A method for evaluating urban flood risk of a coupled flood and storm surge action dam type city

Abstract

The application provides a kind of coupling flood and storm surge action flood risk assessment method of dam type city, belong to city flood control and risk assessment technical field.The method includes: constructing the future climate and land use coupling scenario of research area;Based on the SWAT model, simulate the runoff process, generate future flood process;Sample calculation flood level, wind dam height and wave climbing, determine the dam and calculate the probability;Using HEC-RAS two-dimensional model to simulate flood evolution, get the maximum submerged water depth and other indicators;Integrating risk and vulnerability to calculate the risk of urban area.The application solves the problem of insufficient future environmental adaptability of existing method, uncoupling flood-wave action, rough dam mechanism description and ignoring the spatial heterogeneity of city, improves the accuracy and forward-looking of dam type city flood risk assessment, provides scientific support for city flood control engineering planning and emergency management, and is suitable for city dam overtopping risk assessment and precise flood control under the background of climate change.

Description

Technical Field

[0001] This invention relates to the field of urban flood control, disaster reduction and risk assessment technology, and in particular to a method for assessing flood risk in dam-type cities that couples the effects of floods and wind waves. Background Technology

[0002] With the intensification of global climate change and the increase in the frequency and intensity of extreme precipitation events, cities face a significantly increased risk from the combined effects of multiple disaster-causing factors, including floods, tides, and waves. As key urban flood control projects, dams may experience overtopping at their crests under the combined influence of high-water floods and strong winds and waves, due to wave rise, overtopping accumulation, or dam instability, which could then trigger widespread flooding in downstream urban areas.

[0003] The existing urban flood risk assessment methods have the following shortcomings: (1) Insufficient adaptability to the future environment: Most methods rely on historical meteorological and hydrological data to build models, but do not include the precipitation-runoff variation patterns and extreme wind and wave evolution trends under future climate patterns, resulting in assessment results that cannot support long-term flood control planning; (2) Lack of flood-wave coupling effect: Traditional assessments only focus on static flood water levels, ignoring the superposition effect of wind and waves on the dynamic load of the dam surface on the overtopping process, thus underestimating the actual overtopping risk; (3) Rough characterization of the overtopping mechanism: The difference between "flood-dominated overtopping" and "wind and wave superimposed overtopping" is not distinguished, resulting in the risk assessment results deviating from the actual disaster-causing process; (4) Ignoring urban spatial heterogeneity: The urban topographic relief, underlying surface type and key infrastructure distribution characteristics are not combined, making it difficult to accurately identify high-risk areas and formulate targeted prevention and control measures. Summary of the Invention

[0004] This invention provides a method for assessing flood risk in flood-prone cities by coupling the effects of floods and wind waves, in order to address the problems of insufficient adaptability and failure to consider the coupling effect of floods and waves in existing assessment methods, thereby improving the accuracy and foresight of the assessment.

[0005] To achieve the above objectives, the present invention adopts the following technical solution: A method for assessing flood risk in flood-prone cities that couples flood and wave effects includes the following steps: S1. Acquire historical / future meteorological data, hydrological data, land use data, basic data of earth-rock dams, population and economic data of Baise City in the study area, and perform data preprocessing to construct a coupled scenario of climate and land use in the future period of the study area. Step S1 includes: Step 11: Obtain future meteorological data corresponding to the CMIP6 climate model, including future precipitation data, future temperature data, future wind speed / direction data, etc., and downscale the future data.

[0006] Step 12: Obtain basic information data for the earth-rock dam project, including the reservoir-capacity curve, water level-reservoir area curve, and reservoir discharge flow curve.

[0007] Step 13: Obtain the future land use type demand data of the study area corresponding to the CMIP6 climate model scenario through the LUH2 (Land Use Harmonization project) future land use dataset, and then use the FLUS (Future Land Use Simulation) model to predict the spatial distribution of watershed land use.

[0008] The FLUS model contains the following two modules: (1) Probability of occurrence calculation module: The FLUS model uses an artificial neural network method module (ANN-based Probability-of-occurrence Estimation) to simulate the probability and spatial distribution of land use types. Its calculation formula is as follows: ; ; ; In the formula, Land use type In time In grid The probability of; These are the weighting coefficients between the output layer and the hidden layer; This is the association function between the hidden layer and the output layer; For signal factors; This refers to the signal between the input layer and the hidden layer.

