Hidden danger identification and risk assessment method for plateau lake submerging disasters

Abstract

The invention discloses a plateau lake submerging disaster hidden danger identification and risk assessment method, which comprises the following steps: establishing a background data set of a plateau lake based on lake causes and high-resolution optical satellite images; based on long-time-sequence optical remote sensing monitoring, long-time-sequence change analysis is conducted on the historical area of the lake in the background data set, the change trend of the historical area of the lake is obtained, and a target lake with the lake area having the stable increase trend is screened and marked; based on the occurrence condition of the submerging disaster, the lake with the submerging hidden danger is screened out from the target lake, and the lake with the submerging hidden danger is located in a low-lying area of a drainage basin, is free of a dam body and a leakage channel and is provided with a disaster-bearing body on the lake edge; constructing an annual water level sequence based on multi-source altimetry satellite data; based on the historical area of the lake, the annual water level sequence and the lake edge disaster-bearing manifestation, constructing a lake inundation trend index; and based on a preset relationship between the lake inundation trend index and the inundation risk level, dividing the lake inundation risk level.

Description

Technical Field

[0001] The invention relates to the technical field of hydrological remote sensing, and in particular to a method for identifying hidden dangers of flooding disasters in plateau lakes and conducting risk assessment. Background Art

[0002] Lake inundation disasters are a new type of disaster that has emerged in my country's Qinghai-Tibet Plateau under the background of global climate change. In recent years, the Qinghai-Tibet Plateau and its surrounding areas have continued to warm up, glaciers have retreated, and permafrost has thawed, and the area of ​​lakes has generally expanded. Due to changes in basin environmental conditions, the paradigm of plateau floods has changed from the previous "mainly small glacial lake outbursts" to the trend of "equally important with medium- and large-scale lake inundation disasters". Inundation disasters caused by the rapid expansion of plateau lakes are no longer extreme cases, and there are signs of a shift from sporadic to frequent occurrences. They pose a serious threat to the safety of life and property of the people in the Qinghai-Tibet Plateau and major projects in western my country, and they need to be paid urgent attention.

[0003] Glacial lake outburst is a common type of flood in the Qinghai-Tibet Plateau. In the past, scholars have conducted extensive research on the causes, collapse mechanisms and risk assessment of glacial lake outbursts, and conducted correlation analysis on the long-term changes in water volume and meteorological data related to glacial lake outbursts (Science Bulletin, 2024, 69(27):3999-4001; Journal of Soil and Water Conservation, 2024, 38(3):140-149; Geographical Research, 2022, 41(4):980-996; Progress in Geography, 2018, 37(2):214-223; Acta Geographica Sinica, 2010 , 65(3):313-319; Acta Geologica Sinica, 2006(6):876-884), and simulated the evolution of floods formed after glacial lake outburst (Journal of Hydraulic Engineering, 2024, 55(4):493-504; Progress in Water Science, 2023, 34(5):753-765; Glaciology and Geocryology, 2020, 42(4):1344-1352; CN202311622199.7[P].2024-05-07), but there is still a lack of knowledge on the potential hazards of plateau lake inundation and risk assessment.

[0004] With the continuous development of remote sensing technology, the application of optical imaging in lake changes has become more mature, and satellite altimetry technology has performed well in stably obtaining lake water levels, providing reliable data support for research. Combining the advantages of the two can provide new ideas for solving the above problems. Summary of the invention

[0005] In view of the deficiencies in the prior art, the present invention provides a method for identifying and assessing the hidden dangers of flooding disasters in plateau lakes. By combining multi-source remote sensing satellite technology, multiple factors are comprehensively considered to judge and screen lakes with hidden dangers of flooding. The flooding risk of lakes with hidden dangers is comprehensively assessed in combination with their historical changes, the distribution of surrounding disaster-bearing bodies, and future flooding trends, so as to achieve the basic situation of lakes with flooding conditions in plateau areas and quantitative research on flood risks.

[0006] The present invention provides the following technical solutions:

[0007] A method for identifying hidden dangers and risk assessment of plateau lake flooding disasters, comprising:

[0008] Establish a background data set of plateau lakes based on lake genesis and high-resolution optical satellite images;

[0009] Based on long-term optical remote sensing monitoring, the historical area of ​​lakes in the background data set was analyzed using Mann-Kendall (MK) trend analysis to obtain the trend of the historical area of ​​lakes, and to screen and mark target lakes with a stable growth trend in lake area.

