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A Drone‐Borne Method to Jointly Estimate Discharge and Manning's Roughness of Natural Streams
Author(s) -
Bandini Filippo,
Lüthi Beat,
PeñaHaro Salvador,
Borst Chris,
Liu Jun,
Karagkiolidou Sofia,
Hu Xiao,
Lemaire Grégory Guillaume,
Bjerg Poul L.,
BauerGottwein Peter
Publication year - 2021
Publication title -
water resources research
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.863
H-Index - 217
eISSN - 1944-7973
pISSN - 0043-1397
DOI - 10.1029/2020wr028266
Subject(s) - particle image velocimetry , bathymetry , surf zone , pixel , remote sensing , environmental science , geology , physics , turbulence , meteorology , mechanics , optics , oceanography
Abstract Image cross‐correlation techniques, such as particle image velocimetry (PIV), can estimate water surface velocity ( v surf ) of streams. However, discharge estimation requires water depth and the depth‐averaged vertical velocity ( U m ). The variability of the ratio U m / v surf introduces large errors in discharge estimates. We demonstrate a method to estimate v surf from Unmanned Aerial Systems (UASs) with PIV technique. This method does not require any ground control point (GCP): the conversion of velocities from pixels per frame into length per time is performed by informing a camera pinhole model; the range from the pinhole to the water surface is measured by the drone‐borne radar. For approximately uniform flow, U m is a function of the Gauckler‐Manning‐Strickler coefficient ( K s ) and v surf . We implement an approach that can be used to jointly estimate K s and discharge by informing a system of two unknowns ( K s and discharge) and two nonlinear equations: i) Manning's equation and ii) mean‐section method for computing discharge from U m . This approach relies on bathymetry, acquired in situ a priori, and on UAS‐borne v surf and water surface slope measurements. Our joint (discharge and K s ) estimation approach is an alternative to the widely used approach that relies on estimating U m as 0.85· v surf . It was extensively investigated in 27 case studies, in different streams with different hydraulic conditions. Discharge estimated with the joint estimation approach showed a mean absolute error of 19.1% compared to in situ discharge measurements. K s estimates showed a mean absolute error of 3 m 1/3 /s compared to in situ measurements.