CN115859676B - Multi-level urban waterlogging coupling simulation method considering climate factors - Google Patents
Multi-level urban waterlogging coupling simulation method considering climate factors Download PDFInfo
- Publication number
- CN115859676B CN115859676B CN202211665462.6A CN202211665462A CN115859676B CN 115859676 B CN115859676 B CN 115859676B CN 202211665462 A CN202211665462 A CN 202211665462A CN 115859676 B CN115859676 B CN 115859676B
- Authority
- CN
- China
- Prior art keywords
- rainwater
- data
- flow
- water
- model
- Prior art date
- Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
- Active
Links
- 238000000034 method Methods 0.000 title claims abstract description 83
- 230000008878 coupling Effects 0.000 title claims abstract description 37
- 238000010168 coupling process Methods 0.000 title claims abstract description 37
- 238000005859 coupling reaction Methods 0.000 title claims abstract description 37
- 238000004088 simulation Methods 0.000 title claims abstract description 23
- XLYOFNOQVPJJNP-UHFFFAOYSA-N water Substances O XLYOFNOQVPJJNP-UHFFFAOYSA-N 0.000 claims abstract description 62
- 230000008569 process Effects 0.000 claims abstract description 35
- 238000004364 calculation method Methods 0.000 claims abstract description 18
- 238000004519 manufacturing process Methods 0.000 claims abstract description 14
- 238000009826 distribution Methods 0.000 claims abstract description 12
- 230000008020 evaporation Effects 0.000 claims description 33
- 238000001704 evaporation Methods 0.000 claims description 33
- 239000002352 surface water Substances 0.000 claims description 22
- 230000008859 change Effects 0.000 claims description 19
- 238000004422 calculation algorithm Methods 0.000 claims description 10
- 238000007689 inspection Methods 0.000 claims description 9
- 238000011160 research Methods 0.000 claims description 9
- 238000003860 storage Methods 0.000 claims description 7
- 230000001133 acceleration Effects 0.000 claims description 6
- 239000002689 soil Substances 0.000 claims description 6
- 230000000694 effects Effects 0.000 claims description 5
- 230000008595 infiltration Effects 0.000 claims description 5
- 238000001764 infiltration Methods 0.000 claims description 5
- 238000009825 accumulation Methods 0.000 claims description 4
- 238000012937 correction Methods 0.000 claims description 4
- 230000035699 permeability Effects 0.000 claims description 4
- 230000005855 radiation Effects 0.000 claims description 4
- 238000001035 drying Methods 0.000 claims description 3
- 230000002068 genetic effect Effects 0.000 claims description 3
- 238000012821 model calculation Methods 0.000 claims description 3
- 230000009467 reduction Effects 0.000 claims description 3
- 238000010206 sensitivity analysis Methods 0.000 claims description 3
- 238000005516 engineering process Methods 0.000 abstract description 2
- 238000001556 precipitation Methods 0.000 description 9
- 238000010586 diagram Methods 0.000 description 4
- 230000005484 gravity Effects 0.000 description 2
- 238000007726 management method Methods 0.000 description 2
- 238000012544 monitoring process Methods 0.000 description 2
- 239000010865 sewage Substances 0.000 description 2
- 238000012952 Resampling Methods 0.000 description 1
- 230000009471 action Effects 0.000 description 1
- 230000009286 beneficial effect Effects 0.000 description 1
- 230000000903 blocking effect Effects 0.000 description 1
- 238000009933 burial Methods 0.000 description 1
- 238000009833 condensation Methods 0.000 description 1
- 230000005494 condensation Effects 0.000 description 1
- 238000010276 construction Methods 0.000 description 1
- 230000007547 defect Effects 0.000 description 1
- 238000011161 development Methods 0.000 description 1
- 238000007599 discharging Methods 0.000 description 1
- 239000000428 dust Substances 0.000 description 1
- 239000005431 greenhouse gas Substances 0.000 description 1
- 239000003673 groundwater Substances 0.000 description 1
- 238000012423 maintenance Methods 0.000 description 1
- 230000014759 maintenance of location Effects 0.000 description 1
- 239000000463 material Substances 0.000 description 1
- 238000010309 melting process Methods 0.000 description 1
- 238000012986 modification Methods 0.000 description 1
- 230000004048 modification Effects 0.000 description 1
- 239000011148 porous material Substances 0.000 description 1
- 230000001737 promoting effect Effects 0.000 description 1
- 239000002912 waste gas Substances 0.000 description 1
Landscapes
- Management, Administration, Business Operations System, And Electronic Commerce (AREA)
Abstract
The invention provides a multi-level urban waterlogging coupling simulation method taking climate factors into consideration, which aims to couple a climate module, a surface production converging module and a pipe network converging module to simulate and forecast urban waterlogging process from multiple levels completely and accurately, and comprises the following steps: acquiring climate data of a target area; calculating the space rainfall distribution condition by utilizing the punctiform data of the rainfall station; modeling the urban surface production converging process by adopting a hydrodynamics model based on raster data; calculating the confluence of the surface yield; and (5) converging calculation and coupling water depth calculation of the rainwater pipe network. The invention combines GIS theory and technology, deeply analyzes the multi-module coupling model principle of geographical climate factors, earth surface overflow calculation, pipe network convergence calculation and the like, and more accurately characterizes the space-time evolution of real city storm waterlogging disaster events by establishing a scientific and reasonable model structure.
Description
Technical Field
The invention belongs to the technical field of urban waterlogging in the field of hydrologic water resources, and particularly relates to a multi-level urban waterlogging coupling simulation method considering climate elements.
