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Article

Research on Water Content Spatial Distribution Pattern of Fine—Grained Sediments in Debris Flow—Taking Beichuan Debris Flow as a Case

by
Qinjun Wang
1,2,3,4,5,*,
Jingjing Xie
1,2,3,
Jingyi Yang
1,2,3,
Peng Liu
1,2,3,
Wentao Xu
1,2,3,
Boqi Yuan
1,2,3 and
Chaokang He
1,2,3
1
International Research Center of Big Data for Sustainable Development Goals, Beijing 100094, China
2
Key Laboratory of Digital Earth Science, Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100094, China
3
College of Resources and Environment, Yanqi Lake Campus, University of Chinese Academy of Sciences, Beijing 101408, China
4
Kashi Aerospace Information Research Institute, Kashi 844199, China
5
Key Laboratory of the Earth Observation of Hainan Province, Hainan Aerospace Information Research Institute, Sanya 572029, China
*
Author to whom correspondence should be addressed.
Water 2024, 16(18), 2640; https://doi.org/10.3390/w16182640
Submission received: 15 August 2024 / Revised: 13 September 2024 / Accepted: 13 September 2024 / Published: 17 September 2024
(This article belongs to the Special Issue Coast Sediment Dynamics: Historical Development, Current Situation and Perspectives)
Browse Figures
Review Reports Versions Notes

Abstract

:
Due to being lightweight, fine-grained sediments easily flow with water and thus amplify the destructive effect of debris flow hazards. In such hazards, water content and shear strength are key inter-controlled factors relating to the stability of fine-grained sediments and thus control the density, scale, and danger of debris flow hazards. Although the correlation between water content and sediment stability has been studied, there are still some issues to be solved: what is the changing trend of shear strength with increasing water content? What is the water content spatial distribution pattern of fine-grained sediments in debris flow? What is the role/impact of this pattern on debris flow hazards prevention? Therefore, the objective of this research is to show the spatial distribution pattern of water content and establish a correlation between the water content and the shear strength of fine-grained sediments to provide a scientific basis for debris flow hazard prevention. Taking the Beichuan debris flow for our study, with a length of 37.6 km, and a 341 km2 study area, the results show that (1) the average water content shows an increasing trend, from 9.9% in the upstream of the river (SP01–SP05) to 21.7% in the downstream of the river (SP13–SP15). (2) When unsaturated, the correlation between the water content and shear strength is determined by combining the cohesion, normal stress, and internal friction angle; when saturated, the water content is negatively correlated with shear strength. (3) Water content and shear strength are the key inter-controlled factors relating to the stability of fine-grained sediments, and the water content distribution pattern of this research indicates the key locations that require attention: locations with high water content in the downstream river or with high curvature, which is of some significance for debris flow hazard prevention.

1. Introduction

Fine-grained sediments in debris flows refer to sediments with a grain size of less than 2 mm. With the characteristics of a small grain size, light weight, and easy migration, they not only increase the density of debris flow but also enhance its destructive power, thus amplifying the destructive effect and bringing huge challenges to debris flow prevention. As we know, the higher the initial water content is, the shorter the time it takes for a debris flow to occur [1]. Therefore, the water content of fine-grained sediments seriously affects their stability. Studying the spatial distribution patterns of the water content relating to fine-grained sediments in a debris flow is of great significance for debris flow hazard prevention.
Due to the importance of water content to hazard prevention, most research focuses on its correlation with shear strength, slope stability, inversion methods, etc. (1) There are two viewpoints on the correlation between water content and shear strength: one is that shear strength first increases and then decreases with increasing water content [2,3,4], and the other is that shear strength gradually decreases with increasing water content [5,6,7,8,9,10,11,12,13,14]. (2) There are also two viewpoints on the correlation between water content and cohesion: one is that the cohesion tends to increase first and then decrease with increasing water content [2,14,15,16,17,18,19], and the other is that cohesion gradually decreases with increasing water content [9,20,21,22,23,24]. (3) There is only one viewpoint on the correlation between water content and the internal friction angle: with increasing water content, the internal friction angle gradually decreases [3,9,12,14,15,16,17,18,19,20,21,23,24]. (4) As for the correlation between water content and slope stability, research shows that when saturated, soil structural stability decreases with increasing water content [5,24,25,26,27,28,29,30]. Differences in water ingress rates triggering a critical rise in pore pressures provide insights into the varying rainfall characteristics that are likely to trigger debris flows in different soil types [31]. Both dry and humid deposits located at the highest and lowest elevations from a river’s surface are more easily eroded than intermediate deposits with medium moisture [32]. (5) As to the water content inversion methods used, this paper shows that it is possible to invert the water content using remote sensing images or hydrological models, such as the Normalized Difference Water Index (NDWI), the Modified Normalized Difference Water Index (mNDWI), frequency–magnitude distributions, and empirical cumulative distribution functions (ECDFs) [33,34,35,36,37,38,39]. The results show that considering the factors influencing water and soil reflectivity can support decision-makers in identifying high-risk debris flow locations to develop debris flow hazard alarm systems [40].
Although the correlation between water content and sediment stability has been studied, there are still some issues to be solved: (1) Which viewpoint on the above correlation between water content and shear strength is correct? (2) What role do the water content spatial distribution patterns of fine-grained sediments play in debris flow? What is the role/impact of this pattern on debris flow hazard prevention?
To solve above problems, this research will conduct relevant experiments to summarize the water content spatial distribution patterns of fine-grained sediments in a debris flow and analyze the potential reasons to provide a scientific basis for debris flow hazard prevention.

