Preprints
https://doi.org/10.5194/gmd-2022-92
https://doi.org/10.5194/gmd-2022-92
Submitted as: development and technical paper
24 May 2022
Submitted as: development and technical paper | 24 May 2022
Status: this preprint is currently under review for the journal GMD.

An Improved Algorithm for Simulating the Surface Flow Dynamics based on the Flow-Path Network Model

Qianjiao Wu1,3, Yumin Chen2, Huaming Xie1,3, Tong Xu4, Jiayong Yu5, and Ting Zhang1,3 Qianjiao Wu et al.
  • 1School of Environment and Energy Engineering, Anhui Jianzhu University, Hefei, 230601, China
  • 2School of Resource and Environment Science, Wuhan University, Wuhan, 430079, China
  • 3Institute of Remote Sensing and Geographic Information Systems, Anhui Jianzhu University, Hefei, 230601, China
  • 4School of Architecture and Urban Planning, Anhui Jianzhu University, Hefei, 230601, China
  • 5School of Civil Engineering, Anhui Jianzhu University, Hefei, 230601, China

Abstract. This paper proposes an improved algorithm for simulating the surface flow dynamics based on the flow-path network model. This algorithm utilizes the parallel-multi-point method to extract the critical points and the D8 algorithm to retrieve the drainage networks from the regular-grid digital elevation model (DEM) for constructing a drainage-constrained triangulated irregular network (TIN). Then, it combines the flow directions of triangular facets over TIN with resampled flow source points to track flow lines to generate the flow path network (FPN) based on the flow-path network model. On this basis, the proposed algorithm employs three terrain parameters (slope length factor, topographic wetness index and flow path curvature) to improve the classical Manning equation based on the analytic hierarchy process (AHP) to enhance the accuracy of the flow velocity calculation. The topographic wetness index and flow path curvature are derived by the flow-path-network-triangular-facet-network (FPN_TFN) algorithm, a new flow-path-network-topographic-wetness-index (FPN_TWI) algorithm and the flow-path-network-flow-path-curvature (FPN_C) algorithm, respectively. Finally, the velocity estimation function and surface flow discharge simulation function are parallelized by the Compute Unified Device Architecture (CUDA) to enhance its computational efficiency. The outcomes are compared with the algorithm before improvement (TIN_based algorithm) and the SWAT model. The results demonstrate that the speedup ratio reaches 15.7 compared to the TIN_based algorithm. The Nash coefficient increases by 6.49 %, the correlation coefficient decreases slightly, and the balance coefficient increases by 19.08 %. Compared with the SWAT model, the Nash coefficient and correlation coefficient increase by 97.56 % and 4.60 %, respectively. The balance coefficient is close to 1 and outperforms the compared algorithms.

Qianjiao Wu et al.

Status: open (until 22 Jul 2022)

Comment types: AC – author | RC – referee | CC – community | EC – editor | CEC – chief editor | : Report abuse
  • CEC1: 'Comment on gmd-2022-92', Juan Antonio Añel, 16 Jun 2022 reply
    • AC1: 'Reply on CEC1', Qianjiao Wu, 23 Jun 2022 reply

Qianjiao Wu et al.

Qianjiao Wu et al.

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Short summary
To solve the problems of accuracy and response efficiency of existing simulation methods, an improved algorithm was proposed to simulate the surface flow dynamics quickly and accurately. We considers the influence of terrain parameters on flow velocity to improve Manning’s equation for enhancing simulation accuracy. We also use CUDA to advance the efficiency. Experimental results show that it can quickly and accurately complete the multi-scale simulation and ensure simulation consistence.