Coastal city flood multi-system coupling assessment method and system

Abstract

The application relates to a coastal city flood multi-system coupling evaluation method and system, which comprises the following steps: acquiring multi-source data, uniformly projecting and time resolution normalizing the data; constructing an upstream river network one-dimensional water power model, a city drainage pipe network one-dimensional hydraulic model and a city two-dimensional surface water power model based on the multi-source data, and setting a tidal boundary condition; coupling the models and the tidal boundary; model calibration and error constraint are performed on the coupled multi-dimensional water power coupling model; a composite scenario is constructed, each group of scenarios is taken as input boundary conditions to drive the multi-dimensional water power coupling model, and a flood evolution result is obtained; and risk assessment and sensitivity analysis are performed based on the flood evolution result. By constructing a unified river network-pipe network-surface-tide multi-scale coupling framework, the defects that a traditional single model or a result superposition method cannot reflect multi-system interaction are effectively made up.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of flood disaster assessment, in particular to a coastal city flood multi-system coupling assessment method and system. BACKGROUND

[0002] With the increase in the frequency of extreme weather events caused by climate change and the intensification of imperviousness of the land surface brought about by urbanization, coastal cities are facing the risk of compound flood disasters under the superimposed effects of heavy rainfall, high tide level and upstream flood. Flood disasters have become an important factor restricting the sustainable development of social economy in coastal cities. A large number of studies have shown that the flood process in coastal cities is often not triggered by a single factor, but is formed by the joint action of multiple hydrodynamic factors. For example, when the upstream flood peak flow and local extreme rainfall occur at the same time and are superimposed on the jacking effect of astronomical tide or storm surge, the drainage of the city pipe network is often blocked or even backflow, resulting in a significant amplification of the risk of waterlogging.

[0003] Existing researches are mostly focused on the simulation of a single factor. For example, the upstream inflow is estimated based on a hydrological model, or the urban drainage process is calculated by a one-dimensional pipe network model, or the urban waterlogging evolution is simulated based on a two-dimensional hydrodynamic model. These single-model methods have applicability in specific scenarios, but have obvious limitations when multiple factors are superimposed, and cannot fully reflect the interaction between complex hydrodynamic systems. In particular, in coastal cities, the periodic rise and fall of tides directly affect the drainage efficiency of rivers and pipe networks, while upstream floods and local heavy rainfall are superimposed in a short period of time to cause a dramatic increase in runoff. These factors jointly form a typical "compound" flood disaster process.

[0004] In recent years, some scholars have attempted to superimpose different models to describe the coupling effect of multiple factors. However, such researches often stay at the level of one-way driving or linear superposition, and lack the depiction of dynamic feedback mechanisms between systems. For example, the rise of river water level will inversely affect the drainage capacity of the pipe network, and the tide jacking will further change the river water level conditions. These cross-scale and cross-system hydrodynamic coupling relationships are often simplified or ignored in traditional methods. Therefore, the existing methods cannot accurately predict the evolution characteristics of floods in space and time, and the uncertainty of risk assessment results is large. In addition, the existing researches mostly adopt the "divide and conquer" approach, modeling the river, pipe network and surface process separately, and then superimposing the results. This method cannot realize real-time interaction and bidirectional exchange of multiple systems, cannot reveal the nonlinear amplification effect between tides and pipe network jacking, and cannot be used to assess the risk threshold under extreme working conditions. This defect is particularly prominent in coastal cities, because such cities are often in a complex hydrological and hydrodynamic environment, and are controlled by both land-based inflow and marine boundary conditions.

[0005] Therefore, there is an urgent need for a new technical method that can systematically depict the multi-scale coupling relationship between upstream floods, local rainfall, urban drainage pipe networks and tidal effects in a unified framework, and realize dynamic simulation and risk assessment. SUMMARY

[0006] To this end, the technical problem to be solved by the present application is to overcome the technical defects existing in the prior art, and to propose a coastal city flood multi-system coupling evaluation method and system, which can simultaneously consider the coupling effects of multiple disaster-causing factors such as upstream inflow, urban river drainage, surface water evolution and coastal tide jacking in a system by constructing a unified river network-pipe network-surface-tide multi-scale coupling framework, and effectively makes up for the defects of traditional single model or result superposition method that is difficult to reflect the interaction of multiple systems by realizing the continuity of flow and water level of different hydraulic processes.

[0007] To solve the above technical problems, the present application provides a coastal city flood multi-system coupling evaluation method, comprising the following steps:

[0008] S1, obtaining multi-source data, and performing unified projection and time resolution normalization processing on the data, wherein the multi-source data includes hydrological data, meteorological data, marine data and city data;

[0009] S2, constructing an upstream river network one-dimensional hydrodynamic model, a city drainage pipe network one-dimensional hydraulic model and a city two-dimensional surface water dynamic model based on the multi-source data, and setting tide boundary conditions;

[0010] S3, coupling the upstream river network one-dimensional hydrodynamic model, the city drainage pipe network one-dimensional hydraulic model, the city two-dimensional surface water dynamic model and the tide boundary to obtain a multi-dimensional hydrodynamic coupling model;

[0011] S4, model calibration and error constraint of the multi-dimensional hydrodynamic coupling model;

[0012] S5, after the model calibration and verification are completed, constructing a composite scenario, driving the multi-dimensional hydrodynamic coupling model with each group of scenarios as input boundary conditions to obtain flood evolution results;

[0013] S6, risk assessment and sensitivity analysis based on the flood evolution results.

