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Title of Dataset: Disruptive Role of Vertical Land Motion in Future Assessments of Climate Change-Driven Sea Level Rise and Coastal Flooding Hazards in the Chesapeake Bay.
- Author(s): Sonam Futi Sherpa, Manoochehr Shirzaei, Chandrakanta Ojha
- Corresponding Author Email Address: sfsherpa@vt.edu
- Categories: Climate Science, Earth Sciences not elsewhere classified, Environmental Science
- Group: Geosciences
- Item Type: Dataset
- Keywords: sea-level increase, climate projections, Solid Earth, future coastal changes, Inundation Modeling, disaster resilience, Climate Change Evidence, remote sensing database, Interferometric synthetic aperture radar, LiDAR analysis
Description: Inundation projections from Sea Level Rise (SLR), Vertical Land Motion (VLM), and storm surge for hurricane Isabel at climate scenarios namely Shared Socioeconomic Pathways (SSPs) 1-1.9, 1-2.6, 2-4.5, 3-7.0 and 5-8.5 at three-time scales (2030-2050-2100) at medium confidence, median values, likely ranges upper and lower (for three selected SSPs: see below) are provided. Additionally, the inundated area from SLR, Subsidence at SSP 1-2.6 and 5-8.5 for likely ranges (upper and lower) at low confidence scenarios are provided.
Sentinel-1 and ALOS data from 2007-2020 are processed used the WabInSAR algorithm. We use future SLR scenarios from the Sixth Assessment Report (AR6) following SSPs adopted by the IPCC for projection periods of 2030, 2050 till 2100, relative to a baseline of 1995-2014 with medium confidence and low confidence.
Anyone wishing to use this dataset should cite Sherpa et al. 2022 and contact Sonam Futi Sherpa at sfsherpa@vt.edu for any questions with details of their work, so that we may offer guidance in regards to the best usage of our produced inundation scenarios dataset.
Funding: National Aeronautics and Space Administration, 80NSSC170567, US Geological Survey (USGS), National Science Foundation (NSF) 1735139, Geological Society of America (GSA)
Cite this as:
Sherpa, Sonam Futi; Shirzaei, Manoochehr; Ojha, Chandrakanta (2022): Disruptive Role of Vertical Land Motion in Future Assessments of Climate Change-Driven Sea Level Rise and Coastal Flooding Hazards in the Chesapeake Bay. University Libraries, Virginia Tech. Dataset. https://doi.org/10.7294/20161580
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Title of Dataset: Country-wide flood exposure analysis using Sentinel-1synthetic aperture radar data: Case study of 2019 Iran flood
- Author(s): Sonam Futi Sherpa, Manoochehr Shirzaei
- Corresponding Author Email Address: sfsherpa@vt.edu
- Categories: Environmental Science, Hydrology, Meteorology, Natural Hazards, Climate Science
- Group: Geosciences
- Item Type: Dataset
- Keywords: climate extremes, Flood exposure, remote sensing data, precipitation patterns
Description: We provide county and state-level flood exposure data, precipitation data, and individual flood maps for each SAR frames to understand flood exposure from the 2019 Flood of Iran at the country level utilizing 673 Sentinel-1 Synthetic Aperture Radar intensity images spanning January to February. A complete description of the method used to obtain probabilistic flood maps and exposure can be found in Sherpa and Shirzaei (2020) but is briefly stated below.
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We applied a Bayesian framework to SAR intensity images to calculate the probability of a SAR pixel being flooded (Giustarini et al., 2016; Sherpa et al., 2020), for which a likelihood probability density function was estimated, thereby providing a continuous value between 0 and 1 as a probabilistic flood map.
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To obtain an estimate of likelihood, an image segmentation scheme using the fast marching algorithm (FMA) is implemented (Sethian, 1999).
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The percent area exposed to flooding is estimated as the pixel area's multiplication with its flooding probability for pixels located within each county or state divided by the county or state area.
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The population exposure is calculated by multiplying each county or state's percent area exposure values with their population, assuming a uniform population distribution.
Anyone wishing to use this dataset should cite Sherpa and Shrizaei (2022) and this dataset. Please also contact and contact Sonam Futi Sherpa at sfsherpa@vt.edu for any questions with details of their work, so that we may offer guidance in regard to the best usage of our produced dataset.
