CN116226707A - Storm surge and flood identification method based on cellular automaton - Google Patents
Storm surge and flood identification method based on cellular automaton Download PDFInfo
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Abstract
The invention provides a storm surge and flood identification method based on cellular automata, which comprises the following steps: gridding a research area, preparing geography, hydrology and meteorological data, and constructing an initial grid structure; based on cellular automata and a dry-wet grid algorithm, designing a dry-wet grid transformation rule based on a two-dimensional shallow water momentum equation, and performing iterative calculation on a grid structure; and the GIS technology is utilized to intuitively display the influence range and degree of storm surge and flood. The method mainly aims at the problem that the existing storm surge and flood identification method cannot be met at the same time in high efficiency, stability and accuracy, achieves quick identification, gives attention to identification accuracy, and provides a new choice for scientific research and practical application of storm surge and flood so as to better meet different requirements.
Description
Technical Field
The invention relates to the technical fields of ocean science and ocean engineering, in particular to a storm surge and flood model based on cellular automata.
Background
As a densely populated and economically viable area, coastal areas are subjected to a long-term threat of various marine disasters, with storm surge disasters being the most serious, causing a great deal of casualties and economic losses. Storm tide refers to the phenomenon of abnormal rise of sea surface caused by strong atmospheric disturbance, such as strong wind and sudden change of air pressure, and can cause the tide level to rise and land to be submerged with astronomical tide and sea wave, so as to cause disasters. According to statistics of China ocean disaster gazettes, 16 storm surge occurs in the offshore China in 2021, 24.7 hundred million yuan direct economic losses are caused, 80% of all ocean disaster losses are occupied, along with rapid development of ocean economy and increasing of climate change situation, the ocean disaster in coastal areas, particularly storm surge disaster risks are increasingly prominent, and higher requirements are put forward on disaster risk assessment and disaster prevention and reduction.
Disaster is a consequence of physical processes applying load to the environment, manifesting as property loss and casualties. For storm tide disasters, flood beach and inundation caused by rising tide level are main physical processes affecting human society. In order to develop a proper disaster prevention and reduction strategy, it is necessary to perform risk assessment. Estimating the extent and intensity of disaster occurrence is the first step of risk assessment and is also the most important step. Therefore, the establishment of an accurate and efficient storm surge and inundation model and the estimation of disaster factors including inundation range, inundation water depth and flow rate are important tools for supporting storm surge disaster risk assessment work.
At present, storm surge identification models can be largely divided into two types. One is based on the numerical model of solving shallow water equations, which has more accurate simulation results because of more comprehensive reflection of hydrodynamic principles, and commercial or non-commercial storm surge numerical models such as FVCOM, MIKE21, ADCIRC, etc. are widely used in storm surge research. However, the numerical mode has high demand for computing resources, and the time and cost for simulating a storm surge process in a region with a relatively large range are high, so that the forecasting timeliness of a storm surge disaster is generally difficult to meet, and the stability is insufficient. Although various means including optimizing the program operation mode and simplifying the mode base are put into improvement of the mode operation efficiency, the overall effect is still quite limited. Unlike numerical models, another class of methods for identifying a flood beach, which may be collectively referred to as a conceptual model, is mainly known in high efficiency. The concept model can be divided into two types in principle, one is a model based on a Digital Elevation Model (DEM), only the source water level and the terrain connectivity are considered to identify the submerged range and the submerged depth, and the other is a model based on a Cellular Automaton (CA), and the evolution rule of the wet and dry grid is designed based on a continuity equation to simulate the submerged process. The conceptual model is simpler in design principle, so that the recognition efficiency is higher. But, in contrast, the recognition result of the conceptual model is less accurate. In particular, for the flood beach process caused by storm surge, external force forces such as wind stress, bottom friction force and the like have a certain influence on the movement of water flow, so that a conceptual model forced by the external force is not considered, and larger errors can be generated.
Therefore, in order to better meet the requirements of storm surge disaster risk assessment, a storm surge and inundation model which is accurate, efficient and stable is required to be designed, and a new choice is provided for storm surge scientific research and practical application so as to better meet different requirements.
