Inverse modeling of unsaturated flow using clusters of soil texture and pedotransfer functions

Yonggen Zhang, Marcel Schaap, Alberto Guadagnini, Shlomo P Neuman

Research output: Contribution to journalArticle

13 Citations (Scopus)

Abstract

Characterization of heterogeneous soil hydraulic parameters of deep vadose zones is often difficult and expensive, making it necessary to rely on other sources of information. Pedotransfer functions (PTFs) based on soil texture data constitute a simple alternative to inverse hydraulic parameter estimation, but their accuracy is often modest. Inverse modeling entails a compromise between detailed description of subsurface heterogeneity and the need to restrict the number of parameters. We propose two methods of parameterizing vadose zone hydraulic properties using a combination of k-means clustering of kriged soil texture data, PTFs, and model inversion. One approach entails homogeneous and the other heterogeneous clusters. Clusters may include subdomains of the computational grid that need not be contiguous in space. The first approach homogenizes within-cluster variability into initial hydraulic parameter estimates that are subsequently optimized by inversion. The second approach maintains heterogeneity through multiplication of each spatially varying initial hydraulic parameter by a scale factor, estimated a posteriori through inversion. This allows preserving heterogeneity without introducing a large number of adjustable parameters. We use each approach to simulate a 95 day infiltration experiment in unsaturated layered sediments at a semiarid site near Phoenix, Arizona, over an area of 50 × 50 m2 down to a depth of 14.5 m. Results show that both clustering approaches improve simulated moisture contents considerably in comparison to those based solely on PTF estimates. Our calibrated models are validated against data from a subsequent 295 day infiltration experiment at the site.

Original languageEnglish (US)
Pages (from-to)7631-7644
Number of pages14
JournalWater Resources Research
Volume52
Issue number10
DOIs
StatePublished - Oct 1 2016

Fingerprint

pedotransfer function
unsaturated flow
soil texture
hydraulics
modeling
vadose zone
infiltration
hydraulic property
moisture content
experiment
parameter
sediment
inversion
soil

Keywords

  • heterogeneity
  • hydraulic properties
  • inversion
  • pedotransfer
  • soil texture
  • vadose zone

ASJC Scopus subject areas

  • Water Science and Technology

Cite this

Inverse modeling of unsaturated flow using clusters of soil texture and pedotransfer functions. / Zhang, Yonggen; Schaap, Marcel; Guadagnini, Alberto; Neuman, Shlomo P.

In: Water Resources Research, Vol. 52, No. 10, 01.10.2016, p. 7631-7644.

Research output: Contribution to journalArticle

@article{8604baad05ba4e8796fcfca08cedb646,
title = "Inverse modeling of unsaturated flow using clusters of soil texture and pedotransfer functions",
abstract = "Characterization of heterogeneous soil hydraulic parameters of deep vadose zones is often difficult and expensive, making it necessary to rely on other sources of information. Pedotransfer functions (PTFs) based on soil texture data constitute a simple alternative to inverse hydraulic parameter estimation, but their accuracy is often modest. Inverse modeling entails a compromise between detailed description of subsurface heterogeneity and the need to restrict the number of parameters. We propose two methods of parameterizing vadose zone hydraulic properties using a combination of k-means clustering of kriged soil texture data, PTFs, and model inversion. One approach entails homogeneous and the other heterogeneous clusters. Clusters may include subdomains of the computational grid that need not be contiguous in space. The first approach homogenizes within-cluster variability into initial hydraulic parameter estimates that are subsequently optimized by inversion. The second approach maintains heterogeneity through multiplication of each spatially varying initial hydraulic parameter by a scale factor, estimated a posteriori through inversion. This allows preserving heterogeneity without introducing a large number of adjustable parameters. We use each approach to simulate a 95 day infiltration experiment in unsaturated layered sediments at a semiarid site near Phoenix, Arizona, over an area of 50 × 50 m2 down to a depth of 14.5 m. Results show that both clustering approaches improve simulated moisture contents considerably in comparison to those based solely on PTF estimates. Our calibrated models are validated against data from a subsequent 295 day infiltration experiment at the site.",
keywords = "heterogeneity, hydraulic properties, inversion, pedotransfer, soil texture, vadose zone",
author = "Yonggen Zhang and Marcel Schaap and Alberto Guadagnini and Neuman, {Shlomo P}",
year = "2016",
month = "10",
day = "1",
doi = "10.1002/2016WR019016",
language = "English (US)",
volume = "52",
pages = "7631--7644",
journal = "Water Resources Research",
issn = "0043-1397",
publisher = "American Geophysical Union",
number = "10",