[0014] In an embodiment of the present application, in S2, the method for constructing the upstream river network one-dimensional hydrodynamic model, the city drainage pipe network one-dimensional hydraulic model and the city two-dimensional surface water dynamic model comprises:

[0015] The upstream river network one-dimensional hydrodynamic model is represented by one-dimensional Saint-Venant equation set:

[0016] ;

[0017] ;

[0018] where A is the cross-sectional area of water, Q is the flow rate, q l is the lateral flow rate, S0 is the bed surface slope, S f is the friction slope, and I1 is the hydraulic integral term, where the friction slope S f is expressed using the Manning formula:

[0019] ;

[0020] where n is the Manning roughness, and R is the hydraulic radius;

[0021] The one-dimensional hydraulic model of the urban drainage network is expressed using the energy equation:

[0022] ;

[0023] where Q is the flow rate, B is the pipe cross-sectional area, L is the pipe length, D is the diameter, λ is the friction coefficient, , are the upstream and downstream water heads, respectively;

[0024] The gate control logic is introduced on the basis of the energy equation to simulate the blockage of backflow when the tide level is higher than the node water level:

[0025] ;

[0026] where is the gate cross-sectional area of water, is the control height, and the flow coefficient in the gate flow rate calculation is represented by K;

[0027] The threshold value of the node water level is used to control the pump station flow rate, and the pump station start-stop control formula is:

[0028] ;

[0029] where , is the start-stop threshold water level, is the node water level, is the pump station flow rate, is the maximum pump station flow rate;

[0030] The two-dimensional surface water power model of the city is expressed using the shallow water equation:

[0031] ;

[0032] ;

[0033] ;

[0034] wherein, is the water depth, u, v are velocity components, is the rainfall inflow term, is the infiltration term, z is the ground elevation, , is the friction term, wherein the node exchange term formula is:

[0035] ;

[0036] wherein, is the exchange flow, Cs is the exchange coefficient, is the surface water level.

[0037] In an embodiment of the present application, in S2, the method for setting the tidal boundary condition comprises:

[0038] The tidal boundary condition adopts the formula of harmonic constituent:

[0039] ;

[0040] wherein, H i is the constituent amplitude, ω i is the angular velocity, V i is the initial astronomical angle, g i is the constituent lag angle, f i is the correction factor;

[0041] The tidal boundary condition is adaptively adjusted, when the simulation time step Δt tide does not match the main frequency ω i of the tide, a phase correction term is introduced:

[0042] ;

[0043] wherein, is the tidal elevation at time t, n is the total number of tidal components, is the astronomical factor of the i-th tidal component, is the harmonic constant amplitude of the i-th tidal component, is the angular frequency of the i-th tidal component, t is the time variable, is the astronomical argument of the i-th tidal component, is the initial phase angle of the i-th tidal component, is the phase correction term, used for correction when the boundary condition does not match the main frequency.

[0044] In one embodiment of the present invention, in S3, the method for coupling the one-dimensional hydrodynamic model of the upstream river network, the one-dimensional hydraulic model of the urban drainage network, the two-dimensional surface hydrodynamic model of the city, and the tidal boundary includes:

[0045] Establishing multi-scale hydraulic coupling relationships includes setting water level continuity conditions and flow conservation conditions at the confluence nodes of river networks and pipe networks. , In the pipeline network and on the surface, overflow and backflow are exchanged bidirectionally through the nodal weir flow formula. And applying time-varying water level boundaries at river networks and tidal estuaries. ;

[0046] Interface coupling conditions are set based on multi-scale hydraulic coupling relationships, including flow conservation conditions and water level continuity conditions, as follows:

[0047] ;

[0048] ;

[0049] In the formula, The total inflow to the node. This represents the total outflow of the node. For flow conservation error, The allowable traffic error threshold; Calculate the water level for the river channel. For node water level, For water level difference error, The allowable water level difference threshold;

[0050] An iterative convergence mechanism is set up so that when the flow conservation error and the water level difference error exceed the threshold, the interface flow and water level are updated using an iterative correction algorithm until convergence.

[0051] In one embodiment of the present invention, in S3, after model coupling, a hierarchical time step is proposed. Within each large step, the tidal boundary is updated once, the river network is updated twice, the pipeline network is updated ten times, and the surface is updated twenty times. The synchronization error is monitored by an indicator, which is:

[0052] ;

[0053] In the formula, Let k be the water level at node k obtained in the m-th iteration. Let Et be the result of the m-1th iteration, and Et be the relative error.

[0054] In one embodiment of the present invention, in step S4, the method for model calibration and error constraint of the multidimensional hydrodynamic coupling model includes:

[0055] The performance of the multidimensional hydrodynamic coupling model is measured by the Nash efficiency coefficient (NSE) and the coefficient of determination (R²). 2 verify:

[0056] ;

[0057] ;

[0058] In the formula, For the flow rate observed at time i, For the simulated flow rate at time i, The mean flow rate is the observed value, and n is the sample size.

[0059] Based on NSE and R 2 Introducing a global mass conservation error index:

[0060] ;

[0061] In the formula, The total volume entering the system, The total volume of the outflow system. This refers to changes in the water storage within the system.

[0062] In one embodiment of the present invention, a method for sensitivity analysis based on flood evolution results includes:

[0063] The results of flood evolution include the distribution of water depth h(x,y,t) and the inundated area A. f And the water level process curves at key nodes;

[0064] The sensitivity coefficient method is introduced to quantify the contribution of each factor to the flood outcome:

[0065] ;

[0066] In the formula, A f h represents the inundated area. max Let be the maximum water depth, D be the flood disaster intensity or loss function value, φ be the water level or tide level, Q be the flow rate, and Δt be the maximum water depth. phase The phase difference time of a tidal or flood process. This represents the sensitivity coefficient of water level changes to the flooded area. The sensitivity coefficient of flow rate change to maximum water depth. This is the sensitivity coefficient of phase difference time to disaster intensity.

[0067] Furthermore, this invention also provides a multi-system coupled assessment system for flooding in coastal cities, comprising:

[0068] The data acquisition module is used to acquire multi-source data and perform unified projection and time resolution normalization processing on the data. The multi-source data includes hydrological data, meteorological data, marine data and urban data.

[0069] The model building module is used to construct a one-dimensional hydrodynamic model of the upstream river network, a one-dimensional hydraulic model of the urban drainage network, and a two-dimensional surface hydrodynamic model of the city based on the multi-source data, and to set tidal boundary conditions.