[0009] (2) Cellular automata module based on adaptive inertial mechanism: The cellular automaton module based on the adaptive inertial mechanism is calculated as follows: ; ; ; In the formula, For time Land use types In grid The overall probability; Land use type In the grid The probability, i.e., domain influence; For time Land use types The coefficient of inertia; Land use type To land use type Switching costs; In order to be in window Land use type after the end of the time The total number of grid cells occupied; For variable weights; The value is the CA mole neighborhood value; In order to be in Time-based land use types The difference between demand and the number of grid cells.

[0010] Step 14: Construct a coupled scenario of climate and land use in the study area for the future.

[0011] S2. Based on the climate-land use data from step S1, construct a SWAT distributed watershed hydrological model to simulate the runoff generation and confluence process in the upstream watershed of the dam site, output the design flow process line of the river channel in front of the dam, and couple the historical typical flood process line to generate the future flood process.

[0012] Step S2 includes: Step 21: Based on historical precipitation and runoff data of the study area, construct a SWAT distributed watershed hydrological model to simulate the runoff generation and confluence process of the upstream watershed of the dam site, and determine the parameter scheme of the model in the region through calibration and verification.

[0013] The water balance governing equations of the SWAT hydrological model are:

[0014] In the formula, and These represent the soil moisture content at the beginning and end of the time period, respectively. ; For time, ; , and The first Rainfall, evapotranspiration and surface runoff ; and The first The flow of water from the earth and underground, .

[0015] The calculation of the SWAT model includes the following aspects: (1) Evapotranspiration calculation: The Penman-Montcith method is used to calculate potential evapotranspiration, and the formula is as follows: ; In the formula, For the desired potential evaporation, ; The average daily temperature ; For about and The slope of the relationship curve, ; and These are the daily net surface radiation flux and the daily soil heat flux, respectively. ; These are parameters related to air humidity. ; The wind speed is 2 meters below the ground. ; and These are the saturated water vapor pressure and the actual water vapor pressure. .

[0016] (2) Surface runoff calculation: The SWAT model uses the SCS curve method to calculate surface runoff on a daily timescale. The formula is as follows: ; ; ; In the formula, It is surface runoff. ; This refers to the daily rainfall. ; This is the initial loss amount. ; The maximum allowable retention capacity of the watershed. ; These are the parameters for the SCS curve.

[0017] (3) Groundwater runoff: In the SWAT model, groundwater runoff includes shallow groundwater runoff and deep groundwater runoff, and the calculation formulas are as follows: ; ; In the formula, and The first Tianhe Di The shallow soil water storage capacity of the day ; For the first Shallow groundwater recharge ; This refers to the amount of water that replenishes deep groundwater from shallow groundwater. ; This refers to the evaporation rate of shallow soil. ; The underground recharge of the main river channel ; This refers to the outflow rate of shallow soil. ; and For the first Tianhe Di Deep soil water storage capacity ; For deep soil outflow, .

[0018] (4) Convergence: In the SWAT model, the Muskingum method can be used for flow calculation, and its calculation formula is as follows: ; ; ; ; In the formula, and These are the initial and final inflow rates of the upstream river section, ; and These represent the initial and final outflow rates of the downstream river section. ; For the time of dissemination, ; is the flow rate specificity constant.

[0019] (5) Evaluation of the accuracy of the hydrological model: The simulation accuracy of the SWAT model was analyzed using the Nash Sutcliffe efficiency (NSE), the deterministic coefficient (R²), and the Kling–Gupta efficiency (KGE).

[0020] The calculation formulas are as follows: ; ; ; In the formula, and The first Measured and simulated flow rates, m³ / s; and These are the average values ​​of the measured flow rate and the simulated flow rate, respectively, in m³ / s; This represents the linear correlation coefficient between the measured flow rate and the simulated flow rate. This is the ratio of the standard deviation of the simulated flow series to the standard deviation of the measured flow series. for and The ratio.

[0021] Step 22: Based on the hydrological model constructed in Step 21 and the determined model parameter scheme, use future climate-land use data to predict the inflow process at the earth-rock dam site under various future climate and land use coupled scenarios.

[0022] Step 23: Based on the future inflow results predicted in Step 22, couple the P-III type distribution model and the Copula function to conduct a joint frequency analysis of the flood peak-volume at the dam site, and determine the design results of the flood peak-volume at each set frequency in the future period.

[0023] S3. For the future flood process obtained in step S2, multiple sets of flood peak-flood volume combinations are generated by sampling using the peak volume discarding method and the flood level is calculated by flood regulation calculation. The wind dam height is sampled by the extreme value type I distribution inverse function method and the wave run-up is sampled by the Rayleigh distribution inverse function method. It is determined whether the earth-rock dam will overflow and the probability of overflow is calculated.

