Risk Zoning for Susceptibility to Highway Geohazards in the Sichuan-Chongqing-Hubei Region

JieZhang1

HuaTian2,3✉Emailtianh1@cma.gov.cn

JingjingGao2

YayuZhu1

JianyangSong2

HangXu1

Tongling Meteorological Bureau244100AnhuiChina

2Public Meteorological Service Center of China Meteorological Administration100081BeijingChina

3Key Laboratory of Transportation Meteorology of CMA210009NanjingJiangsuChina

Jie Zhang¹,Hua Tian^2,3,*,Jingjing Gao²,Yayu Zhu¹,Jianyang Song²,Hang Xu¹

1 Tongling Meteorological Bureau, Anhui 244100, China

2 Public Meteorological Service Center of China Meteorological Administration, Beijing 100081, China

3 Key Laboratory of Transportation Meteorology of CMA, Nanjing, Jiangsu 210009, China

* Correspondence: tianh1@cma.gov.cn (H.T.)

Abstract

Highway geohazards are prevalent in the Sichuan–Chongqing–Hubei region, conducting susceptibility risk zoning for these disasters provides crucial scientific support for implementing disaster prevention forecasting and risk assessment along highways. In the present study, data from 2014 to 2022 were utilised, encompassing highway geohazard-induced traffic disruptions, precipitation records, and basic geographic information, to investigate geohazard susceptibility. A total of 11 susceptibility indicators were selected, integrating geological, environmental, topographic and meteorological parameters. Susceptibility indices and triggering probabilities were employed to examine the key influencing factors of highway geohazards, followed by systematic classification of the indicators. To quantify the relative importance of each indicator, the entropy weight method and analytic hierarchy process were applied, facilitating the evaluation and spatial delineation of geohazard susceptibility risk levels.

The results demonstrate that susceptibility index and triggering probabilities effectively reflect the characteristic factors influencing highway geohazards. Key triggering factors include: semi-luvisols as the dominant soil type, grassland as the land-use type, peak ground acceleration (PGA) exceeding 0.3g, fault distance within 3 km, river distance within 0.5 km, 24-hour cumulative precipitation exceeding 100 mm, effective precipitation exceeding 100 mm, and consecutive rainfall events lasting 3–5 days. The risk zoning map generated using the entropy weight method effectively reflects the spatial distribution patterns of highway geohazards. High and moderately high risk zones are primarily concentrated in high-altitude mountainous regions, notably in the central-western and southern areas of Sichuan Province, the eastern and south-western parts of Chongqing Municipality, and the western and eastern regions of Hubei Province—areas characterised by a high incidence of highway geohazard events.

Keywords:

Highway geohazard

Susceptibility index

Triggering probability

Risk zoning

1 Introduction

Precipitation-induced highway geohazards constitute one of the principal threats to traffic safety and highway maintenance. In recent years, China’s rapid expansion of its highway network has resulted in a total expressway length exceeding 180,000 kilometres. However, in the context of a warming climate, the increasing incidence of fatalities and economic losses associated with extreme rainfall-triggered geohazards has highlighted the urgent need to strengthen mitigation strategies. Owing to China’s vast territorial extent and diverse natural conditions, significant regional disparities exist in the types and risks of geohazards encountered. Consequently, susceptibility risk zoning for highway geohazards has emerged as a critical research priority, aimed at minimising infrastructure losses and promoting sustainable integration between highway development and the natural environment.

In recent years, catastrophic highway geohazards such as major landslides and debris flows have garnered significant attention from scholars worldwide. Researchers worldwide have integrated Geographic Information Systems (GIS) into geohazard studies, achieving significant theoretical and case-based advancements in geohazard database systems and risk assessment, alongside progress in highway geohazard science (Kang. 2006; Ma et al. 2006; Tian. 2007; Ma et al. 2010; Zhao et al. 2017; Wang et al. 2018; Liu et al. 2019; Chen et al. 2023). A pioneering contribution was made by Brabb Earl E. of the U.S. Geological Survey (Menlo Park, California), who, in 1986, applied GIS functionalities, such as data processing, digital mapping, and data management, to investigate geohazards in California (Brabb et al. 1986). In 1998, Mejia-Navarro et al. combined GIS with decision-support systems, developing an integrated framework for natural disaster risk assessment using GIS and engineering mathematical models (Mejia-Navarro et al. 1986). Sharma employed GIS to conduct overlay analyses of slope types, geological structures, topography, and land use, synthesising evaluation factors for landslide susceptibility along India's national highways (Sharma et al. 2012). Amatya et al. utilised ultra-high-resolution optical satellite imagery to collect landslide data within a 3 km buffer zone along Nepal’s Karnali Highway, applying a logistic regression model based on eight indicators (e.g., slope gradient, aspect, and elevation) to generate a landslide susceptibility map (Amatya et al. 2019). While these studies emphasise topographic, geological, and environmental influences on highway geohazards, they largely neglect rainfall dynamics. Notably, Regmi incorporated rainfall as a key parameter alongside terrain, geology, land use, and highway infrastructure to develop rockfall hazard maps along the Arniko Highway, marking a partial advancement (Regmi et al. 2016).

