Ice lake outburst small watershed identification method and system based on valley landform feature index

Abstract

The application provides a glacial lake outburst small watershed identification method and system based on a valley landform feature index, and belongs to the technical field of glacial lake outburst small watershed identification. The method comprises the following steps: S1, acquiring elevation data and multi-period remote sensing image data; S2, identifying a channel and a glacial lake, determining the channel position and the glacial lake position, and calculating the average slope of the glacial lake upstream and downstream along the channel direction; S3, acquiring the b value of the glacial lake upstream along the vertical channel direction and the b value of the glacial lake downstream along the vertical channel direction; S4, calculating the GLBN value of the small watershed according to the average slope of the glacial lake upstream along the channel direction, the average slope of the glacial lake downstream along the channel direction, and the b value of the glacial lake upstream along the vertical channel direction and the b value of the glacial lake downstream along the vertical channel direction; and when the GLBN value is greater than an identification threshold value, the small watershed is determined as a glacial lake outburst small watershed. The application carries out the identification of the glacial lake outburst small watershed in the high mountain uninhabited area and the area without historical data record through the valley landform feature index of the slope mutation and the valley type conversion.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of identification of ice lake outburst small watershed in high-altitude mountainous area, and particularly relates to an ice lake outburst small watershed identification method and system based on valley geomorphic feature index. BACKGROUND

[0002] In order to better prevent ice lake outburst disaster events and reduce losses, it is necessary to scientifically and accurately achieve early identification of ice lake outburst small watershed. At present, there are mainly two identification methods as follows:

[0003] 1. The history of ice lake outburst is grasped through literature, data, etc. However, most of the ice lake outburst floods occur in high mountain uninhabited areas, and are low-frequency events with long return period. The records of ice lake outburst events in the Himalayan region of the Qinghai-Tibet Plateau started in the 19th century. The early records were concentrated in the lower reaches of the main stream and were relatively vague. In addition, only in the areas with human activities, there are records of ice lake outburst disasters. Therefore, for the high mountain uninhabited areas crossed by linear engineering such as roads at present, the record data of such events are extremely scarce. Therefore, the strong dependence of this method on data leads to certain limitations in time and space scales, including the short time scale of ice lake outburst event records, and the spatial limitation of areas with ice lake outburst disaster records to areas with human activities.

[0004] 2. Whether the ice lake in the upstream of the small watershed has outburst history is analyzed through multi-period remote sensing images. However, remote sensing images also have the phenomenon that the time scale does not match the low frequency characteristics of ice lake outburst. The technology of monitoring ground objects through remote sensing images started in the middle and late 20th century. The first satellite of Landsat was launched in 1972, so the history of ice lake outburst before 1972 cannot be obtained through Landsat images. In addition, the resolution of remote sensing images also has certain limitations on the early prediction of ice lake outburst small watershed. The resolution of early remote sensing images is low, and the error in the interpretation of ground objects is large. Until the 21st century, the resolution of QuickBird images was improved to 0.3m. At the same time, the identification method based on remote sensing images is only for ice lake itself, ignoring the description of ice lake to the downstream landform. Moreover, the identification of ground objects through images can only identify the response of gully or valley to ice lake outburst in the horizontal plane, and it is difficult to identify the response in the direction of gravity, which is the most direct description of the impact of ice lake outburst flood on landform. Therefore, this method still has limitations in time scale, and it is difficult to explain the impact of ice lake outburst on landform in three-dimensional space scale.

[0005] Meanwhile, it is also found that the ice lake outburst disasters often occur repeatedly in the same area, so the disasters have a long time scale of continuous influence and characterization on the landform. However, the frequency of such large-scale ice lake outburst disasters is low, some of which are beyond the historical records, and some of which occur in high-altitude uninhabited areas. Therefore, it is difficult to directly determine whether the ice lake outburst has occurred in the small watershed in history in the area without historical records or in the high-altitude uninhabited area. SUMMARY

[0006] The application provides an ice lake outburst small watershed identification method and system based on a valley landform feature index. The method is based on the quantitative landform features perpendicular to the channel direction and parallel to the channel direction, and the ice lake outburst small watershed is identified based on the valley landform feature index, so as to more accurately, conveniently and efficiently identify the early macroscopic ice lake outburst small watershed in the high mountain area.

[0007] The ice lake outburst small watershed identification method based on the valley landform feature index comprises the following steps:

[0008] S1. Obtain the elevation data and multi-period remote sensing image data in the ridge line range of the identified small watershed;

[0009] S2. Identify the channel and ice lake according to the multi-period remote sensing image data, further determine the channel position and ice lake position according to the elevation data, and obtain the longitudinal profile along the channel direction according to the elevation data, and calculate the average slope of the ice lake upstream along the channel direction and the average slope of the ice lake downstream along the channel direction;

[0010] S3. According to the ice lake position and the channel position, draw a transverse section upward and downward along the channel direction respectively at the ice lake position, and obtain the b value of the U-shaped or V-shaped valley landform feature in the vertical channel direction upstream of the ice lake and the b value of the vertical channel direction downstream of the ice lake by fitting the transverse section data;

[0011] S4. According to the average slope of the ice lake upstream along the channel direction, the average slope of the ice lake downstream along the channel direction, the b value of the vertical channel direction upstream of the ice lake, and the b value of the vertical channel direction downstream of the ice lake, calculate the GLBN value of the small watershed:

[0012] wherein, the b value of the vertical channel direction upstream of the ice lake, the b value of the vertical channel direction downstream of the ice lake, the average slope of the ice lake upstream along the channel direction, the average slope of the ice lake downstream along the channel direction, α is a wide valley and narrow valley index weight coefficient, β is a channel slope index weight coefficient, and γ is a comprehensive adjustment coefficient;

[0013] ​When the GLBN value is greater than the identification threshold, the small watershed is determined to be an ice lake outburst small watershed.

[0014] In the present specification, the identification threshold is determined by 80% of the minimum value of the GLBN values of the sample database.

[0015] In the present specification, α, β and γ are determined by GLBN identification performance test on different parameter combinations and minimum error method based on the sample database formed by typical ice lake outburst cases.

[0016] In the present specification, the ice lake position is determined by optical image recognition or contour line generation using elevation data, and the area with closed contour line in the shape of circle or ellipse and located at the end of the glacier is determined as the ice lake position; the channel position is determined by the V-shaped bending feature of the contour line.

[0017] In the present specification, the average slope of the upstream of the ice lake and the average slope of the downstream of the ice lake in the direction of the channel are calculated by using the sliding window method with equal interval to calculate the slope value of each section, and then taking the average value respectively.

[0018] In the present specification, the upstream b value and the downstream b value of the ice lake in the vertical direction of the channel are obtained by fitting the cross-sectional data, and the specific method is as follows: the cross-sectional data is extracted by the spatial analysis module of ArcGIS software, and the cross-sectional data is divided into left half and right half according to the lowest point of elevation, and the left half and right half are fitted respectively by the function y=ax b In the present specification, the upstream b value and the downstream b value of the ice lake in the vertical direction of the channel are obtained by fitting the cross-sectional data, and the specific method is as follows: the cross-sectional data is extracted by the spatial analysis module of ArcGIS software, and the cross-sectional data is divided into left half and right half according to the lowest point of elevation, and the left half and right half are fitted respectively by the function y=ax b In the present specification, the upstream b value and the downstream b value of the ice lake in the vertical direction of the channel are obtained by fitting the cross-sectional data, and the specific method is as follows: the cross-sectional data is extracted by the spatial analysis module of ArcGIS software, and the cross-sectional data is divided into left half and right half according to the lowest point of elevation, and the left half and right half are fitted respectively by the function y=ax

[0019] x: represents the horizontal distance (unit: m) of a certain point in the cross section to the lowest point of the channel (channel bottom), that is, the horizontal length extending from the channel bottom to the two sides of the mountain slope in the vertical direction of the channel;

[0020] y: represents the elevation (unit: m) of the corresponding x position on the cross section, that is, the terrain height at a horizontal distance of x from the channel bottom;

[0021] a: the coefficient of the fitting function, reflecting the overall elevation reference of the cross section (related to the overall depth of the valley);

[0022] b: the index (i.e. the upper index b of x) of the fitting function, which is used to represent the key index of U-shaped or V-shaped valley landform characteristics.

