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Methods of Soil Resampling to Monitor Changes in the Chemical Concentrations of Forest Soils

Methods of Soil Resampling to Monitor Changes in the Chemical Concentrations of Forest Soils

Published on: November 25, 2016

Using advanced surface complexation models for modelling soil chemistry under forests: Solling forest, Germany.

Luc T C Bonten¹, Jan E Groenenberg, Henning Meesenburg

¹Alterra-Wageningen UR, Soil Science Centre, PO Box 47, 6700 AA Wageningen, The Netherlands. luc.bonten@wur.nl

Environmental Pollution (Barking, Essex : 1987)

|May 31, 2011

Summary

This study successfully integrated surface complexation models (SCMs) into the SMARTml dynamic soil chemistry model. The enhanced model accurately predicts major and trace element sorption in forest soils without site-specific calibration.

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Published on: February 15, 2019

Area of Science:

Environmental Science
Soil Chemistry
Geochemistry

Background:

Dynamic soil chemistry models are crucial for understanding atmospheric deposition impacts.
Current models often require site-specific calibration for sorption parameters, limiting their applicability.
Surface complexation models (SCMs) show promise for predicting ion sorption in static systems with generic parameters.

Purpose of the Study:

To integrate SCMs into the dynamic soil chemistry model SMARTml.
To test the model's ability to predict soil and soil solution chemistry, including trace metals, at a field site.
To evaluate the performance of generic SCM parameter sets without site-specific calibration.

Main Methods:

Incorporation of an assemblage of SCMs into the SMARTml model.
Application of the integrated model to a spruce forest site in Solling, Germany.
Utilized generic parameter datasets for SCMs, avoiding site-specific calibration.

Main Results:

The integrated model accurately predicted major element concentrations in soil and soil solution.
Sorption of most trace metals was also well-predicted using the SCM framework.
Successful application demonstrates the potential of generic parameters for dynamic soil modeling.

Conclusions:

Integrating SCMs into dynamic models like SMARTml enhances predictive capabilities for soil chemistry.
Generic SCM parameter sets can be effectively used in dynamic models, reducing the need for extensive site-specific calibration.
This approach improves the spatial and temporal extrapolation of soil chemistry model predictions.