Although China has made considerable progress in regional geohazard risk assessment research, studies that specifically examine the influence of meteorological factors, such as extreme rainfall, on mountain highway geohazards remain relatively limited. Existing methodologies for highway geohazard risk assessment predominantly rely on fuzzy comprehensive evaluation, the analytic hierarchy process (AHP), and factor overlay analysis (Chen et al. 2011, Xue et al. 2011, Guo et al. 2017). Over the past decade, scholars such as Zhao and Tao have advanced multi-factor evaluation models integrating terrain, rainfall, gully density, geological types, and vegetation cover, utilising GIS to classify provincial-level highway geohazard risk zones (Zhao et al. 2016, Zhao et al. 2018, Tao et al. 2022). These studies incorporate rainfall parameters; however, the emphasis is predominantly placed on annual mean precipitation. Crucially, the integration of key meteorological variables, such as rainfall intensity, duration, and cumulative precipitation, into hierarchical risk assessment frameworks remains insufficiently explored.

The Sichuan-Chongqing-Hubei Region, located in central-western China (Fig. 1), is characterised by dense mountain ranges, intersecting river systems, and numerous basins and valleys. Located along the eastern margin of the collision zone between the Eurasian and Indo-Australian plates, the region is subject to frequent seismic events, intense tectonic activity, and a high density of active fault zones. The widespread presence of unstable lithosols and fragile geological formations provides a conducive environment for the development of geohazards such as landslides and debris flows. Extreme rainfall events frequently trigger mountain floods, landslides, and debris flows, with statistical analyses indicating that geohazards affecting highways in this region accounted for 45.3% of national occurrences over the past four years. This study focuses on expressways and national highways within the Sichuan–Chongqing–Hubei region. By integrating geological, environmental, topographic, and meteorological datasets, we develop regional susceptibility indicators for highway geohazards. The findings aim to provide actionable insights for forecasting, zoning, and mitigating rainfall-induced geohazards along critical transport corridors.

Fig. 1

Geographical Location Map of the Sichuan-Chongqing-Hubei Region

This map was generated by the authors using ArcGIS (version 10.7.0.10450; Esri, URL: https://www.esri.com/en-us/arcgis/products/arcgis-desktop/overview). ArcGIS® and ArcMap™ are the intellectual property of Esri and are used herein under license. Copyright © Esri. All rights reserved.

2 Materials and Methods

2.1 Data Sources

The highway geohazard dataset used in the present study comprised 1,952 records of traffic disruptions(landslides and debris flows) along highways in the Sichuan-Chongqing-Hubei Region between 2014 and 2022, obtained from the Highway Traffic Condition Information System (HTCIS) under the Ministry of Transport of China. Precipitation data were derived from hourly records (2014–2022) collected by transportation meteorological stations and adjacent stations within a 5 km buffer zone of highways, ensuring spatial relevance to geohazard-prone road segments.

2.2 Basic Geographic Information Data

The foundational geographic datasets included:

30 m digital elevation model (DEM), derived from the Shuttle Radar Topography Mission (SRTM v1) via NASA's official platform;

PGA data, obtained from the Seismic Ground Motion Parameter Zonation Map published by the China Earthquake Administration (CEA);

Soil type classifications, based on the 1:1,000,000 Soil Map of China compiled by the National Soil Survey Office;

Fault distribution data, extracted from the 1:2,500,000 Digital Geological Map Spatial Database maintained by the China Geological Survey (CGS);

Land use/cover types, sourced from the 2020 China Land Use Remote Sensing Monitoring Dataset (Chinese Academy of Sciences, CAS);

Fractional Vegetation Cover (FVC) data (250 m resolution, 2022) provided by the National Tibetan Plateau Data Center (NTPDC);

River network data, extracted from vector line datasets via OpenStreetMap;

Road infrastructure data, sourced from the China Road Spatial Distribution Database (CAS).

