Efi Foufoula-Georgiou
Distinguished Professor, NAE
Departments of Civil and Environmental Engineering
 and Earth System Science

University of California, Irvine (UCI)
Henry Samueli School of Engineering
Engineering Hall 5400 (EH 5400), Irvine, CA 92697-2175
Office +1(949) 824-9643 | Fax +1(949) 824-2117 | Cell +1(651) 470-2038

Funded Research

History of NSF funded grants

Current Research

The group's current research concentrates on the following areas: (1) Hydroclimatology and S2S precipitation forecasting, (2) inverse problems for precipitation estimation from space, (3) stochastic theories of water/sediment transport and landscape evolution, and (4) river delta network topology and dynamics for vulnerability assessment. To read more on these topics, please click on a project below:

Hydroclimatology and S2S precipitation forecasting: A new interhemispheric teleconnection (NZI)

We work on enhancing physical understanding of large-scale climate modes of variability and change and their regional hydroclimatic impacts. We also develop new forecasting schemes to optimally combine climate teleconnections in a changing climate for improving subseasonal-to-seasonal (S2S) predictability of regional precipitation.

Based on recent observational evidence, our work has identified a previously undiscovered teleconnection that has intensified during the last four decades, and which has increased potential for earlier and more accurate prediction of winter precipitation in southwestern US (SWUS). Specifically, we found that sea surface temperature (SST) anomalies in the subtropical Pacific off the coast of New Zealand (30°×15° region of 170°E-200°E and 25°S-40°S, termed the NZI region) have emerged as strong and early (three months ahead) predictors of winter SWUS precipitation, with NZI positive (negative) anomalies in the late summer expected to result in below (above) average precipitation in the coming winter.

Evidence for a new teleconnection in the southwestern Pacific. a, b Correlation coefficients of –NZI and Niño 3.4 with winter precipitation (Nov–Mar) for all climate divisions of southwestern US (SWUS) and for two different periods (Jul–Sep and Sep–Nov) for 1950–2015. Average precipitation is defined as the area-weighted average precipitation amount over climate divisions where Niño 3.4 exhibits statistically significant correlation (see colored numbers in panels a, b, and regions in panel h). c Correlation map between SST (Jul–Sep) and the average winter precipitation in SWUS for 1950–2015. White color indicates statistically insignificant correlations (α= 0.05 significance level); d Same as c, but SST is averaged over Sep–Nov; e, f Correlation maps as in c, d, but using GPH (400 mb). The emergence of a persistent correlation pattern in the southwestern Pacific (coined as the New Zealand Index, NZI) is robust for both SST and GPH; g the location and areal extent of NZI and Niño 3.4; h The selected climate divisions in SWUS (in color), for deriving the regionally averaged precipitation amount, based on their significant correlation with Niño 3.4.

Climate modes which have been physically linked to precipitation variability in the SWUS, such as the El Niño Southern Oscillation (ENSO), do not exhibit significant statistical relations with precipitation. Notable examples of poor association of ENSO conditions with precipitation include:

the Mega El Niño in 2015-2016 which was associated with California drought.

the strong La Niña in 2010-2011 which was associated with wet conditions in southern California.

one of the wettest years in record for southern California was in 1992-1993, when ENSO was neutral.

During the last three decades, NZI associated more strongly with the average winter precipitation in the SWUS than Niño 3.4. NZI and Niño 3.4 anomalies correspond to Sep-Nov, while average winter (Nov-Mar) precipitation is computed over climate divisions of significant correlation with Niño 3.4. In the lower panel, the 5 coolest/warmest NZI years are indicated (lower/upper ~15%).

The teleconnection depends on a western Pacific ocean-atmosphere pathway, whereby sea surface temperature anomalies propagate from the southern to the northern hemisphere during boreal summer.

The New Zealand Index (NZI) teleconnection depends on a western Pacific ocean–atmosphere pathway. a Negative SST anomalies (blue shading) in the NZI region cascade in the northern hemisphere through a late summer interhemispheric atmospheric bridge and are maintained by air-sea coupling until the following winter. The SST anomalies affect the atmospheric pressure in the US west coast and strengthen the regional jet stream which brings more winter storms in the SWUS; b Late-summer positive SST anomalies (red shading) in the NZI region deflect the jet stream to the north, leading to dry conditions over the SWUS.

NZI affects the upper zonal winds in the northeastern Pacific and modulates SWUS precipitation. a Zonal average (220°E–260°E) of zonal wind in m/s for different latitude and pressure levels, during Nov–Mar, for 1982–2015; b Same as (a), but for the 5 coolest NZI years; c Same as (a), but for the 5 warmest NZI years.