[0024] Step S3 includes: Step 31: Based on the frequency analysis of flood peak-volume and the joint design results of peak-volume under various setting frequencies in the future period obtained in step S2, flood sample sampling is carried out using the peak volume rejection method, and flood level can be obtained by performing flood regulation calculation on the sample flood process.

[0025] The specific steps are as follows: (1) Construct the joint distribution function of flood peak volume and deduce the coordinates of the boundary points B and C corresponding to a certain return period (the return period for this sampling is 10,000 years); (2) Generate (0~1) random numbers that conform to a uniform distribution. ,make Then the design value of the flood peak ; (3) Determine whether q is located at Between; if so, then calculate v: ; If not, repeat step (2) until the conditions in step (3) are met, and the flood design value can be obtained. ; (4) Repeat steps (ii) to (iii) N times to obtain several sets of peak quantity combination design values. .

[0026] Step 32: Based on wind speed-direction prediction data from future climate models, the wind bulge height is sampled using the extreme value type I distribution inverse function method.

[0027] The probability distribution function and its inverse function of the extreme value type I distribution are: ; ; In the formula, is a constant parameter of the extreme value type I distribution, and has , Random variables The mean and standard deviation.

[0028] Let the randomly generated uniformly distributed random number between [0,1] be... Then the random numbers of the extreme value type I distribution Then it is ; Therefore, it can be concluded that uniformly distributed random numbers are obtained in the range [0,1]. Then, we only need to obtain the mean and standard deviation of the wind dam height.

[0029] The mean and standard deviation of the wind dam height can be obtained by referring to the relevant formulas in the "Design Code for Rolled Earth-Rock Dams" based on the effective wind speed data at 10 meters above the reservoir water surface. ; ; In the formula, and These are the mean and standard deviation of the wind bulge height, respectively; The comprehensive friction coefficient; its value is [value missing]. , and The mean and standard deviation of the effective wind speed at a depth of 10 meters above the water surface; For the reservoir's pumping process; The average water depth of the water area; This is the acceleration due to gravity; its value is 9.81. .

[0030] Step 33: Based on wind speed-direction prediction data from future climate models, the Rayleigh distribution inverse function method is used to sample wave rise.

[0031] The probability distribution function of the Rayleigh distribution and its inverse function are: ; ; In the formula, Let be the distribution parameter, and have ; Random variables The mean and standard deviation.

[0032] Let the randomly generated uniformly distributed random number between [0,1] be... Then the random numbers of the Rayleigh distribution Then it is: ; Therefore, it can be concluded that uniformly distributed random numbers are obtained in the range [0,1]. Then, we only need to obtain the mean and standard deviation of the wave rise.

[0033] The mean wave run-up can also be calculated using the effective wind speed data at 10 meters above the reservoir water surface, referring to the relevant formulas in the "Design Code for Rolled Earth-Rock Dams". ; ; In the formula, This represents the mean of the wave rise. The slope roughness and permeability coefficient; This is an empirical coefficient, the value of which is related to wind speed and average water depth in the basin, and can be determined by looking up a table; This represents the slope coefficient of the slope. and These represent the wave height and the average wave height. The wavelength is the average wavelength of the wave; the meanings of other variables are the same as described above.

[0034] Step 34, after obtaining the flood level Wind height Wave Climbing Then, it can be determined whether the earth-rock dam will overflow, and the probability of overflow can be calculated: ; In the formula, The probability of flooding; This refers to the water level in front of the dam. This refers to the elevation of the dam crest. The water level in front of the reservoir dam caused by flooding; The height of the wind dam; Climbing for the waves.

[0035] S4. Based on the flood-wave combination under different scenarios in step S3, the HEC-RAS two-dimensional model is used to simulate the flood evolution process after the dam overflows, analyze the evolution characteristics of the dam-break flood in the downstream urban area, and evaluate the impact of urban floods on the maximum inundation depth, maximum inundation velocity, and flood inundation duration as indicators.

[0036] Step S4 includes: Step 41: Based on the probability analysis of overtopping of earth-rock dams under different climates and land use changes, classify and screen the flood-wave combinations of overtopping under different scenarios.

[0037] Step 42: Using the HEC-RAS two-dimensional model, simulate the flood evolution process of the dam overflow after the dam overflow occurs, and analyze the evolution characteristics of the dam-break flood in downstream cities.

[0038] The basic governing equations of the HEC-RAS two-dimensional model are as follows: Assuming the flow is incompressible, the differential form of the continuity equation is: ; In the formula, t For time; , These are the velocity components (m / s) in the x and y directions, respectively. H Water surface elevation (m); h Water depth (m); It is the side inflow (m2 / s).