[0070] The coupling module is used to couple the one-dimensional hydrodynamic model of the upstream river network, the one-dimensional hydraulic model of the urban drainage network, the two-dimensional surface hydrodynamic model of the city, and the tidal boundary to obtain a multi-dimensional hydrodynamic coupling model.

[0071] The model calibration and error constraint module is used to perform model calibration and error constraint on the multidimensional hydrodynamic coupling model.

[0072] The scenario construction-driven evolution module is used to construct composite scenarios after model calibration and validation. Each scenario is used as an input boundary condition to drive the multidimensional hydrodynamic coupling model and obtain flood evolution results.

[0073] The assessment and analysis module is used for risk assessment and sensitivity analysis based on flood evolution results.

[0074] Furthermore, the present invention also provides a computer device, including a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the program to implement the steps of the method described above.

[0075] Furthermore, the present invention also provides a computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the steps of the method described above.

[0076] The technical solution of the present invention has the following advantages over the prior art:

[0077] 1. This invention constructs a unified multi-scale coupling framework of river network-pipe network-surface-tide, which can simultaneously consider the coupling effects of multiple disaster-causing factors such as upstream water inflow, urban river drainage, surface water accumulation evolution and coastal tidal backwater in one system. By realizing the continuity of flow and water level in different hydraulic processes, it effectively makes up for the shortcomings of traditional single model or result superposition method in reflecting the interaction of multiple systems.

[0078] 2. This invention constructs composite scenarios, using each scenario as input boundary conditions to drive a multidimensional hydrodynamic coupling model, obtaining flood evolution results. Based on these results, risk assessment and sensitivity analysis are conducted, enabling quantitative analysis of the impact of tidal rise, flood peak phase difference, and rainfall intensity on inundated area and water depth. This method provides a quantitative tool for revealing nonlinear amplification effects, identifying key risk sources and system weaknesses, thus providing direct support for emergency plan development and engineering deployment.

[0079] 3. The method of this invention is not only applicable to flood risk assessment in coastal cities, but also has good scalability and extensibility. With complete input data, it can be extended to complex areas such as delta cities, coastal industrial zones, and estuary plains. Its simulation results can directly output flood risk maps and lists of key facilities at risk of flooding, providing a scientific basis for urban planning, flood control and drainage engineering renovation, and resource optimization, thus having important application value in improving urban flood resilience and protecting the lives and property of residents. Attached Figure Description

[0080] To make the content of this invention easier to understand, the invention will be further described in detail below with reference to specific embodiments and accompanying drawings.

[0081] Figure 1 This is a flowchart illustrating a multi-system coupled assessment method for flooding in coastal cities proposed in this invention. Detailed Implementation

[0082] The present invention will be further described below with reference to the accompanying drawings and specific embodiments, so that those skilled in the art can better understand and implement the present invention. However, the embodiments described are not intended to limit the present invention.

[0083] Reference Figure 1 As shown in the figure, this invention provides a method for multi-system coupled assessment of flooding in coastal cities, including the following steps:

[0084] Step S1: Acquire multi-source data and perform unified projection and time resolution normalization on the data. The multi-source data includes hydrological data, meteorological data, marine data, and urban data.

[0085] Step S2: Construct a one-dimensional hydrodynamic model of the upstream river network, a one-dimensional hydraulic model of the urban drainage network, and a two-dimensional surface hydrodynamic model of the city based on multi-source data, and set tidal boundary conditions;

[0086] Step S3: Couple the one-dimensional hydrodynamic model of the upstream river network, the one-dimensional hydraulic model of the urban drainage network, the two-dimensional surface hydrodynamic model of the city, and the tidal boundary to obtain a multi-dimensional hydrodynamic coupling model.

[0087] Step S4: Perform model calibration and error constraints on the multidimensional hydrodynamic coupling model;

[0088] Step S5: After the model calibration and verification are completed, construct composite scenarios and use each scenario as input boundary conditions to drive the multidimensional hydrodynamic coupling model to obtain the flood evolution results.

[0089] Step S6: Conduct risk assessment and sensitivity analysis based on flood evolution results.

[0090] This invention constructs a unified multi-scale coupling framework of river network-pipe network-surface-tide, which can simultaneously consider the coupling effects of multiple disaster-causing factors such as upstream water inflow, urban river drainage, surface water accumulation evolution, and coastal tidal backwater in a single system. By achieving the continuity of flow and water level in different hydraulic processes, it effectively compensates for the shortcomings of traditional single models or result superposition methods in reflecting the interaction of multiple systems.

[0091] This invention constructs composite scenarios, using each scenario as input boundary conditions to drive a multidimensional hydrodynamic coupling model, obtaining flood evolution results. Based on these results, risk assessment and sensitivity analysis are conducted, enabling quantitative analysis of the impact of tidal rise, flood peak phase difference, and rainfall intensity on inundated area and water depth. This method provides a quantitative tool for revealing nonlinear amplification effects, identifying key risk sources and system weaknesses, thus providing direct support for emergency plan development and engineering deployment.

[0092] In step S1, the multi-source data of this invention includes hydrological data, meteorological data, marine data, and urban data. Hydrological data includes upstream river flow processes, tributary inflows, and observed flood peak data; meteorological data includes rainstorm duration and rainfall intensity distribution curves; marine data includes astronomical tide levels and storm surge records, with a harmonic analysis method used to obtain the tide level sequence; and urban data includes drainage pipe network diameter, length, slope, node elevation, urban digital elevation model (DEM), and land use types. All data undergo unified projection and temporal resolution normalization to ensure model input compatibility.

