A coastal city river network flood risk quantification method based on hydrodynamic characteristics

Abstract

The application discloses a kind of based on hydrodynamic characteristics of coastal city river network flood risk quantification method, comprising: establishing river network topological connection relationship and catchment runoff relationship;Definition river network import boundary condition, export boundary condition, catchment hydrological boundary condition, combine the above-mentioned relationship to build the one-dimensional hydrology and hydrodynamic model of coastal city river network;Design hydrodynamic boundary condition working condition and hydrological boundary condition working condition, utilize one-dimensional hydrology and hydrodynamic model to carry out hydrology, hydrodynamic combined working condition numerical simulation, extract hydrodynamic index;Coastal city river network flood risk quantification index system framework based on hydrodynamic characteristics is constructed, combine the combined weight of all risk indexes in calculation framework of hydrodynamic index, according to the combined weight calculation each river section unit flood risk index.The method fully considers the uncertainty of boundary conditions such as rainfall, external river flow and ocean tide level, and quantifies the composite flood risk of coastal city river network.

Description

Technical Field

[0001] This invention belongs to the field of hydraulics and river dynamics, and relates to a method for quantifying flood risk in coastal urban river networks based on hydrodynamic characteristics. Background Technology

[0002] Cities with dense river networks are typically located in the alluvial plains downstream of river basins, characterized by developed economies, large populations, and prominent strategic positions. However, these coastal cities often have highly complex river networks, receiving inflows from external rivers and being subject to backwater effects. Combined with the gentle slopes of rivers in the plains, this results in poor river flow, drainage difficulties, and a severe risk of urban flooding. Existing urban flood risk assessments typically employ an indicator system method based on Geographic Information Systems (GIS) to classify land use types. This method can effectively assess the exposure and vulnerability of different urban areas. However, due to the lack of hydrodynamic characteristic analysis, GIS-based risk assessments struggle to quantify the danger of urban flooding, especially in densely populated river-networked cities affected by external river inflows and backwater effects. Therefore, there is an urgent need to construct a quantitative indicator system for the flood risk of coastal city river networks, accurately assessing the flood hydrodynamic characteristics of different river areas, and providing technical support for the joint regulation and control of river network gate and pump projects and flood control expansion and renovation schemes.

[0003] Currently, domestic and international experts' assessments of urban river networks largely focus on water ecology and water environment indicators. For example, in the assessment of the water replenishment effect in the southeastern plain river network area of ​​Ningbo City, only five water ecology indicators were considered: ammonia nitrogen, dissolved oxygen, chlorophyll a, total phosphorus, and chemical oxygen demand. (Authors: Wang Yumeng, Cheng Junrui, Wang Xiao, Wang Kan; "Comprehensive Benefit Assessment of Urban River Network Water Replenishment Based on Hydrodynamic-Water Ecological Coupled Simulation," published in the *Journal of Ningbo University (Science and Engineering Edition)*). Among currently available patent documents, Chinese Invention Patent Publication No. CN 111861193 A discloses a method for constructing an assessment and early warning indicator system for the water environment carrying capacity of plain river networks. It selects 20 secondary indicators based on driving force index, pressure index, state index, response index, and benefit index, belonging to the assessment scope of water resources, water environment, water ecology, water management, and macroeconomic management, respectively. However, it lacks consideration for hydrodynamic characteristic indicators and flood risk. Summary of the Invention

[0004] To address the shortcomings of existing technologies, this invention provides a method for quantifying flood risk in coastal urban river networks based on hydrodynamic characteristics, solving the problem that existing technologies cannot accurately assess the composite flood risk of different river segments in urban river networks.

[0005] The technical solution is as follows: A method for quantifying flood risk in coastal urban river networks based on hydrodynamic characteristics, comprising: S1: Obtain geographic information data of river networks, urban rainfall data, external river flow data, ocean tide data, and water level data of river network distributaries in coastal cities; S2: Establish the topological connectivity of the river network and the runoff generation relationship of the catchment area based on the aforementioned river network geographic information data; S3: Define the boundary conditions of the river network inlet node based on the external river flow data, define the boundary conditions of the outlet node based on the ocean tide data, define the hydrological boundary conditions of the catchment area based on the urban rainfall data, and construct a one-dimensional hydrological and hydrodynamic model of the coastal city river network by combining the river network topological connection relationship and the runoff relationship of the catchment area. S4: Based on the aforementioned external river flow data and ocean tide data, design the hydrodynamic boundary conditions for the river network inlet and outlet nodes. Based on the aforementioned urban rainfall data, enumerate and adopt hydrological boundary conditions with different rainfall return periods and rainfall durations. S5: Based on the hydrodynamic boundary conditions and hydrological boundary conditions, conduct numerical simulation of combined hydrological and hydrodynamic conditions using the one-dimensional hydro-hydrodynamic model, and extract hydrodynamic indicators. S6: Construct a framework for a quantitative index system of flood risk in coastal urban river networks based on hydrodynamic characteristics. This framework includes three layers of risk indicators: river network topology parameters, daily flow characteristics, and rainfall-flood characteristics. The combined weights of all risk indicators are calculated in conjunction with the hydrodynamic indicators. Based on these combined weights, the flood risk index of each river segment unit is calculated.

[0006] Furthermore, the river network geographic information data in step S1 includes watershed topographic data, coastline topographic data, urban water system data, river cross-section data, river network water conservancy facility location data, and surface catchment area division and underlying surface data.

[0007] Further, in step S2, the river network inlet, outlet, and control nodes are divided, river network distributaries are determined, and undirected river segment units are divided. The river segment length, slope, elevation, and cross-sectional shape attributes are extracted to establish the river network topological connection relationship and runoff relationship of the catchment area. This includes: dividing the outer river inlet nodes based on the urban water system data; dividing the downstream outlet nodes based on the coastline topography data; determining the river network distributaries based on the urban water system data and the location data of river network water conservancy facilities; dividing the river segment units; dividing different channel nodes within the river segment unit according to the river cross-sectional spacing to obtain undirected river segment units; and extracting the river segment length, slope, elevation, and cross-sectional shape attributes based on the urban water system data, watershed topography data, and river cross-sectional data; dividing control nodes based on the location of sluice gates and pumping stations in the river network water conservancy facility location data; and establishing the runoff relationship between each channel node and its corresponding sub-catchment area based on the surface catchment area division and underlying surface data.

