About

George Allen is an Associate Professor of Hydrology and Remote Sensing at Virginia Tech, based in the Department of Geosciences. His research utilizes satellites to study Earth’s freshwater resources on a global scale. His group applies various remote sensing data, including multispectral, hyperspectral, thermal, radar, and lidar, to understand the movement of water and other materials through rivers, lakes, and wetlands. In addition to remote sensing, he employs field methods, computer models, and sensor data to investigate how these water bodies and their geomorphology are being altered by land use and climate change. His work is driven by a desire to promote the conservation, sustainable management, and understanding of Earth's surface water resources.

Research topics

• Computer Science
• Environmental science
• Geology
• Geography
• Ecology
• Meteorology
• Climatology
• Cartography
• Biology
• Physical geography
• Physics
• Remote sensing
• Engineering
• Water resource management
• Mechanics
• Telecommunications
• Earth science
• Geomorphology
• Oceanography
• Environmental resource management

Selected publications

• Improving the Spatial Representation of Reservoir Evaporation Using SAR-Based Wind Fields

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

  articleOpen access

  Evaporation losses from reservoirs can be substantial, yet spatial variability in evaporation rates is difficult to quantify, in part due to the difficulty of estimating high-resolution wind fields at the water-air interface. Synthetic aperture radar (SAR) offers a promising approach to estimate high-resolution wind fields over lakes. To test the utility of SAR-based winds to improve spatial representation of reservoir evaporation, we developed a framework fusing Sentinel-1 based wind fields with gridded meteorological estimates of air temperature, humidity, and solar radiation to estimate distributed evaporation rates at Lake Mead, USA using the Penman open water evaporation equation. We validated outputs at over-water buoys and benchmarked the results against wind and evaporation estimates derived solely from the Real-Time Mesoscale Analysis (RTMA) data product. Sentinel-1 wind field accuracy matched or outperformed RTMA, with a wind direction mean absolute error (MAE) of 41.3° compared to 40.3° and a wind speed MAE of 1.3 m/s compared to 1.8 m/s. This framework yielded a 25x increase in spatial resolution of wind fields (100 m) and evaporation (100 m) compared to RTMA-alone (2500 m), enabling identification of greater spatial variability across Lake Mead than was previously possible. This framework could be used globally for high-resolution evaporation mapping to support water resources monitoring, modeling, and management.

  PublisherDOI

• Global River Widths from Landsat (GRWL) Database

  Zenodo (CERN European Organization for Nuclear Research) · 2026-02-12 · 2 citations

  datasetOpen access1st authorCorresponding

  If you use the GRWL Database in your work, please cite: Allen and Pavelsky (2018) Global Extent of Rivers and Streams. Science. https://doi.org/10.1126/science.aat0636 This long-term repository contains three files: 1) Simplified GRWL Vector Product: GRWL_summaryStats_V01.01.zip 2) GRWL Mask (raster): GRWL_mask_V01.01.zip 3) GRWL Vector Product: GRWL_vector_V01.01.zip Other data: - Location map of the individual GRWL tiles: Shapefile download - River and stream surface area totals by drainage basin (Fig. 4 in Allen & Pavelsky, 2018): Shapefile download 1) Documentation for the Global River Width from Landsat (GRWL) Simplified Vector Product V01.01 This zip file contains a single ESRI shapefile polyline of river centerlines. Projection: Geographic WGS84 This file is a simplified version of the raw GRWL vector product (see #3 below). This product is a smaller and more wieldy compared to the raw GRWL vector dataset and most users of GRWL will prefer to use this simplified version. This simplified vector product reduces the number of feature vertices and attributes by simplifying the polyline geometry and by calculating summary statistics along each polyline segment. Polyline segments are roughly the line segments between each tributary junction. For each polyline segment, the shapefile contains the following attributes:1. width_min: the minimum of river width measurements along the segment at mean discharge (meters)2. width_med: the median of river width measurements along the segment at mean discharge (meters)3. width_mean: the mean of river width measurements along the segment at mean discharge (meters)4. width_max: the maximum of river width measurements along the segment at mean discharge (meters)5. width_sd: the standard deviation of river width measurements along the segment at mean discharge (meters)6. lakeflag: integer specifying if segment is located on a river (lakeflag=0), lake/reservoir (lakeflag=1), tidal river (lakeflag=2), or canal (lakeflag=3). This information is of much higher quality in the Global River Width from Landsat (GRWL) Vector Product V01.01 (product #3 below). 8. nSegPx: number of pixels within the segment (N pixels)9. Shape_Leng: length of the segment (kilometers) 2) Documentation for the Global River Width from Landsat (GRWL) Mask V01.01 This zip file contains 830 GeoTIFF tiles of water masks at mean discharge. The assembly of this database is described in Allen and Pavelsky (2018) “Global Extent of Rivers and Streams” published in Science. The GRWL mask is an intermediate product in the production the GRWL vector product and thus is not explicitly validated. Tile coverage: 4 degrees latitude by 6 degrees longitudeFile format: GeoTIFF (unsigned byte)Projection: Geographic WGS84 Resolution: 30 m Pixel classifications: DN = 256 : No DataDN = 255 : RiverDN = 180 : Lake/reservoir DN = 126 : Tidal rivers/delta DN = 86 : CanalDN = 0 : Land/water not connected to the GRWL river network 3) Documentation for the Global River Width from Landsat (GRWL) Vector Product V01.01 This zip file contains 829 ESRI shapefile polylines of river centerlines. Tile coverage: 4 degrees latitude by 6 degrees longitude. Projection: Geographic WGS84 Resolution: 30 m At each GRWL measurement location, the shapefile contains the following attributes:1. utm_east: UTM Easting (UTM Zone is given in tile file name; meters)2. utm_north: UTM Northing (UTM Zone is given in tile file name; meters)3. width_m: wetted width of river (meters)note: width_m == 1 indicates NA (no width data along the centerline) 4. nchannels: braiding index (-)5. segmentID: unique ID of river segment in each tile6. segmentInd: Index of each observation in each segment. Not sorted by upstream or downstream7. lakeflag: integer specifying if observation is located on a river (lakeflag=0), lake/reservoir (lakeflag=1), tidal river (lakeflag=2), or canal (lakeflag=3). 8. lon: Longitude (decimal degrees)9. lat: Latitude (decimal degrees)10. elev: Elevation (meters) – sampled from the Hydro1k DEM

