A method for evaluating ice lake outburst danger in high and steep mountainous area

Abstract

The application discloses a high and steep mountain area ice lake breaching danger assessment method and relates to the technical field of natural disaster assessment. The application comprises the following steps: collecting SAR data, DEM data and ice lake data sets covering a target area and capable of being obtained, so as to read out SAR satellite parameter information, terrain parameter information and ice lake distribution; the surface deformation distribution of the area can be obtained by using the collected parameter information related to the satellite and the terrain and adopting SBAS-InSAR technology; the slope unit of the area is calculated by using the DEM data and is substituted into the method proposed in the application, so that the spatial position of the potential trigger type ice lake in a wide area can be obtained according to the deformation-ice lake colocation principle and the elevation screening; a two-dimensional evaluation matrix is established by considering the rate and size of the deformation and the steepness of the slope, so that the trigger type ice lake breaching danger grade is further obtained.

Description

Technical Field

[0001] This invention belongs to the field of natural disaster assessment technology, and in particular relates to a method for assessing the risk of glacial lake outburst floods in high and steep mountainous areas. Background Technology

[0002] Glacial lake outburst floods (GLOFs) are flood events caused by the sudden and massive release of water from glacial lakes due to the instability of natural dams such as moraine dams or glaciers. Against the backdrop of global warming, GLOF events triggered by glacial instability-induced ice avalanches / landslides impacting lakes have become the most prevalent and destructive disaster mechanism in high-altitude regions of Asia. These triggered outburst floods, due to the enormous kinetic energy conversion from large-scale landslides or ice avalanches, exhibit significant characteristics of being high-altitude, sudden, and geographically distant, often causing disasters far exceeding the scale of other types of GLOFs. There is an urgent need to develop methods for detecting potentially hazardous lakes susceptible to large-scale movement triggering factors to support timely early warning and hazard assessment.

[0003] Current hazard assessments of GLOFs can be broadly categorized into two types: individual glacial lake assessments and wide-area assessments. The former mainly considers the possibility of large-scale movements such as landslides / rockfalls on the slopes surrounding a single glacial lake, and the main factors considered include topographic conditions, climate influence, changes in the area / volume of the glacial lake, and slope stability. Wide-area glacial lake hazard assessments vary significantly depending on the presence of surrounding slopes. When the glacial lake is located on relatively flat terrain without adjacent slopes, the focus is on two main aspects: regional climate variables (such as rainfall and temperature) and glacial lake characteristics (such as volume, type, and altitude). When adjacent slopes are present, wide-area assessments pay particular attention to topographical attributes conducive to large-scale movements, such as the steepness of the dam, the distance from the steepest slope, the average slope around the lake, and proximity to the glacier. Potential trigger zones have also been identified, including areas with slopes greater than 45° (prone to avalanches / icefalls) and slopes greater than 30° (prone to rockfalls). Some researchers have applied these criteria at regional scales ranging from tens to hundreds of square kilometers. Furthermore, some researchers have used InSAR technology to monitor surface deformation and systematically assessed the stability of slopes and moraine dams around glacial lake groups, considering factors such as the steepness of the dam and potential icefalls / rockfalls. This provides a new perspective for wide-area GLOF hazard assessment based on InSAR surface deformation. Although existing wide-area GLOF hazard assessments have detailed analytical studies, they are still limited by the effective acquisition of data within a wide area. They only focus on assessing static geometric indicators such as the size of the glacial lake area and the distance from steep slopes, making it difficult to achieve regional dynamic assessments.

[0004] Therefore, it is particularly important to develop a method for assessing the risk of glacial lake outburst floods (GLOFs) in high-altitude mountainous areas over a wide region. This method will provide fundamental data support for subsequent risk assessments, offer a transferable paradigm for evaluating GLOF hazards in high-altitude regions, and provide scientific decision-making support for developing disaster prevention and mitigation strategies and enhancing regional resilience in the context of climate change. Summary of the Invention

[0005] To solve the above-mentioned technical problems, the present invention is achieved through the following technical solution: This invention relates to a method for assessing the risk of glacial lake outburst floods in steep mountainous areas, comprising the following steps: Step S1: Collect SAR data, DEM data, and glacial lake datasets covering the target area to extract SAR satellite parameter information, terrain parameter information, and glacial lake distribution. Step S2: Using the collected satellite and terrain-related parameter information, the surface deformation distribution of the region can be obtained by employing SBAS-InSAR technology; Step S3: Calculate the slope units of the area using DEM data and substitute them into the method proposed in this invention. Based on the deformation-glacial lake co-location principle and elevation screening, the spatial location of potential triggering glacial lakes in a wide area can be obtained. Step S4: Considering the rate of deformation and the steepness of the slope, establish a two-dimensional evaluation matrix to further obtain the risk level of the triggered glacial lake outburst.

