About

Trevor Keenan is a Professor in the Department of Environmental Science, Policy & Management at UC Berkeley. His research focuses on understanding the response of terrestrial ecosystems to climate variability and long-term change, with particular attention to ecosystem carbon cycling and water use, and their feedbacks to the atmosphere. His work combines large ecological data sets, such as eddy-covariance and remote sensing data, with models of ecosystem state and function, utilizing data assimilation and mining tools. He integrates results from in-situ field studies and experiments to develop a mechanistic understanding of key physical and biological processes. Keenan employs methods from diverse disciplines including ecophysiology, biogeochemistry, micrometeorology, atmospheric science, mathematics, statistics, and high-performance computing. He earned his Ph.D. in Earth System Science from the Autonomous University of Barcelona in 2009, a Master of Research in Mathematics in the Living Environment from the University of York in 2006, and a Bachelor of Science in Mathematics from Dublin City University in 2002.

Research topics

• Geography
• Environmental science
• Geology
• Meteorology
• Ecology
• Archaeology
• Agronomy
• Atmospheric sciences
• Earth science
• Climatology
• Soil science
• Library science
• Biology

Selected publications

• Soil Moisture Buffers the Impact of Precipitation Variability on Ecosystem Productivity

  Water Resources Research · 2026-03-01

  articleOpen accessSenior author

  Abstract Water availability governs ecosystem productivity, yet estimates of vegetation sensitivity to water can differ greatly depending on whether the sensitivity is examined spatially or temporally. In particular, the spatial sensitivity is often reported to be much stronger than temporal sensitivities, leading to highly uncertain projections of ecosystem responses to future climate change when using space‐for‐time substitution. The large difference between spatial and temporal sensitivities remains unexplained. Prior research, however, primarily relied on precipitation as the water availability proxy, whereas vegetation responds to soil moisture. Here, we combined satellite estimates of vegetation productivity with soil moisture data across water‐limited ecosystems of the continental United States (CONUS) to identify a convergent sensitivity of productivity to water availability. Using precipitation, we show that temporal sensitivity is 66% lower than spatial sensitivity overall. Our analysis identified the cause of the difference to be primarily driven by the seasonal variability of water availability, rooting depth, and soil properties. When using soil moisture instead of precipitation, we observed widespread convergence in the spatial and temporal sensitivities—that is, the two sensitivities became much more similar in magnitude across all water‐limited ecosystems within CONUS. These results show that overlooking soil hydrology can inflate perceived discrepancies between spatial and temporal vegetation sensitivities, leading to biased projections of ecosystem dynamics under future hydro‐climatic change.

  PublisherDOI

• Reconciling Cross-Scale Discrepancies in CO₂ Fluxes. Preliminary Findings from the BenchFlux Project

  2026-03-14

  articleOpen access

  The BenchFLUX project represents an important advance in evaluating nature-based climate solutions (NbCS) to address the growing climate crisis. The benchmarking of CO₂ fluxes using flux tower measurements and Earth Observation (EO) data is the project's aim, employing multiple approaches to introduce, compare, and integrate temporal and spatial scales. The methods used account for the nonlinear behavior of carbon flux dynamics across scales. Therefore, measurement harmonizations are fundamental for aligning ground and atmospheric measurements. And thus, BenchFLUX provides reliable models and products that accurately track carbon emissions from small local areas to the global scale.To achieve this goal, the project combines eddy covariance flux tower ground data with multi-source EO data to create harmonized datasets for various advanced machine learning models at different scales. The processes use cloud computing technologies, such as Google Earth Engine and cloud-optimized workflows, to produce spatial CO₂ flux data at multiple spatial resolutions. The proposed methods, including Bayesian and knowledge-guided approaches to achieve accurate and consistent results, and the final products are nested across different temporal and spatial scales among the six international research teams, serving as an integrated element for cross-scale continuity.The spatial scalability of these methods is analyzed in the project prototype results. Preliminary monthly average CO₂ exchange (GPP) results are provided from the highly standardized NEON sites database for the higher spatial resolution models, revealing discrepancies at multiple scales during both the growing and non-growing seasons. The initial results will also compare coarser spatial resolution models with the eddy covariance ground truth data. All these ongoing comparisons aim to identify the most reliable methods for scaling carbon flux estimates. This will help determine the best combination of techniques to ensure high local precision and global consistency, ultimately supporting continuous cross-scale resource management.

  PublisherDOI

• Global distribution and changes of leaf-level intrinsic water use efficiency and their responses to water stress

  Nature Communications · 2026-01-07 · 1 citations

  articleOpen access

  Intrinsic water use efficiency (iWUE) at the leaf level measures water expenditures by terestrial plants during photosynthesis, yet its global spatiotemporal dynamics and responses to water stress remain poorly understood. Using machine-learning models and carbon isotope observations in C3 foliage, here we elucidate global patterns, trends, and water-stress responses of leaf iWUE. We find high iWUE in cold, arid regions and lower values in warm, humid areas. From 2001 to 2020, global iWUE increases at 0.2 ± 0.02 μmol mol-1 year-1, with strong biome specific differences. Grasslands exhibit the highest mean iWUE but the slowest increase, whereas evergreen broadleaf forests show the lowest iWUE yet the fastest increase. iWUE rises with increasing water stress, but the rate of growth diminishes as water stress intensifies. Vapor pressure deficit influence iWUE more broadly than soil moisture. The ecological optimality model reproduces the spatial patterns of leaf iWUE and identifies vapor pressure deficit as the dominant driver, but overestimates mean iWUE and its trend. Our findings suggest that increasing water stress may slow the rate of global iWUE increase as the climate continues to warm. Climate change is altering how plants balance carbon gain and water loss. This study maps global leaf-level water-use efficiency over the past two decades, showing it is highest in regions that are cold or dry, increasing worldwide, and strongly influenced by atmospheric dryness.

