A method for identifying and assessing risks of plateau lake submergence disasters

Abstract

The application discloses a kind of plateau lake submerged disaster hidden danger identification and risk assessment method, comprising: based on the background data set of plateau lake, lake genesis, high-resolution optical satellite image is established;Based on long time sequence optical remote sensing monitoring, the long time sequence change analysis of the historical area of lake in background data set is carried out, the change trend of lake historical area is obtained, and the target lake with stable growth trend of lake area is screened and marked;Based on the occurrence condition of submerged disaster, the lake with submerged hidden danger is screened from target lake, and the lake with submerged hidden danger is the lake in low-lying area of watershed, without dam body and discharge channel, and with disaster body on lake rim;Based on multi-source height measurement satellite data, annual water level sequence is constructed;Based on lake historical area, annual water level sequence, lake rim disaster body present situation, submerged trend index of lake is constructed;Based on the relationship between preset submerged trend index of lake and submerged risk level, the risk level of lake submergence is divided.

Description

Technical Field

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

[0002] Glacial lake outburst is a common type of flood disaster 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 (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 carried out evolution simulation 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; Glacier and Soil Permafrost, 2020, 42(4):1344-1352; CN202311622199.7[P].2024-05-07), but the identification of hidden dangers of plateau lake inundation and risk assessment are still blank.

[0003] 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

[0004] In response to the shortcomings of the existing technology, the present invention provides a method for identifying and assessing the hidden dangers of flooding disasters in plateau lakes. In combination with multi-source remote sensing satellite technology, it comprehensively considers multiple factors to judge and screen lakes with flooding risks, and comprehensively assesses the flooding risks of lakes with flooding risks based on their historical changes, the distribution of surrounding disaster-prone 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.

[0005] The present invention provides the following technical solutions:

[0006] A method for identifying hidden dangers and risk assessment of plateau lake flooding disasters, comprising:

[0007] Establish a baseline dataset of plateau lakes based on lake genesis and high-resolution optical satellite imagery;

[0008] Based on long-term optical remote sensing monitoring, the Mann-Kendall (MK) trend analysis was used to analyze the long-term changes in the historical area of ​​lakes in the background dataset. The trend of the lake's historical area was obtained, and target lakes with a stable growth trend in lake area were screened and marked.

[0009] Based on the conditions for flooding disasters, lakes with potential flooding risks are screened out from the target lakes. The lakes with potential flooding risks are located in low-lying areas of the basin, have no dams or outflow channels, and have hazard-bearing bodies at the lake edge;

[0010] Based on multi-source altimetry satellite data, annual water level series for lakes with flooding risks are constructed;

[0011] Based on the historical area, annual water level series, and the current status of lake margin hazard-bearing bodies of lakes with flooding risks, a lake flooding trend index was constructed to assess lake flooding risks.

[0012] Based on the relationship between the preset lake flooding trend index and the flooding risk level, the flooding risk level of lakes with flooding hazards is divided.

[0013] As a further improvement of the present invention, lakes with a lake MK trend analysis statistic Z>1.96 are screened and marked as target lakes.

[0014] As a further improvement of the present invention, the method of constructing an annual water level sequence of lakes with potential flooding hazards includes:

[0015] Based on data from the altimetry satellites ERS-2L2 GDR, ICEsat L2 GLAH14, Cryosat-2L2 GDR Baseline E, and ICEsat-2 L3AATL13, historical lake water level data from each altimetry satellite are collected on a monthly basis each year. A baseline correction algorithm is used to construct an annual water level series for lakes with flooding risks that have a unified baseline.

[0016] As a further improvement of the present invention, the monthly collection of historical water level data of each altimetry satellite lake includes:

[0017] Based on the ground footprint size of the altimetry satellite, the altimetry footprint points of the altimetry satellite are screened monthly year by year;

[0018] 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.

[0019] As a further improvement of the present invention, after collecting historical lake water level data of each altimetry satellite on a monthly basis year by year, 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;

[0020] Based on the corrected monthly lake water level data, the annual lake water level is calculated, and the annual lake water level is the average of the monthly lake water levels;

[0021] The lake water levels of each year are arranged in chronological order to construct an annual water level sequence.

[0022] As a further improvement of the present invention, 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:

[0023] 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. Based on the average deviation value of the monthly data of the overlapping period, the monthly data to be corrected of other altimetry satellites in the overlapping period are corrected.