2. Study Area

As shown in Figure 1, the study area is located in Beichuan county, Sichuan province, China, with the geographic coordinates of 104°14′–104°33′28.5″ E, 31°47′55.4″–31°53′25.25″ N. It is 31 km long from east to west and 11 km wide from north to south, thus covering an area of 341 km2. Since the Wenchuan earthquake took place on 12 May 2008, there have been six very serious debris flow hazards involving the broken strata caused by the earthquake and loose accumulations on the surface (such as 20 August 2019, 11 July 2018, 28 July 2016, 6 July 2013, 17 August 2011, and 24 September 2008), which resulted in a large number of houses being destroyed and tens of thousands of people leaving their hometowns [41].
The climate of the study area is the subtropical humid monsoon climate of the Sichuan Basin with an annual average temperature of 15.6 °C and average annual rainfall of 1399.1 mm, among which, 70% of the annual rainfall is concentrated from June to September [42,43], thus leading to the climate characteristics of abundant precipitation, easy flooding in summer, continuous rainfall and insufficient sunlight in autumn.
As shown in Figure 2, the terrain of the study area is in a mountainous area, whose altitude is high in the northwest and low in the southeast, leading to streams converging on the Jianjiang River to form a huge water volume in case of rainstorms, thus triggering a large-scale debris flow easily. The following rocks are widely developed in the study area: limestone of Carboniferous strata; limestone, sand stone and shale of Devonian strata; limestone and shale of Permian strata; shale, sand stone and limestone of Silurian strata; shale, argillite and aleurolite of Tertiary strata; shale, siliceous rock and dolomite of Cambrian strata. They are easy to be broken and flow with water to form a large-scale debris flow [44].

3. Materials and Methods

As shown in Table 1, to show the water content spatial distribution pattern of fine-grained sediments in the study area, we obtained GaoFen-2 (GF-2) remote sensing images with a spatial resolution of 1 m and ASTER DEM data with a spatial resolution of 30 m. Based on which, we designed 15 sampling points of fine-grained sediments in the study area according to the “Specification of Comprehensive Survey for Landslide, Landslip and Debris Flow(DZ/T 02161-2014)” [45].

