A gully-type debris flow risk assessment method considering water source supply of material source

Abstract

The application discloses a kind of gully-type debris flow risk assessment method considering source water supply, it is related to geological disaster assessment technical field, the present application includes obtaining target area multi-source remote sensing and meteorological data;Identification slope material source, landslide material source and channel material source and analyze material supply structure;Introduce precipitation and air temperature factor to evaluate water source condition influence;Build material source-water source coupling's risk comprehensive evaluation model, realize the differentiation of different section debris flow risk assessment.The present application distinguishes material supply structure and includes two types of water source factors, precipitation and air temperature, solves the problem of material homogenization and ignoring air temperature driven snowmelt supply in existing method, improves the accuracy of debris flow risk assessment under the background of climate change.

Description

Technical Field

[0001] This invention belongs to the field of geological hazard assessment technology, and in particular relates to a method for assessing the hazard of gully-type debris flows that takes into account the replenishment of material and water sources. Background Technology

[0002] Risk assessment of gully-type debris flows is a crucial technical foundation for disaster prevention and mitigation planning and the safe construction of major projects in high-altitude canyon areas such as the southeastern edge of the Qinghai-Tibet Plateau. Accurate assessment of debris flow hazard is of significant practical importance for engineering route selection, risk zoning, and disaster prevention and mitigation decision-making. Currently, various methods have been applied to debris flow hazard assessment, including assessment methods based on topographic parameters, assessment methods driven by meteorological factors, and comprehensive assessment methods involving multiple factors. However, existing methods generally include source conditions as a general background factor in the evaluation or ignore them entirely, failing to distinguish the essential differences in spatial distribution, recharge methods, and initiation conditions between slope sources, landslide sources, and gully sources. This results in assessment results that cannot reflect the differentiated control effect of different source supply structures on debris flow activity characteristics. This deficiency makes it difficult for assessment conclusions based on existing methods to accurately reveal the spatial differentiation patterns of debris flow hazard in different sections, thus limiting the level of precision in disaster risk assessment.

[0003] The issue of homogenizing the aforementioned source conditions becomes particularly prominent in the context of climate change. Different types of sources exhibit significantly different response mechanisms to precipitation and temperature changes: debris flows primarily originating from gullies rely mainly on rainfall runoff for initiation and transport of loose material, making their response to precipitation changes more direct; while debris flows primarily originating from landslides are more affected by snowmelt, freeze-thaw damage, and slope instability at high altitudes, making them more sensitive to rising temperatures. Existing methods fail to establish a correlation mechanism between source type and water-driving conditions, making it difficult to support dynamic assessments of debris flow hazard and trend analysis under the trend of altered precipitation patterns and increased snowmelt caused by climate warming. Furthermore, existing methods mostly use precipitation as the dominant meteorological factor triggering debris flows, lacking a systematic consideration of the enhanced effects of snowmelt and glacier retreat caused by rising temperatures on high-altitude water supply, further limiting the applicability of assessment methods under climate change scenarios. Therefore, the following solutions are proposed to address these issues. Summary of the Invention

[0004] The purpose of this invention is to provide a method for assessing the hazard of gully-type debris flows that considers material and water sources. By distinguishing between slope, landslide, and gully material sources and introducing precipitation and temperature factors to construct a coupled evaluation model, it can achieve differentiated assessment of debris flow hazard and solve the problems of homogenization of material sources and neglect of temperature-driven snowmelt water supply in existing methods.

[0005] To solve the above-mentioned technical problems, the present invention is achieved through the following technical solution: This invention provides a method for assessing the hazard of gully-type debris flows, taking into account both sediment and water supply, and includes the following steps: Step S1: Collect multi-source remote sensing data and meteorological data covering the target area and complete preprocessing to construct a basic dataset for subsequent analysis; Step S2: Identify debris flow activity points at different times based on multi-period remote sensing images, analyze the spatial migration characteristics of debris flows using the kernel density estimation method, and conduct statistical significance tests. Step S3: Extract key geomorphological parameters of the watershed, identify three types of debris flow sources: slope source, landslide source, and gully source, and estimate their volumes respectively, quantitatively characterizing the source supply structure of a single gully. Step S4: Select precipitation, temperature and geomorphological factors, and use binomial logistic regression combined with hierarchical segmentation to quantitatively assess the independent contribution rate of the two types of water source factors to the spatial distribution of debris flow. Step S5: Determine the water source driving weight based on the matching of material supply structure, construct a risk assessment model by comprehensively considering geomorphological conditions, material supply scale and water source driving intensity, and output the risk level zoning results.

