CN114491927A - Urban ecological environment gas-soil-water coupling simulation forecasting method - Google Patents
Urban ecological environment gas-soil-water coupling simulation forecasting method Download PDFInfo
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Abstract
The invention provides a city ecological environment gas-soil-water coupling simulation forecasting method, which is characterized in that a city ecological environment gas-soil-water full coupling simulation model is constructed based on an improved city atmospheric pollutant diffusion model, a city river network water environment model and a soil attribute estimation model, and the whole process simulation forecasting of the migration and transformation of the city typical pollutants in the gas-soil water is realized by linkage of dry and wet settlement and coupling exchange of the city typical pollutants in the gas-soil water. The invention provides a multi-scale simulation method for urban atmospheric pollutants based on multi-source data, wherein CFD and WRF-Chem modes are fused; a one-dimensional river network and two-dimensional lake hydrodynamic-water quality coupling model is constructed, and the virtual sensing monitoring and water quality dynamic simulation of the urban water area water environment elements from point to line in the one-dimensional water area and from point to surface in the two-dimensional water area are realized; and fusing the multi-source remote sensing data by utilizing a machine learning technology and a remote sensing image processing technology, modeling and mapping the soil attribute, and realizing the inversion of the soil attribute.
Description
Technical Field
The invention belongs to the technical field of environmental monitoring, and particularly relates to a gas-soil-water coupling simulation forecasting method for an urban ecological environment.
Background
In recent years, various environmental pollution problems have been highlighted along with the increasing of human activities. The problems of urban atmospheric pollution, water body pollution, soil pollution and the like are mutually influenced and interacted, and the problems of urban ecological environment have much uncertainty and complexity, and are more and more concerned and paid attention by governments, social public and scientific research workers.
The environment on the earth surface is complex and changeable, and the processes of generating, spreading and converting the pollution elements are not usually completed in an absolute single medium body. The distribution of pollutants in multi-medium environments such as atmosphere, soil, water and the like is realized through cross-medium migration, and the interface of the multi-medium environment is not only a channel for the cross-medium migration of the pollutants, but also a high enrichment area of the pollutants and micro organisms. The cross-media migration involves an interface effect between the media and the media, and the contaminant has the characteristics of converting near the interface and converting and coexisting inside a single-media environment. For example, the atmosphere contains a certain amount of solid suspended matters such as PM2.5 and PM10 and pollution elements such as CO, NO2 and SO2, and the solid suspended matters fall or attach to surface units such as water bodies, vegetation and earth surfaces along with rainfall and strong wind while spreading in the atmosphere, and also permeate into soil or enter the water body to spread along with the action of infiltration and runoff. In order to simplify migration and distribution of chemical substances in an environmental medium, some scholars construct an environmental mathematical model to simulate behaviors of pollutants in an ecological environment on the basis of a mass conservation law, wherein the behaviors comprise an atmospheric pollutant diffusion model, a soil property prediction model, a water environment mathematical model and the like, but researches on a gas, soil and water whole-process coupling interaction simulation method are relatively few, and a gas, soil and water forecasting simulation method based on information coupling is not uncommon.
Disclosure of Invention
Aiming at the problems that typical urban pollutants are influenced by the processes of atmospheric motion, rainfall, runoff, infiltration and the like, the mutual migration process in various media of atmosphere, soil and water is complex, and early warning and forecasting are difficult, the invention designs and develops urban ecological environment air-soil-water coupling forecasting simulation system software based on the idea of information coupling on the basis of fully combing an atmospheric pollutant diffusion model, a soil property prediction model and a water environment mathematical model, provides an urban ecological environment air-soil-water coupling simulation forecasting method, realizes the whole-process and automatic forecasting simulation of typical index elements of urban ecological environment by an informatization means, and further improves the time-space precision and accuracy of the forecasting simulation.
In order to achieve the purpose, the technical scheme of the invention is as follows:
the method for simulating and forecasting the coupling of the gas, the soil and the water in the urban ecological environment comprises the following steps:
the method comprises the following steps: the method comprises the steps of obtaining multi-source data through multiple methods, preprocessing the multi-source data by utilizing a data preprocessing and data fusion technology, and fusing the data, wherein the multi-source data at least comprises the following steps: remote sensing data, environmental data, administrative boundary data, soil attribute data, water conservancy and hydrological data and meteorological data;
step two: constructing an improved atmospheric pollutant diffusion model, an urban water environment virtual sensing monitoring and dynamic prediction model and an urban multi-scale soil element monitoring simulation model by using the multi-source data processed in the step one;
the improved atmospheric pollutant diffusion model is constructed in the following way:
on the basis of a traditional WRF-Chem model, a refined regional underlay is constructed to improve the WRF-Chem, a mesoscale WRF-Chem model is coupled with a CFD model, and the constructed urban atmospheric pollutant diffusion model with high resolution is verified to simulate the pollutant diffusion capacity;
the urban water environment virtual sensing monitoring and dynamic prediction model is constructed in the following way:
extracting the area of the water area in the polder area by using a remote sensing image processing technology on the basis of river network water system basic data and remote sensing image fusion data to construct a two-dimensional river network hydrodynamic water quality refinement model; carrying out calibration and verification on the model, and carrying out simulation;
the urban multi-scale soil element monitoring simulation model is constructed in the following way:
multi-source, multi-temporal and multi-platform remote sensing data and field actual measurement spectrum data are fused to perform modeling prediction of soil key attributes, a machine learning method of partial least squares is adopted to perform modeling prediction so as to screen an optimal estimation model and parameter combination, and precision evaluation and verification are performed on model estimation results;
step three: based on the principles of dry and wet sedimentation, soil moisture infiltration and water evaporation, an atmospheric pollutant diffusion model, a soil attribute estimation model and an urban water environment model are connected in series and coupled to construct a space-time coupling model; and establishing a simulation prediction model aiming at different environmental conditions, setting different threshold conditions of model operation, and performing corresponding model calculation and result output when the threshold conditions are triggered.
