Analysis and Evaluation of Extreme Rainfall Trends and Geological Hazards Risk in the Lower Jinshajiang River
1
School of Software Engineering, Chengdu University of Information Technology, Chengdu 610225, China
2
Sichuan Province Engineering Technology Research Center of Support Software of Informatization Application, Chengdu 610225, China
3
School of Environment and Civil Engineering, Chengdu University of Technology, Chengdu 610059, China
4
Operational System Development and Maintenance Division, National Climate Center, Beijing 100081, China
*
Authors to whom correspondence should be addressed.
Appl. Sci. 2023, 13(6), 4021; https://doi.org/10.3390/app13064021
Submission received: 7 February 2023
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Revised: 10 March 2023
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Accepted: 14 March 2023
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Published: 22 March 2023
(This article belongs to the Special Issue Geospatial AI in Earth Observation, Remote Sensing and GIScience)
Abstract
:This research studied the risk assessment of geological hazards, such as landslides and debris flow, under the time series and trend characteristics of extreme precipitation events in the last 60 years in nine typical regions of the lower Jinshajiang River Basin. Nine indicators, including slope, engineering geological rock group, slope structure type, distance to road, topographic relief, distance to fault, distance to the water system, vegetation cover and profile curvature, were selected as the index factors for landslide susceptibility evaluation, and the information quantity method was used to obtain the landslide susceptibility evaluation of the study area. Based on the susceptibility evaluation, the spatial analysis function of GIS was used to derive the geological hazard zoning under the extreme rainfall trend. The results showed that the areas with high extreme rainfall trends have higher densities of geological hazard development and they are concentrated, while areas with low extreme rainfall trends have relatively less geological hazard development, and what development exists is scattered.
1. Introduction
China has some of the most serious, intense and large-scale geological disasters in the world [1]. The Hengduan Mountains area of the Yangtze River Basin is located in the second and third ladder transition zone of China on the eastern edge of the Qinghai–Tibet Plateau and belongs to several monsoon confluence areas. It has a variety of climate types and extremely complex climatic conditions. Sensitive to the effects of global climate change, the area is a high-incidence area for extreme weather and climate events [2]. Systematic assessment of geological hazards and comprehensive risk evaluation of extreme climatic conditions can provide important references for the effective development of disaster prevention and mitigation work [3]. The demand for the refined evaluation of geological disaster risks in key areas and the accuracy of evaluation results have improved in recent years [4,5,6]. The current risk evaluation methods mainly include qualitative evaluation methods (expert scoring method [3], fuzzy comprehensive evaluation method [4,5,6,7], semi-quantitative evaluation method (information value method [7,8], entropy value method [8,9], frequency ratio method, logistic regression model [10,11], artificial intelligence model [10,11], weight of evidence method [12,13,14], coefficient of determination method [15,16,17,18], analytic hierarchy process [19,20,21,22], quantitative evaluation methods (infinite slope model [22,23,24,25], 3D limit equilibrium method, slope hydrological model [25,26,27], etc.
Climate change has a profound impact on major water conservation projects in the Yangtze River Basin. The Jinshajiang River is an important section of the Yangtze River Basin, and in recent years, extreme weather events have been accompanied by frequent geological hazards, seriously affecting construction and people’s well-being [28]. The downstream section of the Jinshajiang River was selected as a typical area to evaluate the vulnerability of typical landslides to geological hazards, taking into account the formation mechanism of geological hazards and extreme rainfall trends [29,30]. In order to adapt to the impact of climate change and mitigate the risk of geological hazards, there is an urgent need to carry out an assessment of climate change and geological hazard risk and to formulate a targeted disaster reduction strategy for climate change adaptation in the Yangtze River Basin [31].
The purpose of this study is to investigate the relationship between geological hazards and their influencing factors (topography, geology, human engineering activities, rainfall) in the study area, determine the main controlling factors and influencing factors for the occurrence of disasters and use future rainfall trends to predict the occurrence of geological disasters.
2. Materials and Methods
2.1. Characteristics of Changes in Extreme Precipitation Events in the Lower Jinshajiang River Basin
Four reservoir areas in the lower reaches of the Jinshajiang River (Xiangjiaba, Xiluodu, Baihetan, Wudongde) and thirty national meteorological observation stations (Wudongde: 6; White Crane Beach: 9; Xiluodu: 6; Xiangjiaba: 9) were selected for our study. Rainfall data from 1961 to 2020 were compiled for spatiotemporal analysis using representative extreme climate indexes defined by climate change detection and index experts (ETCCDI) following procedures jointly established by the World Meteorological Organization (WMO) and the World Climate Research Program (WCRP). Nine extreme precipitation index were finally selected for analysis as detailed in Table 1.
The time series and trend characteristics of extreme precipitation indices in the last 60 years in 9 typical regions of the lower Jinshajiang River Basin were studied in which the trend characteristics of several extreme precipitation events are not significant (Figure 1). However, the number of heavy rain days, extreme heavy rain days and rainstorm days from 1961 to 2020 can reach 40, 20 and 4 days, respectively, and the total and extreme heavy precipitation totals reach 400 mm and 150 mm, respectively, as shown in Figure 1 and Table 2. An increase in the CDD indicates an increase in drought in the lower Jinshajiang River Basin.