Background
In recent years, global extreme rainfall weather is frequent, and rainstorm waterlogging brings great threat to life, property and social and economic activities of people. Especially, in this century, the climate change caused by the increase of greenhouse gases causes the change of air temperature space-time distribution and global precipitation, the hydrologic cycle elements are continuously changed in various scales, and the frequent occurrence of flood events obviously affects the sustainable development of human society.
Aiming at the problem of urban storm and waterlogging disasters, partial scholars at home and abroad reform the traditional model to adapt to the urban complex geospatial environment, and partial scholars independently develop the model based on new theoretical results. For example, the domestic scholars Wu Xiaodan analyze the principle of storm water accumulation in a central urban area of a certain city based on the simulation result of the storm flood management model Storm Water Management Model (SWMM). SWMM has the advantages of simple concept, capability of simulating drainage facilities and the like, but cannot give out the inundation condition of urban ground surfaces; the one-way coupling simulation of the surface pipeline taking pipe network overflow as main driving can give out the inundation distribution condition of overflow water on land, but the inundation distribution condition is difficult to truly simulate urban inundation ponding and surface runoff, and the coupling models do not consider climate elements to carry out deviation correction, so that the numerical value of the climate elements in a simulation area can not be effectively captured.
Disclosure of Invention
In order to solve the problems, the invention discloses a multi-level urban waterlogging coupling simulation method taking climate elements into consideration, which is used for coupling a climate module, a land production converging module and a pipe network converging module to simulate an urban waterlogging process more completely and accurately from multiple levels.
In order to achieve the above purpose, the technical scheme of the invention is as follows:
a multi-level urban waterlogging coupling simulation method considering climate factors comprises the following steps:
s1: inputting longitude and latitude coordinates, and acquiring climate data of the region from a world climate research plan (World Climate Research Programme) coupling model mutual comparison project (Coupled Model Intercomparison Project) file;
s2: calculating the space rainfall distribution condition by utilizing the punctiform data of the rainfall station;
s3: modeling an urban surface production converging process by adopting a hydrologic hydrodynamic model based on raster data;
s4: calculating the confluence of the surface yield;
s5: converging and calculating a rainwater pipe network;
s6: and (5) calculating the coupling water depth.
Further, the step S1 includes the steps of:
s11: the temperature data is based on Parameter-altitude regression Parameter-elevation Regressions on Independent Slopes Model (PRISM) observations of the independent grade model, and if PRISM data is not available, a global land data assimilation system Global Land Data Assimilation System (GLDAS) is used. If GLDAS data is not available, the nearest grid cell with available GLDAS data is used;
s12: the evaporation value is determined by the Hargreaves method, and the evaporation value E (in mm/day) determines the drying rate of the land surface and soil during urban stormwater:
in the above, R a Is externally radiated water equivalent (unit is MJm -2 d -1 ) Lambda represents the latent heat of evaporation (in MJkg -1 )，T r Average daily temperature range (in degrees celsius) representing a period of time, T a Represents oneAverage daily temperature (in degrees celsius) over time.
External radiation water equivalent R a Calculated by the following formula:
in the above, d r To relative earth-day distance, w s Represents the sunset angle (radian system),representing latitude, delta representing solar declination;
s13: after determining the historical month evaporation rate, applying temperature adjustment to historical temperature data of the simulated scene, and calculating the adjusted month evaporation rate by using a Hargreaves equation;
s14: the change in the month evaporation value is calculated by comparing the adjusted month evaporation value with the historical month evaporation value, thereby determining the climate month change coefficient (temperature change coefficient and evaporation value change coefficient).
Further, the step S3 includes the steps of:
s31: carrying out urban earth surface partial modeling work of a research area;
s32: building a surface grid unit based on the space discrete grid;
s33: based on raster data, modeling an urban surface production converging process by adopting a hydrological hydrodynamic model, and inputting parameter information such as initial water loss, elevation information (DEM), infiltration rate, initial water depth, manning coefficient and the like into a raster unit;
s34: and (3) adding the climate month change coefficient of the specific position in the step S1 into the INPUT file of the INPUT so as to adjust the air temperature and the evaporation rate of the grid unit in the area.
Further, the step S4 includes the steps of:
s41: calculating earth surface production flow, namely coupling rainfall with an earth surface module;
s42: primary and post-loss processes.
Further, the step S42 includes the following steps:
s421: the permeability and interception capacity of the land utilization type data to the surface grid are digitized;
s422: according to the earth surface type classification standard, the land utilization type data and vector data such as lakes, roads, rivers and the like are overlapped and resampled to obtain an earth surface grid classification result;
s423: and simulating the primary loss and the post loss processes in each surface grid unit by using hydrologic knowledge, digitizing the rainwater interception quantity by using a box model, and quantifying the underwater seepage quantity of the rainwater by using a stable seepage model.
S43: and (5) calculating surface confluence.
The method for calculating the confluence of the urban ground surface by adopting the diffuse wave method of the two-dimensional Save Vietnam equation set is as follows:
in the above formula, X and Y respectively represent the distances in the X direction and the Y direction under a plane rectangular coordinate system; h represents the depth of surface water; t represents time; j and K represent the single wide flow in the X direction and the Y direction; g represents gravitational acceleration, z represents surface water level, i.e. water depth and surface elevation accumulation; u and v represent components of the flow velocity vector in the X-direction and Y-direction (vertical direction); n represents the Manning roughness coefficient.
Solving the surface confluence hydrodynamic model by using a hidden-format finite difference method, thereby calculating the flow size and direction between adjacent grid units, and updating the water depth on the grid units according to the flow in different directions;
s44: and (3) adopting a model parameter calibration method of multiple calculation and value selection, and optimizing parameter combinations according to simulation results.
