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BASFOR

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BASFOR

David Cameron and Marcel van Oijen
Centre for Ecology and Hydrology (CEH-Edinburgh)
Bush Estate, Penicuik EH26 0QB, United Kingdom
dcam@ceh.ac.uk ; mvano@ceh.ac.uk


Website
https://github.com/MarcelVanOijen/BASFOR/tree/master/


Description
BASFOR is a BASic FORest model, with simple representation of forest biogeochemistry. Despite this, the aim is wide model applicability by simulating the impacts of a range of environmental drivers.

BASFOR simulates soil-plant-atmosphere processes of deciduous and coniferous forest stands. Interactions with the atmospheric and soil environment are simulated in some detail, as are the impacts of management: thinning and pruning. Three biogeochemical cycles are simulated: carbon, nitrogen and water. BASFOR is a one-dimensional model, so no horizontal heterogeneity of the forest is captured. BASFOR does not simulate wood quality or pests and diseases.

BASFOR has 17 state variables. Nine of those variables quantify the state of the trees and eight variables represent the soil. The tree variables are tree density, carbon pools (stems, branches, leaves, roots, reserves), foliar nitrogen and phenological state (accumulated chill days, thermal time). The soil variables are carbon and nitrogen in litter and two classes of organic matter, mineral N, water.

Inputs to the model include atmospheric CO2 concentration and time series of radiation, air temperature, precipitation, wind speed, humidity. Also required is a calendar indicating days at which the stand is thinned or pruned.

Outputs from the model include, in the default set-up, 36 different output variables: the 17 state variables plus biogeochemical fluxes (including those of greenhouse gases to and from trees and soil) and forest productivity variables (stem diameter, volume, height). This selection of output variables can be altered by the model user.


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Scientific articles

Van Oijen, M., Balkovič, J., Beer, C., Cameron, D., Ciais, P., Cramer, W., Kato, T., Kuhnert, M., Martin, R., Myneni, R., Rammig, A., Rolinski, S., Soussana, J.-F., Thonicke, K., Van der Velde, M. & Xu, L. (2014). Impact of droughts on the C-cycle in European vegetation: A probabilistic risk analysis using six vegetation models. Biogeosciences 11: 6357-6375. http://www.biogeosciences.net/11/6357/2014/bg-11-6357-2014.html . doi:10.5194/bg-11-6357-2014.
BASFOR is used together with five other vegetation models in a probabilistic risk analysis.

Van Oijen, M., Beer, C., Cramer, W., Rammig, A., Reichstein, M., Rolinski, S., Seneviratne, S. & Soussana, J.-F. (2013). A novel probabilistic risk analysis to determine the vulnerability of ecosystems to extreme climatic events.  Environmental Research Letters 8: 015032. http://iopscience.iop.org/1748-9326/8/1/015032
This paper introduces a new method for risk analysis; BASFOR is used in an example.

Cameron, D.R., Van Oijen, M., Werner, C., Butterbach-Bahl, K., Grote, R., Haas, E., Heuvelink, G., Kiese, R., Kros, J., Kuhnert, M., Leip, A., Reinds, G.J., Reuter, H.I., Schelhaas, M.J., de Vries, W. & Yeluripati, J. (2013). Environmental change impacts on the C- and N-cycle of European forests: a model comparison study. Biogeosciences 10: 1751-1773. http://www.biogeosciences.net/10/1751/2013/bg-10-1751-2013.html
Outputs are compared from four models: BASFOR, DailyDayCent, INTEGRATOR and Landscape-DNDC.

Van Oijen, M., Reyer, C., Bohn, F.J., Cameron, D.R., Deckmyn, G., Flechsig, M., Härkönen, S., Hartig, F., Huth, A., Kiviste, A., Lasch, P., Mäkelä, A., Mette, T., Minunno, F. & Rammer, W. (2013). Bayesian calibration, comparison and averaging of six forest models, using data from Scots pine stands across Europe. Forest Ecology and Management 289: 255-268.
Six forest models, including BASFOR, are calibrated (in a Bayesian way, with model-specific priors) using forest inventory data from four countries (Belgium, Austria, Estonia, Finland), and tested against independent data from permanent sample plots. Results from country-specific calibrations are contrasted to those from calibrations using all data together. In a final analysis, posterior model probabilities are used in Bayesian model averaging which is shown to lead to robust predictions.

Van Oijen, M., Cameron, D.R., Butterbach-Bahl, K., Farahbakhshazad, N., Jansson, P.-E., Kiese, R., Rahn, K.-H., Werner, C., Yeluripati, J.B. (2011). A Bayesian framework for model calibration, comparison and analysis: application to four models for the biogeochemistry of a Norway spruce forest. Agriculture and Forest Meteorology 151: 1609-1621.
This paper shows how Bayesian methods for model calibration and comparison can be applied to process-based forest models, and argues that the methods need to be complemented with detailed analysis of prior and posterior model-data mismatch. The models used are BASFOR, COUP, DAYCENT and MoBiLE-DNDC, and the data are on fluxes of NO, N2O and CO2 plus soil water content, all taken from the Höglwald site in southern Germany.

Van Oijen, M. & Thomson, A. (2010). Towards Bayesian uncertainty quantification for forestry models used in the United Kingdom Greenhouse Gas Inventory for land use, land use change, and forestry. Climatic Change 103: 55-67.
This paper shows how BASFOR is calibrated against data from two forest sites in the UK and then applied over a country-wide grid, with uncertainty quantified by sampling from the posterior distribution for model parameters.

Van Oijen, M., Dauzat, J., Harmand, J.-M., Lawson, G. & Vaast, P. (2010). Coffee agroforestry systems in Central America: II. Development of a simple process-based model and preliminary results. Agroforestry Systems 80: 361-378.
This paper introduces a dynamic model for coffee agroforestry systems, CAF2007. The tree component is BASFOR.

Van Oijen, M., Rougier, J. & Smith, R. (2005). Bayesian calibration of process-based forest models: bridging the gap between models and data. Tree Physiology 25: 915-927.
This paper introduces BASFOR and includes a list of model parameters. It explains how the joint probability distribution for the parameters of such process-based models can be calibrated in a Bayesian way using a Markov Chain Monte Carlo (MCMC) algorithm.

 

Technical information
Operating system(s): The model is written in FORTRAN and run from R, so is essentially platform-independent. It comes pre-compiled with Windows-DLLs.

Licence: No license, the model is freely available from GitHub:
https://github.com/MarcelVanOijen/BASFOR/tree/master/

Output(s): See Description of the model above.

Export format(s): The model is run from R, and model results can be exported in a wide variety of formats including txt-files.

Other information: User manual available with the model.

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