Skip to main content
Log in

A stochastic approach to nonlinear unconfined flow subject to multiple random fields

  • Original Paper
  • Published:
Stochastic Environmental Research and Risk Assessment Aims and scope Submit manuscript

Abstract

In this study, the KLME approach, a moment-equation approach based on the Karhunen–Loeve decomposition developed by Zhang and Lu (Comput Phys 194(2):773–794, 2004), is applied to unconfined flow with multiple random inputs. The log-transformed hydraulic conductivity F, the recharge R, the Dirichlet boundary condition H, and the Neumann boundary condition Q are assumed to be Gaussian random fields with known means and covariance functions. The F, R, H and Q are first decomposed into finite series in terms of Gaussian standard random variables by the Karhunen–Loeve expansion. The hydraulic head h is then represented by a perturbation expansion, and each term in the perturbation expansion is written as the products of unknown coefficients and Gaussian standard random variables obtained from the Karhunen–Loeve expansions. A series of deterministic partial differential equations are derived from the stochastic partial differential equations. The resulting equations for uncorrelated and perfectly correlated cases are developed. The equations can be solved sequentially from low to high order by the finite element method. We examine the accuracy of the KLME approach for the groundwater flow subject to uncorrelated or perfectly correlated random inputs and study the capability of the KLME method for predicting the head variance in the presence of various spatially variable parameters. It is shown that the proposed numerical model gives accurate results at a much smaller computational cost than the Monte Carlo simulation.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8
Fig. 9
Fig. 10
Fig. 11
Fig. 12
Fig. 13
Fig. 14
Fig. 15

Similar content being viewed by others

References

  • Bear J (1972) Dynamics of fluids in porous media. Dover, Mineola

    Google Scholar 

  • Chen M, Zhang D, Keller A, Lu Z (2005) A stochastic analysis of steady-state two-phase flow in heterogeneous media. Water Resour Res 41:W1006. doi:10.1029/2004WR003412

    Article  Google Scholar 

  • Chen M, Zhang D, Keller AA, Lu Z, Zyvoloski GA (2006) A stochastic analysis of transient two phase flow in heterogeneous media. Water Resour Res 42:W03425. doi:10.1029/2005WR004257

    Article  Google Scholar 

  • Chen M, Keller AA, Lu Z (2007) Stochastic Analysis of three-phase flow in heterogeneous porous media. Stoch Environ Res Risk Assess. doi:10.1007/s00477-007-0198-y

  • Courmontagne P (1999) A new formulation for the Karhunen–Loeve expansion. Signal Process 79:235–249

    Article  Google Scholar 

  • Dagan G (1989) Flow and transport in porous formations. Springer, New York

    Google Scholar 

  • Gelhar LW (1993) Stochastic subsurface hydrology. Prentice-Hall, Englewood Cliffs

    Google Scholar 

  • Gomez-Hernandez JJ, Gorelick SM (1989) Effective groundwater model parameter values: influence of spatial variability of hydraulic conductivity, leakance and recharge. Water Resour Res 25(3):405–419

    Article  Google Scholar 

  • Graham WD, Tankersley CD (1994) Optimal estimation of spatially variable recharge and transmissivity fields under steady-state groundwater flow, part 1: theory. J Hydrol 157:247–266

    Article  Google Scholar 

  • Li LY, Graham W (1998) Stochastic analysis of solute transport in heterogeneous aquifers subject to spatially random recharge. J Hydrol 206:16–38

    Article  CAS  Google Scholar 

  • Liu G, Zhang D, Lu Z (2006) Stochastic uncertainty analysis for unconfined flow systems. Water Resour Res 42:9412. doi:10.1029/2005WR004766

    Article  Google Scholar 

  • Loeve M (1977) Probability theory, 4th edn. Springer, Berlin

    Google Scholar 

  • Lu Z, Zhang D (2004) Conditional simulations of flow in randomly heterogeneous porous media using a KL-based moment-equation approach. Adv Water Resour 27:859–874

    Article  Google Scholar 

  • Lu Z, Zhang D (2007) Stochastic simulations for flow in nonstationary randomly heterogeneous porous media using a KL-based moment-equation approach. SIAM Multiscale Model Simul 6(1):228–245

