Calibration of subsurface batch and reactive-transport models involving complex biogeochemical processes

In this study, the calibration of subsurface batch and reactive-transport models involving complex biogeochemical processes was systematically evaluated. Two hypothetical nitrate biodegradation scenarios were developed and simulated in numerical experiments to evaluate the performance of three calib...

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Bibliographic Details
Published in:Advances in water resources Vol. 31; no. 2; pp. 269 - 286
Main Authors: Matott, L.S, Rabideau, A.J
Format: Journal Article
Language:English
Published: Oxford Elsevier Science 01.02.2008
Subjects:
algorithms
aquifers
biodegradation
biogeochemistry
calibration
chemical reactions
Earth sciences
Earth, ocean, space
Exact sciences and technology
groundwater
groundwater flow
Hydrogeology
hydrologic models
Hydrology. Hydrogeology
ions
mathematical models
minerals
organic compounds
reactive transport
simulation models
subsurface batch and reactive transport modeling
experimental studies
algorithms
models
Parameter estimation
Multi- start nonlinear regression
biodegradation
transport
Reactive transport
nitrates
subsurface
Particle swarm optimization
water resources
optimization
regression
Subsurface modeling
Biogeochemical processes
particles
calibration
uncertainties
performances
ISSN:0309-1708, 1872-9657
Online Access:Get full text
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Summary:In this study, the calibration of subsurface batch and reactive-transport models involving complex biogeochemical processes was systematically evaluated. Two hypothetical nitrate biodegradation scenarios were developed and simulated in numerical experiments to evaluate the performance of three calibration search procedures: a multi-start non-linear regression algorithm (i.e. multi-start Levenberg-Marquardt), a global search heuristic (i.e. particle swarm optimization), and a hybrid algorithm that combines the particle swarm procedure with a regression-based ''polishing'' step. Graphical analysis of the selected calibration problems revealed heterogeneous regions of extreme parameter sensitivity and insensitivity along with abundant numbers of local minima. These characteristics hindered the performance of the multi-start non-linear regression technique, which was generally the least effective of the considered algorithms. In most cases, the global search and hybrid methods were capable of producing improved model fits at comparable computational expense. In other cases, the multi-start and hybrid calibration algorithms yielded comparable fitness values but markedly differing parameter estimates and associated uncertainty measures.
Bibliography:http://dx.doi.org/10.1016/j.advwatres.2007.08.005
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ISSN:0309-1708
1872-9657
DOI:10.1016/j.advwatres.2007.08.005