  PublisherDOI

• Phenological turnover matters when making trait‐based predictions of plant–pollinator interactions

  Functional Ecology · 2025-07-17

  articleOpen access

  Abstract Understanding the processes determining species' interactions is key to predicting and safeguarding ecological networks under rapid environmental change. One approach to estimating interactions is to use morphologies of taxa interacting across trophic levels to reveal suites of traits they are more likely to interact with (i.e. a morphological trait niche). Previous work studying these morphological trait niches has typically used interactions between species that are pooled in space and time. However, species assemblages, and the traits of individuals within species, can change across even small landscapes over a season, leading to morphological trait space being dynamically reshaped. Therefore, it is unclear how morphological trait turnover affects our inferences of trait niches, and our ability to answer this is in part limited by a lack of individual‐level trait data. Here, we directly address this by studying a montane Arctic plant‐pollinator community over five growing seasons (>1300 h of fieldwork). Specifically, we linked every recorded plant–bumblebee interaction with the traits of the bee individual involved ( n = 1150) to investigate (1) whether plant taxa ( n = 10) exhibited bee trait niches by interacting with specific regions of multidimensional trait space of visiting bumblebees, and (2) how our inference of these trait niches was affected by considering bumblebee trait turnover and plant taxon turnover over space and time. When we did not consider turnover (i.e. interactions in space and time were pooled), plant taxa demonstrated bee trait niches. However, we next considered how bee trait space was reshaped over the elevational and seasonal gradient (for example, with the emergence of different castes), and how this reshaping co‐occurred with different spatiotemporal ranges of the plant taxa. From this, we found plant taxa no longer interacted with a smaller area of bee community trait space than expected by chance (i.e. no longer showed a bee trait niche) and that seasonal reshaping of bee trait space was the primary driver of this trend. Overall, in highly dynamic systems like the Arctic, overlooking community turnover could mask and even overestimate the ability of morphology to explain interactions. Hence, determining how morphological traits of individual interaction partners are in phenological synchrony at localised scales will be fundamental to understanding the role morphology plays in underpinning plant‐pollinator interactions. Read the free Plain Language Summary for this article on the Journal blog.

