spiked with local samples.Crossref | GoogleScholarGoogle Scholar | 1:CAS:528:DC%2BC3MXks1Gg&md5=490e5e8c629a0d03c1d143506761fc50CAS |
Wösten JHM, van Genuchten MTh (1988) Using texture and other soil properties to predict the unsaturated soil hydraulic functions. Soil Science Society of America Journal 52, 1762–1770.
| Using texture and other soil properties to predict the unsaturated soil hydraulic functions.Crossref | GoogleScholarGoogle Scholar |
Wösten JHM, Lilly A, Nemes A, Le Bas C (1999) Development and use of a database of hydraulic properties of European soils. Geoderma 90, 169–185.
| Development and use of a database of hydraulic properties of European soils.Crossref | GoogleScholarGoogle Scholar |
Wösten JHM, Pachepsky YA, Rawls WJ (2001) Pedotransfer functions: bridging the gap between available basic soil data and missing soil hydraulic characteristics. Journal of Hydrology 251, 123–150.
| Pedotransfer functions: bridging the gap between available basic soil data and missing soil hydraulic characteristics.Crossref | GoogleScholarGoogle Scholar |
Wu C, Wu J, Luo Y, Zhang L, DeGloria SD (2009) Spatial prediction of soil organic matter content using cokriging with remotely sensed data. Soil Science Society of America Journal 73, 1202–1208.
| Spatial prediction of soil organic matter content using cokriging with remotely sensed data.Crossref | GoogleScholarGoogle Scholar | 1:CAS:528:DC%2BD1MXos1Ggs7Y%3D&md5=74e65e7dcb70a3122ccd2f5967153a4bCAS |
Yates SR, Warrick AW (1987) Estimating soil water content using cokriging. Soil Science Society of America Journal 51, 23–30.
| Estimating soil water content using cokriging.Crossref | GoogleScholarGoogle Scholar |
Ye M, Khaleel R, Schaap MG, Zhu J (2007) Simulation of field injection experiments in heterogeneous unsaturated media using cokriging and artificial neural network. Water Resources Research 43, W07413
| Simulation of field injection experiments in heterogeneous unsaturated media using cokriging and artificial neural network.Crossref | GoogleScholarGoogle Scholar |
Zhu Q, Lin HS (2010) Comparing ordinary kriging and regression kriging for soil properties in contrasting landscapes. Pedosphere 20, 594–606.
| Comparing ordinary kriging and regression kriging for soil properties in contrasting landscapes.Crossref | GoogleScholarGoogle Scholar |