Further, the step S5 includes the following steps:
s51: establishing a rainwater pipe network model of a research area;
s52: in the pipe network element traversing process, combining a breadth-first traversing algorithm of the directed graph to correct the pipe network data with the attribute and the topology information in error;
s53: and (5) converging and calculating the rainwater pipe network.
Modeling a rainwater pipe network converging process by adopting a Pressimann virtual narrow seam method and a non-constant Santa-Venan equation, wherein a specific calculation equation is as follows:
in the above formula, M represents the cross-sectional area of the pipeline; n represents the width of the virtual narrow slit; t represents time; q represents the flow rate of the section of the pipeline; u is the lateral boundary input flow rate based on the pipe direction; q represents lateral boundary flow; x represents distance (in the direction of the pipe); a is a correction coefficient for momentum; g represents gravitational acceleration; y represents the position head; s is S f Is the friction gradient of the pipeline.
Solving the rainwater pipe network converging model by adopting a display format algorithm, and acquiring pipe network hydrodynamic parameters and geometric forms from S51 rainwater pipe network model data;
s54: the calibration of model parameters in the rainwater pipe network converging process is only carried out aiming at more sensitive parameters, so that the dimension reduction effect is achieved, and the model calculation efficiency is greatly improved. The invention adopts Morris method to carry out global sensitivity analysis to select sensitive parameters;
s55: and (5) carrying out full-automatic calibration on the sensitive parameters in the step S54 by adopting a genetic algorithm.
Further, the step S6 includes the following steps:
s61: and (5) coupling the rainwater grate with the ground surface, and calculating the coupling flow of the rainwater grate. There are three processes when the rainwater grate exchanges flow with the surface water:
s611: when the rainwater grate is in a water storage state and the surface water storage depth is shallow, calculating the flow of the surface water collected into the rainwater grate by using a weir flow formula;
s612: when the depth of the surface water is gradually increased but the rainwater grate is not full of water, calculating the flow of the surface water which is converged into the rainwater grate by using an orifice outflow formula;
s613: when the rainwater grate well is full, the rainwater grate well becomes a pressurized flow state, and the flow direction can be inward or outward (specifically according to the pressure distribution in the grate well).
S62: the rainwater inspection well is coupled with the ground surface, and the overflow quantity of the rainwater well is calculated;
s63: the rainwater outlet is coupled with the ground surface, and the water discharge of the water outlet is calculated;
s64: combining expert experience, adopting a model parameter calibration method of multiple value selection and multiple calculation, and calibrating the orifice outflow coefficient and the weir flow coefficient at the coupling node according to the simulation result;
s65: and (3) coupling the climate month change coefficient in the INPUT file, and adjusting the temperature and the evaporation rate of the area so as to update the grid water depth and the pipeline node flow.
The beneficial effects of the invention are as follows:
1. by combining GIS theory and technology, the principle of a multi-module coupling model such as climate factors, earth surface overflow calculation, pipe network convergence calculation and the like is deeply analyzed, and the hydrologic process under the urban storm waterlogging scene is more completely expressed by establishing a scientific and reasonable model structure;
2. the method overcomes the defect that urban flood simulation results are inaccurate due to the fact that only a single factor is considered, brings climate change prediction into a coupling model, comprehensively utilizes multi-source geographic information data and hydrologic climate data, more accurately characterizes space-time evolution of actual urban storm and waterlogging disaster events, improves accuracy and timeliness of the storm and waterlogging simulation results, and accordingly supports sponge urban construction and flood control and drainage decisions of urban waterlogging-prone areas.
Drawings
FIG. 1 is a block diagram of a method of the present invention;
FIG. 2 is a diagram of the surface grid cell attributes of the present invention;
FIG. 3 is a vector data diagram of the rainwater pipe network of the present invention;
FIG. 4 is a schematic view of the invention with ground water entering the rain grate;
fig. 5 is a diagram of a simulation result of a rainstorm waterlogging water depth of a certain monitoring station according to an embodiment of the invention.
Detailed Description
The present invention is further illustrated in the following drawings and detailed description, which are to be understood as being merely illustrative of the invention and not limiting the scope of the invention.
Taking a certain area as an example, the rainwater drainage pipe network in the area is relatively independent, and the rainwater drainage pipe network has various water storage units such as artificial ditches, lakes and the like. Therefore, the region in this embodiment can be determined to belong to the urban river basin with a relatively clear boundary condition. The climate types of the example areas are the wind moist climate in the northern subtropical season, the season is clear, the spring, the autumn, the winter and the summer are long, the average air temperature is 15℃, the relative humidity is 76%, the average rainfall reaches 1106 mm, and the average rainfall is 110 days throughout the year. The area has a plurality of wind and rain, and the precipitation process has short time and high strength.
In addition, because the urban area has high urban degree and high surface temperature in summer, the automobile exhaust and the air conditioner heat the bottom atmosphere, the building with ultra-high density blocks the atmospheric circulation, deeper air clusters are formed above the area, more condensation nuclei above the area are caused by waste gas pollution and engineering dust, the effect of promoting warm cloud precipitation is achieved, conditions are created for the occurrence of storm weather, and storm water logging disasters are extremely easy to cause.
As shown in fig. 1, the embodiment of the invention discloses a multi-level urban waterlogging coupling simulation method considering climate elements, which mainly comprises the following steps:
s1: inputting longitude and latitude coordinates, and acquiring climate data of the region from a world climate research plan (World Climate Research Programme) coupling model mutual comparison project (Coupled Model Intercomparison Project) file;
s2: and calculating the space rainfall distribution condition of the embodiment area by using the punctiform data of the rainfall station. In this embodiment, there are two rain stations, and the main information of the rain station data includes geographical coordinates, a name of the rain station, a monitoring period, and rainfall in the period.
