The Delta Connectome: Structure and Transport Dynamics of Delta Networks across Scales and Disciplines

Human-induced activities and climatic shifts are significantly impacting deltas around the world. A quantitative description of the form and structure of deltas and their dynamics is fundamental to address how they react to changes in climatic forces and human pressure. As part of this project, we are creating a research/educational framework, the Delta Connectome, based on the general idea of a delta as a directed network of connected paths (physical, functional, and conceptual paths of process coupling) which interact continuously at a broad range of space and time scales and dictate system response to change. Specific goals of this project are (i) the objective quantification of delta morphologic features (channel and island properties) to identify the signature of vegetation, anthropogenic disturbance, and processes responsible for delta formation and evolution; (ii) the development of an automatic image processing-based tool for the extraction of relevant information from remotely sensed data in order to apply the analysis in (i) to a wide range of delta morphologies; (iii) the identification of the environmental controls on channel network and shoreline dynamics through coupled analysis of the extracted features through time and time series of environmental forcings; (iv) the quantification of strength, directionality, statistical significance, and scale of couplings among key variables and the effect of anthropogenic disturbance and change on such couplings.
FundingNational Science Foundation 
Students: Alicia Sendrowski, Leo Isikdogan
Structural, functional, (process-based) and process connectivity in river deltas (reproduced from Passalacqua [2016]). 

Open source tool for automatic delta network extraction from remotely sensed data
As part of the Delta Connectome project, we have developed fully automatic tools for the extraction of channel networks from remotely sensed imagery. RivaMap is based on a multi-scale singularity index, while DeepWaterMap and DeepRiver are Convolutional Neural Network models. 

Student: Leo Isikdogan
Collaborator: Al Bovik

Coastal SEES Collaborative Research: Multi-scale modeling and observations of landscape dynamics, mass balance, and network connectivity for a sustainable Ganges-Brahmaputra delta

This project combines innovative quantitative tools (numerical modeling, network and connectivity analysis) with new and existing observational data to analyze the coupled human-natural system and long-term sustainability of the GBMD. Specifically, we will (i) develop a detailed mass balance for delta-wide sediment dispersal; (ii) quantitatively analyze the connectivity of the delta-system network that disperses this sediment; (iii) integrate this knowledge through numerical modeling at local to global scales; (iv) use observational data of landscape and channel dynamics to understand coupled land-sea interactions; (v) evaluate the quality of regional soil and water resources and their links with physical and anthropogenic processes; (vi) assess the impact of these delta dynamics on the human environment and transportation, and finally (vii) disseminate this knowledge through a variety of educational activities and opportunities for students, researchers, and professionals. The Passalacqua group will focus on the network and connectivity analysis. 

Funding: National Science Foundation
Student: Tess Jarriel

Connectivity analysis of the Ganges-Brahmaputra-Meghna Delta (reproduced from Passalacqua [2016]). 

Feature extraction from high resolution topography data 

The advent of meter-resolution topographic data is revolutionizing the study of geomorphic processes. For the first time, the topographic patterns of surface flow, channelization, and landsliding can be resolved over large areas at resolutions commensurate with the scales of the governing processes. We apply new methods of geomorphic feature extraction and morphological analysis (GeoNet) to a large inventory of lidar DTMs in combination with detailed ground validation at key watersheds. We use the extracted information to improve inundation mapping over the continental US. We additionally use GeoNet as technique for hydro-flattening of high resolution topography data.  

Funding: National Science Foundation and United States Department of Agriculture 
Students: Xing Zheng, Lukas Godbout
Collaborators: David Maidment

Scaling analysis of topography data and development of subgrid-scale parameterizations to advance hydrologic modeling 

Despite the recent availability of high resolution (≤ 1 m) topography data in the United States, the information contained in these data sets has yet to be included and exploited in hydrological modeling. Features such as channels are often extracted with methods developed for coarse resolution data and models rely on discrete cross-section based information. Furthermore, engineering practice still relies mostly on semi-distributed hydrological methods that neglect much of the available geomorphological information. Hence, these methods use very basic basin information obtained at aggregated spatial scales (i.e., basin area, average slope, length of the main water course, location of the basin centroid) to define synthetic responses in terms of few representative variables such as peak flow, time to peak, and base time. The use of these methods is even more common in developing countries such as Chile, were information is much less available. We are working on a coupled data and modeling analysis to quantify the effect of data resolution on the estimated basin hydrological response and explore the potential of subgrid-scale parameterizations to include the effect of small scale features when high resolution data are not available and modeling is performed at coarser resolutions.  

FundingSEED FUND 2016, UT Austin and Pontificia Universidad Católica de Chile 
Students: Emily Poston, Jeff Zheng
Collaborators: Jorge Gironas, Cristian Escauriaza

Improving flood inundation mapping at continental scale

A large scale effort is ongoing to improve flood inundation mapping at continental scale by relying on the National Water Model and the HAND method (Height Above Nearest Drainage). The National Water Model continually forecasts flows on 2.7 million stream reaches in the continental United States. These discharge forecasts are then translated into forecasts of water depths above minimum channel elevation with the HAND method. So far this work has relied on the 10m National Elevation Dataset topographic data. Our research group is exploring the use of GeoNet on high resolution topographic data to translate this procedure into lidar terrain data to increase the accuracy of the estimated flood extent.

Funding: Texas Division of Emergency Management
Collaborator: David Maidment and many others 
Student: Xing Zheng, Lukas Godbout, Jeff Zheng 

Height Above Nearest Drainage (HAND) computed over the continental United States. 

A Delta Dynamics Collaboratory

River deltas represent a major Earth-surface system with societal need. Low-lying, ecologically productive, and inhabited by millions of people, deltas also lie directly in the path of a confluence of ongoing changes: nutrient overloading from agriculture; accelerated subsidence and sea-level rise; effects of land use and navigation; and changing hydrology and sediment supply. The overall objective of this project is to develop tested, high-resolution, quantitative models incorporating morphodynamics, ecology, and stratigraphy to predict river delta dynamics over engineering to geologic time-scales, and to specifically address questions of system dynamics, resiliency, and sustainability. The collaboratory comprises two main work centers: a field observatory and a virtual modeling center, together with supporting experimental facilities. The observatory is at Wax Lake Delta, an actively growing delta about 100 km west of the main Mississippi Delta birdsfoot. The virtual modeling center is hosted by the Community Surface Dynamics Modeling System (CSDMS) at University of Colorado, where it can contribute to an evolving library of modules for computation and visualization of geomorphic and sedimentary systems, including access to many of the existing delta models. This project is in collaboration with Dr. David Mohrig and Dr. Wonsuck Kim (UT Austin) and other 11 PIs at several US institutions. 
Our research group is focusing on the flow exchange between channels and islands and the controls exerted by vegetation, topography, and environmental forcing on channel-island hydrological connectivity. 

Funding: National Science Foundation
Student: Kyle Wright