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SPECIAL SECTION: ZNS'03 VADOSE ZONE RESEARCH

Using TDR and Inverse Modeling to Characterize Solute Transport in a Layered Agricultural Volcanic Soil

^a Instituto Canario de Investigaciones Agrarias (ICIA), Apdo. 60, 38200, La Laguna, Spain
^b Agricultural and Biological Engineering Dep., University of Florida, 101 Frazier Rogers Hall, P.O. Box 110570, Gainesville, FL 32611-0570
^c Dep. of Environmental Sciences and Land Use Planning, Unité Génie Rural, Université Catholique de Louvain, Croix du Sud, 2, BP2, B-1348 Louvain-la-Neuve, Belgium
^* Corresponding author (aritter{at}icia.es)

Received 21 June 2004.

Volcanic soils exhibit particular physical-chemical properties (i.e., strong and stable natural aggregation and high content of variable-charge minerals) that may influence solute transport. To determine if such techniques like TDR and inverse modeling are useful for analyzing solute transport in volcanic soils, we studied the governing transport processes by means of a miscible displacement experiment of Br^– in a large undisturbed soil monolith. Bromide resident concentrations at several depths were monitored successfully with TDR technology, while parameters for the convective–dispersive (CDE) and mobile–immobile (MIM) transport models were estimated by inverse modeling. For the relatively high soil moisture conditions, typical of high frequency-irrigation systems that we considered, Br^– was found to move slowly by convection–dispersion. Simulations with the CDE and MIM transport models yielded very similar results. Although Br^– is generally assumed to behave as a tracer, we found that this anion in our experiment was subject to adsorption at the bottom part of the monolith. This may be explained by the variable-charge nature of the minerals (Fe and Al oxihydroxides) present in this volcanic soil, which exhibited anion exchange when the pH of the soil solution decreased below the zero point of charge.

Abbreviations: BTC, breakthrough curves • CDE, convective–dispersive equation • EC, electrical conductivity • GMCS, global optimization algorithm • MIM, mobile–immobile model • NMS, Nelder–Mead–Simplex • nMSE, normalized mean squared error • TDR, time domain reflectometry

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