[0008] Furthermore, in step S3, the one-dimensional hydrological and hydrodynamic model does not include the drainage network model. In the hydrological model, the surface runoff of the sub-catchment area is based on the nearest associated river segment unit node and is input as the surface runoff flow of the sub-catchment area in the form of the hydrodynamic inlet boundary. Moreover, the one-dimensional hydrological and hydrodynamic model does not consider the regulation process of river network water conservancy facilities, and the opening degree of gates and pumps is set to fully open.

[0009] Further, using the water level data of the river network distributaries obtained in step S2, the model parameters of the one-dimensional hydrological and hydrodynamic model established in step S3 are calibrated and the model is verified. The model parameter calibration refers to calibrating the roughness parameter of the river section using the ensemble smoother method. The model verification is to calculate the Nash efficiency coefficient index of the water level data of the river network distributaries. If the Nash efficiency coefficient index exceeds a predetermined threshold, the model is reliable.

[0010] Further, step S4 specifically involves: performing statistical analysis based on the aforementioned river flow data and ocean tide data to extract the maximum daily flow. Q 0. Maximum tidal range H r And the corresponding normal range of 1.5 times the interquartile range, within which the maximum flow rate is... Q 0. Maximum tidal range H r And in the tidal cycle T C Inner tidal phase t Random sampling is performed, and the hydrodynamic boundary conditions for the river network inlet and outlet nodes are designed based on the following formula: , in Q For the flow at the river network inlet node, H This refers to the water level at the outlet node.

[0011] Furthermore, the hydrodynamic indicators extracted in step S5 include the conventional flow of the river segment. Q ia Consistency of Flow φ q Percentage of time with dampness φ t Theoretical flood storage space S t Flood flow Q rf Actual flood storage capacity S f Overflow volume V o Overflow probability η o ; The regular flow Qia Defined as the start time of numerical computation t s Time until rainfall begins t r Transient flow rate of inland waterway unit Q i The absolute value of the time average; The flow consistency φ q Defined as before rainfall n Net flow rate of river section unit under hydrodynamic conditions Q in Compared to regular traffic Q ia The average of the ratios; The proportion of the time of dampness φ t Defined as before rainfall n In the hydrodynamic working conditions, the net discharge of the river unit is compared with that of the river section. Q in The time of the opposite current Δ t t During the tidal cycle T C The proportion in; The theoretical flood storage space S t Defined as the volume of space between the real-time water level of a river segment before rainfall and the highest point of the riverbank in that segment; The flood flow Q rf Defined as rainfall duration t d Transient flow rate of inland waterway unit Q i The time average; The actual flood storage capacity S f Defined as the volume of space corresponding to the water level of a river segment before and after rainfall; The overflow volume V o Defined as after rainfall m The average overflow rate of the river section unit under the group of hydrodynamic conditions; The overflow frequency η o Defined as after rainfall m Overflow volume of river section unit under hydrodynamic conditions V o The percentage of operating conditions with a value greater than 0.

[0012] Furthermore, within the framework of the quantitative index system for river network flood risk in coastal cities, the river network topology parameter criterion layer involves indicators related to the geographical and topographical parameters of river segment units, including but not limited to river length, slope, width, sub-catchment areas, and cross-sectional shape attributes; the daily flow characteristic criterion layer includes flow, tidal, and flood storage indicators related to the hydrodynamic characteristics of river segment units before rainfall, including but not limited to the regular flow, flow consistency, tidal time ratio, and theoretical flood storage space of river segment units; and the rainfall and flood characteristic criterion layer includes flow, flood storage, and overflow indicators related to the hydrodynamic characteristics of river segment units after rainfall, including but not limited to flood flow, actual flood storage capacity, overflow volume, and overflow probability.

[0013] Further, in step S6, all indicators in the three-layer architecture are compared pairwise to determine their relative importance ranking, a priority judgment matrix is ​​constructed, and a consistency level index is calculated based on the largest eigenvalue of the priority judgment matrix to verify the importance ranking. After verification, the subjective weight of each indicator is calculated using the geometric mean method based on the priority judgment matrix; the objective weight of each indicator is calculated using the CRITIC method based on the river network topology connection relationship, the runoff generation relationship of the catchment area, and the hydrodynamic characteristic indicators; and the subjective and objective weights are combined based on the minimum entropy theory, and the Lagrange multiplier method is used to solve for the indicators. i Combined weights Z i : , in, ω i As an indicator i Subjective weighting, β i As an indicator i The objective weights are given by n, where n is the number of indicators.

[0014] Furthermore, the aforementioned flood risk index λ i The calculation formula is: , in, It is an indicator i Normalized dimensionless data, Z i As an indicator i The combined weights.

[0015] The technical solutions provided by the embodiments of this application may include the following beneficial effects: (1) This invention constructs a quantitative index system for flood risk of coastal urban river networks based on hydrodynamic characteristics. A one-dimensional hydrodynamic model is constructed based on river network attributes, topological relationships, rainfall, upstream inflow and downstream tides. Based on numerical simulation, the hydrodynamic characteristics of daily flow and rainfall-induced floods of the river network are obtained. The subjective and objective weights of various indicators are coupled using the minimum entropy theory, thereby quantifying the composite flood risk of coastal urban river networks.

[0016] (2) The present invention uses hydrodynamic simulation to calculate the hydrodynamic characteristics of river segment units, and fully considers the impact of the uncertainty of boundary conditions such as rainfall, external river flow and ocean tide level on the objective weight of hydrodynamic characteristic indicators such as daily flow and rainfall flood. It is applicable to the quantification of flood risk in urban river network systems with different regions, different topological structures and different combinations of rainfall, inflow and tide boundary conditions.

[0017] (3) This invention obtains different combinations of hydrological and hydrodynamic boundary conditions of river networks in coastal cities by combining random sampling and enumeration sampling. Based on the statistical analysis of regional historical data, the sampling range of external river flow, ocean tide level and rainfall is divided. The hydrodynamic characteristic index data of daily flow and rainfall flood are obtained by hydrodynamic simulation and result statistics of random working condition combinations. (4) This invention incorporates indicators such as river network topology parameters, daily flow characteristics and rainfall and flood characteristics into the quantitative indicator system for flood risk in coastal urban river networks. It emphasizes the assessment scale based on river segment units and the selection of indicators based on hydrodynamic characteristics. Compared with the traditional flood risk assessment system based on geographic information division units at the large scale and watershed level, it is more targeted and representative. (5) The hydrodynamic characteristic index of the present invention covers the flow, tide, flood storage and overflow characteristics of urban river network. Combined with the classification of flood risk index, it improves the interpretability of the flood risk index of river section unit and is more instructive for the proposal of flood management schemes such as flood control facility regulation and river channel reconstruction in coastal urban river network.