Disclosure of Invention
The invention aims to solve the problems of the conventional storm surge and flood identification method, and provides a storm surge and flood identification method based on cellular automata, so that the influence range and degree of the storm surge and flood can be quickly and accurately identified.
In order to achieve the above purpose, the invention is realized by adopting the following technical scheme:
a storm surge and inundation identification method based on cellular automata comprises the following steps:
s1, collecting geographic, hydrological and meteorological data, and constructing an initial grid structure of an area to be identified;
s2, establishing a dry-wet grid transformation rule based on a cellular automaton and a dry-wet grid algorithm and based on a two-dimensional shallow water momentum equation; iterative calculation and pre-judging of grid structures in the area are carried out, the grid wet and dry state is updated, and a prediction recognition result is obtained: the extent and extent of storm surge and flooding effects.
The construction of the initial grid structure comprises the following steps:
a. collecting data: the geographic data comprises terrain elevation raster data, surface coverage type raster data, river and dam line vector data, the hydrologic data comprises coastal storm surge highest water level and flow velocity data, and the meteorological data comprises wind field data during storm surge;
b. determining the size of a grid, and rasterizing the region to be detected; resampling the terrain elevation data to a grid model, and determining an initial wet and dry state grid according to the terrain elevation by taking 0m as a threshold value;
c. according to the national land coverage data standard, converting the surface coverage type into a corresponding Manning coefficient, and resampling to a model grid to obtain a surface Manning coefficient grid; interpolating the maximum wind speed and the average wind direction in the storm tide process into a model grid to obtain a wind field grid; as an external force forcing factor to influence the flood beach and the flooding process;
d. and interpolating the highest water level and flow rate in the storm tide process to a grid with a wet initial state in the model grid, and assigning 0 to a grid with a dry initial state to obtain a water level grid and a flow rate grid, wherein the water level grid and the flow rate grid are used as boundary conditions to drive the flood tide process.
The constructed dry-wet grid transformation rule is specifically deduced as follows:
two-dimensional shallow water momentum equation (x direction):
where v is the flow velocity in the x direction, eta is the water level, tau a Is wind stress in x direction, τ b Is the bottom friction force in the x direction, ρ is the sea water density, g is the gravitational acceleration, and D is the water depth;
neglecting local acceleration term in the two-dimensional shallow water momentum equation, and rewriting the two-dimensional shallow water momentum equation (x direction) into an energy form, wherein the physical meaning is that the external force forces to cause the energy height change:
discretizing the formula (2) between the target grid and the adjacent grid by adopting a finite difference method:
wherein v is tar Is the flow velocity, eta, in the x-direction of the target grid tar Is the target grid water level, v ner Is the flow velocity, eta, in the x-direction of the adjacent grid ner Is the adjacent grid water level, Δx is the distance between the adjacent grid and the center of the target grid;
assuming that the friedel numbers of water flows within the target mesh and adjacent mesh are approximately equal:
wherein Fr is a Friedel number, h tar Is the target grid elevation, h ner Is the elevation of the neighboring grid;
and (3) to (4) of the simultaneous formulas, and solving to obtain the target grid water level and the flow rate:
according to the wetting condition that the grid water level is higher than the grid elevation, obtaining the condition that the dry grid is converted into the wet grid:
among other things, consider the effect of wind stress and bottom friction on storm surge and inundation: wind stress τ a And bottom friction force tau b Expressed as:
τ a ＝ρ a C d v 2 (8)
X d ＝(0.75+0.067|v wind |)×10 -3 (9)
τ b ＝ρC f |v wind |v wind (10)
wherein τ a Is wind stress, τ b Is the bottom friction, ρ is the sea water density, ρ a Is the air density, v wind Is the projection of the relative wind speed in the flow velocity direction, C d Is the drag coefficient, C f Is the bottom coefficient of friction, n is the surface Manning roughness coefficient, and is related to the surface coverage type.
