About

Dara Entekhabi is a professor at the Massachusetts Institute of Technology in the Department of Civil and Environmental Engineering. He holds the Bacardi and Stockholm Water Foundations Professorship. His research focuses on water security, climate, and environmental systems, contributing to the understanding and management of water resources and environmental challenges. As a faculty member, he is involved in advancing knowledge in these areas through research and education, supporting MIT's mission to address global water and environmental issues.

Research topics

• Environmental science
• Geology
• Atmospheric sciences
• Meteorology
• Computer Science
• Geography
• Remote sensing
• Climatology
• Ecology
• Soil science
• Physics
• Telecommunications
• Algorithm

Selected publications

• Meteorological to Agricultural Drought Transitions Compounded by Heat Waves in Historical and Future Climates

  Water Resources Research · 2026-03-01

  articleOpen accessSenior author

  Abstract Meteorological droughts (persistent precipitation deficits) often, but not always, transition into agricultural droughts (persistent soil moisture deficits). The intensity of agricultural drought, however, can vary for a given precipitation deficit due to a number of catalyzing co‐factors beyond precipitation such as atmospheric evaporative demand and temperature. In this study we use Earth System Model data to quantify (a) how warm temperature anomalies affect this evolution from meteorological‐to‐agricultural drought and (b) how the evolution of droughts from historical and future climate scenarios differ. We benchmark these results against observational data and use a multi‐model ensemble to quantify agreement on future drought propagation. Broadly speaking, drought temperatures in the upper third of local distributions correspond with shifts on the order of 5 percentile of the soil moisture distribution. We would expect today's meteorological droughts to propagate into agricultural droughts roughly one drought classification more severe in the SSP3‐7.0 scenario in most regions. Even regions with increases in precipitation are likely to see more intense meteorological‐to‐agricultural drought propagation by the end of the 21st century. Models disagree on drought propagation changes in Africa for the same precipitation deficit, but suggest that all historical droughts would have had worse agricultural droughts in Europe and Eastern North America if they happened under SSP3‐7.0. When accounting for precipitation changes—which tend toward more frequent accumulated precipitation deficits—the increased severity of meteorological‐to‐agricultural drought evolution leads to predictions of major increases in moderate to extreme (D1–D3) drought events in all regions globally by the end of the century.

  PublisherDOI

• Satellite Microwave Radiometry at L-Band for Monitoring Earth’s Essential Climate Variables: From Fundamental Physics to Sixteen Years of Global Climate Observations and Beyond

  IEEE Geoscience and Remote Sensing Magazine · 2026-01-01

  articleOpen access

  L-band satellite radiometry has emerged as an important tool for monitoring Earth’s essential climate variables (ECVs). It relies on spaceborne radiometers operating in a protected band (1.4–1.427 GHz) to measure Earth’s surface thermal microwave emission in brightness temperatures. Observations at this band experience minimal atmospheric attenuation and radio-frequency interference (RFI), and can be acquired continuously from day to night, ensuring a short global revisit time. Moreover, L-band radiation can partially penetrate natural materials, allowing for the assessment of subsurface state parameters. These features make L-band radiometry satellites valuable for continuous global climate monitoring, offering unique advantages in tracking specific ECVs that are difficult to measure using other remote sensing techniques. This article reviews recent advances in satellite microwave radiometry at L-band, with a focus on the contributions of key missions—SMOS, Aquarius and SMAP—in retrieving six critical ECVs: 1) surface soil moisture (SM), 2) soil freeze/thaw (FT) status, 3) vegetation aboveground biomass (AGB), 4) sea surface salinity (SSS), 5) sea surface wind (SSW) speed, and 6) sea ice thickness (SIT). It summarizes the rationale behind satellite microwave radiometry and its role in understanding of the spatiotemporal dynamics of these ECVs on a global scale based on more than 16 years of continuous data. Furthermore, it identifies the state-of-the-art status of the discussed ECV products, highlights current challenges, and outlines future directions for the application of microwave radiometry in monitoring ECVs.