[0018] It should be understood that the above general description and the following detailed description are exemplary and explanatory only, and do not limit this application. Attached Figure Description

[0019] The accompanying drawings, which are incorporated in and form part of this specification, illustrate embodiments consistent with this application and, together with the description, serve to explain the principles of this application.

[0020] Figure 1 This is a flowchart illustrating a method for quantifying flood risk in coastal urban river networks based on hydrodynamic characteristics, according to an exemplary embodiment.

[0021] Figure 2This is a framework diagram illustrating flood risk assessment indicators and criteria for river networks in coastal cities, based on an exemplary embodiment.

[0022] Figure 3 The location information and river segment unit naming diagram are shown in the example.

[0023] Figure 4 The hydrodynamic model and model verification diagram are shown in the example.

[0024] Figure 5 The calculation results and distribution map of the flood risk index are shown in the example.

[0025] Figure 6 This is a block diagram illustrating a device for quantifying flood risk in coastal urban river networks based on hydrodynamic characteristics, according to an exemplary embodiment.

[0026] Figure 7 This is a schematic diagram of an electronic device according to an exemplary embodiment. Detailed Implementation

[0027] Exemplary embodiments will now be described in detail, examples of which are illustrated in the accompanying drawings. When the following description relates to the drawings, unless otherwise indicated, the same numbers in different drawings represent the same or similar elements. The embodiments described in the following exemplary embodiments do not represent all embodiments consistent with this application.

[0028] The terminology used in this application is for the purpose of describing particular embodiments only and is not intended to be limiting of the application. The singular forms “a,” “the,” and “the” used in this application and the appended claims are also intended to include the plural forms unless the context clearly indicates otherwise. It should also be understood that the term “and / or” as used herein refers to and includes any or all possible combinations of one or more of the associated listed items.

[0029] It should be understood that although the terms first, second, third, etc., may be used in this application to describe various information, such information should not be limited to these terms. These terms are only used to distinguish information of the same type from one another. For example, without departing from the scope of this application, first information may also be referred to as second information, and similarly, second information may also be referred to as first information. Depending on the context, the word "if" as used herein may be interpreted as "when," "when," or "in response to determination."

[0030] This application provides a method for quantifying flood risk in coastal urban river networks based on hydrodynamic characteristics, such as... Figure 1 As shown, the method may include the following steps: Step S1: Obtain river network geographic information data, urban rainfall data, external river flow data, ocean tide data, and river network distributary water level data for coastal cities; The river network geographic information data of the coastal city includes watershed topographic data, coastline topographic data, urban water system data, river cross-section data, location data of river network water conservancy facilities, and surface catchment area division and underlying surface data. Specifically, watershed topographic data includes basic geographic elevation data on the surface undulations and spatial elevation distribution throughout the entire study area, which is usually obtained from a digital elevation model (DEM).

[0031] Specifically, the coastline topographic data includes the coastline contour elevation data of the land-sea interface zone throughout the entire study area. In this application, only the coastline boundary coordinates are extracted.

[0032] Specifically, urban water system data includes data on the spatial distribution, geometric shape, and topological relationships of natural water bodies within the entire urban watershed, specifically including external rivers, river networks, and lakes.

[0033] Specifically, river cross-sectional data represents the measured geometric and vertical dimensions of the river channel cross-section perpendicular to the river flow direction. It typically includes data such as riverbed elevation, cross-sectional width, and riverbed shape, which are used to construct the river channel topology for subsequent hydrodynamic models.

[0034] Specifically, the location data of river network water conservancy facilities includes the type, coordinates and scale parameters of artificial water conservancy projects throughout the study area, typically including sluice gates, pumping stations, reservoirs, etc.

[0035] Specifically, surface catchment areas are divided according to river nodes and the Thiessen polygon method, and underlying surface data such as impermeable area are obtained based on land use classification of different surface catchment areas.

[0036] Preferably, the geographic information data accuracy prioritizes watershed topography and coastline topography data with a resolution of 5 meters × 5 meters, and river channel cross-section data with an average interval of 100 meters. Acquire urban rainfall data, external river flow data, ocean tide data, and river network inlet water level data for coastal cities; preferably, prioritize the density of hydrological, hydrodynamic, and tidal data, using hourly data of urban rainfall, external river flow, ocean tide, and river network inlet water level for more than 2 years.

[0037] Step S2: Establish the river network topological connectivity and runoff generation relationship in the catchment area based on the river network geographic information data; Specifically, based on the river network geographic information data, the river network inlet, outlet and control nodes are divided, the river network distributaries are determined and the undirected river segment units are divided, the river segment length, slope, elevation and cross-sectional shape attributes are extracted, and the river network topological connection relationship and the runoff generation relationship of the catchment area are established. Furthermore, based on urban water system data, inlet nodes of external rivers are identified, downstream outlet nodes are identified based on coastline boundary coordinates, and river network distributaries are determined based on urban water system data and location data of river network water conservancy facilities, thereby dividing river segment units. Within each river segment unit, different channel nodes are divided according to the distance between river cross sections, resulting in undirected river segment units. The length, slope, elevation, and cross-sectional shape attributes of the river segment are extracted by combining urban water system data, watershed topographic data, and river cross-sectional data. Control nodes are identified based on the location of sluice gates and pumping stations in the location data of river network water conservancy facilities. The runoff relationship between each channel node and its corresponding sub-catchment area is established based on the division of surface catchment areas and underlying surface data.

[0038] Step S3: Define the boundary conditions of the river network inlet node based on the external river flow data, define the boundary conditions of the outlet node based on the ocean tide data, define the hydrological boundary conditions of the catchment area based on the urban rainfall data, and construct a one-dimensional hydrological and hydrodynamic model of the coastal city river network by combining the river network topological connection relationship and the runoff relationship of the catchment area. Specifically, the inlet boundary is defined as the inflow boundary of the external river, the outlet boundary is defined as the ocean tidal level boundary, the urban hydrological inflow boundary is defined based on the rainfall-intensity duration curve, and the inflow boundary of the pipeline network model is defined based on the runoff generated by different sub-catchment areas and land use types.