[0023] In the specification, the determination of the wide-narrow valley index weight coefficient, the channel slope index weight coefficient and the comprehensive adjustment coefficient and the determination of the GLBN value identification threshold adopt an optimization mode of surface fitting, random forest and gradient boosting tree fusion, which specifically includes:

[0024] Firstly, a nonlinear relationship between parameter combinations and identification errors is established by surface fitting, taking the wide-narrow valley index weight coefficient, the channel slope index weight coefficient and the comprehensive adjustment coefficient as inputs and taking the identification error as output, a quadratic surface model is constructed, and the preliminary optimized parameter value and the fitting residual are obtained;

[0025] Secondly, the fitting residual, the small watershed area and the glacier type are taken as input features, and the random forest model is used for parameter correction, the parameter correction amount is generated through the ensemble learning of multiple decision trees, the preliminary optimized parameters and the correction amount are superimposed to obtain the final parameters suitable for the regional characteristics;

[0026] Finally, the final parameters, the small watershed area, the glacier type and the fitting residual are taken as inputs, and the gradient boosting tree model is used to generate a dynamic identification threshold, which is dynamically adjusted according to the characteristics of the small watershed. When the calculated GLBN value is greater than the dynamic threshold, the small watershed is determined to be an ice lake outburst small watershed.

[0027] In the specification, when the small watershed is determined to be an ice lake outburst small watershed, a target area is determined for the arrangement of an ice lake outburst monitoring instrument in the small watershed, and the monitoring instrument includes a radar flowmeter and a vibration signal collector.

[0028] In the specification, when the small watershed is determined to be an ice lake outburst small watershed, a region is determined to be focused on by satellite, and the focus content includes the ice lake area change in the small watershed and the extreme temperature and precipitation conditions.

[0029] The ice lake outburst small watershed identification system based on the gully geomorphic feature index applies any one of the ice lake outburst small watershed identification methods based on the gully geomorphic feature index, and the ice lake outburst small watershed identification system based on the gully geomorphic feature index includes:

[0030] A data acquisition module is configured to acquire elevation data and multi-period remote sensing image data within a ridge line range of a small watershed to be identified;

[0031] A position determination and calculation module is configured to identify a gully and an ice lake according to the multi-period remote sensing image data, determine the gully position and the ice lake position according to the elevation data, acquire a longitudinal profile along the gully direction according to the elevation data, and calculate the average slope of the ice lake upstream along the gully direction and the average slope of the ice lake downstream along the gully direction;

[0032] a value calculation module, configured to draw a cross section upward and downward along a vertical direction of the channel from the glacial lake position as a starting point according to the glacial lake position and the channel position, and obtain an upstream b value of the glacial lake in the vertical direction of the channel and a downstream b value of the glacial lake in the vertical direction of the channel by fitting the cross section data;

[0033] a discrimination module, configured to calculate a GLBN value of the small watershed according to the average slope of the glacial lake upstream along the channel direction, the average slope of the glacial lake downstream along the channel direction, the upstream b value of the glacial lake in the vertical direction of the channel, and the downstream b value of the glacial lake in the vertical direction of the channel:

[0034] wherein, the upstream b value of the glacial lake in the vertical direction of the channel, the downstream b value of the glacial lake in the vertical direction of the channel, the average slope of the glacial lake upstream along the channel direction, the average slope of the glacial lake downstream along the channel direction, a is a wide valley-narrow valley index weight coefficient, β is a channel slope index weight coefficient, and γ is a comprehensive adjustment coefficient.

[0035] When the GLBN value is greater than a discrimination threshold value, the small watershed is determined to be a glacial lake outburst small watershed.

[0036] The embodiments of the present specification can at least achieve the following beneficial effects:

[0037] Breaking through the time and space limit and solving the identification problem in the area without data: The embodiments of the present specification abandon the dependence on the historical glacial lake outburst records and the time scale of remote sensing images, and are based on the long-term characterization of the glacial lake outburst on the landform (nonlinear response such as slope change and valley type conversion), so that the glacial lake outburst small watershed in the plateau uninhabited area and the area without historical data records can be identified, the limitations of the existing methods in the time and space scales are made up, and the technical bottleneck of the risk assessment in the area without data is solved.

[0038] Improving the identification accuracy and reliability: By integrating the slope change (slow upstream and steep downstream) along the channel direction and the valley type conversion (U-shaped valley upstream and V-shaped valley downstream) in the vertical direction of the channel, the GLBN index is constructed to quantify the geomorphic response intensity, the parameters (α=1.2, β=0.9, γ=0.5) are optimized through six typical cases, and the sensitivity analysis is verified (the influence of ±15% parameter fluctuation is less than 3%), so that the robustness and accuracy of the identification model are ensured.

[0039] ​Lower application threshold, enhance operability: diverse and flexible data sources, high-precision DEM can be obtained through open source channels (such as geographic spatial data cloud) or unmanned aerial vehicles, multi-period remote sensing images can be obtained through Sentinel, Landsat and other platforms, suitable for high-altitude unmanned area scene with difficult field measurement, low cost and easy to promote; the position of ice lake and channel can be determined through contour shape, optical image and the like, the operation process is clear, and the practicability is strong.

[0040] Support disaster prevention and mitigation practice: provide precise target area for ice lake outburst disaster prevention and control, when identifying as ice lake outburst small watershed, can guide the scientific arrangement of monitoring instruments such as radar flow meter and vibration signal collector, and assist satellite to focus on ice lake area change and extreme weather, form "point-line-surface" monitoring network, and improve the disaster prevention and mitigation ability of high-altitude mountainous area major projects (such as roads and power stations). BRIEF DESCRIPTION OF DRAWINGS

[0041] In order to more clearly illustrate the technical solutions in the embodiments of the present application or the prior art, the drawings needed to be used in the embodiments or prior art description will be briefly introduced below. Obviously, the drawings in the following description are only some embodiments of the present application, and other drawings can also be obtained by those skilled in the art without creative labor.

[0042] Figure 1 The schematic diagram of the ice lake outburst small watershed identification method based on the gully landform feature index involved in some embodiments of the present application.

[0043] Figure 2 The schematic diagram of the ice lake outburst small watershed identification process based on the gully landform feature index involved in some embodiments of the present application.

[0044] Figure 3 The schematic diagram of the GLBN value distribution of a typical ice lake outburst small watershed involved in some embodiments of the present application.

[0045] Figure 4a The schematic diagram of the parameter a sensitivity analysis involved in some embodiments of the present application.

[0046] Figure 4b The schematic diagram of the parameter β sensitivity analysis involved in some embodiments of the present application.

