CN114372685A - Urban rainstorm waterlogging risk assessment method based on SWMM model - Google Patents
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Abstract
The invention discloses an urban rainstorm waterlogging risk assessment method based on an SWMM model, which overcomes the defect that the traditional assessment system cannot fully consider climate change elements, and on the basis of comprehensively considering the influences of climate change and urbanization level on urban rainstorm waterlogging, the method simulates different climate change and urbanization level based on the SWMM model, quantitatively assesses the influences of the urban rainstorm waterlogging disaster under different scenes, realizes effective simulation of the urban rainstorm flood process, realizes control and prediction of the urban rainstorm waterlogging disaster, and realizes risk control and attribution analysis of the urban rainstorm waterlogging disaster; the invention can provide scientific basis for engineering operation and maintenance and emergency decision of management departments.
Description
Technical Field
The invention relates to the technical field of urban waterlogging risk assessment, in particular to an urban rainstorm waterlogging risk assessment method based on an SWMM model.
Background
The urban rainstorm flood Management Model (SWMM) is a dynamic rainfall-runoff Model based on hydrodynamics developed by the U.S. Environmental Protection Agency (EPA), can simulate the Water quality and quantity process of a single field or a long-term (continuous) rainfall event in a city, is rapidly developed in recent years, and is widely applied to the aspects of effect evaluation of the influence of the surface runoff flow and Low Influence Development (LID) of the urban rainstorm flood in a small river basin on the Water quality and quantity of the urban surface runoff, urban drainage system planning, rainfall flood regulation and storage treatment, Water quality influence evaluation and the like. The main functions of the SWMM model can be realized include a rainfall ground production convergence simulation process, a drainage pipe network convergence simulation process, a river channel hydrodynamic water quality simulation process and the like, all the processes can track and simulate the water quantity and the water quality of each sub-convergence area at any moment, and the water quantity, the water depth, the flow rate and other hydrodynamic factors of the drainage pipe network and the river channel as well as the water quality factor conditions of COD, ammonia nitrogen, total phosphorus and the like can be known; the SWMM model has the display degree and is characterized in that the integration coupling of three water processes of hydrology, hydrodynamic force and water quality is realized, and a rainfall-driven hydrology production convergence process provides a boundary for a river hydrodynamic process through nodes; based on the characteristics of the model, the model becomes a research hotspot of scholars at home and abroad in recent years, and is widely applied to a plurality of small watersheds and cities.
Disaster risk evaluation and management starts in the 30 s of the 20 th century and is originated from a flood disaster risk management method proposed by the administrative department of the watershed of the tennessee of the united states. On the basis, qualitative analysis is developed into semi-quantitative and quantitative evaluation research, a mature disaster risk evaluation system and a theoretical method are gradually formed, and the method is widely applied to prevention and treatment research of a series of natural disasters such as earthquake, typhoon, flood and the like. The three-element theory of natural disasters considers that the natural disasters consist of disaster causing factors, pregnant disaster environments and disaster bearing bodies. The risk of natural disasters (R) can be considered as a function of risk of disaster-causing factors (H), vulnerability of pregnant environments (E), and vulnerability of disaster-bearing bodies (S). The disaster-causing factor is a factor for inducing waterlogging disasters, and for urban waterlogging, the disaster risk is not only related to precipitation, but also influenced by an urban drainage system to a great extent; the pregnant disaster environment is an environment for forming waterlogging disasters; the disaster-bearing body is a disaster-bearing environment, and the vulnerability of the disaster-bearing body refers to the loss degree of the disaster-bearing environment when a disaster is imminent, and is related to factors such as population density, economy, land utilization type and the like. Climate change may cause increased intensity of rainfall and frequency of rainstorms, thereby causing serious urban waterlogging problems; in addition, the rapid urbanization process has obvious influence on the hydrological process of the urban surface by changing the type and the pattern of the urban underlying surface, so that the urban waterlogging risk is aggravated.
In an urban rainstorm waterlogging evaluation system constructed by the conventional SWMM model, the influence of subsequent change elements and urbanization level on urban rainstorm waterlogging disasters cannot be fully considered, so that a method based on various climate change and urbanization development situations needs to be designed, and the influence of climate change and urbanization progress on urban rainstorm waterlogging risks is evaluated by adopting the SWMM model to perform simulation analysis on urban rainstorm flood damage processes under different situations.
