# Effects of Bridge Piers on Flood Hazards: A Case Study on the Jialing River in China

^{*}

## Abstract

**:**

## 1. Introduction

## 2. Materials and Methods

#### 2.1. Study Area

^{2}, and the average bed slope of the river is 1.4%. This is a warm temperate mountain climate area with an annual average temperature of 11.4 °C, an average annual rainfall of 623.6 mm, a maximum rainfall of 939.3 mm (in 1958), and a minimum rainfall of 422.3 mm (in 1969). The precipitation from April to October (wet season) is on average 577.3 mm, which is 92% of the entire year’s precipitation. From November to March (dry season), the precipitation is on average 46.3 mm (8% of the entire year’s precipitation). The elevation on both sides of the Jialing River is in the range of 1400–1800 m, the relative height is 500–800 m with a steep slope of about 30–40 degree. Due to the narrow and steep river channel, the floods are of short durations and high peaks and most have a single peak. According to the statistics of the measured data, the floods in the upper reach of the Jialing River are mainly formed by high-intensity rain [30]. For example, the 1933 rainstorm, with a rainfall intensity of 73.6 mm/day, produced 500 m

^{3}/s peak flow at the upper reach and 970 m

^{3}/s peak flow in the lower reach, which is approximately a 10-year flood. Heavy rains generally occur from July to September.

^{2}, 2722 km

^{2}and 19,206 km

^{2}, respectively. The maximum peak flow rate measured at Fengzhou Station was 972 m

^{3}/s (in 1990). The maximum peak flow rate measured at Ciba Station was 4670 m

^{3}/s (in 1981). The Lueyang Station’s maximum flood peak flow in 1981 reached 8630 m

^{3}/s.

#### 2.2. Model Description

#### 2.3. Mesh Generation and Model Parameters

^{2}, a smallest allowable angle of 29°, and a maximum number of nodes of 100,000. By using an unstructured mesh, the circular bridge piers with diameters of 2 m are directly described (i.e., solid wall conditions) (Figure 2). Two sets of meshes with and without piers were used to investigate the impacts of bridge piers on the flow fields. Mesh independence is analyzed in Section 2.4. The input topography for the study area is presented in Figure 3.

^{−1/3}s. In this study, a constant Manning coefficient (n = 0.038 m

^{−1/3}s) was used in the entire study area for the sake of safety. A sensitivity analysis was also carried out to investigate the effects of the Manning coefficient on the computed results, as described in Section 2.4.

#### 2.4. Sensitivity Analysis

^{3}and a smallest allowable angle of 29° were used for the simulations both with and without piers.

^{−1/3}s, and the results were compared under the 100-year flood condition. The computed water levels at four cross-sections near the bridges are presented in Figure 5. The results show that a smaller Manning coefficient resulted in a lower water level, as expected. The maximum difference for this test was 0.36 m. The effects of the Manning coefficient on the computed results are thus limited in this study.

#### 2.5. Validation of the Numerical Model

^{3}/s measured at Fengzhou Station and 2750 m

^{3}/s measured at Ciba Station. The surveyed flood elevation at the cross-section, 1260 m from the upstream boundary, was 976.80 m. These data were used for validation of the numerical model. The corresponding flow discharge was 1440 m

^{3}/s, calculated by the interpolation of values from the Fengzhou Station and the Ciba Station. From our model, the computed water level at the cross-section under these flood conditions was 977.25 m, 0.45 m higher than the surveyed flood level. When comparing the effects of mesh resolution and the Manning coefficient, the difference between the computed and surveyed water levels is acceptable. Additionally, due to river bed evolution, the topography used in the numerical model may be different from the topography in 1990 and this may be the reason for the difference [32]. Generally, the numerical model developed was validated to be accurate enough for further study.

