About

Guido Salvucci is a Professor in the Department of Earth & Environment at Boston University. He earned his Ph.D. from the Massachusetts Institute of Technology in 1994. His research includes the estimation of water vapor convergence over the Mississippi River Basin using top-of-atmosphere net radiation and the moist-static energy budget as constraints. He also works on the nonparametric estimation of the relation between soil moisture and land surface fluxes and how that relation scales spatially. Additionally, his projects evaluate the impact of large-scale irrigation on boundary layer fluxes in Southeastern Turkey using remote sensing data, mesoscale modeling, and the Bouchet-Morton complementarity framework. His teaching includes courses such as Introduction to Hydrology, Environmental and Geophysical Fluid Dynamics, and Dynamic Landsurface Hydrology.

Research topics

• Atmospheric sciences
• Meteorology
• Geology
• Environmental science
• Climatology
• Geography
• Soil science
• Ecology

Selected publications

• Simplified Cloud‐Topped Mixed Layer Model Explains Observed Spatial Pattern of Soil Moisture‐Precipitation Feedback Across the Conterminous United States

  Geophysical Research Letters · 2025-11-03

  articleOpen accessSenior author

  Abstract Inconsistent findings in soil moisture (SM)‐precipitation feedback literature motivate further research into the role of the boundary layer in these feedbacks. The present study explores mechanisms that can explain the spatial patterns found in a previous analysis employing satellite measured SM: positive feedback in the semi‐arid western U.S. (higher morning SM predicting greater likelihood of afternoon rainfall), and negative feedback in the humid east. Using a cloud–topped boundary layer model, we examine how evaporative fraction (EF, a proxy for SM) influences cloud mass flux (CMF). We then use logistic regression to relate CMF to precipitation. The results are consistent with the previous analysis: in semi‐arid areas, increased humidification with increased EF dominates CMF strength, yielding net positive feedbacks; in humid areas, reductions in convective velocity with increasing EF dominate the CMF, yielding net negative feedbacks. Such offsetting feedbacks may contribute to inconsistencies reported in the literature.

  PublisherOA PDFDOI

• Estimating contrasting soil moisture-precipitation feedbacks across global landmass using data from the Soil Moisture Active Passive satellite mission

  Science of Remote Sensing · 2025-06-14 · 2 citations

  articleOpen accessSenior author

  The effect of soil moisture on future precipitation is complex and has been the subject of significant research. Observational and modeling studies have been carried out on a variety of spatial (e.g., mesoscale to continental) and temporal (e.g., daily to monthly) timescales. Our methodology statistically measures the interaction between morning soil moisture and subsequent probability of precipitation using the Granger Causality approach, with global data from remotely sensed estimates of soil moisture from the Soil Moisture Active Passive (SMAP) and Advanced Microwave Scanning Radiometer 2 (AMSR2) instruments. The SMAP and AMSR2 results are compared, and found to be consistent with each other and with a previous analysis that applied a Granger Causality framework with data from the Advanced Microwave Scanning Radiometer for Earth Observing System (AMSR-E) over the conterminous U.S. We find that the impact of soil moisture on next day precipitation likelihood varies across the global landmass, with patterns related to overall aridity and land cover. • The effect of soil moisture on precipitation likelihood is complex • SM-precipitation feedback strength and sign vary throughout global landmass • Similar environments often have similar feedback behaviors • MODIS landcover and published aridity index allow cross-region comparison • SMAP and AMSR2 results are highly similar

  PublisherDOI

• Climate Change Is Altering Ecosystem Water Use Efficiency in Water‐Limited Ecosystems

