A coupled stochastic inverse/sharp interface seawater intrusion approach for coastal aquifers under groundwater parameter uncertainty
Author
dc.contributor.author
Llopis-Albert, Carlos
Author
dc.contributor.author
Merigó Lindahl, José
Author
dc.contributor.author
Xu, Yejun
Admission date
dc.date.accessioned
2017-01-04T19:20:49Z
Available date
dc.date.available
2017-01-04T19:20:49Z
Publication date
dc.date.issued
2016
Cita de ítem
dc.identifier.citation
Journal of Hydrology 540 (2016) 774–783
es_ES
Identifier
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10.1016/j.jhydrol.2016.06.065
Identifier
dc.identifier.uri
https://repositorio.uchile.cl/handle/2250/142259
Abstract
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This paper presents an alternative approach to deal with seawater intrusion problems, that overcomes some of the limitations of previous works, by coupling the well-known SWI2 package for MODFLOW with a stochastic inverse model named GC method. On the one hand, the SWI2 allows a vertically integrated variable-density groundwater flow and seawater intrusion in coastal multi-aquifer systems, and a reduction in number of required model cells and the elimination of the need to solve the advective-dispersive transport equation, which leads to substantial model run-time savings. On the other hand, the GC method allows dealing with groundwater parameter uncertainty by constraining stochastic simulations to flow and mass transport data (i.e., hydraulic conductivity, freshwater heads, saltwater concentrations and travel times) and also to secondary information obtained from expert judgment or geophysical surveys, thus reducing uncertainty and increasing reliability in meeting the environmental standards. The methodology has been successfully applied to a transient movement of the freshwater-seawater interface in response to changing freshwater inflow in a two-aquifer coastal aquifer system, where an uncertainty assessment has been carried out by means of Monte Carlo simulation techniques. The approach also allows partially overcoming the neglected diffusion and dispersion processes after the conditioning process since the uncertainty is reduced and results are closer to available data. (C) 2016 Elsevier B.V. All rights reserved