[0006] Furthermore, in step S1, SAR data, DEM data, and glacial lake datasets covering the target area are collected; SAR data is used for subsequent wide-area surface deformation monitoring, and terrain parameter information can be obtained from DEM data. It is mainly used in InSAR technology to remove terrain phase difference and geocoding, and subsequent hazard assessment and screening of disaster trigger points require its elevation information.

[0007] Further, in step S2, SBAS-InSAR technology is used to monitor the slope stability around the glacial lake; after filtering N+1 SLC images to reduce image noise, one image is selected as the super master image and registered with other images; and an appropriate spatiotemporal baseline constraint threshold is selected to generate M interferometric pairs, where M satisfies ≤M≤ Interference pairs are generated based on spatial and temporal baseline thresholds. Then, using precise orbital data and external DEM data, each generated interference pair is differentially processed to remove flat and topographic effects, and phase unwrapping is performed using a minimum cost flow method. Interference pairs with low coherence, suboptimal interference, or incorrect unwrapping are initially removed to ensure subsequent error control. Simultaneously, a first linear inversion is performed for optimization and re-flattening, estimation of deformation rate and residual topography, and a second unwrapping. This operation yields elevation and rate accuracy. Based on the obtained deformation rate, further optimization and re-flattening are performed, along with atmospheric filtering to estimate and remove atmospheric phase, resulting in a more accurate deformation rate. Finally, the final line-of-sight deformation rate is obtained using least squares or singular value decomposition methods.

[0008] Furthermore, step S3 includes the following steps: Step S31: Use the r.watershed module in the GRASS GIS environment to extract hydrological features from the digital elevation model covering the study area; calculate the unit catchment area by accumulating the water collection paths of each pixel; Step S32: Based on the expected spatial scale of the slopes surrounding the glacial lake in the study area and the regional geomorphological evolution characteristics, a critical catchment area threshold is set; the initial channel is automatically identified and the catchment area boundary is delineated through this threshold; when the cell catchment area is greater than the threshold, it is identified as a skeleton river network, and this is used as the topographic skeleton to initially divide the continuous geographic space into sub-basin units with independent hydrological attributes. Step S33: Using the r.slopeunits module, the initial units are finely adjusted by introducing slope aspect homogeneity and shape compactness as constraints; slope units with clear topographic significance and reasonable spatial boundaries are formed, providing mapping units for subsequent deformation rate statistics and hazard level assessment.

[0009] Furthermore, step S4 includes the following steps: Step S41: Using the glacial lake boundary and its buffer zone as high-incidence areas for triggered glacial lake outbursts, the deformation and glacial lake within the buffer zone are taken as research objects; by spatially connecting the glacial lake and the deformation area to the slope unit respectively, it is determined which slope unit each glacial lake and deformation area belongs to, and the spatial co-location screening of the deformation area and the glacial lake within the same slope unit is limited. For each pair of glacial lake and deformation area, it is checked whether the elevation of the glacial lake is less than the minimum elevation of the deformation area, and the screening is carried out with the elevation constraint that the elevation of the glacial lake is lower than the elevation of the deformation area, and finally the potential disaster triggering points that may form triggered glacial lake outbursts such as ice avalanches / rockfalls / landslides are determined.

[0010] Step S42: The selected deformation-glacial lake is used as the key trigger point for disaster. This method focuses on monitoring disaster-causing factors. The slope values ​​of the monitored deformation area are divided into three categories (equal interval quantiles) from smallest to largest, and assigned a risk level (low, medium, high). The absolute value of the average deformation rate of the deformation area (note that the original value is negative, and the absolute value is positive. The smaller the absolute value, the lower the risk, and the larger the absolute value, the higher the risk) is divided into three categories (equal interval quantiles) from smallest to largest, and assigned a risk level (low, medium, high).

[0011] Step S43: Combine the above slope and deformation size classification results to form a 3×3 matrix. After combination, 9 categories are obtained, each representing a combination of two hazard dimensions. At this time, 5 levels of hazard are formed, namely, very low hazard, low hazard, medium hazard, high hazard, and very high hazard.

[0012] Step S44: Establish a spatial index for the trigger points in step S3, quickly find the danger level of each trigger point in the buffer zone, and assign the highest danger priority principle (extremely high danger > high danger > medium danger > medium-low danger > low danger) to the buffer zone. At this time, there will still be 5 danger levels. By adopting the diagonal principle, the area above the diagonal is the final high danger, the area below the diagonal is the final medium danger, and the area below the diagonal is the final low danger, thus obtaining the final trigger-type glacial lake outburst danger assessment map.