## 3. Results and Discussion

#### 3.1. Backwater Effects of Bridge Piers

#### 3.2. Effects on Flow Velocity Fields

## 4. Conclusions

## Author Contributions

## Funding

## Acknowledgments

## Conflicts of Interest

## References

- Luo, P.; Zhou, M.; Deng, H.; Lyu, J.; Cao, W.; Takara, K.; Nover, D.; Schladow, S.G. Impact of forest maintenance on water shortages: Hydrologic modeling and effects of climate change. Sci. Total Environ.
**2018**, 615, 1355–1363. [Google Scholar] [CrossRef] [PubMed] - Davor, K.; Roger, A.F.; Michaela, B. Flood hazard assessment for extreme flood events. Nat. Hazards
**2016**, 84, 1569–1599. [Google Scholar][Green Version] - Beniston, M. Trends in joint quantiles of temperature and precipitation in Europe since 1901 and projected for 2100. Geophys. Res. Lett.
**2009**, 36. [Google Scholar] [CrossRef] - Horritt, M.; Bates, P. Evaluation of 1D and 2D numerical models for predicting river flood inundation. J. Hydrol.
**2002**, 268, 87–99. [Google Scholar] [CrossRef] - Merkuryeva, G.; Merkuryev, Y.; Sokolov, B.V.; Potryasaev, S.; Zelentsov, V.A.; Lektauers, A. Advanced river flood monitoring, modelling and forecasting. J. Comput. Sci.
**2015**, 10, 77–85. [Google Scholar] [CrossRef] - Teng, J.; Jakeman, A.J.; Vaze, J.; Croke, B.F.; Dutta, D.; Kim, S. Flood inundation modelling: A review of methods, recent advances and uncertainty analysis. Environ. Model. Softw.
**2017**, 90, 201–216. [Google Scholar] [CrossRef] - Kossi, K.; Jeffrey, N.; Mark, A.T.; Bernd, D. Modelling of flood hazard extent in data sparse areas: A case study of the Oti River basin, West Africa. J. Hydrol. Reg. Stud.
**2017**, 10, 122–132. [Google Scholar] - Hou, J.; Wang, T.; Li, P.; Li, Z.; Zhang, X.; Zhao, J.; Hinkelmann, R. An implicit friction source term treatment for overland flow simulation using shallow water flow model. J. Hydrol.
**2018**, 564, 357–366. [Google Scholar] [CrossRef] - Xia, X.L.; Liang, Q.H.; Ming, X.D.; Hou, J.M. An efficient and stable hydrodynamic model with novel source term discretization schemes for overland flow and flood simulations. Water Resour. Res.
**2017**, 53, 3730–3759. [Google Scholar] [CrossRef] - Liu, Q.; Qin, Y.; Li, G.D. Fast simulation of large-scale floods based on GPU parallel computing. Water
**2018**, 10, 589. [Google Scholar] [CrossRef] - Lee, E.; Kim, J.; Choo, Y.; Jo, D. Application of flood nomograph for flood forecasting in urban areas. Water
**2018**, 10, 53. [Google Scholar] [CrossRef] - Tomasz, D.; Joanna, W.; Mariusz, S. Assessment of the impact of new investments on flood hazard-study case: the bridge on the Warta River near Wronki. Water
**2015**, 7, 5752–5767. [Google Scholar] - Costabile, P.; Macchione, F. Enhancing river model set-up for 2-D dynamic flood modelling. Environ. Model. Softw.
**2015**, 67, 89–107. [Google Scholar] [CrossRef] - Biglari, B.; Sturm, T.W. Numerical modeling of flow around bridge abutments in compound channel. J. Hydraul. Eng.
**1998**, 124, 156–164. [Google Scholar] [CrossRef] - Hoa, L.T.; Shigeko, H.; Nhan, N.H.; Cong, T.T. Infrastructure effects on floods in the Mekong River Delta in Vietnam. Hydrol. Process.
**2008**, 22, 1359–1372. [Google Scholar] [CrossRef] - Siregar, R.I. Hydraulic modeling of flow impact on bridge structures: A case study on Citarum bridge. IOP Conf. Ser. Mater. Sci. Eng.
**2018**, 309, 012015. [Google Scholar] [CrossRef] - Petaccia, G.; Natale, E. ORSADEM: A one-dimensional shallow water code for flood inundation modelling. Irrig. Drain.
**2013**, 62, 29–40. [Google Scholar] [CrossRef] - Costabile, P.; Macchione, F.; Natale, L.; Petaccia, G. Flood mapping using LIDAR DEM Limitations of the 1-D modeling highlighted by the 2-D approach. Nat. Hazards
**2015**, 77, 181–204. [Google Scholar] [CrossRef] - Costabile, P.; Macchione, F.; Natale, L.; Petaccia, G.; Schleiss, A.J.; De Cesare, G.; Franca, M.J.; Pfister, M. Representing skewed bridge crossing on 1-D and 2D flood propagation models: Compared analysis in practical studies. In Proceedings of the 7th International Conference on Fluvial Hydraulics (River Flow), Lausanne, Switzerland, 3–5 September 2014. [Google Scholar]
- Gallegos, H.A.; Schubert, J.E.; Sanders, B.F. Two-dimensional, high-resolution modeling of urban dam-break flooding: A case study of Baldwin Hills, California. Adv. Water Resour.
**2009**, 32, 1323–1335. [Google Scholar] [CrossRef] - Luo, P.; Mu, D.; Xue, H.; Thanh, N.; Kha, D.; Kaoru, T.; Daniel, N.; Groffrey, S. Flood inundation assessment for the Hanoi Central Area, Vietnam under historical and extreme rainfall conditions. Sci. Rep.
**2018**, 8, 12623. [Google Scholar] [CrossRef] - Liang, Q.H.; Smith, L.S. A high-performance integrated hydrodynamic modelling system for urban flood simulations. J. Hydroinform.
**2015**, 17, 518–533. [Google Scholar] [CrossRef] - Meesuk, V.; Vojinovic, Z.; Mynett, A.E.; Abdullah, A.F. Urban flood modelling combining top-view LiDAR data with ground-view SfM observations. Adv. Water Resour.
**2015**, 75, 105–117. [Google Scholar] [CrossRef] - Noh, S.J.; Lee, J.H.; Lee, S.; Kawaike, K.; Seo, D.J. Hyper-resolution 1D-2D urban flood modelling using LiDAR data and hybrid parallelization. Environ. Model. Softw.
**2018**, 103, 131–145. [Google Scholar] [CrossRef] - Hou, J.M.; Liang, Q.H.; Zhang, H.B.; Hinkelmann, R. An efficient unstructured MUSCL scheme for solving the 2D shallow water equations. Environ. Model. Softw.
**2015**, 66, 131–152. [Google Scholar] [CrossRef][Green Version] - Costabile, P.; Macchione, F.; Natale, L.; Petaccia, G. Comparison of scenarios with and without bridges and analysis of backwater effect in 1-D and 2-D river flood modeling. Comput. Model. Eng. Sci.
**2015**, 109, 181–204. [Google Scholar] - Geng, Y.; Wang, Z.L. Two-dimensional unstructured finite volume model for bridge pier flow. Hydro-Sci. Eng.
**2008**, 4, 015. [Google Scholar] - Jiang, Y.X.; Wen, C. Application of 2D flow mathematical model in flood control evaluation of Tongjiang River Bridge. J. Water Resour. Archit. Eng.
**2012**, 6, 028. (In Chinese) [Google Scholar] - Luo, P.; He, B.; Takara, K.; Xiong, Y.; Nover, D.; Duan, W.; Fukushi, K. Historical assessment of Chinese and Japanese flood management policies and implications for managing future floods. Environ. Sci. Policy
**2015**, 48, 265–277. [Google Scholar][Green Version] - Li, T.S. Study on the Historic Flood in Jialing River Basin. J. Catastrophol.
**2005**, 20, 113–115. (In Chinese) [Google Scholar] - Danish Hydraulic Institute. MIKE 21& MIKE 3 Flow Model FM Hydrodynamic and Transport Module: Scientific Documentation; Danish Hydraulic Institute: Hørsholm, Denmark, 2012. [Google Scholar]
- Zhang, B.; Ai, N.S.; Huang, Z.W.; Yi, C.B.; Qin, F.C. Meanders of the Jialing River in China: morphology and formation. Chin. Sci. Bull.
**2008**, 53, 267–281. [Google Scholar] [CrossRef]

