Sensitivity, uncertainty, and reliability in groundwater modelling

Research output: Chapter in Book/Report/Conference proceedingChapter

Abstract

Groundwater numerical models are powerful and efficient tools for groundwater management, protection, and remediation. However, groundwater modelling, which requires a huge amount of data, is not an easy. To build a predictive model, and to get reliable results, input data should be accurate and representative of the real situation in the field. Because of the randomness inherent in nature and heterogeneity of aquifers, it is very difficult to accurately determine the hydrological properties of the aquifers. Classical groundwater models usually handle input parameters in a deterministic way, without considering any variability, uncertainty, or randomness in these parameters. Thus, the results of deterministic modelling are questionable. To account for uncertainty in physical, chemical, and geological data, stochastic modelling is usually used. Many approaches have been developed and used including sampling approaches, reliability methods, and Monte Carlo simulation. In this chapter, different approaches of stochastic and probabilistic modelling are introduced and discussed.

Original languageEnglish
Title of host publicationWater Resources Research Progress
PublisherNova Science Publishers, Inc.
Pages327-350
Number of pages24
ISBN (Print)160021973X, 9781600219733
Publication statusPublished - 2008
Externally publishedYes

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ASJC Scopus subject areas

  • Environmental Science(all)

Cite this

Baalousha, H. (2008). Sensitivity, uncertainty, and reliability in groundwater modelling. In Water Resources Research Progress (pp. 327-350). Nova Science Publishers, Inc..