[0013] The present invention has the following beneficial effects: 1. This invention proposes a method for assessing the risk of triggered glacial lake outbursts at high altitudes, aiming to overcome the limitations of traditional static assessment models in capturing the dynamic evolution characteristics of disaster chain sources. By integrating SBAS-InSAR time-series surface deformation monitoring technology with a hydrologically based method for fine-grained slope unit subdivision, this method can quantitatively identify and extract potential unstable source areas (such as rockfalls, ice avalanches, landslides, and glacial moraine deformation) around glacial lakes from a geomorphological dynamics perspective. By constructing a spatially coupled evaluation matrix of surface deformation rate, slope, and glacial lake, this method achieves the assessment of triggered outburst hazard. It is mainly used for the rapid screening and hazard classification management of large-scale potentially hazardous glacial lakes (PDGLs) in high-altitude and cold environments. Especially in extreme geographical units lacking ground-based measured data, it ultimately provides sufficient scientific support and a high-precision quantitative assessment paradigm for disaster risk prevention and control, early monitoring and warning schemes in high-altitude areas, and mitigation strategies to enhance regional disaster resilience in the context of global climate change.

[0014] 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

[0015] 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.

[0016] Figure 1 This is a simplified schematic diagram of the hazard assessment results of the present invention; Figure 2 This is a flowchart illustrating the technical process of the present invention. Figure 3 This is the result of wide-area InSAR deformation detection in this invention; Figure 4 This is the result of the wide-area triggering glacial lake outburst risk assessment of the present invention; Figure 5 This is a flowchart illustrating the hazard assessment method for glacial lake outburst floods in high and steep mountainous areas according to the present invention. Detailed Implementation

[0017] 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.

[0018] Please see Figure 1-5 As shown, this invention provides a method for assessing the risk of glacial lake outburst floods in steep mountainous areas, comprising the following steps: Step S1: Collect SAR data, DEM data, and glacial lake datasets covering the target area to extract SAR satellite parameter information, terrain parameter information, and glacial lake distribution. Step S2: Using the collected satellite and terrain-related parameter information, the surface deformation distribution of the region can be obtained by employing SBAS-InSAR technology; Step S3: Calculate the slope units of the area using DEM data and substitute them into the method proposed in this invention. Based on the deformation-glacial lake co-location principle and elevation screening, the spatial location of potential triggering glacial lakes in a wide area can be obtained. Step S4: Considering the rate of deformation and the steepness of the slope, establish a two-dimensional evaluation matrix to further obtain the risk level of the triggered glacial lake outburst.

[0019] In step S1, SAR data, DEM data, and glacial lake datasets covering the target area are collected. SAR data is used for subsequent wide-area surface deformation monitoring. Topographic parameter information can be obtained from DEM data. It is mainly used in InSAR technology to remove topographic phase difference and geocoding. The elevation information is required for subsequent hazard assessment to screen disaster trigger points.

[0020] In step S2, SBAS-InSAR technology is used to monitor the stability of the slopes around the glacial lake. After filtering N+1 SLC images to reduce image noise, one image is selected as the super master image and registered with other images. A suitable spatiotemporal baseline constraint threshold is selected to generate M interferometric pairs, where M satisfies... ≤M≤ Interference pairs are generated based on spatial and temporal baseline thresholds. Then, using precise orbital data and external DEM data, each generated interference pair is differentially processed to remove flat and topographic effects, and phase unwrapping is performed using a minimum cost flow method. Interference pairs with low coherence, suboptimal interference, or incorrect unwrapping are initially removed to ensure subsequent error control. Simultaneously, a first linear inversion is performed for optimization and re-flattening, estimation of deformation rate and residual topography, and a second unwrapping. This operation yields elevation and rate accuracy. Based on the obtained deformation rate, further optimization and re-flattening are performed, along with atmospheric filtering to estimate and remove atmospheric phase, resulting in a more accurate deformation rate. Finally, the final line-of-sight deformation rate is obtained using least squares or singular value decomposition methods.