[0006] Furthermore, the multi-source remote sensing data and meteorological information in step S1 include high-resolution orthophoto DOM, airborne LiDAR point cloud data, digital elevation model (DEM), multi-period historical remote sensing images, precipitation reanalysis data, and daily temperature data from meteorological stations; preprocessing includes denoising, ground point filtering, and spatial interpolation processing of the LiDAR point cloud to generate a DEM with a specified resolution and auxiliary data for mountain shadows.

[0007] Furthermore, in step S2, the debris flow activity points are divided into multiple time-series stages based on the time gradient; a rank-sum test is used to perform significance verification, a preset significance threshold is set, and the verification results are used to distinguish whether there are statistically significant differences in the spatial distribution of debris flow activities in different time-series stages.

[0008] Furthermore, the key geomorphic parameters extracted in step S3 include the watershed elevation difference and the longitudinal gradient of the gully bed. The watershed boundary is determined by tracing the upstream confluence area using the D8 algorithm. The three types of sediment sources are spatially identified and their extent interpreted based on a remote sensing identification marker system established by DOM image texture features and LiDAR-derived topographic features.

[0009] Furthermore, in step S3, the volumes of the three types of material sources are estimated using corresponding methods: landslide material sources are calculated block by block using the area-thickness method and then summed; channel material sources are calculated segment by segment using the cross-sectional integration method and then summed; and slope material sources are calculated using the geometric estimation method of multiplying the distributed area by the average erodible thickness.

[0010] Furthermore, in step S3, the material supply structure is characterized by the proportion of landslide material sources. The proportion of landslide material sources is the ratio of the volume of landslide material sources to the total volume of the three types of material sources and is expressed as a percentage. It is used to classify the dominant material source type of the valley.

[0011] Furthermore, in step S4, the independent gully catchment areas divided based on DEM and gully network are used as the basic spatial analysis units, and the occurrence status of debris flows in the corresponding stage within the catchment area is used as the binary response variable. Precipitation, temperature, average elevation, gully slope and watershed elevation difference are selected as explanatory variables to construct a binary logistic regression model.

[0012] Furthermore, in step S4, the independent explanatory quantities of each explanatory variable are decomposed by hierarchical segmentation, and the relative independent contribution rate of each factor to the spatial distribution of debris flow is obtained after normalization, which is used to quantitatively characterize the independent driving capacity of different water source factors.

[0013] Furthermore, in step S5, the weight allocation is adjusted based on the independent contribution rate of precipitation and temperature factors, combined with the dominant material source type: the weight of precipitation factor is increased for gully-type material source dominant gullies, and the weight of temperature factor is increased for landslide-type material source dominant gullies; the hazard assessment model adopts a comprehensive evaluation index, which is obtained by weighted summation of geomorphic condition factors, material source supply factors, and water source driving factors.

[0014] Furthermore, in step S5, the comprehensive weight coefficients of the three types of factors are determined by the hierarchical segmentation method, and the debris flow hazard is divided into multiple hazard levels using the natural breakpoint method, ultimately generating a spatial zoning map of debris flow hazard in the target area.

[0015] The present invention has the following beneficial effects: This invention provides a quantitative assessment of the hazard of gully-type debris flows in high mountain and canyon areas, taking into account differences in both source type and water-driven mechanisms. Its main advantage lies in the fact that, given the source supply structure of debris flows directly determines their initiation conditions and activity characteristics, while differences in water supply types affect their triggering mechanisms and response patterns, this invention establishes methods for classifying and estimating the volume of slope sources, landslide sources, and gully sources. Combined with a hierarchical segmentation method to quantitatively decompose the independent explanatory contributions of precipitation and temperature to the spatial distribution of debris flows, it can identify differences in source composition and water response in different debris flow sections. This assessment, which considers the coupling relationship between source and water, effectively supports the differentiated classification of debris flow hazard levels under different source supply modes, the scientific assessment of the evolution of debris flow activity patterns under climate change trends, and the accurate identification of high-risk sections. This provides data support and decision-making basis for major engineering projects to select routes that avoid high-risk debris flow gullies and to formulate targeted disaster prevention and mitigation measures.