[0093] The method for constructing the one-dimensional hydrodynamic model of the upstream river network, the one-dimensional hydraulic model of the urban drainage network, and the two-dimensional surface hydrodynamic model of the city in step S2 includes:

[0094] (1) The one-dimensional hydrodynamic model of the upstream river network is represented by the one-dimensional Saint-Venant equations:

[0095] ;

[0096] ;

[0097] In the formula, A is the cross-sectional area of ​​the water passage, Q is the flow rate, and q lS is the lateral flow rate, S0 is the bed slope, and S f Let S be the friction gradient, I1 be the hydraulic integral term, and S be the friction gradient. f Expressed using Manning's formula:

[0098] ;

[0099] In the formula, n is the Manning roughness coefficient, and R is the hydraulic radius;

[0100] To ensure the stability of numerical computation, this invention introduces the Courant stability condition (CFL condition) during the numerical discretization process to constrain the time step:

[0101] ;

[0102] In the formula, CFL is the Courant number (taken as 0.8) to ensure the stability of the river network calculation, Δtriver is the time step of the river network calculation, Δx is the spatial discrete step size, g is the gravitational acceleration, h is the water depth, and u is the flow velocity.

[0103] (2) The one-dimensional hydraulic model of the urban drainage network is represented by an energy equation:

[0104] ;

[0105] In the formula, Q is the flow rate, B is the pipe cross-sectional area, L is the pipe length, D is the diameter, and λ is the friction coefficient. , Upstream and downstream water heads respectively

[0106] Based on the energy equation, gate control logic is introduced to simulate backflow prevention when the tide level is higher than the node water level.

[0107] ;

[0108] In the formula, The water passage area of ​​the gate. To control the height, This represents the flow coefficient in gate flow calculation;

[0109] Based on the threshold control of the node water level, the pump station flow rate is controlled, and the pump station start-up and shutdown control formula is as follows:

[0110] ;

[0111] In the formula, , The threshold water level for starting and stopping. For node water level, For the pump station flow rate, This represents the maximum flow rate of the pumping station.

[0112] (3) The two-dimensional surface hydrodynamic model of the city is represented by the shallow water equation:

[0113] ;

[0114] ;

[0115] ;

[0116] In the formula, Let be the water depth, and u and v be the velocity components, respectively. For rainfall inflow, The term represents infiltration, and z represents the ground elevation. , The friction term is given by the formula for the nodal commutation term:

[0117] ;

[0118] In the formula, For the exchange flow, Cs is the exchange coefficient. This refers to the surface water level.

[0119] (4) The tidal boundary conditions adopt the harmonic tidal formula:

[0120] ;

[0121] In the formula, H i For the tidal amplitude, ω i V is the angular velocity. i For the initial astronomical angle, g i To determine the tidal lag angle, f i As a correction factor;

[0122] Adaptive adjustment of tidal boundary conditions is performed when the simulation time step Δt tide With the dominant tidal frequency ω i When there is a mismatch, a phase correction term is introduced. :

[0123] ;

[0124] In the formula, Let be the tidal level at time t, and n be the total number of tidal components. Let i be the astronomical factor of the i-th tidal component. Let be the harmonic constant amplitude of the i-th tidal component. Let be the angular frequency of the i-th tidal component, and t be the time variable. Let be the astronomical argument of the i-th tidal component. Let be the initial phase angle of the i-th tidal component, and be the phase correction term used to correct for mismatches between boundary conditions and the dominant frequency.

[0125] In step S3, the flood simulation of coastal cities requires simultaneous consideration of the interactions between upstream river networks, urban drainage networks, urban surface conditions, and offshore tides. Therefore, this invention first establishes the following multi-scale hydraulic coupling relationships within a unified framework:

[0126] (1) River network-pipeline network: Set water level continuity conditions and flow conservation conditions at the confluence nodes:

[0127] ;

[0128] In the formula, The total inflow to the node. The total outflow of the node; the calculated water level of the river channel. The water level at the node.

[0129] (2) Pipeline-Surface: Overflow and backflow are exchanged bidirectionally through the nodal weir flow formula:

[0130] ;

[0131] In the formula, For node water level, This refers to the surface water level.

[0132] (3) River network-tidal: Time-varying water level boundary applied at the river mouth:

[0133] ;

[0134] In the formula, Indicates the time-varying water level at the river mouth;

[0135] Through the above interface conditions, dynamic interaction between river networks, pipeline networks, land surface and tides can be achieved.

[0136] Then, based on the physical relationships, this invention further proposes mathematical expressions and numerical constraints for interface conditions to ensure the stability and accuracy of coupled calculations:

[0137] (1) Flow conservation condition:

[0138] ;

[0139] In the formula, For flow conservation error, This is the allowable traffic error threshold.

[0140] (2) Water level continuity condition:

[0141] ;

[0142] In the formula, For water level difference error, The allowable water level difference threshold.

[0143] (3) Iterative convergence mechanism: When the residual exceeds the threshold, the interface flow and water level are updated by iterative correction algorithm until convergence.

[0144] By setting the interface conditions as described above, this invention achieves bidirectional coupling and dynamic feedback between different subsystems, and can realistically reproduce the evolution mechanism of complex hydraulic processes in coastal cities.

[0145] Furthermore, in step S3, after model coupling, a hierarchical time step is proposed. Within each major step, the tidal boundary is updated once, the river network twice, the pipeline network ten times, and the surface twenty times. Synchronization error is monitored by indicators, which are:

[0146] ;

[0147] In the formula, Let k be the water level at node k obtained in the m-th iteration. Let Et be the result of the (m-1)th iteration, and Et be the relative error.

[0148] In step S4, model calibration uses historical flood data, with parameters primarily including channel roughness, pipeline resistance coefficient, and surface roughness. Model performance is assessed using the Nash efficiency coefficient (NSE) and the coefficient of determination R0. 2 verify:

[0149] ;

[0150] ;

[0151] In the formula, For the flow rate observed at time i, For the simulated flow rate at time i, The mean flow rate is the observed value, and n is the sample size.

[0152] Based on NSE and R 2 Introducing a global mass conservation error index:

[0153] ;

[0154] In the formula, The total volume entering the system, The total volume of the outflowing system is denoted by , and the change in water storage within the system is denoted by . If Em > 0.02, local mesh refinement or time step shortening is triggered.