[0039] The equation in vector form is: ; In the formula, It is a velocity vector; it is The given partial differential operator vector.

[0040] For horizontal region and boundary normal vector Integrating, we obtain the integral form of the equation: ; In the formula, This represents the three-dimensional spatial volume (m3) occupied by the fluid. This represents the area of ​​the lateral boundary (m2). Indicates passage Any flow rate (m³ / s) at the bottom (infiltration) or top (evaporation or rainfall) of the system.

[0041] Its momentum equation is: ; ; In the formula, t Indicates time; , These represent the horizontal and vertical coordinate axes of the Cartesian coordinate system, respectively. , They represent Flow velocity component in the direction (m / s); The acceleration due to gravity (m² / s²); The viscosity in the horizontal direction (m² / s); The coefficient of friction at the bottom; H For water depth; The parameter is the Coriolis force.

[0042] The differential vector expression for the momentum equation is: ; In the formula, It is a velocity vector; It is by The vector of the partial differential operators is given; k is the unit vector in the vertical direction. Each term in the vector equation has a specific physical counterpart, which, from left to right, are acceleration, gravity, horizontal kinematic viscosity, bottom friction coefficient, and Coriolis force coefficient.

[0043] Step 43: Based on the two-dimensional flood evolution simulation results, the maximum inundation depth, maximum inundation velocity, and flood inundation duration are used as the main indicators to analyze the degree of flood impact on the urban area.

[0044] S5. Based on the indicators in step S4, construct an urban dam-break flood risk model, and use the entropy weight method to assign weights and quantify the risk; combine population density, GDP and land use to construct a vulnerability model and quantify the loss potential of disaster-bearing bodies; integrate the risk and vulnerability to calculate the risk level of downstream urban areas during dam-break floods, and complete the comprehensive flood risk assessment.

[0045] Step S5 includes: Step 51: Based on the maximum inundation depth, maximum inundation velocity, and flood inundation duration indicators corresponding to step S4, construct an urban dam-break flood hazard model; where the maximum inundation depth can characterize the water volume characteristics of the flood, the maximum inundation duration can characterize the duration characteristics of the flood, and the maximum velocity can characterize the energy characteristics of the flood.

[0046] Firstly, the index method is used to calculate the risk of urban dam break floods: : In the formula, Indicates each risk indicator; The weights are assigned to each indicator. Due to the significant differences between the numerical values ​​and physical meanings of the various indicators, the model uses a conversion function assignment method to convert the risk indicators into risk levels.

[0047] Finally, the entropy weight method is used to calculate the weight of each indicator.

[0048] The expression is as follows: ; In the formula, The risk level is represented by a value between 0 and 1; 'a' is a coefficient. As a risk indicator, At its maximum, there is The maximum value is 1, depending on the different dam failure scenarios. The value is used to deduce the corresponding flood. value.

[0049] Step 52: Based on three indicators—urban population density, average GDP, and land use type—a vulnerability model for urban dam-break floods is constructed. Population density and average GDP are directly proportional to vulnerability; that is, areas with higher population density and GDP per capita experience greater flood damage, indicating higher vulnerability. To quantify the impact of production stagnation in different regions, the value of each type of land is used to characterize the impact of production stagnation.

[0050] Vulnerability is calculated using the index method: ; In the formula, Indicates various vulnerability indicators; The weights for each indicator are defined. Because there are significant differences in the numerical values ​​and physical meanings of the various indicators, a conversion function is used to transform the fragility indicator into fragility level, as shown in the following expression: ; In the formula, This is a vulnerability index, representing the value per unit area of ​​different disaster-bearing bodies. At its maximum, The maximum value is 1, according to the maximum The value can be used to deduce the b value of the corresponding indicator.

[0051] Step 53: Based on the hazard and vulnerability results, construct a risk assessment model for the downstream urban area during a flood overflow. The model formula is as follows: ; In the formula, R Risk level of downstream urban areas during flood overflow, with a value ranging from 0 to 1; H Flood risk level is used to characterize the likelihood and consequences of a flood, and its value ranges from 0 to 1. V Vulnerability is a measure of the potential losses that socioeconomic entities exposed to floods may suffer, with a value ranging from 0 to 1.

[0052] Compared with the prior art, the present invention has the following beneficial effects: 1. By constructing a climate-land use coupled scenario and integrating the warming scenario predicted by the IPCC with the future precipitation-runoff evolution, the assessment results are significantly enhanced to support long-term flood control planning.

[0053] 2. The system couples wind and wave dynamic loads with flood processes to accurately identify "flood-dominated" and "wind and wave superposition" overtopping mechanisms, effectively solving the problem of underestimating the risk of dam overtopping by traditional methods.