2.3 Research Methodology

2.3.1 Susceptibility Index

To quantitatively evaluate the triggering effects of different classified characteristic factors of geological, environmental, and topographic influencing indicators on highway geohazards, the susceptibility index(SI) can be defined as:

$\:{x}_{i}=\frac{{N}_{i}}{{s}_{i}/{s}_{sum}}$

where

$\:{x}_{i}$

is SI;

$\:i$

is the number of influence indicator categories;

$\:{N}_{i}$

is the frequency of highway geohazard occurrences under a specific classified characteristic factor of an influence indicator (unit: times);

$\:{s}_{i}$

is the number of highway and national road grid cells corresponding to the classified characteristic factor;

$\:{s}_{sum}$

is the total number of grid cells for highways and national roads under the influence indicator; and

$\:{s}_{i}/{s}_{sum}$

represents the proportion of highway segments associated with the classified characteristic factor. SI comprehensively characterises the occurrence frequency across various classified characteristic factors of influencing indicators in the Sichuan-Chongqing-Hubei region.

2.3.2 Antecedent Effective Rainfall

The antecedent effective rainfall model proposed by Crozier (Crozier et al. 2019) was adopted, which can be expressed as:

$\:{P}_{a0}=\sum\:_{i=1}^{n}{K}^{i}{R}_{i}$

where

$\:{P}_{a0}$

is the antecedent effective rainfall (unit: mm);

$\:n$

is the number of days preceding the highway geohazard event (unit: d, n = 15);

$\:K$

is the decay coefficient (

$\:K=0$

.8);

$\:{K}^{i}$

is represents the decay coefficient raised to the power of the i-th day; and

$\:{R}_{i}$

is the daily rainfall on the i-th day prior to the geohazard (unit: mm).

2.3.3 Triggering Probability

The triggering probability(TP) of highway geohazards under different classification factors of a rainfall indicator can be defined as:

$\:{P}_{i}=\sum\:_{k=1}^{n}{N}_{ik}/\sum\:_{k=1}^{n}{M}_{ilk}$

where:

$\:{P}_{i}$

is the TP of a rainfall indicator classification factor;

$\:i$

is the number of classification factors for the rainfall indicator;

$\:n$

is the total number of highway geohazard event samples;

$\:{N}_{ik}$

is the occurrence frequency of a specific rainfall indicator classification factor

$\:i$

within the statistical samples;

$\:{M}_{ilk}$

is the occurrence frequency of the rainfall indicator classification factor

$\:i$

in the specific month

$\:l$

; and

$\:l$

is the month during which the highway geohazard event occurred.

2.3.4 Analytic Hierarchy Process

The Analytic Hierarchy Process (AHP), introduced by Saaty in the 1970s, is a multi-criteria decision-making method that facilitates the transformation of semi-qualitative and semi-quantitative problems into quantitative analyses. It achieves this by hierarchically structuring the influencing factors and gradually comparing their correlations to derive comprehensive weighting coefficients for evaluation purposes (Saaty et al. 1990, Saaty et al. 2008). AHP begins with establishing a rational hierarchical model, typically comprising three levels: the Objective Layer, the Criterion Layer, and the Indicator Layer. The Criterion Layer governs the Indicator Layer while remaining subordinate to the Objective Layer, thereby maintaining a logical and structured hierarchy of dependencies.

In accordance with the AHP scoring protocol, multiple experts from the transportation and meteorology sectors were invited to conduct pairwise comparisons and quantitative scoring of elements within the alternatives level. The eigenvalue method was employed to derive weighting coefficients for each element, thereby establishing a mathematically rigorous prioritisation framework. A judgement matrix is considered to exhibit acceptable consistency when the consistency ratio is less than 0.1; if this threshold is exceeded, the consistency principle is violated, rendering the expert evaluations invalid (Wang et al. 1990, Jiang. 1993, Chen. 1995, Saaty et al. 2008).

2.3.5 Entropy Weight Method

The Entropy Weight Method (EWM) is an objective weighting method that uses entropy weights to represent the relative importance of evaluation indicators. It determines indicator weights according to the information-carrying capacity inherent in the data. Indicators exhibiting greater variability are assigned higher weights, as they convey more effective information, whereas indicators with lower variability receive reduced weights. The EWM is characterised by its computational efficiency, strong capability to manage interrelationships among multiple evaluation objects under the same indicator, and low sensitivity to outliers, thereby effectively minimising subjective bias within the evaluation process (Jia et al. 2007).

