C. J. Gleason

University of Massachusetts Amherst

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L. C. Smith

Planetary Science Institute

Tamlin M. Pavelsky

University of North Carolina at Chapel Hill

L. H. Pitcher

Cooperative Institute for Research in Environmental Sciences

Dongmei Feng

University of Cincinnati

Michael Durand

The Ohio State University

V. W. Chu

University of California, Santa Barbara

Theodore Langhorst

University of North Carolina at Chapel Hill

Peirong Lin

Beijing Institute of Big Data Research

George H. Allen

Virginia Tech

Merritt E. Harlan

United States Geological Survey

Cooperative Institutions

Jet Propulsion Laboratory

University of Massachusetts Amherst

University of Washington

University of North Carolina at Chapel Hill

Centre National de la Recherche Scientifique

University of California, Los Angeles

United States Geological Survey

California Institute of Technology

The Ohio State University

Institut de Recherche pour le Développement

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An intercomparison of remote sensing river discharge estimation algorithms from measurements of river height, width, and slope

Water Resources Research (2016)

Michael Durand C. J. Gleason Pierre‐André Garambois David M. Bjerklie L. C. Smith

Abstract The Surface Water and Ocean Topography (SWOT) satellite mission planned for launch in 2020 will map river elevations and inundated area globally for rivers >100 m wide. In advance of this launch, we here evaluated the possibility of estimating discharge in ungauged rivers using synthetic, daily “remote sensing” measurements derived from hydraulic models corrupted with minimal observational errors. Five discharge algorithms were evaluated, as well as the median of the five, for 19 rivers spanning a range of hydraulic and geomorphic conditions. Reliance upon a priori information, and thus applicability to truly ungauged reaches, varied among algorithms: one algorithm employed only global limits on velocity and depth, while the other algorithms relied on globally available prior estimates of discharge. We found at least one algorithm able to estimate instantaneous discharge to within 35% relative root‐mean‐squared error (RRMSE) on 14/16 nonbraided rivers despite out‐of‐bank flows, multichannel planforms, and backwater effects. Moreover, we found RRMSE was often dominated by bias; the median standard deviation of relative residuals across the 16 nonbraided rivers was only 12.5%. SWOT discharge algorithm progress is therefore encouraging, yet future efforts should consider incorporating ancillary data or multialgorithm synergy to improve results.

Discharge

10.1002/2015wr018434

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Citations (199)

Fluvial-UMass/Gleason-et-al-TC-2021: Pre-publication archive creation

Zenodo (CERN European Organization for Nuclear Research) (2021)

C. J. Gleason

First release of verified code to recreate figures for Gleason et al TC 2021 "Hourly surface meltwater routing for a Greenlandic supraglacial catchment across hillslopes and through a dense topological channel network"

10.5281/zenodo.4646423

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Remotely Sensing River Greenhouse Gas Exchange Velocity Using the SWOT Satellite

Global Biogeochemical Cycles (2022)

Craig Brinkerhoff C. J. Gleason Christopher J. Zappa Peter A. Raymond Merritt E. Harlan

Abstract Two decades of research has shown that the global river network emits significant amounts of greenhouse gas. Despite much progress, there is still large uncertainty in the temporal dynamics of gas exchange and thus carbon emissions to the atmosphere. Much of this uncertainty stems from a lack of existing tools for studying the spatiotemporal dynamics of gas exchange velocity (the rate of this diffusive transport). We propose that the NASA/CNES/UKSA/CSA Surface Water and Ocean Topography (SWOT) satellite can provide new insights to fluvial gas exchange modeling upon launch and subsequent data collection in 2022. Here, we exploit the distinct geomorphology of SWOT‐observable rivers (>50 m wide) to develop a physical model of gas exchange that is remotely sensible and explains 50% of log‐transformed variation across 166 field measurements of . We then couple this model with established inversion techniques to develop BIKER, the “Bayesian Inference of the Exchange Rate” algorithm. We validate BIKER on 47 SWOT‐simulated rivers without an in‐situ calibration, yielding an algorithm that predicts the timeseries solely from SWOT observations with a by‐river median Kling‐Gupta Efficiency of 0.21. BIKER is better at inferring the temporal variation of gas exchange (median correlation coefficient of 0.91), than reproducing the absolute rates of exchange (median normalized RMSE of 51%). Finally, BIKER is robust to measurement errors implicit in the SWOT data. We suggest that BIKER will be useful in mapping global‐scale fluvial gas exchange and improving emissions estimates when coupled with river models.

