About

Rafael L. Bras holds the K. Harrison Brown Family Chair and is a Professor at the Georgia Institute of Technology. He is a Regents' Professor, indicating a distinguished academic status. His research focuses on water resources engineering, as he is the Group Coordinator for this program and the Director of the Georgia Water Resources Institute. His work involves addressing issues related to water, energy, and environmental systems, contributing to the development of sustainable infrastructure and resilient communities. As a prominent figure in civil and environmental engineering, he has made significant contributions to advancing knowledge in water resources and sustainable systems, supporting Georgia Tech's mission to solve complex societal problems through engineering.

Research topics

• Environmental science
• Geography
• Artificial Intelligence
• Geology
• Computer Science
• Operations research
• Oceanography
• Risk analysis (engineering)
• Geotechnical engineering
• Ecology
• Soil science
• Remote sensing
• Meteorology
• Engineering

Selected publications

• Deriving the conditional distribution of soil moisture and its use in estimating memory in the water-soil system

  2026-03-14

  articleOpen accessSenior author

  Soil moisture plays a crucial role in both ecosystems and human activities. It serves as a primary water source for plants and soil microorganisms. People use soil moisture information in irrigation strategies, predicting soil-borne plant diseases, and more. Soil moisture memory (SMM) refers to the soil’s ability to reflect signals of perturbations caused by anomalies such as intense storms or prolonged dry periods, over time. Its significance stems from its direct link to soil moisture dynamics, as understanding SMM characteristics helps predict soil moisture behavior over time. Many studies have investigated SMM, employing various metrics for its measurement, for example, the e-folding autocorrelation timescale. The time scale of SMM ranges from a couple of days to several months, but its duration and seasonality vary by location, depending on soil types, local hydrological settings, climatic regimes, and vegetation ecosystems. This study introduces a novel SMM metric based on the differences between the conditional and marginal distributions of soil moisture. First, a soil moisture simulation model is calibrated using modified ERA5 Potential Evapotranspiration (PET) and NASA’s GPM IMERG precipitation data as inputs, with SMAP soil moisture data as target values on a daily scale. Next, 2,000 years of daily precipitation and minimum/maximum temperature are generated using the stochastic weather generator WeaGETS, driven by GPM IMERG and CPC temperature data. PET is then estimated from the simulated temperature using the temperature-based Hargreaves-Samani equation. Using the generated 2,000-year input data, daily soil moisture is simulated. The simulation bias is then corrected using the CDF-matching method. With the bias-corrected daily soil moisture, the joint, marginal, and conditional probability distributions of soil moisture are analyzed at multiple lead times (3, 7, 14, 21 days) across four seasons and two study sites in Iowa and Ukraine. Results show that conditional distributions converge toward marginal distributions within 7-14 days in Iowa and 14-21 days in Ukraine in most seasons, with winter and spring exhibiting the longest SMM time scale for Iowa and Ukraine, respectively. This study shows how the conditional distributions of soil moisture gradually converge to the marginal distributions as lead prediction time increases. The time to convergence, dependent on soils, climate and season is a measure of the memory of soil moisture in the system. The conditional distributions are key to applications like irrigation scheduling.

  PublisherDOI

• Supplementary material to "Spatially distributed water content thresholds for rainfall-induced landslide initiation"

  2026-05-04

  article
  PublisherDOI

• Spatially distributed water content thresholds for rainfall-induced landslide initiation

  2026-05-04

  articleOpen access

  Abstract. Rainfall-induced shallow landslides are among the most widespread natural hazards in mountainous regions, where intense precipitation, steep topography, and subsurface hydrological processes interact to trigger slope failures. Physically based approaches commonly derive rainfall-triggering thresholds using the framework proposed by Montgomery and Dietrich (1994), which defines instability conditions as a function of groundwater table position. However, this formulation neglects the stabilizing contribution of matric suction in unsaturated soils, potentially limiting its applicability. This study introduces a complementary metric, the Critical Soil Moisture (CSM), which, together with the classical Critical Wetness Index (CWI), provides a continuous hydro‑mechanical description of stability across the full range of hillslope moisture states. The methodology is applied to the 28.6 km² Pontaiba basin in the Carnic Alps (northeastern Italy), a region characterized by steep terrain, high precipitation, and documented shallow landslides. Spatially distributed analyses based on topographic, soil, and landslide inventory data are combined with sensitivity analyses and an ensemble calibration procedure using Receiver Operating Characteristic (ROC) metrics to constrain uncertain parameters. Results delineate three stability regimes, unconditionally stable terrain, groundwater-controlled instability (CWI), and moisture-controlled instability (CSM), and identify slope-dependent hydrological thresholds that can support landslide early warning by focusing on state variables (groundwater, soil moisture) rather than rainfall alone.