[0010] Based on the conditions for the occurrence of flooding disasters, lakes with flooding risks are screened out from the target lakes. The lakes with flooding risks are located in low-lying areas of the basin, have no dams and spillways, and have disaster-bearing bodies at the edge of the lake;

[0011] Based on multi-source altimetry satellite data, annual water level series of lakes with flooding risks are constructed;

[0012] Based on the historical area, annual water level series, and current status of lake edge hazards of lakes with flooding risks, a lake flooding trend index for assessing lake flooding risks is constructed;

[0013] Based on the relationship between the preset lake flooding trend index and the flooding risk level, the flooding risk level of lakes with flooding risks is divided.

[0014] As a further improvement of the present invention, lakes with lake MK trend analysis statistics Z>1.96 are screened and marked as target lakes.

[0015] As a further improvement of the present invention, the method of constructing an annual water level sequence of a lake with a potential flooding risk includes:

[0016] Based on the data from the altimetry satellites ERS-2L2 GDR, ICEsat L2 GLAH14, Cryosat-2L2 GDR Baseline E, and ICEsat-2 L3A ATL13, the historical water level data of lakes from each altimetry satellite are collected on a monthly basis each year, and the annual water level series of lakes with flooding risks with a unified baseline are constructed through a baseline correction algorithm.

[0017] As a further improvement of the present invention, the historical water level data of each altimetry satellite lake is collected on a monthly basis year by year, including:

[0018] Based on the ground footprint size of the altimetry satellite, the altimetry footprint points of the altimetry satellite are screened monthly year by year;

[0019] The lake water level data corresponding to each altimetry footprint point of the altimetry satellite are obtained. After removing outliers, the average value of the lake water level data corresponding to each altimetry footprint point is calculated as the monthly lake water level data of the altimetry satellite.

[0020] As a further improvement of the present invention, after collecting the historical water level data of each altimetry satellite lake on a monthly basis year by year, the water level data of the altimetry satellite ICEsat-2 L3AATL13 is selected as the correction baseline, and the water level data of other altimetry satellites are corrected on a monthly basis year by year;

[0021] Based on the corrected monthly lake water level data, the lake water level of each year is calculated. The lake water level of each year is the average of the lake water levels of each month.

[0022] The lake water levels of each year are arranged in chronological order to construct an annual water level sequence.

[0023] As a further improvement of the present invention, the water level data of the altimetry satellite ICEsat-2 L3AATL13 is selected as the correction baseline, and the water level data of other altimetry satellites are corrected on a monthly basis year by year, including:

[0024] If the water level data of other altimetry satellites overlap with the water level data of the altimetry satellite ICEsat-2 L3A ATL13, the monthly data of the overlapping period are directly matched to obtain the average deviation value of the monthly data of the overlapping period, and the monthly data to be corrected of other altimetry satellites in the overlapping period are corrected based on the average deviation value of the monthly data of the overlapping period;

[0025] If the water level data of other altimetry satellites have no overlapping period with the water level data of the altimetry satellite ICEsat-2 L3A ATL13, the monthly data of the non-overlapping period are matched with the public water level data to obtain the average deviation value of the monthly data of the non-overlapping period, and the monthly data to be corrected of other altimetry satellites in the non-overlapping period are corrected based on the average deviation value of the monthly data of the non-overlapping period.

[0026] As a further improvement of the present invention, the calculation formula for the average deviation value of the monthly data of the overlap period is:

[0027]

[0028] Among them, Bias cov is the average deviation value of the monthly data in the overlapping period, N is the number of matching months in the overlapping period, and H cov is the monthly data to be corrected of other altimetry satellites in the overlap period, H t Monthly data of the altimetry satellite ICEsat-2 L3AATL13 during the overlapping period;

[0029] The calculation formula for the average deviation value of the monthly data in the non-overlapping period is:

[0030]

[0031] Among them, Bias noncov is the average deviation value of the monthly data in the non-overlapping period, N1 is the number of months that the monthly data to be corrected in the non-overlapping period matches the public water level data, N2 is the number of months that the monthly data of the altimetry satellite ICEsat-2 L3AATL13 matches the public water level data, and H noncov H is the monthly data to be corrected of other altimetry satellites during the non-overlapping period. v Monthly data for public water level data.

