Modelling changes in forest soil chemistry at 16 Swedish coniferous forest sites following deposition reduction

Belyazid, Salim; Westling, Olle; Sverdrup, Harald

doi:10.1016/j.envpol.2006.01.018

Cited by 60 publications

(55 citation statements)

References 20 publications

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“…Atmospheric deposition is known to have a severe impact on forest ecosystem functioning by influencing soil biogeochemistry and nutrients balance, and consequently tree growth and overall forest health and biodiversity (Probst et al, 1995;Belyazid et al, 2006;Jonard et al, 2012).…”

Section: Introductionmentioning

confidence: 99%

Combined effect of atmospheric nitrogen deposition and climate change on temperate forest soil biogeochemistry: A modeling approach

Gaudio

Belyazid²,

Gendre

et al. 2015

Ecological Modelling

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A B S T R A C TAtmospheric N deposition is known to severely impact forest ecosystem functioning by influencing soil biogeochemistry and nutrient balance, and consequently tree growth and overall forest health and biodiversity. Moreover, because climate greatly influences soil processes, climate change and atmospheric N deposition must both be taken into account when analysing the evolution of forest ecosystem status over time. Dynamic biogeochemical models have been developed to test different climate and atmospheric N deposition scenarios and their potential interactions in the long term. In this study, the ForSAFE model was used to predict the combined effect of atmospheric N deposition and climate change on two temperate forest ecosystems in France dominated by oak and spruce, and more precisely on forest soil biogeochemistry, from today to 2100. After a calibration step and following a careful statistical validation process, two atmospheric N deposition scenarios were tested: the current legislation in Europe (CLE) and the maximum feasible reduction (MFR) scenarios. They were combined with three climate scenarios: current climate scenario, worst-case climate scenario (A2) and best-case climate scenario (B1). The changes in base saturation and inorganic N concentration in the soil solution were compared across all scenario combinations, with the aim of forecasting the state of acidification, eutrophication and forest ecosystem recovery up to the year 2100.Simulations highlighted that climate had a stronger impact on soil base saturation, whereas atmospheric deposition had a comparative effect or a higher effect than climate on N concentration in the soil solution. Although deposition remains the main factor determining the evolution of N concentration in soil solution, increased temperature had a significant effect. Results also highlighted the necessity of considering the joint effect of both climate and atmospheric N deposition on soil biogeochemistry.

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Section: Introductionmentioning

confidence: 99%

Combined effect of atmospheric nitrogen deposition and climate change on temperate forest soil biogeochemistry: A modeling approach

Gaudio

Belyazid²,

Gendre

et al. 2015

Ecological Modelling

Self Cite

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“…Until recently, calibration of most dynamic acidification models was performed by fitting the simulation results to (a series of) observations by 'trial and error' procedures: the model is re-run with different settings until the observation(s) are reproduced well. Sometimes a set of parameters is calibrated using various soil (solution) concentrations simultaneously , whereas in other studies only one parameter, such as the initial base saturation, is calibrated (Aherne et al, 1998;Belyazid et al, 2006). A more advanced model calibration was performed by Kros et al (2002) who calibrated the SMART2 acidification model to soil observation data scaled up to 5×5 km blocks using the Gauss-Levenberg-Marquardt algorithm.…”

Section: Introductionmentioning

confidence: 99%

Bayesian calibration of the VSD soil acidification model using European forest monitoring data