[0039] Specifically, since urban extreme rainfall is usually short in duration, the urban rainfall hydrological boundary conditions should prioritize short-duration rainstorms, with a recommended rainfall duration of 1 to 3 hours. The input time step of the hydrodynamic boundary and the hydrological boundary of the catchment area at the river network inlet and outlet nodes should preferably be 1 to 5 minutes. Based on the river network topological connectivity and runoff generation relationship established in step S2, as well as the boundary conditions defined above, a one-dimensional hydrological and hydrodynamic model of the river network of a coastal city is constructed.

[0040] Specifically, the preferred one-dimensional hydrological and hydrodynamic model for the coastal city river network is the SWMM (Storm Water Management Model). In practice, the one-dimensional hydrological and hydrodynamic model does not include the drainage network model. In the hydrological model, the surface runoff of the sub-catchment is based on the Euclidean distance closest association to the river segment unit node, and the surface runoff flow of the sub-catchment is input in the form of the hydrodynamic inlet boundary. Furthermore, the one-dimensional hydrological and hydrodynamic model does not consider the regulation process of river network water conservancy facilities such as pumping stations and sluice gates, and the opening degree of sluice gates and pumps is set to fully open.

[0041] Based on the river network distributary water level data obtained in step S1, model parameter calibration and model validation are performed. Specifically, model parameter calibration involves using an ensemble smoother method to calibrate the river segment roughness parameters. Model validation prioritizes using the Nash efficiency coefficient of the river network distributary water levels. NSE ) Indicators, for continuous m Measured values ​​at each time point y oi Compared with simulated values y si The formula for statistical analysis is as follows: (1) Preferably, minute-by-minute flow / hydrological data are used for calculation, and the number of samples m at consecutive time points is usually greater than 50. The reliability of the model can be verified by the calculated Nash efficiency coefficient NSE > 0.75.

[0042] Step S4: Based on the external river flow data and ocean tide data, design the hydrodynamic boundary conditions for the river network inlet and outlet nodes. Based on the urban rainfall data, enumerate and adopt hydrological boundary conditions with different rainfall return periods and durations. Specifically, based on the river flow data and ocean tide data from step S1, statistical analysis is performed to extract the maximum daily flow. Q 0. Maximum tidal range H r And the corresponding normal range of 1.5 IQR (1.5 times interquartile range), within which the maximum flow rate is... Q 0. Maximum tidal range H r And in the tidal cycle T C Inner tidal phase t 0. Random sampling was performed, and the hydrodynamic boundary conditions of the river network inlet and outlet nodes were designed based on formula (2). The boundary condition combinations of different external river flow and ocean tides and the corresponding flow / water level time series data were obtained. Specifically, the flow at the river network inlet node Q and outlet node water level H The calculation formula is as follows: (2) According to the statistical results, values ​​within 1.5 IQR are considered normal. The data provided in step S1 are measured values, which may contain outliers. These outliers are not considered in this step. t It is time. H As time changes.

[0043] Based on the urban rainfall data, different rainfall recurrence periods and rainfall duration hydrological boundary conditions are defined by enumeration sampling. Step S5: Based on the hydrodynamic and hydrological boundary conditions, conduct numerical simulations of combined hydrological and hydrodynamic conditions using the one-dimensional hydrodynamic and hydrological model, and extract the conventional flow of the river segment.Q ia Consistency of Flow φ q Percentage of time with dampness φ t Theoretical flood storage space S t Flood flow Q rf Actual flood storage capacity S f Overflow volume V o Overflow probability η o Hydrodynamic parameters; Specifically, the regular flow of the river segment unit Q ia Defined as the start time of numerical computation t s Time until rainfall begins t r Transient flow rate of inland waterway unit Q i The absolute value of the time average: (3) River segment flow consistency φ q Defined as before rainfall n Net flow rate of river section unit under hydrodynamic conditions Q in Compared to regular traffic Q ia The average of the ratios: (4) Percentage of tidal time in river section φ t Defined as before rainfall n In the hydrodynamic working conditions, the net discharge of the river unit is compared with that of the river section. Q in The time of the opposite current Δ t t During the tidal cycle T C Percentage in: (5) Theoretical flood storage space of river section unit S t Defined as the volume of space between the real-time water level of a river segment before rainfall and the highest point of the riverbank in that segment; River section unit flood flow Q rf Defined as rainfall duration t dTransient flow rate of inland waterway unit Q i The time average: (6) Actual flood storage capacity of river section unit S f Defined as the volume of space corresponding to the water level of a river segment before and after rainfall; River section unit overflow volume V o Defined as after rainfall m The average overflow rate of the river section unit under the group of hydrodynamic conditions; River section unit overflow frequency η o Defined as after rainfall m Overflow volume of river section unit under hydrodynamic conditions V o Percentage of operating conditions >0; Step S6: Construct a framework for a quantitative index system of flood risk in coastal urban river networks based on hydrodynamic characteristics. This framework includes three layers of risk indicators: river network topology parameters, daily flow characteristics, and rainfall-flood characteristics. Combine the hydrodynamic indicators to calculate the combined weights of all risk indicators, and calculate the quantitative flood risk index of each river segment unit based on the combined weights. Specifically, such as Figure 2 As shown, the river network topology parameter criterion layer mainly includes indicators related to the geographical and topographic parameters of the river segment unit, such as river length, slope, width, sub-catchment area, and cross-sectional shape attributes. The daily flow characteristic criterion layer mainly includes flow, tide, and flood storage indicators related to the hydrodynamic characteristics of the river segment unit before rainfall, such as the regular flow of the river segment unit, flow consistency, proportion of tidal time, and theoretical flood storage space. The rainfall and flood characteristic criteria layer mainly includes flow, flood storage, and overflow indicators related to the hydrodynamic characteristics of river sections after rainfall, such as flood flow, actual flood storage capacity, overflow volume, and overflow probability.