Disclosure of Invention
In order to solve the technical problems, the invention provides an urban rainstorm waterlogging risk assessment method based on an SWMM model, which overcomes the defect that the traditional assessment system cannot fully consider climate change elements, and on the basis of comprehensively considering the influences of climate change and urbanization level on urban rainstorm waterlogging, the method simulates different climate change and urbanization level based on the SWMM model, quantitatively evaluates the influences of the urban rainstorm waterlogging disasters under different scenes, realizes effective simulation of the urban rainstorm flood process, realizes control and prediction of the urban rainstorm waterlogging disasters, and realizes risk control and attribution analysis of the urban rainstorm waterlogging disasters; the invention can provide scientific basis for engineering operation and maintenance and emergency decision of management departments.
In order to achieve the technical features, the invention is realized as follows: a city rainstorm waterlogging risk assessment method based on an SWMM model comprises the following steps:
step S1: basic data collection and processing:
s101, collecting relevant basic data of the research area, wherein the relevant basic data comprises the following steps: the method comprises the following steps of (1) researching area range boundaries, Digital Elevation Model (DEM) data, rainfall site coordinate positions and corresponding rainfall observation time sequences, evaporation amount observation time sequences in a researching area, researching area land utilization data, researching area pipe network data, researching area water system distribution, researching area river channel water conditions and river channel terrain data;
s102, collecting data of hydraulic engineering in a research area, comprising the following steps: engineering position and engineering scale;
s103, performing format processing on the collected data: generalizing a pipe network and a water system, cutting and distributing land utilization, and extracting river cross sections according to river landforms; processing the various sequence data into a format recognizable by the model;
step S2: constructing an SWMM model and developing parameter calibration:
dividing sub-catchment areas based on the pipe network, land utilization, DEM, rainfall and evaporation basic data collected and preprocessed in S1, constructing a SWMM pipe network runoff model skeleton, and using rainfall data as drive calibration model parameters;
step S3: climate change and urbanization level scenarios:
s301, selecting multiple GCMs output data in CMIP6 to construct a climate change scene SSP-RCP, wherein the scene comprises population, economic development, an ecosystem, resources, a system and social factors and also comprises the effort measures for slowing down, adapting and coping with climate change in the future;
preferably, 3 future scenarios, namely SSP1-2.6, SSP2-4.5 and SSP5-8.5 in the ScenarioMIP sub-plan are selected for simulation; wherein SSP1-2.6 is the updated scene of RCP2.6 scene in CMIP5 in CMIP6, under SSP1-2.6 scene, the land use type changes violently, especially the global forest area increases significantly, the scene can reflect the comprehensive influence of low social vulnerability, low slow-down pressure and low radiation compelling; SSP2-4.5 is the updated RCP4.5 scene in CMIP5 in CMIP6, which represents the combination of moderate social vulnerability and moderate radiation forcing; SSP5-8.5 is the scene of CMIP5 after RCP8.5 scene is updated in CMIP6, SSP5 is the only shared socioeconomic path that can realize artificial radiation compelling to reach 8.5W/m2 in 2100 years;
s302, according to the urbanization development situation, different SSP-USM parameters are set in combination with a research area overall planning scheme to simulate the urban sustainable development level under different development conditions;
preferably, to assess the potential impact of urbanization and further understand the interaction of climate change and land utilization change in Urban rainfall flood Management (USM), some studies set up an Urban development scenario (called SSP-USM) whose considerations include population, land utilization, and technologies and policies directly related to Urban rainfall flood Management; the method preferably selects three SSP-USM scenes as examples for simulation, and the three SSP-USM scenes are respectively named as SSPa, SSPb and SSPc to reflect the urban development states under high sustainability, medium sustainability and low sustainability; by referring to the existing research, the main characteristics of urbanization under the three SSP-USM scenes are obtained:
the city is in a high sustainable development state under the SSPa scene, and the development is rapid and reasonable: the situation generally assumes that the development of scientific and technical and educational levels is rapid, the social development mode is environment-friendly, and the population growth is relatively slow; under the condition, the city planning is proper, and more LID measures are adopted to manage the urban rainfall flood;
the sustainability of city development under the SSPc scenario is low, and the city faces severe challenges in all aspects: under the situation, the city development is relatively slow, the investment on technology, education and human capital is less or even almost zero, and no additional LID measures are provided;
