Efi Foufoula-Georgiou, NAE
Distinguished Professor
Henry Samueli Endowed Chair in Engineering
Departments of Civil and Environmental Engineering
 and Earth System Science

University of California, Irvine (UCI)
Henry Samueli School of Engineering
3076 Interdisciplinary Science and Engineering Building (3076 ISEB), Irvine, CA 92697
Office +1(949) 824-9643 | Fax +1(949) 824-2117 | Cell +1(651) 470-2038
Google Scholar


GeoNet is a computational tool for the automatic extraction of channel networks and channel heads from high resolution topography. GeoNet combines nonlinear filtering for data preprocessing and cost minimization principles for feature extraction. The use of nonlinear filtering achieves noise removal in low gradient areas and edge enhancement in high gradient areas, i.e., near feature boundaries. After preprocessing, GeoNet extracts channels as geodesics--lines that minimize a cost function based on fundamental geomorphic characteristics of channels such as flow accumulation and curvature.

The most recent version is GeoNet 2.2. The tool is now also available in Python.


Passalacqua, P., T. Do Trung, E. Foufoula-Georgiou, G. Sapiro, and W. E. Dietrich (2010), A geometric framework for channel network extraction from lidar: Nonlinear diffusion and geodesic paths, J. Geophys. Res., 115, F01002, doi:10.1029/2009JF001254.

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Precipitation Passive Microwave Retrieval

Estimation of precipitation from space is one of the most exciting uses of earth remote sensing. The upwelling earth radiation in microwave bands contains spectral signatures that allow us to measure global precipitation from space. In the past few years, our team has developed a new passive retrieval algorithm to obtain improved estimates of precipitation from space using passive radiometric measurements provided by the Tropical Rainfall Measuring Mission (TRMM) and Global Precipitation Measurement (GPM) satellites.

Our retrieval algorithm, called ShARP (Shrunken Locally Linear Embedding for Passive Microwave Retrieval of Precipitation), relies on manifold learning via Bayesian sparse approximation and promises improved passive retrieval of precipitation, especially over land and at the vicinity of coastlines. ShARP promises improved retrievals over arid and semi-arid (e.g., Sahara Desert) regions and mitigates the commonly observed over-estimation of rainfall over snow-covered land surface (e.g., Tibetan highlands).


Ebtehaj, A.M., R.L. Bras, and E. Foufoula-Georgiou, Shrunken locally linear embedding for passive microwave retrieval of precipitation, IEEE Trans. on Geosci. and Remote Sens., 53(7), 3720-3736, doi:10.1109/TGRS.2014.2382436, 2015.

Ebtehaj, A.M., R.L. Bras, and E. Foufoula-Georgiou, On evaluation of ShARP passive rainfall retrievals over snow-covered land surfaces and coastal zones, Journal of Hydrometeorology, 17, 2016.

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Compressive Earth Observatory

The Compressive Earth Observatory (CEO) is a new conceptual framework that uses Compressive Sensing (CS) theory for the efficient estimation and sampling of land atmosphere state variables and fluxes from space. Using the retrievals of Atmospheric Infrared Sounder (AIRS) on board of NASA’s Aqua satellite, we demonstrated that: 1) the geophysical fields such as temperature and moisture fields are sparse in the wavelet domain, throughout the depth of atmosphere and 2) using a small set (30%) of random samples of temperature and moisture fields, the CS theory enables us to recover the entire field with high degree of accuracy. The main messages are: a) we may be able to design a next generation of sensors that allow to collect a smaller number of samples without compromising the accuracy of the earth observatory systems. b) With current sensing protocols, we may be able to design compatible and operationally viable random sampling schemes that enable significant reduction of the sampling density from space, leading to increased life span of the spacecraft, reduction in latency time of data transfer, and speedy retrievals for early warning systems.


Ebtehaj, A.M., E. Foufoula-Georgiou, G. Lerman, and R.L. Bras, Compressive Earth Observatory: An insight from AIRS/AMSU retrievals, Geophys. Res. Lett., 42(2), 362-369, doi:10.1002/2014GL062711, 2015.