  PublisherDOI

• Global River Topology (GRIT): A Bifurcating River Hydrography

  Water Resources Research · 2025-05-01 · 11 citations

  articleOpen access

  Abstract Existing global river networks underpin a wide range of hydrological applications but do not represent channels with divergent river flows (bifurcations, multi‐threaded channels, canals), as these features defy the convergent flow assumption that elevation‐derived networks (e.g., HydroSHEDS, MERIT Hydro) are based on. Yet, bifurcations are important features of the global river drainage system, especially on large floodplains and river deltas, and are also often found in densely populated regions. Here we developed the first raster and vector‐based Global RIver Topology that not only represents the tributaries of the global drainage network but also the distributaries, including multi‐threaded rivers, canals and deltas. We achieve this by merging a 30 m Landsat‐based river mask with elevation‐generated streams to ensure a homogeneous drainage density outside of the river mask for rivers narrower than approximately 30 m. Crucially, we employ the new 30 m digital terrain model, FABDEM, based on TanDEM‐X, which shows greater accuracy over the traditionally used SRTM derivatives. After vectorization and pruning, directionality is assigned by a series of elevation, flow angle and continuity approaches. The new global network and its attributes are validated using gauging stations, comparison with existing networks, and randomized manual checks. The new network represents 19.6 million km of streams and rivers with drainage areas greater than 50 km 2 and includes 67,495 bifurcations. With the advent of hyper‐resolution modeling and artificial intelligence, GRIT is expected to greatly improve the accuracy of many river‐based applications such as flood forecasting, water availability and quality simulations, or riverine habitat mapping.

  PublisherOA PDFDOI

• A First Look at River Discharge Estimation From SWOT Satellite Observations

  Geophysical Research Letters · 2025-05-03 · 36 citations

  articleOpen access

  Abstract The Surface Water and Ocean Topography (SWOT) satellite has the potential to transform global hydrologic science by offering simultaneous and synoptic estimates of river discharge and other hydraulic variables. Discharge is estimated from SWOT observations of water surface elevation, width, and slope. A first assessment using just the highest quality SWOT measurements, over the first 15 months (March 2023–July 2024) of the mission evaluated at 65 gauged reaches shows results consistent with pre‐launch expectations. SWOT estimates track discharge dynamics without relying on any gauge information: median correlation is 0.73, with a correlation interquartile range of 0.51–0.89. SWOT estimates capture discharge magnitude correctly in some cases but are biased (median bias is 50%) in others. There are already a total of 11,274 ungauged global locations with highest quality SWOT measurements where SWOT discharge is expected to accurately track discharge variations: this value will increase as SWOT data record length grows, algorithms are refined and SWOT measurements are reprocessed. This first look indicates that SWOT discharge is performing as expected for SWOT data that achieve performance requirements, providing observed information on discharge variations in ungauged basins globally.