The specific methods for calculating the space rainfall distribution condition are as follows:
calculating the average precipitation quantity observed by the rain gauge in the same time period in the simulation area by using an arithmetic average method, dividing the average precipitation quantity by the total time to obtain the effective precipitation strength of the simulation area in the time period;
drawing a rainfall contour line according to the point rainfall data by using an equal rainfall line method, obtaining regional rainfall data by using a spatial interpolation algorithm, and dividing the regional rainfall data by the total time to obtain effective rainfall intensity in an observation period;
dividing the simulation area into a plurality of polygons by utilizing a Thiessen polygon method, wherein a rainfall measuring station is positioned at the gravity center of each polygon, and dividing the measured rainfall by time to obtain the effective rainfall intensity in each polygon;
s3: modeling an urban surface production converging process by adopting a hydrologic hydrodynamic model based on raster data;
s4: calculating the confluence of the surface yield;
s5: converging and calculating a rainwater pipe network;
s6: and (5) calculating the coupling water depth.
Further, the step S1 includes the steps of:
s11: the temperature data is based on Parameter-altitude regression (Parameter-elevation Regressions on Independent Slopes Model, PRISM) observations of the independent grade model, and if PRISM data is not available, a global land data assimilation system (Global Land Data Assimilation System, GLDAS) is used. If GLDAS data is not available, the nearest grid cell with available GLDAS data is used. The air temperature is used for simulating the snow melting process and also influencing the evaporation value;
s12: the evaporation value is determined by the Hagrives method, and the evaporation value E (in mm/day) determines the drying rate of the land surface and soil during urban stormwater:
in the above, R a Is externally radiated water equivalent (unit is MJm -2 d -1 ) Lambda represents the latent heat of evaporation (in MJkg -1 )，T r Average daily temperature range (in degrees celsius) representing a period of time, T a Average daily temperature (in degrees celsius) is represented for a period of time.
The lowest and highest daily air temperatures are found at all sites where precipitation data is available. Where possible, PRISM was used as the daily temperature source. When not available, GLDAS data is used. If GLDAS data is not available, the nearest grid cell with available GLDAS data is used. External radiation water equivalent R a Calculated by the following formula:
in the above, d r To relative earth-day distance, w s Represents the sunset angle (radian system),representing latitude, delta representing solar declination;
s13: after determining the historical month evaporation rate, applying temperature adjustment to historical temperature data of the simulated scene, and calculating the adjusted month evaporation rate by using a Hargreaves equation;
s14: the change in the month evaporation value is calculated by comparing the adjusted month evaporation value with the historical month evaporation value, thereby determining the climate month change coefficient (temperature change coefficient and evaporation value change coefficient).
Further, the step S3 includes the steps of:
s31: the embodiment city surface is modeled. In order to develop the modeling work of the urban earth surface part of the embodiment region, the invention acquires the geographic space data such as land utilization type, remote sensing image, building and river network water system distribution and the like from the urban planning and natural resource bureau, and adopts a WGS-84 coordinate system, and when adopting a plane coordinate system, map projection uniformly adopts UTM zone 50N projection. The main geographic information data are shown in table 1:
table 1: basic geographic information data:
s32: building a surface grid unit based on the space discrete grid;
s33: modeling an urban surface production converging process by adopting a hydrological hydrodynamic model based on raster data, and inputting parameter information such as initial water loss, elevation information (DEM), infiltration rate, initial water depth, manning coefficient and the like into a raster unit, as shown in figure 2;
s34: and (3) adding the climate month change coefficient of the specific position in the step S1 into the INPUT file of the INPUT so as to adjust the air temperature and the evaporation rate of the grid unit in the area.
Further, the step S4 includes the steps of:
s41: and calculating the surface production flow, namely coupling rainfall with a surface module. Calculating the accumulated rainfall in a period of time of the rainfall station data by adopting a uniform method, namely multiplying the calculation step length in a period of time by the effective rainfall intensity to obtain the rainfall and adding the rainfall to the surface grid unit in the step S3;
s42: primary and post-loss processes. The initial loss process after precipitation falls to the ground surface is caused by entrapment of hollow soil and vegetation, while the subsequent loss process is mainly represented by infiltration of rainwater into the soil through ground surface pores, and the residual precipitation after initial loss and subsequent loss is surface runoff. Urban underground is broadly classified into grasslands, bare lands, hard lands, and flat hard lands according to rainwater interception capability. The underlying surface may be divided into an impermeable surface, a semi-permeable surface, a permeable surface, and a strong permeable surface according to the soil permeability.
Further, the step S42 includes the following steps:
s421: the permeability and retention capacity of the land use type data to the surface grid are quantified. Considering the flow characteristics of the urban underlying surface comprehensively, the urban surface grid is divided into 8 types: grasslands, water bodies, farmlands, roads, commercial land, woodlands, wetlands and bare lands;
s422: according to the earth surface type classification standard, overlapping and resampling 5 m-precision land utilization type data and 3 m-precision vector data such as lakes, roads and rivers to obtain an earth surface grid classification result;
s423: and simulating the primary loss and the post loss processes in each surface grid unit by using hydrologic knowledge, digitizing the rainwater interception quantity by using a box model, and quantifying the underwater seepage quantity of the rainwater by using a stable seepage model.