For a system with

$\:m$

evaluation indicators and

$\:n$

evaluation objects, the entropy of the

$\:i$

-th indicator can be defined as:

$\:{H}_{i}=-\frac{1}{\text{ln}n}{\sum\:}_{j=1}^{n}{f}_{ij}\text{ln}{f}_{ij},i=\text{1,2},3\cdots\:,m$

where

$\:{f}_{ij}=\frac{{r}_{ij}}{{\sum\:}_{j=1}^{n}{r}_{ij}}$

.When

$\:{f}_{ij}=0$

$\:{f}_{ij}\text{ln}{f}_{ij}=0$

. Here,

$\:{r}_{ij}$

is the standard value of the

$\:j$

-th evaluation object on the

$\:i$

-th evaluation indicator,

$\:{r}_{ij}\in\:\left[\text{0,1}\right]$

The entropy weight

$\:{w}_{i}$

of the i-th indicator can be calculated as:

$\:{w}_{i}=\frac{1-{H}_{i}}{m-{\sum\:}_{i=1}^{m}{H}_{i}}$

$\:\text{w}\text{h}\text{e}\text{r}\text{e}\:0\le\:{w}_{i}\le\:1\:\text{a}\text{n}\text{d}\:{\sum\:}_{i=1}^{m}{w}_{i}=1.$

2.3.6 Risk Zoning for Susceptibility to Highway Geohazards

Highway geohazards are primarily influenced by geological, environmental, topographic, and meteorological factors. In domestic and international geohazard risk assessment studies, features such as fault proximity and slope gradient are commonly employed as evaluation indicators. In the present study, 11 evaluation indicators were adopted, which can be categorised as follows:

Geological Factors included Soil Type, Seismic Peak Acceleration and Fault Distance.

Environmental Factors included Land Use Type and River Distance.

Topographic Factors included Digital Elevation Model (DEM), Slope Aspect and Gradient.

Meteorological Factors included 24-hour cumulative precipitation, Previous 15-day Consecutive Rainy Days and Effective Precipitation.

Based on the frequency of highway geohazard occurrences, SI and TP associated with 11 evaluation indicators, the susceptibility-influencing characteristics of these indicators were systematically analysed. The indicators were categorised into four susceptibility tiers, low, moderate, moderate high, and high, and assigned normalised values ranging from 1 to 4 using min–max standardisation. The weights of the evaluation indicators were independently calculated using the AHP and the EWM (Table 1). Subsequently, the geohazard susceptibility risk index for each pixel was computed using a linear weighted combination of the normalised indicator values and their corresponding weights as follows:

$\:J=\sum\:_{i=1}^{n}{k}_{i}{M}_{i}$

where

$\:n$

represents the total number of evaluation indicators (assigned a value of 11);

$\:{k}_{i}$

denotes the weight coefficient of the

$\:i$

-th indicator; and

$\:{M}_{i}$

corresponds to the graded numerical value of the

$\:i$

-th indicator.

Table 1
Susceptibility Risk Assessment Indicators and Grading for Highway Geohazards
Objective Layer	Criterion Layer	Evaluation Indicators	Indicator Weights		Indicator Levels
Objective Layer	Criterion Layer	Evaluation Indicators	AHP	EWM	1(Low)	2(Moderate)	3(Moderately High)	4 (High)
Highway Geohazard Susceptibility Risk Assessment A	Geological Factors B1	Soil Types C1	0.0625	0.0832	Alpine soils, Anthrosols,Rivers, Hydromorphic soils,Semi-hydromorphic soils	Primosols	Ferralsols,Luvisols	Semi-luvisols
		Seismic Peak Acceleration C2	0.0625	0.1323	≦ 0.1g	0.15g	0.2g	≧ 0.3g
		Fault Distances C3	0.1250	0.1479	> 8km	5-8km	3-5km	≤ 3km
	Environmental Factors B2	Land Use Types C4	0.1250	0.0576	Residential land	Cultivated land	Woodland	Grassland, Water bodies
	Environmental Factors B2	River Distances C5	0.1250	0.1726	> 1.5km	1-1.5km	0.5-1km	≦ 0.5km
	Topographic Factors B3	Slope Aspect C6	0.0499	0.0937	345-0°、0–75°	255–345°	165–255°	75–165°
		Slope Gradient C7	0.1502	0.0820	≤ 5°	5–15°	15–30°	>30°
		Elevation C8	0.0499	0.1062	≤ 0.5km、>3.5km	0.5-1km	1-2km	2-3.5km
	Meteorological Factors B4	24-hour precipitation C9	0.0625	0.0368	0-10mm	10-50mm	50-100mm	>100mm
		Effective Precipitation C10	0.0625	0.0771	≤ 30mm	30-60mm	60-100mm	>100mm
		Consecutive Rainy Days C11	0.1250	0.0107	1–2 days	≥ 9 days	6–8 days	3–5 days

3 Characteristics of Highway Geohazard Susceptibility Impact Indicators

3.1 Geological Factors

3.1.1 Soil Types

Figure 2 illustrates the frequency of highway geohazard occurrences and SI variations across nine soil types in the Sichuan-Chongqing-Hubei region. Significant differences in geohazard frequency were observed among soil types. Semi-luvisols, Ferralisols, Luvisols, Primosols, and Anthrosols exhibited the highest geohazard frequencies, all exceeding 15% (Fig. 1).