  PublisherOA PDFDOI

• Global methane budget 2000--2020

  ENLIGHTEN (Jurnal Bimbingan dan Konseling Islam) · 2025-05-09 · 218 citations

  articleOpen access

  Understanding and quantifying the global methane (CH4) budget is important for assessing realistic pathways to mitigate climate change. CH4 is the second most important human-influenced greenhouse gas in terms of climate forcing after carbon dioxide (CO2), and both emissions and atmospheric concentrations of CH4 have continued to increase since 2007 after a temporary pause. The relative importance of CH4 emissions compared to those of CO2 for temperature change is related to its shorter atmospheric lifetime, stronger radiative effect, and acceleration in atmospheric growth rate over the past decade, the causes of which are still debated. Two major challenges in quantifying the factors responsible for the observed atmospheric growth rate arise from diverse, geographically overlapping CH4 sources and from the uncertain magnitude and temporal change in the destruction of CH4 by short-lived and highly variable hydroxyl radicals (OH). To address these challenges, we have established a consortium of multidisciplinary scientists under the umbrella of the Global Carbon Project to improve, synthesise, and update the global CH4 budget regularly and to stimulate new research on the methane cycle. Following Saunois et al. (2016, 2020), we present here the third version of the living review paper dedicated to the decadal CH4 budget, integrating results of top-down CH4 emission estimates (based on in situ and Greenhouse Gases Observing SATellite (GOSAT) atmospheric observations and an ensemble of atmospheric inverse-model results) and bottom-up estimates (based on process-based models for estimating land surface emissions and atmospheric chemistry, inventories of anthropogenic emissions, and data-driven extrapolations). We present a budget for the most recent 2010–2019 calendar decade (the latest period for which full data sets are available), for the previous decade of 2000–2009 and for the year 2020. The revision of the bottom-up budget in this 2025 edition benefits from important progress in estimating inland freshwater emissions, with better counting of emissions from lakes and ponds, reservoirs, and streams and rivers. This budget also reduces double counting across freshwater and wetland emissions and, for the first time, includes an estimate of the potential double counting that may exist (average of 23 Tg CH4 yr−1). Bottom-up approaches show that the combined wetland and inland freshwater emissions average 248 [159–369] Tg CH4 yr−1 for the 2010–2019 decade. Natural fluxes are perturbed by human activities through climate, eutrophication, and land use. In this budget, we also estimate, for the first time, this anthropogenic component contributing to wetland and inland freshwater emissions. Newly available gridded products also allowed us to derive an almost complete latitudinal and regional budget based on bottom-up approaches. For the 2010–2019 decade, global CH4 emissions are estimated by atmospheric inversions (top-down) to be 575 Tg CH4 yr−1 (range 553–586, corresponding to the minimum and maximum estimates of the model ensemble). Of this amount, 369 Tg CH4 yr−1 or ∼ 65 % is attributed to direct anthropogenic sources in the fossil, agriculture, and waste and anthropogenic biomass burning (range 350–391 Tg CH4 yr−1 or 63 %–68 %). For the 2000–2009 period, the atmospheric inversions give a slightly lower total emission than for 2010–2019, by 32 Tg CH4 yr−1 (range 9–40). The 2020 emission rate is the highest of the period and reaches 608 Tg CH4 yr−1 (range 581–627), which is 12 % higher than the average emissions in the 2000s. Since 2012, global direct anthropogenic CH4 emission trends have been tracking scenarios that assume no or minimal climate mitigation policies proposed by the Intergovernmental Panel on Climate Change (shared socio-economic pathways SSP5 and SSP3). Bottom-up methods suggest 16 % (94 Tg CH4 yr−1) larger global emissions (669 Tg CH4 yr−1, range 512–849) than top-down inversion methods for the 2010–2019 period. The discrepancy between the bottom-up and the top-down budgets has been greatly reduced compared to the previous differences (167 and 156 Tg CH4 yr−1 in Saunois et al. (2016, 2020) respectively), and for the first time uncertainties in bottom-up and top-down budgets overlap. Although differences have been reduced between inversions and bottom-up, the most important source of uncertainty in the global CH4 budget is still attributable to natural emissions, especially those from wetlands and inland freshwaters. The tropospheric loss of methane, as the main contributor to methane lifetime, has been estimated at 563 [510–663] Tg CH4 yr−1 based on chemistry–climate models. These values are slightly larger than for 2000–2009 due to the impact of the rise in atmospheric methane and remaining large uncertainty (∼ 25 %). The total sink of CH4 is estimated at 633 [507–796] Tg CH4 yr−1 by the bottom-up approaches and at 554 [550–567] Tg CH4 yr−1 by top-down approaches. However, most of the top-down models use the same OH distribution, which introduces less uncertainty to the global budget than is likely justified. For 2010–2019, agriculture and waste contributed an estimated 228 [213–242] Tg CH4 yr−1 in the top-down budget and 211 [195–231] Tg CH4 yr−1 in the bottom-up budget. Fossil fuel emissions contributed 115 [100–124] Tg CH4 yr−1 in the top-down budget and 120 [117–125] Tg CH4 yr−1 in the bottom-up budget. Biomass and biofuel burning contributed 27 [26–27] Tg CH4 yr−1 in the top-down budget and 28 [21–39] Tg CH4 yr−1 in the bottom-up budget. We identify five major priorities for improving the CH4 budget: (i) producing a global, high-resolution map of water-saturated soils and inundated areas emitting CH4 based on a robust classification of different types of emitting ecosystems; (ii) further development of process-based models for inland-water emissions; (iii) intensification of CH4 observations at local (e.g. FLUXNET-CH4 measurements, urban-scale monitoring, satellite imagery with pointing capabilities) to regional scales (surface networks and global remote sensing measurements from satellites) to constrain both bottom-up models and atmospheric inversions; (iv) improvements of transport models and the representation of photochemical sinks in top-down inversions; and (v) integration of 3D variational inversion systems using isotopic and/or co-emitted species such as ethane as well as information in the bottom-up inventories on anthropogenic super-emitters detected by remote sensing (mainly oil and gas sector but also coal, agriculture, and landfills) to improve source partitioning.