the SSPb scenario is an intermediate state of SSPa and SSPc: setting an SSPb scenario representing an intermediate state of SSPa and SSPc based on a prediction of population growth, social development and technical revolution trends in the past decades, the sustainable development level being moderate;
s303, constructing a specific scene which comprehensively reflects the climate change and the urbanization, and coupling the SSP-RCP scene with the SSP-USM scene; naming the coupled scene as SSPx-y, where x refers to a specific SSP-RCP scene and y refers to a specific SSP-USM scene; simultaneously selecting a scene without considering the influence of climate change and urbanization as an original scene;
step S4: and (3) evaluating the risk of rainstorm waterlogging disasters: inputting the comprehensive scene SSPx-y of the climate change and the urbanization level constructed in the S3 into the SWMM model constructed in the S2 as a boundary for simulation, calculating the evolution process of urban rainstorm waterlogging under different situations, and finally finishing the risk evaluation of the climate change and the urbanization on the urban waterlogging disaster; the specific method comprises the following steps:
s401, constructing an inland inundation disaster risk evaluation model according to an SMI-P method, wherein the evaluation model is divided into a target layer, a standard layer and an index layer; the target layer is an inland inundation disaster risk index, and the criterion layer comprises a disaster causing factor, a pregnant disaster environment and a disaster bearing body; the risk indexes of the disaster-causing factors comprise total overflow amount, overflow duration and maximum overflow amount; the vulnerability indexes of the pregnant disaster environment comprise terrain elevation and terrain standard deviation; the vulnerability index of the disaster-bearing body comprises the land utilization type and the construction completion degree of the sponge city;
s402, carrying out single-index quantization standardization processing on the selected various indexes;
s403, obtaining subjective weight through an analytic hierarchy process, obtaining objective weight through an entropy weight process, and then obtaining the weight of the index; after the weight calculation is finished, adding the risk quantification results of all indexes according to the weight to obtain a risk index of the evaluation object; the evaluation principle is as follows:
in the formula, R is an urban inland inundation disaster risk index, Hi, Ej and Sk are indexes i, j and k selected when H (disaster-causing factor risk index), E (pregnant disaster environment vulnerability index) and S (disaster-bearing body vulnerability index) are calculated respectively, XHi, XEj and XSk are corresponding index weights, and A, B, C is an index weight corresponding to H, E, S respectively;
according to the difference of urban inland inundation disaster risk indexes (R), the risk grades are divided into five grades: low risk, [0,0.2 ]; lower risk, (0.2,0.4 ]; intermediate risk, (0.4,0.6 ]; higher risk (0.6,0.8] and high risk (0.8, 1).
Further, in the step S402, the piecewise linear membership function is preferably used as the method for quantizing the single index, and in the selected index, different functions are adopted for normalization of the forward and reverse indexes:
preferably, the specific method for constructing and developing the SWMM model in step S2 is as follows:
s201, generalization of pipe networks and sub-catchment areas: determining a research area range according to river network water system characteristics and a catchment area, extracting pipe network and drainage node information in the research area range by combining drainage system planning information in the research area, acquiring a simulated sub-catchment area of the research area based on a Thiessen polygon method according to a pipe network topological relation, and determining a corresponding relation between the sub-catchment area and a catchment node;
s202, assigning: according to the elevation of the pipe network and the DEM, carrying out elevation assignment on each node in the pipeline, and determining the upstream and the downstream of the pipeline; assigning the land types to each sub-catchment area according to the land utilization division result, and calculating the area proportion of the land types in each sub-catchment area; assigning the soil type to each sub-catchment area according to the soil type division result, determining the soil type of each sub-catchment area, and calculating the soil infiltration coefficient of each sub-catchment area; determining the outlet of the model, namely an output boundary according to the range of the catchment area and the distribution of the water outlets of the pipe network;
s203, construction and verification of the SWMM pipe network runoff model: importing the information of the assigned pipe network, the sub-catchment areas and the nodes into SWMM model software, defining parameters of the sub-catchment areas and the pipelines in the SWMM software, using rainfall data and watershed evaporation data of a rainfall station as a drive to realize the operation of the model, comparing data of an output boundary with measured data, and finishing parameter calibration and model verification; the calibration parameters comprise pipe network runoff model parameters of pipeline roughness, sub-catchment area characteristic width, water impermeability runoff coefficient, water permeability runoff coefficient, water impermeability depression water storage capacity, water permeability depression water storage capacity and sub-catchment area Manning coefficient.
