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Impact analysis of highways in China under future extreme precipitation

Natural Hazards volume 110, pages 1097–1113 (2022)Cite this article

Abstract

With worsening global climate change, we still do not fully understand how to cope with possible extreme precipitation events or secondary disasters on highway networks. Correctly estimating the impact on the highway network from extreme precipitation plays a vital role in decision making regarding future highway investment. This study uses datasets from 21 NASA Earth Exchange Global Daily Downscaled Projections (NEX-GDDP) under the RCP (Representative Concentration Pathway) 4.5 and RCP8.5 scenarios. We use the percentile method to select the extreme precipitation threshold. A set of system performance measures for an impact analysis of Chinese highways under different scenarios is developed from the perspectives of physical exposure, network function and sensitivity analyses for high-impact areas in China. The results show that the intensity of extreme precipitation will increase in the future. More than 10,000 km and at least 4,000 intersections will be affected by extreme precipitation in 2030 and 2050. Based on a functional analysis of the highway network in Guangdong and Guangxi, more than 80% of the mileage of highways in Guangdong and Guangxi will be exposed to extreme precipitation. The network function of Chinese highways will dramatically decrease when precipitation reaches a critical value, which will shed light on highway fortification standards and planning.

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Abbreviations

1d:

The maximum precipitation in one day

3d:

The maximum precipitation in three consecutive days

1d-RCP4.5–2030:

1D-RCP4.5–2030 represents the maximum precipitation in the one day scenario in 2030 under the RCP4.5 scenario

1d-RCP4.5–2050:

1D-RCP4.5–2050 represents the maximum precipitation in the one day scenario in 2050 under the RCP4.5 scenario

1d-RCP8.5–2030:

1D-RCP8.5–2030 represents the maximum precipitation in the one day scenario in 2030 under the RCP8.5 scenario

1d-RCP8.5–2050:

1D-RCP8.5–2050 represents the maximum precipitation in the one day scenario in 2050 under the RCP8.5 scenario

3d-RCP4.5–2030:

3D-RCP4.5–2030 represents the maximum precipitation in the three day scenario in 2030 under the RCP4.5 scenario

3d-RCP4.5–2050:

3D-RCP4.5–2050 represents the maximum precipitation in the three day scenario in 2050 under the RCP4.5 scenario

3d-RCP8.5–2030:

3D-RCP8.5–2030 represents the maximum precipitation in the three day scenario in 2030 under the RCP8.5 scenario

3d-RCP8.5–2050:

3D-RCP8.5–2050 represents the maximum precipitation in the three day scenario in 2050 under the RCP8.5 scenario.

C :

Connectivity

E :

Distance efficiency

G :

The maximum connectivity subgraph relative size

KN :

The number of exposed nodes with degrees greater than 2

L :

Exposed kilometers

N :

The number of exposed nodes

S :

Extreme precipitation area

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Acknowledgements

This work was financially supported by the Fundamental Research Funds for the Central Universities 2019RC043, the National Key Program of China (No.2016YFA0602403), Creative Research Groups of National Natural Science Foundation of China (No.41621061), and National Key Research and Development Program of China (No. 2018YFC1508903), the China Postdoctoral Science Foundation under Grant No. 2019M660435 and the National Natural Science Foundation of China No.7200010568.

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Authors and Affiliations

  1. State Key Laboratory of Earth Surface Processes and Resources Ecology, Beijing Normal University, Beijing, 100875, China

    Liang Jia, Saini Yang & Xinlong Zhang

  2. Academy of Disaster Reduction and Emergency Management, Ministry of Emergency Management and Ministry of Education, Beijing, 100875, China

    Liang Jia, Saini Yang & Xinlong Zhang

  3. Faculty of Geographical Science, Beijing Normal University, Beijing, 100875, China

    Liang Jia, Saini Yang & Xinlong Zhang

  4. Institute of Transportation System Science and Engineering, Beijing Jiaotong University, Beijing, 100044, China

    Weiping Wang

  5. Key Laboratory of Transport Industry of Big Data Application Technologies for Comprehensive Transport, Ministry of Transport, Beijing Jiaotong University, Beijing, 100044, China

    Weiping Wang

Authors
  1. Liang Jia
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  2. Saini Yang
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  4. Xinlong Zhang
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Contributions

Liang Jia was involved in the conceptualization, data preparation, methodology and writing; Saini Yang contributed to the idea, methodology, writing, reviewing and editing; Weiping Wang was involved in the network construction, reviewing and editing; Xinlong Zhang was involved in the data preparation.

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Correspondence to Saini Yang.

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Jia, L., Yang, S., Wang, W. et al. Impact analysis of highways in China under future extreme precipitation. Nat Hazards 110, 1097–1113 (2022). https://doi.org/10.1007/s11069-021-04981-6

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Keywords

  • Extreme precipitation
  • Highways
  • Impact analysis
  • Sensitivity analysis