[0054] 3. Based on the HEC-RAS two-dimensional model, the evolution of dam-break floods is simulated, the impact of flooding on downstream urban areas is quantified by combining key indicators, and the risk and vulnerability factors are integrated by the entropy weight method to achieve precise risk classification.

[0055] 4. By comprehensively considering the topography, underlying surface, and distribution of key infrastructure, high-risk areas are accurately identified, providing a scientific basis for dam reinforcement, wave dissipation facility deployment, and emergency evacuation, significantly improving the scientific nature and foresight of urban flood control decisions. Attached Figure Description

[0056] The present invention will be further described below with reference to the accompanying drawings and embodiments: Figure 1 This is a schematic diagram of the method flow of the present invention; Figure 2 This is a map showing the study area and distribution of hydrological and meteorological stations in an embodiment of the present invention. Figure 3 These are land use result maps for different future scenarios according to embodiments of the present invention; Figure 4 The figure shows the calibration and verification results of the hydrological model in an embodiment of the present invention. Figure 5 This is a diagram showing the maximum inundation depth of an earth-rock dam reservoir under different scenarios of flood overflow in an embodiment of the present invention; Figure 6 This is a diagram showing the distribution of maximum flow velocity during overtopping floods in earth-rock dam reservoirs under different scenarios according to embodiments of the present invention. Figure 7 The figures show the duration of flood inundation of earth-rock dam reservoirs under different scenarios according to embodiments of the present invention. Figure 8 This is a diagram showing the risk assessment results for the urban area downstream of the flood in an embodiment of the present invention. Detailed Implementation

[0057] The technical method of the present invention will be further described below with reference to the accompanying drawings and specific examples.

[0058] like Figure 1 As shown in this embodiment, a method for assessing the flood risk of a flood-prone city under future environmental changes, coupled with the effects of floods and wind waves, includes the following steps: S1. Acquire historical / future meteorological data, hydrological data, land use data, basic data of earth-rock dams, population and economic data of Baise City in the study area, and perform data preprocessing to construct a coupled scenario of climate and land use in the future period of the study area. S2. Based on the climate-land use data from step S1, construct a SWAT distributed watershed hydrological model to simulate the runoff generation and confluence process in the upstream watershed of the dam site, and output the design flow process curve of the river channel in front of the dam; further couple the historical typical flood process curves to generate future flood processes; S3. After obtaining the future flood process under each scenario, multiple sets of flood peak-flood volume combinations are generated by sampling using the peak volume discarding method and the flood level is calculated by flood regulation. The wind dam height is sampled by the extreme value type I distribution inverse function method and the wave run-up is sampled by the Rayleigh distribution inverse function method. The three factors are combined to determine whether the earth-rock dam will overflow and then the probability of overflow is calculated. S4. Based on the flood-wave combination under different scenarios in S3, the HEC-RAS two-dimensional model is used to simulate the flood evolution process after the dam overflows. Based on its continuity, momentum equation and vector form, the evolution characteristics of the dam-break flood in the downstream urban area are analyzed. Finally, the maximum inundation depth, flow velocity and duration are used as indicators to assess the impact of urban floods. S5. Based on the maximum inundation depth, flow velocity, and duration of S4, an urban dam-break flood hazard model is constructed, and the hazard is quantified by using the entropy weight method. A vulnerability model is constructed by combining population density, GDP, and land use to quantify the loss potential of disaster-bearing bodies. Finally, the hazard and vulnerability are integrated to calculate the risk of downstream urban areas during dam overflow floods and assess the comprehensive flood risk.

[0059] Taking Baise City and its upstream Chengbihe Reservoir (an earth-rock dam) as an example, the runoff simulation study area is the Chengbihe River basin, while the dam-break flood risk study area is the Baise City area, including the Bo'ai River and Youjiang River channels from Bailin Village to Jiuse Village, and the Chengbihe River channel from the Chengbihe Reservoir to Baise City, which are the key assessment areas for dam-break flood risk, involving Yongle Town, Baicheng Subdistrict, and Longjing Subdistrict. Ground station data collected included daily meteorological data (rainfall, temperature, and evaporation) and daily flow data from 1970 to 2019. Daily rainfall data was monitored by 12 hydrological and meteorological stations: Donghe Station, Lingyun Station, Xiajia Station, Jiefu Station, Haokun Station, Chaoli Station, Nongtang Station, Xiatang Station, Linhe Station, Bailian Station, Pingtang Station, and Bashou Station. Figure 2Daily flow data were monitored by the Pingtang and Xiajia hydrological stations. Daily temperature data were monitored by the dam head hydro-meteorological station, while evaporation data was estimated using the Penman-Monteith formula based on solar radiation, wind speed, relative humidity, and other relevant meteorological data from the Baise meteorological station provided by the China Meteorological Data Network (http: / / data.cma.cn / ). Future climate data employed seven climate models from the Coupled Model Intercomparision Project phase 6 (CMIP6), and three projection scenarios (hereinafter referred to as projection scenarios) were selected, taking into account anthropogenic emission trends and socio-economic pathways. Basic information on the climate models and projection scenarios is shown in Tables 1 and 2. In addition, data such as watershed DEM data, land use type data, soil type data, basic data of the Chengbihe Reservoir, and population and GDP data of Baise city were also collected.