  PublisherDOI

• Satellite Requirements to Capture Water Propagation in Earth's Rivers

  Reviews of Geophysics · 2025-07-22 · 6 citations

  articleOpen access

  Abstract The water in Earth's rivers propagates as waves through space and time across hydrographic networks. A detailed understanding of river dynamics globally is essential for achieving accurate knowledge of surface water storage and fluxes to support water resources management and water‐related disaster forecasting and mitigation. Global in situ information on river flows are crucial to support such an investigation but remain difficult to obtain at adequate spatiotemporal scales, if they even exist. Many expectations are placed on remote sensing techniques as key contributors. Despite a rapid expansion of satellite capabilities, however, it remains unclear what temporal revisit, spatial coverage, footprint size, spatial resolution, observation accuracy, latency time, and variables of interest from satellites are best suited to capture the space‐time propagation of water in rivers. Additionally, the ability of numerical models to compensate for data sparsity through model‐data fusion remains elusive. We review recent efforts to identify the type of remote sensing observations that could enhance understanding and representation of river dynamics. Key priorities include: (a) resolving narrow water bodies (finer than 50–100 m), (b) further analysis of signal accuracy versus hydrologic variability and relevant technologies (optical/SAR imagery, altimetry, microwave radiometry), (c) achieving 1–3 days observation intervals, (d) leveraging data assimilation and multi‐satellite approaches using existing constellations, and (e) new variable measurement for accurate water flux and discharge estimates. We recommend a hydrology‐focused, multi‐mission observing system comprising: (a) a cutting‐edge single or dual‐satellite mission for advanced surface water measurements, and (b) a constellation of cost‐effective satellites targeting dynamic processes.

  PublisherDOI

• SAEM: Satellite Altimetry-based Extension of global-scale in situ river discharge Measurements

  2025-03-14

  preprintOpen access

  Accurate river discharge monitoring is essential for understanding hydrological processes, yet the availability of in situ measurements is increasingly limited due to a declining number of operational gauges and temporal gaps in gauge records. Satellite altimetry offers a robust alternative to address these limitations. Here, we introduce the Satellite Altimetry-based Extension of the global-scale in situ river discharge Measurements (SAEM) dataset, which integrates data from multiple satellite altimetry missions to estimate river discharge and enhance global hydrological monitoring networks. Our analysis evaluated 47,000 discharge gauges and successfully derived height-based discharge estimates for 8,730 gauges, expanding the coverage of current remote sensing datasets by a factor of three. These gauges collectively represent approximately 88% of the globally gauged discharge volume. The SAEM dataset achieves a median Kling-Gupta Efficiency (KGE) of 0.48, demonstrating superior performance compared to existing global datasets.In addition to discharge time series, SAEM offers three supplementary products: (1) a catalog of Virtual Stations (VSs) with metadata, including geographic coordinates, altimetry mission details, distances to discharge gauges, and quality flags; (2) for VSs with quality-controlled discharges, we provide IDs from L3 databases such as Hydroweb.Next (formerly Hydroweb), the Database of Hydrological Time Series of Inland Waters (DAHITI), the Global River Radar Altimeter Time Series (GRRATS), and HydroSat, and for VSs without corresponding time series in these L3 products, we have generated water level time series (SAEM WL) as an additional product; (3) rating curves that map water levels to discharge using the Nonparametric Stochastic Quantile Mapping Function approach. The SAEM dataset can enhance hydrological research, support water resource management, and allow addressing complex water-related challenges in the context of a changing climate.

  PublisherDOI

• Satellites reveal hotspots of global river extent change

  UNC Libraries · 2025-03-01

  articleOpen access
  PublisherDOI

• The Surface Water and Ocean Topography Mission (SWOT) Prior Lake Database (PLD): Lake mask and operational auxiliaries

  2025-06-20

  preprintOpen access

  The preprint version of this work has now been formally published. Going forward, please cite the final WRR publication instead of the preprint : Wang, J., Pottier, C., Cazals, C., Battude, M., Sheng, Y., Song, C., Sikder, M.S., Yang, X., Ke, L., Delhoume, M., Gosset, M., Oliveira, R.R.A., Grippa, M., Girard, F., Allen, G.H., Xu, X., Zhu, X., Biancamaria, S., Smith, L.C., Crétaux, J.-F., and Pavelsky, T. (2025). The Surface Water and Ocean Topography Mission (SWOT) Prior Lake Database (PLD): Lake mask and operational auxiliaries. Water Resources Research , 61, e2023WR036896. https://doi.org/10.1029/2023WR036896

  PublisherOA PDFDOI

Recent grants

• CAREER: Integrating river hydrology across scales: advancing understanding of the global river-atmosphere interface

  NSF · $192k · 2022–2022

Frequent coauthors

• Tamlin M. Pavelsky

  107 shared

• Peirong Lin

  59 shared

• Ryan Riggs

  48 shared

• Ming Pan

  42 shared

• Cédric H. David

  Jet Propulsion Laboratory

  42 shared

• Jida Wang

  39 shared

• Dai Yamazaki

  The University of Tokyo

  33 shared

• Michael Durand

  32 shared

Labs

• Department of GeosciencesPI

Education

• PhD, MS, Geological Sciences

  University of North Carolina

  2017

• BS Geology, Geology

  University of California Davis

  2008