[0016] Of course, any product implementing this invention does not necessarily need to achieve all of the advantages described above at the same time. Attached Figure Description

[0017] To more clearly illustrate the technical solutions of the embodiments of the present invention, the accompanying drawings used in the description of the embodiments will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0018] Figure 1 This is a flowchart illustrating a method for assessing the hazard of gully-type debris flows that takes into account the replenishment of material and water sources, according to the present invention. Figure 2 Schematic diagram of remote sensing interpretation markers for different source types; Figure 3 This is a schematic diagram of the debris flow hazard assessment results based on material-water source coupling. Detailed Implementation

[0019] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0020] Please see Figure 1-3 As shown, this invention provides a method for assessing the hazard of gully-type debris flows considering material and water source recharge, comprising the following steps: Step S1: Collect multi-source remote sensing data and meteorological data covering the target area and complete preprocessing to construct a basic dataset for subsequent analysis; Step S2: Identify debris flow activity points at different times based on multi-period remote sensing images, analyze the spatial migration characteristics of debris flows using the kernel density estimation method, and conduct statistical significance tests. Step S3: Extract key geomorphological parameters of the watershed, identify three types of debris flow sources: slope source, landslide source, and gully source, and estimate their volumes respectively, quantitatively characterizing the source supply structure of a single gully. Step S4: Select precipitation, temperature and geomorphological factors, and use binomial logistic regression combined with hierarchical segmentation to quantitatively assess the independent contribution rate of the two types of water source factors to the spatial distribution of debris flow. Step S5: Determine the water source driving weight based on the matching of material supply structure, construct a risk assessment model by comprehensively considering geomorphological conditions, material supply scale and water source driving intensity, and output the risk level zoning results.

[0021] The multi-source remote sensing data and meteorological information in step S1 include high-resolution orthophoto imagery (DOM), airborne LiDAR point cloud data, digital elevation model (DEM), multi-period historical remote sensing images, precipitation reanalysis data, and daily temperature data from meteorological stations; preprocessing includes denoising, ground point filtering, and spatial interpolation of the LiDAR point cloud to generate a DEM with a specified resolution and auxiliary data for mountain shadows.

[0022] Step S2 divides debris flow activity points into multiple time-series stages based on the temporal gradient; uses the rank-sum test to verify significance, presets a significance threshold, and uses the verification results to distinguish whether there are statistically significant differences in the spatial distribution of debris flow activity in different time-series stages.

[0023] The key geomorphic parameters extracted in step S3 include the watershed elevation difference and the longitudinal gradient of the gully bed. The watershed boundary is determined by tracing the upstream confluence area using the D8 algorithm. The three types of sediment sources are spatially identified and their extent interpreted based on a remote sensing identification marker system established by DOM image texture features and LiDAR-derived topographic features.

[0024] In step S3, the volumes of the three types of material sources are estimated using corresponding methods: landslide material sources are calculated block by block using the area-thickness method and then summed; channel material sources are calculated segment by segment using the cross-sectional integration method and then summed; and slope material sources are calculated using the geometric estimation method of multiplying the distributed area by the average erodible thickness.

[0025] In step S3, the material supply structure is characterized by the proportion of landslide material sources. The proportion of landslide material sources is the ratio of the volume of landslide material sources to the total volume of the three types of material sources and is expressed as a percentage. It is used to classify the dominant material source type of the valley.

[0026] In step S4, the independent gully catchment areas divided based on DEM and gully network are used as the basic spatial analysis units. The occurrence status of debris flow in the corresponding stage within the catchment area is used as the binary response variable. Precipitation, temperature, average elevation, gully slope and watershed elevation difference are selected as explanatory variables to construct a binary logistic regression model.