  PublisherDOI

• A subnational social vulnerability index for use in drought decision support systems

  Environmental Science & Policy · 2026-03-02

  articleOpen access
  PublisherDOI

• Integrating Satellite Retrievals, Numerical Models, and Machine Learning for Global Groundwater Recharge Estimation

  Water Resources Research · 2026-03-01

  articleOpen access

  Abstract Knowledge of the groundwater recharge rate determines whether aquifer use is sustainable. However, accurately measuring recharge globally presents significant challenges due to the complexity of subsurface processes and the lack of direct observational methods. This study addresses these challenges by developing a methodology that integrates satellite data, numerical models, and machine learning to estimate groundwater recharge globally. The methodology involves two steps. First, we run a numerical model, Hydrus‐1D, to simulate soil moisture fluxes in the unsaturated zone by solving the Richards equation in the vertical direction for 235 different points representing various climates and soil types across the globe. Second, using Hydrus‐1D inputs and outputs, we train a supervised ensemble machine‐learning model, specifically a Gaussian Process Regression model, as an emulator to mimic Hydrus‐1D. This enables us to process satellite observations efficiently to estimate annual recharge flux globally. Inputs for the model include NASA's SMAP soil moisture and GPM precipitation observations, ERA5 climate reanalysis data, and soil hydraulic properties. Rainfall, unsaturated hydraulic conductivity, and soil moisture are identified as the most significant predictors of groundwater recharge. The approach effectively captures global recharge patterns, particularly in regions with high rainfall, though it shows some limitations in arid areas with minimal recharge and heavily irrigated areas. We confirm the reasonableness of recharge estimates by comparing them with observed changes in subsurface water storage from the GRACE satellite mission. The method effectively captures the observed trends in water storage, demonstrating the model's capability to estimate recharge using large‐scale satellite and reanalysis data.

  PublisherDOI

• Control of Vegetation and Temperature on Topsoil Water Losses

  Water Resources Research · 2026-04-01

  articleOpen accessSenior author

  Abstract Due to its location at the interface between land surface and atmosphere, soil moisture (SM) plays an important role in modulating energy, water and carbon fluxes. During periods of decreasing SM, SM loss is dependent on evapotranspiration (ET), drainage and changes in plant water storage. Investigating SM loss can give important insights into these processes. Here we use 25 years of global remote sensing data to investigate how SM loss is controlled by vegetation and temperature. We find that positive vegetation anomalies lead to slower SM loss in most areas, except for cold boreal forests. We hypothesize that these effects arise from competing effects of soil shading, transpiration and root water uptake by the vegetation. The effect whereby positive vegetation anomalies increase SM loss is limited to high SM conditions and disappears at lower SM, likely due to water stress limiting transpiration. By analyzing temperature and vegetation anomalies jointly we find that the relationship between SM loss and temperature varies between regions, but vegetation cover effects persist across the full range of temperature anomalies. Using a simple energy and moisture budget model, we can reproduce observed vegetation and temperature effects, supporting the interpretation that vegetation controls topsoil SM loss through shading and transpiration. We also find widespread positive SM loss trends which indicates accelerated topsoil water cycling, likely due to higher atmospheric water demand driven by increasing temperatures.

  PublisherDOI

• Control of vegetation and temperature on topsoil water losses

  2026-03-13

  articleOpen accessSenior author

  Due to its location at the interface between land surface and atmosphere, soil moisture (SM) plays an important role in modulating energy, water and carbon fluxes. During periods of decreasing SM, SM loss is dependent on evapotranspiration (ET), drainage and changes in plant water storage. Investigating SM loss can give important insights into these processes. Here we use 24 years of global remote sensing data to investigate how SM loss is controlled by vegetation and temperature. We find that positive vegetation anomalies lead to slower SM loss in most areas, except for cold boreal forests. We hypothesize that these effects arise through competing effects of soil shading, transpiration and root water uptake by the vegetation. The effect that positive vegetation anomalies increase SM loss is limited to high SM conditions and disappears at lower SM, likely due to water stress limiting transpiration. By analyzing temperature and vegetation anomalies jointly we find that the relationship between SM loss and temperature varies between regions, but vegetation cover effects persist across the full range of temperature anomalies. Using a simple energy and moisture budget model we can reproduce observed vegetation and temperature effects, supporting the interpretation that vegetation controls topsoil SM loss through shading and transpiration. We also find widespread positive SM loss trends which indicates accelerated topsoil water cycling, likely due to higher atmospheric water demand driven by increasing temperatures.