Figure 2 shows the time series characteristics of the extreme precipitation events for the nine lower Jinshajiang River hydropower reservoirs, with four reservoirs showing a consistent overall change in the extreme precipitation events. Here, r10 mm and r20 mm show that the four reservoirs have similar extreme precipitation day characteristics, with a maximum of 40 and 20 days, respectively, while r50mm shows that the number of extreme rainstorm days in the Xiangjiaba reservoir is significantly higher than the other reservoirs at 6 days. The same extreme precipitation characteristics can be seen in Rx1 day, Rx5 day, R95p and R99p, i.e., the extreme precipitation is higher in the Xiangjiaba reservoir area. CDD indicates that the number of dry days is higher in the Wudongde reservoir area, which is related to the relatively low extreme precipitation in the Wudongde compared to the other reservoirs at Rx1day, Rx5day, R95p and R99p.
In summary, extreme precipitation in the lower Jinshajiang River hydropower reservoir area is more prominent in the Xiangjiaba reservoir area, and persistent dryness is more prominent in the Wudongde reservoir area, indicating overall aridity in Wudongde in the lower Jinshajiang River reservoir area. The linear trend of extreme precipitation in the lower Jinshajiang River reservoir area from 1961 to 2020 (Table 3) indicates that extreme precipitation events are not significant, and the overall extreme precipitation is not very variable.
2.2. Current Status of Geological Hazards
In recent years, China has carried out county-level geological hazard risk investigation and evaluation work nationwide. Through remote sensing interpretation and field investigation, detailed geological hazard catalog data of each county and district have been obtained as shown in Table 4. Through this catalog data, we can understand the spatial distribution and temporal distribution characteristics of geological disasters. At the same time, the mechanism of some typical geological hazards is analyzed, and the background and disaster mechanism of different types of geological hazards in the region are grasped. The 22 districts and counties in the study area are areas with frequent geological disasters, and the main types of geological disasters are landslides and debris flows.
As shown in Figure 3, geological hazards are distributed in 22 districts and counties in the study area. This study mainly deals with collapse, landslide and debris flow. There were 2643 landslides in the study area, accounting for 74.3%, followed by 619 debris flows, accounting for 17.4%. There were 295 land collapses in the study area, accounting for 8.3%. Refer to Table 4 for statistical information on various districts and counties according to disaster types. Among them, many geological disasters developed in Huize County, Ningnan County and Huidong County.
2.3. Analysis of the Formation Mechanism of Geological Hazards
The occurrence and development of geological hazards are closely related to rainfall, which is the main precipitating factor for geological hazards in the study area [32]. The average annual rainfall over the past five years in the study area was 960.0 mm, with an annual maximum of 1547.8 mm. Precipitation is mostly concentrated in the annual rainy season (May–September), accounting for about 75% of the annual precipitation. Moreover, due to the complex topography of the mountainous area, localized strong rainstorm weather processes are often formed and induce serious geological disasters, such as landslides, cave-ins and mudslides.
According to the statistics of the time of year when geological hazards occur or show signs of deformation and damage, of the 3557 geological hazards in the county, 22 occurred or showed signs of deformation and damage in January–April, 3513 in May–September and 22 in October–December. It can be seen that all geological hazards in the study area occur during the rainy season (May to September), accounting for 98.77% of the total number of occurrences. Moreover, the geological hazards tend to be concentrated on the day when local heavy rainfall occurs.
- 1.
- Rainfall and mudslides
The role of rainfall in the formation of mudslides is reflected in two aspects: first, it provides hydrodynamic conditions for the formation of mudslides. Rainfall causes the soil to initiate the formation of mudslides through surface confluence and erosion; second, it becomes one of the important components of mudslide bodies and is an indispensable condition for mudslide outbreaks.
According to the results of visits to mudslide disasters in the study area in recent years and a survey, the mudslides in the territory all occur from May to September, and there are no mudslides in other months. Moreover, there are multiple outbreaks in many places during the rainy season each year, consistent with the distribution of monthly rainfall, indicating that precipitation is an important trigger for mudslides in this area.
- 2.
- Rainfall and slope hazards
Slope hazards include collapses, landslides and unstable slopes. The effect of rainfall on slope hazards is mainly reflected in three aspects: first, it increases the self-weight of the slope body and accelerates the destabilization of the slope under the effect of gravity; second, it infiltrates the weak structural surface of the slope and weakens its shear strength; third, rainfall forms slope floods, which scour and erode the slope body, hollowing out the front edge, the two wings and the foot of the slope, thereby reducing the stability of the slope [33,34,35].
According to the results of geological hazard surveys and visits, the time of occurrence and intensification of deformation of the 2938 identified landslides and crumbling geological hazards were mainly concentrated in May to September each year, and the time of slope deformation coincides with heavy rainfall in the territory, indicating that slope deformation or instability is closely related to rainfall.
