Application of the Bayesian calibration methodology for the parameter estimation in CoupModel

Abstract. This study provides results for the optimization strategy of highly parameterized models, especially with a high number of unknown input parameters and joint problems in terms of sufficient parameter space. Consequently, the uncertainty in model parameterization and measurements must be considered when highly variable nitrogen losses, e.g. N leaching, are to be predicted. The Bayesian calibration methodology was used to investigate the parameter uncertainty of the process-based CoupModel. Bayesian methods link prior probability distributions of input parameters to likelihood estimates of the simulation results by comparison with measured values. The uncertainty in the updated posterior parameters can be used to conduct an uncertainty analysis of the model output. A number of 24 model variables were optimized during 20 000 simulations to find the "optimum" value for each parameter. The likelihood was computed by comparing simulation results with observed values of 23 output variables including soil water contents, soil temperatures, groundwater level, soil mineral nitrogen, nitrate concentrations below the root zone, denitrification and harvested carbon from grassland plots in Northern Germany for the period 1997–2002. The posterior parameter space was sampled with the Markov Chain Monte Carlo approach to obtain plot-specific posterior parameter distributions for each system. Posterior distributions of the parameters narrowed down in the accepted runs, thus uncertainty decreased. Results from the single-plot optimization showed a plausible reproduction of soil temperatures, soil water contents and water tensions in different soil depths for both systems. The model performed better for these abiotic system properties compared to the results for harvested carbon and soil mineral nitrogen dynamics. The high variability in modeled nitrogen leaching showed that the soil nitrogen conditions are highly uncertain associated with low modeling efficiencies. Simulated nitrate leaching was compared to more general, site-specific estimations, indicating a higher leaching during the seepage periods for both simulated grassland systems.

[1]  Nicola Fohrer,et al.  Modelling of nitrogen leaching under a complex winter wheat and red clover crop rotation in a drained agricultural field , 2009 .

[2]  Gerard B. M. Heuvelink,et al.  Bayesian calibration of the VSD soil acidification model using European forest monitoring data , 2008 .

[3]  Klaus Butterbach-Bahl,et al.  Simulation of NO and N2O emissions from a spruce forest during a freeze/thaw event using an N-flux submodel from the PnET-N-DNDC model integrated to CoupModel , 2008 .

[4]  P. Jansson,et al.  Estimating the Fate of De-icing Salt in a Roadside Environment by Combining Modelling and Field Observations , 2008 .

[5]  David Gustafsson,et al.  Bayesian calibration method used to elucidate carbon turnover in forest on drained organic soil , 2008 .

[6]  A. Lundmark,et al.  Monitoring transport and fate of de-icing salt in the roadside environment: modelling and field measurements , 2008 .

[7]  Mark E. Borsuk,et al.  Approaches to Evaluate Water Quality Model Parameter Uncertainty for Adaptive TMDL Implementation1 , 2007 .

[8]  F. Feichtinger,et al.  Nitrogen leaching losses under crops fertilized with biowaste compost compared with mineral fertilization , 2007 .

[9]  H. Grip,et al.  Modelling the effects of mulching and fallow cropping on water balance in the Chinese Loess Plateau , 2007 .

[10]  K. Mengel,et al.  Nitrogen turnover in bare soil planted subsequently with grass as investigated by electro-ultrafiltration (EUF) , 2007 .

[11]  David Gustafsson,et al.  Modeling Carbon Turnover in Five Terrestrial Ecosystems in the Boreal Zone Using Multiple Criteria of Acceptance , 2006, Ambio.

[12]  S. Sorooshian,et al.  Application of stochastic parameter optimization to the Sacramento Soil Moisture Accounting model , 2006, Journal of Hydrology.

[13]  F. Pappenberger,et al.  Ignorance is bliss: Or seven reasons not to use uncertainty analysis , 2006 .

[14]  Jeffrey G. Arnold,et al.  CUMULATIVE UNCERTAINTY IN MEASURED STREAMFLOW AND WATER QUALITY DATA FOR SMALL WATERSHEDS , 2006 .

[15]  Ron Smith,et al.  Bayesian calibration of process-based forest models: bridging the gap between models and data. , 2005, Tree physiology.

[16]  A. Herrmann,et al.  Performance of grassland under different cutting regimes as affected by sward composition, nitrogen input, soil conditions and weather—a simulation study , 2005 .

[17]  M. Wachendorf,et al.  Performance and environmental effects of forage production on sandy soils. II. Impact of defoliation system and nitrogen input on nitrate leaching losses , 2004 .

[18]  Michael Wachendorf,et al.  Performance and environmental effects of forage production on sandy soils. III. Energy efficiency in forage production from grassland and maize for silage , 2004 .

[19]  Lars R. Bakken,et al.  Nitrogen dynamics of grass as affected by N input regimes, soil texture and climate: lysimeter measurements and simulations , 2003, Nutrient Cycling in Agroecosystems.

[20]  C. Coning,et al.  Report to the Water Research Commission , 2004 .

[21]  I. McTaggart,et al.  Simulating field-scale nitrogen management scenarios involving fertiliser and slurry applications , 2003 .

[22]  Gert Jan Reinds,et al.  Intensive monitoring of forest ecosystems in Europe: 1. Objectives, set-up and evaluation strategy , 2003 .

[23]  Markus Casper,et al.  Die Identifikation hydrologischer Prozesse im Einzugsgebiet des Dürreychbaches (Nordschwarzwald) , 2002 .

[24]  Keith A. Smith,et al.  Soil and environmental analysis : physical methods , 2000 .

[25]  D. Zwart,et al.  Intensive monitoring of forest ecosystems in Europe; technical report 2002 , 1997 .

[26]  Kl Greenwood,et al.  A double-puncture technique for improving the accuracy of puncture tensiometer measurements , 1996 .

[27]  J. Monteith Evaporation and environment. , 1965, Symposia of the Society for Experimental Biology.