My research interests include:
• Hillslope geomorphology and landscape evolution; image: hillslope and river channel morphology near Te Puka Valley, Willington, New Zealand; source: link
•• Fault mechanics and earthquake physics; image: San Andreas Fault zone, California, USA; source: Tom Bean/Corbis
••• Transport and surficial patterns in gravel bed rivers; image: gravel bar formation and armoring (grain sorting, coarsening) phenomenon in Elbow river, Canada; source: link
•••• Fluvial geomorphology; image: Rhône river flowing from Valais into Lake Geneva, bringing fresh muddy sediments to its delta; source: link
••••• (Aeolian?) transport and surficial patterns on planetary bodies and its relation to their forcings and climatic history; image: dunes of Mars; source: link
•••••• Aeolian transport on Earth; image: sand dunes in Death Valley, California, USA; source: link


Welcome to my webpage! I hope you find the materials and brief discussions presented here interesting. This page is being regularly updated, so please feel free to come back soon! Also, if you have any questions or have some related or unrelated ideas or suggestions for potential collaboration, please do not hesitate to get in touch with me.
I am Behrooz Ferdowsi, currently a Hess Fellow and Postdoctoral Research Associate at the Department of Geosciences, Princeton University. I am at the moment mostly collaborating with Prof. Allan M. Rubin and Prof. Troy Shinbrot (Rutgers University) on physics of earthquake fault friction (also known as rate- and state-dependent friction laws), granular friction and granular flows, and planetary surface and subsurface processes. Before coming to Princeton, I was a Postdoctoral Researcher at the University of Pennsylvania, Sediment Dynamics Laboratory (PennSeD) and a Synthesis Postdoctoral Fellow of the National Center for Earth-Surface Dynamics (NCED). At PennSeD, I worked with Prof. Douglas J. Jerolmack (UPenn, Earth and Environmental Science) on bimodal sediment transport, subsurface to surface evolution of riverbeds and also granular controls of hillslope creep and landscape evolution. I also worked with my NCED-synthesis postdoctoral team (a team of highly motivated early career Earth scientists) to develop what we call an Earthcasting framework that we hope will shape the future of applied and fundamental research in Earth surface science.
I received my PhD (Dr. sc.) from ETH Zurich (Switzerland) under supervision of Prof. Jan Carmeliet and Dr. Michele Griffa and in collaboration with Dr. Paul Johnson (Los Alamos National Laboratory) and Prof. Chris Marone (Pennsylvania State University). For my PhD, I studied Dynamic Earthquake Triggering phenomenon, that is triggering of earthquakes by other (seismic) sources.
A latest copy of my CV is available here and my publications are also separately listed here. My contact info can be found below:

Research interests

I have a broad research interest in several (and expanding) areas of geosciences, including earthquake physics, hillslope and landscape evolution, fluvial and aeolian transport, stratigraphy and sedimentology. The unifying concept of my research interests however lies in soft condensed matter physics and the statistical physics of particles and fields. I search for universal laws that can describe evolution of different sections (and cross-sections) of Earth and other planets across the scales of time (sub-second to million years) and length (asperities and grains to geo/log/phy/ical scales). For my works, I often use a combination of laboratory-scale experiments, numerical (Discrete Element Method and Molecular Dynamics) simulations, and analytical (sometimes continuum) modeling, with continuous inspiration and insight from direct (by myself) and indirect (previously published, collected, documented) field observations.

News, short notes and views

Brief summary on current and past research

a) Evolution of riverbed surface in gravel-bed rivers (2015-2017)

In collaboration with Dr. Carlos P. Ortiz and Dr. Morgane Houssais and under direction of Prof. Doug Jerolmack, I worked on sediment transport in bimodal (two major grain size) systems in an idealized laboratory river. Separation or segregation of grains with different size (and shape, and mass, and surface characteristics, and ...) is a frequently observed phenomenon in rivers, streams (where it is commonly known as sorting, coarsening, or armoring when larger grains cover the riverbed surface, and is known as gravel bars and fans when segregation and separation takes place intermittently) and granular physics! Geologists and geomorphologists tend to think that formation of a coarse surface layer shields the finer underlying grains from erosion. They also traditionally assume that armor develops due to sorting of surficial grains by the fluid flow. In this research, we showed how motion of grains deep beneath the surface delivers larger grains to the surface. Using experiments in a laboratory river, and discrete and continuum models, we further demonstrated that river-bed armoring is driven by vertical granular segregation and that the fluid has little effect. Results also revealed different segregation mechanisms for deep (creeping) versus shallow (dense and rapidly flowing) grains, which has broader implications for all manner of granular flows. Please see our Nature Communications (2017) and University of Pennsylvania News article: `Brazil Nut Effect` Helps Explain How Rivers Resist Erosion on this work for more information.