  PublisherDOI

• Delineating conditionally stable areas and critical soil water content maps for initiation of rainfall-induced landslides 

  2025-03-14

  preprintOpen access

  Initiation of rainfall-induced landslides is intricately linked to hydrological conditions, mainly soil water content (SWC), which directly reflects precipitation intensity and patterns. Initiation may occur only on areas that are susceptible to the movement, i.e., the so-called conditionally stable areas. Existing methods delineate unconditionally and conditionally stable areas in “partially saturated” soils based on topography, mechanical properties, and a steady state wetness index (WI) or depth of groundwater level.This study presents a methodology that delineates conditionally stable areas under fully unsaturated soil water conditions, i.e., in the absence of groundwater. In particular, the methodology identifies (i) the ‘partially-saturated’ conditionally stable areas previously mentioned in terms of groundwater level or positive pressure head, and (ii) an ‘unsaturated’ conditionally stable areas, assessed in terms of SWC or negative pressure head. This is obtained computing the factor of safety (FoS) by using two equations of the infinite slope model, which account for both saturated and unsaturated soil conditions. The region delineation ultimately depends on the spatial heterogeneity of topographic and hydro-mechanical properties of the terrain. Finally, for the conditionally stable areas, both ‘partially saturated’ and ‘unsaturated,’ we derive critical maps of landslide initiation, either in terms of SWC or pressure head, respectively. In order to provide efficient and easy-to-interpret maps, the methodology generates Homogeneous Soil Units (HSUs) where each unit is represented by a unique combination of slope and hydro-mechanical properties of the terrain. A unique critical value of SWC or pressure head will result for each HSU at a given hypothetical failure surface, i.e., soil depth.We apply the methodology over the Friuli Venezia Giulia region, Italy, and central Puerto Rico, where thousands of shallow landslides were triggered by Hurricane Maria in September 2017.This research received funding from European Union NextGenerationEU – National Recovery and Resilience Plan (PNRR), Mission 4, Component 2, Investiment 1.1 -PRIN 2022 – 2022ZC2522 - CUP G53D23001400006.

  PublisherDOI

• Low‐Rank Gap Filling and Downscaling for SMAP Soil Moisture Datasets

  Ecohydrology · 2025-04-01

  articleOpen access

  ABSTRACT Soil moisture is the linchpin of the surface hydrologic cycle, controlling the partitioning of water and energy fluxes at the surface. Without it, vegetation, and hence life on the solid Earth as we know it, would not exist. Understanding ecohydrology is understanding the availability of soil moisture to vegetation. Until recently, measuring soil moisture was difficult, expensive, intrusive, and local. NASA's Soil Moisture Active Passive (SMAP) mission changed that by providing global estimates at reasonable frequencies. Ecohydrology and many other hydrologic applications are best when high spatiotemporal resolution soil moisture datasets are available. The SMAP and SMAP‐Sentinel soil moisture products currently possess contrasting spatial and temporal resolutions, but their coincident nature presents an opportunity to learn how to enhance the spatial resolution of SMAP retrievals to obtain a global, high spatiotemporal resolution dataset. However, a challenge in learning from SMAP‐Sentinel data is the presence of missing pixels. In this work, we propose a low‐rank approach to both gap‐fill SMAP‐Sentinel and downscale SMAP and evaluate its performance globally on both held‐out SMAP‐Sentinel data and measurements from SMAPVEX validation datasets. The proposed method outperformed baselines globally on SMAP‐Sentinel data but had mixed performance against retrievals from airborne measurements. A procedure for filling in missing pixels in SMAP‐Sentinel measurements using the low‐rank models was found to outperform alternative interpolation methods. Overall, the results show that the proposed method can recover missing pixels in soil moisture measurements and can be used to compute estimates of high‐resolution SMAP‐Sentinel retrievals from low‐resolution SMAP data.