SWOT Analysis

10.1029/2022gb007419

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Citations (8)

Allen et al., Similarity of stream width distributions across headwater systems, data 2017

Zenodo (CERN European Organization for Nuclear Research) (2017)

George H. Allen Tamlin M. Pavelsky Eric Barefoot Michael P. Lamb David Butman

These files contain the results from Allen et al. "Similarity of stream width distributions across headwater systems". locationStreamSurveys.zip : contains stream hydromorphology data collected in seven headwater catchments in North America and New Zealand. repeatStreamSurveys.zip : contains stream hydromorphology data collected in six repeat surveys in the Stony subcatchment in Duke Forest, NC. streamWidthModelOutput.zip : contains stream width model parameters and output data presented in Allen et al. Note: the code used to analyze these data can be found at https://github.com/geoallen/streamWidthAnalysis2017/

Similarity (geometry)

10.5281/zenodo.1034384

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Global Long-term River Width (GLOW)

Zenodo (CERN European Organization for Nuclear Research) (2022)

Dongmei Feng C. J. Gleason Xiao Yang George H. Allen Tamlin M. Pavelsky

This repository archives the Global LOng term river Width (GLOW) dataset measured from Landsat for 1984-2020. There are three datasets in this repository: Width, Cross_section, and Environmental_parameter_global. Width contains the GLOW dataset, which has three attributes: ID, date (mm/dd/yyyy), and width (unit: meters). Each cross-section is identified by a unique ID in the format of R########XS#######. The dataset is grouped into eight regions based on the geospatial location. Cross-section contains the shapefiles indicating the GLOW cross-section locations, which have three attributes: ID, lat (latitude), and lon (longitude). Environmental_parameter_global contains the environmental parameters generated in this study for the Random Forest analysis, which has eighteen parameters for all global rivers at the river reach level, including the temporal trend slope, interquartile range, 25th percentile, and 75th percentile of air temperature, snowmelt, precipitation, evapotranspiration, and river discharge. Please refer to the following paper for more details: Feng, D., Gleason C.J., Yang X., Allen G.H., and Pavelsky T.M., How have global river widths changed over time? Water Resources Research

10.5281/zenodo.6425656

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CryoSheds: a GIS modeling framework for delineating land-ice watersheds for the Greenland Ice Sheet

GIScience & Remote Sensing (2016)

L. H. Pitcher L. C. Smith C. J. Gleason Kang Yang

Choice of watershed delineation technique is an important source of uncertainty for cryo-hydrologic studies of the Greenland Ice Sheet (GrIS), with different methods yielding different watersheds for a common pour point. First, this paper explores this uncertainty for the Akuliarusiarsuup Kuua River Northern Tributary, Western Greenland. Next, a standardized, semi-automated modeling framework for generating land-ice watersheds for GrIS land-terminating ice (henceforth referred to as CryoSheds) using geographic information systems (GIS) hydrologic modeling tools is presented. The framework uses ArcGIS and the ArcPy geoprocessing library to delineate two types of land-ice watersheds, namely those defined by: (1) a hydraulic pressure potential with varying water to ice overburden pressure ratios (k-value), which determines theoretical flow paths from the hydrostatic equation, using surface and bedrock digital elevation models (DEMs) and (2) a surface topography DEM alone. Lastly, a demonstration of the CryoSheds method is presented for seven remotely sensed proglacial pour points along the Aussivigssuit River (AR), Western Greenland, and its largest tributaries. GrIS meltwater runoff from these seven nested land-ice watersheds is estimated using Modele Atmospherique Regional (MAR) v.3.2 and runoff uncertainties due to watershed delineation parameter selection is estimated.