In addition, there is a direct correlation between the development of geological hazards and the spatial distribution of rainfall, as shown in Figure 4 and Figure 5, which show the relationship between monthly cumulative rainfall and the frequency and density of geological hazards, with the density of hazards showing an approximately exponential relationship with rainfall contours [36]. When the monthly cumulative rainfall contour is >400 mm, the density of hazard sites increases exponentially.
2.4. Preliminary Analysis of the Relationship between Extreme Rainfall Trends and Geological Hazards
The geomorphology of the Jinshajiang River Basin is cut by the Hengduan Mountains, with high mountains and deep valleys, and the terrain is severe. The structure in the area is complex, mainly based on a series of north–south upsurge and oblique thrust faults, and there are many north–south compound folds and fold groups with strong tectonic movements. The strata in the area are completely exposed, and the strata are almost distributed from all ages. The valley area is mainly composed of the metamorphic rock series of the Pre-Devonian, mainly composed of various schists, quartz sandwiched marble and metamorphic basic igneous rocks; carboniferous sedimentary and igneous rocks of Permian, Triassic and Jurassic; dispersive distribution of Tertiary and Quaternary rock layers.
The Jinshajiang River Basin has a strong physical and geological effect and impacts the development of collapses and landslides. The main stream section of the river has formed a large-scale bank collapse stream or gully five times in the past 100 years, and the branch gully has been the source of more collapses. The distribution law of geological hazards is the comprehensive embodiment of its control and influence factors and its formation mechanism. The distribution of geological hazards in the study area is strictly affected by the combined action of geological environment conditions, climatic factors, and human engineering economic activities, and has obvious regularity, which is generally manifested in the concentrated distribution of strips along the water system and fault structures.
The main induced factors of landslides in the area are natural. According to the geological investigation, the formation of a landslide caused by rainfall is mainly reflected in two aspects: first, continuous heavy rainfall makes the slope body saturated and reduces physical and mechanical properties; second, the erosion of the slope foot ditch caused by rainfall.
As geological hazards are mainly closely related to rainfall, the relationship between extreme rainfall trends and geological hazard distribution in the study area was mapped based on the climatic analysis of extreme rainfall time series in the Jinshajiang River Basin. As shown in Figure 6, the high probability of extreme rainfall in the study area is mainly concentrated in two places: The left bank area of the Jinshajiang River in Leibo County; The areas on both sides of the Jinshajiang River valley in Ningnan County–Qiaojia County. By superimposing the hazard point data, it can be found that the areas with high extreme rainfall trends have a high density of geohazard development and are concentrated, while the areas with low extreme rainfall trends have relatively less geohazard development and are scattered.
2.5. Establish Evaluation Index System
For the evaluation of susceptibility, the construction of the index system is quite an important step. Generally speaking, the commonly used indicators for the evaluation of landslide susceptibility are topography, elevation, slope, the density of gullies, relief of the terrain, etc.; geomorphology: geomorphological units, micro-geomorphological patterns, overall terrain, etc.; stratigraphic lithology: lithological characteristics, the thickness of rock layers, type of rock genesis, etc.; geological structure: faults, folds, joints and fissures, etc.; earthquake: intensity, dynamic peak acceleration, historical seismic activity, etc.; geology: stability of regional crust, depth of bedrock, lithology of main bearing layers, bearing capacity, engineering geological zoning of geotechnical bodies, etc. [37,38].
It is almost impossible in practice to build a model that encompasses all geo-environmental factors to evaluate susceptibility [34,35]. This is because the parameters included in certain factors are sometimes difficult to obtain, and with many evaluation parameters, there may be intricate synergies or interactions between the parameters. Therefore, it is the key to evaluation factors to select the stable and quantifiable participation factors that play a leading role in evaluation objectives.
In terms of the geological background of the investigation area, the topography, stratigraphic lithology, geological structure, hydrogeology and other geological environmental conditions in the study area are very complex, but when combined, it can be found that the entire geological background is mainly determined and controlled by the tectonic pattern of the Minjiang River Fracture Zone–Xueshan Fracture Zone–HuYa Fracture Zone. For example, although the whole area is covered with gullies, the gullies around Huanglong, Xiaohe and Baiyang are obviously denser and more deeply cut, thus causing numerous geological hazards, while the distribution of outcrops of stratigraphic lithology and the distribution of tectonic patterns are also somewhat related. Therefore, from this level, it is possible to select geological formations, engineering geological rock groups and gully density factors.
For example, in the case of landslides with a large number of creep–slip–slip mechanisms, the main controlling condition is the slope of the terrain, which is why the development of landslides is well correlated with the slope of the terrain in the statistical analysis of the gestation conditions. Based on this, the factors that influence the occurrence of geological hazards under heavy rainfall conditions through the analysis of the incubation conditions are, in summary, the following factors: slope, plane curvature, slope structure, human engineering activities and river geological process [39,40].
Table 5 shows that the following factors can be preliminarily selected for landslide susceptibility evaluation (1:50,000) in the study area.