Reinds

Oijen²,

Heuvelink

et al. 2008

Geoderma

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Over the past years, Bayesian calibration methods have been successfully applied to calibrate ecosystem models. Bayesian methods combine prior probability distributions of model parameters, based on assumptions about their magnitude and uncertainty, with estimates of the likelihood of the simulation results by comparison with observed values. Bayesian methods also quantify the uncertainty in the updated posterior parameters, which can be used to perform an analysis of model output uncertainty. In this paper, we apply Bayesian techniques to calibrate the VSD soil acidification model for 182 intensively monitored forest sites in Europe. Prior distributions for the model parameters were based on available literature. Since the available literature shows a strong dependence of some VSD parameters on, for example, soil texture, prior distributions were allowed to depend on soil group (i.e. group of soils with similar texture or C/N ratio). The likelihood was computed by comparing modelled soil solution concentrations with observed concentrations for the period 1996-2001. Markov Chain Monte Carlo (MCMC) was used to sample the posterior parameter space. Two calibration approaches were applied. In the singe-site calibration, the plots are calibrated separately to obtain plot-specific posterior distributions. In the multi-site approach priors are assumed constant in space for each soil group, and all plots are calibrated simultaneously yielding one posterior probability distribution for each soil group.Results from the single-site calibrations show that the model performs much better after calibration compared to a run with standard input parameters. Posterior distributions for HAl equilibrium constants narrow down, thus decreasing parameter uncertainty. For base cation weathering of coarse texture soils the posterior distribution is shifted to larger values, indicating an initial underestimation of the weathering rate for these soils. Results for the 2 parameters related to nitrogen modelling show that the nitrogen processes model formulations in VSD may have to be reconsidered as no evidence is found for a relationship between nitrogen immobilization and the C/N ratio of the soil, as assumed in VSD. The multi-site calibration also strongly decreases model error for most model output parameters, but model error is larger than the median model error from the singe-site calibration.Because the large number of plots calibrated at the same time provides very many observations, the Markov chain converges to a very narrow parameter space, leaving little room for posterior parameter uncertainty. For an uncertainty analysis with VSD on the European scale, this study provides promising results, but more work is needed to investigate how the results can be used on a European scale by looking at regional patterns in calibrated parameters from the site calibration or by calibrating for regions instead of all of Europe.

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“…Steady-state mass balance modeling is based on estimating the net loss or accumulation of N inputs and outputs over the long term under the assumption that the ecosystem is at steady state with respect to N inputs. Dynamic models also use a mass balance approach, but consider timedependent processes and require detailed data sets for parameterization and testing (Belyazid et al 2006, de Vries et al 2007.…”

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confidence: 99%

Effects of nitrogen deposition and empirical nitrogen critical loads for ecoregions of the United States

Pardo

Fenn

Goodale

et al. 2011

Ecological Applications

398

342

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Abstract. Human activity in the last century has led to a significant increase in nitrogen (N) emissions and atmospheric deposition. This N deposition has reached a level that has caused or is likely to cause alterations to the structure and function of many ecosystems across the United States. One approach for quantifying the deposition of pollution that would be harmful to ecosystems is the determination of critical loads. A critical load is defined as the input of a pollutant below which no detrimental ecological effects occur over the long-term according to present knowledge.The objectives of this project were to synthesize current research relating atmospheric N deposition to effects on terrestrial and freshwater ecosystems in the United States, and to estimate associated empirical N critical loads. The receptors considered included freshwater diatoms, mycorrhizal fungi, lichens, bryophytes, herbaceous plants, shrubs, and trees. Ecosystem impacts included: (1) biogeochemical responses and (2) individual species, population, and community responses. Biogeochemical responses included increased N mineralization and nitrification (and N availability for plant and microbial uptake), increased gaseous N losses (ammonia volatilization, nitric and nitrous oxide from nitrification and denitrification), and increased N leaching. Individual species, population, and community responses included increased tissue N, physiological and nutrient imbalances, increased growth, altered root : shoot ratios, increased susceptibility to secondary stresses, altered fire regime, shifts in competitive interactions and community composition, changes in species richness and other measures of biodiversity, and increases in invasive species.The range of critical loads for nutrient N reported for U.S. ecoregions, inland surface waters, and freshwater wetlands is 1-39 kg NÁha , spanning the range of N deposition observed over most of the country. The empirical critical loads for N tend to increase in the following sequence for different life forms: diatoms, lichens and bryophytes, mycorrhizal fungi, herbaceous plants and shrubs, and trees.The critical load approach is an ecosystem assessment tool with great potential to simplify complex scientific information and communicate effectively with the policy community and the public. This synthesis represents the first comprehensive assessment of empirical critical loads of N for major ecoregions across the United States.

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Modelling changes in forest soil chemistry at 16 Swedish coniferous forest sites following deposition reduction

Cited by 60 publications

References 20 publications

Combined effect of atmospheric nitrogen deposition and climate change on temperate forest soil biogeochemistry: A modeling approach

Combined effect of atmospheric nitrogen deposition and climate change on temperate forest soil biogeochemistry: A modeling approach

Bayesian calibration of the VSD soil acidification model using European forest monitoring data

Effects of nitrogen deposition and empirical nitrogen critical loads for ecoregions of the United States

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