Meltwater

Greenland ice sheet

Hydrological modelling

10.1080/15481603.2016.1230084

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Citations (14)

RODEO: An algorithm and Google Earth Engine application for river discharge retrieval from Landsat

Environmental Modelling & Software (2021)

Ryan Riggs George H. Allen Cédric H. David Peirong Lin Ming Pan

10.1016/j.envsoft.2021.105254

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Citations (24)

Technical Note: Semi-automated classification of time-lapse RGB imagery for a remote Greenlandic river

C. J. Gleason L. C. Smith D. C. Finnegan A. L. LeWinter L. H. Pitcher

Abstract. River systems in remote environments are often challenging to monitor and understand where traditional gauging apparatus are difficult to install or where safety concerns prohibit field measurements. In such cases, remote sensing, especially terrestrial time lapse imaging platforms, offer a means to better understand these fluvial systems. One such environment is found at the proglacial Isortoq River in southwest Greenland, a river with a constantly shifting floodplain and remote Arctic location that make gauging and in situ measurements all but impossible. In order to derive relevant hydraulic parameters for this river, two RGB cameras were installed in July of 2011, and these cameras collected over 10 000 half hourly time-lapse images of the river by September of 2012. Existing approaches for extracting hydraulic parameters from RGB imagery require manual or supervised classification of images into water and non-water areas, a task that was impractical for the volume of data in this study. As such, automated image filters were developed that removed images with environmental obstacles (e.g. shadows, sun glint, snow) from the processing stream. Further image filtering was accomplished via a novel automated histogram similarity filtering process. This similarity filtering allowed successful (mean accuracy 79.6%) supervised classification of filtered images from training data collected from just 10% of those images. Effective width, a hydraulic parameter highly correlated with discharge in braided rivers, was extracted from these classified images, producing a hydrograph proxy for the Isortoq River between 2011 and 2012. This hydrograph proxy shows agreement with historic flooding observed in other parts of Greenland in July 2012 and offers promise that the imaging platform and processing methodology presented here will be useful for future monitoring studies of remote rivers.

RGB color model

10.5194/hessd-12-1311-2015

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Sensitivity of lake and wetland methane emissions upscaling to littoral zone area using airborne synthetic aperture radar

AGU Fall Meeting Abstracts (2020)

Ethan D. Kyzivat L. C. Smith Tamlin M. Pavelsky David Butman C. J. Gleason

Methane Emissions

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Reconciling at‐a‐Station and at‐Many‐Stations Hydraulic Geometry Through River‐Wide Geomorphology

Geophysical Research Letters (2019)

Craig Brinkerhoff C. J. Gleason David W. Ostendorf

Abstract At‐many‐stations hydraulic geometry (AMHG), while useful for estimating river discharge from satellite data, remains empirical and has yet to be reconciled with the at‐a‐station hydraulic geometry (AHG) from which it was originally derived. Here we present evidence, using United States Geological Survey field measurements of channel hydraulics for 155 rivers, that AMHG can be hydraulically and geomorphically reconciled with AHG. Our results indicate that AMHG is rightly understood as an expression of a river‐wide model of hydraulics driven by changes in slope imposed upon AHG physics. The explanatory power of AHG and this river‐wide model combine to determine whether AMHG exists: if both AHG and the river‐wide model adequately describe hydraulics, then we show that AMHG is a necessary mathematical consequence of these two phenomena. We also orient these findings in the context of river discharge estimation and other applications.

Hydraulics

10.1029/2019gl084529

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Citations (32)