    Article  Google Scholar 

  • Phoon KK, Huang SP, Quek ST (2002) Implementation of Karhunen–Loeve expansion for simulation using a wavelet-Galerkin Scheme. Probabilistic Eng Mech 17:293–303

    Article  Google Scholar 

  • Simunek J, Vogel T, van Genuchten M (1992) The SWMS_2D code for simulating water flow and solute transport in two-dimensional variably saturated media. Version 1.1, Research Report No.126.US Salinity Lab

  • Xiu D, Karniadakis G (2002) Modeling uncertainty in steady state diffusion problems via generalized polynomial chaos. Comput Methods Appl Mech Eng 191:4927–4948

    Article  Google Scholar 

  • Yang J, Zhang D, Lu Z (2003) Stochastic analysis of saturated–unsaturated flow in heterogeneous media by combing Karhunen–Loeve expansion and perturbation method. J Hydrol 294(1–3):18–38

    Google Scholar 

  • Yeh TC, Gelhar LW, Gutjahr AL (1985) Stochastic analysis of unsaturated flow in heterogeneous soils, 2, statistically anisotropic media with variable a. Water Resour Res 21(4):457–464

    Article  Google Scholar 

  • Zhang D (2002) Stochastic methods for flow in porous media: copying with uncertainties. Academic Press, San Diego

    Google Scholar 

  • Zhang D, Lu Z (2004) An efficient, high-order perturbation approach for flow in random porous media via Karhunen–Loeve and polynomial expansions. J Comput Phys 194:773–794

    Article  Google Scholar 

Download references

Acknowledgments

This work is partially supported by Natural Science Foundation of China (NSFC) under grants 40672164, 0620631, and 50688901. And the first author would like to acknowledge the support by China Scholarship Council through grant 2007101645.

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Liangsheng Shi.

Appendix: The derivation of second-order equations

Appendix: The derivation of second-order equations

For notation brevity, we set \( \left\{ {\psi_{1} ,\psi_{2} ,\psi_{3} ,\psi_{4} } \right\} = \left\{ {\xi ,\eta ,\varphi ,\varsigma } \right\} \) in the following derivation. By collecting the terms with s=2 \( \left( {p,q,r,s \ne 1} \right), \) we have

$$ S\frac{\partial }{\partial t}\left( {\sum\limits_{k,l = 1}^{4} {\sum\limits_{i,j = 1}^{\infty } {h_{ij}^{{\psi_{k} \psi_{l} }} \left( {\psi_{k} } \right)_{i} \left( {\psi_{l} } \right)_{j} } } } \right) = \nabla \left[ {K_{G} h^{\left( 0 \right)} \nabla \left( {\sum\limits_{k,l = 1}^{4} {\sum\limits_{i,j = 1}^{\infty } {h_{ij}^{{\psi_{k} \psi_{l} }} \left( {\psi_{k} } \right)_{i} \left( {\psi_{l} } \right)_{j} } } } \right)} \right] + g_{ij} $$
$$ \begin{aligned} g_{ij} = & \nabla \left[ {K_{G} \left( {\sum\limits_{k,l = 1;k \le l}^{4} {\sum\limits_{i,j = 1}^{\infty } {h_{ij}^{{\psi_{k} \psi_{l} }} \left( {\psi_{k} } \right)_{i} \left( {\psi_{l} } \right)_{j} } } } \right)\nabla h^{\left( 0 \right)} } \right] + \nabla \left[ {K_{G} h^{\left( 0 \right)} \left( {\sum\limits_{k = 1}^{4} {\sum\limits_{i,j = 1}^{\infty } {k_{i} \xi_{i} \left( {\psi_{k} } \right)_{j} \nabla h_{j}^{{\psi_{k} }} } } } \right)} \right] \\ & + \nabla \left[ {K_{G} \left( {\sum\limits_{k = 1}^{4} {\sum\limits_{i,j = 1}^{\infty } {k_{i} \xi_{i} h_{j}^{{\psi_{k} }} \left( {\psi_{k} } \right)_{j} } } } \right)\nabla h^{\left( 0 \right)} } \right] + \nabla \left[ {K_{G} \left( {\sum\limits_{k,l = 1}^{4} {\sum\limits_{i,j = 1}^{\infty } {\left( {h_{i}^{{\psi_{k} }} \nabla h_{j}^{{\psi_{l} }} } \right)\left( {\psi_{k} } \right)_{i} \left( {\psi_{k} } \right)_{j} } } } \right)} \right] \\ & + \nabla \left[ {K_{G} \frac{1}{2}\left( {\sum\limits_{i,j = 1}^{\infty } {k_{i} k_{j} \xi_{i} \xi_{j} } } \right)h^{\left( 0 \right)} \nabla h^{\left( 0 \right)} } \right] \\ \end{aligned} $$
(27)