[0044] The Analytic Hierarchy Process (AHP) is used to compare all indicators in the three-layer architecture pairwise to determine their relative importance ranking, constructing a priority judgment matrix. A consistency level index is calculated based on the largest eigenvalue of the priority judgment matrix to verify the importance ranking. After successful verification, the subjective weight of each indicator is calculated using the geometric mean method based on the priority judgment matrix. Using the river network topology, runoff generation relationship, and hydrodynamic characteristics as samples, the CRITIC method is used to calculate the objective weight of each indicator. Based on the minimum entropy theory, the subjective and objective weights are combined, and the Lagrange multiplier method is used to solve for the combined indicator weights.

[0045] Specifically, the comparison criteria include national and regional industry standards, expert experience and scoring, relevant academic works, and other materials. The importance ranking between each pair of indicators is determined based on the comparison criteria.

[0046] Specifically, indicators i and indicators j The relative importance of pairwise comparisons t ij The sorting needs to pass a consistency verification based on the priority decision matrix. T The largest eigenvalue λ m Calculated consistency level index CR Need to meet n Average random consistency index of matrix order k The calculation verification prioritizes the determination matrix. T Consistency level indicators CR The calculation formula is as follows: (7) (8) index i Subjective weight ω i The formula for calculating using the geometric mean method is as follows: (9) index j The objective weights are evaluated using the CRITIC method, and the standard deviation is [not specified]. S j Conflict coefficient A j and objective weight β i The calculation formula is as follows: (10) (11) in n For the number of indicators, m This represents the number of samples.

[0047] index i Combined weights Z i The calculation formula is as follows: (12) Subsequently, based on the combined weights Z i Calculate the flood risk index of the river section unit λ iWhen the flood risk index is in the range of 0.8 to 1.0, the river section is at extremely high flood risk; when the flood risk index is in the range of 0.6 to 0.8, the river section is at relatively high flood risk; when the flood risk index is in the range of 0.4 to 0.6, the river section is at moderate flood risk; when the flood risk index is in the range of 0.2 to 0.4, the river section is at low flood risk; and when the flood risk index is in the range of 0 to 0.2, the river section is at no flood risk. Specifically, the flood risk index λ i The calculation formula is as follows: (13) Specifically, It is an indicator i Normalized dimensionless data means that the original data of each indicator are uniformly scaled to the range between 0 and 1.

[0048] Example 1 Figure 1 This is a schematic diagram of the overall process for constructing a quantitative indicator system for river network flood risk in coastal cities. The method may include the following steps: Step 1: Collect urban river network geographic information data of the Qianshan River Basin in Zhuhai City, Guangdong Province; Specifically, water system data, satellite remote sensing imagery, and digital elevation model (DEM) data for the target area were acquired. GIS software was used to perform preprocessing operations on the data, including cropping, coordinate system resetting, and filling. This yielded 5m x 5m resolution topographic data of the Qianshan River basin (DEM); nearshore water depth and coastline data (China Maritime Safety Administration nautical chart, 2008); and river cross-section data at average intervals of 100 meters (local hydrological bureau).

[0049] Step 2: Collect hydrological, hydrodynamic, and tidal data on the external rivers, river networks, and oceans of the Qianshan River Basin in Zhuhai City, Guangdong Province; Specifically, the project aims to obtain flow observation data from the Wuzhou station in the upper reaches of the Xijiang River in the target area from 2021 to 2023, tidal observation data from the Dahengqin station in the lower reaches of the river, and rainfall data from the Zhuxiandong station in Zhuhai District, Zhuhai City, Guangdong Province.

[0050] Step 3: Divide the river segment units of the urban river network in the Qianshan River Basin, clarify the outer rivers, tidal boundaries and river network inlet and outlet nodes, establish the topological connection relationship of the river network, and divide the sub-catchment area distribution; Specifically, the urban river network area was selected with the sluice gate as the boundary, and the river channel was segmented with the river network distributaries as the boundary, resulting in 34 different river segment units labeled as follows: Figure 3 As shown.

[0051] Step 4: Define the hydrological and hydrodynamic boundary conditions for the rainfall-outer river-urban river network-ocean system; Specifically, the upper reaches of the outer river are set as flow inlet nodes, with the node flow being 70% of the flow at Wuzhou Station in the upper reaches of the Xijiang River; the two estuaries in the lower reaches of the outer river are set as water level outlet nodes, with the outlet water level being the water level observed by the tide at Dahengqin Station; each sub-catchment area is set as a rainfall inlet, with the rainfall amount being the rainfall intensity curves of different rainfall return periods at Zhuxiandong Station in Zhuhai District, Zhuhai City, Guangdong Province; and the infiltration area is simulated using the Horton model with nonlinear reservoir simulation of slope confluence.

[0052] Step 5: Construct a one-dimensional hydrological and hydrodynamic SWMM model of the rainfall-outer river-urban river network-ocean system in the Qianshan River Basin, and complete the calibration of the roughness parameter of the river section and the model verification. Specifically, a river network topology model is constructed based on river cross-section and elevation data from geographic information data, comprising a total of 205 nodes and 210 river segments; 30 sub-catchment areas are set up, with the runoff of each catchment area directly connected to the nearest river node as the inlet boundary.

[0053] Specifically, 70% of the flow at Wuzhou Station between 8:00 on May 31, 2021 and 8:00 on June 31, 2021 was selected as the inlet flow boundary, and the tidal observation water level at Dahengqin Station was used as the outlet water level node for the two estuaries. The ensemble smoother method was used to calibrate the river roughness, and the roughness of the river network section was between 0.025 and 0.040, while that of the outer river was between 0.015 and 0.030.

[0054] Specifically, the Nash efficiency coefficient was calculated using water level monitoring data from different river network distributaries (#1 to #7). NSE The indicators were used for model validation at each monitoring point. NSE A value between 0.91 and 0.98 meets the accuracy requirements for simulation calculations.

[0055] Specifically, the hydrodynamic model and model validation results are as follows: Figure 4 As shown in Table 1.

[0056] Table 1

[0057] Specifically, Figure 4 (a) shows the location information of different river network distributaries #1 to #7 used for model validation within the river network. Figure 4 (b) shows the measured and simulated water levels at monitoring points #1 and #6 between 8:00 AM on May 31, 2021 and 8:00 AM on June 31, 2021. Table 1 shows the calculated values ​​for each monitoring point. NSE value.

[0058] Step 6: Calculate the maximum daily flow rate at Wuzhou Station and the maximum daily tidal range at Dahengqin Station, and design the hydrodynamic boundary conditions for the inlet and outlet nodes in the hydrodynamic simulation. Specifically, the daily maximum flow rate at Wuzhou Station exhibits numerous abnormally high values, displaying a clearly skewed distribution. The normal range (1.5 IQR) is 2219 m³ / h. 3 / s to 9940m 3 / s and the largest outlier is close to 30000m 3 / s.

