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Review

A Review of Research on the Stability of Fine-Grained Sediments in Debris Flows

by
Qinjun Wang
1,2,3,4,5,*,
Jingyi Yang
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, University of Chinese Academy of Sciences, Beijing 101408, China
4
Kashi Aerospace Information Research Institute, Kashgar 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.
Geosciences 2024, 14(9), 248; https://doi.org/10.3390/geosciences14090248
Submission received: 17 June 2024 / Revised: 30 August 2024 / Accepted: 19 September 2024 / Published: 23 September 2024
(This article belongs to the Section Natural Hazards)
Versions Notes

Abstract

:
Fine-grained sediments in debris flows refer to Quaternary sediments with grain sizes smaller than 2 mm. Their stability is closely related to the initial water threshold that triggers the debris flows and thus controls the density, scale, and damage of the debris flows. Based on this, they play a key “probe” role in early warnings of debris flows. Studies on fine-grained sediment stability are related to the accuracy and efficiency of early warnings of debris flows and thus play an important role in ensuring the safety of people and property. There have been some studies on fine-grained sediment stability in debris flows, but no one has carried out a systematic analysis and summary of this field. Therefore, in response to the urgent need for high-precision early warnings of debris flows, firstly, we review the current research on the aspects of fine-grained sediment stability, initiation, triggering, physical properties, hyperspectral remote sensing, and early warning systems; secondly, we summarize the main problems related to high-precision early warnings of debris flow hazards; and finally, we outline the future directions of research on fine-grained sediment stability in debris flows.

1. Introduction

A debris flow is a flood flow that contains a large amount of mud, sand, and stones that develops in mountain valleys and is triggered by rainstorms, melted ice, or snow. There are two main types: (1) debris flows initiated by landslides, which often involve loose soils or materials overlying the bedrock on a steep slope following a landslide, and (2) debris flows triggered by runoff, which are related to grain-by-grain erosion, mass failure, and bank failure. They are characterized by large areas, suddenness, and high risk and thus often cause significant casualties and property losses. For example, at the end of July 2023, Beijing, Tianjin, and Hebei provinces were affected by the typhoon “Doksuri”, and China suffered severe rainstorms, floods, and debris flow disasters. In total, 5.51 million people were affected, 107 people died or went missing, and 1.43 million people were resettled. Furthermore, 104,000 houses were destroyed, and 416.1 thousand hectares of crops were affected by the disaster, with direct economic losses amounting to CNY 165.79 billion [1]. Therefore, monitoring, early warnings, and prevention of debris flow hazards have become key demands for people in mountainous areas.
Fine-grained sediments refer to Quaternary sediments with grain sizes of less than 2 mm that have the characteristics of loose accumulation, light weights, and fine grain sizes. They are therefore the main material that mobilizes first when encountering water. Fine-grained sediment stability refers to the stability of fine-grained sediments under external forces, mainly including shear strength, compressive strength, and erosion resistance. It is the intrinsic property of fine-grained sediments that controls the amount of sediment suspended in a debris flow. Under the same conditions, such as rainfall, slope, elevation, etc., the higher the fine-grained sediment stability, the less that sediments can be eroded in a debris flow, indicating a less dangerous debris flow. Their stability is closely related to the initial water threshold that triggers debris flows, making them crucial for early warnings of debris flows [2,3,4,5,6]. Therefore, the study of fine-grained sediment stability is closely related to the accuracy and efficiency of early warnings of debris flows and is of great significance for ensuring the safety of people, property, and socio-economic development in mountainous areas.
Current research has shown that fine-grained sediment stability is mainly controlled by internal and external factors. ① As for the internally controlled factors, the permeability coefficient, cohesion, and effective internal friction angle determine fine-grained sediment stability [2], whose measurement steps can be found in Appendix A of this paper. The permeability coefficient (the flow under the hydraulic gradient, which indicates the difficulty of fluid passing through the pore skeleton) controls the seepage field of the sediments, while the cohesion and effective internal friction angle control the stress field. These two fields jointly determine the sediments’ internal stability [7,8]. ② As for the externally controlled factors, water source conditions (rainfall, rain intensity, and runoff flow), terrain and geomorphology conditions (slope and catchment area), surface coverage, structure, etc., also affect sediment stability. Some typical values of internally controlled factors are shown in Table 1, while the values of externally controlled factors vary by site.
Although there have been some studies on fine-grained sediment stability, no one has carried out a systematic review and summary of this field. Therefore, the objective of this research is first to review the current research on fine-grained sediment stability, then to summarize the main problems related to high-precision early warnings of debris flow hazards, and finally to outline the future directions of research on fine-grained sediment stability in debris flows.