[0032] As a further improvement of the present invention, the construction of a lake flooding trend index for assessing lake flooding risk includes:

[0033] The lake water level trend index is constructed based on the lake annual water level series, the lake flood trend index is constructed based on the lake historical area and lake annual water level series, and the lake edge disaster-bearing body vulnerability index is constructed based on the current status of the lake edge disaster-bearing body;

[0034] The lake flooding trend index is constructed based on the lake water level trend index, the lake rising trend index, and the lake edge disaster-bearing body vulnerability index. The calculation formula of the lake flooding trend index is:

[0035] ξ=θ p ·θ s ·θ f

[0036] Where ξ is the lake flooding trend index, ξ≥0, dimensionless; θ p is the lake level trend indicator, θ s is the vulnerability index of the lake edge disaster-bearing body, θ f These are indicators of lake rising trend, and all indicators are dimensionless.

[0037] As a further improvement of the present invention, the calculation formula of the lake water level trend index is:

[0038]

[0039] Among them, H t is the current water level of the lake, H risk is the elevation of the disaster-bearing body closest to the lake, H low is the lowest water level of the lake in the annual water level sequence of the lake, H i is the annual water level of the lake in year i, and I is the total number of years calculated;

[0040] The calculation formula of the lake flood trend index is:

[0041]

[0042] Among them, ΔQ is the change in lake water volume between two consecutive years, ΔQ t is the change in lake water volume in the last two years, ΔQ min is the minimum value of the historical water volume change of the lake between two consecutive years, ΔQ max is the maximum value of the historical water volume change of the lake between two consecutive years, ΔQ p is the average value of the change in lake water volume between two consecutive years, and σ is the standard deviation of the change in lake water volume between two consecutive years;

[0043] The calculation formula of the vulnerability index of the lake edge disaster-bearing body is:

[0044]

[0045]

[0046] Among them, l old is the shortest distance from the disaster-stricken point closest to the lake to the ancient lake shoreline, l lake is the shortest distance from the disaster-prone point closest to the lake to the current lake shoreline; the area between the current lake shoreline and the ancient lake shoreline is evenly divided into square grids of the same size. is the Moran index of the distribution of hazard-bearing bodies around the lake, which is used to characterize the aggregation of hazard-bearing bodies. The index ∈ [-1, 1] is dimensionless. J is the total number of grids in the area between the current lake shoreline and the ancient lake shoreline. w jk is the spatial weight of the relationship between grid j and grid k, and W is w jk The sum of L is the land use of each grid in the area between the current lakeshore and the ancient lakeshore, and L j is the land use situation of grid j, L kis the land use situation of grid k, L is the average land use situation of each grid in the area between the current lakeshore and the ancient lakeshore, and the grid with L = N / A does not participate in any calculation.

[0047] As a further improvement of the present invention, the relationship between the preset lake flooding trend index and the flooding risk level is:

[0048]

[0049] Among them, G is the lake flooding risk assessment function, and ξ is the lake flooding trend index, which is used to quantify the lake flooding trend.

[0050] Compared with the prior art, the present invention has the following beneficial effects:

[0051] Plateau lake flooding disaster is a new type of disaster caused by climate change in the Qinghai-Tibet Plateau. At present, there is no systematic and effective method for comprehensive identification of lake flooding disaster hazards and flooding risk assessment. The method provided by the present invention comprehensively analyzes the causes of lake formation, long-term change analysis, and flooding disaster conditions, accurately screens lakes with flooding hazards, and comprehensively assesses the flooding risk of lakes with flooding hazards by combining the historical changes of lakes, the distribution of surrounding disaster-bearing bodies, and future flooding trends.

[0052] By integrating multi-source remote sensing satellite data, we can obtain high-precision historical lake area, annual water level series and water volume change data, providing data support for risk assessment of lakes with flooding risks.

[0053] The quantitative characterization of water level changes in lakes with flooding risks, the vulnerability of the hazard-bearing bodies at the lake edge, and the characteristics of lake rising trends provides an analytical basis for risk assessment of flooding hazards in plateau lakes.