}

TY - JOUR

T1 - Inverse modeling of unsaturated flow using clusters of soil texture and pedotransfer functions

AU - Zhang, Yonggen

AU - Schaap, Marcel

AU - Guadagnini, Alberto

AU - Neuman, Shlomo P

PY - 2016/10/1

Y1 - 2016/10/1

N2 - Characterization of heterogeneous soil hydraulic parameters of deep vadose zones is often difficult and expensive, making it necessary to rely on other sources of information. Pedotransfer functions (PTFs) based on soil texture data constitute a simple alternative to inverse hydraulic parameter estimation, but their accuracy is often modest. Inverse modeling entails a compromise between detailed description of subsurface heterogeneity and the need to restrict the number of parameters. We propose two methods of parameterizing vadose zone hydraulic properties using a combination of k-means clustering of kriged soil texture data, PTFs, and model inversion. One approach entails homogeneous and the other heterogeneous clusters. Clusters may include subdomains of the computational grid that need not be contiguous in space. The first approach homogenizes within-cluster variability into initial hydraulic parameter estimates that are subsequently optimized by inversion. The second approach maintains heterogeneity through multiplication of each spatially varying initial hydraulic parameter by a scale factor, estimated a posteriori through inversion. This allows preserving heterogeneity without introducing a large number of adjustable parameters. We use each approach to simulate a 95 day infiltration experiment in unsaturated layered sediments at a semiarid site near Phoenix, Arizona, over an area of 50 × 50 m2 down to a depth of 14.5 m. Results show that both clustering approaches improve simulated moisture contents considerably in comparison to those based solely on PTF estimates. Our calibrated models are validated against data from a subsequent 295 day infiltration experiment at the site.

AB - Characterization of heterogeneous soil hydraulic parameters of deep vadose zones is often difficult and expensive, making it necessary to rely on other sources of information. Pedotransfer functions (PTFs) based on soil texture data constitute a simple alternative to inverse hydraulic parameter estimation, but their accuracy is often modest. Inverse modeling entails a compromise between detailed description of subsurface heterogeneity and the need to restrict the number of parameters. We propose two methods of parameterizing vadose zone hydraulic properties using a combination of k-means clustering of kriged soil texture data, PTFs, and model inversion. One approach entails homogeneous and the other heterogeneous clusters. Clusters may include subdomains of the computational grid that need not be contiguous in space. The first approach homogenizes within-cluster variability into initial hydraulic parameter estimates that are subsequently optimized by inversion. The second approach maintains heterogeneity through multiplication of each spatially varying initial hydraulic parameter by a scale factor, estimated a posteriori through inversion. This allows preserving heterogeneity without introducing a large number of adjustable parameters. We use each approach to simulate a 95 day infiltration experiment in unsaturated layered sediments at a semiarid site near Phoenix, Arizona, over an area of 50 × 50 m2 down to a depth of 14.5 m. Results show that both clustering approaches improve simulated moisture contents considerably in comparison to those based solely on PTF estimates. Our calibrated models are validated against data from a subsequent 295 day infiltration experiment at the site.

KW - heterogeneity

KW - hydraulic properties

KW - inversion

KW - pedotransfer

KW - soil texture

KW - vadose zone

UR - http://www.scopus.com/inward/record.url?scp=84992443500&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=84992443500&partnerID=8YFLogxK

U2 - 10.1002/2016WR019016

DO - 10.1002/2016WR019016

M3 - Article

AN - SCOPUS:84992443500

VL - 52

SP - 7631

EP - 7644

JO - Water Resources Research

JF - Water Resources Research

SN - 0043-1397

IS - 10

ER -