Further, the method also comprises the fourth step of: providing model interfaces of atmosphere-water environment-soil, and providing corresponding interfaces of all modules to realize the automation of a coupled space-time model; providing a data processing interface to ensure that multi-source data such as monitoring data, satellite/unmanned aerial vehicle remote sensing data, artificial monitoring data and the like can be transmitted to a single model and a coupling model in real time, so that data transmission automation is realized; providing each model parameter configuration interface to realize remote configuration of model parameters and model schemes; providing a model calculation interface to realize remote calling of model calculation; and providing a model calculation result display interface to realize real-time transmission of multi-type model results.
The invention has the beneficial effects that:
1. the method is based on an improved urban atmospheric pollutant diffusion model, an urban river network water environment model and a soil attribute estimation model to construct an urban ecological environment gas-soil-water full-coupling simulation model, and realizes the full-process simulation prediction of the migration and transformation of the urban typical pollutants in the gas-soil water by linking the dry-wet settlement with the coupling exchange of the urban typical pollutants in the gas-soil water.
2. Based on multi-source data, a CFD and WRF-Chem mode fused urban atmospheric pollutant multi-scale simulation method is provided, the urban atmospheric pollutant simulation precision is further improved, and the atmospheric pollutant simulation forecast is refined; a one-dimensional river network and a two-dimensional lake hydrodynamic-water quality coupling model are constructed, and on the basis, the point-to-line one-dimensional water area and the point-to-surface two-dimensional water area virtual sensing monitoring and water quality dynamic simulation of urban water area water environment factors are realized; and fusing the multi-source remote sensing data by utilizing a machine learning technology and a remote sensing image processing technology, modeling and drawing the soil attribute of Nanjing city center, and realizing the inversion of the soil attribute.
3. An informatization interface of an atmospheric pollutant diffusion model, an urban water environment model and a soil moisture model is provided, and the automation of a coupling model calculation process is realized on the basis of an air-soil-water coupling model.
Drawings
FIG. 1 is a schematic view of the general flow of the method for simulating and forecasting the coupling of gas, soil and water in the urban ecological environment provided by the invention;
FIG. 2 is a schematic view of a business process of the present invention;
FIG. 3 is a technical route diagram for the construction of an atmospheric pollutant model;
FIG. 4 is a distribution diagram of the daily average variation of NO2 concentration in the Main City of Nanjing;
FIG. 5 is a typical regional river network construction diagram of the Jinchuan river;
FIG. 6 shows the two-dimensional diffusion evolution of contaminants;
FIG. 7 is a schematic representation of the correlation of sentinel number 2 with Landsat 8 data;
FIG. 8 is the variance interpretation ratio and prediction residual (S2-12+ OLI7) of the first 10 principal components;
FIG. 9 shows the predicted results;
FIG. 10 shows the results of atmospheric pollutant simulation;
FIG. 11 shows results of soil property simulation;
FIG. 12 shows simulation results of urban water environment;
FIG. 13 is a graph of simulated prediction of the trend of multi-element, multi-media contamination;
FIG. 14 is a schematic diagram of the analysis and prediction of the transfer process of contaminants in a multimedia medium according to the method of the present invention.
Detailed Description
The technical solutions provided by the present invention will be described in detail below with reference to specific examples, and it should be understood that the following specific embodiments are only illustrative of the present invention and are not intended to limit the scope of the present invention. The methods provided by the present invention may be performed in a computer system, such as a set of computer-executable instructions.
According to the invention, by improving the subentry model, a regional space-time model facing the urban ecological environment is established, the sky-ground multi-source monitoring perception data and information are fused, and a time-driven coupling gas-soil-water simulation method is used to form full-process automatic simulation. The invention discloses a method for simulating and forecasting air-soil-water coupling of an urban ecological environment, the general flow of which is shown in figure 1, and the method comprises the following steps:
the method comprises the following steps: the method comprises the steps of fully utilizing space, aviation and ground monitoring stations and an artificial observation method to obtain multi-source data such as remote sensing data, environmental data, administrative boundary data, soil attribute data, water conservancy hydrological data and meteorological data, and utilizing data preprocessing and data fusion technology to preprocess and fuse the multi-source data.