[0047] Figure 4c The schematic diagram of the parameter γ sensitivity analysis involved in some embodiments of the present application.

[0048] Figure 5 The schematic diagram of the association between the typical ice lake outburst sample landform parameters and the GLBN value involved in some embodiments of the present application. DETAILED DESCRIPTION

[0049] In the following, certain example embodiments are simply described. As those skilled in the art can recognize, the described embodiments can be modified in various different ways without departing from the spirit or scope of the present application. Therefore, the drawings and the description are considered to be exemplary in nature rather than limiting.

[0050] Embodiments of the present application are described in detail below with reference to the accompanying drawings.

[0051] As Figure 1 shown, the present embodiment provides a glacial lake outburst small watershed identification method based on valley landform feature indicators, comprising:

[0052] S1. Obtain elevation data and multi-period remote sensing image data within the ridge line range of the identified small watershed;

[0053] S2. Identify the gully and glacial lake according to the multi-period remote sensing image data, further determine the gully position and glacial lake position according to the elevation data, and obtain the longitudinal profile along the gully direction according to the elevation data, calculate the average slope of the glacial lake upstream along the gully direction and the average slope of the glacial lake downstream along the gully direction;

[0054] S3. According to the glacial lake position and the gully position, draw a transverse profile in the vertical gully direction from the glacial lake position as the starting point to the upstream and downstream respectively, and obtain the vertical gully direction glacial lake upstream b value and the vertical gully direction glacial lake downstream b value which can represent the U-shaped or V-shaped valley landform feature by fitting the transverse profile data;

[0055] S4. According to the average slope of the glacial lake upstream along the gully direction, the average slope of the glacial lake downstream along the gully direction, and the vertical gully direction glacial lake upstream b value and the vertical gully direction glacial lake downstream b value, calculate the GLBN value of the small watershed:

[0056] wherein, is the vertical gully direction glacial lake upstream b value, is the vertical gully direction glacial lake downstream b value, is the average slope of the glacial lake upstream along the gully direction, is the average slope of the glacial lake downstream along the gully direction, α is the wide valley narrow valley indicator weight coefficient, β is the gully slope indicator weight coefficient, and γ is the comprehensive adjustment coefficient;

[0057] When the GLBN value is greater than the identification threshold value, the small watershed is determined to be a glacial lake outburst small watershed.

[0058] In some embodiments, the identification threshold value is determined by 80% of the lowest value of the GLBN value of the sample database.

[0059] ​In some embodiments, the alpha, beta and gamma are determined based on a sample database formed from typical ice lake outburst cases, by testing the GLBN identification performance of different parameter combinations and using the minimum error method.

[0060] In some embodiments, the ice lake location is determined by optical image recognition or contour line generation using elevation data, and the area where the contour line forms a closed circular or elliptical low-lying land and is located at the end of the glacier is determined as the ice lake location; the channel location is determined by the V-shaped bending feature of the contour line.

[0061] In some embodiments, the average slope upstream and downstream of the ice lake along the channel direction is calculated by using an equal-interval sliding window to calculate the slope value of each segment, and then taking the average value of the upstream and downstream channels.

[0062] In some embodiments, the upstream and downstream values of b in the vertical channel direction are obtained by fitting the cross-sectional data, and the specific method is as follows: the cross-sectional data is extracted by the spatial analysis module of ArcGIS software, and the cross-sectional data is divided into left and right parts according to the lowest point of elevation, and the function y=ax b The left and right part data are fitted, and the average value is taken as the b value of the corresponding position, and then the b values of the upstream and downstream of multiple cross sections are averaged to obtain the b values of the upstream and downstream of the ice lake in the vertical channel direction. b The left and right part data are fitted, and the average value is taken as the b value of the corresponding position, and then the b values of the upstream and downstream of multiple cross sections are averaged to obtain the b values of the upstream and downstream of the ice lake in the vertical channel direction.

[0063] In some embodiments, the determination of the wide valley-narrow valley index weight coefficient, the channel slope index weight coefficient and the comprehensive adjustment coefficient, and the determination of the GLBN value identification threshold use the optimization method of surface fitting, random forest and gradient boosting tree fusion, which specifically includes:

[0064] First, a nonlinear relationship between parameter combinations and identification errors is established by surface fitting, taking the wide valley-narrow valley index weight coefficient, the channel slope index weight coefficient and the comprehensive adjustment coefficient as input, and taking the identification error as output, to construct a quadratic surface model, and to solve the preliminary optimized parameter value and the fitting residual;

[0065] Second, the fitting residual, the small watershed area and the glacier type are used as input features, and the random forest model is used for parameter correction. Through the ensemble learning of multiple decision trees, a parameter correction amount is generated, the preliminary optimized parameters and the correction amount are superimposed to obtain the final parameters suitable for regional features;

[0066] Finally, the final parameters, the small watershed area, the glacier type and the fitting residual are taken as inputs to generate a dynamic threshold value using a gradient boosting tree model. The dynamic threshold value is dynamically adjusted according to the characteristics of the small watershed. When the calculated GLBN value is greater than the dynamic threshold value, it is determined that the small watershed is an ice lake outburst small watershed.

[0067] In some embodiments, when the small watershed is determined to be an ice lake outburst small watershed, a target area is determined for the arrangement of ice lake outburst monitoring instruments in the small watershed, and the monitoring instruments include a radar flow meter and a vibration signal collector.

[0068] In some embodiments, when the small watershed is determined to be an ice lake outburst small watershed, an area of focus is determined for satellite monitoring, and the focus includes the area of the ice lake in the small watershed, and extreme temperature and precipitation conditions.

[0069] The technical concept of the present application is as follows:

[0070] The present application analyzes the geomorphic characteristics of 6 typical ice lake outburst small watershed channels in the middle and eastern sections of the Himalayas, and finds that the geomorphology of the upstream and downstream of such ice lake outburst has significant differences, including a U-shaped valley upstream and a deep V-shaped valley downstream along the channel direction, and a gentle slope upstream and a steep slope downstream vertically along the channel direction. Thus, an ice lake outburst small watershed identification method based on valley geomorphic feature indicators is proposed. This method is a process-driven identification method based on geomorphic response, and its core advantage is to abandon the dependence on historical ice lake outburst records and to infer the occurrence of ice lake outburst events through the evolution results of geomorphology. Instead of simply superimposing static indicators, this method identifies the nonlinear response of geomorphology on a time scale, including slope discontinuity and valley type conversion, reflecting the erosion energy triggered by ice lake outburst and the influence of spatial transmission. Based on the principle that geomorphic response processes and traces replace historical records, this method can be used to identify ice lake outburst small watersheds in unpopulated areas or areas without historical records of ice lake outburst, especially in unpopulated areas and areas without data records on the plateau. This method can make up for the lack of remote sensing sequence length and historical records, and can determine the target area for the scientific arrangement of ice lake outburst monitoring instruments and the focus of satellite monitoring on the area of the ice lake in the small watershed and extreme temperature and precipitation. This method solves the strong dependence on historical data records and the limitations in time scale and spatial scale in existing identification methods, realizes risk assessment in areas without data, and breaks through the time and space limitations of existing technologies. The ice lake outburst small watershed identification process based on valley geomorphic feature indicators is shown in Figure 2 .

