About

Tamlin M. Pavelsky is a Kenan Distinguished Professor in the Department of Earth, Marine and Environmental Sciences at the University of North Carolina at Chapel Hill. His research interests focus on the intersections between hydrology, satellite remote sensing, and climate change. He works on scales ranging from the entire globe to a single large wetland, with a particular emphasis on Arctic and Subarctic regions. His current projects aim to understand how satellite remote sensing can be used to analyze the storage, movement, and quality of surface water, assess the accuracy of regional climate models in simulating hydrologic processes, and investigate the impacts of human-created warming on Arctic and boreal hydrologic and climatic systems. A significant part of his research involves the Surface Water and Ocean Topography (SWOT) satellite mission and its applications to rivers, lakes, and wetlands.

Research topics

• Computer Science
• Environmental science
• Geography
• Ecology
• Remote sensing
• Geology
• Cartography
• Biology
• Climatology
• Physical geography
• Oceanography
• Meteorology
• Forestry
• Earth science
• Agroforestry
• Chemistry
• Environmental chemistry
• Data science
• Statistics
• Mathematics

Selected publications

• SWOT Satellite Observations of the Kakhovka Dam Break Flood Highlight Limitations of Outburst Flood Models

  Geophysical Research Letters · 2026-04-17

  articleOpen access

  Abstract The 6 June 2023 failure of the Kakhovka Dam generated a catastrophic outburst flood, leading to loss of life and infrastructure damage. During the flood, daily measurements of water surface elevation were collected by the SWOT satellite, providing the first direct, high‐resolution 2D measurements of a large outburst flood. We compared SWOT measurements with 2D numerical hydraulic simulations to evaluate the ability of flood models to reproduce outburst flood stage. Simulations reliant on minimally constrained, globally available reservoir bathymetry significantly underestimate flood stage. Modifying reservoir and channel bathymetry to reflect geomorphology produces better agreement with SWOT, and is closer to in situ reservoir bed elevations, yet still fails to reproduce both flood stage and timing. SWOT's unprecedented direct measurements suggest that 2D flood models may not capture outburst flood dynamics at a level useful for geohazard decision making and demonstrate the need for more observational validation of this destructive and costly geohazard.

  PublisherDOI

• Global River Widths from Landsat (GRWL) Database

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

  datasetOpen accessSenior author

  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

• 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

• Estimating Daily Suspended Sediment Flux From Multiple Data Sources Using Deep Learning

  UNC Libraries · 2025-10-16

  articleOpen access

  Suspended sediment concentration, flux, and river discharge are essential indicators of river ecosystem health and reflect watershed‐scale processes. Monitoring these variables is labor‐intensive, leading to sparse and geographically biased observations and the development of models to fill in the observational gaps. These models generally use either climatological data or satellite images to estimate one of these variables. In this work, we present a novel deep learning model that can leverage multiple data sources with different temporal characteristics to produce continuous daily estimates of suspended sediment concentration (SSC), suspended sediment flux (SSF), and discharge. The model first encodes daily hydrological data from the ERA5‐Land reanalysis using a Long Short‐Term Memory network and water color data from Landsat satellites using a Multi‐Layer Perceptron network, then merge these encoded data sources using a cross‐attention decoder. We train and test the model on a large data set of in situ observations from 630 river sites over 43 years in the contiguous United States, covering a wide range of watersheds and conditions. We produce SSC, SSF, and discharge predictions with respective relative errors of 49%, 57%, and 44%, and relative bias of −2.5%, 2.6%, and 3.7%. We use our model to create a data set of continuous daily SSC, SSF, and discharge for all large rivers in the contiguous United States. This new model architecture provides a valuable tool for monitoring river systems, addressing limitations of single‐source models and offering a framework applicable to other Earth systems monitoring problems where integrating diverse data streams may be useful. Suspended sediment concentration, flux, and river discharge are essential indicators of river ecosystem health and reflect watershed‐scale processes. Monitoring these variables is labor‐intensive, leading to a lack of global data. In this work, we build a new type of model that can use multiple data sources to predict all three of these variables, compared to existing models that generally use one data source to predict one of the variables. We find that combining data sources and predicting multiple physically related variables improves model accuracy. We develop a model that uses daily meteorological data and irregularly timed optical satellite imagery to predict daily fluvial sediment flux Our model performs as well as existing models with the benefit of predicting daily sediment flux, concentration, and discharge at once This model approach can be extended to other Earth systems processes that need to integrate diverse and sparse data sets for daily estimates We develop a model that uses daily meteorological data and irregularly timed optical satellite imagery to predict daily fluvial sediment flux Our model performs as well as existing models with the benefit of predicting daily sediment flux, concentration, and discharge at once This model approach can be extended to other Earth systems processes that need to integrate diverse and sparse data sets for daily estimates

  PublisherDOI

• Integrating Information from Hydrologic Models and SWOT Remote Sensing Measurements to Estimate Discharge across River Networks

  2025-09-29

  articleOpen access

  The Surface Water and Ocean Topography (SWOT, launched in December 2022) satellite has the potential to improve knowledge of river discharge globally, but preliminary SWOT discharge estimates are prone to timeseries bias, mainly induced by globally available prior information. To address this issue, we present a new algorithm designed to improve SWOT discharge. SFOI analytically integrates basin-scale information by applying mass conservation to time averaged SWOT discharge and estimates of regionalized runoff. We test the algorithm on three river basins with drainage areas on the order of 105 km2. We find that when the time averaged SWOT discharge observations are spatially unbiased (i.e. their spatially averaged error is close to zero) the algorithm significantly improves SWOT discharge: the median (interquartile range) absolute value of the normalized SFOI error is 15% (10-19%) for the Willamette River basin, and 12% (7-48%) for the upper Ohio River basin, significant improvements over SWOT discharge error (median values of 30% and 31%, respectively). However, for the Loire River basin, which has high (~60%) spatial bias, the algorithm does not improve SWOT discharge. These results imply that understanding and reducing spatial bias at the basin scale is key for reducing temporal bias in river discharge timeseries. Assessing spatial bias globally, we find that SWOT discharge should achieve its target accuracy (30% discharge error, with most of it temporal bias) even in ungaged basins, enabling SWOT discharge to provide significant improvements to the global water budget.

  PublisherOA PDFDOI

• Overlooked and extensive ghost forest formation across the US Atlantic coast

  Nature Sustainability · 2025-12-03 · 4 citations

  article
  PublisherDOI

• 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

• Author Correction: World’s landlocked basins drying

  UNC Libraries · 2025-09-09

  articleOpen access1st authorCorresponding
  PublisherDOI

• Corrigendum

  UNC Libraries · 2025-09-06

  articleOpen access

  Corrigendum to "Comparison of Methods to Estimate Snow Water Equivalent at the Mountain Range Scale: A Case Study of the California Sierra Nevada"

  PublisherDOI

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

  2025-06-20

  preprintOpen accessSenior author

  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

• NNA Track 1: Collaborative Research: Navigating Convergent Pressures on Arctic Development

  NSF · $191k · 2020–2025

Frequent coauthors

• George H. Allen

  107 shared

• Xiao Yang

  Southern Methodist University

  101 shared

• Michael Durand

  100 shared

• L. C. Smith

  Providence College

  91 shared

• C. J. Gleason

  University of Massachusetts Amherst

  80 shared

• L. H. Pitcher

  Oak Ridge Institute for Science and Education

  61 shared

• Wayana Dolan

  Denver Federal Center

  50 shared

• Theodore Langhorst

  University of Massachusetts Amherst

  47 shared