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Cited by 28Hydrology and Sediment Transport ProcessesHydraulic flow and structuresFlood Risk Assessment and Management

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Unpiloted Aerial Vehicle (UAV) image velocimetry for validation of two-dimensional hydraulic model simulations

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Masafu, Christopher, Williams, Richard, Shi, Xiaogang, Yuan, Qiangqiang, Trigg, Mark·2022·Journal of Hydrology
DOI 10.1016/j.jhydrol.2022.128217
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Abstract

Non-intrusive image-based techniques for measuring surface river velocities have rapidly evolved as a cost-effective and safe means for quantifying flow patterns. Large-scale particle image velocimetry (LSPIV) can provide instantaneous surface velocities over a large spatial footprint rapidly and with little pre-calibration as compared to traditional techniques. Assessment of the spatial distribution of flow velocities in hydraulic models has been comparatively harder to achieve than assessment of depth due to logistical challenges but would be aided using large observational datasets that represent the variability and distribution of flow hydraulics. Additionally, the efficacy of image velocimetry in assessing the accuracy of outputs from 2D hydraulic models has not been addressed. Here, we demonstrate how LSPIV can be used to calibrate and validate 2D model predictions in a gravel bed river reach. LSPIV velocities are depth-averaged using standard velocity coefficients ( α ) and then using the Probability Concept (PC) - a probabilistic formulation of velocity distributions that accounts for non-standard velocity profiles, typical in field settings. UAV surveys were used to acquire video for LSPIV and imagery for Structure from Motion (SfM) topographic modelling. We use spatially dense acoustic doppler current profiler (aDcp) velocity data for benchmark assessment of the velocity outputs of HEC-RAS 2D model simulations. 2D model prediction error, based on seeded LSPIV velocities, was within range (4.2%) of the aDcp parametrised model, with improvements in modelled versus predicted velocity correlations (up to 7.7%) when using PC to depth average LSPIV velocities. Validation bias reduced significantly (11%) with tighter error distributions when compared to the aDcp based model. Although additional hydraulic measurements are required to parametrise the Probability Concept algorithm, the performance of 2D hydraulic models calibrated/validated with LSPIV velocities is on par with traditional techniques, demonstrating the potential of this non-intrusive, low-cost approach.