[0059] Specifically, the daily maximum tidal range at Dahengqin Station generally follows a normal distribution with few outliers, and the normal range (1.5IQR) is 0.36m to 1.52m.

[0060] Specifically, the ocean tides in this area are mixed tides, and the tidal cycle is defined as one day.

[0061] Specifically, the inflow of the river network Q The sampling range is 2000~20000m 3 / s, outlet water level H The sampling range is 0.5~1.5m, tidal phase t The sampling range for 0 was 0~1440 min. Random sampling was performed using the Monte Carlo method, resulting in 195 sets of hydrodynamic boundary conditions.

[0062] Step 7: Select hydrological boundary conditions, conduct hydrodynamic numerical simulation, and obtain hydrodynamic characteristic calculation results; Specifically, a 2-hour short-duration rainstorm was selected from the Zhuxiandong Station in Zhuhai District, and rainfall curves with different return periods of 5, 10, 20, 30 and 50 years were obtained for hydrological condition design calculations, with the corresponding rainfall intensity in the range of 62.5~105 mm / h.

[0063] Specifically, 195 different hydrodynamic boundaries and 6 different hydrological boundaries (including no rainfall and 5 different rainfall return periods) were set up, totaling 1170 hydrodynamic simulation conditions. Batch calculations and data processing were performed using pyswmm.

[0064] Specifically, the hydrodynamic indicators of 34 different river segments were statistically analyzed, including conventional flow, flow consistency, proportion of tidal time, theoretical flood storage space, flood flow, actual flood storage capacity, overflow volume, and overflow probability. The results were then normalized to obtain a dimensionless value of 0 to 1. The hydrodynamic indicators of each river segment were ranked according to the standard that 1 indicates an increased risk of flooding and 0 indicates a decreased risk of flooding.

[0065] Step 8: Construct a framework for a quantitative index system of flood risk in coastal urban river networks based on hydrodynamic characteristics; Specifically, the framework targets the overflow risk of river segment units and includes three criterion layers: river network topology parameters, daily flow characteristics, and rainfall and flood characteristics.

[0066] Specifically, the river network topology parameters include four indicators: river segment unit length, slope, width, and sub-catchment area.

[0067] Specifically, the daily flow characteristics include the regular flow, flow consistency, tidal time percentage, and hydrodynamic characteristics of the theoretical flood storage space for 195 river section units under no-rainfall conditions.

[0068] Specifically, the rainfall and flood characteristics include 975 sets of rainfall conditions, including the flood flow, actual flood storage capacity, overflow volume, and overflow probability hydrodynamic characteristics of river sections.

[0069] Step 9: Calculate the subjective weight, objective weight, and combined weight of each indicator.

[0070] Specifically, based on expert scoring and literature research, the relative importance of each indicator was ranked in the following order: overflow probability > overflow volume > flood flow > actual flood storage capacity > conventional flow > theoretical flood storage space > flow consistency > proportion of tidal time > sub-catchment area > river segment unit width > river segment unit length > river segment unit slope.

[0071] Specifically, the priority decision matrix is ​​obtained. T as follows: (14) Specifically, the average random consistency index of a 12th-order matrix is ​​calculated to be 1.56, and the consistency level index of the matrix is ​​prioritized for determination. CR The value is 0.089, which is less than 0.1, satisfying the consistency requirement.

[0072] Specifically, using the geometric mean method, the subjective weights of each indicator were calculated as follows: river segment unit length 0.0187, river segment unit slope 0.0156, river segment unit width 0.0114, sub-catchment area 0.0544, conventional flow 0.0688, flow consistency 0.031, tidal time percentage 0.0258, theoretical flood storage space 0.0686, flood flow 0.1319, actual flood storage capacity 0.1136, overflow volume 0.2169, and overflow probability 0.2434.

[0073] Specifically, based on geographic information systems and hydrodynamic simulations, the standard deviation and conflict coefficient of length, slope, width, sub-catchment area, regular flow, flow consistency, tidal time ratio, theoretical flood storage space, flood flow, actual flood storage capacity, overflow volume, and overflow probability index data of different river segment units are extracted, and the objective weighting coefficient is calculated using the CRITIC method.

[0074] Specifically, the objective weights of each indicator were calculated as follows: river segment unit length 0.0647, river segment unit slope 0.0772, river segment unit width 0.0686, sub-catchment area 0.0580, regular flow 0.0872, flow consistency 0.1121, tidal time percentage 0.1032, theoretical flood storage space 0.0561, flood flow 0.0818, actual flood storage capacity 0.1069, overflow volume 0.0607, and overflow probability 0.1234.

[0075] Specifically, the comprehensive weights of each indicator calculated based on the minimum entropy weight method are as follows: river segment unit length 0.0384, river segment unit slope 0.0328, river segment unit width 0.0361, sub-catchment area 0.0621, regular flow 0.0855, flow consistency 0.0651, tidal time proportion 0.0570, theoretical flood storage space 0.0685, flood flow 0.1147, actual flood storage capacity 0.1217, overflow volume 0.1267, and overflow probability 0.1914.

[0076] Step 10: Calculate the flood risk index of the river segment and assess the risk level.

[0077] Specifically, the flood risk index and its distribution for 34 different river segment units were statistically analyzed, as follows: Figure 5 As shown in Table 2.

[0078] Table 2

[0079] Specifically, river segments R5, R20, R30, R19, R31, and R26 have a relatively high risk of flooding; river segments R9, R21, R23, R24, R32, R18, R27, R8, R34, R10, R13, R7, R29, R12, R1, R25, R2, R22, and R16 have a moderate risk of flooding; and river segments R6, R15, R28, R11, R4, R3, R14, R17, and R33 have a relatively low risk of flooding. Overall, the urban river network in the Qianshan River Basin of Zhuhai City, Guangdong Province, has a relatively high risk of flooding.

[0080] Specifically, the four river segments R5, R20, R30, and R26 are tributaries that are connected to the river network on one side. Due to their large sub-catchment areas, high probability of river overflow, and large overflow volume, they have a high risk of flooding.

