About

Alexandra Konings is an Associate Professor of Earth System Science and, by courtesy, of Geophysics at Stanford University. She is also a Senior Fellow, by courtesy, at the Woods Institute for the Environment. Her research focuses on passive and active microwave remote sensing and the global mapping of ecohydrological variables. Additionally, she works on AI-driven flood and drought forecasting, integrating advanced remote sensing techniques with environmental and climate science. Through her leadership in the Remote Sensing Ecohydrology Group, she advances understanding of the terrestrial water cycle and its interactions with plant and climate systems, employing innovative approaches that combine AI, physical models, and large datasets for hydrology and earth systems modeling.

Research topics

• Environmental science
• Geology
• Geography
• Ecology
• Atmospheric sciences
• Biology
• Meteorology
• Remote sensing
• Computer Science
• Soil science
• Cartography
• Chemistry
• Botany
• Agroforestry
• Environmental engineering
• Agronomy

Selected publications

• Feldman et al. rainfall frequency and intensity trends dataset

  Zenodo (CERN European Organization for Nuclear Research) · 2026-02-23

  datasetOpen access

  These data and scripts are those that were used in the Feldman et al. manuscript accepted in Geophysical Research Letters titled "Widespread co-location of less frequent and more intense daily precipitation over land." Please consult the README.txt file, which details the workflow and input and output file naming conventions. The "Scripts_FigureGenerationData.zip" contains the python (.py) scripts used in all steps of the workflow. It also contains the necessary files to only plot the main and supplemental figures in python (only Step 3 in the README.txt file). The "StudyDatasets.zip" contains all input and output files used in the full workflow. These files are in compressed .npz files, which are typically agile if mainly using python and numpy (as these scripts here primarily use). However, "StudyDatasets_netcdf_PartX.zip" provide these same datasets as .nc files, which is accessible for a wider range of programming applications. Please consult the README.txt file for more information about python and names of files and scripts being used. For any use of the datasets and scripts, please cite (1) the Feldman et al "Widespread co-location of less frequent and more intense daily precipitation over land" publication in Geophysical Research Letters. Additionally, (2) please acknowledge the use of these scripts and/or datasets in this Zenodo repository in the acknowledgements section of your publication. The use of any scripts or data are not allowed unless this paper is officially in print. For questions, please contact Andrew Feldman at andrew.feldman@nasa.gov or afeld24@umd.edu. The authors thank the following funding sources: the NASA Postdoctoral Program (NPP) as well as a NASA ROSES A.63 Ecohydrology Grant (80NSSC25K0156). Both grants were acquired and led by PI A. Feldman.

  PublisherDOI

• Feldman et al. rainfall frequency and intensity trends dataset

  Open MIND · 2026-02-23 · 1 citations

  dataset

  These data and scripts are those that were used in the Feldman et al. manuscript accepted in Geophysical Research Letters titled "Widespread co-location of less frequent and more intense daily precipitation over land." Please consult the README.txt file, which details the workflow and input and output file naming conventions. The "Scripts_FigureGenerationData.zip" contains the python (.py) scripts used in all steps of the workflow. It also contains the necessary files to only plot the main and supplemental figures in python (only Step 3 in the README.txt file). The "StudyDatasets.zip" contains all input and output files used in the full workflow. These files are in compressed .npz files, which are typically agile if mainly using python and numpy (as these scripts here primarily use). However, "StudyDatasets_netcdf_PartX.zip" provide these same datasets as .nc files, which is accessible for a wider range of programming applications. Please consult the README.txt file for more information about python and names of files and scripts being used. For any use of the datasets and scripts, please cite (1) the Feldman et al "Widespread co-location of less frequent and more intense daily precipitation over land" publication in Geophysical Research Letters. Additionally, (2) please acknowledge the use of these scripts and/or datasets in this Zenodo repository in the acknowledgements section of your publication. The use of any scripts or data are not allowed unless this paper is officially in print. For questions, please contact Andrew Feldman at andrew.feldman@nasa.gov or afeld24@umd.edu. The authors thank the following funding sources: the NASA Postdoctoral Program (NPP) as well as a NASA ROSES A.63 Ecohydrology Grant (80NSSC25K0156). Both grants were acquired and led by PI A. Feldman.

