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A Compartmental–Spatial System Dynamics Approach to Ground Water Modeling
Author(s) -
Roach Jesse,
Tidwell Vince
Publication year - 2009
Publication title -
groundwater
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.84
H-Index - 94
eISSN - 1745-6584
pISSN - 0017-467X
DOI - 10.1111/j.1745-6584.2009.00580.x
Subject(s) - modflow , environmental science , context (archaeology) , evapotranspiration , system dynamics , groundwater , computer science , hydrology (agriculture) , groundwater flow , aquifer , geography , geology , ecology , geotechnical engineering , artificial intelligence , biology , archaeology
High‐resolution, spatially distributed ground water flow models can prove unsuitable for the rapid, interactive analysis that is increasingly demanded to support a participatory decision environment. To address this shortcoming, we extend the idea of multiple cell (Bear 1979) and compartmental (Campana and Simpson 1984) ground water models developed within the context of spatial system dynamics (Ahmad and Simonovic 2004) for rapid scenario analysis. We term this approach compartmental–spatial system dynamics (CSSD). The goal is to balance spatial aggregation necessary to achieve a real‐time integrative and interactive decision environment while maintaining sufficient model complexity to yield a meaningful representation of the regional ground water system. As a test case, a 51‐compartment CSSD model was built and calibrated from a 100,000+ cell MODFLOW (McDonald and Harbaugh 1988) model of the Albuquerque Basin in central New Mexico (McAda and Barroll 2002). Seventy‐seven percent of historical drawdowns predicted by the MODFLOW model were within 1 m of the corresponding CSSD estimates, and in 80% of the historical model run years the CSSD model estimates of river leakage, reservoir leakage, ground water flow to agricultural drains, and riparian evapotranspiration were within 30% of the corresponding estimates from McAda and Barroll (2002), with improved model agreement during the scenario period. Comparisons of model results demonstrate both advantages and limitations of the CCSD model approach.