Tobias Houska

SPOTting Model Parameters Using a Ready-Made Python Package.

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.

Comparing molecular composition of dissolved organic matter in soil and stream water: Influence of land use and chemical characteristics

Electrospray ionization Fourier transform ion cyclotron resonance mass spectrometry (ESI-FT-ICR-MS) was used to examine the molecular composition of dissolved organic matter (DOM) from soils under different land use regimes and how the DOM composition in the catchment is reflected in adjacent streams. The study was carried out in a small area of the Schwingbach catchment, an anthropogenic-influenced landscape in central Germany. We investigated 30 different soil water samples from 4 sites and different depths (managed meadow (0-5cm, 40-50cm), deciduous forest (0-5cm), mixed-coniferous forest (0-5cm) and agricultural land (0-5cm, 40-50cm)) and 8 stream samples. 6194 molecular formulae and their magnitude-weighted parameters ((O/C)w, (H/C)w, (N/C)w, (AI-mod)w, (DBE/C)w, (DBE/O)w, (DBE-O)w, (C#)w, (MW)w) were used to describe the molecular composition of the samples. The samples can be roughly divided in three groups. Group 1 contains samples from managed meadow 40-50cm and stream water, which are characterized by high saturation compared to samples from group 2 including agricultural samples and samples from the surface meadow (0-5cm), which held more nitrogen containing and aromatic compounds. Samples from both forested sites (group 3) are characterized by higher molecular weight and O/C ratio. Environmental parameters vary between sites and among these parameters pH and nitrate significantly affect chemical composition of DOM. Results indicate that most DOM in streams is of terrestrial origin. However, 120 molecular formulae were detected only in streams and not in any of the soil samples. These compounds share molecular formulae with peptides, unsaturated aliphatics and saturated FA-CHO/FA-CHOX. Compounds only found in soil samples are much more aromatic, have more double bonds and a much lower H/C ratio but higher oxygen content, which indicates the availability of fresh plant material and less microbial processed material compared to stream samples.