With respect to the SI, the highest values were observed in Semi-luvisols, which exhibited partial lyophilisation and were particularly susceptible to erosion and dissolution by water, thereby increasing their vulnerability to geohazards. Ferralsols and Luvisols displayed the second highest SI. Ferralsols are characterised by low permeability and high viscosity, conditions that promoted excessive soil moisture retention and reduced interparticle friction. Luvisols, due to their strong leaching properties and loose structure, are similarly prone to geohazards. Primosols also exhibited a notably high SI. As newly formed soils undergoing natural recovery, Primosols possess an unstable structure, making them particularly sensitive to external disturbances such as rainfall and seismic activity, and thus susceptible to events such as landslides and debris flows. In contrast, alpine soils demonstrated greater stability due to the presence of permafrost or ice layers. Anthrosols, those altered through human activities such as excavation and landfilling, tend to be more stable following intervention and proper management. Other soil types, including fluvial soils, hydromorphic and semi-hydromorphic soils, typically retained high moisture content, while calcareous soils contained significant calcium carbonate, which enhanced their structural stability and reduced geohazard risk. Based on the SI and soil characteristics, Semi-luvisols were classified as high susceptibility; Ferralsols and Luvisols as moderately high; Primosols as moderate; and all remaining soil types as low susceptibility. The subsequent indicator analyses followed a similar classification approach, as summarised in Table 1.

Fig. 2

Frequency and SI of Highway Geohazards with variation of Soil Types

3.1.2 Seismic Peak Acceleration

Earthquakes cause damage to the internal structure of slope geotechnical bodies, leading to tension cracking and relaxation of pre-existing structural surfaces. These disturbances often trigger a rise in the water table and alter runoff conditions, thereby increasing the likelihood of geological disasters. According to the China Earthquake Parameter Zoning Map (GB 18306 − 2015), the values of seismic defence parameters for roads, bridges, and other general engineering works are standardised. Additionally, the Seismic Design Standard for Buildings (GB/T 50011 − 2010), revised and implemented in 2024, clearly specifies design requirements for building structures under different levels of seismic acceleration.

The frequency of highway geohazards has generally decreased with increasing PGA (Fig. 3). Most highway geohazard events were found to be concentrated at PGA levels below 0.2g, with a frequency of 0.05g accounting for 31.3%. In contrast, the frequency of events at or above 0.3g remained relatively low. However, the SI has exhibited an increasing trend with rising PGA values, clearly indicating that both higher earthquake frequency and greater seismic intensity elevate the risk of highway geohazard occurrence. This correlation highlights that increased seismic activity significantly amplifies the risk characteristics associated with geological disasters along highways.

Fig. 3

Frequency and SI of Highway Geohazards with variationof Seismic Peak Acceleration

3.1.3 Fault Distances

The distribution of faults reflects the tectonic activity, and geotechnical bodies in fault-prone areas tend to be more fragmented, with well-developed joints and fissures. Generally, the influence of faults on the integrity of geotechnical bodies is closely related to proximity. The frequency and SI of highway geohazards exhibited a consistent pattern, both showing elevated values within 3 km of fault lines, followed by a sharp decline as the distance from faults increases. Notably, the SI decreased significantly beyond 8 km (Fig. 4). This pattern aligns with faulting mechanisms and supports existing findings that faults located more than 5–10 km away have no substantial impact on the structural integrity of geotechnical bodies (Rodríguez et al. 1999, Huang et al. 2009, Chong et al. 2013, Jiang et al. 2014).

Fig. 4

Frequency and SI of Highway Geohazards with variation of Fault Distance

3.2 Environmental factors

3.2.1 Land use types

Land use types reflect the extent and nature of land exploitation and utilisation driven by human socio-economic activities. Inappropriate land development can disrupt surface stability and induce geohazards. As shown in the distribution of highway geohazard frequency and SI across land use types (Fig. 5), the highest frequency was found to occur in areas categorised as cropland and woodland. The SI for these two land types were moderate and comparable, which reflects the fact that highways in the Sichuan, Chongqing, and Ezhou regions traverse substantial areas of forest and farmland. The soil layers in woodland and cropland are relatively stable, making them less prone to geological disasters. Grassland areas exhibited the second highest frequency, but they recorded the highest SI. This suggests that the steeper topography common in these regions, combined with the looser soil structure under grassland vegetation, contributes to increased vulnerability to geohazards. Residential land accounted for the second lowest frequency and also recorded the lowest SI. Although urban roads are densely developed and contain numerous underground utilities, effective governance and proactive disaster mitigation strategies in urban areas contribute to a lower incidence of geohazards. Water-related land types exhibited the lowest frequency of highway geohazards; however, they showed the second highest SI. This reflects the elevated risk along riverside roads and bridge-tunnel sections of highways in the Sichuan, Chongqing, and Hubei regions, which are particularly vulnerable to flooding, landslides, and debris flows triggered by intense rainfall.

