Inci Guneralp
Inci Guneralp
Associate professor
Texas A&M University
Geography
R 810 Eller O&M Building, 3147 TAMU
College Station
TX
77843-3147
Email
Phone
Fields of interest
Hydrodynamics and morphodynamics of lowland river landscapes, river flooding, river-floodplain connectivity, large wood and rivers, remote sensing of rivers, machine learning applications in river science
Description of scientific projects
The scientific projects that I am involved with are clustered in three major groups. These projects commonly integrate hydrodynamic and/or morphodynamic modeling, remote sensing data/tools (multispectral and hyperspectral satellite, aerial photography, and lidar), geospatial technologies, and field-based data.
(1) Flooding and river-floodplain connectivity in lowland rivers: We have been studying multiple rivers systems within the coastal bend of Texas to understand the interactions among river hydrology, geomorphology, and land cover that create the observed flood inundation patters and surface-water connectivity for a gradient of river flows. Past and current project are:
• Estimating flood-pulse dynamics of a coastal river floodplain
• Towards a framework for quantifying hydrologic surface connectivity and its implications to a coastal river-floodplain system
• Tree-blowdown impacts of hurricanes on river-floodplain connectivity within a lowland river system
• Hydrological and ecological impacts of hurricane-induced tree blowdowns on a coastal river floodplain in Texas and improved community understanding through public education and outreach
(2) Remote sensing of large wood in lowland rivers: These studies focus on developing tools and methods for detection of large wood under closed/relatively closed forest canopies of floodplain, riparian, and bottomland hardwood forests of lowland rivers from high-density lidar, satellite remote sensing, and aerial photographs using machine learning and deep learning. The current projects are:
• Uncovering effects of fallen trees on flooding and carbon estimation in hurricane-impacted landscapes with deep learning
• Deep learning for large wood detection in hurricane-prone lowland river landscapes
(3) Morphodynamics and biomorphodynamics of lowland rivers: These studies have a strong emphasis on topology and morphology of river channels and their floodplains in a spectrum of environments, from tropics to Arctic. We have also been studying co-evolution of geomorphology and riparian vegetation in lowland rivers at multiple temporal scales ranging from decades to millennia. The current projects are:
• Long-term biomorphodynamic evolution of lowland river landscapes
• Coevolution of river-channel geomorphology and riparian vegetation on the Lower Brazos River, Texas
• Tracing the signature of human-induced deforestation on lowland tropical river landscapes using simulation modeling and deep learning
• Topological signatures of meandering rivers
• Morphodynamic signatures of Arctic and Subarctic meandering rivers
(1) Flooding and river-floodplain connectivity in lowland rivers: We have been studying multiple rivers systems within the coastal bend of Texas to understand the interactions among river hydrology, geomorphology, and land cover that create the observed flood inundation patters and surface-water connectivity for a gradient of river flows. Past and current project are:
• Estimating flood-pulse dynamics of a coastal river floodplain
• Towards a framework for quantifying hydrologic surface connectivity and its implications to a coastal river-floodplain system
• Tree-blowdown impacts of hurricanes on river-floodplain connectivity within a lowland river system
• Hydrological and ecological impacts of hurricane-induced tree blowdowns on a coastal river floodplain in Texas and improved community understanding through public education and outreach
(2) Remote sensing of large wood in lowland rivers: These studies focus on developing tools and methods for detection of large wood under closed/relatively closed forest canopies of floodplain, riparian, and bottomland hardwood forests of lowland rivers from high-density lidar, satellite remote sensing, and aerial photographs using machine learning and deep learning. The current projects are:
• Uncovering effects of fallen trees on flooding and carbon estimation in hurricane-impacted landscapes with deep learning
• Deep learning for large wood detection in hurricane-prone lowland river landscapes
(3) Morphodynamics and biomorphodynamics of lowland rivers: These studies have a strong emphasis on topology and morphology of river channels and their floodplains in a spectrum of environments, from tropics to Arctic. We have also been studying co-evolution of geomorphology and riparian vegetation in lowland rivers at multiple temporal scales ranging from decades to millennia. The current projects are:
• Long-term biomorphodynamic evolution of lowland river landscapes
• Coevolution of river-channel geomorphology and riparian vegetation on the Lower Brazos River, Texas
• Tracing the signature of human-induced deforestation on lowland tropical river landscapes using simulation modeling and deep learning
• Topological signatures of meandering rivers
• Morphodynamic signatures of Arctic and Subarctic meandering rivers