For the initially selected vulnerability evaluation factors, these factors are not independent of each other but are correlated with each other to a certain extent. If they are not processed, the influence weights between the indicator factors may be superimposed, which in turn may lead to inaccurate evaluation results. Therefore, to ensure the mutual independence of the indicator factors and to meet the accuracy of the input parameters of the model, it is necessary to screen each of the selected indicator factors. To sum up, the correlation analysis of all the preliminary selected indicator factors in the ArcGIS spatial analysis tool was applied to measure the correlation degree between the indicators by calculating the correlation coefficient R. The value of R takes the range [−1, 1], and the closer R is to 1, the higher the correlation between the two indicators as shown in Table 6.
The attribute data of each factor layer was extracted by ArcGIS software Pro 2.8. The correlation coefficient matrix between the factors calculated using spatial analysis tools is shown in Table 7.
Combining the calculated correlation matrix of each factor, according to the |R| characterization correlation table shown in Table 6, the highly correlated factor: topographic relief was eliminated, although this process should not be entirely based on mathematical methods to eliminate the selected factors, but should be combined with geological understanding and judgment of the development of geological hazards. In the end, combining the distribution rules of geological hazards, the analysis of the main controlling factors, and the results of the field survey, nine indicators such as slope, engineering geological rock group, slope structure type, distance from the road, terrain undulation, distance from the fault, distance from the water system, vegetation cover and curvature of the profile were initially determined as the evaluation indicators of landslide susceptibility.
2.6. Amount of Information on Evaluation Indicators
The preliminary evaluation of landslide susceptibility in the study area was carried out using the information quantity model method [36,37]. After grading each factor layer, the graded area of each factor and the hazard area of the landslide hazard surface distributed in the area were calculated, and then the data were substituted into the above equation to obtain the information value of each factor gradation, and the software reclassification function was used to assign the value of each factor layer to form the basic data for the next step of susceptibility overlay as shown in Table 8.
2.7. Weighting of Evaluation Indicators
The weight values of nine indicators, including slope, engineering geological rock group, slope structure type, distance to road, topographic relief, distance to fault, distance to the water system, vegetation cover and profile curvature, are derived according to the above hierarchical analysis method and are detailed in Table 9.
3. Results
Based on the GIS platform, the grid layers assigned with information values were overlaid with weights to obtain a comprehensive index of landslide susceptibility, and according to the results, it can be seen that the comprehensive susceptibility index is approximately normally distributed (Figure 7).
After several experimental analyses, it is not reasonable to classify the susceptibility classes by the natural interval method. To facilitate the analysis, the information quantity values were normalized into [0, 1], and the normalized information quantity values were made into the information quantity cumulative frequency distribution curve (Figure 8). According to the required susceptibility degree of high, medium and low. Combined with the distribution curve distribution characteristics, the distribution curve has obvious inflection points at 0.64 and 0.84. Therefore, the landslide susceptibility degree of the study area is classified as “low-susceptibility degree” (0–0.64), “medium-susceptibility degree” (0.64–0.84) and “high-susceptibility degree” (0.84–1.0).
At the end of the model evaluation results, the results were validated. The working characteristic curve (ROC) is a measure of the prediction accuracy of the evaluation model, using the percentage of cumulative grids in the area with the highest to lowest information value to the total grids as the horizontal axis, and the percentage of cumulative points of geohazards in the corresponding information interval to the total number of geohazards as the vertical axis, and when the area under the ROC curve is closer to 1, it means that the more consistent with the real situation. According to the validation results, the area under the ROC curve is 0.81, indicating a good vulnerability prediction as shown in Figure 9.
According to the evaluation results, the area of the high-landslide-susceptibility zone in the study area is about 3388 km2, accounting for 6.51%; the area of the medium-susceptibility zone is about 14,554 km2, accounting for 27.96%; the area of the low-susceptibility zone is about 34.95 km2, accounting for 65.53% as shown in Table 10.
The susceptibility assessment map (Figure 10) shows that the high-vulnerability areas of geological hazards in the study area are mainly located on both sides of the main streams and first-class tributaries of the Jinshajiang River and in the urban development areas. The Baihetan Hydropower Station reservoir, Xiluodu reservoir and Xiangjiaba reservoir are mostly in the high- and medium-vulnerability areas, while the Wudongde reservoir is in the low- and medium-vulnerability areas.
4. Discussion
The geological disasters in the research area are the most densely populated areas on both sides of the Jinshajiang River and urban areas, which seriously threaten people’s lives and property safety, and also threaten the infrastructure of four cascade hydropower stations. According to the results of this vulnerability evaluation, the following suggestions are preliminarily put forward.
- 1.
- Strengthen early identification of hidden dangers of geological hazards.
Geological hazard exploration can find out the type, distribution and development characteristics of hidden danger points of geological hazards to master the deformation and damage mechanism and further judge the stability of geological hazards and provide a basis for engineering design. The geological hazard survey is the basis of monitoring and early warning and engineering governance design. The geological hazard survey needs to be carried out in the early stage of professional monitoring or engineering governance. The measures of monitoring and early warning can be adopted for the hidden danger points of geological hazards with great hazards and small dangers, and the necessary geological analysis models and reasonable calculation parameters can be provided for monitoring and early warning using geological hazard exploration, which lays a reliable foundation for setting effective early warning indicators.