The parameters of the initial and the final losses of the rainfall of the different grid units are shown in table 2:
table 2 values of parameters of post-primary loss for different types of grids:
s43: and (5) calculating surface confluence. The modeling concept of representing urban surfaces using regular grids is consistent with describing surface properties using grid data in GIS. The data structure of the grid is generic and easy to pair with other modules. The modeling of the surface confluence process based on regular grid data expression refers to calculating water quantity exchange among grid units by utilizing a water power method, namely simulating water flow movement under the action of gravity and structure blocking, and outputting a water depth distribution result consistent with the shape of a terrain grid. The method for calculating the confluence of the urban ground surface by adopting the diffuse wave method of the two-dimensional Save Vietnam equation set is as follows:
in the above formula, X and Y respectively represent the distances in the X direction and the Y direction under a plane rectangular coordinate system; h represents the depth of surface water; t represents time; j and K represent the single wide flow in the X direction and the Y direction; g represents gravitational acceleration, z represents surface water level, i.e. water depth and surface elevation accumulation; u and v represent components of the flow velocity vector in the X-direction and Y-direction (vertical direction); n represents the Manning roughness coefficient.
Solving the surface confluence hydrodynamic model by using a hidden-format finite difference method, thereby calculating the flow size and direction between adjacent grid units, and updating the water depth on the grid units according to the flow in different directions;
s44: and (3) adopting a model parameter calibration method of multiple calculation and value selection, and optimizing parameter combinations according to simulation results. Parameters required in the process of confluence of surface production mainly refer to Manning roughness coefficient, initial loss interception rainfall and infiltration rate. The final parameter calibration results of the surface production and confluence process are shown in Table 3:
table 3: surface yield confluence parameter calibration results:
further, the step S5 includes the following steps:
s51: and establishing a rainwater pipe network model of the embodiment area. The embodiment area has complete drainage system thematic data, and can be used for establishing a rainwater pipe network model in the embodiment area, including drainage nodes, drainage pipelines, pump stations and gate station information. The drainage nodes and the drainage pipelines are divided into rainwater and sewage. The drainage nodes comprise water outlets, inspection wells and grates, and the key attribute information is ground elevation, node codes, well depths, rain sewage types and accessories. Key attribute information for the drain pipe elements are length, material, code for start and end points, start and end point burial depths, cut-off shape and size. The attribute information of the pump station element only includes geographic coordinates and pump station type. The attribute information of the gate station includes only geographic coordinates. Vector data of each element of the rainwater pipe network are shown in figure 3;
s52: and in the pipe network element traversing process, combining a breadth-first traversing algorithm of the directed graph to correct the pipe network data with the attribute and topology information errors. After finishing traversing, correcting the traversed nodes, and extracting a rainwater pipeline, a rainwater outlet, a rainwater grate and a rainwater inspection well according to the data mark;
s53: and (5) converging and calculating the rainwater pipe network. The water body on the urban surface automatically flows to the low-lying position, enters a rainwater pipe network through a rainwater grate, and forms an upper boundary condition of a rainwater pipe network converging model by utilizing the calculated overflow or drainage of the coupling of the pipe network and the ground surface. The dynamic wave is adopted to calculate the hydraulic elements and the rainwater nodes of the pipeline after the rainwater enters the pipe network, and the flow state of the water flow in the pipeline is continuously switched between the open channel flow and the pressurized pipe flow after the rainwater enters the pipe network, so that the process of alternately changing the two flow states is smooth. Modeling a rainwater pipe network converging process by adopting a Pressimann virtual narrow seam method and a non-constant Santa-Venan equation, wherein a specific calculation equation is as follows:
in the above formula, M represents the cross-sectional area of the pipeline; n represents the width of the virtual narrow slit; q represents the flow rate of the section of the pipeline; u is the lateral boundary input flow rate based on the pipe direction; q represents lateral boundary flow; x represents distance (in the direction of the pipe); a is the correction system of momentumA number; g represents gravitational acceleration; y represents the position head; s is S f Is the friction gradient of the pipeline.
Solving the rainwater pipe network converging model by adopting a display format algorithm, and acquiring pipe network hydrodynamic parameters and geometric forms from S51 rainwater pipe network model data;
s54: the calibration of model parameters in the rainwater pipe network converging process is only carried out aiming at more sensitive parameters, so that the dimension reduction effect is achieved, and the model calculation efficiency is greatly improved. The invention adopts Morris method to carry out global sensitivity analysis to select sensitive parameters;
s55: and (5) carrying out full-automatic calibration on the sensitive parameters in the step S54 by adopting a genetic algorithm. Wherein the coefficient calibration results of the pipeline Manning roughness rate are shown in Table 4:
table 4: the rainwater pipe network converging parameter calibration results:
further, the step S6 includes the following steps:
s61: and (5) coupling the rainwater grate with the ground surface, and calculating the coupling flow of the rainwater grate. There are three processes when the gully grating exchanges flow with the surface water, as shown in fig. 4:
s611: when the rainwater grate is in a water storage state and the surface water storage depth is shallow, calculating the flow of the surface water collected into the rainwater grate by using a weir flow formula;
s612: when the depth of the surface water is gradually increased but the rainwater grate is not full of water, calculating the flow of the surface water which is converged into the rainwater grate by using an orifice outflow formula;
s613: when the rainwater grate well is full, the rainwater grate well becomes a pressurized flow state, and the flow direction can be inward or outward (specifically according to the pressure distribution in the grate well).