3.1. Background Data

Launched on 19 August 2014, Gaofen-2 satellite (GF-2) is the first civilian optical remote sensing satellite independently developed by China with a spatial resolution of 1 m. As shown in Table 2, it is equipped with a 1 m high-resolution panchromatic camera and a 4 m multi-spectral camera. With high positioning accuracy and fast attitude maneuverability, it effectively improves the comprehensive observation efficiency and marks China’s remote sensing satellites entering a “high-resolution era”.
GF-2 remote sensing image is acquired from the Land Satellite Remote Sensing Application Center, China to provide the background image for designing sampling points. From these, debris flow hazards are extracted, and the locations of 15 sampling points are determined with the characteristics of non-vegetation, gray color and near to rivers on the image.
With a spatial resolution of 30 m, ASTER DEM data are acquired from the Ministry of International Trade and Industry, Japan, to provide background terrain info to analyze the causes of debris flow formation. Contour lines are drawn out to show the terrain characteristics of the study area: it is about 2000 m in altitude in the northwest and 600 m in the southeast, thus leading to the Jianjiang River flow from the western study area to the eastern study area.
Based on GF-2 remote sensing image and ASTER DEM data, debris flow hazards are investigated from the aspects of geological conditions (terrain, lithology, fault, earthquake, climate, vegetation and human activities in debris flow formation area, running area and accumulated area), formation characteristics (type, water source, sediments stability, accumulated area and history debris flow hazard events), triggering factors (rainfall, melted snow or glacier, ground water), danger (scale, accumulation, dangerous targets, dangerous areas, future trend/development of debris flow), and prevention and controlled measures (status of debris flow exploration, monitoring and engineering protection measures) according to the “Specification of Comprehensive Survey for Landslide, Landslip and Debris Flow (DZ/T 02161-2014)” [45].
The results of debris flow investigation show that locations in the downstream Jianjiang River with high water content or with high curvature are more dangerous than other locations. They need prevention and controlled measures before summer. For the abundant precipitation in summer, the water flow of the Jianjiang River is very strong due to the convergence of tributaries from neighboring mountains. Furthermore, there are a large amount of accumulations along the river beds with high curvature in downstream river s(such as SP09 in Figure 1), which makes it easily flooded to form debris flow hazards.

3.2. Sampling

From 19 to 25 March 2021, 200 samples were collected from these 15 sampling points (about 13 samples per sampling points) using a ring cutter according to “Soil Quality-Guidance on Sampling Techniques (GB/T 36197-2018)” [46], whose details are described from Table 3, Table 4 and Table 5. Causes of the measured data variability are “intrinsic” to the place from which the samples were extracted. Because the water content, grain size and shear strength are tested using standard instruments with corresponding measurement methodology, the results of the tested cohesion–permeability coefficient from the same experiment are published in [39] and in [41]. With a depth of about 5 cm, each sample is 600 mL in volume and about 1 kg in weight. After sampling, the sample is immediately put into a plastic bag, sealed and then put into a cotton bag for transportation. At the same time, GPS (Global Positioning System) is used to record the location of sampling points and describe them in detail. For the small area of fine-grained sediments, we can only collect one sample at the SP14 sampling point.
According to the cohesion box diagram (Figure 3) and on-site photo, we establish the soil hardness classification criteria in the study area as follows.

3.3. Water Content Measurement

The water content of each sample was measured using the electric constant temperature drying oven (the Oven, Figure 4) with a temperature sensitivity of ±1 °C [47,48,49,50]. Based on which, the water content was the averaged water content of samples near the sampling point.
Steps for water content measurement are described as follows.
(1)
Part of the sample weighting m0 g is loaded into an aluminum box and then placed in the oven;
(2)
Turn on the switch, and set the temperature to be 110 °C. When the temperature rises up to the set temperature, the oven will enter a constant temperature mode;
(3)
Maintain the constant temperature for no less than 8 h;
(4)
After drying, take out the sample and weight it as m1 g;
(5)
Calculate the water content of the sample as W = (m0/m1 − 1) × 100%.

3.4. Shear Strength Measurement

Shear strength was measured using the ZJ strain-controlled direct shear instrument (SDJ-1, Figure 5) with the controlled shear rate between 0.02 and 2.4 mm/min [47,48,49,50]. Based on which, the shear strength of the sampling point was the averaged shear strength of samples near the point.
Steps for shear strength measurement are described as follows.
(1)
Rotate the stabilizer to make the lever level;
(2)
Make part of the soil sample using a ring cutter; then push it into the shear box;
(3)
Close the cutting box cover and press the pressure screw on the cover;
(4)
Tighten the force-measured ring and set the dial gauge to zero;
(5)
Add weights (1.275 kg, 2.55 kg, representing shear normal stresses of 50 kPa and 100 kPa, respectively) to the lever to obtain shear normal stresses ranging from 50 to 300 kPa.
(6)
Push the switch to “Shear” to start cutting and observe the force-measured ring. Record the reading when the ring pointer no longer moves forward or starts moving backward;
(7)
Push the switch to “Reverse” to end cutting; then, take out the shear box and pour out the damaged soil sample. Repeat steps (2)–(6), conduct 4 experiments on each sample (loading weights of 1.275 kg, 2.55 kg, 5.1 kg, and 7.65 kg in sequence, representing shear normal stresses (σ) of 50 kPa, 100 kPa, 200 kPa, and 300 kPa respectively), and record the force-measured ring reading (R) of each experiment. Then, the shear stress τ (kPa) of each experiment can be obtained by the product of the coefficient (f) and the reading (R) of the force-measured ring as τ = f × R.
(8)
Fit a straight line between normal stress σ and shear stress τ by taking σ as the horizontal axis and τ as the vertical axis. Here, the intercept of the straight line is the cohesive force c (kPa), and the inclination angle is the effective internal friction angle φ (°) of the sample.
Experimental results of the average cohesion and average effective internal friction angle are shown in Table 6.