[0060] Table 1. Basic Information on Climate Models

[0061] Table 2 Basic Information on the Projected Scenario

[0062] After acquiring future climate data, the climate models were downscaled and averaged using multiple models. Then, combined with the Baise City territorial spatial planning data (expansion of construction land, changes in green space ratio, and adjustments to the proportion of hardened surfaces over the next 20-50 years), spatial distribution data of future urban underlying surface types under different scenarios were generated by coupling a land use dynamic model (FLUS model). The results are as follows: Figure 3 At this point, the future climate and land use scenarios for the study area can be constructed.

[0063] After constructing a coupled scenario of future climate and land use for the study area, a SWAT distributed watershed hydrological model was built to simulate the runoff generation and concentration processes in the upstream watershed of the dam site, outputting the design flow process curve of the upstream channel. The SWAT distributed watershed hydrological model was simulated and validated based on precipitation-runoff data from 1970 to 2019, and the results are as follows: Figure 4 As shown. The SWAT hydrological model in the rate-determining period. It is 0.820. It is 0.824. It was 0.839, while during the verification period... It is 0.847. It is 0.880. It is 0.734. and Compared to the previous period, the rates increased by 0.026 and 0.056 respectively. This represents a decrease of 0.105 compared to the rate period. The changes in all three evaluation indicators were relatively small. The SWAT hydrological model demonstrated slightly better simulation accuracy during the validation period than during the rate period, effectively simulating the runoff generation and confluence processes in the upstream basin of the dam site. Once the model is finalized, inputting future climate and land use data allows for the simulation of future inflow processes at the dam site. Furthermore, it can be coupled with historical typical flood hydrographs to generate future flood events.

[0064] After obtaining flood sample data for various future scenarios, a joint frequency analysis of peak-volume inflow floods at the dam site was conducted by coupling the P-III type distribution model and the Copula function to determine the design results of peak-volume floods at various frequencies in the future. Then, a peak-volume rejection method was used to sample flood data, resulting in 10,000 flood events. Flood levels were then calculated using flood regulation calculations. Based on wind speed-direction prediction data from future climate models, wind uplift height and wave run-up were calculated using the extreme value type I distribution inverse function method and the Rayleigh distribution inverse function method, respectively. Finally, the overtopping probability of the earth-rock dam under various future climate and land use scenarios was calculated, and the results are shown in Table 3. Table 3 shows that under the future climate and land use scenarios SSP1-2.6 and SSP2-4.5, the reservoir overtopping rate is 2.52 × 10⁻⁶. -6 and 6.26×10 -6 The dam overtopping risk level is classified as low. However, under the SSP5-8.5 future climate and land use scenario, the dam overtopping risk rate is 4.26 × 10⁻⁶. -5 The risk level of the dam overflow has increased.

[0065] Table 3. Probability of Overtopping of Earth-Rock Dams

[0066] Based on the probability analysis of overtopping of earth-rock dams under different climates and land use changes, flood-wave combinations under different overtopping scenarios were classified and screened. Then, the HEC-RAS two-dimensional model was used to simulate the flood evolution process after overtopping, and the evolution characteristics of dam-break floods in downstream cities were analyzed. The degree of flood impact on urban areas was analyzed using maximum inundation depth, maximum inundation velocity, and flood inundation duration as the main indicators. The results are as follows: Figures 5-7 As shown in the figure, from SSP5-8.5 to SSP1-2.6, the maximum inundation depth decreases, and the maximum inundation depth is distributed near the downstream of the dam site. The maximum inundation depth in Baise city is 0m~10m. The distribution of maximum flow velocity conforms to the laws of hydrodynamics. The flow velocity is the highest near the thalweg line of the river channel, followed by the flow velocity at river bends or narrow valleys, while the flow velocity decreases at the open river channel or urban streets. The distribution of inundation duration is somewhat similar to the distribution of maximum flow velocity. Among them, the inundation duration range of the SSP5-8.5 scenario is the widest, and the inundation duration is the longest at the downstream of the dam site and at the river outlet, reaching more than 40 hours.