[0027] In step S4, the independent explanatory values ​​of each explanatory variable are decomposed by hierarchical segmentation. After normalization, the relative independent contribution rate of each factor to the spatial distribution of debris flow is obtained, which is used to quantitatively characterize the independent driving capacity of different water source factors.

[0028] In step S5, the independent contribution rates of precipitation and temperature factors are used as the base weights, and the weight allocation is adjusted in combination with the dominant material source type: the weight of precipitation factor is increased for gully material source dominant type gullies, and the weight of temperature factor is increased for landslide material source dominant type gullies; the risk assessment model adopts a comprehensive evaluation index, which is obtained by weighted summation of geomorphic condition factors, material source supply factors and water source driving factors.

[0029] In step S5, the comprehensive weight coefficients of the three types of factors are determined by the hierarchical segmentation method. The natural breakpoint method is used to classify the debris flow hazard into multiple hazard levels, and finally a spatial zoning map of debris flow hazard in the target area is generated.

[0030] The specific application of this embodiment is as follows: Step S1: Multi-source data acquisition and preprocessing Multi-source heterogeneous data covering the target area were collected to construct a comprehensive dataset encompassing remote sensing imagery, high-precision topographic data, meteorological data, and field geological survey information. Specific data types, resolutions, and applications are shown in Table 1. Table 1 Datasets used in the study

[0031] The airborne LiDAR point cloud data, after denoising, ground point filtering, and spatial interpolation, generates a 5 m resolution DEM, and further generates auxiliary data for hillshade and skyview factor (SVF). The hillshade calculation formula is as follows:

[0032] In the formula, The solar altitude angle is set to 45°. The solar azimuth angle is set to 315°. Slope; It is a slope.

[0033] ERA5 reanalysis precipitation data is used to characterize the spatiotemporal variation of regional precipitation; daily temperature data from meteorological stations at different altitudes are used to analyze indicators such as regional temperature change trends.

[0034] Step S2: Analysis of Spatial Activity Patterns of Debris Flows Based on multiple high-resolution remote sensing images and field survey data, debris flow activity points in the target area at different times were identified and divided into three stages according to the activity period: 15-30 years ago, 5-15 years ago, and 1-5 years ago. This provides a basic dataset for subsequent analysis of the differences in debris flow material and water sources at different stages.

[0035] The spatial density distribution of debris flow activity points at each stage was calculated using the kernel density estimation method. The calculation formula is as follows:

[0036] In the formula, For position The kernel density estimate at that location; This represents the total number of debris flow activity points. For bandwidth parameters; For kernel functions; For the first Location of the debris flow activity point.

[0037] Based on the identification of spatial clustering characteristics, the significance test method is used to verify the statistical significance of the differences in the location of debris flow activity in different periods. The calculation formula is as follows:

[0038] In the formula, The total number of samples from debris flow activity points across all periods; This represents the number of independent sample groups. For the first Group sample size; For the first The sum of the ranks of the group samples after global merge and sorting. The significance level is set to... ,when At that time, it was believed that there were significant differences in the location of debris flow activity at different times.

[0039] Step S3: Geomorphological parameter extraction and material supply structure analysis This step includes three stages: geomorphological parameter extraction, source type identification, and source volume estimation.

[0040] Step S31, Extraction of geomorphological parameters: Based on a 5m resolution DEM, key geomorphic parameters of each debris flow gully were extracted. The watershed elevation difference is defined as the difference in elevation between the highest and lowest points within the watershed boundary.

[0041] The longitudinal gradient of the ditch bed reflects the steepness of the ditch's longitudinal profile, and the calculation formula is as follows:

[0042] In the formula, The elevation of the highest point of the main ditch; The elevation of the ditch mouth; The length of the main channel. The watershed boundary is determined by tracing the upstream confluence area using the D8 algorithm.

[0043] Step S32, Source Type Identification: By combining high-resolution orthophotos (DOM) and LiDAR images, a remote sensing identification system for three types of material sources—slope source, landslide source, and gully source—was established (Table 2). Based on this system, the spatial distribution range of each type of material source was interpreted and verified through field surveys.