**Figure 7.**Distribution of water depth near the Fengxian Bridge for 10-year flood conditions (circles denote bridge piers).

**Figure 9.**Distributions of the computed velocity magnitude for 10-year flood conditions. (

**a**) Results without bridge piers, and (

**b**) results with bridge piers.

**Figure 10.**Distributions of the computed velocity magnitude for 100-year flood conditions. (

**a**) Results without bridge piers, and (

**b**) results with bridge piers.

Mesh | Number of Elements | Smallest Allowable Angle | Maximum Element Area (m^{3}) |
---|---|---|---|

1 | 14,412 | 29° | 200 |

2 | 33,865 | 29° | 80 |

3 | 41,524 | 29° | 50 |

Location | Distance to the Upstream Boundary (m) | Designed Elevations at the Top of Levees (m) | Computed (with Piers) Elevations at the Top of Levees (m) | Differences (m) |
---|---|---|---|---|

Xizhuang Bridge | 631 | 978.83 | 978.98 | −0.15 |

Fengxian Bridge | 1220 | 976.64 | 976.97 | −0.33 |

E Ramp Bridge | 2400 | 973.24 | 973.6 | −0.36 |

Jialing River Bridge | 2856 | 971.03 | 971.38 | −0.35 |

© 2019 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (http://creativecommons.org/licenses/by/4.0/).

## Share and Cite

**MDPI and ACS Style**

Wang, W.; Zhou, K.; Jing, H.; Zuo, J.; Li, P.; Li, Z. Effects of Bridge Piers on Flood Hazards: A Case Study on the Jialing River in China. *Water* **2019**, *11*, 1181.
https://doi.org/10.3390/w11061181

**AMA Style**

Wang W, Zhou K, Jing H, Zuo J, Li P, Li Z. Effects of Bridge Piers on Flood Hazards: A Case Study on the Jialing River in China. *Water*. 2019; 11(6):1181.
https://doi.org/10.3390/w11061181

**Chicago/Turabian Style**

Wang, Wen, Kaibo Zhou, Haixiao Jing, Juanli Zuo, Peng Li, and Zhanbin Li. 2019. "Effects of Bridge Piers on Flood Hazards: A Case Study on the Jialing River in China" *Water* 11, no. 6: 1181.
https://doi.org/10.3390/w11061181