The iterative computation is carried out on the grid structure in the area, the dry and wet states of the grids are prejudged and updated, and the predicted identification result is obtained, and the method comprises the following steps:
traversing all grids, and circularly iterating the following steps a-d to enable the grid structure to continuously iterate and evolve until all grids are unchanged, obtaining a final wet grid area which is the maximum possible submerged range, and obtaining submerged water depth and flow velocity;
step a, if the target grid is a wet grid or no adjacent wet grid exists, the state of the target grid is unchanged;
step b, if the target grid is a dry grid and at least one adjacent wet grid exists, calculating the residual energy height and the Friedel number of the adjacent wet grid;
c, if a plurality of wet grid transformation rules are met, updating the state of the current target grid into a wet grid; otherwise, the state of the current target grid is unchanged;
and d, updating and calculating the water level and the flow rate of the wet grid.
The calculating the remaining energy height and friedel number of the adjacent wet grid includes:
wherein H is ner Is the remaining energy level, fr, of the neighboring grid ner Friedel number, v, of adjacent grid ner Is the flow velocity, eta, in the x-direction of the adjacent grid ner Is adjacent to the grid water level, tau a Is wind stress in x direction, τ b Is the bottom friction in the x-direction, ρ is the sea water density, g is the gravitational acceleration, D is the water depth, and Δx is the distance between the adjacent grid and the center of the target grid.
If a plurality of adjacent wet grids exist, the residual energy height and the Friedel number are averaged to be regarded as a single grid to participate in calculation.
The method further comprises the steps of: and S3, visually displaying the range and degree of the influence of storm surge and flood by using a GIS technology.
Compared with the existing storm surge and flood identification method, the method has the following beneficial effects:
1. based on a two-dimensional shallow water momentum equation, the influence of wind stress and bottom friction on storm surge and flood is fully considered, and the recognition result of the model is more accurate compared with the existing rapid recognition model;
2. the energy-based angle design model not only considers the influence of storm surge and flood driving factors on the water level, but also considers the influence of the storm surge and flood driving factors on the flow rate. The identification result not only comprises a submerged range and a submerged water depth, but also comprises an important storm surge disaster factor of the flow rate;
3. based on the principle design model of cellular automaton, the method has higher efficiency and stability compared with a perfect numerical mode.
Drawings
FIG. 1 is a flow chart of an implementation of the present invention for storm surge and flood identification;
FIG. 2 is a flow chart of the procedure of the storm surge and inundation identification model of the present invention;
FIG. 3 is a view of selected areas and typhoon pathways for a verification experiment of the present invention;
FIG. 4 is a graph comparing the present invention with actual disaster investigation results (Hebei Cangzhou);
FIG. 5 is a graph comparing the present invention with the actual disaster investigation result (Guangdong Shenzhen);
FIG. 6 is a graph comparing results of the present invention with the flood beach mode (ADCIRC-SWAN).
Detailed Description
In order that the above objects, features and advantages of the invention will be readily understood, a more particular description of the invention will be rendered by reference to the appended drawings. In the following description, numerous specific details are set forth in order to provide a thorough understanding of the present invention. The invention may be embodied in many other forms than described herein and similarly modified by those skilled in the art without departing from the spirit or scope of the invention, which is therefore not limited to the specific embodiments disclosed below.
Unless defined otherwise, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this invention belongs. The terminology used in the description of the invention herein is for the purpose of describing particular embodiments only and is not intended to be limiting of the invention.
A storm surge and inundation model based on cellular automata comprises the construction of model principles and the realization of model programs.