Because geological disasters such as landslides, collapses and debris flows are mostly sudden, short in history, fierce and destructive, and mainly occur in mountainous areas with complex topographic conditions and inconvenient transportation, which brings a lot of inconvenience to the field investigation, the field investigation is difficult and the research area is wide and the natural conditions are bad. Remote sensing technology should be fully used for regular interpretation and identification of hidden dangers of ground disasters. In the region, optical remote sensing images are combined with radar data for analysis, and InSAR, PS-InSAR and other technologies are fully used to identify deformation areas, and high-resolution optical remote sensing data are combined to identify hidden dangers. In areas with a high risk of ground disasters, important infrastructure and population gathering areas, aerial image data of UAV are collected regularly, and time series data are identified by artificial visual interpretation and deep learning.
- 2.
- Combination of group survey and prevention with professional monitoring.
Mass survey and mass prevention is mass monitoring and mass prevention of geological disasters. The mass survey and mass prevention system are a series of disaster prevention systems, measures and other contents constructed to achieve mass survey and mass prevention. For the hidden dangers of ground disasters that have been discovered, special personnel shall be arranged to patrol regularly, professional monitoring shall be carried out on the hidden danger points that are more threatening and have no relocation conditions, and early warning thresholds shall be set according to the specific conditions of the hidden danger points to minimize the loss of people’s lives and property.
According to the characteristics of the development and distribution of geological disasters and the level of regional economic and social development, the prevention and control of geological disasters such as geological disaster risk investigation and evaluation, monitoring and early warning, risk aversion and relocation, danger removal and engineering management are planned as a whole and they are implemented step by step according to priorities and actively promoted. Emphasis will be placed on engineering governance in areas that threaten population gatherings such as market towns, where hidden dangers of geological hazards that threaten more than 50 people can be relocated, and hidden dangers of geological hazards that threaten less than 50 people will be given priority to relocation.
- 3.
- Strengthen publicity and training on mass geological hazard risks.
Strengthen publicity on geological disaster prevention and control in areas prone to geological disasters, and conduct training on geological disaster prevention and control knowledge and escape and avoidance exercises for key threat objects. Through the implementation of disaster reduction and prevention education, training, and publicity, we will establish a correct concept of disaster prevention and reduction among the masses, further enhance the awareness of disaster prevention, teach the masses how to identify disasters, understand disasters and establish a correct concept of risks, to improve residents’ crisis awareness, train residents’ self-rescue and life-saving skills and improve emergency response capabilities.
5. Conclusions
Geological disasters occur frequently in the study area, among which the main inducing factor of landslides is rainfall. According to the results of geological survey and statistical analysis, the formation of landslides caused by rainfall is mainly manifested in two aspects: first, continuous heavy rainfall saturates the slope and reduces the physical and mechanical properties; second, slope foot ditch erosion caused by rainfall. There is a strong distribution law along the river valley in the spatial distribution, and it is related to the rainfall height in the temporal distribution. Based on the analysis and evaluation of extreme precipitation trends in the study area, the vulnerability of the study area was evaluated by using the informational quantity model (AUC (area under the curve) = 0.81). The proportion of the areas with high incidence, medium incidence and low incidence in the study area was 6.51%, 27.96% and 65.53%, respectively.
The analysis of susceptibility evaluation results shows that the high-susceptibility areas of geological disasters in the study area are mainly distributed on both sides of the main stream of the Jinshajiang River and the first-level tributaries, and concentrated urban development areas. The areas along both sides of the main stream of the Jinshajiang River are mostly the medium-prone area except for the high-prone area. Most of the Baihetan Hydropower Station Reservoir Area, Xiluodu Reservoir Area and Xiangjiaba Reservoir Area are in the high- to medium-prone area to geological disasters, while the Wudongde Reservoir area is in the medium- to low-prone area to geological disasters.
The results of the susceptibility assessment and the distribution of hazard sites are a good fit. The high-susceptibility areas are mostly located in areas with strong topographic cuts. At the same time, there are more human activities such as road construction, residential land, and construction of industrial facilities. The topography is the main controlling factor for the occurrence of geological hazards, and human engineering activities and rainfall are the main triggering factors. Geographic information system (GIS) has strong spatial expression abilities and spatial analysis abilities. It can analyze the quantitative relationship between geographical phenomena and their influencing factors through GIS, and is widely used in geological hazard vulnerability assessment, environmental impact assessment, mineral potential assessment, etc. [41,42]. The spatial analysis function of ArcGIS is used to study the factors affecting the risk of geological hazards under extreme rainfall trends and to derive the risk zones through superposition analysis. The study will provide a reference basis for the planning of geological hazard prevention and control by formulating targeted mitigation strategies and geological hazard risk management measures to adapt to climate change in the Yangtze River Basin.