Deeply analyzing urban characteristics based on a plurality of data such as DEM (Digital Elevation Model) data, topographic data, multispectral data, hyperspectral data, administrative boundary data, ecological environment basic data and the like to form refined underlying surface data, wherein the refined underlying surface data mainly comprise the following contents: acquiring underlying surface data, opening the underlying surface data by using Arcgis, building new document in the Arcgis, loading the underlying surface data, opening properties attribute of an image, selecting a Classified function under a symbology menu, visually displaying the land utilization classification condition of the current underlying surface data, then loading 30m dem data, 30m topographic data, 30m multispectral data and hyperspectral data, comprehensively analyzing whether the image underlying surface data and the remote sensing data are corresponding to each other, replacing inconsistent areas in an editor option with corresponding land utilization results, increasing the original land utilization result from 500m to 30m by upsampling, and improving the accuracy of the underlying surface data by manual inspection and image spatial resolution; the urban river channel foundation profile is researched based on watershed water system data, water conservancy project basic data, river channel section data and the like. The data are shown in table 1:
TABLE 1 space-time model construction elements Table
Step two: the method comprises the steps of acquiring multi-source data such as atmosphere, water quality, soil moisture content and the like by using all-around space-air-ground-human monitoring means such as satellite remote sensing, unmanned aerial vehicle remote sensing, a ground monitoring station, artificial observation and the like, constructing an improved atmospheric pollutant diffusion model, a virtual sensing monitoring and dynamic prediction model of the urban water environment and a multi-scale soil element monitoring simulation model of the city, inputting all data into the atmospheric model, the water environment model and the soil model for rolling calculation, and realizing the simulation prediction of the urban atmospheric pollutants, the urban water environment and the urban soil moisture content. The method specifically comprises the following steps:
1. high resolution atmospheric pollutant diffusion model
On the traditional WRF-Chem model, the WRF-Chem is improved by constructing a refined area under pad, and the improvement comprises the following steps: after refined underlying surface data are obtained, loading an HDF import Tool in matlab, editing the region by using a Geographic Box, obtaining a proper region by inputting the longitude and latitude of the upper left, the lower left, the upper right and the lower right, renaming the band name, and storing and importing the band name; modifying the latitude and longitude information of the upper left, the lower left, the upper right and the lower right through the vi.landump.m, and finally converting the underlying surface data into the nc format by using the run landump.m; and finally, opening an executable program for storing the key information of the underlying surface in fortran, replacing nx and ny (namely longitude and latitude information), and finally executing an f90 file to finish the file replacement in the model. After the space precision of the WRF-Chem model in the aspect of atmospheric pollutant diffusion simulation is improved, the remote driving and calling module of the WRF-Chem model is developed to improve the usability of the WRF-Chem model, and the mesoscale WRF-Chem model is coupled with the CFD model, wherein a Fluent solver in the CFD is used for testing the turbulence simulation of the k-epsilon model of the two equations, a standard wall function and a SIMPLE algorithm are adopted, the initial parameter setting uses a setting scheme of standard air, the density is set to be 1.225kg/m3 which is constant, and the viscosity is set to be 1.7894e-05 kg/m-s. The physical properties, concentration, diffusion speed and the like of the pollutants are determined according to the investigation result of the pollutant diffusion point. The air in urban wind farms flows at low speed, where the density variation of the air is very small and negligible, so it is considered as an incompressible fluid in the simulation. And verifying the simulation capability of the constructed high-resolution urban atmospheric pollutant diffusion model on pollutant diffusion. The method specifically comprises the following steps:
the technical route diagram of the atmospheric pollutant model construction is shown in figure 3. Firstly, acquiring meteorological related data, wherein the data mainly comprises meteorological station observation data (national station hourly instantaneous wind speed, national station hourly instantaneous wind direction, automatic station hourly instantaneous wind speed and automatic station hourly wind direction), urban building geographic information (building position and building height), national basic geographic information, meteorological station information, land utilization data, atmospheric pollution source information, pollutant concentration information and the like.
Prior to using WRF-Chem to simulate pollutants, the previously made global artificial emissions source was replaced with a desublimed emissions source, taking into account the impact of the artificial emissions source on the simulation effect. For 2016 MEIC inventory data, processing and production are required for the emission source data. In addition, the rough underlying surface carried by WRF-Chem is replaced by a fine underlying surface, and the land utilization classification after replacement is more in line with the situation of a performance area and is more fine.
1-b, then selecting different grid division modes according to the complexity of the calculation domain boundary of the region, wherein quadrilateral units are commonly used for the construction of structural grids in two-dimensional grids, and triangular units are commonly used for the construction of non-structural grids; in the three-dimensional grid, hexahedral units are commonly used for construction of structural grids, and tetrahedral and pentahedral units are commonly used for construction of non-structural grids, wherein the pentahedral units comprise pyramidal units and prismatic or wedge-shaped units, and as most of research areas have complicated building models, a mode of combining tetrahedrons and pyramids is selected.