Mamalakis, A., J.-Y. Yu, J.T. Randerson, A. AghaKouchak, and E. Foufoula-Georgiou (2018) A new interhemispheric teleconnection increases predictability of winter precipitation in southwestern US, Nature Communications, DOI: 10.1038/s41467-018-04722-7

Data for New Zealand Index (NZI)

Estimating precipitation from space

We work on new and innovative formalisms for inverse estimation problems (downscaling, data fusion, retrieval, and data assimilation) of precipitation using multi-sensor, multi-scale measurements from space. The proposed frameworks draw upon: (1) recent observations that precipitation fields exhibit "sparsity" in a gradient or wavelet domain (a manifestation of the coherent multicellular structure of rainfall and the presence of sharp fronts), and (2) new theoretical developments in the signal processing community for non-linear, non-smooth data recovery from noisy, blurred and downsampled signals based on Compressive Sensing (CS). In contrast to the widely used formulation of these problems using least-squares variational frameworks, the proposed approach incorporates a non-smooth term (L1 norm) in the wavelet domain, consistent with sparsity and allowing preservation of extremes and localized features. We also work on developing methodologies for non-parametrically handling biases in data assimilation of land-surface states based on the theory of Optimal Mass Transport (OMT).

Rainfall retrievals for the TRMM overpass on November 15, 2007 above the cyclone Sidr at UTC 13:59, including the results from: ShARP (left panel), 2A25 (middle panel) and 2A12 (right panel). The results show the promises of ShARP to improve the rainfall retrieval at the vicinity of coastlines, relevant to coastal hydrologic analysis and deltaic systems.

The three month rainfall [mm] retrievals--October-December of 2013--over the southwest Asia including the Tibetan Highlands and Ganges-Brahmaputra-Meghna river basin, shown at 0.1-degree. The standard TMI-2A12 (left panel), radar PR-2A25 (right panel) and ShARP retrievals. The results clearly indicate that ShARP can significantly mitigate the effects of background noise due to snow cover lands, which results in rainfall over estimation over the Himalayan range and headwaters of the Brahmaputra river basin. These results promise improved hydrologic analysis of mid-latitude and in cold climates.

Compressive Sensing (CS) reconstruction of the surface skin temperature [Kelvin] (left column, 01/01/2002) and surface water vapor mass mixing ratio [g/kg dry air] (right column, 09/09/2002) for all daily ascending orbital tracks. The originally retrieved fields (top row), the randomly under-sampled fields (middle row), and the CS reconstructed fields (bottom row). The under-sampled fields only contain 45% of randomly chosen pixels, which are also corrupted with a white Gaussian noise. The data are obtained from level II, AIRS/AMSU retrievals.

Water/sediment transport and landscape evolution

We explore new methodologies for quantifying the stochastic nature of bedload sediment transport using multiscale analysis and dynamical system theory. We seek to understand the relations between near bed turbulence, river bed morphodynamics and sediment transport using experimental, theoretical, and numerical research. At the basin scale, we investigate network-based frameworks for identifying potential synchronizations and amplifications of sediment delivery to basin outlets and also for identifying hotspots of fluvial geomorphic change based on dynamic connectivity. Dynamical frameworks for the analysis of river meandering dynamics, inferring process from form, and quantifying response to perturbations are also studied. Finally, landscape re-organization under climate change is studied using both controlled laboratory experiments and theoretical approaches.

Process interaction network showing the coupled hydro-geo-biological system that is incorporated into a dynamic model and applied to the Minnesota River Basin. Black dashed lines show weak interactions and black solid lines show strong interactions, which are either positive (+) or negative (-).

Photograph (a) and the DEM (b) of the evolved landscape at the steady state obtained from the eXperimental Landscape Evolution (XLE) facility at the St. Anthony Falls laboratory of University of Minnesota. The river network at the steady state extracted from the DEM at steady state is shown in (c) with the color bar indicating Strahler channel order. Notice the 5th order channels present in the drainage basins. These experiments were conducted to understand how landscapes respond to climate dynamics in terms of macro-scale (average topographic features) and micro-scale (landform re-organization) reorganization.

Results of the long-term simulated meandering river. (a) 30,000 years of modeled centerline realizations. Older centerlines are darker; the blue centerline shows t=30,000 years. The upstream boundary condition fixes the first centerline node in place, leading to the formation of the spiral pattern at the upstream boundary. No restrictions are placed on the downstream node so the river may migrate freely. (b) A reach of simulated centerline selected shows the growth and cutoff of all three atom types. Realizations are 300 years apart. Note the complex multilobe meander that starts as double lobed but develops a third lobe before cutting off between 900 and 1200 years.

