Intelligent Adaptation to Runoff Generation Mechanism for Hydrological Forecasting: A Case Study of the Xun River Basin

Huiyuan Liu; Xiaoxue Gan; Weijian Guo; Jing Guo; Lu Chen; Bin Yi

doi:10.2991/978-94-6463-728-1_67

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Intelligent Adaptation to Runoff Generation Mechanism for Hydrological Forecasting: A Case Study of the Xun River Basin

Authors

Huiyuan Liu¹, Xiaoxue Gan²^{, 3}^{, *}, Weijian Guo¹, Jing Guo¹, Lu Chen²^{, 3}^{, 4}^{, *}, Bin Yi²^{, 3}

¹Powerchina Huadong Engineering Corporation Limited, Hangzhou, 311122, China

²School of Civil and Hydraulic Engineering, Huazhong University of Science and Technology, Wuhan, 430074, China

³Hubei Key Laboratory of Digital Valley Science and Technology, Wuhan, 430074, China

⁴School of Water Resources and Civil Engineering, Tibet Agricultural & Animal Husbandry University, Linzhi, 860000, China

^*Corresponding author. Email: ganxx@hust.edu.cn

^*Corresponding author. Email: chen_lu@hust.edu.cn

Corresponding Authors

Xiaoxue Gan, Lu Chen

Available Online 19 May 2025.

DOI: 10.2991/978-94-6463-728-1_67 How to use a DOI?
Keywords: Runoff Generation Mechanism; Index Construction; Hydrological Modelling
Abstract: Runoff generation is a key process of the hydrologic cycle. In studies, most hydrological models can’t explain how changes in the runoff generation mechanism caused by rainfall and soil conditions. Accounting for this issue, this article proposed a runoff generation mechanism index (RGMI) to realize intelligent adaptation to runoff generation mechanism. The posteriori RGMI was constructed with runoff coefficient (C) and curve number (CN) by weighting, and the priori RGMI was calculated based on rainfall and antecedent soil moisture through nonlinear fitting. Further, Xin’anjiang model (XAJ), Green-Ampt model (GA), and Vertically Mixed Runoff Model (VMM) were adopted to compose a novel adaptive runoff generation module. In the Xun River basin, the performances of the proposed runoff generation method and three fixed runoff generation methods were compared. The forecasting results of flood events were evaluated using relative peak error (Q_p), error between simulated and observed peak times (T_p), Nash-Sutcliffe efficiency (E_NS), and relative flood volume error (W_p). The results showed that the adaptive runoff generation module can simulate the flood peak flow and peak time with higher accuracy than fixed runoff generation module. In addition, the dominant runoff generation processes in the Xun River basin mostly are saturation-excess and hybrid runoff generation processes.
Open Access: Open Access This chapter is licensed under the terms of the Creative Commons Attribution-NonCommercial 4.0 International License (http://creativecommons.org/licenses/by-nc/4.0/), which permits any noncommercial use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made.

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Volume Title: Proceedings of the 3rd International Conference on Green Building, Civil Engineering and Smart City (GBCESC 2024)
Series: Advances in Engineering Research
Publication Date: 19 May 2025
ISBN: 978-94-6463-728-1
ISSN: 2352-5401
DOI: 10.2991/978-94-6463-728-1_67 How to use a DOI?
Open Access: Open Access This chapter is licensed under the terms of the Creative Commons Attribution-NonCommercial 4.0 International License (http://creativecommons.org/licenses/by-nc/4.0/), which permits any noncommercial use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made.

Cite this article

ris enw bib

TY  - CONF
AU  - Huiyuan Liu
AU  - Xiaoxue Gan
AU  - Weijian Guo
AU  - Jing Guo
AU  - Lu Chen
AU  - Bin Yi
PY  - 2025
DA  - 2025/05/19
TI  - Intelligent Adaptation to Runoff Generation Mechanism for Hydrological Forecasting: A Case Study of the Xun River Basin
BT  - Proceedings of the 3rd International Conference on Green Building, Civil Engineering and Smart City (GBCESC 2024)
PB  - Atlantis Press
SP  - 719
EP  - 726
SN  - 2352-5401
UR  - https://doi.org/10.2991/978-94-6463-728-1_67
DO  - 10.2991/978-94-6463-728-1_67
ID  - Liu2025
ER  -

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