[0155] In step S5, after the model calibration and verification are completed, this embodiment proposes a method for generating and analyzing composite flood scenarios based on parameter vectorization.

[0156] (1) Definition of composite scenario parameters

[0157] Construct a composite scenario parameter vector:

[0158] ;

[0159] In the formula, P represents the intensity of the rainstorm or the total rainfall, Q represents the upstream peak flow, η represents the offshore tide level, and Δt represents the total tidal range. phase To account for the phase difference between various disaster-causing factors, a large number of composite scenario inputs can be automatically generated by random sampling and orthogonal design of different return period combinations.

[0160] (2) Scenario simulation process

[0161] Each scenario serves as the input boundary condition to drive the coupled model, outputting flood evolution results, including water depth distribution h(x,y,t) and inundation area A. f And the water level process curves at key nodes.

[0162] In step S6, this embodiment introduces a sensitivity coefficient method to quantify the contribution of each factor to the flood outcome:

[0163] ;

[0164] In the formula, A f h represents the inundated area. max D represents the maximum water depth and the duration of submersion.

[0165] Then, based on the simulation results, risk indicators are calculated:

[0166] (1) Inundated area A f :

[0167] ;

[0168] In the formula, Si is the unit area. Because of the water depth, The threshold water depth.

[0169] (2) Disaster-affected population :

[0170] ;

[0171] In the formula, pi represents the population of a unit.

[0172] Then, a sensitivity analysis was conducted. By adjusting the boundary conditions (tide level +0.3m, rainfall +20%), the changes in flood indicators were compared, and the contribution of each factor was quantified.

[0173] After completing scenario simulation and sensitivity analysis, the method of this invention can generate a series of applied results, which can be directly used for urban flood control, disaster reduction and emergency management:

[0174] (1) Output of flood evolution results

[0175] Flood evolution curve: Output the water level-discharge-time curves h(t), Q(t) of key sections or nodes;

[0176] Urban inundation map: Outputs the distribution of inundation range and depth at different times and with different return periods;

[0177] List of high-risk facilities: Automatically identifies the flood depth and duration of key locations such as hospitals, subway stations, and power facilities.

[0178] (2) Risk map and zoning results

[0179] Based on the results of multi-scenario simulations, an urban flood risk map and zoning levels (low, medium, high, and extremely high risk areas) are generated, which makes it easier for government departments to intuitively identify key protection areas.

[0180] (3) Sensitivity curve and risk contribution analysis

[0181] The system outputs sensitivity curves for tidal rise, flood peak phase difference, and rainfall intensity on inundated area and water depth, quantitatively representing the risk contribution rate of each factor and supporting the prioritization of flood control investment.

[0182] (4) Emergency response plan support

[0183] The results of this invention can be embedded in urban flood control and drainage emergency plans to formulate tiered response measures. For example, when the tide level reaches the warning value, the pumping station is automatically activated and the gates are closed; when the predicted flooded area exceeds the threshold, traffic control and resident evacuation are arranged in advance.

[0184] The method of this invention is not only applicable to flood risk assessment in coastal cities, but also has good scalability and extensibility. With complete input data, it can be extended to complex areas such as delta cities, coastal industrial zones, and estuary plains. Its simulation results can directly output flood risk maps and lists of key facilities at risk of flooding, providing a scientific basis for urban planning, flood control and drainage engineering renovation, and resource optimization. Thus, it has important application value in improving urban flood resilience and protecting the lives and property of residents.

[0185] To verify the effectiveness and applicability of this invention, a multi-scale hydraulic coupling model was constructed and applied in the central urban area of ​​B city in province A.

[0186] 1.1 Data Preparation: The study area includes the upstream river network, urban drainage network, and coastal tidal boundary; upstream hydrological data are selected from the measured flow processes of the main control sections within City B, covering peak flows from 10-year to 200-year return periods; urban rainfall data are based on historical rainstorm records from 1970 to 2020, and typical rainstorm duration curves are used for design; tidal data are from stations C and D, and astronomical tide levels and storm surge increase sequences are obtained through harmonic analysis; pipeline data includes the diameter, slope, and node elevation of approximately 500 kilometers of pipelines; topographic data uses a 5m resolution DEM, and urban surface roughness distribution is generated by combining land use information.

[0187] 1.2 Model Construction and Calibration: First, a one-dimensional hydrodynamic model of the upstream river network was established, and the river roughness coefficient n=0.035 was calibrated, with a verification accuracy (NSE) of 0.87. Next, an urban pipeline network model was established, and the resistance coefficients of key trunk pipes were calibrated, with the node water level error being less than 0.1m during the verification period. Simultaneously, a two-dimensional surface model was established, using the Manning coefficient n=0.015–0.030 for zonal assignment, and compared with the measured water depth at water accumulation points to determine the coefficient of determination (R). 2 =0.82. Finally, the river network-pipeline network-surface and tidal boundary were coupled, and the overall model was validated in the "7.18" rainstorm case in 2018. The deviation of the water accumulation area was controlled within 10%, indicating that the model has high reliability.

[0188] 1.3 Scenario Simulation: After model validation, set up the following scenario:

[0189] Single-factor scenario: Only considers the upstream flood peak that occurs once every 50 years, only considers the rainstorm that occurs once every 100 years, and only considers the high tide level;

[0190] Two-factor scenarios: upstream flood peak + high tide level, heavy rain + high tide level, heavy rain + upstream flood peak;

[0191] Three-element scenario: heavy rain + upstream flood peak + high tide level (i.e., a typical "three-in-one" situation).

[0192] Simulation results show that under the single-factor scenario, the area of ​​urban flooding is limited, mainly occurring in low-lying areas of the pipe network, with a maximum water depth of about 0.4m; under the two-factor scenario, the flooded area expands by about 1.5 times, and some main roads experience traffic disruptions; under the three-factor scenario, large areas of the southern part of the city and the old city are flooded, with a maximum water depth exceeding 1.2m and the flooding duration extending to more than 36 hours.