Coastal deltas and vulnerability assessment

We explore quantitative frameworks for studying river delta topology and dynamics based on graph-theoretic approaches, where deltaic systems are represented by rooted directed acyclic graphs. Using results from spectral graph theory we develop methods for systematic identification of the upstream and downstream subnetworks, computing steady flux propagation in the network, and finding partition of the flow at any channel among the downstream channels. Using these attributes we construct vulnerability maps that quantify the relative change of sediment and water delivery to the shoreline outlets in response to possible perturbations in hundreds of upstream links. We also develop metrics that capture unique physical, topological, and dynamical aspects of delta networks with the ultimate objective that deltas projected in such a "metric space" can be compared and contrasted and also analyzed for relative vulnerability (or resilience) to change.

Quantitative framework for studying delta channel network connectivity based on spectral graph theory. The river delta, characterized by its channel network, is represented by a directed graph, i.e., a collection of vertices (bifurcations and junctions in the delta) and directed edges (channels in-between vertices, where the direction is given by the flow). All information about the network connectivity can be stored in a sparse adjacency matrix that allows us to extract important network topologic information by straightforward algebraic manipulations.

Location of seven deltas and their corresponding channel networks numbered according to size (largest to smallest area). We used the Smart and Moruzzi [1971] networks for (1) Niger, (2) Parana, (3) Yukon, (4) Irrawaddy, and (5) Colville Deltas. For (6) Wax Lake we used the network extracted by Edmonds et al. [2011]. We have extracted the network of (7) Mossy from Google Earth. Satellite images are copyrighted by Digital Globe Inc. 2014.

Topo-dynamic complexity space for deltas. We project deltas into a topo-dynamic space whose coordinates are given by the topologic and dynamic delta complexity metrics, and show that this space provides a basis for delta comparison and physical insight into their dynamic behavior. Here, the x-axis corresponds to the dynamic exchange of the different subnetworks measured by the Leakage Index (LI), and the y-axis corresponds to the topologic complexity measured by the Number of alternative paths from apex to outlet (Nap). Each colored cross corresponds to a different delta, and the orange dot corresponds to a binary tree. The vertical (horizontal) component of each cross runs from the 25th until the 75th percentile of the Number of alternative paths (Leakage Index).

Current Funding

Please click an award to learn about our research:

NSF: Water Sustainability and Climate - REACH (REsilience under Accelerated CHange)

REACH (REsilience under Accelerated CHange) is a multi-institution, interdisciplinary effort led by the University of Minnesota, with the overall goal to develop a "framework" within which the vulnerabilities of a natural-human system can be assessed to guide decision-making towards sustainability and resilience. A unique element of the framework is identifying and focusing on places, times, and processes of accelerated or amplified change. One specific hypothesis to be tested is that of Human Amplified Natural Change (HANC), which states that areas of the landscape that are most susceptible to human, climatic, and other external changes are those that are undergoing the highest natural rates of change. To test the HANC hypothesis and turn it into a useful paradigm for enabling water sustainability studies, a predictive understanding of the cascade of changes and local amplifications between climatic, human, hydrologic, geomorphologic, and biologic processes are being developed to identify "hot spots" of sensitivity to change and inform mitigation activities.

The developed framework is being tested in the Minnesota River Basin (MRB) where pervasive landscape disturbances due to geological history and human actions are affecting changes in ecosystem health and water quantity and quality. Underlying our research efforts are the following science questions (hypotheses): (1) What are the major drivers of change in the MRB, and how do natural and anthropogenic changes interact, propagate and amplify across scales and across processes (physical, biological, and socio-economic)? (2) How can the history and present state of landscape organization inform the identification of "hot spots" of change? (3) What modeling frameworks (from fully distributed to reduced complexity network-based models) can capture the cascade of physical, biological, and socio-economic changes in data-limited environments and for short to long-term predictions? and (4) How can policy and management decisions be informed by understanding the system vulnerabilities and the places/times most sensitive to change? In other words, how can we preserve and improve water quality, ecosystem functioning, and resilience while still meeting the needs of ecosystems and society?

A schematic of the challenges associated with the Minnesota River Basin (MRB) highlighting the intense crop productivity during the growing season, the artificial drainage that has contributed to an accelerated hydrologic cycle, the geologic legacy of the landscape that predisposes it to increased sediment production and the eventual cascade of these changes to diminishing aquatic species richness in many streams of the basin.

