Thomas Wutzler

Interoperability among Parallel DEVS Simulators and Models Implemented in Multiple Programming Languages

FleXible yet efficient eXecution of heterogeneous simulations benefits from concepts and methods that can support distributed simulation eXecution and independent model development. To enable formal model specification with submodels implemented in multiple programming languages, we propose a novel approach called the Shared Abstract Model (SAM) approach, which supports simulation interoperability for the class of Parallel Discrete Event System Specification (DEVS) compliant simulation models. Using this approach, models written in multiple programming languages can be eXecuted together using alternative implementations of the Parallel DEVS abstract simulator. In this paper, we describe the SAM concept, detail its specification and eXemplify its implementation with two disparate DEVS-simulation engines. We demonstrate the simplicity of integrating simulation of component models written in the programming languages Java, C++ and Visual Basic. We describe a set of illustrative eXamples that are developed in an integrated DEVSJAVA and Adevs environment. Further, we stage simulation eXperiments to investigate the eXecution performance of the proposed approach and compare it with alternatives. We conclude that application domains, in which independently-developed heterogeneous component models consistent with the Parallel DEVS formalism, benefit from a rigorous foundation and are also interoperable across different simulation engines.

Balancing multiple constraints in model-data integration: Weights and the parameter block approach

Model data integration (MDI) studies are key to parameterize ecosystem models that synthesize our knowledge about ecosystem function. The use of diverse data sets, however, results in strongly imbalanced contributions of data streams with model fits favoring the largest data stream. This imbalance poses new challenges in the identification of model deficiencies. A standard approach for balancing is to attribute weights to different data streams in the cost function. However, this may result in overestimation of posterior uncertainty. In this study, we propose an alternative: the parameter block approach. The proposed method enables joint optimization of different blocks, i.e., subsets of the parameters, against particular data streams. This method is applicable when specific parameter blocks are related to processes that are more strongly associated with specific observations, i.e., data streams. A comparison of different approaches using simple artificial examples and the DALEC ecosystem model is presented. The unweighted inversion of a DALEC model variant, where artificial structural errors in photosynthesis calculation had been introduced, failed to reveal the resulting biases in fast processes (e.g., turnover). The posterior bias emerged only in parameters related to slower processes (e.g., carbon allocation) constrained by fewer data sets. On the other hand, when weighted or blocked approaches were used, the introduced biases were revealed, as expected, in parameters of fast processes. Ultimately, with the parameter block approach, the transfer of model error was diminished and at the same time the overestimation of posterior uncertainty associated with weighting was prevented.

Vegetation impacts soil water content patterns by shaping canopy water fluxes and soil properties

Friedrich Schiller University, Institute of Geoscience, Burgweg 11, 07749 Jena, Germany Max‐Planck‐Institute for Biogeochemistry, Hans‐Knöll‐Straße 10, 07745 Jena, Germany Friedrich Schiller University, Institute of Ecology, Dornburger Straße 159, 07743 Jena, Germany The German Centre for Integrative Biodiversity Research (iDiv) Halle‐Jena‐ Leipzig, Leipzig, Germany Correspondence Johanna Clara Metzger, Friedrich Schiller University, Institute of Geoscience, Burgweg 11, 07749 Jena, Germany. Email: johanna.clara.metzger@uni‐jena.de