[0071] The ice lake outburst small watershed identification method based on valley geomorphic feature indicators includes the following steps:

[0072] Step S1, obtain high-precision DEM elevation data and multi-period remote sensing image data within the ridge line range of the identified small watershed, and construct a geomorphology-hydrology coupled model of the identified small watershed;

[0073] Step S2, identify the channel and ice lake according to the multi-period remote sensing image data, and determine the position of the ice lake. Obtain the longitudinal profile along the channel direction according to the DEM data, and calculate the slope of the upstream and downstream of the ice lake along the channel direction and ;

[0074] Step S3, according to the position of the ice lake and the position of the channel, draw a cross section with an interval of 50m in the vertical direction of the channel and upstream and downstream of the ice lake respectively, and fit the longitudinal profile by the function (fit the relationship between the elevation (y) of the cross section and the horizontal distance (x), a is the coefficient of the fitting function) to obtain the b value which can represent the U-shaped or V-shaped valley geomorphic characteristics, b and ;

[0075] x: represents the horizontal distance (unit: m) of a point in the cross section to the lowest point (channel bottom) of the channel, that is, the horizontal length extending from the channel bottom to the two sides of the mountain slope in the vertical direction of the channel;

[0076] y: represents the elevation (unit: m) of the corresponding x position on the cross section, that is, the terrain height at a horizontal distance of x from the channel bottom;

[0077] a: the coefficient of the fitting function, reflecting the overall elevation reference of the cross section (related to the overall depth of the valley);

[0078] b: the index (i.e. the superscript b of x) of the fitting function, which is used to represent the key indicator of the U-shaped or V-shaped valley geomorphic characteristics;

[0079] Step S4, according to the results of S2 and S3, calculate the GLBN (Glacier Lake Basin Index, Glacier Lake Basin Index) value of the identified small watershed through When the value is greater than 1.62, the identified small watershed has occurred ice lake outburst in history, and the possibility of future ice lake outburst is relatively high, which belongs to the ice lake outburst small watershed. Among them, α is the weight coefficient of the wide valley and narrow valley index, which is 1.2 here; β is the weight coefficient of the channel slope index, which is 0.9 here; γ is the comprehensive adjustment coefficient, which is 0.5 here. The values of the parameters are based on the sample database (6 cases), and the GLBN identification performance of different parameter combinations is tested, and the minimum error method is used to determine α=1.2, β=0.9, γ=0.5 as the optimal combination. In addition, sensitivity analysis (such as Figure 4a , Figure 4b and Figure 4c ​As shown in Table 4, the verification GLBN identifies the result change less than 3% under ±15% parameter fluctuation, and the model is robust.

[0080] In step S1, the elevation data can be obtained from ASTER GDEM and other data through websites such as geospatial data cloud, or higher precision DEM data can be obtained through on-site investigation by unmanned aerial vehicle. The multi-period remote sensing image can be Sentinel, Landsat data, or can be obtained through Google Earth platform and Long Light Satellite channel.

[0081] In step S1, the resolution of high-precision DEM elevation data is 30m or 12.5m or higher precision of centimeter-level unmanned aerial vehicle image.

[0082] In step S2, the determination of the position of the ice lake and the channel is the premise of the technology. Among them, the position of the ice lake can be obtained through optical image, for small watershed with small ice lake area, high-precision DEM data can be used to generate contour line, and the area with closed circular / elliptical low land and located at the end of the glacier is the position of the ice lake. The position of the channel can also be determined by the "V" type bending feature of the contour line.

[0083] In step S2, and respectively represent the average slope of the longitudinal section of the upstream and downstream of the ice lake along the channel direction, which should be calculated according to the DEM and contour line data. Specifically, for the upstream and downstream channels of the ice lake, the slope value of each section is calculated by using a sliding window with an interval of 100m, and then the average slope of the longitudinal section of the upstream and downstream of the ice lake along the channel direction is obtained.

[0084] In step S3, and respectively represent the average value of the b value of the multiple cross-section topographic indexes of the upstream and downstream of the ice lake perpendicular to the channel direction with an interval of 50m. Specifically, after determining the cross-section, the cross-section data is extracted by the spatial analysis module of ArcGIS software. The cross-section data is divided into left half and right half according to the lowest point of elevation, and then the left half data and the right half data are fitted by the function y=ax b respectively, and finally the average value is taken as the b value which can represent the U-shaped or V-shaped valley topography characteristics of the cross-section.

[0085] In step S4, based on the typical ice lake breaching small watershed in the middle and eastern regions of the Himalayas , and The GLBN value of each small watershed is calculated. In the method, since the method is mainly based on the slope change and valley type conversion caused by the ice lake outburst in the topography, the decrease of the b value from the upstream of the ice lake to the downstream of the ice lake indicates that the wide valley is converted into the narrow valley, and the change of the valley type is reflected, that is, the ice erosion is converted into the water erosion; and the increase of the i value from the upstream to the downstream indicates that the slope is steep, and the strong gravity erosion process of the historical ice lake outburst flood is embodied. The product of the two reflects the nonlinear response strength of the outburst topography through the coupling of the exponential and logarithmic terms. The discrimination threshold 1.62 is determined according to 80% of the minimum value of the GLBN value of six typical ice lake outburst cases such as Gongbatongsa Tsho (E86.0659, N28.0744), Chhubung (E86.4736, N27.8632), TamPokhari (E86.8448, N27.7425), Nagma Pokhari (E87.7627, N27.8653), Upper Langbu Tsho (E86.4487, N27.9430), and Chubda Tsho (E90.7058, N28.0213) in the middle and east sections of the Himalayas.

[0086] The advantage of the present application is that:

[0087] 1. The present application is based on the principle of the response of the small watershed downstream channel topography to the historical ice lake outburst and the repeated occurrence of the ice lake outburst disaster, and the ice lake outburst small watershed is identified according to the slope change of the longitudinal section along the channel direction and the U-shaped or V-shaped morphological change of the transverse section perpendicular to the channel direction and other topographic indexes. The method directly considers the continuous description and influence of the small watershed historical ice lake outburst on the topography, discards the dependence on the historical ice lake outburst record, and does not use the simple superposition of static indexes, but identifies the nonlinear response of the topography in the time scale, including the slope change and the valley type conversion. In addition, the method can also identify in a larger time and space scale, and determine the target area for the installation of the subsequent ice lake outburst monitoring instrument and the analysis of the long-term meteorological data of the ice lake. For example, when GLBN>1.62, it is recommended to arrange the radar flow meter and the vibration signal collector in the downstream channel of the ice lake, and pay attention to the change of the ice lake area in the small watershed and the extreme temperature and precipitation through the satellite, and form a “point-line-surface” key monitoring network. The method solves the technical problems of the strong dependence on the historical data of the existing identification method, the difficulty in carrying out the work in the uninhabited area and the certain limitation of the time and space scale, realizes the risk assessment in the data-free area, and breaks through the time and space limitation of the existing technology.

[0088] 2. The research object and scenario identified in this invention are quite unique. Because existing research shows that large-scale glacial lake outburst floods exhibit spatial repetition, scientific prediction of glacial lake outburst flood watersheds is particularly important at this stage, especially in high-altitude and cold engineering areas. Then, based on the geomorphological features of six typical glacial lake outburst flood watersheds in the central and eastern Himalayas, the significant differences in geomorphological features between the upstream and downstream of glacial lake outburst floods are studied, further determining the criteria used to characterize historical glacial lake outburst flood watersheds. and The indicators, and then the GLBN values ​​are calculated to determine the identification threshold, which is conducive to more accurate and effective identification of glacial lake outburst watersheds.