Collaborators: Jacques C. Finlay (Univ. of Minnesota), Karen B. Gran (Univ. of Minnesota - Duluth), Gillian H. Roehrig (Univ. of Minnesota), Patrick Belmont (Utah State Univ.), Peter R. Wilcock (Johns Hopkins University now at Utah State Univ.), Gary Parker (Univ. of Illinois at Urbana-Champaign), Praveen Kumar (Univ. of Illinois at Urbana-Champaign), Catherine L. Kling (Iowa State Univ.), Sergey Rabotyagov (Univ. of Washington).

NSF Water Sustainability and Climate (WSC) project EAR-1209402.

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NSF-Belmont Forum: DELTAS project - Catalyzing action towards sustainability of deltaic systems with an integrated modeling framework for risk assessment

Deltas are dynamic landforms at the land-ocean boundary, involving intricate mazes of river channels, estuarine waterways, and vast, often flooded landscapes. They cover 1% of Earth, yet are home to over half a billion people. Deltas sustain biodiverse and rich ecosystems, such as mangroves, reedlands and marshes. They are also economic hotspots that support major fisheries, forest production, and agriculture, as well as major urban centers, ports, and harbors. Yet, worldwide delta systems, including the people, economies, infrastructure, and ecology they support, are under threat from a range of natural and anthropogenic activities.

The DELTAS project, led by the University of Minnesota, is funded by the Belmont Forum to develop a science-based integrative modeling framework that can be used to assess delta vulnerability and guide sustainable management and policy decisions at the regional and local scales. The DELTAS project will investigate several key questions in delta functioning and vulnerability, towards proposing sustainable solutions. These questions include: (1) How do climate change, pressure on resources, and engineering/infrastructure development make the inhabitants, biodiversity, and ecosystems of deltas vulnerable? (2) How is this vulnerability to be measured? (3) How do delta areas absorb extreme events? What are the hydrological and ecological thresholds underlying the integrity of a delta region? (4) What are the relevant local and regional biophysical and social stressors for a particular delta system, how do these interact, and how do they vary spatially and over time? (5) How can regional delta sustainability be balanced with economic growth? and (6) How can one reduce future risk while attaining sustainable development?

Our research framework comprises five different components:

Delta-SRES: Develop a theoretical framework for assessing delta vulnerability and the possibility for transitions to undesired biophysical or socio-economic states under various scenarios of change.

Delta-RADS: Develop an open-access, science-based, integrative modeling framework called the Delta Risk Assessment and Decision Support (RADS) Tool. This tool will be a GIS modeling system that will support quantitative mapping and definition of functional relationships of the bio-physical environment of deltas as well as their social and economic dynamics.

Delta-DAT: Consolidate data on bio-physical, social, and economic parameters into an international repository of integrated data sets and make these readily available relevant data for use by the community at large to assess critical parameters, compute vulnerability metrics, and provide input data to the Deltas-RADS modeling framework.

Delta-GDVI: Develop Global Delta Vulnerability Indices (GDVI) that capture the current and projected physical-social-economic status of deltas around the world ("delta vulnerability profiles").

Delta-ACT: Work with regional teams and stakeholders to put the products of Delta-SRES, Delta-RADS and Delta-DAT into action by demonstrating the implementation of the developed framework to three major deltas. The selected deltas are the Ganges-Brahmaputra-Meghna (GBM), Mekong, and Amazon deltas.

Amazon River Delta (ARD). The ARD delta is the world's largest delta (~450,000 km2) influencing the coastal economies of Brazil, French Guiana, Surinam, Guyana and Venezuela. In terms of environmental challenges that are typically associated with delta systems, the ARD is often classified as low risk because of its limited damming and water/oil extraction. However, deforestation proceeds at a rapid pace, and population, economy, and infrastructure in Amazonia are growing quickly. Ultimately, like all deltas, the ARD faces multiple, imminent environmental threats.

Ganges-Brahmaputra-Meghna (GBM) Delta. The GBM delta is the world's second largest delta (~100,000 km2) draining land from Bangladesh, Bhutan, China, India and Nepal. The delta covers most of Bangladesh and part of West Bengal, India, with many of the 147 million people (in 2000) living in the delta under extreme poverty and facing multiple challenges. The population is expected to increase by 28% in 2015. Already 30% of Bangladesh is within 5 m of sea level, experiencing tidal water movement 100 km inland during the dry season; and relative sea-level rise that exceeds global mean sea-level rise, demonstrating subsidence. Together these factors make the GBM delta one of the most vulnerable coastal regions in the 21st century.