After determining the meshing manner, determining a boundary condition by:
in the formula, z0、
The standard reference height in China is 10m, and the average wind speed at the standard reference height is obtained; z, z,
Is the average wind speed at any altitude and at any altitude; alpha is the roughness index of the ground, the rougher the terrain is, the stronger the blocking effect of the ground surface on airflow is, and the larger alpha is.
And 1-c, on the basis of finishing the step 1-b, constructing an atmospheric pollutant diffusion simulation model according to the preprocessing data, the underlying surface and other basic data. First, a WRF pre-processing system (WPS) was constructed, consisting of geogid, Ungrib and Metgrid. Determining a mode area in Geogrid, interpolating static terrain data to grid points, extracting meteorological element fields from GRIB format data in Ungrib, and horizontally interpolating the extracted meteorological element fields from Metgrid to grid points determined by Geogrid.
The method comprises the steps of constructing a WRF-Chem model after the preprocessing system is set, firstly configuring namelist and iput parameters in the WRF-Chem, and mainly setting simulated time control, output time interval (min), the number of files contained in each file, simulated integral time step length, vertical direction end grid values, the number of vertical layers of input data and the number of soil layers of the input data. The mode selects a three-layer nested area simulation scheme, the longitude and latitude of the center are 32.022N and 118.7825E, the projection mode is Lambert projection, and the two standard latitudes are 30-degree N and 60-degree N respectively. The number of outer layer grids was 59, the horizontal resolution was 4500km, the number of inner layer grids was 61, the horizontal resolution was 0.5km, and the mode ceiling was 50 hPa. The physical parameter scheme adopts a Lin micro physical scheme, a Grel I-3 cloud convection parameterization scheme, a Dudhia short wave radiation scheme, an RRTM long wave radiation scheme, a YSU boundary layer scheme and a Noah land surface process mode; the chemical parameter protocol employed the RADM2 gas phase chemical reaction mechanism and the MOSAIC aerosol mechanism. And linking the output file of the metagrid to the operation path of the WRF-Chem model, executing real.exe to realize the initialization of the WRF-Chem model, and operating wrf.exe to start model calculation after the initialization is finished.
And 1-d, setting the simulation time to be 24 hours and the time interval to be hours in the model in the step 1-c, calculating to obtain the distribution characteristics of the atmospheric pollutants in the main city of Nanjing city, and verifying the result, wherein the simulation result is shown in figure 4, and the result shows that part of the atmospheric pollutants in the main city of Nanjing city presents obvious weekend effect characteristics.
The verification formula adopts average deviation (MB), normalized average deviation (NMB), normalized average error (NME) and Root Mean Square Error (RMSE), and is as follows.
In the above expression, CmIs an analog value, C0Is the monitoring value, and n is the number of samples. The average deviation degree of the NMB simulation forecast value and the monitored value is reflected, the average error of the simulation value and the monitored value is reflected by the NME, and the deviation degree of the simulation value and the monitored value is reflected by the RMSE. Comparing and verifying the hourly simulation values and the observed values in Nanjing, wherein the verification result shows that the R values of all stations of NO2 reach more than 0.5, the R values of eight stations reach more than 0.6, and the R values of two stations reach more than 0.7; for O3, the R values for all stations reached above 0.7, with four stations reaching above 0.8.
2. Urban water environment virtual sensing monitoring and dynamic prediction model
Extracting the area of the water area in the polder area by using a remote sensing image processing technology on the basis of river network water system basic data and remote sensing image fusion data to construct a two-dimensional river network hydrodynamic water quality refinement model; carrying out difference on the Saint-Vietnam equation set by using formulas (6) to (8), and processing the river channel discrete equation set by adopting a double-pursuit method; and setting a river channel concern node based on the constructed hydrodynamic water quality model and the river channel boundary conditions, and realizing the virtual sensing monitoring and dynamic early warning prediction of the urban water environment through a dynamic early warning prediction function.
Where Δ t is a time step, Δ x is a distance step, θ is a weight coefficient, j is a section number, n is an nth period, and Δ f is fn+1-fn。
The method specifically comprises the following steps:
2-a, selecting the Nanjing Jinchuan river as a research object, and completing basic data collection in modes of on-site investigation, field exploration and the like, wherein the basic data comprises basic data of river water systems, hydraulic engineering scheduling, land utilization, hydrodynamic water quality (water quality concentration, flow and water level), river terrain and the like; analyzing and extracting characteristic parameters of the river water system by utilizing high-resolution remote sensing, wherein the characteristic parameters comprise the topological relation, the water surface rate, the river channel length and the like of the river water system, and the characteristic parameters and the basic base map are constructed by using the characteristics parameters as a model; and (3) utilizing an ecological environment fusion data platform to butt against real-time and forecast information of elements such as weather, hydrology, hydrodynamic force, water quality, soil water content and the like, and supporting model construction and simulation calculation.