3.5. Grain Size Measurement

As shown in Figure 6, the grain size of each sample was measured using the Microtrac S3500 laser grain size analyzer produced by Microtrac Inc., Montgomeryville, PA, USA with a precision of ±0.6% [47,48,49,50]. Based on which, the grain size of each sampling point was the averaged grain size of samples near the point.
Steps for grain size measurement are described as follows.
(1)
Turn on the computer, the S3500 host and sample delivery controller;
(2)
Open the Microtrac FLEX software on the desktop;
(3)
Select “dry” mode for this measurement in the Tools menu of hardware configuration;
(4)
Select the instrument type in the measurement tab and open the testing page;
(5)
In the “File” menu, select the established data file or create a new data file for saving data;
(6)
Click the “Set up” button to set the sample ID and parameters;
(7)
Lay flat the sample powder in the sampler tray;
(8)
Click the “AUTO” button to start automatic measurement;
(9)
After measurement, the software will automatically save data to the established file and then clean the pipeline. After completing the measurement, turn off the instrument and computer.

4. Results and Discussion

Based on the measured data, the spatial distribution pattern of the average water content is shown in Figure 7. Here, we can see the following:
(1)
The average water content of fine-grained sediments in the debris flow shows an increasing trend from upstream (9.9% in SP01–SP05) to downstream (21.7% in SP13–SP15) with a standard deviation of 7.28%, indicating the relatively high groundwater level in the downstream river.
(2)
When unsaturated, the correlation between the average water content and the average shear strength is determined by a combination of cohesion, normal stress, and internal friction angle.
The correlation between the average water content and average cohesion is shown in Figure 8 with a correlation coefficient of 0.56, a standard deviation of 2.63 and X variable’s p-value of 0.03, showing that the average cohesion is significantly correlated with the average water content at more than a 95% confidence level. It just shows the correlation significance between average water content and average cohesion and thus does not require reporting the standard error associated with the means.
From Figure 8, we can see that when unsaturated, the average water content is positively related to the average cohesion with a correlation coefficient of 0.56. It is mainly because cohesion is the mutual attraction between adjacent particles within the same materials (such as electrostatic attraction, van der Waals force, bonding force between particles, and valence bonds at indirect contact points of particles). When unsaturated, with the increase in water content, a water film is formed between fine-grained particles, and the product of matrix suction and water film area increases, leading to an increase in the tensile force and the formation of aggregates in the clay, thus making the cohesive force increase. However, when saturated, a thick water film is formed around the fine-grained particles, increasing the distance between particles, resulting in a smaller product of matrix suction and water film area between particles, which decreases the pulling force and results in less cohesion [17].
The effective internal friction angle indicates the frictional force between fine-grained sediments particles, such as the surface friction force, the embedding and interlocking forces between particles. With the increase in water content, the matrix suction between particles weakens, reducing the surface friction and bite force between particles, and thus reducing the effective internal friction angle [17,19].
According to shear strength formula
τ = c + σtanφ
where τ is shear strength, c is cohesion, σ is normal stress, and φ is internal friction angle, we simulated the correlation between the water content and shear strength of unsaturated fine-grained sediments with the increase in normal stress. The results are shown in Figure 9.
From Figure 9, we can see that with the increase in normal stress, the simulated correlation coefficient (R) between the water content and shear strength increases slowly when the normal stress is less than 80 kPa, while it decreases sharply when the normal stress is more than 80 kPa. It indicates that there is a normal stress threshold for the shear strength of unsaturated fine-grained sediments. When the normal stress is less than the threshold, the shear strength increases slowly with the water content, while when the normal stress is more than the threshold, the shear strength tends to be less or have no correlation with the water content. As shear strength is the key parameter of fine-grained sediments stability, it also indicates that the fine-grained sediments tend to be unstable when normal stress is more than its threshold. We believe that the normal stress thresholds are different at other sites, which can be determined by the same methodology as that used in Section 4 of this paper.
Therefore, when unsaturated, the correlation between water content and shear strength is determined by the combination of cohesion, normal stress, and internal friction angle rather than a single positive or negative correlation. This is mainly because when unsaturated, the water content is positively correlated with cohesion, while it is negatively with internal friction angle [17,18]. As shown by the shear strength formula (1), the normal stress likes a weight whose value determines the positive or negative correlation between the water content and shear strength. Furthermore, it also explains two viewpoints presented in this paper: one viewpoint is that the water content is positively correlated with their shear strength, while the other indicates the opposite. The results of this research indicate that when the normal stress is less than the threshold, the shear strength increases slowly with water content, while when the normal stress is more than the threshold, the shear strength tends to be less or have no correlation with the water content.
However, when fine-grained sediments are saturated, the average water content is negatively correlated with its shear strength. This is mainly because when saturated, with the increase in water, the thickening of the water film between fine-grained particles leads to a decrease in cohesion and internal friction angle, resulting in the lower shear strength [19,41,47]. Furthermore, with the increase in water content, the internal pressure of fine-grained sediments becomes higher. When the pressure is more than the maximum shear strength, the disintegration and flow of fine-grained sediments will happen to provide materials for debris flows [15,17,33].
Because shear strength controls the stability of fine-grained sediments, the study area is safe with small rainfall, while it is danger with heavy rainfall. The danger increases from upstream to downstream of the river, drawing people to pay more attention to locations with high water content, such as SP09 and SP14. Therefore, the results point out key locations for debris flow hazard prevention: locations with high water content in downstream rivers or with high curvature.