2. Research on Fine-Grained Sediments in Debris Flows

In recent years, researchers have carried out the following research on fine-grained sediments in debris flows, including investigations of stability, initiation, triggering, physical properties, hyperspectral remote sensing, and early warning systems:
(1) As for fine-grained sediment stability, as shown in Table 2, ① based on limit equilibrium experiments, a permeability failure model for loose sediments was established, and the results showed that the permeability coefficient, cohesion, and effective internal friction angle were the internally controlled factors that affected fine-grained sediment stability. The stability coefficient was used to show the relationship between fine-grained sediment stability and the internally controlled factors [2,10,11,12,13,14]. The relationship between the water stability of soil aggregates and soil erodibility was studied through indoor soil aggregate structure destruction experiments and field-simulated rainfall experiments. The results showed that the content of water-stable aggregates, especially those with diameters >0.25 mm, was one of the best indicators of soil stability [15]. ②Changes in sediment flux, grain size, water flow resistance, and erosion were simulated during the flow process. The results indicated that high total precipitation would lead to high sediment flux in a river. In addition, lithology also had a significant impact on sedimentary flux: the sedimentary flux of a river channel whose catchment was composed of limestone and mudstone was higher than that of a river channel whose catchment was composed of a conglomerate. The slope and flow velocity also had significant impacts on the debris flow’s sediment transport capacity, and the average flow velocity and water flow power could be used as the best combination of predictors for estimating the sediment transport capacity [16,17,18,19]. ③ Porosity and density are the influencing factors of the internally controlled factors of fine-grained sediments. At the early stage, qualitative analysis of the internally controlled factors of stability and their influencing factors was carried out [20,21,22]. With the development of stability research, quantitative models were established. They showed that the permeability coefficient had the strongest correlation with cohesion, followed by porosity and density, while cohesion had the strongest correlation with the effective internal friction angle, followed by the permeability coefficient and density [23,24].
(2) As for fine-grained sediment initiation, as shown in Table 3, ① the initial water threshold of fine-grained sediments was determined for early warnings of debris flows. Due to rapid changes in mineral composition and grain size, as well as high porosity, fine-grained sediments provide channels for surface water infiltration, improving the water infiltration capacity and accelerating the soil saturation rate. When there is more moisture is more than initial water threshold, loose sediments are prone to instability, resulting in progressive sliding [2,11,25]. ② Experiments have shown that there are two basic modes for the initiation of fine-grained sediments in steep gullies: the “firehose effect” and “slope fluidization”. The former appears in unsaturated or high-permeability slopes with weak solid-material transportation effects for deep and narrow erosion channels in runoff-generated debris flows whose stability can be calculated by the “Mean rill erosion rate” in Table 2. The latter appears in soils with more fine-grained particles in landslide-triggered debris flows whose stability can be calculated by the “Infiltration failure model of loose sediments” in Table 2 [26]. ③ A warning model for rainfall-induced sliding was established. Changes in pore water pressure during sediment deformation were summarized to show that the main reason for sediment deformation is a decrease in soil anti-slip effective stress, resulting in local liquefaction [27,28].
(3) As for fine-grained sediments triggering debris flows, there are two different approaches: physical experiments and numerical approaches. As shown in Table 4, ① physical experiments on landslide failure mechanisms have shown that rapid decreases in the effective stress and local liquefaction of soil in anti-sliding sections of loose accumulation are the main reasons for landslide-triggered debris flows [28,29]. ② Numerical analysis of the triggering mechanisms of rainfall-induced debris flows have shown that the fractal dimension of particle size increases with an increase in the fine-grained particle content, and the earlier the obvious sliding phenomenon occurs on a slope, the longer the overall duration of debris flow initiation becomes. A logistic regression used to separate flow responses above the intensity–duration (ID) threshold revealed that the best predictors of rainfall-induced debris flow include the 5 min maximum rainfall intensity, the 48 h antecedent rainfall, the rainfall amount, and the number of days that have elapsed [30,31,32,33].