  PublisherOA PDFDOI

• Exploiting EGMS data in a thickness inversion methodology to enhance shallow landslide assessment

  2025-03-14

  preprintOpen access

  Physically-based models for rainfall-triggered landslides enhance understanding of the interactions between rainfall, soil hydrology, and slope stability. Pre-event landslide modeling presents significant challenges, primarily due to uncertainties in estimating landslide volumes, which depend on the complex geometries of natural and basal sliding surfaces. Furthermore, physically-based distributed models often face challenges in acquiring datasets that are both spatially and temporally comprehensive.This study introduces a methodology leveraging recent advancements in remote sensing technologies, which offer promising non-contact solutions for estimating landslide characteristics. A key focus is on calculating soil thickness, a critical parameter influencing mobilized soil weight and the factor of safety (FS) for physically based modeling. We integrate InSAR data from the European Ground Motion Service (EGMS), which provides freely accessible, continental-scale ground motion and displacement rate observations over stable targets (the so-called persistent scatterers, or PS), generally identified with man-made infrastructures or rock outcrops, with the mass conservation method. This method assumes minimal changes in the sliding base geometry during the observed deformation period, linking the rate of landslide thickness change to the spatial variation of the vertical deformation mean yearly velocity, enabling soil thickness estimation and sliding geometry definition. The experiment involved selecting landslides with a minimum number of PS falling on their surface, then setting up the system of differential linear equations applied to the selected PS targets. Tikhonov regularization was employed to overcome ill-posedness, and the equations were solved by finite difference methods implemented in Matlab. The Tikhonov regularization introduces a smoothing parameter which assigns a weight to the Laplacian term of the thickness model. The methodology is being tested in a case study area within the Friuli-Venezia Giulia region, in Italy, known for well-documented shallow landslides in the Italian Landslide Inventory (IFFI).Preliminary results demonstrate that the soil thickness and sliding geometry can be retrieved with reasonable accuracy, although measurements are highly sensitive to the choice of the smoothing parameter used in the regularization process.

  PublisherDOI

• Modeling the impact of Hurricane Maria on Puerto Rico with an eco-hydrological landslide model

  2024-03-09

  preprintOpen accessSenior author

  This study proposes an advanced hydrologic/landslide modeling application to assess the spatial distribution of rainfall-induced landslides for a sub-basin in central Puerto Rico. The framework implements a stability component into a spatially distributed physically-based hydrological model coupled to a model of plant physiology. Puerto Rico is an ideal study site to assess the performance of landslide modeling efforts due to the availability of thousands of catalogued landslides triggered by Hurricane Maria (HMA) during September 19-22, 2017. The main objective of the study is to simulate the observed landslide events forcing a coupled eco-hydrological-stability model, the tRIBS-VEGGIE-Landslide, with weather data of HMA. The tRIBS-VEGGIE-Landslide model has the advantage of accounting for the vegetation dynamics that affect the soil moisture patterns at an hourly scale and for the soil-water characteristic curve and the saturated shear strength parameters (cohesion and friction angle) to assess the factor of safety (FS) in space and time, using an infinite slope model.The modeling application focuses on two small sub-basins of the Rio Saliente watershed, each smaller than 1 km2. The small study area allows for the use of a 5m DEM resolution topography, which has been derived from a 1m resolution LiDAR measurements. Since many radar and ground stations were destroyed during the hurricane, the hourly time series of the HMA event has been reconstructed by using the NCEP (National Centers for Environmental Prediction) – Environmental Modeling Center (EMC) gridded Stage IV data, produced by NOAA National Weather Service. The precipitation data resulted in a maximum hourly intensity of 64.52 mm/hr, maximum daily intensity of 294.56 mm/day, and rainfall total of 332.15 mm, consistent with other daily reconstructions. Preliminary results demonstrate the importance of the spatial computational mesh and accurate characterization of soil parameters, which play an essential role in simulating landslides with mechanistic models.

  PublisherDOI

• Mit's Master Of Engineering Program In Civil And Environmental Engineering A First Professional Degree