Fig. 5

Frequency and SI of Highway Geohazards with variation of Land Use Types

3.2.2 River distances

The erosive and scouring effects of river systems on adjacent bank slopes result in an unloading effect within the slope zone, which can lead to slope deformation. Consequently, the proximity to rivers constitutes a significant factor influencing the occurrence of highway geohazards. Both the frequency of highway geohazards and the SI exhibited a similar pattern, showing a rapid decline as the distance from the river increased. This indicates that highway geohazards are more likely to occur in areas closer to river systems. Specifically, the highest frequency and the greatest SI were observed within 0.5 km of rivers. Between 0.5 km and 1 km, both the frequency and susceptibility decreased moderately. Beyond 2 km, the SI declined sharply, further confirming the strong inverse relationship between river proximity and geohazard risk (Fig. 6).

Fig. 6

Frequency and SI of Highway Geohazards with variation of River distances

3.3 Topographic factors

3.3.1 Slope Aspect

Slope aspect refers to the direction of the projection of the normal line of the slope surface on the horizontal plane. Different slope orientations receive varying amounts of solar radiation, which influences vegetation cover, the degree of rock surface weathering, rainfall accumulation, and evapotranspiration. These factors, in turn, affect the physical and mechanical properties of the slope’s rock and soil bodies, as well as the distribution of groundwater pore pressure. As such, different slope orientations will not only affect the vegetation coverage and rainfall size of the environment for geohazard development, but also the degree of development of weathering joints in the rocks and the degree of rock fragmentation. This will affect the possibility of geohazard development. Slope direction is consistent with the trend of the frequency and SI of road geohazards (Fig. 7). The highest frequency of geohazard occurrences was observed on slopes facing 75°–165° (easterly direction), with more than 120 recorded events and the highest associated SI. Slopes oriented between 165°–255° (southerly facing) exhibited the second highest frequency of geohazards and a correspondingly high SI. In contrast, slopes facing 345°–75° (northerly facing) experienced the lowest frequency of geohazards and the lowest SI.

Fig. 7

Frequency and SI of Highway Geohazards with variation of Aspect

3.3.2 Slope Gradient

Geohazards frequently occur on specific slope bodies, and slope gradient, as a fundamental component of natural geography, plays a critical role in determining the stability of slope rock and soil masses. The frequency of road geohazards was found to decrease with increasing slope gradient (Fig. 8). Slopes with gradients below 10° accounted for the highest frequency of geohazard events, comprising 49% of all recorded cases. In contrast, the SI exhibited an increasing trend with rising slope gradient. Slopes exceeding 30° corresponded to the highest susceptibility levels (high index level), while slopes between 15° and 30° showed a moderately high susceptibility. Slopes ranging from 5° to 15° displayed a relatively low SI (intermediate level), and those below 5° had the lowest susceptibility (low index level.

Fig. 8

Frequency and SI of Highway Geohazards with variation of Slope Gradient

3.3.3 Elevation

From the perspective of elevation and the frequency of highway geohazards (Fig. 9), the occurrence of such hazards gradually decreased with increasing elevation, with the majority recorded below 1,500 m. Specifically, the highest frequency occurred at elevations below 500 m, accounting for 25.7% of all events. The second highest frequency was observed between 500–1,000 m, representing 23.6%. Beyond 1000 m, the frequency declined rapidly, and the occurrence of geohazards became negligible above 3500 m. The SI for highway geohazards exhibited a trend of initially increasing and then decreasing with elevation. It peaked within the 2000–3500 m range, indicating that high-elevation mountainous areas, characterised by intense precipitation and deep surface incision, were more prone to flash floods, landslides, debris flows, and related hazards. Based on this analysis, elevations between 2000–3500 m were classified as high susceptibility, 1,000–2000 m as moderately high, 500–1000 m as moderate, and elevations below 500 m and above 3500 m as low susceptibility.