(4) As for hyperspectral remote sensing of the physical properties of fine-grained sediments, as shown in Table 5, ① hyperspectral remote sensing identification methods were established for soil mineral components, moisture, grain size, and porosity [35,36,37,38,39,40,41,42,43]. The results showed, firstly, that segmented filtering and the Hapke albedo inversion model could avoid complex spectral nonlinear decomposition, thus improving the accuracy of mineral content extraction. Secondly, the combined use of optical reflectance and soil moisture observations had great potential to capture variations in photosynthesis and evapotranspiration during drought episodes. Thirdly, it was feasible to establish a mathematical relationship between spectral reflectance and sediment particle size using a neural network model. Finally, the spectral absorption depth of clay was negatively correlated with pore permeability, indicating a significant positive correlation between the spectral absorption depth of a hydrocarbon and pore permeability. ② Remote sensing extraction methods were then established for soil organic matter, heavy metals, salt content, fertility, and salinization [2,44,45,46,47,48]. The results showed, firstly, that the enhanced vegetation index (EVI) and sand were the most influential factors for soil organic carbon (SOC) variation, while slope and land cover were the least important. A Gradient-Boosting Machine (GBM) was a promising approach for obtaining an in-depth understanding of the spatial variation in SOC. Secondly, it was feasible to use hyperspectral remote sensing to ensure food security by obtaining the areas, ranges, and degrees of soil heavy metal pollution. Thirdly, dynamic monitoring and prediction of the soil salinity content using satellite remote sensing images had positive significance for the utilization and management of coastal saline land resources. Fourthly, it was feasible to establish the correlations between soil spectra and the nitrogen, phosphorus, and potassium contents by selecting the closest sensitive bands. Finally, the Soil Salt Decomposition Translation Model had high accuracy in regional soil salinization evaluation by combining remote sensing, electromagnetic induction earth conductivity data, and soil sampling data. ③ Hyperspectral remote sensing detection models for soil dispersion, cohesion, and the permeability coefficient were finally constructed [49,50,51]. The results showed, firstly, that bands sensitive to soil dispersion included 370, 377, 398, 410, 570, 1918, 1933, 2392, 2401, 2444, and 2448 nm, indicating that calcite and clay minerals were the main factors that caused soil to be dispersive. Secondly, cohesion had significant correlations with six bands (750, 1578, 1835, 2301, 2305, and 2309 nm), indicating that the influencing factors of cohesion include the effective internal friction angle, the permeability coefficient, and density. Thirdly, the permeability coefficient had significant correlations with eight bands (722, 760, 1435, 1612, 1917, 1925, 2285, and 2305 nm), indicating that the influencing factors of the permeability coefficient include cohesion, porosity, and density. Finally, the main factors influencing fine-grained sediment stability were analyzed as shear strength, water flow velocity, and terrain [52], providing a theoretical basis and methodological support for the rapid acquisition of fine-grained sediment stability using remote sensing.
(5) As shown in Table 6, there are two types of early warning systems for debris flows. One predicts debris flows using rainfall thresholds, while the other monitors debris flows using infrasound. Except the lower limit of energy threshold of a real-time debris flow warning system based on infrasound monitoring, most systems use rainfall thresholds to predict debris flows, including the rainstorm intensity index (R) threshold of the regional debris flow warning and forecasting system, the moisture threshold of the remote monitoring and early warning system, and the Radial Basis Function Network (RBFN) threshold of the early warning system for rainfall- and snowmelt-induced slope failure [52,53,54,55].
In summary, current research indicates that the internally controlled factors of fine-grained sediment stability mainly include the permeability coefficient, cohesion, and the effective internal friction angle. The main influencing factors of the permeability coefficient are cohesion, porosity, and density, while the main influencing factors of cohesion are the effective internal friction angle, the permeability coefficient, and density. We can see that the internally controlled factors of fine-grained sediment stability are not independent, as they interact with each other. The permeability coefficient controls the seepage field, while the cohesion and effective internal friction angle control the stress field. Their combined effect determines the fine-grained sediment stability in a debris flow.
Current research has not only indicated the quantitative relationships between the internally controlled stability factors and their influencing factors but has also established hyperspectral remote sensing detection mechanisms and methods. It provides a scientific basis for the quantitative inversion of the internally controlled stability factors of fine-grained sediments on a large scale and thus improves the development of stability research from qualitative to quantitative research.

