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Verification of STIV Analysis of Flood Discharge at Nakano Observatory in the Ota River by Flow Analysis Using ALB Data

Author(s): Keisuke Yoshida; Shinya Nigo; Shiro Maeno; Koji Mano; Mitsutaka Nakano

Linked Author(s): Keisuke Yoshida

Keywords: Airborne laser bathymetry; River discharge observations; River flow analysis; Space-time image velocimetry

Abstract: Flooded river discharge has been measured using floating rods in Japan for reasons of cost-efficiency and feasibility. However, recent severe flood conditions that surpass planned severity pose new challenges for measurement with human works. For such practical issues related to river engineering, non-contact type measurement methods using cost-efficient devices and improved algorithms for flow analysis have been developed. These methods are anticipated for wider use in practical works of data acquisition for both river channel planning and disaster reduction. Nevertheless, their applicability must be verified and improved for various river conditions. We conducted flow measurements using space-time image velocimetry (STIV) analysis at Nakano Observatory in the Ota River in Japan during heavy rainfall in July 2018. Results revealed that the hourly discharge values obtained using an H–Q rating curve were overestimated by approximately 10 percent compared to those obtained using the present STIV analysis. Therefore, this study examined the accuracy of the present STIV analysis for river discharge estimation during flooding, using numerical simulation results and water level observations. First, we obtained data for both the topo-bathymetry and vegetation properties within the targeted reach from airborne laser bathymetry (ALB) measurement results. Secondly, we conducted numerical simulations of the flood flows using discharge hydrography of two kinds: The rating curve and the STIV analysis. Finally, we compared simulated water levels with observations made during flooding. Results demonstrated that the STIV analysis provides more accurate discharge data than data shown by the rating curve.


Year: 2020

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