Further, the specific method of the thiessen polygon method is as follows: determining nodes and a catchment area according to pipe network information, taking drainage nodes as discrete points, performing neighborhood Analysis by using ArcGIS to complete division of Thiessen polygons (Toolbox-Analysis Tools-maximum-Create Thiessen Polygon), cutting according to the catchment area to generate a simulation sub-catchment area (Toolbox-Analysis Tools-Extract-Clip) of a research area, and finally manually adjusting the sub-catchment area.
Further, in the data acquisition of the disaster causing factor in step S401, a rainstorm design is performed in advance, the rainstorm intensity is calculated through a local rainstorm intensity calculation formula, a suitable rain type is selected to perform rainfall distribution on the designed rainstorm, and a rainstorm peak coefficient, a rainfall duration and multiple recurrence periods are selected for the risk index analysis of the disaster causing factor.
The urban rainstorm waterlogging risk assessment method based on the SWMM model has the following beneficial effects:
the method comprises the steps that 1, influences of climate change and urbanization level on rainfall flood are considered at the same time, an urban rainstorm waterlogging risk assessment method comprehensively considering the climate change and the urbanization development level is constructed, technical support is provided for flood control and waterlogging drainage management of regional roads, bridges, tunnels, waterlogging risk points and low-lying and waterlogging-prone regions, a model assessment result can well provide guidance for urban waterlogging risk management and control and emergency plan formulation, the urban flood risk management level can be improved comprehensively, urban updating is facilitated, industrial layout upgrading is achieved, and urban high-quality development is assisted;
2, the method is particularly suitable for plain district small watershed and urban area with detailed pipe network data.
Drawings
FIG. 1 is a risk evaluation model of urban inland inundation disaster according to the present invention;
FIG. 2 is a flow chart of an example of urban rainstorm waterlogging risk assessment based on the SWMM model according to the present invention;
Detailed Description
In the case of the example 1, the following examples are given,
as shown in fig. 1 to 2, a method for assessing risk of urban rainstorm waterlogging based on SWMM model includes the following steps:
step S1: basic data collection and processing:
s101, collecting relevant basic data of the research area, wherein the relevant basic data comprises the following steps: the method comprises the following steps of (1) researching area range boundaries, Digital Elevation Model (DEM) data, rainfall site coordinate positions and corresponding rainfall observation time sequences, evaporation amount observation time sequences in a researching area, researching area land utilization data, researching area pipe network data, researching area water system distribution, researching area river channel water conditions and river channel terrain data;
s102, collecting data of hydraulic engineering in a research area, comprising the following steps: engineering position and engineering scale;
s103, performing format processing on the collected data: generalizing a pipe network and a water system, cutting and distributing land utilization, and extracting river cross sections according to river landforms; processing the various sequence data into a format recognizable by the model;
step S2: constructing an SWMM model and developing parameter calibration:
dividing sub-catchment areas based on the pipe network, land utilization, DEM, rainfall and evaporation basic data collected and preprocessed in S1, constructing a SWMM pipe network runoff model skeleton, and using rainfall data as drive calibration model parameters;
step S3: climate change and urbanization level scenarios:
s301, selecting multiple GCMs output data in CMIP6 to construct a climate change scene SSP-RCP, wherein the scene comprises population, economic development, an ecosystem, resources, a system and social factors and also comprises the effort measures for slowing down, adapting and coping with climate change in the future;
preferably, 3 future scenarios, namely SSP1-2.6, SSP2-4.5 and SSP5-8.5 in the ScenarioMIP sub-plan are selected for simulation; wherein SSP1-2.6 is the updated scene of RCP2.6 scene in CMIP5 in CMIP6, under SSP1-2.6 scene, the land use type changes violently, especially the global forest area increases significantly, the scene can reflect the comprehensive influence of low social vulnerability, low slow-down pressure and low radiation compelling; SSP2-4.5 is the updated RCP4.5 scene in CMIP5 in CMIP6, which represents the combination of moderate social vulnerability and moderate radiation forcing; SSP5-8.5 is the scene of CMIP5 after RCP8.5 scene is updated in CMIP6, SSP5 is the only shared socioeconomic path that can realize artificial radiation compelling to reach 8.5W/m2 in 2100 years;