Rearranging (27) as

$$ \sum\limits_{k,l = 1;k \le l}^{4} {\sum\limits_{i,j = 1}^{\infty } {L_{ij}^{{\psi_{k} \psi_{l} }} \left( {h_{ij}^{{\psi_{k} \psi_{l} }} } \right)\left( {\psi_{k} } \right)_{i} \left( {\psi_{l} } \right)_{j} } } = 0 $$
(28)

where

$$ \begin{gathered} L_{ij}^{{\psi_{k} \psi_{k} }} \left( {h^{{\psi_{k} \psi_{k} }} } \right) = S\frac{{\partial h_{ij}^{{\psi_{k} \psi_{k} }} }}{\partial t} - \nabla \left[ {K_{G} h^{\left( 0 \right)} \nabla h_{ij}^{{\psi_{k} \psi_{k} }} } \right] - \nabla \left[ {K_{G} h_{ij}^{{\psi_{k} \psi_{k} }} \nabla h^{\left( 0 \right)} } \right] - \nabla \left[ {K_{G} h_{i}^{{\psi_{k} }} \nabla h_{j}^{{\psi_{k} }} } \right] - \hfill \\ \delta \left( {k,1} \right)\nabla \left[ {\frac{1}{2}K_{G} k_{i} k_{j} h^{\left( 0 \right)} \nabla h^{\left( 0 \right)} } \right] - \delta \left( {k,1} \right)\nabla \left[ {K_{G} k_{i} h_{j}^{\xi } \nabla h^{\left( 0 \right)} } \right] - \delta \left( {k,1} \right)\nabla \left[ {K_{G} h^{\left( 0 \right)} k_{i} \nabla h_{j}^{\xi } } \right]\quad k \ge 1 \hfill \\ L_{ij}^{{\psi_{k} \psi_{l} }} \left( {h^{{\psi_{k} \psi_{k} }} } \right) = - \nabla \left[ {K_{G} \left( {h_{i}^{{\psi_{k} }} \nabla h_{j}^{{\psi_{l} }} + h_{i}^{{\psi_{l} }} \nabla h_{j}^{{\psi_{k} }} } \right)} \right] - \hfill \\ \delta \left( {k,1} \right)\nabla \left[ {K_{G} k_{i} h_{j}^{{\psi_{l} }} \nabla h^{\left( 0 \right)} } \right] - \delta \left( {k,1} \right)\nabla \left[ {K_{G} h^{\left( 0 \right)} k_{i} \nabla h_{i}^{{\psi_{l} }} } \right]\quad k,l \ge 1;k < 1 \hfill \\ \end{gathered} $$
(29)

By considering the following properties of ξ i and η i , we have

$$ \begin{gathered} \left\langle {\xi_{i} \xi_{j} \xi_{m} \xi_{n} } \right\rangle = \delta_{ij} \delta_{mn} + \delta_{im} \delta_{jn} + \delta_{in} \delta_{jm} \hfill \\ \left\langle {\eta_{i} \eta_{j} \xi_{m} \xi_{n} } \right\rangle = \delta_{ij} \delta_{mn} \hfill \\ \left\langle {\varepsilon_{i} \eta_{j} \xi_{m} \xi_{n} } \right\rangle = 0 \hfill \\ \end{gathered} $$
(30)