5. Conclusions

By studying the water content spatial distribution pattern of fine-grained sediments in debris flow and its correlation with shear strength, we can draw the following conclusions:
(1)
The water content of fine-grained sediments in debris flow shows a linear upward trend from the upstream to the downstream of the river. Due to the climate, terrain and rock types in the study area, the amount of water gradually rises from the upstream to the downstream of the river, leading to high water content in locations with downstream rivers (SP14) or with high curvature (SP09).
(2)
When fine-grained sediments are unsaturated, the correlation between the water content and shear strength is determined by a combination of cohesion, normal stress, and internal friction angle. However, when fine-grained sediments are saturated, the water content is negatively correlated with shear strength.
Therefore, the water content distribution pattern of this research points out the key locations needing to be paid more attention to: locations with high water content in downstream rivers or with high curvature, which is of some significance for debris flow hazard prevention in the future. It can also contribute to the similar conditions worldwide, such as investigating the relationship between water infiltration and debris flow development, detecting soil moisture as an indicator to predict high-risk debris flow areas, or to calculate materials supplied to stream networks when planning erosion control, etc. [33,51,52].

Author Contributions

Conceptualization, Q.W. and J.X.; methodology, Q.W., J.X., J.Y. and P.L.; validation, Q.W., J.X. and P.L.; formal analysis, Q.W. and J.X.; investigation, Q.W., J.X., P.L., W.X., B.Y. and C.H.; data curation, J.X., J.Y. and P.L.; writing—original draft preparation, Q.W., J.X. and J.Y.; writing—review and editing, Q.W.; visualization, W.X.; supervision, B.Y.; project administration, Q.W.; funding acquisition, Q.W. All authors have read and agreed to the published version of the manuscript.

Funding

This research was funded in part by the National Natural Science Foundation of China (grant number 42071312), the National Key R&D Program (grant number 2021YFB3900503), and the Second Tibetan Plateau Scientific Expedition and Research (STEP) (grant number 2019QZKK0806).

Data Availability Statement

All data generated or analyzed during this research are included in this research.

Conflicts of Interest

The authors declare no conflicts of interest.