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River Mussel-Sediment Interaction Model: Simulates freshwater mussel populations' response to changes in suspended sediment

This model simulates the interaction between suspended sediment, chlorophyll-a, and mussel population density. Discharge is the driver; it modulates suspended sediment and its interactions in the system. The model is suitable for simulating mussel densities at-a-site. It was originally developed to test the hypothesis that increased sediment loads in Minnesota Rivers are a plausible cause of observed mussel population declines.


Hansen, A.T., J.A. Czuba, J. Schwenk, A. Longjas, M. Danesh-Yazdi, D.J. Hornbach, and E. Foufoula-Georgiou, Coupling freshwater mussel ecology and river dynamics using a simplified dynamic interaction model, Freshwater Science, 35(1), 200-215, doi:10.1086/684223, 2016.

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Matlab toolbox for mapping and measuring river planform changes

This toolbox was constructed to help analyze changing river planforms (aerial views). Given a binary mask of a river, tools are provided to efficiently compute - channel centerline - banklines - channel width (two methods) - centerline direction - centerline curvature.

If multiple input mask images contain georeferenced information, a tool is provided to "stitch" the masks together--before or after analysis. Stitching can be done for both images and vectors of x,y coordinates. The mapping toolbox is required for this functionality.

If multiple masks (realizations) of the river are available, RivMAP includes tools to - compute centerline migrated areas - compute erosional and accretional areas - identify cutoff areas and quantify cutoff length, chute length, and cutoff area - generate channel belt boundaries and centerline - measure and map changes (in width, migration areas or rates, centerline elongation, accreted/eroded areas) in space and time


Schwenk, J., A. Khandelwal, M. Fratkin, V. Kumar, and E. Foufoula-Georgiou, High spatio-temporal resolution of river planform dynamics from Landsat: the RivMAP toolbox and results from the Ucayali River, Earth and Space Science, 4, 46–75, doi:10.1002/2016EA000196, 2017.

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River Network Bed-Material Sediment

Bed-material sediment transport and storage dynamics on river networks.

Network-based modeling framework of Czuba and Foufoula-Georgiou as applied to bed-material sediment transport.


Czuba, J.A., E. Foufoula-Georgiou, K. Gran, P. Belmont, and P. Wilcock, Interplay between Spatially-Explicit Sediment Sourcing, Hierarchical River-Network Structure, and In-Channel Bed-Material Sediment Transport and Storage Dynamics, JGR Earth Surface, 122, 1090-1120,doi:10.1002/2016JF003965, 2017.

Czuba, J.A., A Network-Based Framework for Hydro-Geomorphic Modeling and Decision Support with Application to Space-Time Sediment Dynamics, Identifying Vulnerabilities, and Hotspots of Change, http://hdl.handle.net/11299/181713, 2016.

Gran, K. B., and J.A. Czuba, Sediment pulse evolution and the role of network structure, Geomorphology, 277, 17-30. 10.1016/j.geomorph.2015.12.015, 2017.

Czuba, J.A., and E. Foufoula-Georgiou, Dynamic connectivity in a fluvial network for identifying hotspots of geomorphic change, Water Resources Research, 51(3), 1401-1421, doi:10.1002/2014WR016139, 2015.

Czuba, J.A., and E. Foufoula-Georgiou, A network-based framework for identifying potential synchronizations and amplifications of sediment delivery in river basins, Water Resources Research, 50(5), 3826-3851, doi:10.1002/2013WR014227, 2014.

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Nitrate Network Model

Nitrate and organic carbon dynamics on a wetland-river network.

Network-based modeling framework of Czuba and Foufoula-Georgiou as applied to nitrate and organic carbon on a wetland-river network.


Czuba, J. A., A. T. Hansen, E. Foufoula-Georgiou, and J. C. Finlay, Contextualizing Wetlands Within a River Network to Assess Nitrate Removal and Inform Watershed Management, Water Resources Research, doi:10.1002/2017WR021859, 2018.

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