[0044] Table 2 Remote Sensing Identification Markers for Debris Flow Source Types

[0045] Step S33, Source Volume Estimation: Different methods were used to estimate the volume of the three types of sediment sources, and the sediment supply structure of each debris flow gully was quantitatively characterized.

[0046] The volume of landslide source material was calculated using the area-thickness method. The total volume of landslide source material in a single debris flow gully is:

[0047] In the formula, For the first The planar area of ​​each landslide body was obtained through remote sensing interpretation; The average thickness of the landslide body.

[0048] The volume of the channel source material was calculated using the cross-sectional integration method:

[0049] In the formula, For the first The cross-sectional area of ​​the nth cross section; Lᵢ is the area of ​​the cross section of the nth cross section. The length of the channel centered on the cross-section is represented by the channel.

[0050] The volume of material source on the slope is estimated using a geometric method:

[0051] In the formula, The area of ​​the slope where the material source is distributed; The average erodible thickness of loose material on the slope.

[0052] Based on this, the volume proportions of the three types of sediment sources in each debris flow gully were statistically analyzed to quantitatively characterize its sediment supply structure. The sediment supply structure was summarized into two typical modes: a sediment supply structure dominated by gully sediment sources and a sediment supply structure dominated by landslide sediment sources.

[0053] Step S4: Analysis of Contribution of Debris Flow Water Source Factors To quantitatively reveal the influence of water source conditions such as precipitation and snowmelt on the spatial distribution of debris flows at different stages and their relative contributions, this step employs an analytical framework combining binomial logistic regression and hierarchical segmentation. Precipitation is used to characterize the supply of rainwater, and air temperature is used to characterize the thermal conditions of snowmelt, identifying the independent explanatory role of water source factors and their stage-specific changes.

[0054] Using independent gully catchment areas delineated based on DEM and gully network as the basic spatial analysis unit, a binary response variable for debris flow distribution is constructed: a value of 1 is assigned if debris flow occurs at the corresponding stage within the catchment area, and a value of 0 is assigned otherwise. Precipitation (P), temperature (T), mean elevation (ME), gully slope (CG), and watershed elevation difference (R) are selected as explanatory variables.

[0055] Establish binomial logistic regression models for different stages:

[0056] In the formula, For the first Distribution of debris flows in the catchment areas of individual gullies; This represents the probability of a mudslide occurring in the valley. For the first The values ​​of each environmental factor; The intercept; is the regression coefficient.

[0057] Based on logistic regression, a hierarchical segmentation method is used to decompose the independent explanatory power of each factor:

[0058] In the formula, For variables The number of independent explanatory powers; Not included Any combination of explanatory variables; Combination of variables The goodness of fit of the corresponding model. The independent explanatory values ​​of each variable are normalized to obtain the relative independent contribution rates:

[0059] In the formula, For variables The relative independent contribution rate to the spatial distribution of debris flows; the higher the value, the stronger the independent explanatory power of the corresponding factor.

[0060] Step S5: Comprehensive assessment of debris flow hazard based on material-water source coupling The hazard of debris flows is jointly controlled by the material supply conditions and the water driving conditions. This step uses the type of material source as the basis for water source weighting: for debris flow gullies dominated by sediment source, the loose material mainly relies on the scouring, initiation, and transportation of rainfall runoff, so the weight of the precipitation factor is increased; for debris flow gullies dominated by landslide source, the water source is more affected by snowmelt, freeze-thaw damage, and slope instability caused by rising temperatures, so the weight of the temperature factor (temperature is used to characterize the thermodynamic conditions of snowmelt and freeze-thaw activity) is increased; for debris flow gullies with mixed source, a comprehensive weighting is applied between precipitation and temperature based on the composition ratio of different types of material sources.

[0061] Based on the material supply structure obtained in step S3 and the contribution rates of precipitation and temperature factors obtained in step S4, a water source driving weight under the constraint of material source type is constructed. Furthermore, by integrating geomorphological conditions, material supply scale and water source driving intensity, a comprehensive evaluation model for the hazard of gully debris flows based on material-water source coupling is established.