Sherpa, S. F., & Shirzaei, M. (2021). Country‐wide flood exposure analysis using Sentinel‐1 synthetic aperture radar data: Case study of 2019 Iran flood. Journal of Flood Risk Management, 15(1), e12770.
https://doi.org/10.1111/jfr3.12770
Sherpa, Sonam Futi; Shirzaei, Manoochehr (2022): Country-wide flood exposure analysis using Sentinel-1synthetic aperture radar data: Case study of 2019 Iran flood. University Libraries, Virginia Tech. Dataset. https://doi.org/10.7294/21764222
Additional references:
Sherpa, S. F., Shirzaei, M., Ojha, C., Werth, S., & Hostache, R. (2020). Probabilistic mapping of august 2018 flood of Kerala, India, using space-borne synthetic aperture radar. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 13, 896-913. 10.1109/JSTARS.2020.2970337
Giustarini, Laura, Renaud Hostache, Dmitri Kavetski, Marco Chini, Giovanni Corato, Stefan Schlaffer, and Patrick Matgen. "Probabilistic flood mapping using synthetic aperture radar data." IEEE Transactions on Geoscience and Remote Sensing 54, no. 12 (2016): 6958-6969.
Sethian, J. A. (1999). Level set methods and fast marching methods: evolving interfaces in computational geometry, fluid mechanics, computer vision, and materials science (Vol. 3). Cambridge university press.
Funding: National Aeronautics and Space Administration, 80NSSC170567
Resource Title: Country-wide flood exposure analysis using Sentinel-1 synthetic aperture radar data: Case study of 2019 Iran flood
Resource DOI: 10.1111/jfr3.12770
Other References:
License: CC0 1.0 Universal (CC0 1.0) Public Domain Dedication
Publisher: University Libraries, Virginia Tech
Location: Blacksburg, Virginia
Corresponding Author Name: Sonam Futi Sherpa
Corresponding Author E-mail Address: sfsherpa@vt.edu
Files/Folders in Dataset and Description of Files
The dataset contains:
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Flood_Maps.zip: Probabilistic flood maps for 673 Sentinel-1 SAR frames over the entire county of Iran. Some of these maps are found in the Supporting Information section of the associated publication.
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State_Monthly_FloodAreaPer.csv: Monthly flooding area exposure percentile at the state-level flooding months January, February, and March 2019.
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County_Monthly_FloodAreaPer.csv: Monthly flooding area exposure percentile at the county level for the flooding months, January, February, and March 2019.
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State_Monthly_Floodexposed_Population.csv : Monthly flooding population exposure map at the state level for flooding months, January, February, and March 2019
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County_Monthly_Floodexposed_Population.csv: Monthly flooding population exposure map at the county level for flooding months, January, February, and March 2019.
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State_Monthly_Precipitation.csv: Monthly total precipitation aggregated over states for flooding months, January, February, and March 2019.
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County_Monthly_Precipitation.csv: Monthly total precipitation aggregated over counties for flooding months, January, February, and March 2019.
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Title of Dataset: Global VLM and Relative Sea Level Acceleration Derived from Global GNSS Datasets
Note that the datasets will be public once the publication (Sherpa and Shirzei under review.) is published.
Author(s): Sonam Futi Sherpa, Manoochehr Shirzaei
Corresponding Author Email Address: sonam_sherpa@brown.edu
Description
We provide global Vertical Land Motion (VLM) and Relative Sea Level acceleration datasets using global GNSS data from 1994 to 2023. Selected GNSS station information (rate and acceleration) is assigned to the global coastal segment from the Dynamic Interactive Vulnerability Assessment (DIVA) model (Nicholls et al., 2021), as well as global tide gauge stations. We project these changes to 2050. A comprehensive description of the method used to derive acceleration from global GNSS datasets can be found in Sherpa et al. (2023). The method is briefly summarized below.
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We perform step correction on the GNSS time series. Then, we obtained rates, acceleration, and other variables if the data length was longer than 10 years.
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Following (Sherpa et al., 2023), We apply Singular Spectrum Analysis (SSA) to the time series of continental GNSS stations with observation longer than 10 years to investigate VLM rate variations.
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We use refined time series from SSA to obtain VLM rates, acceleration, and their uncertainties.
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We project VLM (relative sea level) to 2050, using the current rate of VLM and acceleration.