The core of the model principle is to construct a dry-wet grid transformation rule, which comprises the following deduction processes:
two-dimensional shallow water momentum equation (x direction):
where v is the flow velocity in the x direction, eta is the water level, tau a Is wind stress in x direction, τ b Is the bottom friction force in the x direction, ρ is the sea water density, g is the gravitational acceleration, and D is the water depth;
neglecting local acceleration term in the two-dimensional shallow water momentum equation, and rewriting the two-dimensional shallow water momentum equation (x direction) into an energy form, wherein the physical meaning is that the external force forces to cause the energy height change:
discretizing the formula (2) between the target grid and the adjacent grid by adopting a finite difference method:
wherein v is tar Is the flow velocity, eta, in the x-direction of the target grid tar Is the target grid water level, v ner Is the flow velocity, eta, in the x-direction of the adjacent grid ner Is the adjacent grid water level, Δx is the distance between the adjacent grid and the center of the target grid;
assuming that the friedel numbers of water flows within the target mesh and adjacent mesh are approximately equal:
wherein Fr is a Friedel number, h tar Is the target grid elevation, h ner Is the elevation of the neighboring grid;
and (3) to (4) of the simultaneous formulas, and solving to obtain the target grid water level and the flow rate:
according to the wetting condition that the grid water level is higher than the grid elevation, obtaining the condition that the dry grid is converted into the wet grid:
wherein the wind stress τa Friction force with bottom τb Expressed as:
τ a ＝ρ a C d v 2 (8)
C d ＝(0.75+0.067|v wind |)×10 -3 (9)
τ b ＝ρC f |v wind |v wind (10)
wherein τ a Is wind stress, τ b Is the bottom friction, ρ is the sea water density, ρ a Is the air density, v wind Is the projection of the relative wind speed in the flow velocity direction, C d Is the drag coefficient, C f Is the bottom coefficient of friction, n is the surface Manning roughness coefficient, and is related to the surface coverage type.
As shown in fig. 1, the implementation of the model program includes the following modules:
1) A data preparation module; gridding a research area, preparing geography, hydrology and meteorological data, constructing an initial grid structure, and inputting the initial grid structure as a model;
2) Flood beach and flood identification modules; performing iterative computation on the grid structure based on the cellular automaton and a wet-dry grid algorithm;
3) A result visualization module; and the GIS technology is utilized to intuitively display the influence range and degree of storm surge and flood.
The data preparation module specifically comprises the following steps:
1) And determining a research area and storm tide process, and acquiring geographic, hydrological and meteorological data during the storm tide process of the research area. The geographic data comprises geographic elevation raster data of a research area, surface coverage type raster data and river and dyke line vector data; the hydrologic data comprise highest tide level and flow speed data in the coastal storm surge process, simulation is carried out based on a storm surge numerical mode, and the model adopts an ADCIRC-SWAN coupling mode; the meteorological data comprises wind field data during storm surge, including wind speed and wind direction;
2) Determining the size of the grid, and rasterizing the research area to obtain a model grid. The terrain elevation raster data is resampled to the model raster. And obtaining buffer area surface vector data according to the river and dyke line vector data and the characteristic horizontal scale, converting the buffer area into raster data according to the elevation of the raster data, and resampling the raster data to a model raster. Correcting the terrain elevation of the grid where the river and the dykes are positioned to obtain a corrected terrain elevation grid;
3) With 0m as a threshold, determining the initial wet and dry state of the grid according to the terrain elevation. In the program, the grid dry and wet binary states are represented by assignment, and the grid state value with the height of more than or equal to 0m is set to be 0 to represent a dry grid; gao Chengxiao the grid state value at 0m is set to 255, representing a wet grid. Obtaining an initial wet and dry state grid;
4) According to the national land coverage data standard, converting the surface coverage type into corresponding Manning coefficients, and resampling to a model grid to obtain a surface Manning coefficient grid;
5) Interpolating the maximum wind speed and the average wind direction in the storm tide process into a model grid to obtain a wind field grid which is used as an external force to forcedly influence the flood beach process;
6) And interpolating the highest water level and the maximum flow rate in the storm tide process to a grid with a wet initial state and a grid with a dry initial state, and assigning 0 to obtain a water level grid and a flow rate grid, wherein the water level grid and the flow rate grid are used as boundary conditions to drive the flood tide process.
Thus, an initial grid structure is obtained, and grid attributes including dry and wet states, elevation, surface Manning coefficients, water level, flow velocity, wind speed and wind direction are used as input of a model. Wherein the grid wet and dry state, water level and flow rate evolve with iteration.