  DOI

• Plant responses to rainfall frequency and intensity variations from field to global scales

  2026-03-13

  articleOpen accessCorresponding

  Regardless of annual rainfall amount changes, daily rainfall events are becoming more intense but less frequent across Earth’s land surfaces. Larger rainfall events and longer dry spells ­have complex and sometimes opposing effects on plant photosynthesis and growth, challenging abilities to understand broader consequences on the carbon cycle. Cross-scale analyses are ultimately needed to quantify responses of vegetation function to fewer, larger rainfall from different data sources, disentangle the complex driving mechanisms of the plant responses, and scale findings from field to global scales.Here, we ask, to what degree is global vegetation function sensitive to shifts in daily rainfall frequency and intensity, especially when compared with variations in annual rainfall totals? Is global vegetation function (and terrestrial carbon uptake via photosynthesis) higher or lower in years with less frequent, more intense rainfall?First, we collate field, model, and satellite studies that investigate the effects of fewer, larger rainfall events, while controlling for annual rainfall amounts. Plant function responses vary between -28% to 29% (5th to 95th percentile) in years with fewer, larger rainfall events compared to nominal years, with the sign of response contingent on climate; productivity increases are more common in dry ecosystems (46% positive; 20% negative), whereas responses are typically negative in wet ecosystems (28% positive; 51% negative) in years with fewer, larger rainfall events. Field scale analyses and analytical models applied to site data reveal that non-linear plant responses to soil moisture are a major mechanism responsible for these differences in sign. Second, using vegetation indices from four different satellites and a statistical approach, we draw similar conclusions about the changing sign of response across dry to wet ecosystems. Furthermore, the satellite analysis reveals that global vegetation is sensitive to daily rainfall variability across 42% of Earth’s vegetated land surfaces. Surprisingly, vegetation is almost (95%) as sensitive to daily rainfall variability as vegetation is to annual rainfall totals.These findings across scales suggest that daily rainfall variability impacts on terrestrial ecosystems are likely having a substantial impact on the global carbon cycle and food security. Observational results, included mechanisms revealed in these analyses, are pivotal for benchmarking models and an analysis on this topic is ongoing.

  PublisherDOI

• Continuous monitoring of plant water potential: sensor‐based approaches and best practices

  New Phytologist · 2026-05-05

  article

  Plant water potential is a central integrator of plant water status, linking hydraulic function with physiological performance and ecosystem water dynamics across species and systems. This review is motivated by the need to capture these dynamics under rapidly changing environmental conditions, which are often missed by discrete measurements. We evaluate the main approaches for continuous monitoring of plant water potential, including direct in situ sensors, indirect methods based on plant water content, and remote-sensing proxies. We discuss the principles, measurement mechanisms, practical constraints, and environmental sensitivities of each approach. Relative to traditional methods, such as pressure chambers, continuous measurements offer major advantages by resolving rapid variation in water status and strengthening inference on plant-soil-atmosphere interactions. These approaches are especially valuable under dynamic field conditions, where temporal variability in vapor pressure deficit, soil moisture, temperature, and radiation strongly shapes hydraulic behavior. We conclude that continuous monitoring has substantial potential to advance plant and ecosystem science, but wider application will depend on careful interpretation and greater harmonization across comparable methodologies. By synthesizing core principles, methodological challenges and best practices, this review provides a practical framework for researchers and practitioners applying continuous water potential measurements.

  PublisherDOI

• Supporting data for "Parameterizing stomatal conductance based on trait-environment relationships often improves land surface model predictions of evapotranspiration and streamflow"

  Zenodo (CERN European Organization for Nuclear Research) · 2026-05-12

  datasetOpen accessSenior author

  Supporting maps of g1, streamflow error, and ET error across the study domain. Maps exist for all three Catchment-CN4.5 runs discussed in the paper: the PFT-default run, the PFT-optimized run, and the TER-optimized run.

  PublisherDOI

• Reconciling remote sensing and reanalysis land surface temperatures: How surface conditions shape systematic differences between GOES-16 and MERRA-2 across the contiguous US

  Journal of Applied Meteorology and Climatology · 2026-05-22

  article

  Abstract Land surface temperature (LST) is a key variable governing land–atmosphere energy and water exchanges. Despite the importance of LST, satellite observations and reanalysis products often differ in how they define the effective LST depth and in the assumptions underlying their estimates, making comparisons and interpretation challenging. In this study, we present a detailed comparison of LST from GOES-16 (satellite) and MERRA-2 (reanalysis) across the contiguous United States for 2022 and 2023. The results reveal systematic diurnal and seasonal differences: GOES-16 tends to be warmer than MERRA-2 in the afternoon and at night, but cooler in the morning. The magnitude of these differences varies by season. At night, GOES-16 is warmest relative to MERRA-2 for forests; in the morning, it is coolest for croplands and grasslands; and in the afternoon, it is warmest for barren and shrublands. Within individual land cover types, variability in surface conditions—such as soil moisture and elevation—modulates the differences at night and in the morning, with GOES-16 LST being warmer at night and cooler in the morning for wetter soils and at higher elevations. Our analysis also indicates that Leaf Area Index plays a role during spring and autumn, likely due to the association of temperature with leaf emergence and senescence. These findings provide new insights into the mechanisms underlying LST differences between these datasets, and highlight the importance of accounting for surface condition variability when developing LST fusion and assimilation workflows.

  PublisherDOI

• Daily surface flux equilibrium (SFE) ET across CONUS

  Zenodo (CERN European Organization for Nuclear Research) · 2026-03-12

  datasetOpen accessSenior author

  Daily, 4km estimates of surface flux equilibrium (SFE) ET across CONUS from "Triple collocation validates CONUS-wide evapotranspiration inferred from atmospheric conditions, McCormick et al. (In Minor Revision), Hydrology and Earth System Sciences.