[0021] Step S3 includes the following steps: Step S31: Use the r.watershed module in the GRASS GIS environment to extract hydrological features from the digital elevation model covering the study area; calculate the unit catchment area by accumulating the water collection paths of each pixel; Step S32: Based on the expected spatial scale of the slopes surrounding the glacial lake in the study area and the regional geomorphological evolution characteristics, a critical catchment area threshold is set; the initial channel is automatically identified and the catchment area boundary is delineated through this threshold; when the cell catchment area is greater than the threshold, it is identified as a skeleton river network, and this is used as the topographic skeleton to initially divide the continuous geographic space into sub-basin units with independent hydrological attributes. Step S33: Using the r.slopeunits module, the initial units are finely adjusted by introducing slope aspect homogeneity and shape compactness as constraints; slope units with clear topographic significance and reasonable spatial boundaries are formed, providing mapping units for subsequent deformation rate statistics and hazard level assessment.

[0022] Step S4 includes the following steps: Step S41: Using the glacial lake boundary and its buffer zone as high-incidence areas for triggered glacial lake outbursts, the deformation and glacial lake within the buffer zone are taken as research objects; by spatially connecting the glacial lake and the deformation area to the slope unit respectively, it is determined which slope unit each glacial lake and deformation area belongs to, and the spatial co-location screening of the deformation area and the glacial lake within the same slope unit is limited. For each pair of glacial lake and deformation area, it is checked whether the elevation of the glacial lake is less than the minimum elevation of the deformation area, and the screening is carried out with the elevation constraint that the elevation of the glacial lake is lower than the elevation of the deformation area, and finally the potential disaster triggering points that may form triggered glacial lake outbursts such as ice avalanches / rockfalls / landslides are determined.

[0023] Step S42: The selected deformation-glacial lake is used as the key trigger point for disaster. This method focuses on monitoring disaster-causing factors. The slope values ​​of the monitored deformation area are divided into three categories (equal interval quantiles) from smallest to largest, and assigned a risk level (low, medium, high). The absolute value of the average deformation rate of the deformation area (note that the original value is negative, and the absolute value is positive. The smaller the absolute value, the lower the risk, and the larger the absolute value, the higher the risk) is divided into three categories (equal interval quantiles) from smallest to largest, and assigned a risk level (low, medium, high).

[0024] Step S43: Combine the above slope and deformation size classification results to form a 3×3 matrix. After combination, 9 categories are obtained, each representing a combination of two hazard dimensions. At this time, 5 levels of hazard are formed, namely, very low hazard, low hazard, medium hazard, high hazard, and very high hazard, as shown in Table 1.

[0025] Table 1: Hazard Classification

[0026] Step S44: Establish a spatial index for the trigger points in step S3, quickly find the danger level of each trigger point in the buffer zone, and assign the highest danger priority principle (extremely high danger > high danger > medium danger > medium-low danger > low danger) to the buffer zone. At this time, there will still be 5 danger levels. By adopting the diagonal principle, the area above the diagonal is the final high danger, the area below the diagonal is the final medium danger, and the area below the diagonal is the final low danger, thus obtaining the final trigger-type glacial lake outburst danger assessment map.

[0027] One specific application of this embodiment is: Step 1: Collect Sentinel-1 satellite SAR data, 30m spatial resolution SRTM-DEM data, and ice lake dataset covering the target area, as shown in Table 2.

[0028] Table 2. Datasets used in this method

[0029] Step Two: Using the collected SAR and DEM data, the stability of the entire region, including Cho Oyu (elevation 8201 meters, total area 3600 km²), the world's sixth highest peak, was assessed. Deformation areas were automatically delineated using SBAS-InSAR technology with 2σ of the annual average deformation rate as the standard, retaining only deformation zones with an area > 0.05 km². A total of 313 deformation zones were detected. Figure 3 As shown.

[0030] Step 3: This method uses 410 glacial lake boundaries and a surrounding 1 km buffer zone as the default high-incidence area for triggered glacial lake outbursts; 1503 slope units were extracted as basic analysis units based on the r.slopeunits hydrological process method; and 79 pairs of deformation-glacial lake co-location areas were selected from the 410 glacial lake locations through the above spatial co-location screening; and 35 potential disaster triggering points that may form glacial lake outbursts such as ice avalanches / landslides were finally identified through elevation constraint screening.

[0031] Step 4: Using the above principles for classifying the hazard of the buffer zone where the disaster trigger point is located, generate a hazard assessment map for triggered glacial lake outburst floods, such as... Figure 4 Ultimately, 10 high-risk buffer zones and 11 medium-risk buffer zones were identified.

[0032] 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.