s302, according to the urbanization development situation, different SSP-USM parameters are set in combination with a research area overall planning scheme to simulate the urban sustainable development level under different development conditions;
preferably, to assess the potential impact of urbanization and further understand the interaction of climate change and land utilization change in Urban rainfall flood Management (USM), some studies set up an Urban development scenario (called SSP-USM) whose considerations include population, land utilization, and technologies and policies directly related to Urban rainfall flood Management; the method preferably selects three SSP-USM scenes as examples for simulation, and the three SSP-USM scenes are respectively named as SSPa, SSPb and SSPc to reflect the urban development states under high sustainability, medium sustainability and low sustainability; by referring to the existing research, the main characteristics of urbanization under the three SSP-USM scenes are obtained:
the city is in a high sustainable development state under the SSPa scene, and the development is rapid and reasonable: the situation generally assumes that the development of scientific and technical and educational levels is rapid, the social development mode is environment-friendly, and the population growth is relatively slow; under the condition, the city planning is proper, and more LID measures are adopted to manage the urban rainfall flood;
the sustainability of city development under the SSPc scenario is low, and the city faces severe challenges in all aspects: under the situation, the city development is relatively slow, the investment on technology, education and human capital is less or even almost zero, and no additional LID measures are provided;
the SSPb scenario is an intermediate state of SSPa and SSPc: setting an SSPb scenario representing an intermediate state of SSPa and SSPc based on a prediction of population growth, social development and technical revolution trends in the past decades, the sustainable development level being moderate;
s303, constructing a specific scene which comprehensively reflects the climate change and the urbanization, and coupling the SSP-RCP scene with the SSP-USM scene; naming the coupled scene as SSPx-y, where x refers to a specific SSP-RCP scene and y refers to a specific SSP-USM scene; simultaneously selecting a scene without considering the influence of climate change and urbanization as an original scene;
step S4: and (3) evaluating the risk of rainstorm waterlogging disasters: inputting the comprehensive scene SSPx-y of the climate change and the urbanization level constructed in the S3 into the SWMM model constructed in the S2 as a boundary for simulation, calculating the evolution process of urban rainstorm waterlogging under different situations, and finally finishing the risk evaluation of the climate change and the urbanization on the urban waterlogging disaster; the specific method comprises the following steps:
s401, constructing an inland inundation disaster risk evaluation model according to an SMI-P method, wherein the evaluation model is divided into a target layer, a standard layer and an index layer; the target layer is an inland inundation disaster risk index, and the criterion layer comprises a disaster causing factor, a pregnant disaster environment and a disaster bearing body; the risk indexes of the disaster-causing factors comprise total overflow amount, overflow duration and maximum overflow amount; the vulnerability indexes of the pregnant disaster environment comprise terrain elevation and terrain standard deviation; the vulnerability index of the disaster-bearing body comprises the land utilization type and the construction completion degree of the sponge city;
s402, carrying out single-index quantization standardization processing on the selected various indexes;
s403, obtaining subjective weight through an analytic hierarchy process, obtaining objective weight through an entropy weight process, and then obtaining the weight of the index; after the weight calculation is finished, adding the risk quantification results of all indexes according to the weight to obtain a risk index of the evaluation object; the evaluation principle is as follows:
in the formula, R is an urban inland inundation disaster risk index, Hi, Ej and Sk are indexes i, j and k selected when H (disaster-causing factor risk index), E (pregnant disaster environment vulnerability index) and S (disaster-bearing body vulnerability index) are calculated respectively, XHi, XEj and XSk are corresponding index weights, and A, B, C is an index weight corresponding to H, E, S respectively;
according to the difference of urban inland inundation disaster risk indexes (R), the risk grades are divided into five grades: low risk, [0,0.2 ]; lower risk, (0.2,0.4 ]; intermediate risk, (0.4,0.6 ]; higher risk (0.6,0.8] and high risk (0.8, 1).