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Water 16 02640 g001
Figure 1. Study area (left) and its location in western China (right).
Figure 1. Study area (left) and its location in western China (right).
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Figure 2. Geological map of the study area.
Figure 2. Geological map of the study area.
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Figure 3. Average cohesion box diagram.
Figure 3. Average cohesion box diagram.
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Figure 4. Electric constant temperature drying oven.
Figure 4. Electric constant temperature drying oven.
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Figure 5. Principle of the ZJ strain-controlled direct shear instrument.
Figure 5. Principle of the ZJ strain-controlled direct shear instrument.
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Figure 6. Microtrac S3500 laser grain size analyzer.
Figure 6. Microtrac S3500 laser grain size analyzer.
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Figure 7. Spatial distribution pattern of the average water content with error bars (dotted line is the trend line).
Figure 7. Spatial distribution pattern of the average water content with error bars (dotted line is the trend line).
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Figure 8. Correlation between the average water content and average cohesion in unsaturated fine-grained sediments (dotted line is the trend line).
Figure 8. Correlation between the average water content and average cohesion in unsaturated fine-grained sediments (dotted line is the trend line).
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Figure 9. Simulated correlation coefficient between water content and shear strength (R) with the increase in normal stress in unsaturated fine-grained sediments.
Figure 9. Simulated correlation coefficient between water content and shear strength (R) with the increase in normal stress in unsaturated fine-grained sediments.
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Table 1. Data and equipment.
Table 1. Data and equipment.
MaterialsData/EquipmentManufacturer/ProviderFunction
Remote sensing imagesGaofen-2 (GF-2)Land Satellite Remote Sensing Application Center, Beijing, ChinaDesign sampling points according to standard DZ/T 02161-2014 [45]
Digital Elevation Model (DEM)Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER)Ministry of International Trade and Industry, Tokyo, JapanAcquire terrain info to analyze the causes of debris flow formation
Fine-grained sedimentsRing knife (200 mL)Longnian Hardware Tools Store, Nanjing, Jiangsu, ChinaCollect samples according to standard GB/T 36197-2018 [46]
Water contentElectric constant temperature drying ovenShanghai-southern Electric Furnace Oven Factory, Shanghai, ChinaDetermine water content of each sample
Shear strengthZJ strain-controlled direct shear instrumentNanjing soil instrument factory Company Limited (Co., Ltd.), Nanjing,
Jiangsu, China		Determine shear strength of each sample
Grain sizeMicrotrac S3500 laser grain size analyzerMicrotrac MR B, Montgomeryville,
PA, USA		Determine grain size of each sample
Table 2. GF-2 parameters.
Table 2. GF-2 parameters.
CameraBandWavelength
(um)		Spatial Resolution (m)Scan Width (km)Revisit Cycle
(d)
Panchromatic camera10.45~0.901455
Multi-spectral camera20.45~0.524
30.52~0.59
40.63~0.69
50.77~0.89
Table 3. Parameters of each sampling point.
Table 3. Parameters of each sampling point.
Sampling PointAverage Water Content (%)Average Water Standard Deviation (%)Average Grain Size (um)Average Grain Size Standard Deviation (um)
SP0119.982.8827.1815.51
SP027.022.4721.409.93
SP036.831.4421.9319.64
SP048.001.2828.1614.12
SP057.680.4716.001.74
SP065.560.4515.511.49
SP0711.381.0722.359.47
SP0812.030.8620.0310.07
SP0924.614.4512.0311.91
SP1022.791.5319.4516.71
SP1114.291.7244.4337.83
SP1211.770.8917.644.22
SP1320.263.9519.465.78
SP1428.57/52.19/
SP1516.360.3328.0514.80
Table 4. Soil hardness classification criteria in the study area.
Table 4. Soil hardness classification criteria in the study area.
ClassificationMinimum Average Cohesion (kPa)Maximum Average Cohesion (kPa)
hardened silty loam23.9
hardened-loose silty loam2323.9