As shown in fig. 2, the flood beach and flood identification module comprises the following steps:
1) Inputting an initial grid structure;
2) Traversing all grids, performing the following procedure:
if the target grid is a wet grid or there is no adjacent wet grid, the target grid remains unchanged. Wherein adjacent wet meshes are defined as molar neighbors, i.e., eight meshes adjacent to the target mesh;
if condition 1 is satisfied: the target grid is a dry grid and there is at least one adjacent wet grid, then the following steps are performed:
calculating the remaining energy height and friedel number of the adjacent wet grid:
wherein Hner is the remaining energy level of the neighboring grid, frner is the Friedel number of the neighboring grid, v ner Is the flow velocity, eta, in the x-direction of the adjacent grid ner Is adjacent to the grid water level, tau a Is wind stress in x direction, τ b Is the bottom friction in the x-direction, ρ is the sea water density, g is the gravitational acceleration, D is the water depth, and Δx is the distance between the adjacent grid and the center of the target grid.
A plurality of adjacent wet grids, and then the residual energy height and the Friedel are processed in an average way, and the average treatment is regarded as a single grid to participate in calculation;
if condition 2 is satisfied: the target grid is changed from a dry grid to a wet grid when the target grid meets the formula (7), and the water level and the flow rate after the change are solved according to the formulas (5) - (6); if the target grid does not meet the formula (7), the target grid is kept unchanged;
after traversing all grids, the grid state changes to obtain an iterated grid structure;
3) The iterative program is circulated, so that the grid structure is continuously and iteratively evolved until all grids are unchanged;
4) And extracting grids with dry initial states and wet final states to obtain a submerged range, and converting the submerged range into face vector data. And calculating the difference between the grid water level and the elevation to obtain the submerged water depth. Submerged water depth and flow rate within the extraction submerged range. Outputting results, including submerged range plane vector data, submerged water depth raster data and flow rate raster data;
a result visualization module comprising the steps of:
and the submerged range, the submerged water depth and the flow velocity are superimposed on the map by using a GIS technology, so that the influence range and degree of storm surge and flood are intuitively reflected.
The verification of the accuracy of the invention comprises the following comparison experiments:
in order to verify the accuracy of the model identification result, two types of comparison experiments are designed and respectively compared with the actual disaster investigation result and the numerical mode result. The comparison with the actual disaster investigation results is to explain the reliability of the model for practical application, and the comparison with the numerical mode results is to explain the rationality of the model principle.
Comparison with actual disaster investigation results includes the following experiments:
and selecting the Hebei and Guangdong Shenzhen from Bohai sea and south sea respectively as research areas, and selecting typhoon storm tide processes which have great influence on the two areas respectively. Typhoons No. 1909 land in Shandong province at 10.11.2019, and the central maximum wind speed is 9 levels, and serious storm surge disasters occur in Bohai Bay and Laozhou Bay. After the disaster, the national ocean environment prediction center organizes disaster investigation aiming at the south shore of Bohai Bay, so No. 1909 typhoon storm tide is selected as an experimental storm tide process in the Cangzhou market. Typhoons 9216 originated in the pacific northwest at 8.20 of 2017, and suffered from strong winds and severe storm surge near the mouth of the bead because of their travel path and strength. The ocean monitoring and forecasting center in Shenzhen city performs the expansion investigation of the important disaster-affected area in the storm tide process, so that the 1713 typhoon storm tide at the west coast of Shenzhen city is selected as the experimental storm tide process. The experimental area and the experimental typhoon path are shown in fig. 3.
Because the actual disaster investigation data belongs to post-disaster investigation, errors exist for the submerged condition during disaster, especially for the specific numerical value of the submerged depth. Thus, the comparison with the actual disaster investigation results only involves comparison of the submerged range, and does not compare the submerged water depth.
Comparison with the numerical mode results included the following experiments:
comparison with numerical mode results the ADCIRC-SWAN coupling mode was selected as a control with the Laozhou bay coast as the study area. Due to the particularity of the geographic position, storm surge has higher water increasing intensity and is frequently suffered from storm surge disasters. Typhoons 1909 and 9216 were chosen as experimental storm surge processes, representing two typhoons paths affecting the shandong peninsula, respectively, with the center of typhoons 1909 going directly through the lyzhou bay and typhoons 9216 moving approximately along the south of the shandong peninsula.
