Modeling Variability in the Earth System: From Rainfall, to River Networks, to Landscape Processes


Modeling Variability in the Earth System: From Rainfall, to River Networks, to Landscape Processes

Rainfall is the driver of land surface processes, including erosion and sedimentation and the flow of water and pollutants in streams, floodplains, and river networks. Meandering rivers with high rates of channel migration can deliver large quantities of sediment to rivers, indirectly affect water quality and biotic functioning, and increase the risk to public and private property. In order to understand how, and ultimately predict where, a river channel will migrate, the challenge lies in linking the meandering dynamics to the sinuous river channel planform geometry. To this end these researchers are using numerical modeling to gain insight on the interplay between a meandering river’s form (i.e. shape) and its migration dynamics. These simulations often require solving complex and computationally intensive nonlinear coupled differential equations; hence, having access to powerful computational resources and parallel computational capacity is essential for efficient implementation of this research.

In attempt to better understand long-term mussel population dynamics in watersheds, this group has developed a process-based interaction framework describing the interdependent set of dynamic environmental variables affecting mussels. A dynamic, process-based interaction model was then developed under the premise that chronic exposure to increased suspended sediment and food limitation are the primary factors limiting native mussel population growth. The model incorporates empirical and theoretical functional relationships to define interactions between mussel population density, streamflow, suspended sediment concentration, and phytoplankton population density and simulates these interactions at a daily timescale over several decades.

The availability of high-resolution topography from LiDAR (light detection and ranging, an optical remote sensing technology) and advanced tools such as wavelets to explore landscape geomorphological characteristics call for the development of new methodologies able to not only extract geomorphologic features (channel heads, channel banks, channel networks, slopes, curvatures, etc.) automatically, but also present frameworks to study the topographical features locally. This research group has developed a geometric framework that combines nonlinear diffusion for the pre-processing of the data and geodesic minimization principles for the extraction of channels. The nonlinear filtering operation allows to focus the analysis on the scales of interest and to enhance features that are critical to the channel extraction. Channels are extracted as geodesics, or curves of minimal effort, where the effort is measured based on fundamental geomorphological characteristics such as flow accumulation and iso-height contours curvature. Because of the large size of LiDAR datasets, computationally efficient algorithms need to be developed to model flow and transport in large watersheds.

Finally, the researchers are investigating new concepts, such as that of a process-scaling formulation for transport of mud, sand, and gravel and also a dynamic connectivity framework capable of investigating the internal dynamics of fluxes toward identifying places and times where fluxes concentrate

A bibliography of this group’s publications is attached.

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