loose silty loam 23
Table 5. Samples and their description.
Table 5. Samples and their description.
Sampling PointsDescriptionOn-Site Photo and Its Number
SP01With the average water content of 19.97%, the average grain size of 27.18 um and the average cohesion of 30.6 kPa, SP01 is a hardened silty loam sample.Water 16 02640 i001
567
SP02With the average water content of 7.02%, the average grain size of 21.40 um and the average cohesion of 23.9 kPa, SP02 is a hardened-loose silty loam sample.Water 16 02640 i002
563
SP03With the average water content of 6.83%, the average grain size of 21.93 um and the average cohesion of 21.9 kPa,, SP03 is a loose silty loam sample.Water 16 02640 i003
560
SP04With the average water content of 8.00%, the average grain size of 28.16 um and the average cohesion of 22.1 kPa,, SP04 is a loose silty loam sample.Water 16 02640 i004
557
SP05With the average water content of 7.68%, the average grain size of 16 um and the average cohesion of 22.9 kPa, SP05 is a loose silty loam sample.Water 16 02640 i005
553
SP06With the average water content of 5.56%, the average grain size of 15.51 um and the average cohesion of 19.2 kPa, SP06 is a loose silty loam sample.Water 16 02640 i006
549
SP07With the average water content of 11.38%, the average grain size of 22.34 um and the average cohesion of 21.2 kPa,, SP07 is a loose silty loam sample.Water 16 02640 i007
545
SP08With the average water content of 12.03%, the average grain size of 20.03 um and the average cohesion of 20.9 kPa, SP08 is a loose silty loam sample.Water 16 02640 i008
541
SP09With the average water content of 24.61%, the average grain size of 12.03 um and the average cohesion of 22.8 kPa, SP09 is a loose silty loam sample.Water 16 02640 i009
571
SP10With the average water content of 22.79%, the average grain size of 19.45 um and the average cohesion of 23.3 kPa, SP10 is a hardened-loose silty loam sample.Water 16 02640 i010
536
SP11With the average water content of 14.29%, the average grain size of 44.43 um and the average cohesion of 20.1 kPa, SP11 is a loose silty loam sample.Water 16 02640 i011
516
SP12With the average water content of 11.77%, the average grain size of 17.64 um and the average cohesion of 22.3 kPa,, SP12 is a loose silty loam sample.Water 16 02640 i012
518
SP13With the average water content of 20.26%, the average grain size of 19.46 um and the average cohesion of 21.6 kPa, SP13 is a loose silty loam sample.Water 16 02640 i013
522
SP14With the average water content of 28.57%, the average grain size of 52.19 um and the average cohesion of 29.1 kPa,, SP14 is a hardened silty loam sample.Water 16 02640 i014
527
SP15With the average water content of 16.36%, the average grain size of 28.05 um and the average cohesion of 22.9 kPa, SP15 is a loose silty loam sample.Water 16 02640 i015
531
Table 6. Average cohesion and average effective internal friction angle of each sampling point.
Table 6. Average cohesion and average effective internal friction angle of each sampling point.
Sampling PointAverage Cohesion (c/kPa)Average Effective Internal Friction Angle (φ/°)
SP0130.6019.25
SP0223.9419.34
SP0321.8920.22
SP0422.1420.56
SP0522.9119.99
SP0619.2021.71
SP0721.2020.20
SP0820.8520.52
SP0922.8120.93
SP1023.3220.43
SP1120.1020.49
SP1222.3420.51
SP1321.6020.01
SP1429.0818.91
SP1522.9120.58
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Wang, Q.; Xie, J.; Yang, J.; Liu, P.; Xu, W.; Yuan, B.; He, C. Research on Water Content Spatial Distribution Pattern of Fine—Grained Sediments in Debris Flow—Taking Beichuan Debris Flow as a Case. Water 2024, 16, 2640. https://doi.org/10.3390/w16182640

AMA Style

Wang Q, Xie J, Yang J, Liu P, Xu W, Yuan B, He C. Research on Water Content Spatial Distribution Pattern of Fine—Grained Sediments in Debris Flow—Taking Beichuan Debris Flow as a Case. Water. 2024; 16(18):2640. https://doi.org/10.3390/w16182640

Chicago/Turabian Style

Wang, Qinjun, Jingjing Xie, Jingyi Yang, Peng Liu, Wentao Xu, Boqi Yuan, and Chaokang He. 2024. "Research on Water Content Spatial Distribution Pattern of Fine—Grained Sediments in Debris Flow—Taking Beichuan Debris Flow as a Case" Water 16, no. 18: 2640. https://doi.org/10.3390/w16182640

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