The boundary input maximum water level and flow rate of the model are taken from the coastal simulation results of the ADCIRC-SWAN mode to ensure that the same boundary conditions are adopted as the mode. Furthermore, it is considered that the expression form of the friction coefficient of the sole in the ADCIRC-SWAN mode is different from that of the model, and is related to the water depth and is irrelevant to the Manning coefficient. Thus, in the comparative test, the Manning coefficients in the present model will be replaced with equivalent Manning coefficients related to water depth, so that the model has underlying conditions consistent with the pattern. The equivalent Manning coefficient is obtained by combining the formulas (11) and (14).
The expression of the midsole coefficient of friction in ADCIRC-SWAN mode:
wherein C is f Is the coefficient of friction of the base and,
is the minimum bottom friction coefficient, H break The crushing depth, H is the water depth, θ is a dimensionless parameter indicating the speed at which the bottom friction coefficient approaches its limit, and γ is a dimensionless parameter indicating the speed at which the bottom friction coefficient increases with decreasing water depth; />
And γ were set to 0.0015,1, 10 and 1/3 in the ADCIRC-SWAN mode, respectively.
Equivalent Manning coefficient expression adopted by the model in the comparison experiment:
where n is the surface Manning roughness coefficient, g is the gravitational acceleration, and D is the water depth.
Comparison with the numerical mode results includes both submerged range and submerged water depth.
Considering that the model and the ADCIRC-SWAN mode respectively adopt different calculation grids, the comparison of the model and the ADCIRC-SWAN mode simulation results is based on the calculation nodes of the ADCIRC-SWAN mode. The simulation result of the model is interpolated to the nodes, and quantitative comparison is carried out on two aspects of the submerged range and the water depth respectively.
And comparing the submerged ranges, defining a submerged node of model and pattern recognition by taking the consistency rate delta as a measurement index, wherein the ratio of the number of intersection nodes to the number of union nodes is as follows:
wherein N is a Is the number of submerged nodes identified by the model, N m The number of submerged nodes, N, is ADCIRC-SWAN mode identification o The number of submerged nodes which are overlapped by the two methods is represented.
To the coincident submerged jointComparing the submerged depths of the points by a correlation coefficient R 2 And root mean square error RMSE as a measure:
wherein, the liquid crystal display device comprises a liquid crystal display device,
representing the submerged water depth of the model identification at the inode,/->
Representing submerged water depth of ADCIRC-SWAN pattern recognition at inode, +.>
Representing the mean value of the submerged depths of the model identification at the coincident node,/->
Representing the submerged water depth average of ADCIRC-SWAN pattern recognition on the coincident nodes.
The comparative experiment results are as follows:
as can be seen from fig. 4, the simulation result of the present model has a high degree of coincidence with the actual measurement of the boundary line of the submerged range caused by the typhoon storm surge No. 1909 in the cangzhou market. The coastal seawall in Cangzhou plays a remarkable role, and the submerged boundary line is mostly consistent with the trend of the seawall. The portion in which the submerged boundary line curves to the land side is caused by the reverse flow of the river sea water. The inundation investigation of the 1713 typhoon storm tide on the Western coast of Shenzhen city only comprises local disaster sites, wherein three sites are seriously damaged. As can be seen in fig. 5, the three disaster sites substantially coincide with the model simulated flooding scope.
Besides the difference between DEM data and real terrain and errors caused by other factors such as rainfall, the simulation result of the model is more consistent with the actual disaster investigation result.
As can be seen from FIG. 6, the submerged range identified by the model is basically consistent with the ADCIRC-SWAN mode identification result, the consistency ratio of the submerged ranges in the two typhoons is 0.92 and 0.95 respectively, and the comparison of the submerged water depth scatter diagram shows that the error of the model and the ADCIRC-SWAN mode identification water depth is basically within 0.3 m. Wherein, the water depth of the typhoon storm surge process No. 1909 is compared with R 2 Water depth contrast R for typhoon storm surge process No. 9216 with nmse=0.13 m, 0.96 =0.96 2 Nmse=0.12 m, indicating that the submerged water depth identified by the present model substantially corresponds to the ADCIRC-SWAN pattern.