  PublisherDOI

• Daily surface flux equilibrium (SFE) ET across CONUS

  Zenodo (CERN European Organization for Nuclear Research) · 2026-03-12

  datasetOpen accessSenior author

  Daily, 4km estimates of surface flux equilibrium (SFE) ET across CONUS from "Triple collocation validates CONUS-wide evapotranspiration inferred from atmospheric conditions, McCormick et al. (In Minor Revision), Hydrology and Earth System Sciences.

  PublisherDOI

• Supporting data for "Parameterizing stomatal conductance based on trait-environment relationships often improves land surface model predictions of evapotranspiration and streamflow"

  Zenodo (CERN European Organization for Nuclear Research) · 2026-05-12 · 1 citations

  datasetOpen accessSenior author

  Supporting maps of g1, streamflow error, and ET error across the study domain. Maps exist for all three Catchment-CN4.5 runs discussed in the paper: the PFT-default run, the PFT-optimized run, and the TER-optimized run.

  PublisherDOI

• Tracking Water Status and Drought Response with GNSS-T VOD Across Tropical to Temperate Forest Ecosystems

  2026-03-14

  articleOpen accessCorresponding

  Monitoring vegetation water status is key to understanding forest canopy hydraulics, stomatal regulation, and ultimately the biosphere's drought response under a changing climate. Yet direct, in situ measurements of hydraulic state are labor-intensive and rarely sustained long enough to produce the multi-year time series needed for model development and drought-impact forecasting. Continuous proxies such as sap flow or stem water potential provide vital information about fluxes, but their representativeness for entire trees and stand-scale canopy water status remains very limited.Here, we highlight the potential of Global Navigation Satellite Systems Transmissometry (GNSS-T) to bridge this observation gap. GNSS-T retrieves vegetation optical depth (VOD), the effective canopy opacity at L-band (1-2 GHz), by measuring one-way attenuation of GNSS microwave signals along their path from the transmitting satellite to a receiver located below the canopy. GNSS-T VOD integrates information on canopy biomass and water content of the canopy (plant and interception storage) and has demonstrated sensitivity to stand-scale vegetation water dynamics. However, its sensitivity to changes in vegetation water dynamics is expected to vary with stand biomass and canopy cover, species hydraulic strategies, and climatic conditions. These dependencies remain poorly quantified. To date, progress has been limited due to the novelty of this emerging technique as existing GNSS-T records are rather short in time and largely confined to individual sites.In this contribution, we present the first data from VODnet, a community-driven network that builds, maintains, and advances GNSS-T for ecological research. The emerging dataset spans 10 forest stations across diverse biomes, including temperate, Mediterranean, savanna, and tropical ecosystems in South America, and Southern and Central Europe, enabling cross-site analyses of GNSS-T VOD sensitivity under contrasting climate conditions and vegetation properties.The goal of this study is to understand the sensitivity of GNSS-T VOD to changes in vegetation water status across climate gradients, plant traits, and forest structural conditions. We do this by calculating partial correlations of VOD with hydrological drivers such as soil moisture deficit, sap flow and water potential anomalies while accounting for structural properties such as LAI, total biomass and canopy cover, and measure the degree to which site factors drive this correlation. Beyond in situ applications, VODnet provides a unique opportunity to study uncertainty in widely used spaceborne VOD data sets (e.g., SMAP, AMSR-2) through validation across forest ecosystems. Based on our results, we can now provide a first assessment of whether GNSS-T can serve as a validation reference for satellite-derived VOD.

  PublisherDOI

Recent grants

• Collaborative Research: Hydrologic Disturbance in Tropical Peatlands: Linking Drainage, Soil Moisture, Flammability, and Carbon Fluxes

  NSF · $330k · 2019–2024

• CAREER: Elucidating Large-Scale Spatial Patterns of Ecosystem Traits with Data Assimilation

  NSF · $665k · 2020–2027

Frequent coauthors

• Dara Entekhabi

  Massachusetts Institute of Technology

  86 shared

• Kaighin A. McColl

  Harvard University

  43 shared

• Pierre Gentine

  43 shared

• A. Anthony Bloom

  Jet Propulsion Laboratory

  39 shared

• Hamed Alemohammad

  39 shared

• Gregory R. Quetin

  University of California, Santa Barbara

  32 shared

• María Piles

  Universitat de València

  31 shared

• P. R. Moorcroft

  Harvard University

  31 shared

Labs

• Remote Sensing Ecohydrology GroupPI

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Education

• Ph.D., Civil and Environmental Engineering

  Massachusetts Institute of Technology

  2015

• M.S., Nicholas School of the Environment

  Duke University

  2011

• S.B., Civil and Environmental Engineering

  Massachusetts Institute of Technology

  2009