  Global Change Biology · 2025-08-01 · 6 citations

  article

  ABSTRACT Dryland ecosystems are expected to expand globally as a result of rising atmospheric water demand and vapor pressure deficit. However, the nature and magnitude of how water‐limited ecosystems are adapting to increases in aridity is unclear. Here we examine changes in ecosystem water use efficiency (WUE), defined as the ratio of gross primary productivity (GPP) to evapotranspiration (ET), in global water‐limited regions over the past two decades. Our analysis uses remotely sensed data, process‐based models, and reanalysis datasets to quantify changes in WUE and examine the role that changes in atmospheric CO 2 , atmospheric water demand, and soil moisture exert on WUE dynamics in water‐limited ecosystems. Our results show that on average WUE increased by 17% in water‐limited regions worldwide. Asia, North America, and Africa showed the largest increases in WUE (24%, 17%, and 17%, respectively), followed by Europe, South America, and Oceania (15%, 10%, and 9%, respectively). Ecosystems with low mean annual WUE showed the largest increases of WUE. CO 2 fertilization from increasing atmospheric CO 2 concentrations was the dominant driver behind observed changes in WUE, especially in the Northern Hemisphere. Our findings indicate that vegetation in water‐limited ecosystems is adapting to climate change by optimizing water use efficiency but also suggest that the ability of many ecosystems to adapt may decrease as they become drier.

  PublisherDOI

• Estimating Contrasting Soil Moisture-Precipitation Feedbacks Across Global Landmass Using Data from the Soil Moisture Active Passive Satellite Mission

  SSRN Electronic Journal · 2024-01-01

  preprintOpen accessSenior author
  PublisherDOI

• Modifications to the CLASS Boundary Layer Model for Improved Interaction Between the Mixed Layer and Clouds

  Journal of Advances in Modeling Earth Systems · 2024-06-28 · 1 citations

  articleOpen accessSenior author

  Abstract The impact of clouds on the mixed layer (ML) is critical for understanding the evolution of boundary layer humidity and temperature over the course of a day. We found that accounting for moistening of the cloud layer (CL) by humidity originating in the ML dramatically alters the interaction between the ML and the CL in a one‐dimensional cloud‐topped boundary layer model: Chemistry Land‐surface Atmosphere Soil Slab (CLASS) (Vilà‐Guerau de Arellano et al., 2015, https://doi.org/10.1017/CBO9781316117422 ). We demonstrate that enabling CLASS to moisten the lower CL improves the prediction of humidity (and the flux of humidity) both above and below the ML top ( h ). To account for this moistening, we propose a length scale, L , above h , over which mixing of mass fluxes into the environment occurs. The mass fluxes are assumed to decrease linearly from h to a height L meters above h , analogous to a convective plume detraining into the environment at a height‐independent rate. Accounting for this process is accomplished by modifying the differential equations representing the growth of the jumps (sharp changes in humidity and temperature) at h . From analysis of a large number of diurnal Large Eddy simulations (covering approximately 11,000 different early morning initial conditions), we provide a regression model for parameterizing L from early morning weather variables. With the regression‐based estimate of L , the modified model (CLASS‐L) accounts for moistening the lower CL, and as a result, yields improved humidity dynamics, humidity flux profiles, and cloud growth under a broad range of conditions.

  PublisherOA PDFDOI

• Estimating Contrasting Soil Moisture-Precipitation Feedbacks Across Global Landmass Using Data from the Soil Moisture Active Passive Satellite Mission

  SSRN Electronic Journal · 2024-01-01

  preprintOpen accessSenior author
  PublisherDOI

• Tropical surface temperature response to vegetation cover changes and the role of drylands

  Global Change Biology · 2022-09-28 · 53 citations

  articleOpen access

  Vegetation cover creates competing effects on land surface temperature: it typically cools through enhancing energy dissipation and warms via decreasing surface albedo. Global vegetation has been previously found to overall net cool land surfaces with cooling contributions from temperate and tropical vegetation and warming contributions from boreal vegetation. Recent studies suggest that dryland vegetation across the tropics strongly contributes to this global net cooling feedback. However, observation-based vegetation-temperature interaction studies have been limited in the tropics, especially in their widespread drylands. Theoretical considerations also call into question the ability of dryland vegetation to strongly cool the surface under low water availability. Here, we use satellite observations to investigate how tropical vegetation cover influences the surface energy balance. We find that while increased vegetation cover would impart net cooling feedbacks across the tropics, net vegetal cooling effects are subdued in drylands. Using observations, we determine that dryland plants have less ability to cool the surface due to their cooling pathways being reduced by aridity, overall less efficient dissipation of turbulent energy, and their tendency to strongly increase solar radiation absorption. As a result, while proportional greening across the tropics would create an overall biophysical cooling feedback, dryland tropical vegetation reduces the overall tropical surface cooling magnitude by at least 14%, instead of enhancing cooling as suggested by previous global studies.