[0067] Based on the maximum inundation depth, maximum inundation velocity, and flood duration, an urban dam-break flood hazard model was constructed to analyze the urban overtopping flood hazard under various scenarios. The flood hazard was then statistically classified and categorized, with results shown in Tables 4-6. Under scenario SSP1.2-6, the inundation area was predominantly of medium-to-low risk, with medium-risk areas accounting for 33.91% and low-risk areas accounting for approximately 45.92%. High-risk areas were relatively evenly distributed, accounting for 20.06%. Under scenario SSP2.4-5, the proportion of high-risk areas increased to 45%, mainly distributed in Longjing Subdistrict. Overall, the area remained predominantly of medium-to-low risk, accounting for over 50%. Under scenario SSP5.8-5, the proportion of high-risk areas increased significantly, approaching 60%, and was also mainly distributed in Longjing Subdistrict.

[0068] Table 4SSP1.2-6 Statistics on the Zoning and Classification of Dam Break Flood Hazard

[0069] Table 5.4-5 Statistics on the Zoning and Classification of Dam Break Flood Hazard

[0070] Table 6. SSP5.8-5 Statistics on the Zoning and Classification of Dam Break Flood Hazard

[0071] Based on three indicators—urban population density, average GDP, and land use type—an urban dam-break flood vulnerability model was constructed to analyze the vulnerability of urban areas to inundation floods under various scenarios. It should be noted that the analysis revealed little difference in urban population density, average GDP, and land use type across different climate and land use scenarios. Therefore, to reduce workload and uncertainty, based on the worst-case scenario principle, the vulnerability results for each climate and land use scenario were adopted using the SSP5-8.5 land use type vulnerability model (and adapted to local development plans). Flood vulnerability was then statistically analyzed by region and level, with results shown in Table 7. The table shows that, overall, most areas in the study area have a vulnerability level of medium or higher, with the most vulnerable areas accounting for approximately 67.51%; followed by highly vulnerable areas at approximately 25.64%; and the least vulnerable areas at approximately 6.85%.

[0072] Table 7. Percentage of Comprehensive Vulnerability Zoning and Classification in the Study Area for Future Dam-Break Floods

[0073] Finally, based on the hazard and vulnerability results, a risk assessment model for downstream urban areas under overtopping floods was constructed to evaluate the risk of urban overtopping floods under various scenarios. The results are as follows: Figure 8As shown in the figure, overall, with the increase in the degree of dam overtopping, the risk area caused by overtopping floods is larger, and the proportion of high-risk areas (greater than 0.6) is also larger. Specifically, in SSP5-8.5 scenarios, the risk area of ​​overtopping floods is mainly concentrated on both banks of the Bo'ai River and the Youjiang River, and near the downstream of the Chengbihe Reservoir dam site; in SSP2-4.5, the risk area of ​​overtopping floods in the study area decreases, but the risk area is still larger on both banks of the Youjiang River, and is mainly distributed in Longjing Street; in SSP1-2.6, the risk area further decreases, but the flood-inundated area is still concentrated on both banks of the Youjiang River, mainly distributed in Longjing Street.

[0074] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and are not intended to limit it. Although the present invention has been described in detail with reference to preferred embodiments, those skilled in the art should understand that modifications or equivalent substitutions can be made to the technical solutions of the present invention without departing from the spirit and scope of the technical solutions of the present invention, and all such modifications or substitutions should be covered within the scope of the claims of the present invention.

Claims

1. A method for assessing flood risk in dam-type cities by coupling flood and wave effects, characterized in that, Includes the following steps: S1. Obtain historical / future meteorological data, hydrological data, land use data, basic data of earth-rock dams, and urban population and economic data of the study area. After data preprocessing, construct a coupled scenario of climate and land use in the future period of the study area. S2. Based on the climate-land use data from step S1, construct a SWAT distributed watershed hydrological model to simulate the runoff generation and confluence process in the upstream watershed of the dam site, output the design flow process line of the river channel in front of the dam, and couple the historical typical flood process line to generate the future flood process. S3. For the future flood process obtained in step S2, multiple sets of flood peak-flood volume combinations are generated by sampling using the peak volume discarding method and the flood level is calculated by flood regulation. The wind dam height is sampled by the extreme value type I distribution inverse function method and the wave run-up is sampled by the Rayleigh distribution inverse function method. The earth-rock dam is determined to overflow and the probability of overflow is calculated. S4. Based on the flood-wave combination under different scenarios in step S3, the HEC-RAS two-dimensional model is used to simulate the flood evolution process after the dam overflows, analyze the evolution characteristics of the dam-break flood in the downstream urban area, and use the maximum inundation depth, maximum inundation velocity and flood inundation duration as indicators to assess the impact of urban floods. S5. Based on the indicators in step S4, construct an urban dam-break flood risk model, and use the entropy weight method to assign weights and quantify the risk; combine population density, GDP and land use to construct a vulnerability model and quantify the loss potential of disaster-bearing bodies; integrate the risk and vulnerability to calculate the risk level of downstream urban areas during dam-break floods, and complete the comprehensive flood risk assessment.