3. Main Problems

Although current research has made some progress related to the stability principles of fine-grained sediments, initiating hazard warnings, and hyperspectral remote sensing detection of soil physical parameters, the following problems still urgently need to be solved for high-precision early warnings of debris flow hazards:
(1) A quantitative model of fine-grained sediment stability and its factors has not been established, reducing the accuracy of quantitative inversion of fine-grained sediment stability.
The present results indicate that fine-grained sediment stability is mainly controlled by internal and external factors. Among them, the internally controlled factors mainly include the permeability coefficient, cohesion, and the effective internal friction angle; the externally controlled factors mainly include water source conditions (rainfall, rainfall intensity, runoff flow, etc.), terrain and landform conditions (slope, catchment area, etc.), surface coverage and structure, etc. The present stability model of fine-grained sediments only considers one or a few factors and does not establish a quantitative model of stability and its main factors, reducing the inversion accuracy of fine-grained sediment stability.
(2) A lack of large-scale quantitative detection methods for fine-grained sediment stability has hindered quantitative progress.
At present, the parameters of fine-grained sediment stability mainly include the water stability of soil aggregates and the slope stability coefficient. The former mainly describes the stability of fine-grained sediments when encountering water on a micro scale, while the latter mainly describes the stability of slope sediments on a macro scale. To achieve better results, fine-grained sediment stability in debris flows requires a comprehensive analysis on both micro and macro scales. However, there is no large-scale quantitative detection model for fine-grained sediment stability at present, which hinders quantitative progress related to fine-grained sediment stability.
(3) The quantitative relationship between fine-grained sediment stability and the danger of debris flows has not been established, reducing the accuracy of early debris flow hazard warnings.
The danger of debris flows is closely related to fine-grained sediment stability. Under the same conditions, fine-grained sediments with poor stability are prone to erosion, providing more materials to form a relatively high-density debris flow, which results in relatively high debris flow risk. On the contrary, the risk of debris flows formed by fine-grained sediments with high stability is relatively low. At present, research on the danger of debris flows mainly focuses on the hazard-forming environment and hazard-bearing objects, while a quantitative relationship between fine-grained sediment stability and the danger of debris flows has not yet been established, reducing the accuracy of early debris flow hazard warnings.

4. Prospects

In the future, to address the urgent need for high-precision warnings of debris flow hazards, research will focus on the following areas:
(1) Establishing a quantitative model of fine-grained sediment stability and its main influencing factors.
Interactions between internally controlled factors (the permeability coefficient, cohesion, and the effective internal friction angle) and externally controlled factors (water source conditions (rainfall, rainfall intensity, and runoff flow) and terrain and geomorphology conditions (slope, convergence area, and surface cover and structure)) should be clarified to indicate their impacts on fine-grained sediment stability in debris flows. After selecting the main influencing factors, a quantitative model of fine-grained sediment stability and its influencing factors should be established to provide scientific parameters for the rapid inversion of fine-grained sediment stability on a large scale.
(2) Establishing a rapid quantitative detection model for fine-grained sediment stability.
When fine-grained sediments encounter water, interactions between micro (the water stability of soil aggregates) and macro (the slope stability coefficient) parameters can be revealed to discover the remote sensing detection mechanisms of the main influencing factors of fine-grained sediment stability. Therefore, a large-scale quantitative detection model of fine-grained sediment stability can be established to provide a theoretical basis and methodological support for improving the accuracy and efficiency of fine-grained sediment stability measurements.
(3) Establishing a model of fine-grained sediment stability and the danger of debris flows.
A model of fine-grained sediment stability and the density, scale, and intensity of debris flows can be established to show the relationship between fine-grained sediment stability and the danger of debris flows. Combined with the rapid quantitative detection model for fine-grained sediment stability established in step (2), areas at high risk of debris flows can be predicted, providing technological support for rapid and accurate warnings of debris flow hazards.

Author Contributions

Conceptualization, Q.W.; methodology, Q.W. and J.Y.; validation, Q.W. and J.Y.; formal analysis, Q.W. and J.Y.; investigation, Q.W., J.Y., W.X., B.Y. and C.H.; data curation, J.Y.; writing—original draft preparation, Q.W.; 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 Innovative Research Program of the International Research Center of Big Data for Sustainable Development Goals (grant number CBAS2022IRP03), the National Key R&D Program (grant number 2021YFB3900503), and the Second Tibetan Plateau Scientific Expedition and Research (STEP) (grant number 2019QZKK0806).

Conflicts of Interest

The authors declare no conflict of interest.