  2024-01-31

  articleOpen accessSenior author

  Abstract NOTE: The first page of text has been automatically extracted and included below in lieu of an abstract Session 2515 MIT's Master of Engineering Degree in Civil and Environmental Engineering--a first professional degree E. Eric Adams, Rafael L. Bras Dept. of Civil and Environmental Engineering Massachusetts Institute of Technology Introduction Engineering is one of the few disciplines in which professional status is claimed with only a four-year undergraduate degree. It is becoming evident that such a model is not sustainable in an increasingly complicated and technological world. Employers have responded by effectively requiring a masters as entry level degree for premium civil and environmental engineering positions. Society, in turn, has responded by devaluing engineers relative to other professions. In recognition of this situation, the Board of Direction of the American Society of Civil Engineers has approved a policy statement supporting the Master's degree as the First Professional Degree for the practice of Civil Engineering at a professional level1. Four years ago MIT's Department of Civil and Environmental Engineering developed a new degree, seeking a unique and different post-baccalaureate experience that we hope will become the model of the first professional degree. Following is a summary of our experiences after three graduating classes. MIT's Master of Engineering Degree in Civil and Environmental Engineering In 1995 the Department of Civil and Environmental Engineering at MIT introduced the Master of Engineering degree. The M.Eng. degree provides additional technical depth beyond the B.S. and an educational experience aimed toward professional practice. All M.Eng. students are expected to have undergraduate degrees in engineering (mostly civil or environmental), about one-third have one or more year's of previous work experience, and about half are U.S. citizens (though many of the foreign students are U.S. permanent residents and/or have studied in the U.S.). Except for the engineering degree, which is not required for other graduate programs, admission to the Master of Engineering program is based on the same quality criteria as other programs. The profile of students, though, tends to be different, with practice the clear immediate professional objective. Graduates of our Bachelor of Science program with "B" or higher career cumulative averages are offered automatic admission into the M.Eng. program. With proper planning, our undergraduates can develop a seamless transition between undergraduate and graduate programs culminating in the B.S. and M.Eng. degrees in 5 years. They have the advantage of being able to pace requirements better and to experience a somewhat less crowded 5th year. Additional characteristics of the M.Eng. program, include:

  PublisherOA PDFDOI

• Modeling Landslides in Puerto Rico with a Distributed Eco-hydrological Model

  Soil Erosion Research Under a Changing Climate, January 8-13, 2023, Aguadilla, Puerto Rico, USA · 2023-01-01

  articleSenior author

  This presentation provides a compilation of results from an eco-hydrological-landslide model: tRIBS-VEGGIE-Landslide. This effort couples a well-established distributed basin simulator with a slope stability model that considers topography, soil moisture and vegetation. The research explores the impact of soil parameter uncertainty, of vegetation and roots, and of numerical resolution on predictions of rainfall triggered landslides. The Luquillo Mountains on the northeast of Puerto Rico are used as a case study. The uncertainty associated to the soil hydrological and geotechnical parameters is considered by implementing the First- Order Second Moment (FOSM) probabilistic method. The model produces maps and times series of two main representative quantities of probability (hereinafter p.) of failure: (i) the p. of plane of failure at a given soil depth (PrPFi), which indicates dynamically the most probable depth of failure, based on soil moisture dynamics and soil parameters; (ii) the p. of failure of the column, PrFC, that can be generated anywhere within the column. Figure 1 shows the time series of the two probabilities evaluated for the rainfall event reported in Figure 1a. The most probable failure surfaces occurred at depths between 300 and 1000 mm, indicating high probability of shallow landslides. <fig><graphic xlink:href=23500_files/23500-09.jpg id=ID_2d696a77-3005-4db4-94c1-b7c38b941b03></graphic></fig> The hydrological and mechanical effects of roots on slope stability were investigated assessing the role of two different vegetation species, shrubs and trees, in comparison to a case without vegetation cover. The model estimates the additional root reinforcement exerted by roots, in addition to the apparent cohesion due to soil suction under unsaturated soil. Figure 2 shows representative results in terms of Factor of Safety (FS) obtained with or without additional root cohesion (∆FSr). The stabilizing effect of the hydrological process is more effective in the case of trees. The resulting drier conditions, due to transpiration, over the most critical depths led to an increase of FS. Finally, to evaluate the influence of terrain resolution on the hydro-geomorphological processes involved in slope stability, we run tRIBS-VEGGIE-Landslide by using five grid-DEM resolutions of the case study basin, i.e., 10, 20, 30, 50, and 70 m (Arnone et al., 2021). Since the model implements Triangulated Irregular Network (TIN), a grid-DEM to TIN algorithm is involved. Using irregular meshes reduces the loss of accuracy with coarser resolutions in the derived slope distribution, in comparison to that estimated using the original grid-DEM. Additionally, from the hydrological perspective, the ultimate impact of resolution on slope stability is significant mostly when lateral water exchanges are allowed within the model framework. <fig><graphic xlink:href=23500_files/23500-08.jpg id=ID_1aa8eec6-32ce-42b3-97c2-172c37aa7cbf></graphic></fig> <b>REFERENCES</b> Arnone, E., Francipane, A., Dialynas, Y.G., Noto, & Bras, R.L. (2021). Implications of terrain resolution on modeling rainfall-triggered landslides using a TIN-based model. Environ. Model. Softw., 141, 105067. Arnone, E., Dialynas, Y.G., Noto, L.V., & Bras, R.L. (2016a). Accounting for soils parameter uncertainty in a physically based and distributed approach for rainfall-triggered landslides. Hydrol. Process., 30, 927–944. Arnone, E., Caracciolo, D., Noto, L.V., Preti, F., & Bras, R.L. (2016b). Modeling the hydrological and mechanical effect of roots on shallow landslides. Water Resour. Res., 52 (11), 8590-8612.

