14 Dec 2022
 | 14 Dec 2022
Status: a revised version of this preprint is currently under review for the journal GI.

Towards Affordable 3D Physics-Based River Flow Rating: Application Over Luangwa River Basin

Hubert T. Samboko, Sten Schurer, Hubert H.G. Savenije, Hodson Makurira, Kawawa Banda, and Hessel Winsemius

Abstract. Unmanned aerial vehicles (UAVs), affordable precise Global Navigation Satellite System hardware, echo sounders, open-source 3D hydrodynamic modelling software, and freely available satellite data have opened up opportunities for a robust, affordable, physics-based approach to monitor river flows. In short, the hardware can be used to produce the geometry. 3D hydrodynamic modelling offers a framework to establish relationships between river flow and state variables such as width and depth, while satellite images with surface water detection methods or altimetry records can be used to operationally monitor flows through the established rating curve. Uncertainties in the data acquisition may propagate into uncertainties in the relationships found between discharge and state variables. Variations in acquired geometry emanate from the different ground control point (GCP) densities and distributions which are used during photogrammetry-based terrain reconstruction. In this study, we develop a rating curve using affordable data collection methods and basic principles of physics. The specific objectives were to: determine how the rating curve based on a 3D hydraulic model compares with conventional methods; investigate the impact of geometry uncertainty on estimated discharge when applied in a hydraulic model; and investigate how uncertainties in continuous observations of depth and width from satellite platforms propagate into uncertainties in river flow estimates using the rating curves obtained. The study shows comparable results between the 3D and traditional river rating discharge estimations. The rating curve derived on the basis of 3D hydraulic modelling was within a 95 % confidence interval of the traditional gauging based rating curve. The physics-based estimation requires determination of the roughness coefficient within the permanent bed and the floodplain using field observation as both the end of dry and wet season. Furthermore, the study demonstrates that variations in the density of GCPs beyond an optimal number (9) has no significant influence on the resultant rating relationships. Finally, the study observes that it depends on the magnitude of the flow which state variable approximation (water level & river width) is most promising to use. Combining stage appropriate proxies (water level when the floodplain is entirely filled, and width when the floodplain is filling) in data limited environments yields more accurate discharge estimations. The study was able to successfully apply low cost technologies for accurate river monitoring through hydraulic modelling. In future studies, a larger amount of in-situ gauge readings may be considered so as to optimise the validation process.

Hubert T. Samboko et al.

Status: final response (author comments only)

Comment types: AC – author | RC – referee | CC – community | EC – editor | CEC – chief editor | : Report abuse
  • RC1: 'Comment on gi-2022-21', Anonymous Referee #1, 10 Feb 2023
  • RC2: 'Comment on gi-2022-21', Anonymous Referee #1, 10 Feb 2023
  • RC3: 'Comment on gi-2022-21', Anonymous Referee #1, 10 Feb 2023
    • AC1: 'Reply on RC3', Hubert Samboko, 13 Feb 2023
      • RC4: 'Reply on AC1', Anonymous Referee #1, 20 Feb 2023
  • RC5: 'Comment on gi-2022-21', Anonymous Referee #2, 24 Feb 2023
    • AC2: 'Reply on RC5', Hubert Samboko, 21 Mar 2023

Hubert T. Samboko et al.

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Python Scripts Hubert Samboko

Hubert T. Samboko et al.


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Short summary
The study investigates how low cost technology can be applied in data scarce catchments to improve water resource management. More specifically, we investigate how drone technology can be combined with low-cost RTK GNSS equipment and subsequently applied in a 3 D hydraulic model so as to generate more physically based rating curves.