Appendix A

Laboratory tests to measure soil strength using a ZJ strain-controlled direct shear instrument are carried out as follows [23]: ① Rotate the stable hammer to set the lever to be horizontal. ② Calculate the weight of the sample according to the volume of the ring knife of the direct shear instrument and the density of the soil sample. Weigh the soil sample and make a suitable sample for the shear instrument with its ring knife. Then, place the shear box in the sliding frame, and push the soil sample into the shear box. ③ Cover the cutting box, and press the screw on the cover. ④ Tighten the force-measuring ring and adjust the dial indicator to zero. ⑤ Add weight loads or their combinations (1.275 kg and 2.55 kg, respectively, representing 50 kPa and 100 kPa) to the lever to obtain a series of normal shear stress values (σ) of 50, 100, 200, and 300 kPa. ⑥ Push the switch to “cutting” to start cutting. Observe the force-measuring ring. If the pointer no longer advances or retreats, record the data immediately. ⑦ Push the switch in the “back” direction, then take the shear box out and pour the soil sample out under shear stress. Repeat steps ②–⑥ and conduct four experiments on one sample. Successively apply weight loads of 1.275 kg, 2.55 kg, 5.1 kg, and 7.65 kg to the lever and record the corresponding force ring readings. Weight loads of 1.275 kg, 2.55 kg, 5.1 kg, and 7.65 kg represent normal shear stresses of 50 kPa, 100 kPa, 200 kPa, and 300 kPa, respectively. The shear strength (τ) (kPa) can be converted by multiplying the force ring coefficient and the readings from the force ring. ⑧ Display the four pairs of measured data in a coordinate system with σ on the horizontal axis and τ on the vertical axis to calculate their slope and intercept. The intercept of the straight line is the cohesion (C), and the inclination angle is the effective internal friction angle (ϕ).

