Response of maize biomass and soil water fluxes on elevated CO2 and drought-From field experiments to process-based simulations.

The rising concentration of atmospheric carbon dioxide (CO2 ) 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 CO2 (550 ppm). To project future yields of maize under climate change, an accurate representation of the effects of eCO2 and drought on biomass and soil water conditions is essential. Current crop growth models reveal limitations in simulations of maize biomass under eCO2 and limited water supply. We use the coupled process-based hydrological-plant growth model Catchment Modeling Framework-Plant growth Modeling Framework to overcome this limitation. We apply the coupled model to the maize-based FACE experiment in Braunschweig that provides robust data for the investigation of combined CO2 and drought effects. We approve hypothesis I that CO2 enrichment has a small direct-fertilizing effect with regard to the total aboveground biomass of maize and hypothesis II that CO2 enrichment decreases water stress and leads to higher yields of maize under restricted water conditions. Hypothesis III could partly be approved showing that CO2 enrichment decreases the transpiration of maize, but does not raise soil moisture, while increasing evaporation. We emphasize the importance of plant-specific CO2 response factors derived by use of comprehensive FACE data. By now, only one FACE experiment on maize is accomplished applying different water levels. For the rigorous testing of plant growth models and their applicability in climate change studies, we call for datasets that go beyond single criteria (only yield response) and single effects (only elevated CO2 ).

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. To fill these gaps, we developed the FLOTATION (FLuorescent dOm Transport And TransformatION) model, which includes three processes: biodegradation, photodegradation, and primary production formation. We applied the model to the Nanfei River to understand the source, spatial distribution, and fate of DOM under low flow conditions. The model was set up and calibrated with the longitudinal measurements of four humic-like components (C1-C4) and one protein-like component (C5) identified by excitation-emission matrix parallel factor analysis (EEM-PARAFAC). The results showed that the simulation reproduced the longitudinal variations of all components well. The photodegradation process removed 18 %, 15 % and 21 % of the total input loadings of the humic-like components C1, C2 and C4, respectively. Algal primary production contributed 18 % of the downstream transport loading, constituting an important autochthonous source. For the protein-like C5, photodegradation and biodegradation together removed 7 % of the input loading. Our newly developed FLOATATION model can facilitate a comprehensive understanding of the fate and transport of DOM in aquatic environments.

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. Stream water, soil water and soil horizons were sampled at four sites representing typical settings within a forested catchment in the Ore Mountains (Erzgebirge, Germany) from winter 2020 to spring 2022. The samples were analyzed using pyrolysis-gas chromatography/mass spectrometry (Py-GC/MS). The resulting data were successfully subjected to semi-automatic processing of the molecular composition of DOM, reaching a percentage of identified peaks up to 98 %. Principal component analysis (PCA) and cluster analyses were carried out to identify distinct differences between DOM from the potential sources and in the streams. According to the PCA, organic soil horizons, soil water, and stream water samples could be clearly distinguished. Cluster analysis revealed that soil water DOM at all depths of Peats and deeper horizons of the Peaty Gleysols contributed the most to DOM in the stream section dominated by organic soils. In areas dominated by mineral soils, stream DOM resembled the DOM from the deeper mineral horizons of Cambisols and Podzols. Overall, our results suggested that most of the DOM exported from the catchment was derived from deeper mineral soil horizons, with little contribution of DOM derived from organic soils. Therefore, DOM fingerprint analysis of in-situ soil water proved to be a promising approach for tracing back the main sources of stream water DOM.

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. Specifically, the structure of such models seldom conforms to the physics of water flow and solute transport in soils. The equilibrium relationships may fail to predict seasonality in radionuclide transfer between environmental compartments; model transferability between sites is often hampered by its empirical nature. Numerous studies have highlighted the need to circumvent these limitations. In this paper, we introduce dynamic and process-based modelling to a conventional radioecological model by coupling an empirical plant module to a process-based soil module that simulates water flow, solute transport and root uptake in the soil column. Illustrative simulations are presented using the coupled model and stable chlorine cycling in a temperate Scots pine (Pinus sylvestris L.) stand as an example. The model satisfactorily reproduced soil moisture dynamics and the inventory of inorganic chlorine in the tree and forest floor compartments. The inventory of organic chlorine in the stand, however, was overestimated, indicating that processes pertinent to organochlorine cycling at the stand were missing from the model. The approach proposed in this paper is a step towards dynamic and process-based modelling in terrestrial radioecology and impact assessment. It can be particularly useful for modelling transfer of elements, such as redox-sensitive radionuclides, whose behaviour in soil-plant systems is moisture-dependent.

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. To accomplish this we built five models of differing complexity, ranging from a simple lumped model to a semi-lumped model with eight spatial subdivisions. All models are built within the same modelling framework, use the same data, and are calibrated with the same algorithm. We also consider two different methods for the potential evapotranspiration. We found that that there is a clear trade-off along the axis of complexity. While the more simple models can constrain their parameters quite well, they fail to get the hydrological signatures right. It is the other way around for the more complex models. The method of evapotranspiration only influences the parameters directly related to it. This study highlights that it is important to focus not only on parametric uncertainty. Tightly constrained parameters can be misguiding as they give credibility to oversimplified model structures.

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.

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.