  PublisherDOI

• Vegetation water content mediates decoupling between leaf-out and rainfall onset in the African dry tropics

  2026-03-14

  articleOpen accessSenior author

  Vegetation phenology and productivity in water-limited ecosystems are tightly coupled to plant hydraulic functioning, particularly the capacity to access and store water across seasonal dry periods. Across the African dry tropics, many woodland ecosystems initiate leaf green-up prior to the onset of seasonal rainfall, suggesting complex water-use mechanisms that are not directly observable through precipitation or surface soil moisture data alone. Understanding the hydraulic basis of these phenological strategies is critical for interpreting remotely sensed vegetation water signals and for predicting ecosystem responses to shifting rainfall regimes.Here, we integrate satellite observations and reanalysis data to investigate how vegetation phenology and ecosystem productivity are mediated by plant water status across Africa. We identify three water-stress regimes based on the sensitivity of gross primary productivity (GPP) to rainfall frequency, intensity, and rainy season length, and assess the extent to which these regimes explain the widespread decoupling between rainfall onset and vegetation green-up across dry tropical woodlands. Furthermore, using observations of vegetation optical depth (VOD) as an integrative proxy for vegetation water content, we evaluate the role of plant-stored water in facilitating pre-rain leaf-out. We find that 64% of Africa's terrestrial ecosystems are subject to chronic water stress, and another 22% experience acute water stress. These acutely water-stressed regions initiate green-up when soil moisture is lower relative to chronically water-stressed regions, indicating decoupling between onset of rainfall and leaf-out. Notably, seasonal trajectories of LAI and VOD are asynchronous in regions with pre-rain green-up, consistent with the mobilization of plant-stored water to support early leaf-out. Our results demonstrate how satellite-derived vegetation water content metrics can reveal hydraulic strategies that decouple vegetation dynamics from surface moisture forcing. This work highlights the value of microwave-based observations for diagnosing plant hydraulic functioning at ecosystem scales and underscores vulnerabilities of water-limited ecosystems to shifts in rainfall timing and seasonality under climate change.

  PublisherDOI

• A Semiempirical Modeling for Soil Moisture Retrieval Using High-Resolution SAR Data

  IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing · 2026-01-01

  articleOpen access

  The NASA-ISRO Synthetic Aperture Radar (NISAR) mission will advance global soil moisture monitoring through high-resolution L-band observations. However, accurate fine-scale retrieval remains challenging due to the 12-day revisit interval and complex, scale-dependent influences of vegetation and surface roughness on backscatter. This study introduces the SMAP-AVS (Attenuation-Volume scattering-Surface scattering) model, a semi-empirical framework evaluated using ALOS-2 PALSAR observations. Formulated within the Water Cloud Model framework, the methodology moves beyond conventional land-cover-based parameters by adopting pixel-wise parameterization. Leveraging soil moisture temporal stability, the framework assumes strong correlation between coarse-scale (9 km) SMAP L3 products and fine-scale (1 km) variations, enabling separation of vegetation and surface roughness contributions to the SAR signal. The SMAP-AVS framework is resolution-agnostic; while demonstrated at 1 km, it scales to meter-resolution expected from NISAR. Once parameterized, the model operates in snapshot retrieval mode, deriving soil moisture from single SAR acquisitions and NDVI data without further temporal information. Validation across four hydro-climatically diverse U.S. regions (2021–2024) included temporal comparison against International Soil Moisture Network observations and spatial validation using Multi-Radar Multi-Sensor precipitation fields to distinguish physical moisture heterogeneity from artifacts. SMAP-AVS resolves rainfall-driven patterns often missed by coarser products, with spatial correlation coefficients exceeding 0.5 in semi-arid and agricultural regions, demonstrating that the framework captures physically significant hydrological variability and offers a scalable methodology for operational high-resolution soil moisture products.