After multiplying Eq. 28 with ξ m ξ n and taking expectation of the resulting equations, we have

$$ \begin{gathered} \sum\limits_{i,j = 1}^{\infty } {L_{ij}^{\xi \xi } \left( {h_{ij}^{\xi \xi } } \right)\left( {\delta_{ij} \delta_{mn} + \delta_{im} \delta_{jn} + \delta_{in} \delta_{jn} } \right)} + \sum\limits_{i,j = 1}^{\infty } {L_{ij}^{\eta \eta } \left( {h_{ij}^{\eta \eta } } \right)\delta_{ij} \delta_{mn} } + \sum\limits_{i,j = 1}^{\infty } {L_{ij}^{\varphi \varphi } \left( {h_{ij}^{\varphi \varphi } } \right)\delta_{ij} \delta_{mn} } + \hfill \\ \sum\limits_{i,j = 1}^{\infty } {L_{ij}^{\varsigma \varsigma } \left( {h_{ij}^{\varsigma \varsigma } } \right)\delta_{ij} \delta_{mn} } = 0\quad m,n = 1,2, \ldots \hfill \\ \end{gathered} $$
(31)

Similarly, by multiplying Eq. 31 with η m η n , φ m φ n and ς m ς n , respectively, and taking expectation of the resulting equations, we can obtain the three sets of equations similar to Eq. 31. These equations can be expressed as a uniform representation

$$ \begin{gathered} \sum\limits_{i,j = 1}^{\infty } {L_{ij}^{{\psi_{k} \psi_{k} }} \left( {h_{ij}^{{\psi_{k} \psi_{k} }} } \right)\left( {\delta_{ij} \delta_{mn} + \delta_{im} \delta_{jn} + \delta_{in} \delta_{jn} } \right)} + \hfill \\ \left( {1 - \delta \left( {k,l} \right)} \right)\sum\limits_{l = 1}^{4} {\sum\limits_{i,j = 1}^{\infty } {L_{ij}^{{\psi_{l} \psi_{l} }} \left( {h_{ij}^{{\psi_{l} \psi_{l} }} } \right)\delta_{ij} \delta_{mn} } } = 0\quad m,n = 1,2, \ldots \hfill \\ \end{gathered} $$
(32)

After accumulating Eq. 32 for k = 1, 2, 3 and 4 and because of the symmetry of \( L_{mn}^{\varepsilon \varepsilon } ,L_{mn}^{\eta \eta } ,L_{mn}^{\varphi \varphi } \) and \( L_{mn}^{\varsigma \varsigma } , \) we have

$$ L_{mn}^{\varepsilon \varepsilon } \left( {h_{mn}^{\varepsilon \varepsilon } } \right) + L_{mn}^{\eta \eta } \left( {h_{mn}^{\eta \eta } } \right) + L_{mn}^{\varphi \varphi } \left( {h_{mn}^{\varphi \varphi } } \right) + L_{mn}^{\varsigma \varsigma } \left( {h_{mn}^{\varsigma \varsigma } } \right) = 0,\quad m \ge n \ge 1 $$
(33)

By substituting Eq. 29 into Eq. 33, we obtain the second-order equations

$$ \begin{gathered} S\frac{{\partial h_{ij} }}{\partial t} = \nabla \left[ {K_{G} h^{\left( 0 \right)} \nabla h_{ij} } \right] + g_{ij} \hfill \\ g_{ij} = \nabla \left[ {K_{G} h_{ij} \nabla h^{\left( 0 \right)} } \right] + \sum\limits_{k = 1}^{4} {\left( {\nabla \left[ {K_{G} h_{i}^{{\psi_{k} }} \nabla h_{j}^{{\psi_{k} }} } \right]} \right)} + \nabla \left[ {K_{G} h^{\left( 0 \right)} k_{i} \nabla h_{j}^{\xi } } \right] + \hfill \\ \nabla \left[ {\frac{1}{2}K_{G} k_{i} k_{j} h^{\left( 0 \right)} \nabla h^{\left( 0 \right)} } \right] + \nabla \left[ {K_{G} k_{i} h_{i}^{\xi } \nabla h^{\left( 0 \right)} } \right]\quad i \ge j \ge 1 \hfill \\ \end{gathered} $$
(34)

where \( h_{ij} = h_{ij}^{\xi \xi } + h_{ij}^{\eta \eta } + h_{ij}^{\varphi \varphi } + h_{ij}^{\varsigma \varsigma } . \)

Rights and permissions

Reprints and permissions

About this article

Cite this article

Shi, L., Yang, J. & Zhang, D. A stochastic approach to nonlinear unconfined flow subject to multiple random fields. Stoch Environ Res Risk Assess 23, 823–835 (2009). https://doi.org/10.1007/s00477-008-0261-3

Download citation

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s00477-008-0261-3

Keywords

Navigation