[0024] If the water level data of other altimetry satellites do not overlap 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. Based on the average deviation value of the monthly data of the non-overlapping period, the monthly data to be corrected of other altimetry satellites in the non-overlapping period are corrected.

[0025] As a further improvement of the present invention, the calculation formula for the average deviation value of the monthly data of the overlapping period is:

[0026]

[0027] 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 overlapping period, H t The monthly data of the altimetry satellite ICEsat-2 L3AATL13 are for the coincident time period;

[0028] The calculation formula for the average deviation value of the monthly data of the non-overlapping period is:

[0029]

[0030] 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 L3A ATL13 matches the public water level data, and H noncov is the monthly data to be corrected of other altimetry satellites during the non-overlapping period, H v This is the monthly data of public water level data.

[0031] As a further improvement of the present invention, the construction of a lake flooding trend index for assessing lake flooding risk includes:

[0032] Build lake water level trend indicators based on the lake's annual water level series, build lake flood trend indicators based on the lake's historical area and annual water level series, and build lake edge hazard-bearing body vulnerability indicators based on the current status of the lake edge hazard-bearing body.

[0033] The lake inundation trend index is constructed based on the lake water level trend index, the lake rising trend index, and the lake edge hazard-bearing body vulnerability index. The calculation formula of the lake inundation trend index is:

[0034] ξ=θ p ·θ s ·θ f

[0035] 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 trends, and all indicators are dimensionless.

[0036] As a further improvement of the present invention, the calculation formula of the lake water level trend index is:

[0037]

[0038] Among them, H t is the current water level of the lake, H risk H is the elevation of the hazard-bearing body closest to the lake. low is the lowest water level of the lake in the annual water level series, H i is the annual water level of the lake in year i, and I is the total number of years calculated;

[0039] The calculation formula of the lake rising trend index is:

[0040]

[0041] Among them, ΔQ is the change in lake water volume between two consecutive years, ΔQ tis 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 lake water volume change between two consecutive years, and σ is the standard deviation of the lake water volume change between two consecutive years;

[0042] The calculation formula for the vulnerability index of the lake edge disaster-bearing body is:

[0043]

[0044] Among them, l old is the shortest distance from the disaster-stricken point closest to the lake to the ancient lakeshore, l lake The shortest distance from the disaster-stricken 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 the land use conditions of each grid in the area between the current lakeshore and the ancient lakeshore, 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 conditions 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.

[0045] As a further improvement of the present invention, the relationship between the preset lake flooding trend index and the flooding risk level is:

[0046]

[0047] Where G is the lake inundation risk assessment function, and ξ is the lake inundation trend index, which is used to quantify the lake inundation trend.

[0048] Compared with the prior art, the present invention has the following beneficial effects:

[0049] Plateau lake inundation is a new type of disaster caused by climate change in the Qinghai-Tibet Plateau. Currently, there is no systematic and effective method for comprehensively identifying lake inundation hazards and assessing inundation risk. The method provided by this paper integrates the causes of lake formation, long-term temporal change analysis, and inundation hazard conditions to accurately screen lakes with inundation risks. Furthermore, the method combines historical lake changes, the distribution of surrounding hazard-bearing bodies, and future inundation trends to comprehensively assess the inundation risk of lakes with inundation risks.

[0050] 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.

[0051] The quantitative characterization of water level changes in lakes with flooding risks, the vulnerability of lake edge hazard-bearing bodies, and lake rising trend characteristics provides an analytical basis for the risk assessment of flooding hazards in plateau lakes.

[0052] Establishing a scientific system for identifying and assessing the hidden dangers of flooding in plateau lakes can be widely applied to plateau lakes in the Qinghai-Tibet Plateau, enabling the basic understanding of lakes with flooding conditions in the plateau area and quantitative research on flood risks. BRIEF DESCRIPTION OF THE DRAWINGS

[0053] Figure 1 A flow chart of the method provided by the present invention. DETAILED DESCRIPTION

[0054] To make the objectives, technical solutions, and advantages of the embodiments of the present invention more clear, the technical solutions in the embodiments of the present invention will be clearly and completely described below in conjunction with the accompanying 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 making creative efforts shall fall within the scope of protection of the present invention.