  PublisherDOI

• Dataset on the global distribution of shallow groundwater

  Data in Brief · 2023-02-13 · 6 citations

  articleOpen accessSenior author

  Shallow groundwater (GW), defined as the water table of unconfined or perched aquifers that is near enough to the land surface to influence the vadose zone and the surface soil moisture, impacts land surface water, energy, and carbon cycles by providing additional moisture to the root zone via capillary fluxes. Although the interactions of shallow GW and the terrestrial land surface are widely recognized, incorporating shallow GW into the land surface, climate, and agroecosystem models is not yet possible due to the lack of groundwater data. Groundwater systems are affected by various factors, including climate, land use/land cover, ecosystems, GW extractions, and lithology. Although GW wells are the most direct and accurate way of monitoring water table depths at point scales, upscaling GW levels from point scale to areal or regional scale poses significant challenges. Here, we provide high spatiotemporal resolution global maps of the terrestrial land surface areas influenced by shallow GW from mid-2015 to 2021 (a separate NetCDF file for each year) in a 9 km spatial and daily temporal resolution. We derived this data from NASA's Soil Moisture Active Passive (SMAP) mission spaceborne soil moisture observations with a temporal resolution of 3 days and approximately 9 km grid resolution. This spatial scale corresponds to SMAP's "Equal Area Scalable Earth" (EASE) grids. The central assumption is that the monthly moving average of soil moisture observations and their coefficient of variation are sensitive to shallow GW regardless of the prevailing climate. We process the Level-2 enhanced passive soil moisture SMAP (SPL2SMP_E) product to detect shallow GW signals. The presence of shallow GW data is calculated by an ensemble machine learning model, which is trained using simulations from a variably saturated soil moisture flow model (Hydrus-1D). The simulations span various climates, soil textures, and lower boundary conditions. The spatiotemporal distribution of shallow GW data based on SMAP soil moisture observations is provided for the first time with this dataset. The data are of value in a wide variety of applications. The most direct use is in climate and land surface models as lower boundary conditions or as a diagnostic tool to verify model results. Some other applications may include flood risk analyses and regulation, identifying geotechnical issues such as shallow GW-triggered liquefaction, global food security, ecosystem services, watershed management, crop yield, vegetation health, water storage trends, and tracking mosquito-borne diseases by identifying wetlands, among other applications.

  PublisherDOI

Recent grants

• New Approaches to Real Time Flood Forecasting: Weather Radar and Fluvial Geomorphology in Hydrology

  NSF · $337k · 1989–1994

• Collaborative Research: An Optimality Principle of Evaporation over the Land Surface

  NSF · $259k · 2003–2007

• Collaborative Research: WCR: Is Deforestation Changing the Hydrologic Climate and Vegetation Dynamics of the Amazon?

  NSF · $400k · 2005–2011

• An Application of the Theory of Maximum Entropy Production in Modeling Evapotranspiration

  NSF · $353k · 2011–2015

• Collaborative Research: Co-Organization of River Basin Geomorphology and Vegetation

  NSF · $219k · 2009–2011

Frequent coauthors

• I. Rodriguez‐Iturbe

  72 shared

• Ede Ijjász‐Vásquez

  48 shared

• Dara Entekhabi

  Massachusetts Institute of Technology

  45 shared

• Junmei Wang

  University of Pittsburgh

  39 shared

• V. Y. Ivanov

  37 shared

• Jingfeng Wang

  Georgia Institute of Technology

  36 shared

• Marcos Longo

  Brazilian Agricultural Research Corporation

  33 shared

• Enrique R. Vivoni

  Arizona State University

  33 shared

Education

• Ph.D., Civil and Environmental Engineering

  Massachusetts Institute of Technology (MIT)

• M.S., Civil and Environmental Engineering

  Massachusetts Institute of Technology (MIT)

• B.S., Civil and Environmental Engineering

  Massachusetts Institute of Technology (MIT)