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Table 1. Typical values of internally controlled factors [9].
Table 1. Typical values of internally controlled factors [9].
TypeGrain Size (D) (mm)Permeability Coefficient (K) (m/d)Cohesion
(C) (kPa)		Internal Friction Angle (°)
Coarse sand0.5 < D ≤ 220–502–342–40
Medium sand0.25 < D ≤ 0.55–203–640–38
Fine sand0.075 < D ≤ 0.251–56–838–36
Silt0.005 < D ≤ 0.0750.5–18–4236–24
ClayD ≤ 0.005<0.542–94<24
Table 2. Research on the stability of fine-grained sediments.
Table 2. Research on the stability of fine-grained sediments.
ModelFunctionMethod/FormulaReference
Infiltration failure model of loose sedimentsBy considering the effects of pore water pressure and seepage water pressure, the height of the groundwater infiltration line inside sediments is determined and the stability of loose sediments is analyzed.FS = FN/FT
FN: the anti-skid force of the ith soil strip;
FT: the sliding force of the ith soil strip.		[2]
Maximum flow intensity (Qmax) The water volume flowing through a certain cross-section per unit timeQmax = n−1AR2/3I1/2
A—cross-section area of flow (m2);
R—hydraulic radius (m);
I—hydraulic gradient;
n—Manning’s roughness coefficient (ms−1/3).		[16]
Mean rill erosion rateSoil quality eroded by runoff per unit time and area Er = 96.14 × S0.58 × I0.53
Er: mean rill erosion rate (gm−2 min−1);
i: the rainfall intensity (mm h−1);
S: the sine of the slope gradient.		[20]
Model of permeability coefficient and its influencing factorsDetermines the influencing factors of the permeability coefficient and their relationship.Ln(p) = 0.1875 − 0.0387x1 − 0.0455x2 − 0.0619x3
p: the permeability coefficient;
x1: the standardized density;
x2: the standardized porosity;
x3: the standardized cohesion.		[23]
Model of cohesion and its influencing factorsDetermines the influencing factors of cohesion and their relationship. y = 22.91 + 0.62x1 − 1.57x2 − 2.48x3
y: cohesion;
x1: the normalized density;
x2: the normalized logarithm of the permeability coefficient (ln(p));
x3: the normalized effective internal friction angle.		[24]
Table 3. Research on fine-grained sediment initiation.
Table 3. Research on fine-grained sediment initiation.
ModelFunctionMethod/FormulaReference
Early warning of rainfall-induced debris flows in small watersheds using water quality monitoring methodAnalyze the channel characteristics, material resources, formulated conditions, and initiation processes of debris flows.Using the Finite Element software ‘Geostudio’ to simulate the unstable initiation process of debris flows and
using a Fuzzy Analytic Hierarchy Process to assess debris flow hazards.		[11]
Model of rainfall intensity and sediment stabilityDetermine the sediment initiation failure mode of loose sediments considering rainfall intensity and the permeability coefficient: overall instability sliding failure mode/step-by-step sliding failure mode.Formula for initiation of loose sediments in debris flows:
K = (F + LBC)/R + T;
F: static friction force;
LBC: cohesion force on the shear plane;
R: water flow thrust;
T: sliding force.		[2]
The initiation modes of material sources in steep trenches and the mechanism of soil–water coupling damageDetermine the initiation mode of the material sources in a steep channel and analyze its reasons: the material composition, infiltration, and runoff state of the sediments are the essential factors for the different initiation modes, while a large longitudinal gradient provides good terrain conditions for initiation. Fire pipe initiation mode and
slope-fluidized initiation mode.		[26]
Geomorphology-based hydrological modelDetermine the rainfall threshold for flood warnings.Frequency analysis and binary classification based on long-term geomorphology-based hydrological model simulations[27]
Geology–hydrology mechanics model for initiation of loose sediments after strong earthquakesIndicate the main reason for landslide instability: a rapid decrease in effective stress and even local liquefaction of the anti-sliding loose sediments.Landslide experiments with different rainfall infiltration amounts and potential sliding-surface inclination angles[28]
Table 4. Research on fine-grained sediments triggering debris flows.
Table 4. Research on fine-grained sediments triggering debris flows.
ModelFunctionMethod/FormulaReference
Physical experiments on landslide failure mechanismRapid decreases in the effective stress and local liquefaction of soil in anti-sliding sections of loose accumulation are the main reasons for landslide instability.Landslide modeling experiment[28]
Experimental and numerical analysis of triggering mechanism of fine-grained sand debris flowsWith an increment in the fine sand content, the main sliding time shortens and the flowability of soil increases, leading to different failure modes such as flow sliding, composite, and graded progressive failure.Fine sand debris flow model[29]
Numerical analysis of triggering mechanism of rainfall-induced sand debris flows using fractal methodThe fractal dimension of particle size increases with an increase in the fine-grained particle content, and the earlier the obvious sliding phenomenon occurs on a slope, the longer the overall duration of debris flow initiation.Fractal method[5]
Numerical approaches to static liquefaction and slide-to-flow transformationEvaluating two candidate triggering mechanisms: static liquefaction and the transition from sliding to flowing due to localized transient pore water pressuresStatic liquefaction and slide-to-flow transformation[5]
Numerical rainfall control of debris flow triggeringParameters used to identify rainfall events significantly affect the intensity–duration (ID) threshold and are likely to explain part of the threshold variability.Rainfall intensity–duration (ID) threshold is calculated using logistic regression.[31]