[0054] The establishment of a scientific plateau lake flooding hazard identification and risk assessment system can be widely applied to plateau lakes in the Qinghai-Tibet Plateau, and can achieve the basic understanding of lakes with flooding conditions in the plateau area and quantitative research on flood risks. BRIEF DESCRIPTION OF THE DRAWINGS

[0055] Figure 1 A flow chart of the method provided by the present invention. DETAILED DESCRIPTION

[0056] In order to make the purpose, technical solution and advantages of the embodiments of the present invention clearer, the technical solution in the embodiments of the present invention will be clearly and completely described below in conjunction with the drawings in the embodiments of the present invention. Obviously, the described embodiments are part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0057] The present invention is further described in detail below in conjunction with the accompanying drawings:

[0058] See also Figure 1 The present invention provides a method for identifying hidden dangers of plateau lake flooding disasters and risk assessment, comprising:

[0059] S1. Establish a background dataset of plateau lakes based on lake genesis and high-resolution optical satellite images;

[0060] S2. Based on long-term optical remote sensing monitoring, the historical area of ​​lakes in the background data set was analyzed using Mann-Kendall (MK) trend analysis to obtain the change trend of the historical area of ​​lakes, and to screen and mark target lakes with a stable growth trend in lake area;

[0061] S3. Based on the conditions for the occurrence of flooding disasters, lakes with potential flooding hazards are selected from the target lakes. The lakes with potential flooding hazards are lakes located in low-lying areas of the basin, without dams and spillways, and with disaster-bearing bodies at the lake edges;

[0062] S4. Construct annual water level series of lakes with flooding risks based on multi-source altimetry satellite data;

[0063] S5. Construct a lake flooding trend index for assessing lake flooding risk based on the historical area, annual water level series, and current status of lake margin hazard-bearing bodies of lakes with flooding risks;

[0064] S6. Based on the relationship between the preset lake flooding trend index and the flooding risk level, the flooding risk level of lakes with flooding risks is divided.

[0065] The method provided in this embodiment comprehensively considers the causes of lake formation, long-term change analysis, and conditions for the occurrence of flooding disasters to accurately screen lakes with potential flooding risks. It also comprehensively assesses the flooding risk of lakes with potential flooding risks based on characteristics such as the historical changes in the lakes, the distribution of surrounding disaster-prone bodies, and future flooding trends.

[0066] In step S1, human-computer interaction recognition is performed based on high-resolution optical satellite images to identify the 1km area of ​​the Qinghai-Tibet Plateau. 2 The above lakes were traversed and extracted, and interference such as glacial lakes and barrier lakes were eliminated according to the causes of lake formation to establish a background dataset of plateau lakes.

[0067] Lakes are important water bodies in nature, and their formation causes vary. According to their causes, lakes can be divided into different types, such as tectonic lakes, fluvial lakes, marine lakes, karst lakes, glacial lakes, and barrier lakes.

[0068] A glacial lake is a lake formed by glacial erosion and sedimentation. A barrier lake is a lake formed by the storage of water after a volcanic lava flow, moraine, or a landslide caused by earthquake activity or other reasons blocks a valley, river valley or riverbed.

[0069] The water level changes of glacial lakes and barrier lakes are affected by many factors, such as climate change and natural disasters. The water level changes have great uncertainty and are difficult to predict, and do not have the characteristics of long-term continuous and stable changes. The method provided in this embodiment is aimed at the continuous expansion of medium and large lakes under the background of global climate change. Therefore, the interference of glacial lakes and barrier lakes is eliminated, making the subsequent long-term change analysis more accurate, so as to ensure the accuracy and reliability of lake flooding disaster risk assessment.

[0070] In step S2, based on long-term optical remote sensing monitoring, the historical area of ​​the lakes in the background data set is analyzed using the Mann-Kendall (MK) trend analysis to obtain the statistical value Z value of each lake. The change trend of the historical area of ​​the lake is judged according to the Z value. The specific judgment criteria are as follows:

[0071] If Z>1.96, it is considered that the historical area of ​​the lake shows a significant upward trend.

[0072] If Z < -1.96, it is considered that the historical area of ​​the lake shows a significant downward trend.

[0073] If -1.96≤Z<1.96, it is considered that there is no significant change trend in the historical area of ​​the lake.

[0074] When the terrain around the lake is low-lying, the lake lacks an effective drainage system, and there are disaster-prone objects such as houses, buildings, and infrastructure around the lake, when there is an abnormal climate (such as long-term heavy rain and other extreme precipitation), the lake is very likely to be flooded. Therefore, in step S3, based on the conditions for flooding disasters, lakes with flooding hazards are screened out from the target lakes, which are located in low-lying areas of the basin, have no dams and external spillways, and have disaster-prone objects at the edge of the lake, and a quantitative indicator is constructed to evaluate the flooding risk of lakes with flooding hazards.