The data collected at present comprises water systems of golden river and rivers, hydraulic engineering, scheduling and monitoring station partial water level and water quality monitoring data processes. Wherein the monitoring station information is shown in table 2 below:
TABLE 2 Jinchuan river survey station statistics
| Name of survey station | River in which it is located | Longitude (G) | Latitude |
| Pagoda bridge | Jinchuan river | 118.859888888889 | 32.1086388888889 |
| Water station for closing bridge | River of Wai jin Chuan | 118.752484070834 | 32.1010175269051 |
| Water station for protecting south of city river | Jinchuan river | 118.752624222714 | 32.0895606870731 |
| Red mountain south road bridge water station | Long channel of nan Shi Li | 118.784523355735 | 32.0932914891514 |
| Jinchuan river station | Jinchuan river | 118.759054488699 | 32.0900201593893 |
| Pagoda bridge water station | River of Wai jin Chuan | 118.745778333913 | 32.1087318806775 |
| Water station for three rivers | North city protects town river | 118.759661084072 | 32.0923561311238 |
| Jinling county water station | Erxian gou (the root of the Chinese immortal Fall) | 118.749535699326 | 32.1085933513208 |
Parameters mainly monitored by the water quality monitoring station comprise COD, turbidity, total phosphorus, total nitrogen, ammonia nitrogen, ph, temperature and the like, and COD is selected as a simulation index by data collection and water environment model simulation elements below.
2-b, constructing a river network hydrodynamic-water quality model based on the step 2-a, wherein the research area range north is bounded by a Jinchuan river, a Dong-to basalt lake, a Nanda-Qinhun-Huaihe river and a West-to Yangtze river, wherein the basalt lake uses a normal water level as a water level boundary, the Yangtze river selects a real-time tide level as a water level boundary, water conservancy projects in the area adopt planning and scheduling, and inflow afflux and water quality monitoring data of rivers such as a Nanshi long ditch and the like are mainly considered in interval inflow. The specific construction range is shown in FIG. 5.
The motion of the water body in the river channel in the research area has obvious one-dimensional characteristics, the river network is described by adopting a 1D model, and St.Vennant equation set is solved and applied for description:
the continuous equation:
equation of motion:
in the formula: q is the flow (m 3/s); h is the section water depth (m); s0 is the source term; a is the cross-sectional area (m 2); b is the total width (m) of the river surface; vx is the flow velocity component (m/s) of the side inflow in the water flow direction; k is the flow modulus:
g is the acceleration of gravity; α is a momentum correction coefficient:
the basic equation of the one-dimensional channel unsteady flow is a group of quasi-linear partial differential equations, and the numerical calculation method adopted in the embodiment is a finite difference method.
The generation of non-point source pollution includes two processes, dry-term accumulation and rain-term flushing. Both the pollutant accumulation and the scouring process are plotted using exponential equations, as shown below. In the formula: m is the actual accumulated amount of pollutants, the variable amount of the actual accumulated amount of pollutants is equal to the runoff pollutant load, and the unit is kg & ha & lt-1 & gt; b ismaxThe unit is kg & ha-1 for the maximum accumulated amount of pollutants; kBThe rate of contaminant accumulation is given in day-1; c1And C2Respectively, a non-point source pollution scouring coefficient and a scouring index. The meaning of the contaminant accumulation process is that the longer the length of the dry period, the closer the actual accumulation of the contaminant is to the maximum accumulation. The meaning of the pollutant scouring process is that runoff pollutant load is positively correlated with runoff flow and the actual accumulated amount of pollutants.
In order to facilitate standardized construction of a later model and encapsulation of model services, the model is constructed according to certain standards, and the standards mainly comprise a river section naming standard, a point naming standard, a hydraulic engineering (sluice and pump station) naming standard, a river reach naming standard, a dike naming standard, a road naming standard, a polygon naming standard, a 2D interval naming standard and the like.
And 2-c, calibrating and verifying the model based on the step 2-b, and simulating. And selecting historical water level observation data to finish initial parameter calibration work of the model, respectively selecting a high water level in a flood season and a low water level in a non-flood season to carry out a prototype observation experiment, calibrating and verifying the model, and improving the accuracy of the model.
The roughness value of the river channel is preliminarily planned to be 0.02-0.04 according to experience values of relevant artificial channels and natural river channels in relevant references such as a hydrodynamics manual and river channel improvement planning and design specifications. And then, carrying out parameter calibration on the Jinchuan river model by adopting duration data, and finally determining the roughness of each river channel in the Jinchuan river area.
The roughness of the main river channels of the endothelium corneum gigeriae galli, the exotica, the northwest protected urban rivers and the northwest protected urban rivers is finally determined to be 0.025-0.03 through the calibration of the model, and the roughness of the river channels of other river channels is 0.03-0.035.