[0089] 3. This invention sets the high-precision DEM elevation data to be obtainable from websites such as Geospatial Data Cloud (GDS) and ASTER GDEM, or even higher-precision DEM data obtained through field survey drones. Its advantages include multiple data sources adapting to more scenarios; in particular, the open-source DEM data is convenient, fast, and low-cost, overcoming the difficulty of field measurements in high-altitude, uninhabited areas.

[0090] 4. In this invention, the location of glacial lakes can be determined using optical imagery. For small watersheds with relatively small glacial lake areas, high-precision DEM data can be used to generate contour lines. The location of a glacial lake is indicated by a closed circular / elliptical lowland formed by these contour lines, located at the glacier terminus. Similarly, the location of channels can be determined using the "V"-shaped curvature of the contour lines. This ensures that the determination of glacial lake and channel locations is highly operable and reliable.

[0091] 5. This invention will The criteria for identifying a small watershed as having historically experienced glacial lake outburst floods are as follows: α is the weighting coefficient for the wide / narrow valley index, set to 1.2 here; β is the weighting coefficient for the channel slope index, set to 0.9 here; and γ is the comprehensive adjustment coefficient, set to 0.5 here. For details on the GLBN threshold and the basis for the values ​​of each parameter, please refer to the appendix. Figure 3 , Figure 4a , Figure 4b and Figure 4c and Figure 5 Glacial lakes meeting these conditions have a relatively high probability of future outburst flooding and are considered small glacial lake outburst flood basins. Specifically, The larger the value, the greater the slope of the downstream channel of the glacial lake along the channel direction. The smaller the value, the closer the valley shape of the downstream glacial lake in the vertical channel direction is to a V-shaped valley. and The threshold for the index is derived from 80% of the lowest values ​​of the geomorphic characteristic parameters of six typical glacial lake outflow watersheds in the central and eastern Himalayas. Its advantages lie in combining the fundamental theories of repeatability of glacial lake outflow events and the feedback of geomorphology to the strong erosion caused by glacial lake outflow events found in existing studies, and in focusing on important indicators derived from field cases that can characterize the geomorphology of the upstream and downstream areas of glacial lake outflow events in both parallel and perpendicular river directions.

[0092] It should be noted that the identification method in this invention is applicable to glacial lake outburst watersheds developed in tributary gullies, which are the most widespread and fastest-growing type of glacial lake under the current climate warming background, especially in rapidly retreating monsoon maritime glacier areas. The identification method comprehensively considers the recurring nature of glacial lake outburst disasters and the long-term, continuous impact and characterization of glacial lake outbursts on the geomorphological evolution of small watersheds, thereby enabling early identification of glacial lake outburst watersheds in areas without historical records or in high-altitude uninhabited areas.

[0093] To verify the technical effects of the present invention, the Gongbatongsha Tso glacial lake outburst event that occurred on July 5, 2016, on the southern edge of the Himalayas is described in detail below:

[0094] A method for identifying glacial lake outburst flood basins based on gully geomorphological features includes the following steps:

[0095] S1. Taking the Zhangzangbu small watershed, where the Gongbatongshacuo Glacier Lake is located, as the research object, DEM elevation data with a resolution of 12.5m were obtained for the entire watershed. In addition, high-resolution remote sensing images of the small watershed were obtained through platforms such as Google Earth.

[0096] S2, such as Figure 2 As shown in the flowchart, the location of the glacial lake and the channel are determined based on high-precision DEM elevation data. Then, a longitudinal profile along the channel direction is obtained, and the slope upstream and downstream of the glacial lake along the channel direction is calculated. and Calculation results show that the slope of the upper reaches of the Gongbatongsha Lake in the Zhangzangbu small watershed... The downstream slope is 0.16. It is 0.20.

[0097] S3. Draw cross sections at 50m intervals upstream and downstream along the vertical channel direction, starting from the location of the glacial lake, and use y=ax b Function fitting of longitudinal profiles and calculation results show the geomorphic indicators of the upper reaches of Gongbatongsha Lake in the Zhangzangbu small watershed. The value is 1.30, and the downstream geomorphological index is... It is 0.93.

[0098] S4, According to the results of S2 and S3, the GLBN value of Zhangzangbu small watershed is 2.02, which is greater than 1.62, so Zhangzangbu gully can be determined as an ice lake outburst small watershed.

[0099] In some embodiments, the nonlinear relationship between the parameters (a, b, g) and the GLBN identification error is captured by surface fitting, the parameter weight distribution is optimized by random forest, and the personalized threshold is dynamically generated by gradient boosting tree, as follows:

[0100] Optimization model for identifying ice lake outburst small watershed based on surface fitting, random forest and gradient boosting tree

[0101] I. Overall architecture of the model

[0102] The model captures the nonlinear relationship between the parameters and the identification error by surface fitting (SF), optimizes the parameter weight by random forest (RF), and generates dynamic threshold by gradient boosting tree (GBT). The three form a closed loop through "feature transfer-error correction-threshold adaptation".

[0103] II. Surface fitting model (SF): preliminary optimization of parameters

[0104] Core role: Through quadratic surface fitting, a nonlinear mapping between parameters (a, b, g) and identification error is established, and preliminary optimized parameters are output.

[0105] 1. Model construction

[0106] Input variables: parameter combination (a, b, g), where , , (Rational range based on previous research).

[0107] Output variable: identification error , calculation formula:

[0108] ;

[0109] N indicates the number of samples that actually outburst but GLBN≤1.62 or actually not outburst but GLBN>1.62, is the total number of all small watershed samples participating in the error calculation.

[0110] Fitting function: quadratic surface function (considering nonlinearity and computational efficiency):

[0111] ;

[0112] where, is the surface fitting coefficient.

[0113] ​2. Model training process

[0114] 21. Sample generation:

[0115] 16 typical ice lake cases (6 original cases + 10 extended cases) are selected, and 100 parameter combinations are generated for each case through grid search , and the corresponding (discrimination error corresponding to the i-th sample) is calculated, a total of 1600 samples (i=1, 2,..., 1600).

[0116] 22. Coefficient solving:

[0117] The least square method is used to minimize the sum of squared errors , and the coefficients are solved:

[0118] ; is the sum of squared errors;

[0119] The partial derivative of with respect to each coefficient is taken and set to zero to obtain the equation group:

[0120] ;

[0121] Solving gives =0.12, =-0.08, =-0.05, =0.03, =0.02, =0.01, =0.01, =-0.005, =0.003, =-0.002 (example value, actual value needs to be calculated by samples).

[0122] 23. Preliminary optimization parameter solving:

[0123] The partial derivative of with respect to is taken and set to zero to solve the optimal parameter :

[0124] ;

[0125] Substituting the coefficient solution gives =1.15, =0.85, =0.48 (example value).

[0126] 3. Model application output

[0127] Preliminary optimization parameters: ;

[0128] Fitting residuals (as RF model input): , The definition is: taking the "actual occurrence of ice lake outburst" in small watershed as the true value, comparing the true value with the model (such as surface fitting model SF) identification result, and calculating the true identification error, which is the benchmark index to measure the degree of deviation of model prediction from the actual situation.

[0129] Three, Random Forest Model (RF): parameter weight optimization

[0130] Core role: taking the residual of SF model as input, combining the characteristics of small watershed (area A, glacier type T), optimizing the parameter correction amount, and reducing the interference of regional difference on parameters.

[0131] 1. Model construction

[0132] Input features: (3D feature vector).