Mekong River Delta (MRD). The MRD is the world's third largest delta (~50,000 km2) and considered as one of Asia's main food baskets. The major challenges in the MRD can be attributed to socio-economic transformation and urbanization processes leading to the degradation of natural forests and wetland areas, accompanied by increasing water pollution. The MRD is now undergoing large-scale erosion, especially in the muddy mangrove-rich western part, increasing its vulnerability under projected sea-level rise and impacting future food security. Other activities include large-scale sand mining in the river and delta reaches, mangrove removal for shrimp farms, dikes and embankments to protect shrimp farms from flooding, and future large-scale hydropower development upstream. The fact that the Mekong river catchment is shared among six countries (China, Myanmar, Lao PDR, Thailand, Cambodia and Vietnam) is a potential source of conflict in harnessing the resources of the basin, especially hydropower development.

Participating countries: USA, India, Japan, Canada, Germany, France, Norway, China, UK, Netherlands, Vietnam, and Bangladesh.

Partner Institutions: University of Minnesota (lead institution, USA), University of Dhaka (Bangladesh), Aix-Marseille Univ. (France), Indiana University (USA), Deltares (Netherlands), German Aerospace Center (Germany), World Wide Fund for Nature - Greater Mekong Program (Vietnam), Vanderbilt University (USA), International Union for the Conservation of Nature (USA), United Nations Environment Programme (France), Univ. of Southampton (UK), Vietnam Academy of Science and Technology (Vietnam), University of Colorado, Boulder (USA), Anna University (India), United Nations University (France), Geological Survey of Japan (Japan), Bangladesh Univ. of Engineering and Technology (Bangladesh), Norwegian Institute for Air Research (Norway), Natural Resources Canada (Canada), University of Waterloo (Canada), and Nanjing University (China).

Belmont Forum and G8 Research Councils Initiative on Multilateral Research Funding - International Opportunities Fund G8MUREFU3-2201-037.

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NSF: International Collaboratory - LIFE (Linked Institutions for Future Earth)

Among the many dimensions of the accelerating pace of global environmental change, the Earth's surface - the environment where most life and human activity evolves - is perhaps the most central but until recently among the least comprehensively studied. The Earth's surface is undergoing profound change due to human activities (land-use change, intensive agriculture to feed the world's increasing population, urbanization, etc.), natural hazards caused by increased extreme events in a warming climate (landslides, droughts, floods), and sea level rise causing coastal erosion and ecosystem degradation in deltas around the world. Building on the success of the National Center for Earth surface Dynamics (NCED) in providing a national collaboratory for Earth surface dynamics research, this project extends our efforts internationally by establishing a Virtual Institute called LIFE (Linked Institutions for Future Earth). This virtual institute aims at coordinating international research built on sharing unique experimental facilities, theoretical strengths, and field observations for advancing the quantitative, predictive understanding of the Earth surface system and its response to change.

Collaborators: Chris Paola (Univ. of Minnesota), Vaughan Voller (Univ. of Minnesota), William Dietrich (Univ. of California - Berkeley), Paola Passalacqua (Univ. of Texas - Austin), Praveen Kumar (Univ. of Illinois), Patrick Hamilton (Science Museum of Minnesota), Vladimir Nikora (Univ. of Aberdeen), Liam Reinhardt (Univ. of Exeter), Francois Metivier (Institut de Physique du Globe de Paris), Antonio Parodi (CIMA Research Foundation), Daniel Conde (Universidad de la Republica - Uruguay), Cristian Escauriaza (Pontifica Universidad Catolica de Chile), Rina Schumer (Desert Research Institute), Laurel Larsen (Univ. of California - Berkeley).

NSF Science Across Virtual Institutes (SAVI) project EAR-1242458.

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NASA: Next Generation of Multi-sensor Multi-scale Precipitation Fusion Products for the GPM era

We develop a new and innovative formalism for statistical estimation (fusion, retrieval, and resolution enhancement) of multi-sensor, multi-scale precipitation measurements in the GPM era and steps for operational implementation. We pose the fusion problem in a nonlinear variational formulation setting and use a Maximum a Posteriori (MAP) estimation which is constrained by a sparse prior in the wavelet domain. In contrast to the widely used least-squares approaches which yield smoother estimates, the proposed approach incorporates a non-smooth term (L1 norm) and, as such, allows preservation of extremes and localized features. Within the same general formalism, we propose new ideas for improving the performance of precipitation retrieval and resolution enhancement of multi-sensor products using a non-linear dictionary-based inverse estimation approach with sparse priors. The ultimate goal of the proposed research, in collaboration with NASA researchers, is to develop state-of-the-art and operationally viable algorithms for optimal fusion, downscaling, and retrieval of multi-instrument and multi-satellite precipitation data for enhanced utilization of the TRMM/GPM products in climate, weather, and hydrologic prediction studies.

NASA Precipitation Measurement Missions Science Team

Software Packages


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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