The calibration verification of the model is mainly used for adjusting relevant parameters in the model so as to improve the accuracy of a simulation result. The main influence factors in the one-dimensional model include space step length, time step length, river roughness and the like. According to the requirements of relevant specifications of the model calibration verification, the effectiveness of the model is evaluated by adopting a Nash-Sutcliffe coefficient NSE and a determinable coefficient R2 in the calibration verification. The Nash-Sutcliffe coefficient is used for representing the approximation degree of the magnitude order of the simulated calculation value series and the actual measurement series, and the coefficient R2 can be used for representing the shape matching degree of the simulated calculation value series and the actual measurement series, and the main formula is as follows.
In the formula: NSE is a Nash-Sutcliffe coefficient for simulating a field flood process; y isi obsThe ith data of the measured sequence is obtained; y isi simCalculating the ith data of the sequence; y isi meanThe measured sequence mean value is obtained; n is the total number of measured data.
In the formula: r2For simulating flood processDetermining a coefficient; y isobsActual measurement sequence data;
the measured sequence mean value is obtained; y issimTo calculate sequence data;
to calculate the sequence mean.
2-d, performing simulation research on migration and diffusion of the Jinchuan river pollutants in Nanjing according to the step 2-c, selecting a water body sensory index (transparency) and a typical water quality index (COD, TN and DO) as analysis objects based on the real-time monitoring data of the cross section and the water quality of a discharge port, establishing a relation between the turbidity and the transparency of the water body, simulating the migration and diffusion process of the pollutants in the water body, analyzing the influence of convection, diffusion and dispersion physical processes and related parameters such as roughness and degradation coefficient on the migration and diffusion process of the pollutants, completing model calibration verification based on the Internet of things perception data and combining historical water quality data, analyzing and researching the two-dimensional diffusion evolution process of the pollutants and the time concentration change process of the pollutants at a focus, wherein the simulation result is shown in figure 6.
3. City multi-scale soil element monitoring simulation model
The method comprises the steps of fusing multi-source, multi-temporal and multi-platform remote sensing data and field actual measurement spectrum data, carrying out modeling prediction on soil key attributes such as soil moisture content (soil moisture condition) and soil texture, adopting a partial least square machine learning method to carry out modeling prediction so as to screen an optimal estimation model and parameter combination, and carrying out precision evaluation and verification on a model estimation result so as to improve the prediction precision of soil attribute information. Meanwhile, a machine learning method is adopted to construct a soil-landscape model by combining environmental parameters such as microtopography, temperature, rainfall, vegetation indexes and the like, so that the regional detection of key soil attributes is realized. The method specifically comprises the following steps:
and 3-a, acquiring main research data, acquiring earth surface spectrum, unmanned aerial vehicle imaging data and remote sensing image data in a research area, shooting a digital photo for extracting vegetation coverage and other information, and acquiring a soil sample. Besides the arrangement of soil sampling points, ground control points are arranged at road intersections to carry out geometric fine correction on the remote sensing images. The method mainly comprises the following steps:
wild in-situ spectrum collection
The field in-situ spectrum is measured by a Fieldspec 4Hi-Res type surface texture spectrometer of American ASD company, the wavelength range of the field in-situ spectrum is 350-2500 nm, the sampling interval is 1.4nm at 350-1050 nm and is 2nm at 1000-2500 nm, and the interval of the output spectrum after interpolation is 1 nm. And (3) carrying out white board correction once before each point collects the spectrum, collecting 20 in-situ spectrums at each sampling point, and taking the average value of the spectrums as the average spectrum of the sampling point.
② unmanned plane/remote sensing image acquisition
Before gathering soil sample, before the earth's surface has not been destroyed promptly, under the clear and non-cloud condition, adopt eight rotor electronic unmanned aerial vehicle (unmanned aerial vehicle dead weight is about 6kg, flight time 20min, flying height 100m), carry on the imager in step and carry out flight monitoring to the research area to acquire unmanned aerial vehicle remote sensing image in the research area. Remote sensing images such as a high score No. 5 hyperspectral image, a high score No. 1 radar image, a high score No. 2 radar image, a high score No. 3 radar image, AMSR-2 image, OLI image, SMAP image and a sentinel No. 1 image are synchronously collected, and part of the remote sensing data can be downloaded freely through a website and can be acquired through a programming ordering mode.
And the downloaded multi-source remote sensing image is subjected to conventional preprocessing such as geometric fine correction, atmospheric correction, radiation correction and the like, and then is matched with the spatial resolution, the spectral range and the spectral sampling interval. And extracting the spectrum of the matched remote sensing image grid by grid.
Atmospheric correction: conventional atmospheric correction methods such as FLAASH and 6S models are used.
And (3) geometric correction: and performing geometric fine correction by using a Ground Control Point (GCP) method.
The base graph is first collected and preprocessed. And collecting basic data of the region DEM, the sentinel No. 2, the Landsat 8 and the like, and finishing preprocessing work.