[0193] 1.4 Sensitivity Analysis: Further sensitivity analysis was conducted on the tide level. Simulation results show that when the high tide level rises by 0.3m, the urban flooded area increases by 22%, and the average water depth increases by 0.18m; when the upstream flood peak overlaps with the rainstorm 6 hours earlier, the maximum water depth increases by 30%. This indicates that the tidal backwater effect and the flood peak superposition effect have a nonlinear amplification effect on urban drainage efficiency.

[0194] 1.5 Application of Results: Based on the simulation results, key flood-prone areas can be identified, including overloaded pipe networks in the old city, along the coastal avenue, and low-lying areas in the port area. The research findings can provide scientific reference for local governments to revise the city's flood prevention and mitigation plan for severe rainstorms in City B. It is recommended to add regulating reservoirs and upgrade pumping station capacity in high-risk areas, and optimize the connection methods between pipe network nodes and tidal channels.

[0195] 1.6 Implementation Results: As demonstrated in this embodiment, the method of the present invention can effectively integrate multi-source data to uniformly describe the complex interactions between river networks, pipe networks, land surfaces, and tides, realistically reproducing the urban waterlogging process under complex flood scenarios. Compared with traditional methods, the present invention can more accurately identify key risk sources, and the results can directly serve urban flood control planning and emergency response, possessing significant engineering practical value.

[0196] Corresponding to the above method embodiments, the present invention also provides a multi-system coupled assessment system for flooding in coastal cities, comprising:

[0197] The data acquisition module is used to acquire multi-source data and perform unified projection and time resolution normalization processing on the data. The multi-source data includes hydrological data, meteorological data, marine data and urban data.

[0198] The model building module is used to construct a one-dimensional hydrodynamic model of the upstream river network, a one-dimensional hydraulic model of the urban drainage network, and a two-dimensional surface hydrodynamic model of the city based on the multi-source data, and to set tidal boundary conditions.

[0199] The coupling module is used to couple the one-dimensional hydrodynamic model of the upstream river network, the one-dimensional hydraulic model of the urban drainage network, the two-dimensional surface hydrodynamic model of the city, and the tidal boundary to obtain a multi-dimensional hydrodynamic coupling model.

[0200] The model calibration and error constraint module is used to perform model calibration and error constraint on the multidimensional hydrodynamic coupling model.

[0201] The scenario construction-driven evolution module is used to construct composite scenarios after model calibration and validation. Each scenario is used as an input boundary condition to drive the multidimensional hydrodynamic coupling model and obtain flood evolution results.

[0202] The assessment and analysis module is used for risk assessment and sensitivity analysis based on flood evolution results.

[0203] The coastal city flood multi-system coupling assessment system of this embodiment is used to implement the aforementioned coastal city flood multi-system coupling assessment method. Therefore, its specific implementation can be referred to the description of the corresponding embodiments, and will not be described in detail here.

[0204] Furthermore, since the coastal city flood multi-system coupling assessment system in this embodiment is used to implement the aforementioned coastal city flood multi-system coupling assessment method, its function corresponds to the function of the above method, and will not be repeated here.

[0205] Corresponding to the above method embodiments, this embodiment of the invention also provides a computer device, including:

[0206] Memory, which is used to store computer programs;

[0207] A processor, used to execute computer programs, implements the steps of the aforementioned method for evaluating the multi-system coupling of flooding in coastal cities.

[0208] In this embodiment of the invention, the processor may be a central processing unit (CPU), an application-specific integrated circuit, a digital signal processor, a field-programmable gate array, or other programmable logic devices.

[0209] The processor can call programs stored in memory. Specifically, the processor can execute the operations described in the above embodiment of the multi-system coupling assessment method for flooding in coastal cities.

[0210] The memory is used to store one or more programs, which may include program code, including computer operation instructions.

[0211] In addition, the memory may include high-speed random access memory, and may also include non-volatile memory, such as at least one disk storage device or other volatile solid-state storage device.

[0212] Corresponding to the above method embodiments, this embodiment of the invention also provides a computer-readable storage medium storing a computer program, which, when executed by a processor, implements the steps of the above-described method for evaluating the multi-system coupling of flooding in coastal cities.

[0213] Those skilled in the art will understand that embodiments of this application can be provided as methods, systems, or computer program products. Therefore, this application can take the form of a completely hardware embodiment, a completely software embodiment, or an embodiment combining software and hardware aspects. Furthermore, this application can take the form of a computer program product embodied on one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0214] This application is described with reference to flowchart illustrations and / or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of this application. It will be understood that each block of the flowchart illustrations and / or block diagrams, and combinations of blocks in the flowchart illustrations and / or block diagrams, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, special-purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, generate instructions for implementing the flowchart... Figure 1 One or more processes and / or boxes Figure 1 A device that provides the functions specified in one or more boxes.

[0215] These computer program instructions may also be stored in a computer-readable storage medium that can direct a computer or other programmable data processing device to function in a particular manner, such that the instructions stored in the computer-readable storage medium produce an article of manufacture including instruction means, which are implemented in a process Figure 1 One or more processes and / or boxes Figure 1 The function specified in one or more boxes.

[0216] These computer program instructions may also be loaded onto a computer or other programmable data processing equipment to cause a series of operational steps to be performed on the computer or other programmable equipment to produce a computer-implemented process, thereby providing instructions that execute on the computer or other programmable equipment for implementing the process. Figure 1 One or more processes and / or boxes Figure 1 The steps of the function specified in one or more boxes.

[0217] Obviously, the above embodiments are merely illustrative examples for clear explanation and are not intended to limit the implementation. Those skilled in the art will recognize that other variations or modifications can be made based on the above description. It is neither necessary nor possible to exhaustively list all possible implementations here. However, obvious variations or modifications derived therefrom are still within the scope of protection of this invention.