Further, in the step S402, the piecewise linear membership function is preferably used as the method for quantizing the single index, and in the selected index, different functions are adopted for normalization of the forward and reverse indexes:
example 2:
the specific method for constructing and developing the SWMM model in the step S2 to calibrate the parameters is as follows:
s201, generalization of pipe networks and sub-catchment areas: determining a research area range according to river network water system characteristics and a catchment area, extracting pipe network and drainage node information in the research area range by combining drainage system planning information in the research area, acquiring a simulated sub-catchment area of the research area based on a Thiessen polygon method according to a pipe network topological relation, and determining a corresponding relation between the sub-catchment area and a catchment node;
s202, assigning: according to the elevation of the pipe network and the DEM, carrying out elevation assignment on each node in the pipeline, and determining the upstream and the downstream of the pipeline; assigning the land types to each sub-catchment area according to the land utilization division result, and calculating the area proportion of the land types in each sub-catchment area; assigning the soil type to each sub-catchment area according to the soil type division result, determining the soil type of each sub-catchment area, and calculating the soil infiltration coefficient of each sub-catchment area; determining the outlet of the model, namely an output boundary according to the range of the catchment area and the distribution of the water outlets of the pipe network;
s203, construction and verification of the SWMM pipe network runoff model: importing the information of the assigned pipe network, the sub-catchment areas and the nodes into SWMM model software, defining parameters of the sub-catchment areas and the pipelines in the SWMM software, using rainfall data and watershed evaporation data of a rainfall station as a drive to realize the operation of the model, comparing data of an output boundary with measured data, and finishing parameter calibration and model verification; the calibration parameters comprise pipe network runoff model parameters of pipeline roughness, sub-catchment area characteristic width, water impermeability runoff coefficient, water permeability runoff coefficient, water impermeability depression water storage capacity, water permeability depression water storage capacity and sub-catchment area Manning coefficient.
Further, the specific method of the thiessen polygon method is as follows: determining nodes and a catchment area according to pipe network information, taking drainage nodes as discrete points, performing neighborhood Analysis by using ArcGIS to complete division of Thiessen polygons (Toolbox-Analysis Tools-maximum-Create Thiessen Polygon), cutting according to the catchment area to generate a simulation sub-catchment area (Toolbox-Analysis Tools-Extract-Clip) of a research area, and finally manually adjusting the sub-catchment area.
Further, in the data acquisition of the disaster causing factor in step S401, a rainstorm design is performed in advance, the rainstorm intensity is calculated through a local rainstorm intensity calculation formula, a suitable rain type is selected to perform rainfall distribution on the designed rainstorm, and a rainstorm peak coefficient, a rainfall duration and multiple recurrence periods are selected for the risk index analysis of the disaster causing factor.
The above-described embodiments are merely preferred embodiments of the present invention, and should not be construed as limiting the present invention, and features in the embodiments and examples in the present application may be arbitrarily combined with each other without conflict. The protection scope of the present invention is defined by the claims, and includes equivalents of technical features of the claims. I.e., equivalent alterations and modifications within the scope hereof, are also intended to be within the scope of the invention.
Claims (2)
1. A city rainstorm waterlogging risk assessment method based on an SWMM model is characterized by comprising the following steps: it comprises the following steps:
step S1: basic data collection and processing:
s101, collecting relevant basic data of the research area, wherein the relevant basic data comprises the following steps: the method comprises the following steps of (1) researching area range boundaries, Digital Elevation Model (DEM) data, rainfall site coordinate positions and corresponding rainfall observation time sequences, evaporation amount observation time sequences in a researching area, researching area land utilization data, researching area pipe network data, researching area water system distribution, researching area river channel water conditions and river channel terrain data;
s102, collecting data of hydraulic engineering in a research area, comprising the following steps: engineering position and engineering scale;
s103, performing format processing on the collected data: generalizing a pipe network and a water system, cutting and distributing land utilization, and extracting river cross sections according to river landforms; processing the various sequence data into a format recognizable by the model;
step S2: constructing an SWMM model and developing parameter calibration:
dividing sub-catchment areas based on the pipe network, land utilization, DEM, rainfall and evaporation basic data collected and preprocessed in S1, constructing a SWMM pipe network runoff model skeleton, and using rainfall data as drive calibration model parameters;
step S3: climate change and urbanization level scenarios:
