👩‍💻 Website
📎 Team documents and guidelines

Hi there! We're the Computational Limnology team from the Freshwater Ecology section at Aarhus University, Denmark.

To advance aquatic ecosystem modelling, we integrate model theory (developing novel models and improving existing code), model application (running models on study sites for scenario analysis), and data science/software development (developing scientific software, including R package development, for quality assurance and data processing) into our research. Traditionally, our focus is on vertical one-dimensional process-based modeling of aquatic ecosystems by coupling hydrodynamic with water quality models. These coupled models are powerful tools to explore long-term ecosystem dynamics under various stressors such as climate change or eutrophication, and to quantify the causality between abiotic and biotic events across different time scales.

Our current focus areas are

📈 Aquatic Ecosystem Modeling by Robert Ladwig: Using mathematical or computational models to simulate the coupled physical, chemical, biological, and ecological processes in aquatic ecosystemss.

and

🌐 Landscape Limnology by Tuba Bucak: Exploring how spatial patterns and interactions between terrestrial and aquatic landscapes influence the ecological structure and functioning of aquatic ecosystems.

Current team members:

• Tuba Bucak (Tenure-Track Assistant Professor)
• Robert Ladwig (Tenure-Track Assistant Professor)
• Lipa Gutani Terrence Nkwalale (PhD student, co-supervised by Robert)
• Marthin Pedersen (Development Engineer)
• Tom Davidson (Professor)

As the Computational Limnology team at Aarhus University, we address limnological questions using a plethora of methods, ranging from simple toy models, to data-driven approaches, and up to complex numerical models. We are also conducting field measurements and long-term monitoring to better capture ecosystems processes on multiple time scales. To complement our research, we work closely across sections to explore lake water quality dynamics, biodiversity changes, metabolism, and carbon cycling.

Here is an overview of our current projects:

🔮 Knowledge-Guided Machine Learning: coupling process-based models and/or scientific knowledge (physical/ecological laws and constraints) with innovative deep learning models

🌎 Landscape Water Quality Analysis: upscaling and exploring spatiotemporal monitoring data to understand nutrient limitation, primary production, carbon sequestration and greenhouse gas emissions

👥 LakeEnsemblR-Water Quality: building a software framework to faciliate setting up, running and post-processing an ensemble of aquatic ecosystem models for structural model-intercomparison