Abteilung Oberflächengewässer

Pastoral farming formed a key element of Norse subsistence strategies in South Greenland but with climatic changes of the Little Ice Age they may have reached their limit. Most recently, studies into hydrological changes across the Norse period (10th–15th century AD) revealed a severe drying trend that was coincident with the Norse demise during the early to mid-15th century AD. This study examines lake sediments from a central area of the Norse Eastern Settlement in Greenland. By means of palynology this study investigates whether climatic changes were responsible for decreasing hay yields and a consequent lack of winter fodder. The results suggest that droughts were likely only minor drivers of vegetation change. In fact, we demonstrate a complex entanglement of cooling trends, substrate impoverishment in the catchment of the sampled lake and human adaptation processes. The latter is manifested in a shift in usage of the farm towards a shieling/ dairy production. We conclude that the high amount of labour required to maintain hay yields while counterbalancing the lack of soil nutrients and the shortening of the growing season could be among the many driving forces in the process of Norse farming reorganization in South Greenland. Furthermore, the results allow for the discussion of a potential first palynological evidence of Norse water management in South Greenland.

Bloom-forming algae present a unique challenge to water managers as they can significantly impair provision of important ecosystem services and cause health risks to humans and animals. Consequently, effective short-term algae forecasts are important as they provide early warnings and enable implementation of mitigation strategies. In this context, machine learning (ML) emerges as a promising forecasting tool. However, the performance of ML models is heavily dependent on the availability of appropriate training data. Consequently, it is essential to determine the volume of data necessary to develop reliable ML forecasts. Understanding this will guide future monitoring strategies, optimize resource allocation, and set realistic expectations for management outcomes. In this study, we used 30 years of fortnightly measurements of 13 different parameters from a lake in the English Lake District (UK) to examine the impact of training data duration on the performance of ML models for forecasting chlorophyll-a two weeks in advance. Once training data availability exceeded four years, a Random Forest model was found to consistently outperform naive benchmarks (mean absolute percentage error 16.4 % lower than the best-performing benchmark). With more than 5 years of training data, model performance generally continued to improve, but with diminishing returns. Furthermore, it was found that equivalent and, in some cases, better performance could be achieved by only using a subset of the most important input features. Additionally, it was found that reducing the sampling frequency had negative impacts on performance, both due to the reduced number of training observations available, and increased forecast horizon. Our findings demonstrate that for lakes ecologically similar to the study site, a consistent and regular sampling programme focused on monitoring a limited number of key parameters can provide sufficient observations for generating short-term algae forecasts after approximately five years of data collection. Importantly, this result provides justification for the initiation of new monitoring programmes for sites where algal blooms are a concern, and suggests that there are likely many pre-existing monitoring datasets which would be suitable for training algae forecast models.

Industrial activities of a silk dyeing factory in Thalwil, on the shore of Lake Zurich, Switzerland, caused extreme Sn contamination of lake sediments. In this study, we determine the contamination source, spread, and age using a multiproxy approach. We used X-ray fluorescence spectroscopy (XRF) core scanning and further geochemical analyses to assess the contamination spreading and thickness in the sedimentary column. We found elevated Sn levels throughout sediments of Lake Zurich, ranging from 177 gkg^-1 in front of the former silk factory to 0.05 gkg^-1 at the southeast end (background: ca. 0.006 gkg^-1). The rapid concentration drop away from the shore suggests quick precipitation of a sparingly soluble inorganic Sn compound, which is confirmed by Scanning Electron Microscope Imaging in tandem with Energy-dispersive XRF spectroscopy (SEM-EDX) data. The Sn XRF profile of a varved core indicates a contamination onset in the early 1890s, a maximum around 1900, and a gradual decrease to low levels in the 1940s. High Sn concentrations in turbidite layers from the deep basin indicate that mass movements physically remobilised Sn. However, in stable conditions, in-situ porewater measurements (conc. < 0.5 μgL^-1) using dialyse plates show little Sn remobilisation into the lake water (0.05 mga^-1m^-2). The low remobilisation, reducing conditions, and high sulphide contents in the contaminated layers suggest that Sn is firmly bound to the sediments. Combined with the low toxicity of Sn, we conclude that the Sn contamination poses no threat to lake biota or drinking water production.