The overarching goal of this project is to revolutionise our understanding of the fundamental principles that govern water regimes in streams and lakes worldwide. The project will capture the multi-dimensional aspects of flow regimes (river discharge) and model hydraulics using a wide range of high resolution geo-datasets integrated with gauging station data in a machine learning framework. The preliminary research below lays the foundation for this challenging project.
Stream networks at global level
- High-resolution stream network delineation using digital elevation models: assessing spatial accuracy
- Watershed and stream network delineation using digital elevation models and spectral satellite information
Geomorphometry/Topography/Environmental variables at global level
- Geomorpho90m: Global high-resolution geomorphometry variables for environmental modelling (preprint-article)
- Geomorpho90m: Global high-resolution geomorphometry variables for environmental modelling (poster)
- Geomorpho90m: Global high-resolution geomorphometry variables for environmental modelling (downloading procedure)
- Geomorpho90m: Global high-resolution geomorphometry variables for environmental modelling (web-GIS visualization)
- A suite of global, cross-scale topographic variables for environmental and biodiversity modelling
- Estimating nitrogen and phosphorus concentrations in streams and rivers across the contiguous United States: a machine learning framework (preprint-article)
- Near-global freshwater-specific environmental variables for biodiversity analyses in 1 km resolution
The project’s research team brings together several years of experience in large geographic data and machine learning model implementation. The team members’ outstanding credentials in the environmental and computer sciences, statistical modelling and hydrology offer the requisite skills to achieve project objectives.
PI: Dr. Giuseppe Amatulli
Research Scientist at Yale University (USA). Expertise: Geocomputation, data analysis, GIS, remote sensing , big geo-dataset processing, hydrology.
Co-PI: Dr. George H. Allen
Assistant Professor at Texas A&M University (USA). Expertise: hydrology, remote sensing, GIS, data analysis, water resources.
Scientists: 2 Post-Docs with expertise hydrology, machine learning and geocomputation (to be selected)
Advisor: Prof. Peter Raymond
Professor at Yale University (USA). Expertise: (USA). Expertise: hydro-chemistry, water resources, freshwater ecology.
Advisor: Dr. Longzhu Shen
Research Scientist at University of Cambridge (UK). Expertise: modeller, data analysis, big-data processing, machine learning, chemistry.
Advisor: Dr. Sami Domisch
Research Scientist at Leibniz-Institute of Freshwater Ecology and Inland Fisheries (Germany). Expertise: freshwater biodiversity, biogeography, modelling and data analysis, massive data processing, machine learning
Collaborator: Mr. Richard Barnes
PhD Student at University of Berkeley. Expertise: computer scientist, developer, data analysis
Collaborator: Mr. Paolo Corti
Geospatial Engineering consultant at Spatial Ecology. Expertise: computer scientist, web-GIS developer
Collaborator: Mr. Tushar Sethi
Head of Operations, Spatial Ecology (UK). Expertise: Water and waste management technologies; translating scientific research into applied solutions.