2. The method for assessing flood risk in flood-prone cities with coupled flood and wave effects as described in claim 1, characterized in that, In step S1, the future meteorological data comes from the CMIP6 climate model, including future precipitation data, temperature data, and wind speed / direction data, and needs to be downscaled.

3. The method for assessing flood risk in flood-prone cities under the combined effects of flood and wind waves, as described in claim 1, is characterized in that... In step S1, the land use data obtains demand data through the LUH2 future land use dataset and uses the FLUS model to predict spatial distribution. The FLUS model includes an occurrence probability calculation module and a cellular automaton module based on an adaptive inertial mechanism.

4. The method for assessing flood risk in flood-prone cities under the combined effects of flood and wind waves, as described in claim 1, is characterized in that... In step S2, the water balance control equation of the SWAT distributed watershed hydrological model is: ； In the formula, and These represent the soil moisture content at the beginning and end of the time period, in units of... ; For time, ; , and The first Rainfall, evapotranspiration, and surface runoff, in units of ; and The first The volume of runoff from the earth and soil and the underground runoff are expressed in units of... .

5. The method for assessing flood risk in flood-prone cities under the combined effects of flood and wind waves, as described in claim 1, is characterized in that... In step S2, when generating future flood events, the P-III type distribution model and the Copula function are coupled to perform a joint frequency analysis of the flood peak-volume at the dam site, and the design results of the flood peak-volume under each set frequency are determined.

6. The method for assessing flood risk in flood-prone cities under the combined effects of flood and wind waves, as described in claim 1, is characterized in that... In step S3, when sampling using the peak volume rejection method, the return period is set to 10,000 years. The boundary point coordinates are derived by constructing a joint distribution function of flood peak volume. 0~1 uniformly distributed random numbers are generated to determine the flood peak design value and flood volume design value. Multiple sets of peak volume combination design values ​​are obtained by repeated sampling.

7. The method for assessing flood risk in flood-prone cities under the combined effects of flood and wind waves, as described in claim 1, is characterized in that... In step S3, the wind dam height is based on the effective wind speed data at 10 meters above the reservoir water surface, and the mean and standard deviation are calculated with reference to the relevant formulas in the "Design Code for Rolled Earth-Rock Dams"; the wave run-up is sampled using the Rayleigh distribution inverse function method, and its mean is calculated based on the above-mentioned effective wind speed data and the relevant formulas in the code.

8. The method for assessing flood risk in flood-prone cities under the combined effects of flood and wind waves, as described in claim 1, is characterized in that... In step S4, the basic governing equations of the HEC-RAS two-dimensional model include a continuity equation and a momentum equation. The differential form of the continuity equation is: ； In the formula, t For time; , They are respectively x, y Velocity component in the direction (m / s); H Water surface elevation (m); h Water depth (m); Side inflow (m2 / s); The differential vector expression for the momentum equation is: ； In the formula, It is a velocity vector; It is by The vector of the partial differential operator is given; k is the unit vector in the vertical direction.

9. The method for assessing flood risk in flood-prone cities under the combined effects of flood and wind waves, as described in claim 1, is characterized in that... In step S5, the hazard model uses a transformation function assignment method to convert hazard indicators into hazard levels, the expression of which is: ； In the formula, Indicates each risk indicator; The weights are the corresponding values ​​for each indicator.

10. The method for assessing flood risk in flood-prone cities under the combined effects of flood and wind waves, as described in claim 1, is characterized in that... In step S5, the risk assessment model formula is: ； In the formula, R Risk level of downstream urban areas during flood overflow, with a value ranging from 0 to 1; H Flood risk level is used to characterize the likelihood and consequences of a flood, and its value ranges from 0 to 1. V Vulnerability is a measure of the potential losses that socioeconomic entities exposed to floods may suffer, with a value ranging from 0 to 1.

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