S62: and the rainwater inspection well is coupled with the ground surface, and the overflow quantity of the rainwater well is calculated. The rainwater inspection well is an auxiliary structure which is convenient for the maintenance of the urban rainwater pipe network, and only a small part of rainwater enters the rainwater pipe network through the inspection well when the urban rainstorm weather occurs. When the position water head of the surface grid is smaller than the total water head in the rainwater inspection well, water in the inspection well overflows to the surface through the gaps between the orifice and the well cover;
s63: the rainwater outlet is coupled with the ground surface, and the water discharge of the water outlet is calculated. In urban rainwater pipe network systems, part of the water outlets are not used for directly discharging water into rivers or lakes, but are discharged into surface grids, and in some embodiments, two-dimensional grids with initial water depths are used for expressing rivers, ponds and lakes. Therefore, the water outlet also needs to be coupled with the ground surface, namely, the drainage quantity of the water outlet is added to the grid where the water outlet is positioned;
s64: according to the method, expert experience is combined, a model parameter calibration method of multiple value selection and multiple calculation is adopted, orifice outflow coefficients and weir flow coefficients at the coupling nodes are calibrated according to simulation results, the orifice outflow coefficients calculated by coupling the rainwater pipe network and the ground surface in the embodiment are 0.42, and the weir flow coefficients are 0.21;
s65: and coupling the climate month change coefficient in the INPUT file to adjust the air temperature and the evaporation rate of the area so as to update the grid water depth and the pipeline node flow.
It should be noted that the foregoing merely illustrates the technical idea of the present invention and is not intended to limit the scope of the present invention, and that a person skilled in the art may make several improvements and modifications without departing from the principles of the present invention, which fall within the scope of the claims of the present invention.
Claims (3)
1. A multi-level urban waterlogging coupling simulation method considering climate elements is characterized by comprising the following steps:
s1: inputting longitude and latitude coordinates, and acquiring climate data of the region from a mutual comparison project file of a world climate research plan coupling model;
the method comprises the following steps:
s11: the temperature data in the regional climate data in S1 is based on the observations of Parameter-elevation Regressions on Independent Slopes Model, using Global Land Data Assimilation System if PRISM data is not available, and using the nearest grid cell with available GLDAS data if GLDAS data is not available;
s12: the evaporation value is determined by the Hargreaves method, and the evaporation value E, in mm/day, determines the drying rate of the land surface and soil during urban stormwater:
in the above, R a For external radiation water equivalent, the unit is MJm -2 d -1 Lambda represents the latent heat of evaporation in MJkg -1 ，T r Average daily temperature range in degrees celsius, T, representing a period of time a Represents the average daily temperature of a period of time, the unit is the degree centigrade, and the external radiation water equivalent weight R a Calculated by the following formula:
in the above, d r To relative earth-day distance, w s Represents the angle of sunset and the like,representing latitude, delta representing solar declination;
s13: after determining the historical month evaporation rate, applying temperature adjustment to historical temperature data of the simulated scene, and calculating the adjusted month evaporation rate by using a Hargreaves equation;
s14: comparing the adjusted month evaporation value with the historical month evaporation value, and calculating the change of the month evaporation value so as to determine the climate month change coefficient;
s2: calculating the space rainfall distribution condition by utilizing the punctiform data of the rainfall station;
s3: modeling an urban surface production converging process by adopting a hydrologic hydrodynamic model based on raster data;
the method comprises the following steps:
s31: carrying out urban earth surface partial modeling work of a research area;
s32: building a surface grid unit based on the space discrete grid;
s33: based on raster data, modeling an urban surface production converging process by adopting a hydrological hydrodynamic model, and inputting parameter information of initial water loss, elevation information, infiltration rate, initial water depth and Manning coefficient into a raster unit;
s34: adding the climate month change coefficient of the specific position in the step S1 into an INPUT INPUT file to adjust the air temperature and the evaporation rate of the grid unit in the area;
s4: calculating the confluence of the surface yield;
the method comprises the following steps:
s41: calculating earth surface production flow, namely coupling rainfall with an earth surface module;
s42: primary damage and post damage processes;
s43: computing surface confluence;
the method for calculating the confluence of the urban ground surface by adopting the diffuse wave method of the two-dimensional Save Vietnam equation set is as follows:
in the above formula, X and Y respectively represent the distances in the X direction and the Y direction under a plane rectangular coordinate system; h represents the depth of surface water; t represents time; j and K represent the single wide flow in the X direction and the Y direction; g represents gravitational acceleration, z represents surface water level, i.e. water depth and surface elevation accumulation; u and v represent components of the flow velocity vector in the X and Y directions; n represents a Manning roughness coefficient, solving the surface confluence hydrodynamic model by using a hidden-format finite difference method, thereby calculating the flow magnitude and direction between adjacent grid units, and updating the water depth on the grid units according to the flow in different directions;
s44: adopting a model parameter calibration method of multiple calculation and value selection, and optimizing parameter combinations according to simulation results;
s5: converging and calculating a rainwater pipe network;
the method comprises the following steps:
s51: establishing a rainwater pipe network model of a research area;
s52: in the pipe network element traversing process, combining a breadth-first traversing algorithm of the directed graph to correct the pipe network data with the attribute and the topology information in error;
s53: the rainwater pipe network converging calculation adopts a Pressimann virtual narrow seam method and a non-constant Santa-Venan equation to model a rainwater pipe network converging process, and a specific calculation equation is as follows:
in the above formula, M represents the cross-sectional area of the pipeline; n represents the width of the virtual narrow slit; q represents the flow rate of the section of the pipeline; u is the lateral boundary input flow rate based on the pipe direction; q represents lateral boundary flow; x represents the distance along the pipeline direction; a is a correction coefficient for momentum; g represents gravitational acceleration; y represents the position head; s is S f For friction gradient of the pipeline, solving the rainwater pipe network converging model by adopting a display format algorithm, and acquiring pipe network hydrodynamic parameters and geometry from S51 rainwater pipe network model dataMorphology;
s54: the calibration of model parameters in the rainwater pipe network converging process is only carried out aiming at more sensitive parameters, so that the dimension reduction effect is achieved, the model calculation efficiency is greatly improved, and the Morris method is adopted for carrying out global sensitivity analysis to select sensitive parameters;
s55: carrying out full-automatic calibration on the sensitive parameters in the S54 by adopting a genetic algorithm;
s6: coupled water depth calculation
The method comprises the following steps:
s61: the coupling flow of the rainwater grate and the ground surface is calculated, namely the rainwater grate and the surface water body are subjected to flow exchange;
s62: the rainwater inspection well is coupled with the ground surface, and the overflow quantity of the rainwater well is calculated;
s63: the rainwater outlet is coupled with the ground surface, and the water discharge of the water outlet is calculated;
s64: the orifice outflow coefficient and the weir flow coefficient at the coupling node are calibrated according to the simulation result by adopting a model parameter calibration method of multiple value selection and multiple calculation;
s65: and (3) coupling the climate month change coefficient in the INPUT file, and adjusting the temperature and the evaporation rate of the area so as to update the grid water depth and the pipeline node flow.