Author Contributions
Conceptualization, X.B.; methodology, Q.F.; software.; L.H.; validation, C.Z.; formal analysis, X.B. and C.Z.; investigation, L.H.; resources, C.Z.; writing—original draft preparation, X.B. and C.Z.; visualization, Q.F.; writing—review and editing, Y.D. and Y.H.; supervision, L.H.; funding acquisition, C.Z. All authors have read and agreed to the published version of the manuscript.
Funding
This research was funded by the Key Projects of Global Change and Response of Ministry of Science and Technology of China under Grant2020YFA0608203, and in part by Research and Demonstration of Key Technologies for Cultivated Land Remote Sensing Intelligent Monitoring 2023YFS0366, in part by China Three Gorges Corporation0704181, in part by Scientific Research Talent Fund of the Chengdu University of Information TechnologyKYTZ202272.
Institutional Review Board Statement
Not applicable.
Informed Consent Statement
Not applicable.
Data Availability Statement
The data presented in this study are available from the corresponding author. Send a request to corresponding author’s email, then you will receive the data.
Conflicts of Interest
The author declares no conflict of interest.
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Figure 1.
Time series of extreme precipitation events in the lower Jinshajiang River Basin.
Figure 2.
Typical regional extreme precipitation events time series. (Blue: Wudongde; Green: Baihetan; Purple: Xiluodu; Orange; Xiangjiaba).
Figure 2.
Typical regional extreme precipitation events time series. (Blue: Wudongde; Green: Baihetan; Purple: Xiluodu; Orange; Xiangjiaba).
Figure 3.
Distribution of geological hazards in the study area.
Figure 4.
Statistical chart of the months in which mudslides and landslides occurred: (a) frequency of mudslides by month; (b) frequency of landslides by month.
Figure 4.
Statistical chart of the months in which mudslides and landslides occurred: (a) frequency of mudslides by month; (b) frequency of landslides by month.
Figure 5.
Correlation analysis of landslides with monthly cumulative rainfall.
Figure 6.
Map of extreme rainfall trends in relation to the distribution of geological hazards.
Figure 7.
Distribution of the combined susceptibility index to landslides in the general survey area.
Figure 7.
Distribution of the combined susceptibility index to landslides in the general survey area.
Figure 8.
Frequency distribution of information volume.
Figure 9.
ROC curve of the results of the susceptibility assessment.
Figure 10.
Preliminary vulnerability assessment map (superimposed hazard points).
Table 1.
Definition of extreme precipitation classifications.
| Index | Definition | Unit |
|---|---|---|
| Maximum Continuous Drought Days (CDD) | Maximum consecutive days without precipitation per year | day |
| Maximum Continuous Rainfall Days (CWD) | Maximum continuous effective precipitation days per year | year |
| Maximum one-day precipitation (RX1day) | Maximum one-day precipitation per year | Mm |
| Maximum five-day precipitation (RX5days) | Maximum annual precipitation for five consecutive days | Mm |
| Heavy precipitation (R95P) | Total daily precipitation greater than 95% of the sub-sites in the base period per year | Mm |
| Extreme Heavy Precipitation (R99P) | Total daily precipitation greater than 99% of sub-sites in the base period per year | Mm |
| Rainstorm days (R50mm) | Number of days with daily precipitation of 50 mm or more per year | day |
| Number of days with moderate rain (R10mm) | Number of days with daily precipitation of 10 mm or more per year | day |
| Heavy rain days (R20mm) | Number of days with daily precipitation of 20 mm or more per year | day |
Table 2.
Linear trends in extreme precipitation events in the lower Jinshajiang River Basin, 1961–2020 (underlining indicates statistically significant through 95%) (units/year).
Table 2.
Linear trends in extreme precipitation events in the lower Jinshajiang River Basin, 1961–2020 (underlining indicates statistically significant through 95%) (units/year).
| rx1 Day | rx5 Day | r10 mm | r20 mm | r50 mm | cdd | cwd | r95p | r99t | |
|---|---|---|---|---|---|---|---|---|---|
| 1901–2020 | −0.02 | −0.01 | 0.00 | 0.01 | 0.00 | 0.05 | −0.02 | 0.31 | 0.02 |
Table 3.
Linear trends in extreme precipitation events for typical regions during 1961–2020. (Underlining indicates statistically significant by 95%) (units/10 years).
Table 3.
Linear trends in extreme precipitation events for typical regions during 1961–2020. (Underlining indicates statistically significant by 95%) (units/10 years).
| rx1 Day | rx5 Day | r10 mm | r20 mm | r50 mm | cdd | cwd | r95p | r99p | ||
|---|---|---|---|---|---|---|---|---|---|---|
| Precipitation | Wudongde | 0.04 | 0.06 | −0.01 | 0.00 | 0.01 | 0.03 | −0.02 | 0.68 | 0.03 |
| Baihetan | 0.22 | 0.21 | −0.02 | 0.01 | 0.01 | 0.08 | −0.03 | 0.87 | 0.63 | |
| Xiluodu | 0.04 | 0.14 | 0.02 | 0.02 | 0.01 | 0.06 | −0.01 | 0.74 | 0.17 | |
| Xiangjiaba | −0.33 | −0.38 | −0.01 | 0.00 | −0.01 | 0.03 | −0.02 | −0.74 | −0.68 | |
Table 4.