Downloading the DEM data of the preprocessing area;
downloading the data of a sentinel 2 number L1C in a research area, wherein the transit time is 22 days in 3 months in 2021, and because the L1C-grade product is an atmospheric apparent reflectivity product subjected to orthorectification and geometric fine correction and is not subjected to atmospheric correction, carrying out radiometric calibration and atmospheric correction on the product to obtain an L2A-grade product so as to obtain base layer reflectivity data;
collecting Landsat 8 remote sensing images in a research area, wherein the transit time is 2021, 3 and 26 months, and performing atmospheric correction to obtain reflectivity data;
then, the data are preliminarily analyzed, and the water content data are respectively subjected to correlation analysis with the image data of the sentinel No. 2 and Landsat 8, and the result is shown in FIG. 7.
Since the spatial resolutions of sentinel 2 and OLI are different, if both are used for subsequent model building, the spatial resolution is firstly matched and then resampled to 30m spatial resolution.
And 3-b, constructing a partial least squares regression model to simulate soil moisture content based on the step 3-a. The partial least square regression method organically combines the multivariate linear regression, the principal component analysis and the typical correlation analysis among the variables in the modeling process, realizes the regression modeling, the data structure simplification and the correlation analysis among two groups of variables, and brings great convenience to the multivariate data analysis. The spectral data has many bands, which are difficult to perform statistical analysis by using a traditional analysis method, and PLSR just solves the problem, so that the method is widely applied to spectral data analysis processing in recent years. The model construction mainly comprises the parameters shown in the following table 3:
TABLE 3
3-c, based on the simulation prediction result obtained in the step 3-b, adopting a verification set decision Coefficient (R2 p), a verification set Root mean square error (RMSEp) and a Ratio of an actual value standard deviation to a predicted value standard deviation (RPD), wherein the specific formula is as follows:
RPD＝SD/RMSEp (17)
μdand
respectively are an actual measured value and a predicted value,
the average value of the measured values is shown as a, the number of samples in the modeling set is shown as b, the number of samples in the verification set is shown as SD, and the standard deviation of the measured values of the samples in the verification set is shown as SD.
The larger R2p and RPD, the smaller RMSEp indicates the better prediction effect.
The verification process is as follows:
the 7 bands of sentinel # 2, 12 bands + OLI were used and the results are shown in figure 8.
The result shows that the method of combining sentinel 2 and OLI data is adopted, the accuracy of the partial least squares regression model is highest, the residual error is not higher than 0.008g/g, prediction mapping is carried out on the soil water content of the research area based on the model, and the result is shown in figure 9.
Step three: and constructing a space-time coupling model based on the principles of dry-wet sedimentation, soil moisture infiltration, water evaporation and the like. Aiming at daily monitoring in a clear sky state, when one or more monitoring elements of the atmosphere, soil and water body are polluted, information collected by a monitoring station is transmitted to a data center, then abnormal information is transmitted into a model through an informatization interface, when the daily information reaches a certain threshold value, a coupling model is triggered to calculate, and hourly simulation prediction is carried out on the change trend of the pollution condition. Aiming at the pollution conditions under abnormal environmental conditions such as rainstorm, sand and dust, high temperature and the like, on the basis of triggering the coupling model to simulate the change conditions of pollutants, the trend and the change conditions of the pollutants transmitted to other media are analyzed, and the multi-element and multi-medium pollutant change trend simulation prediction is formed, wherein the main process is shown in fig. 13.
On the basis, the regional spatial features with strong pertinence are extracted through the fused static basic geographic information; and (3) extracting time sequence characteristics in the multi-type data by combining real-time monitoring data of space-time ground people, and inputting time and space information into a region space-time model, wherein the main operation flow is shown in figure 2.
Firstly, setting corresponding pollutant concentration as a threshold condition for triggering model calculation, driving a corresponding module of a coupling model to calculate when the concentration of a certain pollutant reaches a specified threshold, simulating the diffusion transfer process of the pollutant through a model coupling interface, and finally visually displaying a simulation prediction result. For example, after the pollutants in the air reach a specified threshold, triggering an atmospheric pollutant diffusion model to calculate and roll and simulate the trend of the atmospheric pollutant change, and displaying the result in real time through a time progress bar as shown in fig. 10 while focusing on an area with a high pollutant concentration; after the concentration of the atmospheric pollutants exceeds a certain time, triggering a soil property estimation model to calculate the soil moisture content and the soil property change condition caused by the dry-wet sedimentation, wherein the result is shown in a graph 11; and triggering the urban water environment model to calculate when the concentration of the pollutants in the soil reaches a threshold value, and rolling and simulating the future river water quality change trend as shown in figure 12.