The difference between the model and the numerical mode mainly comes from the difference of the grid structures, and the model simplifies the hydrodynamic principle. But for the design purposes of the present model this degree of error falls within acceptable limits. In addition, different typhoon paths can also cause differences in contrast consistency. In the two typhoons of the experiment, 9216 is better in consistency than 1909 because 1909 typhoons pass through the Laizhou bay directly, the hydrologic and meteorological environments are more complex and changeable in time, and the model adopts extremum and averaging treatment to generate relatively larger errors.
The comparison experiment results are combined, so that the rationality of the design principle of the model and the accuracy of the identification result are enough to be described.
In addition, a sensitivity experiment is designed by comparing the on-off of the expression switch of stroke stress and bottom friction force of the model so as to illustrate the meaning of considering the forced action of external force to the accuracy of the simulation result in the model. The sensitivity test design and results are shown in Table 1.
TABLE 1
Experiment 1 shows a model taking wind stress and bottom friction into consideration, and the result is the result of the comparison test and has better consistency. Experiment 2 shows a model that does not take into account wind stress and bottom friction, and the results show that the submerged range and water depth consistency are lower than those of experiment one. Experiment 3 and experiment 4 show that the consistency is reduced by the model considering only the wind stress and the bottom friction, respectively. Wherein neglecting wind stress greatly reduces the accuracy of the model.
In conclusion, the model considers wind stress and bottom friction force to better accord with the hydrodynamic principle of the storm surge submerging process, so that a more accurate simulation result is generated.
By combining the comparison test and the sensitivity test conclusion, the accuracy of identifying the storm surge and flood beach of the invention is verified, and the accuracy is more accurate than the traditional storm surge flooding conceptual model by relatively improving the hydrodynamic theory basis. Meanwhile, the calculation efficiency of the model program based on the cellular automaton is higher and more stable, so that the model program can be identified more quickly than a storm surge mode. Thus, the unique advantages of the invention in the storm surge and beach identification field are proved.
While the foregoing is directed to the preferred embodiments of the present invention, it will be appreciated by those skilled in the art that various modifications and adaptations can be made without departing from the principles of the present invention, and such modifications and adaptations are intended to be comprehended within the scope of the present invention.
Claims (7)
1. A storm surge and flood identification method based on cellular automata is characterized by comprising the following steps:
s1, collecting geographic, hydrological and meteorological data, and constructing an initial grid structure of an area to be identified;
s2, establishing a dry-wet grid transformation rule based on a cellular automaton and a dry-wet grid algorithm and based on a two-dimensional shallow water momentum equation; iterative calculation and pre-judging of grid structures in the area are carried out, the grid wet and dry state is updated, and a prediction recognition result is obtained: the extent and extent of storm surge and flooding effects.
2. A storm surge and flooding identification method based on cellular automata according to claim 1, wherein said constructing an initial grid structure comprises the steps of:
a. collecting data: the geographic data comprises terrain elevation raster data, surface coverage type raster data, river and dam line vector data, the hydrologic data comprises coastal storm surge highest water level and flow velocity data, and the meteorological data comprises wind field data during storm surge;
b. determining the size of a grid, and rasterizing the region to be detected; resampling the terrain elevation data to a grid model, and determining an initial wet and dry state grid according to the terrain elevation by taking 0m as a threshold value;
c. according to the national land coverage data standard, converting the surface coverage type into a corresponding Manning coefficient, and resampling to a model grid to obtain a surface Manning coefficient grid; interpolating the maximum wind speed and the average wind direction in the storm tide process into a model grid to obtain a wind field grid; as an external force forcing factor to influence the flood beach and the flooding process;
d. and interpolating the highest water level and flow rate in the storm tide process to a grid with a wet initial state in the model grid, and assigning 0 to a grid with a dry initial state to obtain a water level grid and a flow rate grid, wherein the water level grid and the flow rate grid are used as boundary conditions to drive the flood tide process.
