SPOTting Model Parameters Using a Ready-Made Python Package

Tobias Houska; Philipp Kraft; Alejandro Chamorro-Chavez; Lutz Breuer

doi:10.1371/journal.pone.0145180

SPOTting Model Parameters Using a Ready-Made Python Package

PLoS One. 2015 Dec 17;10(12):e0145180. doi: 10.1371/journal.pone.0145180. eCollection 2015.

Authors

Tobias Houska¹, Philipp Kraft¹, Alejandro Chamorro-Chavez¹, Lutz Breuer^{1

2}

Affiliations

¹ Institute for Landscape Ecology and Resources Management, Research Centre for BioSystems, Land Use and Nutrition (IFZ), Justus Liebig University, Giessen, Germany.
² Centre for International Development and Environmental Research, Justus Liebig University, Giessen, Germany.

Abstract

The choice for specific parameter estimation methods is often more dependent on its availability than its performance. We developed SPOTPY (Statistical Parameter Optimization Tool), an open source python package containing a comprehensive set of methods typically used to calibrate, analyze and optimize parameters for a wide range of ecological models. SPOTPY currently contains eight widely used algorithms, 11 objective functions, and can sample from eight parameter distributions. SPOTPY has a model-independent structure and can be run in parallel from the workstation to large computation clusters using the Message Passing Interface (MPI). We tested SPOTPY in five different case studies to parameterize the Rosenbrock, Griewank and Ackley functions, a one-dimensional physically based soil moisture routine, where we searched for parameters of the van Genuchten-Mualem function and a calibration of a biogeochemistry model with different objective functions. The case studies reveal that the implemented SPOTPY methods can be used for any model with just a minimal amount of code for maximal power of parameter optimization. They further show the benefit of having one package at hand that includes number of well performing parameter search methods, since not every case study can be solved sufficiently with every algorithm or every objective function.

Publication types

Research Support, Non-U.S. Gov't

MeSH terms

Ecosystem*
Models, Biological*
Software*

Grants and funding

This work was funded by the LOEWE excellence cluster FACE2FACE of the Hessen State Ministry of Higher Education, Research and the Arts. Further support was received by the DFG for Tobias Houska (BR2238/13-1) and Alejandro Chamorro-Chavez (BR2238/5 2). Lutz Breuer would like to acknowledge funding of the Marie Curie Training Network: Quantifying Uncertainty in Integrated Catchment Studies (QUICS), PITN-GA-2013-607000. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript. There was no additional external funding received for this study.