Claims

1. A multi-system coupled assessment method for flooding in coastal cities, characterized in that: Includes the following steps: S1. Acquire multi-source data and perform unified projection and time resolution normalization processing on the data, wherein the multi-source data includes hydrological data, meteorological data, marine data and urban data; S2. Based on the multi-source data, construct a one-dimensional hydrodynamic model of the upstream river network, a one-dimensional hydraulic model of the urban drainage network, and a two-dimensional surface hydrodynamic model of the city, and set tidal boundary conditions; S3. Couple the one-dimensional hydrodynamic model of the upstream river network, the one-dimensional hydraulic model of the urban drainage network, the two-dimensional surface hydrodynamic model of the city, and the tidal boundary to obtain a multi-dimensional hydrodynamic coupling model. S4. Perform model calibration and error constraints on the multidimensional hydrodynamic coupling model. S5. After the model calibration and verification are completed, construct composite scenarios and use each scenario as input boundary conditions to drive the multidimensional hydrodynamic coupling model to obtain the flood evolution results. S6. Conduct risk assessment and sensitivity analysis based on flood evolution results; In S2, the methods for constructing a one-dimensional hydrodynamic model of the upstream river network, a one-dimensional hydraulic model of the urban drainage network, and a two-dimensional surface hydrodynamic model of the city include: The one-dimensional hydrodynamic model of the upstream river network is represented by the one-dimensional Saint-Venant equations: ； ； In the formula, A is the cross-sectional area of ​​the water passage, Q is the flow rate, and q l S is the lateral flow rate, S0 is the bed slope, and S f Let S be the friction gradient, I1 be the hydraulic integral term, and S be the friction gradient. f Expressed using Manning's formula: ； In the formula, n is the Manning roughness coefficient, and R is the hydraulic radius; The one-dimensional hydraulic model of urban drainage pipe network is represented by an energy equation: ； In the formula, Q is the flow rate, B is the pipe cross-sectional area, L is the pipe length, D is the diameter, and λ is the friction coefficient. , They are the upstream and downstream water heads, respectively; Based on the energy equation, gate control logic is introduced to simulate backflow prevention when the tide level is higher than the node water level. ； In the formula, The water passage area of ​​the gate. To control the height, This represents the flow coefficient in gate flow calculation; Based on the threshold control of the node water level, the pump station flow rate is controlled, and the pump station start-up and shutdown control formula is as follows: ； In the formula, , The threshold water level for starting and stopping. For node water level, For the pump station flow rate, This represents the maximum flow rate of the pumping station. The two-dimensional surface hydrodynamic model of the city is represented by the shallow water equation: ； ； ； In the formula, Let be the water depth, and u and v be the velocity components, respectively. For rainfall inflow, The term represents infiltration, and z represents the ground elevation. , The friction term is given by the formula for the nodal commutation term: ； In the formula, In order to exchange traffic, For the commutation coefficient, This refers to the surface water level. In S2, methods for setting tidal boundary conditions include: The tidal boundary conditions are based on the harmonic tidal formula: ； In the formula, H i For the tidal amplitude, ω i V is the angular velocity. i For the initial astronomical angle, g i To determine the tidal lag angle, f i As a correction factor; Adaptive adjustment of tidal boundary conditions is performed when the simulation time step Δt tide With the dominant tidal frequency ω i When there is a mismatch, a phase correction term is introduced. : ； In the formula, Let be the tidal level at time t, and n be the total number of tidal components. Let i be the astronomical factor of the i-th tidal component. Let be the harmonic constant amplitude of the i-th tidal component. Let be the angular frequency of the i-th tidal component, and t be the time variable. Let be the astronomical argument of the i-th tidal component. Let be the prime phase angle of the i-th tidal component. This is a phase correction term, used to correct for mismatches between boundary conditions and the dominant frequency. In S3, the methods for coupling the one-dimensional hydrodynamic model of the upstream river network, the one-dimensional hydraulic model of the urban drainage network, the two-dimensional surface hydrodynamic model of the city, and the tidal boundary include: Establishing multi-scale hydraulic coupling relationships includes setting water level continuity conditions and flow conservation conditions at the confluence nodes of river networks and pipe networks. , The overflow and backflow in the pipeline network and on the ground surface are exchanged bidirectionally through the nodal weir flow formula. And applying time-varying water level boundaries at river networks and tidal estuaries. ; Interface coupling conditions are set based on multi-scale hydraulic coupling relationships, including flow conservation conditions and water level continuity conditions, as follows: ； ； In the formula, The total inflow to the node. This represents the total outflow of the node. For flow conservation error, The allowable traffic error threshold; Calculate the water level for the river channel. For node water level, For water level difference error, The allowable water level difference threshold; An iterative convergence mechanism is set up so that when the flow conservation error and the water level difference error exceed the threshold, the interface flow and water level are updated using an iterative correction algorithm until convergence.

2. The method for multi-system coupled assessment of flooding in coastal cities according to claim 1, characterized in that: In S3, after model coupling, a hierarchical time step is proposed. Within each large step, the tidal boundary is updated once, the river network twice, the pipeline network ten times, and the surface twenty times. Synchronization error is monitored by indicators, namely: ； In the formula, Let k be the water level at node k obtained in the m-th iteration. Let Et be the result of the m-1th iteration, and Et be the relative error.

3. The method for multi-system coupled assessment of flooding in coastal cities according to claim 1, characterized in that: In S4, the method for model calibration and error constraint of the multidimensional hydrodynamic coupling model includes: The performance of the multidimensional hydrodynamic coupling model is measured by the Nash efficiency coefficient (NSE) and the coefficient of determination (R²). 2 verify: ； ； In the formula, For the flow rate observed at time i, For the simulated flow rate at time i, The mean flow rate is the observed value, and n is the sample size. Based on NSE and R 2 Introducing a global mass conservation error index: ； In the formula, The total volume entering the system, The total volume of the outflow system. This refers to changes in the water storage within the system.