[0055] The present invention is described in further detail below with reference to the accompanying drawings:

[0056] See also Figure 1 The present invention provides a method for identifying hidden dangers of plateau lake flooding disasters and assessing risks, comprising:

[0057] S1. Establish a baseline dataset of plateau lakes based on lake genesis and high-resolution optical satellite images;

[0058] S2. Based on long-term optical remote sensing monitoring, the historical area of ​​lakes in the background dataset was analyzed using Mann-Kendall (MK) trend analysis to obtain the changing trend of the historical area of ​​lakes. Target lakes with a stable growth trend in lake area were screened and marked.

[0059] S3. Based on the conditions for flooding disasters, lakes with potential flooding risks are screened from the target lakes. Lakes with potential flooding risks are located in low-lying areas of the basin, lack dams or spillways, and have hazard-bearing bodies at their edges.

[0060] S4. Construct annual water level series for lakes with flooding risks based on multi-source altimetry satellite data;

[0061] S5. Construct a lake inundation trend index for assessing lake inundation risk based on the historical area, annual water level series, and the current status of lake margin hazard-bearing bodies of lakes with inundation risks;

[0062] S6. Based on the relationship between the preset lake inundation trend index and the inundation risk level, the inundation risk level of lakes with inundation risks is classified.

[0063] The method provided in this embodiment integrates the causes of lake formation, long-term temporal change analysis, and the conditions for the occurrence of flooding disasters to accurately screen lakes with flooding risks. It also comprehensively assesses the flooding risk of lakes with flooding risks by combining characteristics such as the historical changes in the lakes, the distribution of surrounding hazard-bearing bodies, and future flooding trends.

[0064] 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.

[0065] Lakes are important bodies of water in nature, formed for a variety of reasons. Lakes can be categorized into different types based on their origin, such as tectonic lakes, fluvial lakes, marine lakes, karst lakes, glacial lakes, and barrier lakes.

[0066] Glacial lakes are formed by glacial erosion and sedimentation. Barrier lakes are formed when volcanic lava flows, moraine, or landslides caused by earthquakes block valleys, river valleys, or riverbeds, resulting in the accumulation of water.

[0067] The water level changes of glacial lakes and dammed lakes are influenced by a variety of factors, such as sudden climate change and natural disasters. These changes are highly uncertain and difficult to predict, and they rarely exhibit the characteristics of sustained, stable changes over long time series. The method provided in this example addresses the persistent expansion of medium- and large-sized lakes in the context of global climate change. By removing the interference of glacial lakes and dammed lakes, the subsequent long-term change analysis is more accurate, ensuring the accuracy and reliability of lake inundation disaster risk assessment.

[0068] In step S2, based on long-term optical remote sensing monitoring, the Mann-Kendall (MK) trend analysis is used to analyze the long-term changes in the historical area of ​​the lakes in the background dataset, and the statistical value Z value of each lake is obtained. The trend of the change in the historical area of ​​the lake is judged based on the Z value. The specific judgment criteria are as follows:

[0069] If Z>1.96, it is considered that the historical area of ​​the lake has shown a significant upward trend.

[0070] If Z < -1.96, it is considered that the historical area of ​​the lake has shown a significant downward trend.

[0071] If -1.96≤Z<1.96, it is considered that there is no significant change trend in the historical area of ​​the lake.

[0072] When the terrain surrounding a lake is low-lying, the lake lacks an effective drainage system, and there are hazard-prone structures such as buildings and infrastructure around the lake, the lake is highly likely to flood during climate anomalies (such as prolonged heavy rain or extreme precipitation). Therefore, in step S3, based on the flooding hazard conditions, lakes with potential flooding hazards are screened from the target lakes. These lakes are located in low-lying areas of the basin, lack dams or spillways, and have hazard-prone structures at their edges. Quantitative indicators are then constructed to assess the flooding risk of these lakes with potential flooding hazards.

[0073] In step S4, based on data from the altimetry satellites ERS-2L2 GDR, ICEsat L2 GLAH14, Cryosat-2L2 GDRBaseline E, and ICEsat-2 L3A ATL13, historical lake water level data from each altimetry satellite are collected on a monthly basis. Since the data resolution methods of different altimetry satellites vary, a baseline correction algorithm is used to eliminate errors in order to construct a unified annual water level series for lakes with flooding risks.

[0074] The historical water level data of each altimetry satellite lake collected on a monthly basis includes:

[0075] Based on the ground footprint size of the altimetry satellite, the altimetry footprint points of the altimetry satellite are screened monthly year by year;

[0076] Obtain lake water level data corresponding to each altimetry footprint point of the altimetry satellite;

[0077] Visually inspect the lake water level data corresponding to each altimetry footprint point and remove significant outliers;

[0078] 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;

[0079] 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.