  PublisherDOI

• Consistent Soil Moisture and Vegetation Optical Depth From Relatively Calibrated SMOS Brightness Temperatures With SMAP

  IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing · 2026-01-01

  articleOpen access

  The Soil Moisture Active Passive mission (SMAP, since 2015) from The National Aeronautics and Space Administration's (NASA) and Soil Moisture and Ocean Salinity mission (SMOS, since 2009) from The European Space Agency's (ESA) measure polarimetric brightness temperature (TB) at L-band (1.4 GHz). They provide estimates of surface soil moisture (SM) and L-band vegetation optical depth (L-VOD) approximately every 2-3 days at the equator, with a spatial resolution of ∼40 km for a local overpass time of 6 AM/PM. Integrating the AM and PM TB observations from SMAP and SMOS satellite missions can reduce the revisit time to about 1 day over the equator, thus helping to address fast-response hydrologic processes that cannot be addressed with the 2-3 day revisits. This will allow the capture of the SM conditions more often and hence capture the rate of decline due to drainage and recharge to groundwater. This occurs early during dry down after storms. The integration of SMOS measurements also works to fill temporal gaps caused by missing data due to SMAP instrument outages. This paper details the integration of the SMAP and SMOS observations to achieve a combined SM and L-VOD product. The SMOS TB observations interpolated to 40° incidence angle were first relatively calibrated (RC) to generate SMAP-like SMOS TB (RCTB), making the combined TB records consistent spatially and temporally. The SMAP baseline SM and L-VOD retrieval algorithm was then applied to the RCTB records. We showed that after relative calibration (ARC), the bias between the SMAP and SMOS TBs was reduced from 0.5 K to -0.03 K for TB H and from 2.6 K to 0.014 K for TB V in the AM cases. For the PM cases the mean value of differences was reduced from 0.82 K to 0.27 K and from 2.88 K to 0.19 K for TB H and TB V respectively. The comparison of the core validation sites (CVS) in-situ SM to the retrieved SM from the combined TB record showed an unbiased root-mean-square-difference of 0.039 m3/m3 for both AM and PM cases and the retrieved L-VOD demonstrated consistency with independent biomass and tree height estimates. We also showed an improvement in temporal coverage and that the global mean number of visits to each grid went up from 283 (SMAP only) to 446 (SMAP+SMOS) when both AM and PM overpasses are considered.

  PublisherDOI

• Supplementary material to "Interferometric synthetic aperture radar (InSAR) phase data assimilation for Bayesian snow water equivalent estimation"

  2026-04-30

  article
  PublisherDOI

Recent grants

• Collaborative Research: The combined influence of sea ice and snow cover on Northern Hemisphere atmospheric climate variability

  NSF · $284k · 2015–2019

Frequent coauthors

• Narendra N. Das

  Michigan State University

  130 shared

• Thomas Jagdhuber

  University of Augsburg

  104 shared

• Kaighin A. McColl

  Harvard University

  102 shared

• Simon Yueh

  Jet Propulsion Laboratory

  99 shared

• Peggy O’Neill

  Goddard Space Flight Center

  89 shared

• María Piles

  Universitat de València

  88 shared

• Andrew F. Feldman

  Goddard Space Flight Center

  86 shared

• Alexandra G. Konings

  Stanford University

  86 shared

Education

• Ph.D., Hydrology

  Massachusetts Institute of Technology

  1984

• M.S., Hydrology

  Massachusetts Institute of Technology

  1981

• B.S., Civil Engineering

  University of Tehran

  1978

Awards & honors

• American Geophysical Union (AGU), Fellow
• American Meteorological Society (AMS), Fellow
• Institute of Electrical and Electronics Engineers (IEEE), Fe…
• National Academy of Engineering (NAE), Member
• National Science Foundation (NSF), Presidential Young Invest…