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

Abstract

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 Correspondence to: Y. Conrad (yconrad@hydrology-uni-kiel.de) uncertain associated with low modeling efficiencies. Simulated nitrate leaching was compared to more general, sitespecific estimations, indicating a higher leaching during the seepage periods for both simulated grassland systems.

Multi‐year simulation and model calibration of soil moisture and temperature profiles in till soil

“…This indicates whether or not the selection of accepted simulations is sensitive to the parameter. If CV ratio = CV post /CV < 1 then the model is considered sensitive to the parameter, and if CV ratio > 1 the model is considered insensitive (Conrad & Fohrer, ).…”

Calibration of an energy balance model to simulate wintertime soil temperature, soil frost depth, and snow depth for a 14 year period in a highland area of Iran

“…Recently, two uncertainty analysis methods, including Bayesian (Klemedtsson et al, 2008) and GLUE (Lundmark and Jansson, 2008), were added to the model and tested (Conrad and Fohrer, 2009;Wu and Jansson, 2013).…”

Evaluation of denitrification and decomposition from three biogeochemical models using laboratory measurements of N&lt;sub&gt;2&lt;/sub&gt;, N&lt;sub&gt;2&lt;/sub&gt;O and CO&lt;sub&gt;2&lt;/sub&gt;

“…Coup (coupled heat and mass transfer model for soilplant-atmosphere systems) is a complex, adjustable processoriented model that uses a modified approach of PnET-N-DNDC to simulate nitrification and denitrification (Norman et al, 2008). Coup gives users the option to choose between different algorithms, each representing the functionality of a sub-module, with each sub-module addressing a different aspect of the soil-atmosphere-vegetation system (Senapati et al, 2016;He et al, 2016;Norman et al, 2008;Nylinder et al, 2011;Conrad and Fohrer, 2009). This complex modular structure allowed us considerable freedom in adapting the model structure to our experimental setup and the available data (Table S2 in the Supplement).…”