↑ Phenomenology and setup. (A) Bed sediment of the River Wharfe, U.K., that shows a pronounced surface armor. Photo courtesy D. Powell [1]. (B) Sketch of the experiment, showing position of the camera and laser plane used for imaging inside the granular bed. (C-E) Snapshots during armor development for τs* = 3.8τ*cs. Also shown is the fluid boundary stress, which is computed as τ = ηUf/hf [2] where Uf and hf are the top-plate speed and flow depth, respectively. The red curve shows the long-term-averaged streamwise particle velocity ux(z), where I and II correspond to the bed load and creep zones, respectively. The directions x and z are indicated. (F) Temporal evolution of the thickness of the armored layer at different Shields number. Legend indicates Shields number associated with each curve. The brighter continuous lines are predictions from a modified version -to account for creep (slow flow) segregation- of advection-diffusion model [3,4], commonly used to model size-segregation phenomenon in granular mixing. Note the first rapid stage of armoring, which is dependent on Shields number and is associated with bed-load transport, and the second slower stage that exhibits a nearly constant rate for all Shields numbers and is the result of creep.

b) Hillslope evolution and landscape dynamics over geological timescales (2016-present)

Soil creeps imperceptibly downhill, but also fails catastrophically to create landslides. Despite the importance of these processes as hazards and in sculpting landscapes, there is no agreed upon model that captures the full range of behavior. In this work, we examine the granular origins of hillslope soil transport by Discrete Element Method simulations, and re-analysis of measurements in natural landscapes. We find creep for slopes below a critical gradient, where average particle velocity (sediment flux) increases exponentially with friction coefficient (gradient). At critical there is a continuous transition to a dense-granular flow rheology, consistent with previous laboratory experiments. Slow earthflows and landslides thus exhibit glassy dynamics characteristic of a wide range of disordered materials; they are described by a two-phase flux equation that emerges from grain-scale friction alone. This glassy model reproduces topographic profiles of natural hillslopes, showing its promise for predicting hillslope evolution over geologic timescales.
↑ Landslide and creep phenomenology. (A) Rapid landslide in San Salvador, El Salvador; and (B) Slow earthflow in Osh, Kyrgyzstan. (C) Ranges of surface velocities observed for various types of slow and rapid landslides. The datapoints in red, brown, magenta, and green correspond to the observations reported or documented by Cruden & Varnes (1996)[5], Hungr et al. (2001)[6], Hilley et al, (2004)[7], and Saunders & Young (1983)[8], respectively. (D) Schematic cross section of a soil-mantled hillslope. Photo credits: (A) Associated Press/Wide World Photos, (B) Joachim Lent.

↑ General flow behavior and the glassy flux model. (A) DEM results showing normalized local downslope velocity ([(ux(z))/(ux(zc))]) as a function of normalized local friction coefficient ([(μ)/(μc)]) for four different inclinations, below and above the bulk angle of repose. (B) Field data of normalized flux qs/qsc - equivalent to normalized velocity us/usc - versus normalized gradient (S/Sc) for five different studies of natural hillslopes. Data cloud color represents the probability of all field observations, that takes into account not only the probability of an observation at a given value of flux-gradient in the collected datasets, but also the number of sites/hillslopes collected and involved in each study (please see this arXiv:1708.06032 submission for methods and details of the analysis, and Figs. S5 & S6 therein). Dashed line illustrates an exponential scaling for creep regime with critical gradient Sc = 0.4 and a power-law scaling for the range of large flux with a power-law exponent β = [5/2]. The chosen values of exponential and power law scaling coefficients are not a fit to the data, because of inherent variability of the observations. They represent a qualitative comparison between expectations from theory, numerical simulations, and the field.