[0080] Based on the solution formula, the raw data of the altimetry satellite is converted into lake water level elevation (lake water level data).

[0081] The data processing principles of the ERS-2L2 GDR altimeter and the Cryosat-2L2 GDR Baseline E altimeter are the same, and the solution processing formula is as follows:

[0082] h=H alt -H range -R corr -N

[0083] R corr =H dry +H wet +H iono +H set +H pol

[0084] 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 altimeter 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 correction for solid tide; H pol is the extreme tide correction; the constants in both formulas are in meters.

[0085] The solution formula for the ICEsat L2 GLAH14 altimeter satellite is as follows:

[0086] h=H tp -N-0.7

[0087] Among them, H tp is the elevation of the lake in the Topex / Poseidon reference ellipsoid, where the constant is in meters.

[0088] The dataset of the ICEsat-2 L3A ATL13 altimetry satellite contains orthometric parameters and can be used directly as water level data without additional calculation.

[0089] After collecting historical lake water level data from altimetry satellites on a monthly basis, the water level data from the altimetry satellite ICEsat-2L3AATL13 was selected as the calibration baseline. The water level data from other altimetry satellites were calibrated on a monthly basis. The calibration methods include:

[0090] The water level data from the Cryosat-2L2 GDR Baseline E altimeter satellite and the water level data from the altimeter satellite ICEsat-2L3AATL13 overlap in a certain period. 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.

[0091] The calculation formula for the average deviation value of the monthly data in the overlapping period is:

[0092]

[0093] 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 overlapping period, H t These are the monthly data of the altimetry satellite ICEsat-2 L3AATL13 during the overlapping period.

[0094] For the water level data of each altimetry satellite in the period that does not overlap with the altimetry satellite ICEsat-2 L3A ATL13, the monthly data of the non-overlapping period are matched with public water level data (such as Hydroweb data) to obtain the average deviation value of the monthly data in the non-overlapping period. Based on the average deviation value of the monthly data in the non-overlapping period, the monthly data to be corrected of other altimetry satellites in the non-overlapping period are corrected.

[0095] The calculation formula for the average deviation value of the monthly data of the non-overlapping period is:

[0096]

[0097] 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 L3A ATL13 matches the public water level data, and H noncov is the monthly data to be corrected of other altimetry satellites during the non-overlapping period, H v This is the monthly data of public water level data.

[0098] Based on the corrected monthly lake water level data, the annual lake water level is calculated, and the annual lake water level is the average of the monthly lake water levels;

[0099] The lake water levels of each year are arranged in chronological order to construct an annual water level sequence.

[0100] 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 flooding risks.

[0101] In step S5, a lake water level trend index is constructed based on the lake's annual water level sequence, a lake flood trend index is constructed based on the lake's historical area and the lake's annual water level sequence, and a lake edge hazard-bearing body vulnerability index is constructed based on the current status of the lake edge hazard-bearing body;

[0102] Based on the lake water level trend index, lake rising trend index, and lake edge hazard-bearing body vulnerability index, a lake inundation trend index for quantifying lake inundation hazards is constructed. The calculation formula for the lake inundation trend index is:

[0103] ξ=θ p ·θ s ·θ f

[0104] 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 trends, and all indicators are dimensionless.

[0105] The lake water level trend index is used to quantitatively characterize lake water level changes. It is constructed based on the annual lake water level series and the calculation formula is:

[0106]

[0107] Among them, H t is the current water level of the lake, H risk H is the elevation of the hazard-bearing body closest to the lake. low is the lowest water level of the lake in the annual water level series, H i is the annual water level of the lake in year i, I is the total number of years calculated, and the units in the fraction are consistent to eliminate dimension.

[0108] The lake rising trend index is used to quantitatively characterize the characteristics of lake rising trends. It is constructed based on the historical area of ​​the lake and the annual water level series of the lake. The calculation formula is:

[0109]

[0110] ΔQ is the change in lake water volume between two consecutive years (1995-1996, 1996-1997, 1997-1998, and so on to the present), which is calculated based on the historical lake area and annual lake water level series; 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 units in the fraction are consistent to eliminate dimension.