[0081] Specifically, although R19 and R31 have small overflow volumes, their poor drainage (flood flow) and water storage (actual flood storage capacity) capabilities result in a higher risk of flooding.

[0082] Specifically, the R17 and R33 river segments are downstream channels with greater depth, width, and length, and smaller sub-catchment areas, resulting in higher drainage and water storage capacity and lower flood risk.

[0083] Specifically, flood control plans should prioritize pumping or controlling floodwaters through sluice gates to the R17 and R33 river units.

[0084] Specifically, flood storage facility plans should prioritize the four river segments R5, R20, R30, and R26.

[0085] Specifically, the river channel reconstruction and widening plan should give priority to the R19 and R31 river segments.

[0086] Corresponding to the aforementioned embodiments of the method for quantifying flood risk in coastal urban river networks based on hydrodynamic characteristics, this application also provides embodiments of a device for quantifying flood risk in coastal urban river networks based on hydrodynamic characteristics.

[0087] Figure 6 This is a block diagram illustrating a device for quantifying flood risk in coastal urban river networks based on hydrodynamic characteristics, according to an exemplary embodiment. (Refer to...) Figure 6 The device may include: The data acquisition module M1 is used to acquire river network geographic information data, urban rainfall data, external river flow data, ocean tide data, and river network distributary water level data of coastal cities; The topology establishment module M2 is used to establish the topological connection relationship of the river network and the runoff relationship of the catchment area based on the river network geographic information data. The model building module M3 is used to define the boundary conditions of the river network inlet nodes based on the external river flow data, the boundary conditions of the outlet nodes based on the ocean tide data, and the hydrological boundary conditions of the catchment area based on the urban rainfall data. It combines the river network topology connection relationship and the runoff relationship of the catchment area to construct a one-dimensional hydrological and hydrodynamic model of the coastal city river network. The working condition design module M4 is used to design the hydrodynamic boundary conditions for the river network inlet and outlet nodes based on the external river flow data and ocean tide data. Based on the urban rainfall data, it enumerates and adopts hydrological boundary conditions with different rainfall return periods and rainfall durations. The numerical simulation module M5 is used to conduct numerical simulation of combined hydrological and hydrodynamic conditions based on the hydrodynamic boundary conditions and hydrological boundary conditions, and to extract hydrodynamic indicators using the one-dimensional hydro-hydrodynamic model. The risk quantification module M6 is used to construct a framework for a flood risk quantification index system for coastal urban river networks based on hydrodynamic characteristics. It includes three layers of risk indicators: river network topology parameters, daily flow characteristics, and rainfall-flood characteristics. It also calculates the combined weights of all risk indicators in combination with the hydrodynamic indicators, and calculates the flood risk index of each river segment unit based on the combined weights.

[0088] Regarding the apparatus in the above embodiments, the specific manner in which each module performs its operation has been described in detail in the embodiments related to the method, and will not be elaborated upon here.

[0089] For the device embodiments, since they basically correspond to the method embodiments, the relevant parts can be referred to in the description of the method embodiments. The device embodiments described above are merely illustrative. The units described as separate components may or may not be physically separate, and the components shown as units may or may not be physical units, that is, they may be located in one place or distributed across multiple network units. Some or all of the modules can be selected to achieve the purpose of this application according to actual needs. Those skilled in the art can understand and implement this without creative effort.

[0090] Accordingly, this application also provides a computer program product, including a computer program / instruction, which, when executed by a processor, implements the above-described method for quantifying flood risk in coastal urban river networks based on hydrodynamic characteristics.

[0091] Accordingly, this application also provides an electronic device, comprising: one or more processors; a memory for storing one or more programs; and when the one or more programs are executed by the one or more processors, causing the one or more processors to implement the above-described method for quantifying flood risk in coastal urban river networks based on hydrodynamic characteristics. Figure 7 The diagram shown is a hardware structure diagram of any data processing-capable device, including a coastal city river network flood risk quantification device based on hydrodynamic characteristics, provided in an embodiment of the present invention. (Except for...) Figure 7 In addition to the processor, memory, and network interface shown, any data processing device in the embodiment may also include other hardware depending on the actual function of the data processing device, which will not be described in detail here.

[0092] Accordingly, this application also provides a computer-readable storage medium storing computer instructions, which, when executed by a processor, implement the aforementioned method for quantifying flood risk in coastal urban river networks based on hydrodynamic characteristics. The computer-readable storage medium can be an internal storage unit of any data-processing device as described in any of the foregoing embodiments, such as a hard disk or memory. The computer-readable storage medium can also be an external storage device, such as a plug-in hard disk, smart media card (SMC), SD card, flash card, etc., equipped on the device. Furthermore, the computer-readable storage medium can include both internal storage units of any data-processing device and external storage devices. The computer-readable storage medium is used to store the computer program and other programs and data required by the data-processing device, and can also be used to temporarily store data that has been output or will be output.

[0093] Other embodiments of this application will readily occur to those skilled in the art upon consideration of the specification and practice of the disclosure herein. This application is intended to cover any variations, uses, or adaptations of this application that follow the general principles of this application and include common knowledge or customary techniques in the art not disclosed herein.

Claims

1. A method for quantifying flood risk in coastal urban river networks based on hydrodynamic characteristics, characterized in that, include: S1: Obtain geographic information data of river networks, urban rainfall data, external river flow data, ocean tide data, and water level data of river network distributaries in coastal cities; S2: Establish the topological connectivity of the river network and the runoff generation relationship of the catchment area based on the aforementioned river network geographic information data; S3: Define the boundary conditions of the river network inlet node based on the external river flow data, define the boundary conditions of the outlet node based on the ocean tide data, define the hydrological boundary conditions of the catchment area based on the urban rainfall data, and construct a one-dimensional hydrological and hydrodynamic model of the coastal city river network by combining the river network topological connection relationship and the runoff relationship of the catchment area. S4: Based on the aforementioned external river flow data and ocean tide data, design the hydrodynamic boundary conditions for the river network inlet and outlet nodes. Based on the aforementioned urban rainfall data, enumerate and adopt hydrological boundary conditions with different rainfall return periods and rainfall durations. S5: Based on the hydrodynamic boundary conditions and hydrological boundary conditions, conduct numerical simulation of combined hydrological and hydrodynamic conditions using the one-dimensional hydro-hydrodynamic model, and extract hydrodynamic indicators. S6: Construct a framework for a quantitative index system of flood risk in coastal urban river networks based on hydrodynamic characteristics. This framework includes three layers of risk indicators: river network topology parameters, daily flow characteristics, and rainfall-flood characteristics. The combined weights of all risk indicators are calculated in conjunction with the hydrodynamic indicators. Based on these combined weights, the flood risk index of each river segment unit is calculated.