  PublisherOA PDFDOI

• Observed Landscape Responsiveness to Climate Forcing

  Water Resources Research · 2022 · 33 citations

  • Environmental science
  • Climatology
  • Atmospheric sciences

  Abstract Climate variability and change shift environmental conditions on global land surfaces, creating uncertainties in predicting hydrologic flows, crop yields, and land carbon uptake. Land surfaces can present varying degrees of inertia to atmospheric forcing variability (e.g., precipitation). This study asks: are regions with the most variable environmental forcing necessarily the regions with the largest land surface variability? Specifically, it seeks to determine why land surfaces show varying responsiveness to environmental forcing. The degree to which and the mechanisms for how landscapes modulate the forcing are evaluated using a decade‐long satellite observation record of Africa's diverse climates. Surface responsiveness is quantified using intra‐seasonal energy flux variability, based on the observed diurnal temperature amplitude. We map the responsiveness and analyze the underlying mechanisms over intra‐seasonal timescales (especially interstorms). We show that, at a location, land surface responsiveness is dependent on the soil moisture distribution and the nonlinear relationship between energy fluxes and soil moisture. Land surfaces with greater responsiveness to climate are those with soil moisture distributions that span the threshold between evaporation regimes and spend most of their time in the water‐limited regime. Consequently, surface responsiveness mechanisms drive land surface variability beyond high climatic variability. Since we find these results to hold from intra‐seasonal to interannual timescales, we expect that these responsive regions will be most vulnerable to long‐term shifts in climate forcing. The quantification of these phenomena and determination of their geographic distributions based on observations can help assess land surface models used to evaluate hydrologic consequences of climate change.

  PublisherOA PDFDOI

• Observation-Driven Estimation of Surface Water Balance Components from SMAP Measurements

  2020-09-26

  article

  With the availability of global satellite remote sensing observations of surface soil moisture, it is now possible to quantify important hydrological fluxes such as evapotranspiration (ET) and drainage. Furthermore, given the current level of accuracy of remotely sensed soil moisture, these fluxes can be estimated without the need for large-scale land surface or climate modeling. In this work, remote sensing data from the NASA SMAP mission, at 36 [km] scale, and gauge-based precipitation data over the US are utilized within an adjoint-state variation estimation method to obtain time-series daily estimates of ET and drainage. The approach uses only SMAP measurements and precipitation. Neither a hydrology or land surface model nor ancillary hydrologic data are used. ET estimates are compared to eddy covariance measurements from the AmeriFlux network and are shown to capture up to 70% of the in-situ measurements' annual variance. Similarly, Drainage estimates are compared to USGS streamflow measurements. On average Drainage under-estimates streamflow by 1-2 [mm day-1] with seasonal correlation (R2) varying between 0.52-0.77. These estimates close the surface water budget with only SMAP measurements and precipitation information.

  PublisherDOI

• Emergent Climatological Coupling of the Terrestrial Carbon Sink with Water and Energy Availability

  AGU Fall Meeting Abstracts · 2020-12-01

  article
  Publisher

Recent grants

• An Exploration of Multi Decadal, Continental-Scale Evapotranspiration Inferred from Weather Data

  NSF · $334k · 2015–2019

Frequent coauthors

• Dara Entekhabi

  Massachusetts Institute of Technology

  58 shared

• Daniel J. Short Gianotti

  Parsons (United States)

  48 shared

• A. J. Rigden

  University of California, Irvine

  25 shared

• Bruce T. Anderson

  Boston University

  24 shared

• Pierre Gentine

  19 shared

• Kaighin A. McColl

  Harvard University

  19 shared

• Ruzbeh Akbar

  Jet Propulsion Laboratory

  17 shared

• Isabel F. Trigo

  Portuguese Sea and Atmosphere Institute

  15 shared

Education

• Ph.D.

  Massachusetts Institute of Technology

  1994