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Greene, R., Timms, W., Rengasamy, P., Arshad, M., & Cresswell, R. (2016). Soil and Aquifer Salinization: Toward an Integrated Approach for Salinity Management of Groundwater. In A. J. Jakeman, O. Barreteau, R. J. Hunt, J. - D. Rinaudo, & A. Ross (Eds.), Integrated Groundwater Management: Concepts, Approaches and Challenges (pp. 377–412). Cham: Springer International Publishing.
Abstract: Degradation of the quality of groundwater due to salinization processes is one of the key issues limiting the global dependence on groundwater in aquifers. As the salinization of shallow aquifers is closely related to root-zone salinization, the two must be considered together. This chapter initially describes the physical and chemical processes causing salinization of the root-zone and shallow aquifers, highlighting the dynamics of these processes and how they can be influenced by irrigation and drainage practices, thus illustrating the connectivity between soil and groundwater salinization. The processes leading to aquifer salinization in both inland and coastal areas are discussed. The roles of extractive resource industries, such as mining and coal bed methane operations, in causing aquifer salinization are also outlined. Hydrogeochemical changes occurring during salinization of aquifers are examined with the aid of Piper and Mixing Diagrams. The chapter then illustrates the extent of the problem of groundwater salinization as influenced by management and policy using two case studies. The first is representative of a developing country and explores management of salt-affected soils in the Indus Valley, Pakistan, while the second looks at a developed country, and illustrates how through monitoring we can deduce causes of shallow aquifer salinity in the Namoi Catchment of NSW, Australia. Finally, there is a section on integration and conclusions where we illustrate how management to mitigate salinization needs to be integrated with policy to diminish the threat to productivity that occurs with groundwater degradation.
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Seyedmohammadi*, J., Esmaeelnejad, L., & Shabanpour, M. (2016). Spatial variation modeling of groundwater electrical conductivity using geostatistics and GIS. Model. Earth Syst. Environ., 2, 169.
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Satrio, S., Prasetio, R., Hadian, M., Syafri, I. (2016). Stable Isotopes and Hydrochemistry Approach for Determining the Salinization Pattern of Shallow Groundwater in Alluvium Deposit Semarang, Central Java. Indonesian Journal on Geoscience, 4.
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Llopis-Albert, C., Merigó, J. M., & Xu, Y. (2016). A coupled stochastic inverse/sharp interface seawater intrusion approach for coastal aquifers under groundwater parameter uncertainty. Journal of Hydrology, 540, 774–783.
Abstract: 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.
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