2. The method according to claim 1, characterized in that, The river network geographic information data in step S1 includes watershed topographic data, coastline topographic data, urban water system data, river cross-section data, river network water conservancy facility location data, and surface catchment area division and underlying surface data.

3. The method according to claim 2, characterized in that, In step S2, the river network inlets, outlets, and control nodes are delineated, river network distributaries are determined, and undirected river segment units are divided. The length, slope, elevation, and cross-sectional shape attributes of the river segments are extracted to establish the river network topological connectivity and runoff relationship of the catchment areas. This includes: delineating external river inlet nodes based on the urban water system data; delineating downstream outlet nodes based on the coastline topography data; determining river network distributaries based on the urban water system data and the location data of river network water conservancy facilities; dividing river segment units; dividing different channel nodes within each river segment unit based on the river cross-sectional spacing to obtain undirected river segment units; and extracting the length, slope, elevation, and cross-sectional shape attributes of the river segments based on the urban water system data, watershed topography data, and river cross-sectional data. Control nodes are delineated based on the locations of sluice gates and pumping stations in the river network water conservancy facility location data. Runoff relationship between each channel node and its corresponding sub-catchment area is established based on the surface catchment area division and underlying surface data.

4. The method according to claim 1, characterized in that, In step S3, the one-dimensional hydrological and hydrodynamic model does not include the drainage network model. In the hydrological model, the surface runoff of the sub-catchment area is based on the nearest associated river segment unit node and is input as the surface runoff flow of the sub-catchment area in the form of the hydrodynamic inlet boundary. Furthermore, the one-dimensional hydrological and hydrodynamic model does not consider the regulation process of river network water conservancy facilities, and the opening degree of gates and pumps is set to fully open.

5. The method according to claim 1, characterized in that, Using the water level data of the river network distributaries obtained in step S2, the model parameters of the one-dimensional hydrological and hydrodynamic model established in step S3 are calibrated and the model is verified. The model parameter calibration refers to the calibration of the roughness parameter of the river section using the ensemble smoother method. The model verification is to calculate the Nash efficiency coefficient index of the water level data of the river network distributaries. If the Nash efficiency coefficient index exceeds a predetermined threshold, the model is reliable.

6. The method according to claim 1, characterized in that, Step S4 specifically involves: performing statistical analysis based on the aforementioned river flow data and ocean tide data to extract the maximum daily flow. Q 0. Maximum tidal range H r And the corresponding normal range of 1.5 times the interquartile range, within which the maximum flow rate is... Q 0. Maximum tidal range H r And in the tidal cycle T C Inner tidal phase t Random sampling is performed, and the hydrodynamic boundary conditions for the river network inlet and outlet nodes are designed based on the following formula: ， in Q For the flow at the river network inlet node, H This refers to the water level at the outlet node.

7. The method according to claim 1, characterized in that, The hydrodynamic parameters extracted in step S5 include the conventional flow of the river unit. Q ia Consistency of Flow φ q Percentage of time with dampness φ t Theoretical flood storage space S t Flood flow Q rf Actual flood storage capacity S f Overflow volume V o Overflow probability η o ; The regular flow Q ia Defined as the start time of numerical computation t s Time until rainfall begins t r Transient flow rate of inland waterway unit Q i The absolute value of the time average; The flow consistency φ q Defined as before rainfall n Net flow rate of river section unit under hydrodynamic conditions Q in Compared to regular traffic Q ia The average of the ratios; The proportion of the time when the humidity is felt φ t Defined as before rainfall n In the hydrodynamic working conditions, the net discharge of the river unit is compared with that of the river section. Q in The time Δ of the opposing currents t t During the tidal cycle T C The proportion in; The theoretical flood storage space S t Defined as the volume of space between the real-time water level of a river segment before rainfall and the highest point of the riverbank in that segment; The flood flow Q rf Defined as rainfall duration t d Transient flow rate of inland waterway unit Q i The time average; The actual flood storage capacity S f Defined as the volume of space corresponding to the water level of a river segment before and after rainfall; The overflow volume V o Defined as after rainfall m The average overflow rate of the river section unit under the group of hydrodynamic conditions; The overflow frequency η o Defined as after rainfall m Overflow volume of river section unit under hydrodynamic conditions V o The percentage of operating conditions with a value greater than 0.

8. The method according to claim 1, characterized in that, In the framework of the quantitative index system for flood risk in the river network of coastal cities, the river network topology parameter criterion layer involves indicators related to the geographical and topographical parameters of river segment units, including but not limited to river length, slope, width, sub-catchment areas, and cross-sectional shape attributes; the daily flow characteristic criterion layer includes flow, tidal, and flood storage indicators related to the hydrodynamic characteristics of river segment units before rainfall, including but not limited to the regular flow, flow consistency, tidal time ratio, and theoretical flood storage space of river segment units; the rainfall and flood characteristic criterion layer includes flow, flood storage, and overflow indicators related to the hydrodynamic characteristics of river segment units after rainfall, including but not limited to flood flow, actual flood storage capacity, overflow volume, and overflow probability.

9. The method according to claim 1, characterized in that, In step S6, all indicators in the three-layer architecture are compared pairwise to determine their relative importance ranking, a priority judgment matrix is ​​constructed, and a consistency level index is calculated based on the largest eigenvalue of the priority judgment matrix to verify the importance ranking. After verification, the subjective weight of each indicator is calculated using the geometric mean method based on the priority judgment matrix; the objective weight of each indicator is calculated using the CRITIC method based on the river network topology connection relationship, the runoff generation relationship of the catchment area, and the hydrodynamic characteristic indicators. Based on the minimum entropy theory, subjective and objective weights are combined, and the Lagrange multiplier method is used to solve the index. i Combined weights Z i : ， in, ω i As an indicator i Subjective weighting, β i As an indicator i The objective weights are given by n, where n is the number of indicators.

10. The method according to claim 1, characterized in that, The flood risk index λ i The calculation formula is: ， in, It is an indicator i Normalized dimensionless data, Z i As an indicator i The combined weights.

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