2. The method for simulating multi-level urban waterlogging coupling taking climate factors into consideration according to claim 1, wherein the S42 primary loss and post loss process comprises the following steps:
s421: the permeability and interception capacity of the land utilization type data to the surface grid are digitized;
s422: according to the earth surface type classification standard, the land utilization type data and vector data such as lakes, roads, rivers and the like are overlapped and resampled to obtain an earth surface grid classification result;
s423: and simulating the primary loss and the post loss processes in each surface grid unit by using hydrologic knowledge, digitizing the rainwater interception quantity by using a box model, and quantifying the underwater seepage quantity of the rainwater by using a stable seepage model.
3. The method for simulating multi-level urban inland inundation considering climate factors according to claim 1, wherein the step of exchanging the flow between the S61 rainwater grate and the surface water body comprises the following steps:
s611: when the rainwater grate is in a water storage state and the surface water storage depth is shallow, calculating the flow of the surface water collected into the rainwater grate by using a weir flow formula;
s612: when the depth of the surface water is gradually increased but the rainwater grate is not full of water, calculating the flow of the surface water which is converged into the rainwater grate by using an orifice outflow formula;
s613: when the rainwater grate well chamber is full, the rainwater grate well chamber is changed into a pressurized flow state.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202211665462.6A CN115859676B (en) | 2022-12-23 | 2022-12-23 | Multi-level urban waterlogging coupling simulation method considering climate factors |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202211665462.6A CN115859676B (en) | 2022-12-23 | 2022-12-23 | Multi-level urban waterlogging coupling simulation method considering climate factors |
Publications (2)
| Publication Number | Publication Date |
|---|---|
| CN115859676A CN115859676A (en) | 2023-03-28 |
| CN115859676B true CN115859676B (en) | 2024-01-12 |
Family
ID=85654327
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN202211665462.6A Active CN115859676B (en) | 2022-12-23 | 2022-12-23 | Multi-level urban waterlogging coupling simulation method considering climate factors |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN115859676B (en) |
Families Citing this family (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN117332702B (en) * | 2023-11-30 | 2024-03-29 | 南京师范大学 | Waterlogging water depth prediction and multi-factor time sequence analysis method based on ILSTM |
| CN117332544B (en) * | 2023-12-01 | 2024-02-20 | 南京师范大学 | Urban rainfall flood model modeling method by combining vector and grid hydrologic calculation unit |
| CN117892167B (en) * | 2024-03-15 | 2024-05-24 | 南京师范大学 | Urban waterlogging agent model construction method replacing numerical simulation |
Citations (12)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| KR20170005553A (en) * | 2015-07-06 | 2017-01-16 | 주식회사 유일기연 | Floods, drought assessment and forecasting techniques development for intelligent service |
| CN111369059A (en) * | 2020-03-05 | 2020-07-03 | 山东大学 | Urban waterlogging prediction method and system based on rapid waterlogging simulation coupling model |
| AU2020101587A4 (en) * | 2020-07-31 | 2020-09-10 | China Institute Of Water Resources And Hydropower Research | Coupling method of 1D and 2D hydrodynamic models based on spatial topology |
| CN111985129A (en) * | 2020-07-22 | 2020-11-24 | 天津大学 | Urban rainstorm waterlogging fine simulation method |
| CN112084671A (en) * | 2020-09-15 | 2020-12-15 | 武汉大学 | Urban time-varying gain rainfall-runoff process simulation calculation method |
| CN112183890A (en) * | 2020-10-27 | 2021-01-05 | 中德（珠海）人工智能研究院有限公司 | Method and device for determining drainage pipelines of smart city and readable storage medium |
| CN112561172A (en) * | 2020-12-18 | 2021-03-26 | 广西瑞宇建筑科技有限公司 | Rainfall waterlogging house safety early warning system |
| WO2022032872A1 (en) * | 2020-08-14 | 2022-02-17 | 贵州东方世纪科技股份有限公司 | Big data-based hydrologic forecasting method |
| CN114139258A (en) * | 2021-11-26 | 2022-03-04 | 深圳市水务科技发展有限公司 | Urban inland inundation modeling evaluation method and system and computer readable storage medium |
| KR20220046923A (en) * | 2020-10-08 | 2022-04-15 | 국토연구원 | Server and method for providing information about city deluge |
| CN114647881A (en) * | 2022-03-14 | 2022-06-21 | 南京师范大学 | Urban inland inundation modeling method considering building micro hydrological process |
| CN115423188A (en) * | 2022-09-01 | 2022-12-02 | 内蒙古远致科技有限公司 | Accumulated water prediction system |
Family Cites Families (1)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US10664937B2 (en) * | 2018-04-17 | 2020-05-26 | One Concern, Inc. | Flood risk analysis and mapping |
-
2022
- 2022-12-23 CN CN202211665462.6A patent/CN115859676B/en active Active