Statistics of geological disaster distribution in the research area.
| County Name | Collapse | Landslide | Debris Flow | Total |
|---|---|---|---|---|
| Butuo County | 8 | 34 | 35 | 77 |
| Cuiping District | 4 | 38 | 42 | |
| Dongchuan District | 20 | 76 | 96 | |
| Huidong County | 11 | 300 | 41 | 352 |
| Huili County | 19 | 185 | 32 | 236 |
| Huize County | 12 | 375 | 62 | 449 |
| Jinyang County | 24 | 122 | 50 | 196 |
| Leibo County | 59 | 98 | 44 | 201 |
| Ludian County | 4 | 63 | 15 | 82 |
| Luquan Yi and Miao Autonomous County | 13 | 108 | 20 | 141 |
| Ningnan County | 7 | 279 | 82 | 368 |
| Pingshan County | 34 | 212 | 10 | 256 |
| Qiaojia County | 18 | 35 | 31 | 84 |
| Renhe District | 4 | 40 | 1 | 45 |
| Shuifu City | 14 | 81 | 24 | 119 |
| Suijiang County | 22 | 98 | 2 | 122 |
| Wuding County | 11 | 60 | 25 | 96 |
| Syrian District | 11 | 45 | 56 | |
| Yongren County | 1 | 101 | 5 | 107 |
| Yongshan County | 1 | 239 | 20 | 260 |
| Yuanmou County | 52 | 30 | 82 | |
| Zhaoyang district | 18 | 58 | 14 | 90 |
| Total | 295 | 2643 | 619 | 3557 |
Table 5.
Preliminary selection of landslide susceptibility evaluation factors.
| Firstly Factors | Terrain and Landscape Conditions | Geological Formations | Other | ||||||||
|---|---|---|---|---|---|---|---|---|---|---|---|
| Secondary factors | Elevation | Slope | Topographic relief | Plane curvature | Profile curvature | Fracture zone distance | Engineering Geology Rock Group | Sloping structures | Distance from road | Distance from the water system | Vegetation cover |
Table 6.
|R| Table of characterization relevance.
| Range of Values | Degree of Relevance | |
|---|---|---|
| |R| | <0.4 | Low level of relevance |
| 0.4~0.7 | Significant correlation | |
| 0.7~1.0 | Height related |
Table 7.
Correlation matrix of evaluation factors.
| G1 | G2 | G3 | G4 | G5 | G6 | G7 | G8 | G9 | G10 | |
|---|---|---|---|---|---|---|---|---|---|---|
| G1 | 1.000 | |||||||||
| G2 | 0.124 | 1.000 | ||||||||
| G3 | 0.032 | 0.106 | 1.000 | |||||||
| G4 | 0.041 | 0.092 | 0.095 | 1.000 | ||||||
| G5 | 0.027 | 0.065 | 0.085 | 0.006 | 1.000 | |||||
| G6 | 0.006 | 0.005 | 0.121 | 0.210 | −0.054 | 1.000 | ||||
| G7 | 0.050 | 0.537 | 0.066 | 0.056 | −0.018 | 0.004 | 1.000 | |||
| G8 | 0.028 | 0.096 | 0.690 | 0.082 | 0.002 | 0.086 | −0.048 | 1.000 | ||
| G9 | 0.009 | 0.099 | 0.060 | 0.038 | −0.045 | −0.148 | −0.098 | −0.050 | 1.000 | |
| G10 | 0.028 | 0.045 | 0.061 | 0.012 | 0.356 | −0.020 | 0.010 | −0.257 | 0.000 | 1.000 |
G1 faults, G2 engineering geological rock formations, G3 topographic relief, G4 roads, G5 profile curvature; G6 water systems, G7 slope structure, G8 slope, G9 plan curvature, G10 vegetation cover.
Table 8.
Statistics on the amount of information on the evaluation indicators for the evaluation of the vulnerability of landslides.
Table 8.
Statistics on the amount of information on the evaluation indicators for the evaluation of the vulnerability of landslides.