[0075] In step S4, based on the data from the altimetry satellites ERS-2L2 GDR, ICEsat L2 GLAH14, Cryosat-2L2 GDRBaseline E, and ICEsat-2 L3A ATL13, the historical water level data of lakes from each altimetry satellite are collected on a monthly basis year by year. Since the data resolution methods of different altimetry satellites are different, the errors are eliminated through the baseline correction algorithm to construct an annual water level series of lakes with flooding risks with a unified baseline.

[0076] The historical water level data of each altimetry satellite lake collected on a monthly basis each year include:

[0077] Based on the ground footprint size of the altimetry satellite, the altimetry footprint points of the altimetry satellite are screened monthly year by year;

[0078] Obtain lake water level data corresponding to each altimetry footprint point of the altimetry satellite;

[0079] Visually inspect the lake water level data corresponding to each altimetry footprint point and remove significant outliers;

[0080] Using data error processing methods (such as normalized median absolute error), outliers are further eliminated to obtain lake water level data corresponding to each altimetry footprint point that is evenly distributed;

[0081] After removing outliers, the average value of the lake water level data corresponding to each altimetry footprint point was calculated as the monthly lake water level data of the altimetry satellite.

[0082] Based on the solution formula, the raw data of the altimetry satellite is converted into lake water level elevation (lake water level data).

[0083] The data processing principle of ERS-2L2 GDR altimeter is the same as that of Cryosat-2L2 GDR Baseline E altimeter. The calculation formula is as follows:

[0084] h=H alt -H range -R corr -N

[0085] R corr =H dry +H wet +H iono +H set +H pol

[0086] Where h is the orthometric height of the lake surface based on the EGM2008 geoid; H alt H is the ellipsoid height of the altimeter satellite based on the WGS84 reference ellipsoid; range R is the distance from the altimeter satellite to the sub-satellite point measured by the satellite altimeter; corr is the error correction value of the altimetry satellite; N is the geoid height of EGM2008; H dry is the dry tropospheric correction; H wet Correction for moist troposphere; H iono is the ionospheric correction; H set is the solid tide correction; H pol is the extreme tide correction; the constants in both formulas are in meters.

[0087] The solution formula of ICEsat L2 GLAH14 altimetry satellite is as follows:

[0088] h=H tp -N-0.7

[0089] Among them, H tp is the elevation of the lake in the Topex / Poseidon reference ellipsoid, where the constant is in meters.

[0090] The dataset of the ICEsat-2 L3A ATL13 altimetry satellite contains orthometric parameters and can be used directly as water level data without the need for additional calculation.

[0091] After collecting the historical water level data of each altimetry satellite lake on a monthly basis, the water level data of the altimetry satellite ICEsat-2L3AATL13 was selected as the correction baseline, and the water level data of other altimetry satellites were corrected on a monthly basis. The correction methods include:

[0092] The water level data of the Cryosat-2L2 GDR Baseline E altimeter satellite overlap with the water level data of the altimeter satellite ICEsat-2L3AATL13. The monthly data of the overlapping period are directly matched to obtain the average deviation value of the monthly data of the overlapping period. Based on the average deviation value of the monthly data of the overlapping period, the monthly data to be corrected of other altimeter satellites in the overlapping period are corrected.

[0093] The calculation formula for the average deviation value of the monthly data in the overlap period is:

[0094]

[0095] Among them, Bias cov is the average deviation value of the monthly data in the overlapping period, N is the number of matching months in the overlapping period, and H cov is the monthly data to be corrected of other altimetry satellites in the overlap period, H t These are the monthly data of the altimetry satellite ICEsat-2 L3AATL13 during the overlapping period.

[0096] For the water level data of each altimetry satellite in the non-overlapping period with the altimetry satellite ICEsat-2 L3A ATL13, the monthly data of the non-overlapping period are matched with the public water level data (such as Hydroweb data) to obtain the average deviation value of the monthly data in the non-overlapping period, and the monthly data to be corrected of other altimetry satellites in the non-overlapping period are corrected based on the average deviation value of the monthly data in the non-overlapping period.

[0097] The calculation formula for the average deviation value of the monthly data in the non-overlapping period is:

[0098]

[0099] Among them, Bias noncov is the average deviation value of the monthly data in the non-overlapping period, N1 is the number of months that the monthly data to be corrected in the non-overlapping period matches the public water level data, N2 is the number of months that the monthly data of the altimetry satellite ICEsat-2 L3AATL13 matches the public water level data, and H noncov H is the monthly data to be corrected of other altimetry satellites during the non-overlapping period. v Monthly data for public water level data.