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Fig. 1 . Fig. 1. (A) Orthomosaic image of the Swindale (351074E 512754 N, British National Grid) study reach (left) and hillshaded SfM DEM (right) showing the extent of the 2D model domain and investigated sub-reaches; (B, C) aDcp survey transects (black traces); (D, E) LSPIV surface velocity.
Fig. 1 . Fig. 1. (A) Orthomosaic image of the Swindale (351074E 512754 N, British National Grid) study reach (left) and hillshaded SfM DEM (right) showing the extent of the 2D model domain and investigated sub-reaches; (B, C) aDcp survey transects (black traces); (D, E) LSPIV surface velocity.CC-BY · From the PDF
Fig. 2 . Fig. 2. Experimental framework.
Fig. 2 . Fig. 2. Experimental framework.CC-BY · From the PDF
Fig. 3 . Fig. 3. Locations of cross-section velocities acquired using a current meter at sub-reach SW2.
Fig. 3 . Fig. 3. Locations of cross-section velocities acquired using a current meter at sub-reach SW2.CC-BY · From the PDF
Fig. 4 . Fig. 4. Calibration results of 2D model simulations at sub-reach SW1. Model M aDcp (a) and model M LSPIV (b) velocity predictions. Scatter plots of aDcp (c) and LSPIV α (d) velocity observations versus model predictions. (In the box plots, the boundary of the box closest to zero indicates the 25th percentile, the black line within the box marks the median, and the boundary of the box farthest from zero indicates the 75th percentile. Points above or below the plots indicate outliers outside the 10th and 90th percentiles).
Fig. 4 . Fig. 4. Calibration results of 2D model simulations at sub-reach SW1. Model M aDcp (a) and model M LSPIV (b) velocity predictions. Scatter plots of aDcp (c) and LSPIV α (d) velocity observations versus model predictions. (In the box plots, the boundary of the box closest to zero indicates the 25th percentile, the black line within the box marks the median, and the boundary of the box farthest from zero indicates the 75th percentile. Points above or below the plots indicate outliers outside the 10th and 90th percentiles).CC-BY · From the PDF
Fig. 5 . Fig. 5. Validation results of 2D model simulations at sub-reach SW2. Model M aDcp (a) and model M LSPIV (b) velocity predictions. Scatter plots of aDcp (c) and LSPIV α (d) velocity observations versus model predictions.
Fig. 5 . Fig. 5. Validation results of 2D model simulations at sub-reach SW2. Model M aDcp (a) and model M LSPIV (b) velocity predictions. Scatter plots of aDcp (c) and LSPIV α (d) velocity observations versus model predictions.CC-BY · From the PDF
Fig. 6 . Fig. 6. (a) Scatter plots of LSPIV PC vs HEC-RAS velocity magnitudes with linear regression fits, 95% confidence (CI) and prediction (PI) intervals; (b) Variation in absolute LSPIV PC velocity error (ε) as compared to modelled velocities across the six cross-sections. Individual velocity error observations are represented by the coloured dots. Horizontal line in the boxplot indicates the median, box shows the interquartile range (IQR), and the whiskers are 1.5*IQR; (c) Absolute percentage errors (ε) in estimating depth-averaged velocity using the standard velocity index (α, white) and the Probability Concept approach (PC, φ) at cross-sections XS1 -3 (red dot indicates mean error). (For interpretation of the references to colour in this figure legend, the reader is referred to the web version of this article.)
Fig. 6 . Fig. 6. (a) Scatter plots of LSPIV PC vs HEC-RAS velocity magnitudes with linear regression fits, 95% confidence (CI) and prediction (PI) intervals; (b) Variation in absolute LSPIV PC velocity error (ε) as compared to modelled velocities across the six cross-sections. Individual velocity error observations are represented by the coloured dots. Horizontal line in the boxplot indicates the median, box shows the interquartile range (IQR), and the whiskers are 1.5*IQR; (c) Absolute percentage errors (ε) in estimating depth-averaged velocity using the standard velocity index (α, white) and the Probability Concept approach (PC, φ) at cross-sections XS1 -3 (red dot indicates mean error). (For interpretation of the references to colour in this figure legend, the reader is referred to the web version of this article.)CC-BY · From the PDF
Fig. 7 . Fig. 7. Velocity magnitude error histograms for all LSPIV PC cross-sections (relative to model M LSPIV predictions) with overall error in the background (grey).
Fig. 7 . Fig. 7. Velocity magnitude error histograms for all LSPIV PC cross-sections (relative to model M LSPIV predictions) with overall error in the background (grey).CC-BY · From the PDF

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Metadata

Authors
Masafu, C., Williams, R., Shi, X., Yuan, Q., & Trigg, M.
Affiliations
  • University of Glasgow (GB)
  • Wuhan University (CN)
  • University of Leeds (GB)
Corresponding author
  • Masafu, Christopher
  • Shi, Xiaogang
ORCID
Venue
Journal of Hydrology
Publisher
Elsevier BV
Published
2022-09
Volume
612
Pages
128217
ISSN
0022-1694 (ISSN-L)
DOI
10.1016/j.jhydrol.2022.128217
OpenAlex
W4285809653
Cited by
28
References
0
Citations by year
2026: 11 · 2025: 5 · 2024: 7 · 2023: 4 · 2022: 1
FWCI
2.21
Type
journal article
Language
en
Open access
Licence
Locations
  • Journal of Hydrology (publishedVersion, cc-by)
  • ENLIGHTEN (Jurnal Bimbingan dan Konseling Islam) (acceptedVersion, cc-by-sa)
  • White Rose Research Online (University of Leeds, The University of Sheffield, University of York) (acceptedVersion)
Funders
  • Natural Environment Research Council
  • University of Glasgow
Topics
  • Physical Sciences › Earth and Planetary Sciences › Earth-Surface Processes › Hydrology and Sediment Transport Processes
  • Physical Sciences › Engineering › Civil and Structural Engineering › Hydraulic flow and structures
  • Physical Sciences › Environmental Science › Global and Planetary Change › Flood Risk Assessment and Management
Keywords
large-scale particle image velocimetry, model calibration, river surface velocity, gravel-bed rivers, depth-averaged velocity, probability concepts, velocity distribution, HEC-RAS, acoustic Doppler current profiler, non-invasive flow measurement

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Masafu, C., Williams, R., Shi, X., Yuan, Q., & Trigg, M. (2022). Unpiloted Aerial Vehicle (UAV) image velocimetry for validation of two-dimensional hydraulic model simulations. Journal of Hydrology. https://doi.org/10.1016/j.jhydrol.2022.128217