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Retrieval of river discharge solely from satellite imagery and at-many-stations hydraulic geometry: sensitivity to river form and optimization parameters

TitleRetrieval of river discharge solely from satellite imagery and at-many-stations hydraulic geometry: sensitivity to river form and optimization parameters
Publication TypeJournal Article
Year of Publication2014
AuthorsGleason CJ, Smith LC, Lee J
JournalWater Resources Research
Volume50
Issue12
Start Page9604
Pagination9604–9619
Date Published12/2014
Abstract

Knowledge of river discharge is critically important for water resource management, climate modeling, and improved understanding of the global water cycle, yet discharge is poorly known in much of the world. Remote sensing holds promise to mitigate this gap, yet current approaches for quantitative retrievals of river discharge require in situ calibration or a priori knowledge of river hydraulics, limiting their utility in unmonitored regions. Recently, Gleason and Smith (2014) demonstrated discharge retrievals within 20–30% of in situ observations solely from Landsat TM satellite images through discovery of a river-specific geomorphic scaling phenomenon termed at-many-stations hydraulic geometry (AMHG). This paper advances the AMHG discharge retrieval approach via additional parameter optimizations and validation on 34 gauged rivers spanning a diverse range of geomorphic and climatic settings. Sensitivity experiments reveal that discharge retrieval accuracy varies with river morphology, reach averaging procedure, and optimization parameters. Quality of remotely sensed river flow widths is also important. Recommended best practices include a proposed global parameter set for use when a priori information is unavailable. Using this global parameterization, AMHG discharge retrievals are successful for most investigated river morphologies (median RRMSE 33% of in situ gauge observations), except braided rivers (median RRMSE 74%), rivers having low at-a-station hydraulic geometry b exponents (reach-averaged b < 0.1, median RRMSE 86%), and arid rivers having extreme discharge variability (median RRMSE > 1000%). Excluding such environments, 26–41% RRMSE agreement between AMHG discharge retrievals and in situ gauge observations suggests AMHG can meaningfully address global discharge knowledge gaps solely from repeat satellite imagery.

DOI10.1002/2014WR016109