Enhancing insight into DOM fate in aquatic systems: Introducing the FLOTATION model.

Water quality modeling can help to understand the source, transport, transformation and fate of dissolved organic matter (DOM) in aquatic systems. However, water quality models typically use biological oxygen demand as the state variable for DOM, which poorly represents the bio-refractory fraction of the DOM pool. Furthermore, photodegradation, which has a significant impact on the fate of DOM, is often neglected in water quality models.

Tracing sources of dissolved organic matter along the terrestrial-aquatic continuum in the Ore Mountains, Germany.

There is growing concern about the rising levels of dissolved organic matter (DOM) in surface waters across the Northern hemisphere. However, only limited research has been conducted to unveil its precise origin. Compositional changes along terrestrial-aquatic pathways can help determine the terrestrial sources of DOM in streams.

Towards dynamic and process-based modelling of radionuclides cycling in terrestrial radioecology.

Mathematical models are frequently used in terrestrial radioecology to interpret observations and to assess the detrimental impacts of radioactive releases to the environment. Conventional radioecological models are largely based on equilibrium and empirical relationships with reasonable data requirements, making them practical tools for long-term assessments. But conventional models may be inadequate to simulate radionuclide dynamics in terrestrial environments realistically.

Trade-offs between parameter constraints and model realism: a case study.

Tightly constraint parameter ranges are seen as an important goal in constructing hydrological models, a difficult task in complex models. However, many studies show that complex models are often good at capturing the behaviour of a river. Therefore, this study explores the trade-offs between tightly constrained parameters and the ability to predict hydrological signatures, that capture the behaviour of a river.

Response of maize biomass and soil water fluxes on elevated CO and drought-From field experiments to process-based simulations.

The rising concentration of atmospheric carbon dioxide (CO ) is known to increase the total aboveground biomass of several C3 crops, whereas C4 crops are reported to be hardly affected when water supply is sufficient. However, a free-air carbon enrichment (FACE) experiment in Braunschweig, Germany, in 2007 and 2008 resulted in a 25% increased biomass of the C4 crop maize under restricted water conditions and elevated CO (550 ppm). To project future yields of maize under climate change, an accurate representation of the effects of eCO and drought on biomass and soil water conditions is essential.

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.

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.