↑ Hillslope topography of the Oregon Coast Range (OCR) derived from this publicly available airborne lidar data on opentopography platform (link to lidar data). (A) Regional perspective view, showing locations of two example hillslopes. (B) The elevation-distance and (C) gradient-distance relationships for representative profiles of hillslopes (1) (black dots) and (2) (red dots) in panel (A). Blue dashed line is the prediction of the "glassy" flux model with Sc = 0.5 and β = 5/2. Please see supplementary information in this arXiv:1708.06032 submission for more examples.

c) Physics of rate- and state-dependent friction laws for fault gouge (2015-present)

Numerical simulations of earthquake nucleation rely on constitutive rate and state evolution laws to model earthquake initiation and propagation processes. The response of different state evolution laws to large velocity increases is an important feature of these constitutive relations that can significantly change the style of earthquake nucleation in numerical models. However, currently there is not a rigorous understanding of the physical origins of the response of bare rock or gouge-filled fault zones to large velocity increases. This in turn hinders our ability to design physics-based friction laws that can appropriately describe those responses. We here argue that most fault zones form a granular gouge after an initial shearing phase and that it is the behavior of the gouge layer that controls the fault friction. We perform numerical experiments of a confined sheared granular gouge under a range of confining stresses and driving velocities relevant to fault zones and apply 1-3 order of magnitude velocity steps to explore dynamical behavior of the system from grain- to macro-scales. We compare our numerical observations with experimental data from biaxial double-direct-shear fault gouge experiments under equivalent loading and driving conditions. Our intention is to first investigate the degree to which these numerical experiments, with Hertzian normal and Coulomb friction laws at the grain-grain contact scale and without any time-dependent plasticity, can reproduce experimental fault gouge behavior. We next compare the behavior observed in numerical experiments with predictions of the Dieterich (Aging) and Ruina (Slip) friction laws. Finally, the numerical observations at the grain and meso-scales will be used for designing a rate and state evolution law that takes into account recent advances in rheology of granular systems, including local and non-local effects [9,10,11], for a wide range of shear rates and slow and fast deformation regimes of the fault gouge.

d) Pattern formation and charge transfer in vibrated granular beds and implications for landform on Earth and other planets (2017-present)

coming soon!

e) Role of dynamic stress and perturbation in triggering slip in frictional amorphous systems (2011-2014)

coming soon!


Powell, D. M. Patterns and processes of sediment sorting in gravel-bed rivers. Progress in Physical Geography 22, 1-32 (1998).
Houssais, M., Ortiz, C. P., Durian, D. J. & Jerolmack, D. J. Onset of sediment transport is a continuous transition driven by fluid shear and granular creep. Nature communications 6 (2015).
Gray, J. & Ancey, C. Particle-size and-density segregation in granular free-surface flows. Journal of Fluid Mechanics 779, 622-668 (2015).
Gray, J. & Thornton, A. A theory for particle size segregation in shallow granular free-surface flows. In Proceedings of the Royal Society of London A: Mathematical, Physical and Engineering Sciences, vol. 461, 1447-1473 (The Royal Society, 2005).
Cruden, D. M. & Varnes, D. J. Landslides: investigation and mitigation. chapter 3-landslide types and processes. Transportation research board special report (1996).
Hungr, O., Evans, S., Bovis, M. & Hutchinson, J. A review of the classification of landslides of the flow type. Environmental & Engineering Geoscience 7, 221-238 (2001).
Hilley, G. E., Bürgmann, R., Ferretti, A., Novali, F. & Rocca, F. Dynamics of slow-moving landslides from permanent scatterer analysis. Science 304, 1952-1955 (2004).
Saunders, I. & Young, A. Rates of surface processes on slopes, slope retreat and denudation. Earth Surface Processes and Landforms 8, 473-501 (1983).
DeGiuli, E. & Wyart, M. Friction law and hysteresis in granular materials. Proceedings of the National Academy of Sciences (2017). URL
Zhang, Q. & Kamrin, K. Microscopic description of the granular fluidity field in nonlocal flow modeling. Physical Review Letters 118, 058001 (2017).
Henann, D. L. & Kamrin, K. A predictive, size-dependent continuum model for dense granular flows. Proceedings of the National Academy of Sciences 110, 6730-6735 (2013).

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