Fig. 9

Frequency and SI of Highway Geohazards with variation of Elevation

3.4 Meteorological factors

3.4.1 24-hour Antecedent Precipitation

The frequency of highway geohazards showed a significant decreasing trend with increasing rainfall (Fig. 10). The highest frequency was associated with rainfall below the light rainfall threshold, with 884 recorded events, accounting for 45.2% of the total. This was followed by moderate to heavy rainfall, which accounted for 721 occurrences, and rainfall exceeding the heavy rainfall threshold, with 311 occurrences, among which extreme heavy rainfall events numbered only 8. In contrast, the probability of rainfall-induced highway geohazards exhibited a clear increasing trend as rainfall intensified. The probability was lowest for daily rainfall below 10 mm, increased gradually between 10–100 mm, and rose sharply beyond 100 mm, reaching its peak in the 220–230 mm range. These results indicate that rainfall exceeding the heavy rainfall threshold within the preceding 24 hours had a pronounced effect on the likelihood of highway geohazard occurrence. Considering both the frequency and probability of occurrence associated with daily rainfall, rainfall levels were classified into susceptibility grades: 0–10 mm as low, 10–50 mm as moderate, 50–100 mm as moderately high, and above 100 mm as high. The subsequent analyses adhered to the same classification framework.

Fig. 10

Frequency and TP of Highway Geohazards with variation of 24-Hour Precipitation

3.4.2 15-day Antecedent Effective Precipitation

The occurrence of road-related geohazards exhibited not only a strong correlation with 24-hour cumulative precipitation on the preceding day, but also demonstrated a significant time-lag effect. Moreover, the influence of antecedent rainfall progressively weakened as the precipitation events temporally receded. The effective rainfall of road geohazards was mostly concentrated in below 150mm (Fig. 11), with occurrences exceeding 250 mm recorded only 19 times. The overall probability of hazard triggering exhibited a clear increasing trend with rising effective rainfall. The probability was lowest for rainfall below 10 mm, showed gradual growth between 10–30 mm, and accelerated beyond 20 mm. A rapid increase in probability was observed above 60 mm, with the maximum probability recorded for effective rainfall exceeding 250 mm.

Fig. 11

Frequency and TP of Highway Geohazards with variation of 15-day Antecedent Effective Precipitation

3.4.3 15-day Antecedent Maximum Consecutive Rainy Days

The distribution of the number of maximum consecutive rainy days in the previous 15 days showed a single-peak characteristic, and the frequency of disasters showed an increasing and then decreasing trend with the increase in the number of days (Fig. 12). The number of days from 1 to 4 grew rapidly, and the extreme value of its proportion appeared around 4 days. Most highway geohazards were preceded by 2–8 consecutive days of precipitation. The overall probability of hazard triggering exhibited a trend of initially increasing and then decreasing with the number of consecutive rainy days. The probability was low for durations of less than 2 days, increased rapidly after 3 days, and reached its peak at 4 days. Based on the frequency of consecutive rainfall days and the associated TP, the indicator was classified into susceptibility grades: fewer than 2 days was designated as low grade; more than 9 days as intermediate grade; 6–8 days as moderately high grade; and 3–5 days as high grade.

Fig. 12

Frequency and TP of Highway Geohazards with variation of 15-day Antecedent Maximum Consecutive Rainy Days

4 Highway geohazard risk zoning

The 11 types of risk elements were standardised, and the weight coefficients of hierarchical analysis method and entropy weight method were superimposed to generate a national highway geohazard susceptibility risk assessment map. This map was classified using the natural inter-segmental point grading method, resulting in the delineation of highway geohazard susceptibility risk zones (Fig. 13).

A comparative analysis of the area share across risk zones derived from the two methods (Table 2) revealed that both the moderate-risk and moderately High-risk zones occupied a substantial proportion of the total area. However, the area classified as high-risk under the EWM was significantly smaller than that under the AHP. In terms of disaster point distribution, both methods exhibited a clear increasing trend in the proportion of disaster points with rising risk levels, aligning with the observed increase in geohazard frequency at higher risk levels. Notably, the variation in area proportions across risk levels was more pronounced in the EWM results compared to those of the AHP. The high-risk zone in the EWM classification accounted for 40.9% of the total, indicating a stronger correspondence between high-risk areas and actual geohazard occurrences, thereby demonstrating superior performance in capturing the indicative characteristics of highway geohazards.

Table 2
Proportion of Risk Zones
Method	Metric	Low-risk Zone	Moderate-risk Zone	Moderately High-risk Zone	High-risk Zone
AHP	Area Proportion	23.2%	31.7%	29.3%	15.8%
AHP	Hazard Point Ratio	15.4%	21.6%	25.4%	37.6%
EWM	Area Proportion	27.4%	32.7%	27.1%	12.8%
EWM	Hazard Point Ratio	15.2%	18.1%	25.8%	40.9%

According to the AHP, fault distance, land use type, river proximity, slope gradient, and the number of consecutive precipitation days were assigned relatively high weights, indicating their perceived importance. In contrast, the EWM assigned higher weights to peak ground acceleration, fault distance, and river proximity. These factors emerged as the principal influencing variables in both assessment approaches.