[0062] First, based on the proportion of different types of sediment sources in the total sediment source of a single gully, the sediment supply types of debris flow gullies in the study area are classified. A landslide sediment source proportion index is defined (…). ):

[0063] Secondly, based on the formation, initiation, and transport mechanisms of different sediment sources, corresponding water source driving weights are determined. For gully-source-dominated debris flow gullies, loose material is mainly distributed in the gully bed and banks, and its initiation and transport along the course are significantly influenced by short-duration heavy precipitation and the resulting surface runoff; therefore, precipitation is assigned a higher weight. For landslide-source-dominated debris flow gullies, sediment supply is more affected by snowmelt, freeze-thaw damage, and slope instability in high-altitude areas; therefore, air temperature is assigned a higher weight. Here, air temperature does not directly represent water volume but serves as a proxy indicator of the thermal conditions of snowmelt and freeze-thaw activity; its weight reflects the impact of snowmelt, glacier ablation, and related landslide sediment supply processes on debris flow hazard. For mixed-source debris flow gullies, the driving effects of both rainfall runoff and snowmelt conditions are considered.

[0064] Based on the relatively independent contribution rates of precipitation and temperature obtained by the hierarchical segmentation method in step S4, the basic weights of the two types of water source factors are calculated:

[0065] In the formula, and These are the normalized weights for precipitation and air temperature (air temperature is used to characterize the thermal conditions of snowmelt and freeze-thaw water replenishment), respectively. and These represent the relatively independent contribution rates of precipitation and temperature factors obtained by the hierarchical segmentation method.

[0066] Finally, considering the three dimensions of geomorphological conditions, sediment supply scale, and water driving intensity, a comprehensive evaluation index for debris flow hazard is constructed:

[0067] In the formula, The geomorphic condition factor is obtained by normalizing and weighting the watershed elevation difference and the longitudinal ratio of the gully bed. The material supply factor is obtained by normalizing the total volume of the single-channel material source. As the water source driving factor, the snowmelt water supply, characterized by precipitation intensity and temperature, is multiplied by its corresponding weight. and The weighted sum is then obtained; The comprehensive weight coefficients of the three types of factors are determined by the independent interpretation contribution rate of each type of factor obtained by the hierarchical segmentation method. Finally, the natural breakpoint method is used to classify the debris flow hazard into four levels: low, medium, high and extremely high, and generate a debris flow hazard zoning map of the target area.

[0068] In the description of this specification, references to terms such as "an embodiment," "example," "specific example," etc., indicate that a specific feature, structure, material, or characteristic described in connection with that embodiment or example is included in at least one embodiment or example of the invention. In this specification, illustrative expressions of the above terms do not necessarily refer to the same embodiment or example. Furthermore, the specific features, structures, materials, or characteristics described may be combined in any suitable manner in one or more embodiments or examples.

[0069] The preferred embodiments of the present invention disclosed above are merely illustrative of the invention. These preferred embodiments do not exhaustively describe all details, nor do they limit the invention to the specific implementations described. Clearly, many modifications and variations can be made based on the content of this specification. This specification selects and specifically describes these embodiments to better explain the principles and practical applications of the invention, thereby enabling those skilled in the art to better understand and utilize the invention. The invention is limited only by the claims and their full scope and equivalents.

Claims

1. A method for assessing the hazard of gully-type debris flows considering material and water source recharge, characterized in that, The method includes the following steps: Step S1: Collect multi-source remote sensing data and meteorological data covering the target area and complete preprocessing to construct a basic dataset for subsequent analysis; Step S2: Identify debris flow activity points at different times based on multi-period remote sensing images, analyze the spatial migration characteristics of debris flows using the kernel density estimation method, and conduct statistical significance tests. Step S3: Extract key geomorphological parameters of the watershed, identify three types of debris flow sources: slope source, landslide source, and gully source, and estimate their volumes respectively to quantitatively characterize the source supply structure of a single gully. Step S4: Select precipitation, temperature and geomorphological factors, and use binomial logistic regression combined with hierarchical segmentation to quantitatively assess the independent contribution rate of the two types of water source factors to the spatial distribution of debris flow. Step S5: Determine the water source driving weight based on the matching of material supply structure, construct a risk assessment model by comprehensively considering geomorphological conditions, material supply scale and water source driving intensity, and output the risk level zoning results.