[0100] Based on the corrected monthly lake water level data, the lake water level of each year is calculated. The lake water level of each year is the average of the lake water levels of each month.

[0101] The lake water levels of each year are arranged in chronological order to construct an annual water level sequence.

[0102] By integrating multi-source remote sensing satellite data, we can obtain high-precision historical lake area, annual water level series and water volume change data, providing data support for flooding risk assessment of lakes with potential flooding risks.

[0103] In step S5, a lake water level trend index is constructed based on the lake annual water level sequence, a lake flood trend index is constructed based on the lake historical area and the lake annual water level sequence, and a lake edge disaster-bearing body vulnerability index is constructed based on the current status of the lake edge disaster-bearing body;

[0104] Based on the lake water level trend index, lake flooding trend index, and lake edge disaster-bearing body vulnerability index, a lake flooding trend index for quantifying lake flooding hazards is constructed. The calculation formula of the lake flooding trend index is:

[0105] ξ=θ p ·θ s ·θ f

[0106] Where ξ is the lake flooding trend index, ξ≥0, dimensionless; θ p is the lake level trend indicator, θ s is the vulnerability index of the lake edge disaster-bearing body, θ f These are indicators of lake rising trend, and all indicators are dimensionless.

[0107] The lake water level trend index is used to quantitatively characterize the changes in lake water levels. It is constructed based on the annual lake water level series and the calculation formula is:

[0108]

[0109] Among them, H t is the current water level of the lake, H risk is the elevation of the disaster-bearing body closest to the lake, H low is the lowest water level of the lake in the annual water level sequence of the lake, Hi is the annual water level of the lake in the i-th year, I is the total number of years calculated, and the units in the fraction are consistent to eliminate the dimension.

[0110] The lake flooding trend index is used to quantitatively characterize the characteristics of the lake flooding trend. It is constructed based on the historical area of ​​the lake and the annual water level sequence of the lake. The calculation formula is:

[0111]

[0112] Among them, ΔQ is the change in lake water volume between two consecutive years (1995-1996, 1996-1997, 1997-1998...to date), which is calculated based on the historical area of ​​the lake and the annual water level series of the lake; ΔQ t is the change in lake water volume in the last two years, ΔQ min is the minimum value of the historical water volume change of the lake between two consecutive years, ΔQ max is the maximum value of the historical water volume change of the lake between two consecutive years, ΔQ p is the average value of the change in lake water volume between two consecutive years, σ is the standard deviation of the change in lake water volume between two consecutive years, and the units in the fraction are consistent to eliminate the dimension.

[0113] The lake edge hazard-bearing body vulnerability index is used to quantitatively characterize the vulnerability of the lake edge hazard-bearing body. It is constructed based on the current status of the lake edge hazard-bearing body and the calculation formula is:

[0114]

[0115]

[0116] Among them, l old is the shortest distance from the disaster-stricken point closest to the lake to the ancient lake shoreline, l lake is the shortest distance from the nearest disaster-affected point to the current lake shoreline. The units in the fraction are consistent to eliminate the dimension. The area between the current lake shoreline and the ancient lake shoreline is evenly divided into square grids of the same size. is the Moran index of the distribution of hazard-bearing bodies around the lake, which is used to characterize the aggregation of hazard-bearing bodies. The index ∈ [-1, 1] is dimensionless. J is the total number of grids in the area between the current lakeshore and the ancient lakeshore. The ancient lakeshore is the lakeshore of a relatively stable period in history. w jk is the spatial weight of the relationship between grid j and grid k, and W is w jk The sum of L is the land use of each grid in the area between the current lakeshore and the ancient lakeshore. j is the land use situation of grid j, L k is the land use situation of grid k, It is the average value of land use conditions of each grid in the area between the current lakeshore and the ancient lakeshore. The grid where L = N*A does not participate in any calculation.

[0117] In step S6, the relationship between the lake flooding trend index and the flooding risk level is established, and the lake flooding trend index threshold corresponding to each flooding risk level is preset to systematically and quantitatively evaluate the lake flooding risk.

[0118] The relationship between the lake flooding trend index and the flooding risk level is as follows:

[0119]

[0120] Among them, G is the lake flooding risk assessment function, and ξ is the lake flooding trend index, which is used to quantify the lake flooding trend.