[0033] 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 risk of glacial lake outburst floods in steep mountainous areas, characterized in that, Includes the following steps: Step S1: Collect SAR data, DEM data, and glacial lake datasets covering the target area to extract SAR satellite parameter information, terrain parameter information, and glacial lake distribution. Step S2: Using the collected satellite and terrain-related parameter information, the surface deformation distribution of the region is obtained using SBAS-InSAR technology; Step S3: Calculate the slope units of the area using DEM data, and obtain the spatial locations of potential triggering glacial lakes in the wide area based on the deformation-glacial lake co-location principle and elevation screening. Step S31: Use the r.watershed module in the GRASS GIS environment to extract hydrological features from the digital elevation model covering the study area; calculate the unit catchment area by accumulating the water catchment paths of each pixel; Step S32: Based on the expected spatial scale of the slopes surrounding the glacial lake in the study area and the regional geomorphological evolution characteristics, a critical catchment area threshold is set; the initial channel is automatically identified and the catchment area boundary is delineated through this threshold; when the cell catchment area is greater than the threshold, it is identified as a skeleton river network, and this is used as the topographic skeleton to initially divide the continuous geographic space into sub-basin units with independent hydrological attributes. Step S33: Using the r.slopeunits module, the initial units are finely adjusted by introducing slope aspect homogeneity and shape compactness as constraints; thus forming topographic slope units. Step S4: Considering the rate of deformation and the steepness of the slope, establish a two-dimensional evaluation matrix to further obtain the hazard level of the triggered glacial lake outburst flood: Step S41: Using the glacial lake boundary and its buffer zone as high-incidence areas of triggered glacial lake outbursts, the deformation and glacial lake within the buffer zone are taken as research objects; by spatially connecting the glacial lake and the deformation area with the slope unit respectively, it is determined which slope unit each glacial lake and deformation area belongs to, and the spatial co-location screening of the deformation area and the glacial lake being located in the same slope unit is limited. For each pair of glacial lake and deformation area, it is checked whether the elevation of the glacial lake is less than the minimum elevation of the deformation area, and the screening is carried out with the elevation constraint that the elevation of the glacial lake is lower than the elevation of the deformation area, and finally the potential disaster triggering points that may form such triggered glacial lake outbursts (ice avalanches / rockfalls / landslides) are determined. Step S42: The selected deformation-glacial lake is used as the key trigger point for disaster. This method focuses on monitoring disaster-causing factors. The slope values ​​of the monitored deformation zone are divided into three categories from smallest to largest and assigned a hazard level. The absolute value of the average deformation rate of the deformation zone is also divided into three categories from smallest to largest and assigned a hazard level. Step S43: Combine the above slope and deformation classification results to form a 3×3 matrix. After combination, 9 categories are obtained, each representing a combination of two hazard dimensions. At this point, 5 levels of hazard are formed, namely, very low hazard, low hazard, medium hazard, high hazard, and very high hazard. Step S44: Establish a spatial index for the trigger points in step S3, quickly find the danger level of each trigger point in the buffer zone, and assign the highest danger priority to the buffer zone. At this time, there will still be 5 danger levels. By adopting the diagonal principle, the area above the diagonal is the final high danger, the area below the diagonal is the final medium danger, and the area below the diagonal is the final low danger, thus obtaining the final trigger-type glacial lake outburst hazard assessment map.

2. The method for assessing the risk of glacial lake outburst floods in high and steep mountainous areas according to claim 1, characterized in that, In step S1, SAR data, DEM data, and glacial lake datasets covering the target area are collected. SAR data is used for subsequent wide-area surface deformation monitoring. Topographic parameter information is obtained from DEM data. It is mainly used in InSAR technology to remove topographic phase difference and geocoding. The elevation information is required for subsequent hazard assessment to screen disaster trigger points.

3. The method for assessing the risk of glacial lake outburst floods in high and steep mountainous areas according to claim 1, characterized in that, In step S2, SBAS-InSAR technology is used to monitor the stability of the slopes around the glacial lake. After filtering N+1 SLC images to reduce image noise, one image is selected as the super master image and registered with other images. A suitable spatiotemporal baseline constraint threshold is selected to generate M interferometric pairs, where M satisfies... ≤M≤ Interferometric pairs are generated based on spatial and temporal baseline thresholds. Then, using precise orbital data and external DEM data, each generated interferometric pair is differentially processed to remove flat and topographic effects, and phase unwrapping is performed using a minimum cost flow method. Interferometric pairs with low coherence, suboptimal interference, or incorrect unwrapping are initially removed to ensure subsequent error control. Simultaneously, a first linear inversion is performed for optimization and re-flattening, estimation of deformation rate and residual topography, and a second unwrapping. This operation yields elevation and rate accuracy. Based on the obtained deformation rate, further optimization and re-flattening are performed, along with atmospheric filtering to estimate and remove atmospheric phase, resulting in a more accurate deformation rate. Finally, the final line-of-sight deformation rate is obtained using least squares or singular value decomposition methods.

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