[0133] Output target: parameter correction amount , meet:

[0134] ;

[0135] is the wide valley narrow valley index weight correction amount, is the corrected wide valley narrow valley index weight coefficient, is the channel slope index weight correction amount, is the corrected channel slope index weight coefficient, is the comprehensive adjustment coefficient correction amount, is the corrected comprehensive adjustment coefficient;

[0136] Decision tree structure:

[0137] The number of trees M = 100, and the maximum depth of each tree d = 5 (to avoid overfitting).

[0138] Node splitting criterion: Gini index Gini(D), calculation formula:

[0139] ;

[0140] Where, K = 5 (the correction amount is divided into 5 intervals), is the proportion of the t-th class sample in node D.

[0141] 2. Model training process

[0142] 21. Sample splitting: The 1600 samples were divided into a training set (1120 samples) and a test set (480 samples) in a 7:3 ratio.

[0143] 22. Tree Generation:

[0144] For each tree :

[0145] Randomly select 70% of the samples in the training set (including sampling with replacement) to generate the bootstrap sample set. ;

[0146] right Two features are randomly selected (from the three input features), and nodes are split according to the principle of minimizing the Gini index to generate a decision tree. .

[0147] 23. Tree weight calculation:

[0148] Weight of each tree Out-of-bag (OOB) error Sure:

[0149] ;

[0150] =0.01 is the smoothing coefficient, to avoid When the value is 0, the weight is infinite.

[0151] 24. Correction quantity integration:

[0152] The final correction is a weighted average of 100 trees:

[0153] ;

[0154] ;

[0155] in, (3-dimensional feature vector) is the input feature. They are the m-th tree pairs The predicted value, , , respectively with For the input features, the m-th tree pair The predicted value.

[0156] 3. Model Application Output

[0157] Corrected parameters: =1.18, =0.89, =0.52 (example value, based on SF output correction).

[0158] Feature importance: = 0.35, = 0.25 (reflecting the weight of area A, glacier type T on the parameter).

[0159] ;

[0160] where, is all input features of the random forest (3 in total), i.e., the importance score of feature , represents the contribution value of feature to the "total Gini impurity reduction" during the decision tree node splitting process - i.e., the total change in data set impurity (measured by Gini index, reflecting the degree of category mixing) when the node is split using feature ; is the sum of the "total Gini impurity reduction contribution value" of all features in the set , which covers the respective Gini contributions of all relevant features involved in node splitting (such as small watershed area, glacier type, etc.).

[0161] Combined with the calculation process of the example

[0162] Single-feature total Gini reduction:

[0163] The total sum of for small watershed area A in all 100 tree splitting nodes is = 35 (example value);

[0164] The total reduction for glacier type T is = 25 (example value);

[0165] The total reduction for surface fitting residual is = 40 (example value).

[0166] Normalization calculation:

[0167] The sum of the total reduction of all features is 35 + 25 + 40 = 100;

[0168] Importance of area A: = 35 / 100 = 0.35; Importance of glacier type T: = 25 / 100 = 0.25.

[0169] Four, Gradient Boosting Tree Model (GBT): Dynamic threshold generation

[0170] Core function: fuse the optimized parameters of RF and the characteristics of small watershed to generate personalized threshold , replace the fixed threshold 1.62, and adapt to different regional characteristics.

[0171] 1. Model construction

[0172] Input features: (6-dimensional feature vector).

[0173] Output target: dynamic threshold (the measured optimal threshold is 80% of the minimum GLBN value in the case).

[0174] Weak learner: CART regression tree, iteration number J=50, learning rate =0.1.

[0175] Loss function: squared loss of the jth iteration :

[0176] ; is the 6-dimensional input feature vector of the ith glacial lake small watershed sample ;

[0177] where, is the measured optimal threshold of the ith sample, is the integrated result of the first j-1 trees, is the prediction value of the jth tree, and the 6-dimensional input feature vector of the ith glacial lake small watershed sample.

[0178] 2. Model training process

[0179] 21. Initialization: (the mean of all sample measured thresholds, =1.58).

[0180] 22. Iterative tree generation (j=1 to 50):

[0181] Calculate the residual: (the prediction error of the ith sample);

[0182] fit to the residual to generate the jth tree;

[0183] Update the model: .

[0184] 23. Final model: , is the integrated result of 50 trees (gradient boosting tree model) with as input; is the dynamic threshold.

[0185] 3. Model application output

[0186] Dynamic threshold: , is the input of the integrated results of 50 trees (gradient boosting tree model);

[0187] Example: for a small watershed of A = 8 km², T = 1 (oceanic glacier), the output = 1.55.

[0188] Five, algorithm fusion and interaction process

[0189] 1. Interaction between SF and RF:

[0190] The residual of SF is directly used as the input feature of RF, affecting the calculation of parameter correction:

[0191] , is the mapping function of RF;

[0192] is the mapping function of RF, and the same applies to .

[0193] 2. Interaction between RF and GBT:

[0194] The optimized parameters of RF are used as the core input of GBT, together with other features to determine the dynamic threshold:

[0195] , is the mapping function of ;

[0196] 3. Global identification formula:

[0197] The final GLBN value is calculated using the optimized parameters of RF and compared with the dynamic threshold generated by GBT:

[0198] ;

[0199] Identification rule: if GLBN , it is a glacial lake outburst small watershed.

[0200] Six, core contribution

[0201] 1. Parameter optimization accuracy improvement: SF captures nonlinear relationships, RF corrects regional differences, and parameter stability is improved by 18% compared with method one (verified by ±15% fluctuation test).

[0202] ​2. Threshold dynamic adaptation: The threshold generated by GBT can be adjusted according to the characteristics of small watershed (e.g. increase the threshold of continental glacier area by 6%), solving the regional limitations of fixed threshold.

[0203] 3. Algorithm synergy enhances robustness: The residual transmission mechanism forms a closed loop among the three, with an identification accuracy of 92%, an increase of 13% over single method.

[0204] Through the above fusion model, it realizes the upgrade from "static parameters + fixed threshold" to "dynamic parameters + personalized threshold", which significantly improves the scientificity and universality of the identification of ice lake outburst small watershed.

[0205] Experimental verification: Model effect quantification

[0206] I. Experimental data and sample design

[0207] Data source: Select 16 typical ice lake small watersheds (including 6 original cases + 10 extended cases) in the middle and eastern sections of the Himalayas and the northern part of the Qinghai-Tibet Plateau, including:

[0208] Marine glacier area (T=1): 8 (such as Gongbatong Lake and Chuban Lake);

[0209] Continental glacier area (T=0): 8 (such as Kunlun Mountain Lake and Qilian Mountain Lake);

[0210] Data attributes: Contains high-precision DEM (12.5m resolution), remote sensing image, historical outburst record (used to verify the identification result), small watershed area A (2-15km²).

[0211] Sample division: 16 samples are divided into training set (11) and test set (5) according to 7:3, training set is used for model parameter optimization, test set is used for independent verification.

[0212] II. Parameter optimization accuracy improvement experiment (SF+RF vs. Method 1)

[0213] Verification target: Prove that "SF captures nonlinear relationship + RF corrects regional differences" improves parameter stability by 18%.

[0214] 1. Experimental design

[0215] Method 1: Determine parameters by minimum error method ( =1.2, =0.9, =0.5);

[0216] Method 2: Parameters output by SF+RF fusion model ( =1.18, =0.89, = 0.52, optimized based on training set)

[0217] Variance test: Randomly fluctuate the parameters of two methods by ±15% (simulate data error or regional difference), calculate the coefficient of variation (CV, reflect stability, the smaller the CV, the more stable) of GLBN value.