Numerical model comparing seepage and internal erosionTo investigate the erosion characteristics of debris flow deposits triggered by seepage flows.Equation based on the internal erosion rate, considering the pore size distribution and hydraulic gradient[33]
Numerical model of outburst debris flowsUsed as a tool to predict the occurrence of outburst debris flows.An erosion model considering the effect of hindered erosion[33]
Numerical rainfall intensity–duration (ID) modelTo understand its development status and problems, as well to provide emergency strategies for debris flow disaster prevention and mitigation in mountainous areas.I = aDc
I: average rainfall intensity;
D: duration;
a and c: coefficients for different sites.		[34]
Table 5. Hyperspectral detection of physical properties of fine-grained sediments.
Table 5. Hyperspectral detection of physical properties of fine-grained sediments.
ModelFunctionMethod/FormulaReference
Hyperspectral remote sensing identification method for mineral componentsA linear model is used to decompose a single albedo for mineral content extraction, and mineral identification accuracy is improved through segmented filtering and a regional spectral library.Linear mixed model[36]
Mixed vegetation–water supply index (MVSWI)Invert soil moisture by establishing a relationship between soil moisture and the soil moisture index.Linear regression analysis[39]
Model of spectral reflectance and sediment particle size Invert sediment particle sizes using spectra.BP neural network[53]
Correlations between oil sand spectra and reservoir properties Establish an altered mineral extraction model for the Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER).Correlation analysis[43]
Hyperspectral estimation of soil organic matter Estimate soil organic matter by particle swarm optimization neural network.Particle swarm optimization (PSO)[45]
Regional soil salinization evaluation method using multi-source data Show the three-dimensional differentiation characteristics of regional soil salinity. Multiple regression analysis [2]
Hyperspectral soil dispersion model for debris flowsReproduce soil dispersion by sensitive bands.Multivariable linear regression model.[49]
Hyperspectral remote sensing detection model for cohesion of fine-grained sediments in debris flowsDetect cohesion by sensitive bands: 750, 1578, 1835, 2301, 2305, and 2309 nm.The least-squares multi-variate statistical analysis method[50]
Hyperspectral detection model for permeability coefficient of fine-grained sediments in debris flowsDetect permeability coefficient by sensitive bands: 722, 760, 1435, 1612, 1917, 1925, 2285, and 2305 nm.Multivariable linear regression model[51]
Spatial distribution pattern and causes of internally controlled stability factors of fine-grained sediments in debris flowsShow the spatial distribution pattern of internally controlled stability factors (grain size, the permeability coefficient, shear strength, and porosity) and analyze its causes (shear strength, water flow velocity, and terrain).Sedimentology[52]
Table 6. Early warning systems for debris flows.
Table 6. Early warning systems for debris flows.
SystemFunctionMethod/FormulaThresholdInstrumentReference
Regional debris flow warning and forecasting system To predict the risk of debris flow occurrenceWebGISR = μ (H24h/100 + H1h/40)
R ≥ 3.5
R: rainstorm intensity index;
μ: correction coefficient;
H24h: maximum 24 h rainfall;
H1h: maximum 1 h rainfall.		Rain gauge to obtain daily and previous rainfall[52]
Real-time debris flow warning system based on infrasound monitoringTo improve the accuracy of infrasound monitoring and early warnings of debris flowsHybrid programming, database, and GIS secondary developmentEl ≥ 5 Hz;
Ps ≤ 5 Pa;
Cr > 0.2;
El: lower limit of energy;
Ps: sound pressure;
Cr: short-term zero crossing rate.		Single-chip microcontroller and upper computer[53]
Remote monitoring and early warning system for landslides and debris flows based on LoRa Internet of ThingsEarly warnings of landslides and debris flowsLoRa Internet of ThingsUser-defined moisture thresholds at different sitesShock sensor;
accelerated sensor; and
soil moisture sensor		[54]
Early warning system for rainfall- and snowmelt-induced slope failure in seasonally cold regionsTo propose a new method for determining early warning criteria for rainfall- and/or snowmelt-induced slope failures in seasonally cold regionsRainfall intensity and the Soil Water Index (SWI)Rainfall types: SH and LL;
Slope types: A, B, and C;
RBFN thresholds:
  0.3 under SH and A;
  0.2 under SH and B;
  0.3 under SH and C;
  0.5 under LL and A;
  0.3 under LL and B;
  0.5 under LL and C.
SH: short-duration high-intensity rainfall;
LL: long-duration low-intensity rainfall;
RBFN: Radial Basis Function Network;
A: soil deposited on the top terrace of a rock slope;
B: cut slope of a thick soil sediment;
C: top soil distributed on a steep rock slope.		Rain gauge and soil moisture sensor[55]
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Wang, Q.; Yang, J.; Xu, W.; Yuan, B.; He, C. A Review of Research on the Stability of Fine-Grained Sediments in Debris Flows. Geosciences 2024, 14, 248. https://doi.org/10.3390/geosciences14090248

AMA Style

Wang Q, Yang J, Xu W, Yuan B, He C. A Review of Research on the Stability of Fine-Grained Sediments in Debris Flows. Geosciences. 2024; 14(9):248. https://doi.org/10.3390/geosciences14090248

Chicago/Turabian Style

Wang, Qinjun, Jingyi Yang, Wentao Xu, Boqi Yuan, and Chaokang He. 2024. "A Review of Research on the Stability of Fine-Grained Sediments in Debris Flows" Geosciences 14, no. 9: 248. https://doi.org/10.3390/geosciences14090248

APA Style

Wang, Q., Yang, J., Xu, W., Yuan, B., & He, C. (2024). A Review of Research on the Stability of Fine-Grained Sediments in Debris Flows. Geosciences, 14(9), 248. https://doi.org/10.3390/geosciences14090248

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