| Evaluation Factors | Serial Number | Factor Grading | Disaster Area/km2 | Sub-Area AREA/km2 | Amount of Information |
|---|---|---|---|---|---|
| Slope | 1 | 0~15° | 0.37 | 1127.84 | −0.66 |
| 2 | 15~30° | 1.63 | 3168.56 | −0.20 | |
| 3 | 30~45° | 2.70 | 3421.85 | 0.22 | |
| 4 | 45~60° | 0.54 | 591.24 | 0.37 | |
| 5 | >60° | 0.02 | 38.61 | 0.00 | |
| Engineering Geology Rock Group | 1 | Hard | 0.71 | 3210.85 | −1.05 |
| 2 | Semi-hard | 3.16 | 4269.64 | 0.16 | |
| 3 | Soft rock | 1.04 | 197.27 | 2.13 | |
| 4 | Earth body | 0.34 | 197.30 | 1.02 | |
| 5 | Rock | 0.01 | 472.99 | −3.87 | |
| Distance from the fault | 1 | 200 m | 0.43 | 321.82 | 0.75 |
| 2 | 500 m | 0.81 | 436.48 | 1.07 | |
| 3 | 1000 m | 0.60 | 617.94 | 0.44 | |
| 4 | 2000 m | 1.08 | 984.20 | 0.55 | |
| 5 | >2000 m | 2.35 | 5987.61 | −0.48 | |
| Type of slope structure | 1 | Downward slope | 0.87 | 1299.63 | 0.05 |
| 2 | Down-oblique slopes | 0.98 | 1291.19 | 0.19 | |
| 3 | Lateral slopes | 1.59 | 2538.58 | −0.01 | |
| 4 | Back-oblique slope | 0.70 | 1288.95 | −0.15 | |
| 5 | Reverse slope | 0.78 | 1264.44 | −0.03 | |
| 6 | Block slopes | 0.01 | 472.55 | −3.87 | |
| 7 | Soil slopes | 0.35 | 191.48 | 1.06 | |
| Vegetation cover | 1 | 0–0.1 | 0.03 | 457.91 | −2.35 |
| 2 | 0.1–0.3 | 0.14 | 258.76 | −0.15 | |
| 3 | 0.3–0.5 | 0.43 | 329.27 | 0.73 | |
| 4 | 0.5–0.7 | 0.73 | 971.89 | 0.17 | |
| 5 | 0.7–1 | 3.94 | 6330.22 | −0.01 | |
| Distance from the water system | 1 | 20 m | 0.11 | 202.75 | −0.13 |
| 2 | 50 m | 0.27 | 299.26 | 0.34 | |
| 3 | 100 m | 0.59 | 488.38 | 0.65 | |
| 4 | 200 m | 1.27 | 944.56 | 0.75 | |
| 5 | >200 m | 3.03 | 6413.09 | −0.29 | |
| Distance from the road | 1 | 20 m | 0.25 | 28.97 | 2.62 |
| 2 | 50 m | 0.38 | 42.09 | 2.67 | |
| 3 | 100 m | 0.68 | 67.57 | 2.76 | |
| 4 | 200 m | 1.35 | 128.34 | 2.82 | |
| 5 | >200 m | 2.61 | 8081.08 | −0.67 | |
| Profile Curvature | 1 | 14.53 | 1.65 | 2879.984375 | −0.10 |
| 2 | 29.06 | 1.81 | 2748.66 | 0.04 | |
| 3 | 43.59 | 1.09 | 1663.913125 | 0.04 | |
| 4 | 58.12 | 0.52 | 777.43875 | 0.06 | |
| 5 | 72.65 | 0.20 | 278.103125 | 0.11 | |
| Terrain ups and downs degree | 1 | 48 | 0.04 | 421.76125 | −1.78 |
| 2 | 104 | 1.39 | 3102.8325 | −0.33 | |
| 3 | 180 | 3.29 | 4082.47625 | 0.25 | |
| 4 | 312 | 0.52 | 728.066875 | 0.14 | |
| 5 | >312 | 0.03 | 12.915 |
Table 9.
Statistics on the weighting of landslide evaluation factors.
| Evaluation Indicators | Slope | Engineering Geology Rock Group | Distance from the Fault | Sloping Structures | Vegetation Cover |
|---|---|---|---|---|---|
| Weighting | 0.2748 | 0.1664 | 0.0517 | 0.2209 | 0.0398 |
| Evaluation indicators | Distance from the water system | Distance from the road | Profile curvature | Topographic relief | |
| Weighting | 0.027 | 0.027 | 0.0914 | 0.101 |
Table 10.
Susceptibility and hazard statistics.
| Low Ease of Issuance | Medium Ease of Issuance | High Ease of Issuance | ||||||
|---|---|---|---|---|---|---|---|---|
| Area (km2) | Percentage of | Disaster Sites (Locations) | Area (km2) | Percentage of | Disaster Sites (Locations) | Area (km2) | Percentage of | Disaster Sites (Locations) |
| 34,095 | 65.53% | 659 | 14,554 | 27.96% | 608 | 3388 | 6.51% | 239 |
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Bi, X.; Fan, Q.; He, L.; Zhang, C.; Diao, Y.; Han, Y. Analysis and Evaluation of Extreme Rainfall Trends and Geological Hazards Risk in the Lower Jinshajiang River. Appl. Sci. 2023, 13, 4021. https://doi.org/10.3390/app13064021
AMA Style
Bi X, Fan Q, He L, Zhang C, Diao Y, Han Y. Analysis and Evaluation of Extreme Rainfall Trends and Geological Hazards Risk in the Lower Jinshajiang River. Applied Sciences. 2023; 13(6):4021. https://doi.org/10.3390/app13064021
Chicago/Turabian StyleBi, Xiaojia, Qiang Fan, Lei He, Cunjie Zhang, Yifei Diao, and Yanlin Han. 2023. "Analysis and Evaluation of Extreme Rainfall Trends and Geological Hazards Risk in the Lower Jinshajiang River" Applied Sciences 13, no. 6: 4021. https://doi.org/10.3390/app13064021
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