[0121] The lake water level trend index, lake edge hazard-bearing body vulnerability index and lake rise trend index are used to quantitatively characterize the water level changes, lake edge hazard-bearing body vulnerability and lake rise trend characteristics of lakes with flooding risks, providing an analytical basis for the risk assessment of flooding hazards of plateau lakes.

[0122] Through the above method, a scientific plateau lake flooding hazard identification and risk assessment system is established, which can be widely applied to plateau lakes in the Qinghai-Tibet Plateau, and realize the basic situation of lakes with flooding conditions in the plateau area and quantitative research on flood risks.

[0123] The above are only preferred embodiments of the present invention and are not intended to limit the present invention. For those skilled in the art, the present invention may have various modifications and variations. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present invention shall be included in the protection scope of the present invention.

Claims

1. A method for identifying hidden dangers and risk assessment of plateau lake flooding disasters, characterized in that: include: Establish a background data set of plateau lakes based on lake genesis and high-resolution optical satellite images; Based on long-term optical remote sensing monitoring, the historical area of ​​lakes in the background data set was analyzed using Mann-Kendall (MK) trend analysis to obtain the trend of the historical area of ​​lakes, and to screen and mark target lakes with a stable growth trend in lake area. Based on the conditions for the occurrence of flooding disasters, lakes with flooding risks are screened out from the target lakes. The lakes with flooding risks are located in low-lying areas of the basin, have no dams and spillways, and have disaster-bearing bodies at the edge of the lake; Based on multi-source altimetry satellite data, annual water level series of lakes with flooding risks are constructed; Based on the historical area, annual water level series, and current status of lake edge hazards of lakes with flooding risks, a lake flooding trend index for assessing lake flooding risks is constructed; Based on the relationship between the preset lake flooding trend index and the flooding risk level, the flooding risk level of lakes with flooding risks is divided.

2. A method for identifying hidden dangers and risk assessment of plateau lake flooding disasters according to claim 1, characterized in that: Lakes with MK trend analysis statistics Z>1.96 were screened and marked as target lakes.

3. The method for identifying hidden dangers and risk assessment of plateau lake flooding disasters according to claim 1 is characterized in that: The annual water level series of lakes with flooding risks are constructed, including: Based on the data from the altimetry satellites ERS-2L2 GDR, ICEsat L2 GLAH14, Cryosat-2L2 GDR Baseline E, and ICEsat-2 L3A ATL13, the historical water level data of lakes from each altimetry satellite are collected on a monthly basis each year, and the annual water level series of lakes with flooding risks with a unified baseline are constructed through a baseline correction algorithm.

4. A method for identifying hidden dangers and risk assessment of plateau lake flooding disasters according to claim 3, characterized in that: The historical water level data of each altimetry satellite lake collected on a monthly basis each year include: Based on the ground footprint size of the altimetry satellite, the altimetry footprint points of the altimetry satellite are screened monthly year by year; The lake water level data corresponding to each altimetry footprint point of the altimetry satellite are obtained. After removing outliers, the average value of the lake water level data corresponding to each altimetry footprint point is calculated as the monthly lake water level data of the altimetry satellite.

5. A method for identifying hidden dangers and risk assessment of plateau lake flooding disasters according to claim 4, characterized in that: After collecting the historical water level data of each altimetry satellite lake on a monthly basis, the water level data of the altimetry satellite ICEsat-2 L3A ATL13 was selected as the correction baseline, and the water level data of other altimetry satellites were corrected on a monthly basis. Based on the corrected monthly lake water level data, the lake water level of each year is calculated. The lake water level of each year is the average of the lake water levels of each month. The lake water levels of each year are arranged in chronological order to construct an annual water level sequence.

6. A method for identifying hidden dangers and risk assessment of plateau lake flooding disasters according to claim 5, characterized in that: The water level data of the altimetry satellite ICEsat-2 L3A ATL13 is selected as the correction baseline, and the water level data of other altimetry satellites are corrected on a monthly basis year by year, including: If the water level data of other altimetry satellites overlap with the water level data of the altimetry satellite ICEsat-2 L3A ATL13, the monthly data of the overlapping period are directly matched to obtain the average deviation value of the monthly data of the overlapping period, and the monthly data to be corrected of other altimetry satellites in the overlapping period are corrected based on the average deviation value of the monthly data of the overlapping period; If the water level data of other altimetry satellites have no overlapping period with the water level data of the altimetry satellite ICEsat-2 L3A ATL13, the monthly data of the non-overlapping period are matched with the public water level data to obtain the average deviation value of the monthly data of the non-overlapping period, and the monthly data to be corrected of other altimetry satellites in the non-overlapping period are corrected based on the average deviation value of the monthly data of the non-overlapping period.

