Stochastically optimal groundwater management considering land subsidence

Yin Lung Chang*, Tung Lin Tsai, Jinn Chuang Yang, Yeou-Koung Tung

*Corresponding author for this work

Research output: Contribution to journalArticle

9 Scopus citations

Abstract

This paper presents a stochastic groundwater management model explicitly considering land subsidence. Through the use of response matrix technique and one-dimensional consolidation equation, a deterministic management model is first developed. By Latin hypercube sampling technique, along with numerical subsurface flow simulation, statistical features of unit response coefficients due to random hydrogeologic parameters, including hydraulic conductivity (K) and Lame constants (μ and λ), are quantified. The first-order-variance-estimation method is adopted to analyze the uncertainties of drawdown and land subsidence based on which the concept of chance-constrained programming is applied to transfer the original deterministic management model into its stochastic form. The stochastic management model enables the determination of optimal total pumpage subject to the constraints that drawdown and land subsidence do not exceed the allowable values with a specified reliability. A hypothetical example is utilized to demonstrate the applicability of the stochastic model to five cases in which various levels of parameter uncertainty are considered. The results indicate that joint consideration of drawdown and land subsidence is essential, and the proposed stochastic management model can be generally applied for regional groundwater resources management in conjunction with controlling land subsidence.

Original languageEnglish
Pages (from-to)486-498
Number of pages13
JournalJournal of Water Resources Planning and Management
Volume133
Issue number6
DOIs
StatePublished - 1 Nov 2007

Keywords

  • Ground-water management
  • Optimization
  • Stochastic models
  • Subsidence

Fingerprint Dive into the research topics of 'Stochastically optimal groundwater management considering land subsidence'. Together they form a unique fingerprint.

  • Cite this