[0111] The lake margin hazard-bearing body vulnerability index is used to quantitatively characterize the vulnerability of lake margin hazard-bearing bodies. It is constructed based on the current status of the lake margin hazard-bearing bodies and the calculation formula is:

[0112]

[0113] Among them, l old is the shortest distance from the disaster-stricken point closest to the lake to the ancient lakeshore, l lake is the shortest distance from the nearest disaster-stricken 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 lake shoreline and the ancient lake shoreline. The ancient lake shoreline is the lake shoreline at 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 the land use conditions 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 with L = N / A does not participate in any calculation.

[0114] 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.

[0115] The relationship between the lake inundation trend index and the inundation risk level is as follows:

[0116]

[0117] Where G is the lake inundation risk assessment function, and ξ is the lake inundation trend index, which is used to quantify the lake inundation trend.

[0118] The lake water level trend index, lake edge hazard-bearing body vulnerability index, and lake rising trend index are used to quantitatively characterize the water level changes, lake edge hazard-bearing body vulnerability, and lake rising trend characteristics of lakes with flooding risks, providing an analytical basis for the risk assessment of flooding hazards in plateau lakes.

[0119] 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.

[0120] The above are merely preferred embodiments of the present invention and are not intended to limit the present invention. Those skilled in the art will readily appreciate that various modifications and variations of the present invention are possible. Any modifications, equivalent substitutions, or improvements made within the spirit and principles of the present invention shall be included within the scope of protection of the present invention.

Claims

1. A method for identifying hidden dangers and risk assessment of plateau lake flooding disasters, characterized by: include: Establish a baseline dataset of plateau lakes based on lake genesis and high-resolution optical satellite imagery; Based on long-term optical remote sensing monitoring, the Mann-Kendall (MK) trend analysis was used to analyze the long-term changes in the historical area of ​​lakes in the background dataset. The trend of the lake's historical area was obtained, and target lakes with a stable growth trend in lake area were screened and marked. Based on the conditions for flooding disasters, lakes with potential flooding risks are screened out from the target lakes. The lakes with potential flooding risks are located in low-lying areas of the basin, have no dams or outflow channels, and have hazard-bearing bodies at the lake edge; Based on multi-source altimetry satellite data, annual water level series for lakes with flooding risks are constructed; Based on the historical area, annual water level series, and the current status of lake margin hazard-bearing bodies of lakes with flooding risks, a lake flooding trend index was constructed to assess lake flooding risks. Based on the relationship between the preset lake flooding trend index and the flooding risk level, the flooding risk level of lakes with flooding hazards is divided.

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

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 includes: 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 historical lake water level data from each altimetry satellite on a monthly basis, the water level data from the altimetry satellite ICEsat-2 L3A ATL13 was selected as the calibration baseline, and the water level data from other altimetry satellites were calibrated on a monthly basis. Based on the corrected monthly lake water level data, the annual lake water level is calculated, and the annual lake water level is the average of the monthly lake water levels; 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 calibration baseline, and the water level data of other altimetry satellites are calibrated 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. Based on the average deviation value of the monthly data of the overlapping period, the monthly data to be corrected of other altimetry satellites in the overlapping period are corrected. If the water level data of other altimetry satellites do not overlap 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. Based on the average deviation value of the monthly data of the non-overlapping period, the monthly data to be corrected of other altimetry satellites in the non-overlapping period are corrected.

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 overlapping 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 overlapping period, H t The monthly data of the altimetry satellite ICEsat-2 L3A ATL13 for the coincident time period; The calculation formula for the average deviation value of the monthly data of 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 is the monthly data to be corrected of other altimetry satellites during the non-overlapping period, H v This is the monthly data of 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 for assessing lake flooding risk is constructed, including: Build lake water level trend indicators based on the lake's annual water level series, build lake flood trend indicators based on the lake's historical area and annual water level series, and build lake edge hazard-bearing body vulnerability indicators based on the current status of the lake edge hazard-bearing body. The lake inundation trend index is constructed based on the lake water level trend index, the lake rising trend index, and the lake edge hazard-bearing body vulnerability index. The calculation formula of the lake inundation 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 trends, 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 H is the elevation of the hazard-bearing body closest to the lake. low is the lowest water level of the lake in the annual water level series, 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 rising 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 lake water volume change between two consecutive years, and σ is the standard deviation of the lake water volume change between two consecutive years; The calculation formula for 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 lakeshore, l lake The shortest distance from the disaster-stricken 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 the land use conditions of each grid in the area between the current lakeshore and the ancient lakeshore, 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 conditions 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: Where G is the lake inundation risk assessment function, and ξ is the lake inundation trend index, which is used to quantify the lake inundation trend.

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