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We provide this information at Global DIVA segments and Global tide gauge stations.
This dataset consists of,
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Global GNSS Stations Velocity and Acceleration (Current): A CSV file containing derived VLM rates and their corresponding latitude and longitude.
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Global DIVA Segment Velocity and Acceleration (Current) and 2050: A shapefile containing rates, accelerations based on GNSS, GNSS data begin date, GNSS data end date, and GNSS data time duration at global DIVA segments in centimeters and time in days.
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Global IPCC Tide Gauge Station 2050 Stations: A CSV file detailing for 2050 both from Sherpa and Shirzaei (2023) analysis and IPCC at global tide gauge.
For a more detailed description of these datasets see the README file. Anyone wishing to use this dataset should cite Sherpa et al. (2023) and contact Sonam Futi Sherpa at sonam_sherpa@brown.edu with any questions so that we may offer guidance regarding the optimal usage of our dataset.
Cite these datasets as:
Sherpa, S. F., & Shirzaei, M. (2023). Global VLM and Relative Sea Level Acceleration Derived from Global GNSS Datasets (Version V1). Zenodo. https://doi.org/10.5281/zenodo.10205242
Additional references:
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Nicholls, R. J., Lincke, D., Hinkel, J., Brown, S., Vafeidis, A. T., Meyssignac, B., Hanson, S. E., Merkens, J.-L., & Fang, J. (2021). A global analysis of subsidence, relative sea-level change, and coastal flood exposure. Nature Climate Change, 11(4), 338–342. https://doi.org/10.1038/s41558-021-00993-z
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Sherpa, S. F., Shirzaei, M., & Ojha, C. (2023). Disruptive Role of Vertical Land Motion in Future Assessments of Climate Change-Driven Sea-Level Rise and Coastal Flooding Hazards in the Chesapeake Bay. Journal of Geophysical Research: Solid Earth, 128(4), e2022JB025993. https://doi.org/10.1029/2022JB025993
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Title: NASA SWOT Water Surface Elevation (WSE) Data Visualization Dashboard for the Great Salt Lake Basin
Dashboard Link: https://uofu.maps.arcgis.com/apps/dashboards/a11ef620659a4430987f24dcca445543
Authors: Faiyad H. Rishal and Sonam F. Sherpa
Date Created: May 2026
Affiliation: University of Utah - School of Environment, Society and Sustainability
Description
This interactive ArcGIS Dashboard visualizes Water Surface Elevation (WSE) observations derived from NASA's Surface Water and Ocean Topography (SWOT) mission across the Great Salt Lake Basin (GSLB). The dashboard enables users to explore spatial and temporal variations in water levels for lakes throughout the basin using recently available SWOT satellite observations.
The dashboard includes data for 591 lakes and summarizes approximately 24 months of observations, providing both basin-wide statistics and individual lake analyses. Users can interactively examine water surface elevation trends, seasonal fluctuations, and summary statistics through maps, charts, and filtering tools.
The NASA SWOT mission represents a major advancement in monitoring global surface water resources by providing high-resolution measurements of water elevation for lakes, rivers, reservoirs, and coastal regions. Given the ongoing environmental and water resource challenges associated with declining water levels in the Great Salt Lake Basin, this dashboard offers an accessible platform for exploring recent hydrologic conditions and understanding patterns of water-level variability across the region.
Dashboard Features
Basin-Wide Overview
- Interactive map of 591 lakes within the Great Salt Lake Basin.
- Summary statistics of water surface elevation observations.
- Spatial visualization of lake distribution and water-level conditions.
Lake-Level Analysis
- Interactive selection of individual lakes.
- Time-series visualization of water surface elevation changes.
- Basic descriptive statistics, including minimum, maximum, mean, and range of observed elevations.
NASA SWOT Data Integration
- Water surface elevation observations derived from NASA SWOT products.
- Visualization of seasonal and temporal water-level variations.
- Exploration of recent hydrologic trends across the basin.
Acknowledgment
This dashboard was developed as part of the Data Visualization course taught by Dr Sonam F. Sherpa at the University of Utah – School of Environment, Society and Sustainability. The project demonstrates the use of NASA SWOT satellite observations and ArcGIS Dashboard technology to support visualization and exploration of surface water dynamics in the Great Salt Lake Basin.