Patent Citations (12)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| KR20170005553A (en) * | 2015-07-06 | 2017-01-16 | 주식회사 유일기연 | Floods, drought assessment and forecasting techniques development for intelligent service |
| CN111369059A (en) * | 2020-03-05 | 2020-07-03 | 山东大学 | Urban waterlogging prediction method and system based on rapid waterlogging simulation coupling model |
| CN111985129A (en) * | 2020-07-22 | 2020-11-24 | 天津大学 | Urban rainstorm waterlogging fine simulation method |
| AU2020101587A4 (en) * | 2020-07-31 | 2020-09-10 | China Institute Of Water Resources And Hydropower Research | Coupling method of 1D and 2D hydrodynamic models based on spatial topology |
| WO2022032872A1 (en) * | 2020-08-14 | 2022-02-17 | 贵州东方世纪科技股份有限公司 | Big data-based hydrologic forecasting method |
| CN112084671A (en) * | 2020-09-15 | 2020-12-15 | 武汉大学 | Urban time-varying gain rainfall-runoff process simulation calculation method |
| KR20220046923A (en) * | 2020-10-08 | 2022-04-15 | 국토연구원 | Server and method for providing information about city deluge |
| CN112183890A (en) * | 2020-10-27 | 2021-01-05 | 中德（珠海）人工智能研究院有限公司 | Method and device for determining drainage pipelines of smart city and readable storage medium |
| CN112561172A (en) * | 2020-12-18 | 2021-03-26 | 广西瑞宇建筑科技有限公司 | Rainfall waterlogging house safety early warning system |
| CN114139258A (en) * | 2021-11-26 | 2022-03-04 | 深圳市水务科技发展有限公司 | Urban inland inundation modeling evaluation method and system and computer readable storage medium |
| CN114647881A (en) * | 2022-03-14 | 2022-06-21 | 南京师范大学 | Urban inland inundation modeling method considering building micro hydrological process |
| CN115423188A (en) * | 2022-09-01 | 2022-12-02 | 内蒙古远致科技有限公司 | Accumulated water prediction system |
Non-Patent Citations (4)
| Title |
|---|
| 张永勇 ; 侯进进 ; 夏军 ; 佘敦先 ; 吴胜军.中国典型城市小区尺度下垫面对径流过程的调控特征与空间差异.《中国科学:地球科学》.2022,全文. * |
| 朱治州 ; 张书亮 ; 王一梅 ; 杨祺琪 ; 戴强.基于多要素的城市汇水区分级划分研究.《南京师大学报(自然科学版)》.2020,全文. * |
| 社区尺度绿色基础设施暴雨径流消减模拟研究;刘文;陈卫平;彭驰;;生态学报(第06期);全文 * |
| 西安城市暴雨内涝灾害气象预警系统研究;王建鹏;薛春芳;薛荣;金丽娜;;灾害学(第S1期);全文 * |
Also Published As
| Publication number | Publication date |
|---|---|
| CN115859676A (en) | 2023-03-28 |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| CN115859676B (en) | Multi-level urban waterlogging coupling simulation method considering climate factors | |
| CN107832931B (en) | Modularized analysis method for waterlogging risk in plain water network region | |
| CN111369059B (en) | Urban waterlogging rapid prediction method and system based on rain and flood simulation coupling model | |
| CN109657841B (en) | Deep extraction method for urban rainstorm waterlogging | |
| CN113610264B (en) | Refined power grid typhoon flood disaster prediction system | |
| CN110543984B (en) | Urban water surface rate planning method based on water safety | |
| CN114647881B (en) | Urban waterlogging modeling method considering microscopic hydrologic process of building | |
| CN115391712A (en) | Urban flood risk prediction method | |
| CN111475950B (en) | Method for simulating rainfall flood of concave overpass | |
| Tavakol-Davani et al. | Green infrastructure optimization to achieve pre-development conditions of a semiarid urban catchment | |
| CN110222427A (en) | A kind of analysis method based on mathematical model urban waterlogging | |
| CN110188323B (en) | Water surface rate value range measuring and calculating method suitable for urban water ecological protection | |
| CN112242003B (en) | City sub-catchment area division method considering land type and flow direction | |
| CN110287595B (en) | Method for analyzing disaster reduction effect of different underlying surfaces of city | |
| KR20210048227A (en) | Lego type construction block, lego type construction block module, lego type construction block system, and simulation system for the same | |
| Mustafa et al. | Application of swat model for sediment loads from valleys transmitted to Haditha reservoir | |
| CN114528672A (en) | Urban hydrological station network layout method and system based on 3S technology | |
| CN117012004B (en) | Urban waterlogging risk early warning method and system | |
| Lin et al. | Dynamic influencing mechanism of traditional settlements experiencing urbanization: A case study of Chengzi Village | |
| KR20210048447A (en) | Lego type construction block, lego type construction block module, lego type construction block system, and simulation system for the same | |
| CN111985129A (en) | Urban rainstorm waterlogging fine simulation method | |
| CN110889185A (en) | Peak flow analysis method for small watershed and application thereof | |
| Masumoto et al. | Basin‐Scale Irrigation Planning in Areas with Scarce Data | |
| Noori Khaje Balagh et al. | Effects of climate change on quantity and quality of urban runoff in a part of Karaj watershed based on RCP scenarios | |
| Gammoh et al. | Low impact development (LID) efficiency in mitigating urbanization and climate change impacts in Amman, Jordan |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| PB01 | Publication | ||
| PB01 | Publication | ||
| SE01 | Entry into force of request for substantive examination | ||
| SE01 | Entry into force of request for substantive examination | ||
| GR01 | Patent grant | ||
| GR01 | Patent grant |