The risk zoning under the two index selection schemes presented more consistent characteristics (Fig. 13). The high risk areas of highway geological disasters in Sichuan, Chongqing and Hubei were primarily concentrated in the central and western regions, with the most extensive distributions observed in Sichuan Province and Chongqing Municipality. At the provincial level, moderately high and high-risk zones were predominantly located in central, northern, and southern Sichuan Province, the north-eastern part of Chongqing Municipality, and the western and eastern regions of Hubei Province. These areas exhibited a high geohazard risk and corresponded closely with the locations where highway geohazards were most frequently observed, reflecting good agreement with real-world conditions. In contrast, the western and eastern parts of Sichuan Province, the south-central region of Chongqing Municipality, and the central-eastern area of Hubei Province were classified as low to moderate-risk zones, indicating a relatively lower likelihood of highway geohazard occurrence (Fig. 13). Overall, the risk zoning for susceptibility to highway geohazards results effectively captured the spatial distribution characteristics of highway geohazards in the region.

Fig. 13

Distribution of highway geological hazard risk levels (Upper: AHP; Lower: EWM)

5 Conclusion

In the present study, geological, environmental, topographic, and meteorological factors were comprehensively considered to conduct a susceptibility risk zoning analysis of highway geohazards in Sichuan, Chongqing, and Hubei. Using a comparative approach based on SI and TP, the influence of 11 evaluation indicators was analysed: soil type, seismic acceleration,fault distance, land use type, river distance, slope aspect, slope gradient, elevation, 24-hour cumulative precipitation, 15-day antecedent effective precipitation, and 15-day antecedent maximum consecutive rainy days. By examining the relationship between these indicators and the frequency of highway geohazard occurrences, the characteristic influencing factors were identified and corresponding SI grades were determined.

2. Highway geohazards were found to be most likely in the geographic environments of the Sichuan, Chongqing, and Hubei regions characterised by the following conditions: semi-luvisols soil types; peak ground acceleration exceeding 0.3g; grassland land cover; proximity to faults within 3 km; proximity to rivers within 0.5 km; slope orientations between 75° and 165° (east-facing); slope gradients greater than 30°; and elevations ranging from 2000 to 3500 metres. In terms of meteorological factors, 24-hour precipitation exceeding 100 mm on the day prior to the event, effective cumulative precipitation of more than 100 mm over the preceding 15 days, and 3 to 5 consecutive days of rainfall were identified as the most likely conditions to trigger highway geohazards.

3. In terms of highway geological hazard risk levels, high-risk areas were primarily concentrated in the western regions of Sichuan Province and Chongqing Municipality, with these two areas showing the widest spatial distribution of elevated risk. The risk assessment results based on the Entropy Weight Method aligned more closely with the actual distribution patterns of geohazards in the region. Most highway geological hazard events were located within the moderately high and high-risk zones, indicating that the susceptibility risk zoning effectively captured the spatial characteristics of hazard occurrence. These findings offer important theoretical and practical implications for regional highway geohazard prevention, early warning, and forecasting.

Author Contribution

Author Contributions StatementAuthorship provides credit for a researcher's contributions to a study and carries accountability. Use this section to specify how authors contributed to the manuscript.Our authorship policy for Springer (opens in a new window) provides guidance and criteria for authorship.This replaces any statement written within the manuscript and is the one that we will publish.Author Contributions Conceptualization: [Jie Zhang], [Hua Tian]; methodology: [Jie Zhang], [Hua Tian], [Jingjing Gao]; formal analysis and investigation: [Jie Zhang], [Yayu Zhu], [Jianyang Song]; writing – original draft preparation: [Jie Zhang], [Hua Tian]; writing – review and editing: [Jie Zhang], [Hua Tian], [Jingjing Gao]; funding acquisition: [Jie Zhang], [Hua Tian]; resources: [Jie Zhang], [Hua Tian]; supervision: [Jia Li], [Hang Xu]; accuracy evaluation:[Yayu Zhu], [Jianyang Song], [Hang Xu].

Funding

This research was funded by the Joint Research Project for Meteorological Capacity Improvement of the China Meteorological Administration (Grant number 23NLTSZ009), the Innovation Fund Project of the Public Meteorological Service Center of the China Meteorological Administration (Grant number M2023011), and the Special Program for Innovation and Development of the Anhui Provincial Meteorological Bureau (Grant number CXM202206).

Declarations

Confict of interest Not Applicable.

Data Availability

The datasets used and analysed during the current study are available from the corresponding author on reasonable request.

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