2. The method for assessing the hazard of gully-type debris flows considering material and water source recharge as described in claim 1, characterized in that, The multi-source remote sensing data and meteorological information in step S1 include high-resolution orthophoto DOM, airborne LiDAR point cloud data, digital elevation model (DEM), multi-period historical remote sensing images, precipitation reanalysis data, and daily temperature data from meteorological stations; preprocessing includes denoising, ground point filtering, and spatial interpolation of the LiDAR point cloud to generate a DEM with a specified resolution and auxiliary data for mountain shadows.

3. The method for assessing the hazard of gully-type debris flows considering material and water source recharge as described in claim 1, characterized in that, Step S2 divides debris flow activity points into multiple time-series stages based on the time gradient; uses the rank-sum test to perform significance verification, presets a significance threshold, and uses the verification results to distinguish whether there are statistically significant differences in the spatial distribution of debris flow activity in different time-series stages.

4. The method for assessing the hazard of gully-type debris flows considering material and water source recharge as described in claim 1, characterized in that, The key geomorphic parameters extracted in step S3 include the watershed elevation difference and the longitudinal gradient of the gully bed. The watershed boundary is determined by tracing the upstream confluence area using the D8 algorithm. The three types of sediment sources are spatially identified and their extent interpreted based on a remote sensing identification marker system established using DOM image texture features and LiDAR-derived topographic features.

5. The method for assessing the hazard of gully-type debris flows considering material and water source recharge according to claim 4, characterized in that, In step S3, the volumes of the three types of material sources are estimated using corresponding methods: landslide material sources are calculated block by block using the area-thickness method and then summed; gully material sources are calculated segment by segment using the cross-sectional integration method and then summed; and slope material sources are calculated using the geometric estimation method of multiplying the distributed area by the average erodible thickness.

6. The method for assessing the hazard of gully-type debris flows considering material and water source recharge according to claim 5, characterized in that, In step S3, the material supply structure is characterized by the proportion of landslide material sources. The proportion of landslide material sources is the ratio of the volume of landslide material sources to the total volume of the three types of material sources and is expressed as a percentage. It is used to classify the dominant material source type of the valley.

7. The method for assessing the hazard of gully-type debris flows considering material and water source recharge according to claim 1, characterized in that, In step S4, the independent gully catchment area divided based on DEM and gully network is used as the basic spatial analysis unit. The occurrence state of debris flow in the corresponding stage within the catchment area is used as the binary response variable. Precipitation, temperature, average elevation, gully slope and watershed elevation difference are selected as explanatory variables to construct a binary logistic regression model.

8. The method for assessing the hazard of gully-type debris flows considering material and water source recharge according to claim 7, characterized in that, In step S4, the independent explanatory values ​​of each explanatory variable are decomposed by hierarchical segmentation, and the relative independent contribution rate of each factor to the spatial distribution of debris flow is obtained after normalization, which is used to quantitatively characterize the independent driving capacity of different water source factors.

9. The method for assessing the hazard of gully-type debris flows considering material and water source recharge according to claim 1, characterized in that, In step S5, the weight allocation is adjusted based on the independent contribution rate of precipitation and temperature factors, combined with the dominant source type: the weight of precipitation factor is increased in gully-dominated gullies, and the weight of temperature factor is increased in landslide-dominated gullies. The hazard assessment model uses a comprehensive evaluation index, which is obtained by weighted summation of geomorphological condition factors, material supply factors, and water source driving factors.

10. The method for assessing the hazard of gully-type debris flows considering material and water source recharge according to claim 9, characterized in that, In step S5, the comprehensive weight coefficients of the three types of factors are determined by the hierarchical segmentation method. The natural breakpoint method is used to classify the debris flow hazard into multiple hazard levels, and finally a spatial zoning map of debris flow hazard in the target area is generated.

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