  PublisherDOI

• SmithEcophysLab/optimal_vcmax_R: Optimal Vcmax version 4.0

  Zenodo (CERN European Organization for Nuclear Research) · 2026-03-02

  otherOpen accessSenior author

  Version 4.0 of optimal vcmax repository to correspond with submission of optimal photosynthetic strategies manuscript

  PublisherDOI

• Global distribution and changes of leaf-level intrinsic water use efficiency and their responses to water stress

  Repository for Publications and Research Data (ETH Zurich) · 2026-01-07

  otherOpen access

  Intrinsic water use efficiency (iWUE) at the leaf level measures water expenditures by terestrial plants during photosynthesis, yet its global spatiotemporal dynamics and responses to water stress remain poorly understood. Using machine-learning models and carbon isotope observations in C3 foliage, here we elucidate global patterns, trends, and water-stress responses of leaf iWUE. We find high iWUE in cold, arid regions and lower values in warm, humid areas. From 2001 to 2020, global iWUE increases at 0.2 ± 0.02 μmol mol^-1 year^-1, with strong biome specific differences. Grasslands exhibit the highest mean iWUE but the slowest increase, whereas evergreen broadleaf forests show the lowest iWUE yet the fastest increase. iWUE rises with increasing water stress, but the rate of growth diminishes as water stress intensifies. Vapor pressure deficit influence iWUE more broadly than soil moisture. The ecological optimality model reproduces the spatial patterns of leaf iWUE and identifies vapor pressure deficit as the dominant driver, but overestimates mean iWUE and its trend. Our findings suggest that increasing water stress may slow the rate of global iWUE increase as the climate continues to warm.

  PublisherOA PDFDOI

• Incorporating site suitability and carbon sequestration of tree species into China’s climate-adaptive forestation

  2026-03-13

  articleOpen access

  Strategic selection and precise matching of climate-resilient tree species are pivotal for climate-adaptive forestry in terms of forest-based climate change mitigation and adaptation to maximize its full potential. Current forestation plans often fail to account for environmental shifts, particularly at individual species resolution, jeopardizing suboptimal carbon sequestration over the long term. Here we developed a climate-adaptive optimization framework to guide tree species selection and planting in China based on projections of species-specific habitat viability and range redistribution under future climate scenarios. Leveraging over 200,000 tree samples from National Forest Inventories spanning 1999-2018, we quantified habitat viability declines of 12.1-42.9% by 2060 for currently dominant plantation species due to climate threats. Through optimized species-site matching and strategic timber harvesting at peak carbon uptake, we identified 43.2 million hectares sustaining climate-resilient forestation during 2025-2060 - planting approximately 46 billion climate-adapted trees with a total sequestration potential of 3,822.6 Tg of carbon, representing a 28.7% increase compared to unmanaged scenarios. Our study underscores the critical role of optimized adaptive forestation under future climate change scenarios in ensuring carbon mitigation while delivering technical guidance for climate-adaptive forest management plans supporting China’s net-zero aligned goals.

  PublisherDOI

• Leaf temperature and its departure from ambient air temperature

  Nature Plants · 2026-05-08

  article
  PublisherDOI

• Enhanced effect of warming on the leaf-onset date of boreal deciduous broadleaf forest

  Nature Climate Change · 2026-02-01

  article
  PublisherDOI

• Reevaluating the contribution of grain for green program to GPP in the Loess Plateau: Insights from a process-based model

  Agricultural and Forest Meteorology · 2026-02-15

  article
  PublisherDOI

• FLUXNET Data Explorer

  Zenodo (CERN European Organization for Nuclear Research) · 2026-05-21

  otherOpen access1st authorCorresponding

  The FLUXNET Data Explorer is a web-based tool for discovering and accessing flux-tower datasets across FLUXNET-related sources. It combines the FLUXNET Shuttle catalog with supplemental metadata snapshots from regional or source-specific portals, including AmeriFlux, ICOS, JapanFlux, and EFD. The preferred live application is https://www.keenangroup.info/fluxnet-data-explorer/.

  PublisherDOI

Recent grants

• PREEVENTS Track 2: Collaborative Research: Flash droughts: process, prediction, and the central role of vegetation in their evolution.

  NSF · $305k · 2019–2024

Frequent coauthors

• I. Colin Prentice

  138 shared

• Xiangzhong Luo

  National University of Singapore

  98 shared

• Santiago Sabaté

  Centre for Research on Ecology and Forestry Applications

  65 shared

• Benjamin D. Stocker

  University of Bern

  56 shared

• Nicholas G. Smith

  Texas Tech University

  53 shared

• Carlos Gracia

  47 shared

• Andrew D. Richardson

  Northern Arizona University

  46 shared

• Xinchen Lu

  45 shared

Education

• PhD, CREAF

  Autonomous University of Barcelona

  2009

• MRes, Biology

  University of York

  2005

• BSc, Mathematics

  Dublin City University

  2002