s301, selecting multiple GCMs output data in CMIP6 to construct a climate change scene SSP-RCP, wherein the scene comprises population, economic development, an ecosystem, resources, a system and social factors and also comprises the effort measures for slowing down, adapting and coping with climate change in the future;
s302, according to the urbanization development situation, different SSP-USM parameters are set in combination with a research area overall planning scheme to simulate the urban sustainable development level under different development conditions;
s303, constructing a specific scene which comprehensively reflects the climate change and the urbanization, and coupling the SSP-RCP scene with the SSP-USM scene; naming the coupled scene as SSPx-y, where x refers to a specific SSP-RCP scene and y refers to a specific SSP-USM scene; simultaneously selecting a scene without considering the influence of climate change and urbanization as an original scene;
step S4: and (3) evaluating the risk of rainstorm waterlogging disasters: inputting the comprehensive scene SSPx-y of the climate change and the urbanization level constructed in the S3 into the SWMM model constructed in the S2 as a boundary for simulation, calculating the evolution process of urban rainstorm waterlogging under different situations, and finally finishing the risk evaluation of the climate change and the urbanization on the urban waterlogging disaster; the specific method comprises the following steps:
s401, constructing an inland inundation disaster risk evaluation model according to an SMI-P method, wherein the evaluation model is divided into a target layer, a standard layer and an index layer; the target layer is an inland inundation disaster risk index, and the criterion layer comprises a disaster causing factor, a pregnant disaster environment and a disaster bearing body; the risk indexes of the disaster-causing factors comprise total overflow amount, overflow duration and maximum overflow amount; the vulnerability indexes of the pregnant disaster environment comprise terrain elevation and terrain standard deviation; the vulnerability index of the disaster-bearing body comprises the land utilization type and the construction completion degree of the sponge city;
s402, carrying out single-index quantization standardization processing on the selected various indexes;
s403, obtaining subjective weight through an analytic hierarchy process, obtaining objective weight through an entropy weight process, and then obtaining the weight of the index; after the weight calculation is finished, adding the risk quantification results of all indexes according to the weight to obtain a risk index of the evaluation object; the evaluation principle is as follows:
in the formula, R is an urban inland inundation disaster risk index, Hi, Ej and Sk are indexes i, j and k selected when H (disaster-causing factor risk index), E (pregnant disaster environment vulnerability index) and S (disaster-bearing body vulnerability index) are calculated respectively, XHi, XEj and XSk are corresponding index weights, and A, B, C is an index weight corresponding to H, E, S respectively;
according to the difference of urban inland inundation disaster risk indexes (R), the risk grades are divided into five grades: low risk, [0,0.2 ]; lower risk, (0.2,0.4 ]; intermediate risk, (0.4,0.6 ]; higher risk (0.6,0.8] and high risk (0.8, 1).
2. The method for assessing the risk of urban rainstorm waterlogging based on SWMM model according to claim 1, wherein: the specific method for constructing and developing the parameter calibration of the SWMM model in the step S2 is as follows:
s201, generalization of pipe networks and sub-catchment areas: determining a research area range according to river network water system characteristics and a catchment area, extracting pipe network and drainage node information in the research area range by combining drainage system planning information in the research area, acquiring a simulated sub-catchment area of the research area based on a Thiessen polygon method according to a pipe network topological relation, and determining a corresponding relation between the sub-catchment area and a catchment node;
s202, assigning: according to the elevation of the pipe network and the DEM, carrying out elevation assignment on each node in the pipeline, and determining the upstream and the downstream of the pipeline; assigning the land types to each sub-catchment area according to the land utilization division result, and calculating the area proportion of the land types in each sub-catchment area; assigning the soil type to each sub-catchment area according to the soil type division result, determining the soil type of each sub-catchment area, and calculating the soil infiltration coefficient of each sub-catchment area; determining the outlet of the model, namely an output boundary according to the range of the catchment area and the distribution of the water outlets of the pipe network;
s203, construction and verification of the SWMM pipe network runoff model: importing the information of the assigned pipe network, the sub-catchment areas and the nodes into SWMM model software, defining parameters of the sub-catchment areas and the pipelines in the SWMM software, using rainfall data and watershed evaporation data of a rainfall station as a drive to realize the operation of the model, comparing data of an output boundary with measured data, and finishing parameter calibration and model verification; the calibration parameters comprise pipe network runoff model parameters of pipeline roughness, sub-catchment area characteristic width, water impermeability runoff coefficient, water permeability runoff coefficient, water impermeability depression water storage capacity, water permeability depression water storage capacity and sub-catchment area Manning coefficient.
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