7. A method for identifying hidden dangers and risk assessment of plateau lake flooding disasters according to claim 6, characterized in that: The calculation formula for the average deviation value of the monthly data in the overlap period is: Among them, Bias cov is the average deviation value of the monthly data in the overlapping period, N is the number of matching months in the overlapping period, and H cov is the monthly data to be corrected of other altimetry satellites in the overlap period, H t Monthly data of the altimetry satellite ICEsat-2 L3A ATL13 during the coincident period; The calculation formula for the average deviation value of the monthly data in the non-overlapping period is: Among them, Bias noncov is the average deviation value of the monthly data in the non-overlapping period, N1 is the number of months that the monthly data to be corrected in the non-overlapping period matches the public water level data, N2 is the number of months that the monthly data of the altimetry satellite ICEsat-2 L3AATL13 matches the public water level data, and H noncov H is the monthly data to be corrected of other altimetry satellites during the non-overlapping period. v Monthly data for public water level data.

8. The method for identifying hidden dangers and risk assessment of plateau lake flooding disasters according to claim 1 is characterized in that: The lake flooding trend index constructed to assess the lake flooding risk includes: The lake water level trend index is constructed based on the lake annual water level series, the lake flood trend index is constructed based on the lake historical area and lake annual water level series, and the lake edge disaster-bearing body vulnerability index is constructed based on the current status of the lake edge disaster-bearing body; The lake flooding trend index is constructed based on the lake water level trend index, the lake rising trend index, and the lake edge disaster-bearing body vulnerability index. The calculation formula of the lake flooding trend index is: ξ=θ p ·i s ·i f Where ξ is the lake flooding trend index, ξ≥0, dimensionless; θ p is the lake level trend indicator, θ s is the vulnerability index of the lake edge disaster-bearing body, θ f These are indicators of lake rising trend, and all indicators are dimensionless.

9. A method for identifying hidden dangers and risk assessment of plateau lake flooding disasters according to claim 8, characterized in that: The calculation formula of the lake water level trend index is: Among them, H t is the current water level of the lake, H risk is the elevation of the disaster-bearing body closest to the lake, H low is the lowest water level of the lake in the annual water level sequence of the lake, H i is the annual water level of the lake in year i, and I is the total number of years calculated; The calculation formula of the lake flood trend index is: Among them, ΔQ is the change in lake water volume between two consecutive years, ΔQ t is the change in lake water volume in the last two years, ΔQ min is the minimum value of the historical water volume change of the lake between two consecutive years, ΔQ max is the maximum value of the historical water volume change of the lake between two consecutive years, ΔQ p is the average value of the change in lake water volume between two consecutive years, and σ is the standard deviation of the change in lake water volume between two consecutive years; The calculation formula of the vulnerability index of the lake edge disaster-bearing body is: Among them, l old is the shortest distance from the disaster-stricken point closest to the lake to the ancient lake shoreline, l lake is the shortest distance from the disaster-prone point closest to the lake to the current lake shoreline; the area between the current lake shoreline and the ancient lake shoreline is evenly divided into square grids of the same size. is the Moran index of the distribution of hazard-bearing bodies around the lake, which is used to characterize the aggregation of hazard-bearing bodies. The index ∈ [-1, 1] is dimensionless. J is the total number of grids in the area between the current lake shoreline and the ancient lake shoreline. w jk is the spatial weight of the relationship between grid j and grid k, and W is w jk The sum of L is the land use of each grid in the area between the current lakeshore and the ancient lakeshore, and L j is the land use situation of grid j, L k is the land use situation of grid k, It is the average value of land use of each grid in the area between the current lakeshore and the ancient lakeshore. The grid with L=N / A does not participate in any calculation.

10. A method for identifying hidden dangers and risk assessment of plateau lake flooding disasters according to claim 8 or 9, characterized in that: The relationship between the preset lake flooding trend index and the flooding risk level is: Among them, G is the lake flooding risk assessment function, and ξ is the lake flooding trend index, which is used to quantify the lake flooding trend.

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