Advancing knowledge of Arctic lake system dynamics: A data-driven perspective on spatiotemporal patterns
Ecosystems play a pivotal role in addressing climate change but are also highly susceptible to drastic environmental changes. Investigating their historical dynamics can enhance our understanding of how they might respond to unprecedented future environmental shifts. With Arctic lakes currently unde...
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| Format: | Doctoral or Postdoctoral Thesis |
| Language: | English |
| Published: |
2024
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| Online Access: | https://publishup.uni-potsdam.de/frontdoor/index/index/docId/63655 https://nbn-resolving.org/urn:nbn:de:kobv:517-opus4-636554 https://doi.org/10.25932/publishup-63655 https://publishup.uni-potsdam.de/files/63655/pfalz_diss.pdf |
| Summary: | Ecosystems play a pivotal role in addressing climate change but are also highly susceptible to drastic environmental changes. Investigating their historical dynamics can enhance our understanding of how they might respond to unprecedented future environmental shifts. With Arctic lakes currently under substantial pressure from climate change, lessons from the past can guide our understanding of potential disruptions to these lakes. However, individual lake systems are multifaceted and complex. Traditional isolated lake studies often fail to provide a global perspective because localized nuances—like individual lake parameters, catchment areas, and lake histories—can overshadow broader conclusions. In light of these complexities, a more nuanced approach is essential to analyze lake systems in a global context. A key to addressing this challenge lies in the data-driven analysis of sedimentological records from various northern lake systems. This dissertation emphasizes lake systems in the northern Eurasian region, particularly in Russia (n=59). For this doctoral thesis, we collected sedimentological data from various sources, which required a standardized framework for further analysis. Therefore, we designed a conceptual model for integrating and standardizing heterogeneous multi-proxy data into a relational database management system (PostgreSQL). Creating a database from the collected data enabled comparative numerical analyses between spatially separated lakes as well as between different proxies. When analyzing numerous lakes, establishing a common frame of reference was crucial. We achieved this by converting proxy values from depth dependency to age dependency. This required consistent age calculations across all lakes and proxies using one age-depth modeling software. Recognizing the broader implications and potential pitfalls of this, we developed the LANDO approach ("Linked Age and Depth Modelling"). LANDO is an innovative integration of multiple age-depth modeling software into a singular, cohesive ... |
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