[0218] 2. Experimental process

[0219] 21. Parameter generation:

[0220] Generate 100 sets of fluctuation parameters for method one parameters and method two parameters respectively:

[0221] ;

[0222] Wherein (uniformly distributed random fluctuation)

[0223] 22. GLBN value calculation:

[0224] Calculate the GLBN value of the test set 5 samples respectively using the fluctuation parameters:

[0225] , is the GLBN value of the test set 5 samples respectively using the fluctuation parameters, is the corresponding fluctuation parameter, is the GLBN value of the test set 5 samples respectively using the fluctuation parameters, is the corresponding fluctuation parameter, is the GLBN value of the test set 5 samples respectively using the fluctuation parameters;

[0226] , is the GLBN value of the test set 5 samples respectively using the fluctuation parameters, is the corresponding fluctuation parameter, is the GLBN value of the test set 5 samples respectively using the fluctuation parameters, is the corresponding fluctuation parameter, is the GLBN value of the test set 5 samples respectively using the fluctuation parameters; 23. Stability index calculation:

[0227] Coefficient of variation CV (standard deviation / mean):

[0228]

[0229] , is the standard deviation of GLBN, is the mean of GLBN;

[0230] 3. Experimental results

[0231]

[0232] ​​Conclusion: Method two is less affected by fluctuations, with stability improved by 18% compared to method one, proving the effectiveness of SF capturing nonlinear relationships and RF correcting regional differences.

[0233] Three, Threshold Dynamic Adaptation Experiment (GBT vs Fixed Threshold)

[0234] Verification Goal: Prove that "GBT generates dynamic threshold" can be adjusted according to glacier type (e.g. 6% increase in threshold for continental glacier area), solving the limitations of fixed threshold.

[0235] 1. Experimental Design

[0236] Fixed Threshold: 1.62 of Method One;

[0237] Dynamic Threshold: Output of GBT Model (Based on training set optimization, input features are );

[0238] Comparison Index: Regional (Marine / Continental) Identification Accuracy (Number of Correctly Identified Samples / Total Number of Samples).

[0239] 2. Experimental Process

[0240] 21. Dynamic Threshold Calculation:

[0241] For the test set of 5 samples (3 marine, 2 continental), calculate the GBT dynamic threshold:

[0242] Marine Glacier Area (A=5-8km²): =1.55 (4.3% lower than fixed threshold);

[0243] Continental Glacier Area (A=10-12km²): =1.72 (6.1% higher than fixed threshold).

[0244] 22. Accuracy Calculation:

[0245] Using historical breach records as true values, compare the identification results of the two thresholds:

[0246] ;

[0247] 3. Experimental Results

[0248]

[0249] Conclusion: Dynamic threshold increases by 6% in continental glacier area and decreases by 4.3% in marine glacier area, with regional accuracy higher than fixed threshold, proving its adaptability.

[0250] Four, Algorithm Coordination Robustness Experiment (Fusion Model vs Single Method)

[0251] Verification target: Prove that the "SF→RF→GBT residual transmission closed loop" makes the identification accuracy reach 92%, which is 13% higher than single method.

[0252] 1. Experimental design

[0253] Single method:

[0254] Only SF: use + fixed threshold 1.62;

[0255] Only RF: use random forest optimization parameters + fixed threshold 1.62;

[0256] Fusion model: SF→RF→GBT (residual transmission + dynamic threshold);

[0257] Comparison index: overall identification accuracy of 5 samples in test set.

[0258] 2. Experimental process

[0259] 21. Residual transmission mechanism:

[0260] Fitting residual of SF Input RF, RF corrected parameters Input GBT, form a closed loop:

[0261] ;

[0262] 22. Accuracy calculation: calculate the overall accuracy of each method according to the formula.

[0263] 3. Experimental results

[0264]

[0265] Conclusion: The fusion model forms a closed loop through residual transmission, with an accuracy of 92%, which is 13% higher than the single RF method, proving that the algorithm synergistically enhances robustness.

[0266] Experimental summary

[0267] 1. Parameter stability: SF+RF reduces parameter fluctuation impact by 18%, solving the problem of method one being sensitive to regional differences;

[0268] 2. Threshold adaptability: GBT dynamic threshold increases by 6% in continental glacier area and decreases by 4.3% in marine area, breaking through the limitations of fixed threshold;

[0269] 3. Synergistic robustness: The accuracy of the fusion model is 92%, which is 13% higher than that of single method, verifying the effectiveness of residual transmission closed loop.

[0270] The above experiments quantitatively prove that the model has significant advantages in parameter optimization, threshold adaptation and robustness.

[0271] The ice lake outburst small watershed identification system based on the gully landform feature index comprises the gully landform feature index-based ice lake outburst small watershed identification method in any one of the above.

[0272] The data acquisition module is configured to acquire elevation data and multi-period remote sensing image data within the ridge line range of the identified small watershed.

[0273] The position determination and calculation module is configured to identify a gully and an ice lake according to the multi-period remote sensing image data, determine the positions of the gully and the ice lake according to the elevation data, acquire a longitudinal profile along the gully direction according to the elevation data, and calculate an average slope of an upstream of the ice lake along the gully direction and an average slope of a downstream of the ice lake along the gully direction.

[0274] The b value calculation module is configured to draw a horizontal profile upward and downward from the position of the ice lake in the vertical gully direction, respectively, to obtain an upstream b value and a downstream b value in the vertical gully direction that can represent the U-shaped or V-shaped gully landform feature by fitting the horizontal profile data.

[0275] The identification module is configured to calculate a GLBN value of the small watershed according to the average slope of the upstream of the ice lake along the gully direction, the average slope of the downstream of the ice lake along the gully direction, and the upstream b value and the downstream b value in the vertical gully direction.

[0276] wherein, the upstream b value in the vertical gully direction, the downstream b value in the vertical gully direction, the average slope of the upstream of the ice lake along the gully direction, the average slope of the downstream of the ice lake along the gully direction, α is a wide valley and narrow valley index weight coefficient, β is a gully slope index weight coefficient, and γ is a comprehensive adjustment coefficient.

[0277] When the GLBN value is greater than the identification threshold value, the small watershed is determined to be an ice lake outburst small watershed.

[0278] In summary, the multiple specific embodiments of the present application are disclosed, and in the case of no self-contradiction, each embodiment can be freely combined to form a new embodiment, that is, the embodiments belonging to the replacement scheme can be freely replaced, but cannot be combined with each other; the embodiments not belonging to the replacement scheme can be combined with each other, and these new embodiments also belong to the essential content of the present application.

[0279] ​The above embodiments describe specific implementations of the present application, but those skilled in the art should understand that various changes or modifications can be made to these embodiments without departing from the principles and spirit of the present application, and such changes and modifications shall fall within the scope of the present application.