4. The method for multi-system coupled assessment of flooding in coastal cities according to claim 1, characterized in that: Methods for sensitivity analysis based on flood evolution results include: The results of flood evolution include the distribution of water depth h(x,y,t) and the inundated area A. f And the water level process curves at key nodes; The sensitivity coefficient method is introduced to quantify the contribution of each factor to the flood outcome: ； In the formula, A f h represents the flooded area. max Let be the maximum water depth, D be the flood disaster intensity or loss function value, φ be the water level or tide level, Q be the flow rate, and Δt be the maximum water depth. phase The phase difference time of a tidal or flood process. This represents the sensitivity coefficient of water level changes to the flooded area. The sensitivity coefficient of flow rate change to maximum water depth. This is the sensitivity coefficient of phase difference time to disaster intensity.

5. A multi-system coupled assessment system for flooding in coastal cities, characterized in that: include: The data acquisition module is used to acquire multi-source data and perform unified projection and time resolution normalization processing on the data. The multi-source data includes hydrological data, meteorological data, marine data and urban data. The model building module is used to construct a one-dimensional hydrodynamic model of the upstream river network, a one-dimensional hydraulic model of the urban drainage network, and a two-dimensional surface hydrodynamic model of the city based on the multi-source data, and to set tidal boundary conditions. The coupling module is used to couple the one-dimensional hydrodynamic model of the upstream river network, the one-dimensional hydraulic model of the urban drainage network, the two-dimensional surface hydrodynamic model of the city, and the tidal boundary to obtain a multi-dimensional hydrodynamic coupling model. The model calibration and error constraint module is used to perform model calibration and error constraint on the multidimensional hydrodynamic coupling model. The scenario construction-driven evolution module is used to construct composite scenarios after model calibration and validation. Each scenario is used as an input boundary condition to drive the multidimensional hydrodynamic coupling model and obtain flood evolution results. The assessment and analysis module is used for risk assessment and sensitivity analysis based on flood evolution results; Methods for constructing one-dimensional hydrodynamic models of upstream river networks, one-dimensional hydraulic models of urban drainage pipe networks, and two-dimensional surface hydrodynamic models of cities include: The one-dimensional hydrodynamic model of the upstream river network is represented by the one-dimensional Saint-Venant equations: ； ； In the formula, A is the cross-sectional area of ​​the water passage, Q is the flow rate, and q l S is the lateral flow rate, S0 is the bed slope, and S f Let S be the friction gradient, I1 be the hydraulic integral term, and S be the friction gradient. f Expressed using Manning's formula: ； In the formula, n is the Manning roughness coefficient, and R is the hydraulic radius; The one-dimensional hydraulic model of urban drainage pipe network is represented by an energy equation: ； In the formula, Q is the flow rate, B is the pipe cross-sectional area, L is the pipe length, D is the diameter, and λ is the friction coefficient. , They are the upstream and downstream water heads, respectively; Based on the energy equation, gate control logic is introduced to simulate backflow prevention when the tide level is higher than the node water level. ； In the formula, The water passage area of ​​the gate. To control the height, This represents the flow coefficient in gate flow calculation; Based on the threshold control of the node water level, the pump station flow rate is controlled, and the pump station start-up and shutdown control formula is as follows: ； In the formula, , The threshold water level for starting and stopping. For node water level, For the pump station flow rate, This represents the maximum flow rate of the pumping station. The two-dimensional surface hydrodynamic model of the city is represented by the shallow water equation: ； ； ； In the formula, Let be the water depth, and u and v be the velocity components, respectively. For rainfall inflow, The term represents infiltration, and z represents the ground elevation. , The friction term is given by the formula for the nodal commutation term: ； In the formula, In order to exchange traffic, For the commutation coefficient, This refers to the surface water level. Methods for setting tidal boundary conditions include: The tidal boundary conditions are based on the harmonic tidal formula: ； In the formula, H i For the tidal amplitude, ω i V is the angular velocity. i For the initial astronomical angle, g i To determine the tidal lag angle, f i As a correction factor; Adaptive adjustment of tidal boundary conditions is performed when the simulation time step Δt tide With the dominant tidal frequency ω i When there is a mismatch, a phase correction term is introduced. : ； In the formula, Let be the tidal level at time t, and n be the total number of tidal components. Let i be the astronomical factor of the i-th tidal component. Let be the harmonic constant amplitude of the i-th tidal component. Let be the angular frequency of the i-th tidal component, and t be the time variable. Let be the astronomical argument of the i-th tidal component. Let be the prime phase angle of the i-th tidal component. This is a phase correction term, used to correct for mismatches between boundary conditions and the dominant frequency. Methods for coupling one-dimensional hydrodynamic models of upstream river networks, one-dimensional hydraulic models of urban drainage networks, two-dimensional urban surface hydrodynamic models, and tidal boundaries include: Establishing multi-scale hydraulic coupling relationships includes setting water level continuity conditions and flow conservation conditions at the confluence nodes of river networks and pipe networks. , The overflow and backflow in the pipeline network and on the ground surface are exchanged bidirectionally through the nodal weir flow formula. And applying time-varying water level boundaries at river networks and tidal estuaries. ; Interface coupling conditions are set based on multi-scale hydraulic coupling relationships, including flow conservation conditions and water level continuity conditions, as follows: ； ； In the formula, The total inflow to the node. This represents the total outflow of the node. For flow conservation error, The allowable traffic error threshold; Calculate the water level for the river channel. For node water level, For water level difference error, The allowable water level difference threshold; An iterative convergence mechanism is set up so that when the flow conservation error and the water level difference error exceed the threshold, the interface flow and water level are updated using an iterative correction algorithm until convergence.

6. A computer device, comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that: When the processor executes the program, it implements the steps of the method according to any one of claims 1 to 4.

7. A computer-readable storage medium having a computer program stored thereon, characterized in that: When executed by a processor, the program implements the steps of the method according to any one of claims 1 to 4.

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