3. The method for identifying storm surge and flood based on cellular automata according to claim 1, wherein the constructed dry-wet grid transformation rule is specifically deduced as follows:
two-dimensional shallow water momentum equation (x direction):
where v is the flow velocity in the x direction, eta is the water level, tau a Is wind stress in x direction, τ b Is the bottom friction force in the x direction, ρ is the sea water density, g is the gravitational acceleration, and D is the water depth;
neglecting local acceleration term in the two-dimensional shallow water momentum equation, and rewriting the two-dimensional shallow water momentum equation (x direction) into an energy form, wherein the physical meaning is that the external force forces to cause the energy height change:
discretizing the formula (2) between the target grid and the adjacent grid by adopting a finite difference method:
wherein v is tar Is the flow velocity, eta, in the x-direction of the target grid tar Is the target grid water level, v ner Is the flow velocity, eta, in the x-direction of the adjacent grid ner Is the adjacent grid water level, Δx is the distance between the adjacent grid and the center of the target grid;
assuming that the friedel numbers of water flows within the target mesh and adjacent mesh are approximately equal:
wherein Fr is a Friedel number, h tar Is the target grid elevation, h ner Is the elevation of the neighboring grid;
and (3) to (4) of the simultaneous formulas, and solving to obtain the target grid water level and the flow rate:
according to the wetting condition that the grid water level is higher than the grid elevation, obtaining the condition that the dry grid is converted into the wet grid:
among other things, consider the effect of wind stress and bottom friction on storm surge and inundation: wind stress τ a And bottom friction force tau b Expressed as:
τ a ＝ρ a C d v 2 (8)
C d ＝(0.75+0.067|v wind |)×10 -3 (9)
τ b ＝ρC f |v wind |v wind (10)
wherein τ a Is wind stress, τ b Is the bottom friction, ρ is the sea water density, ρ a Is the air density, v wind Is the projection of the relative wind speed in the flow velocity direction, C d Is the drag coefficient, C f Is the bottom coefficient of friction, n is the surface Manning roughness coefficient, and is related to the surface coverage type.
4. The method for identifying storm surge and flood based on cellular automata according to claim 1, wherein the iterative calculation is performed on the grid structure in the area, the dry and wet states of the updated grid are prejudged, and the predicted identification result is obtained, which comprises the following steps:
traversing all grids, and circularly iterating the following steps a-d to enable the grid structure to continuously iterate and evolve until all grids are unchanged, obtaining a final wet grid area which is the maximum possible submerged range, and obtaining submerged water depth and flow velocity;
step a, if the target grid is a wet grid or no adjacent wet grid exists, the state of the target grid is unchanged;
step b, if the target grid is a dry grid and at least one adjacent wet grid exists, calculating the residual energy height and the Friedel number of the adjacent wet grid;
c, if a plurality of wet grid transformation rules are met, updating the state of the current target grid into a wet grid; otherwise, the state of the current target grid is unchanged;
and d, updating and calculating the water level and the flow rate of the wet grid.
5. The cellular automaton-based storm surge and flooding identification method of claim 4 wherein said calculating a remaining energy height and friedel of adjacent wet cells comprises:
wherein H is ner Is the remaining energy level, fr, of the neighboring grid ner Friedel number, v, of adjacent grid ner Is the flow velocity, eta, in the x-direction of the adjacent grid ner Is adjacent to the grid water level, tau a Is wind stress in x direction, τ b Is the bottom friction in the x-direction, ρ is the sea water density, g is the gravitational acceleration, D is the water depth, and Δx is the distance between the adjacent grid and the center of the target grid.
6. The cellular automaton-based storm surge and flooding identification method of claim 4 wherein if there are multiple adjacent wet meshes, then averaging the remaining energy height and friedel is treated as a single mesh to participate in the calculation.
7. A storm surge and flooding identification method based on cellular automata according to any one of claims 1-6, wherein said method further comprises:
and S3, visually displaying the range and degree of the influence of storm surge and flood by using a GIS technology.
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