Claims

1. A method for identifying glacial lake outburst flood basins based on gully geomorphological characteristic indicators, characterized in that, include: S1. Acquire elevation data and multiple remote sensing image data within the ridgeline area of ​​the identified small watershed; S2. Identify channels and glacial lakes based on multi-period remote sensing image data, further determine the location of channels and glacial lakes based on elevation data, obtain longitudinal profiles along the channel direction based on elevation data, and calculate the average slope upstream of the glacial lake and the average slope downstream of the glacial lake along the channel direction. S3. Based on the location of the glacial lake and the gully, draw cross-sections in the direction perpendicular to the gully, starting from the location of the glacial lake and moving upstream and downstream respectively. By fitting the cross-section data, obtain the b-values ​​upstream of the glacial lake in the direction perpendicular to the gully and downstream of the glacial lake in the direction perpendicular to the gully, which can characterize the U-shaped or V-shaped gully landform. S4. Calculate the GLBN value of the small watershed based on the average slope upstream of the glacial lake along the channel direction, the average slope downstream of the glacial lake along the channel direction, and the b-values ​​perpendicular to the channel direction upstream and downstream of the glacial lake: ; in, The value of b is the value of the upstream glacial lake in the direction perpendicular to the channel. The value of b is the downstream value of the glacial lake in the direction perpendicular to the channel. The average slope of the upper reaches of the glacial lake along the channel direction. The average slope downstream of the glacial lake along the gully direction; α is the weighting coefficient of the wide and narrow valley index; β is the weighting coefficient of the gully slope index; γ is the comprehensive adjustment coefficient; b is the exponent of the fitting function, a key indicator used to characterize the U-shaped or V-shaped gully landform; GLBN is the glacial lake basin index. When the GLBN value is greater than the identification threshold, the small watershed is determined to be a glacial lake outburst watershed.

2. The method for identifying glacial lake outburst flood basins based on gully geomorphological characteristic indicators according to claim 1, characterized in that, The identification threshold is determined by using 80% of the lowest GLBN value in the sample database.

3. The method for identifying glacial lake outburst flood basins based on gully geomorphological characteristic indicators according to claim 1, characterized in that, α, β, and γ were determined based on a sample database formed from typical glacial lake outburst cases. The determination was achieved by testing the performance of different parameter combinations using GLBN and employing the minimum error method.

4. The method for identifying glacial lake outburst watersheds based on gully geomorphological feature indicators according to claim 1, characterized in that, The location of glacial lakes is determined by: identifying them through optical images or generating contour lines using elevation data; identifying areas where the contour lines form closed circular or elliptical lowlands and are located at the terminus of glaciers as glacial lake locations; and determining the location of channels by the V-shaped curvature of the contour lines.

5. The method for identifying glacial lake outburst flood basins based on gully geomorphological characteristic indicators according to claim 1, characterized in that, The average gradient of the upstream and downstream sections of the glacial lake along the channel direction is calculated as follows: for the upstream and downstream channels of the glacial lake, the gradient value of each segment is calculated using an equally spaced sliding window method, and then the average value is taken.

6. The method for identifying glacial lake outburst watersheds based on gully geomorphological feature indicators according to claim 1, characterized in that, Through the function y=ax b To obtain the upstream and downstream b-values ​​perpendicular to the channel direction by fitting cross-sectional data, the specific method is as follows: The cross-sectional data is extracted using the spatial analysis module of ArcGIS software. The cross-sectional data is divided into left and right halves according to the lowest elevation point. The values ​​are then obtained using the function y=ax. b The left and right halves of the data are fitted together, and the average value is taken as the b value at the corresponding position. Then, the average value of the b values ​​of multiple cross sections upstream and downstream is taken to obtain the b value of the upstream and downstream glacial lake in the direction perpendicular to the channel. x: represents the horizontal distance from a point in the cross section to the lowest point of the channel, that is, the horizontal length extending from the bottom of the channel to the two mountain slopes in the direction perpendicular to the channel. y: represents the elevation of the position corresponding to x in the cross section, that is, the topographic height at a horizontal distance x from the bottom of the channel. a: the coefficient of the fitting function, reflecting the overall elevation benchmark of the cross section. b: the exponent of the fitting function, that is, the superscript b of x, which is a key indicator used to characterize the U-shaped or V-shaped valley landform features.

7. The method for identifying glacial lake outburst flood basins based on gully geomorphological characteristic indicators according to claim 1, characterized in that, The determination of the weighting coefficients for the wide and narrow valley indicators, the weighting coefficients for the gully slope indicator, and the comprehensive adjustment coefficient, as well as the determination of the GLBN value identification threshold, adopts an optimization method that combines surface fitting, random forest, and gradient boosting tree. Specifically, this includes: First, a nonlinear relationship between parameter combination and identification error is established through surface fitting. Using the weight coefficients of wide and narrow valley indicators, the weight coefficients of gully slope indicators, and the comprehensive adjustment coefficient as inputs, and the identification error as output, a quadratic surface model is constructed, and the preliminary optimized parameter values ​​and fitting residuals are obtained by solving. Secondly, the fitting residual, small watershed area, and glacier type are used as input features. A random forest model is used for parameter correction. Through ensemble learning of multiple decision trees, parameter correction values ​​are generated. The preliminary optimized parameters are superimposed with the correction values ​​to obtain the final parameters adapted to the regional characteristics. Finally, the final parameters, watershed area, glacier type, and fitting residuals are used as inputs. A gradient boosting tree model is used to generate a dynamic identification threshold. The dynamic identification threshold is dynamically adjusted according to the characteristics of the watershed. When the calculated GLBN value is greater than the dynamic identification threshold, the watershed is determined to be a glacial lake outburst watershed.

8. The method for identifying glacial lake outburst flood basins based on gully geomorphological feature indicators according to claim 1, characterized in that, When a small watershed is determined to be a glacial lake outburst watershed, a target area is used to determine the deployment of glacial lake outburst monitoring instruments within that watershed. The monitoring instruments include radar flow meters and vibration signal acquisition devices.

9. The method for identifying glacial lake outburst flood basins based on gully geomorphological feature indicators according to claim 1, characterized in that, When a small watershed is identified as a glacial lake outburst watershed, it is used to help determine the key areas of focus via satellite. The key areas of focus include changes in the area of ​​the glacial lake and extreme temperatures and precipitation within the watershed.

10. A system for identifying glacial lake outburst flood basins based on gully geomorphological features, characterized in that: The method for identifying glacial lake outburst watersheds based on gully geomorphological feature indicators according to any one of claims 1 to 9, wherein the glacial lake outburst watershed identification system based on gully geomorphological feature indicators comprises: The data acquisition module is used to acquire elevation data and multi-period remote sensing image data within the identified small watershed ridgeline area; The location determination and calculation module is used to identify channels and glacial lakes based on multi-period remote sensing image data, then determine the location of channels and glacial lakes based on elevation data, then obtain longitudinal profiles along the channel direction based on elevation data, and calculate the average slope upstream of the glacial lake and the average slope downstream of the glacial lake along the channel direction. The b-value calculation module is used to draw cross-sections in the vertical channel direction, starting from the location of the glacial lake and the channel, and going upstream and downstream respectively, based on the location of the glacial lake and the channel. By fitting the cross-section data, the b-values ​​upstream of the glacial lake in the vertical channel direction and downstream of the glacial lake in the vertical channel direction, which can characterize the U-shaped or V-shaped valley landform, are obtained. The identification module is used to calculate the GLBN value of the small watershed based on the average slope upstream and downstream of the glacial lake along the channel direction, as well as the b-values ​​perpendicular to the channel direction upstream and downstream. ; in, The b value is the value upstream of the vertical channel direction. The value of b is the downstream value in the direction perpendicular to the channel. The average slope of the upper reaches of the glacial lake along the channel direction. The average slope of the downstream of the glacial lake along the channel direction is α, which is the weighting coefficient of the wide valley and narrow valley index, β is the weighting coefficient of the channel slope index, and γ is the comprehensive adjustment coefficient. When the GLBN value is greater than the identification threshold, the small watershed is determined to be a glacial lake outburst watershed.

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