On the basis of refined underlying surface data, urban river foundation data and dynamic time sequence monitoring data, and based on an atmospheric pollutant diffusion model, a soil attribute estimation model and an urban water environment model, the atmosphere, the water environment and the soil model are coupled in series mainly on the basis of the principles of dry-wet settlement, soil water infiltration, water evaporation and the like, wherein the dry-wet settlement formula is as follows:
qj sedimentation ═ a (Cj wet × Vj wet + Cj dry × Vj dry) × (18)
Wherein Qj sedimentation is the sedimentation flux density of the dry and wet sedimentation j element of the atmosphere, Cj wet is the content of the wet sedimentation j element, Vj wet is the volume of the wet sedimentation in the test container, Cj dry is the content of the dry sedimentation j element, Vj is the weight of the dry sedimentation in the test container, a is a conversion constant, and finally the transfer process of the pollutant in the multi-media is analyzed and predicted by using the coupling result, and the result is shown in fig. 14.
And step four, providing model interfaces of atmosphere-water environment-soil based on the models, and providing corresponding interfaces of all modules to realize the automation of the coupled space-time model. Providing a data processing interface to ensure that multi-source data such as monitoring data, satellite/unmanned aerial vehicle remote sensing data, artificial monitoring data and the like can be transmitted to a single model and a coupling model in real time, so that data transmission automation is realized; providing each model parameter configuration interface to realize remote configuration of model parameters and model schemes; providing a model calculation interface to realize remote calling of model calculation; and providing a model calculation result display interface to realize real-time transmission of multi-type model results. The method comprises the following specific steps:
and providing an informationized interface of atmosphere, soil and water environment based on the third step. The interface ID of the atmospheric forecast result is set to GetGasValues, and the main parameters are shown in table 4:
TABLE 4
The soil property estimation model interface ID is set as GetSoilValues, and the main interface parameters are shown in the following table 5:
TABLE 5
The interface ID of the urban water environment model is set as GetWaterValues, and the main interface parameters are shown in the following table 6:
TABLE 6
The ID of the air-soil-water coupling simulation interface is set to getcomplehensevivalues, and the main interface parameters are shown in the following table 7:
TABLE 7
It should be noted that the above-mentioned contents only illustrate the technical idea of the present invention, and the protection scope of the present invention is not limited thereby, and it is obvious to those skilled in the art that several modifications and decorations can be made without departing from the principle of the present invention, and these modifications and decorations fall within the protection scope of the claims of the present invention.
Claims (7)
1. The method for simulating and forecasting the air-soil-water coupling of the urban ecological environment is characterized by comprising the following steps of:
the method comprises the following steps: the method comprises the steps of obtaining multi-source data through multiple methods, preprocessing the multi-source data by utilizing a data preprocessing and data fusion technology, and fusing the data, wherein the multi-source data at least comprises the following steps: remote sensing data, environmental data, administrative boundary data, soil attribute data, water conservancy and hydrological data and meteorological data;
step two: constructing an improved atmospheric pollutant diffusion model, an urban water environment virtual sensing monitoring and dynamic prediction model and an urban multi-scale soil element monitoring simulation model by using the multi-source data processed in the first step;
the improved atmospheric pollutant diffusion model is constructed in the following way:
on the basis of a traditional WRF-Chem model, a refined regional underlay is constructed to improve the WRF-Chem, a mesoscale WRF-Chem model is coupled with a CFD model, and the constructed urban atmospheric pollutant diffusion model with high resolution is verified to simulate the pollutant diffusion capacity;
the urban water environment virtual sensing monitoring and dynamic prediction model is constructed in the following way:
extracting the area of the water area in the polder area by using a remote sensing image processing technology on the basis of river network water system basic data and remote sensing image fusion data to construct a two-dimensional river network hydrodynamic water quality refinement model; carrying out calibration and verification on the model, and carrying out simulation;
the urban multi-scale soil element monitoring simulation model is constructed in the following way:
multi-source, multi-temporal and multi-platform remote sensing data and field actual measurement spectrum data are fused to perform modeling prediction of soil key attributes, a machine learning method of partial least squares is adopted to perform modeling prediction so as to screen an optimal estimation model and parameter combination, and precision evaluation and verification are performed on model estimation results;
step three: based on the principles of dry and wet sedimentation, soil moisture infiltration and water evaporation, an atmospheric pollutant diffusion model, a soil property estimation model and an urban water environment model are connected in series and coupled; and establishing a simulation prediction model aiming at different environmental conditions, setting different threshold conditions of model operation, and performing corresponding model calculation and result output when the threshold conditions are triggered.
2. The urban ecological environment gas-soil-water coupling simulation forecasting method according to claim 1, further comprising the fourth step of: providing model interfaces of atmosphere-water environment-soil, and providing corresponding interfaces of all modules to realize the automation of a coupled space-time model; providing a data processing interface to ensure that multi-source data such as monitoring data, satellite/unmanned aerial vehicle remote sensing data, artificial monitoring data and the like can be transmitted to a single model and a coupling model in real time, so that data transmission automation is realized; providing each model parameter configuration interface to realize remote configuration of model parameters and model schemes; providing a model calculation interface to realize remote calling of model calculation; and providing a model calculation result display interface to realize real-time transmission of multi-type model results.
3. The urban ecological environment gas-soil-water coupling simulation forecasting method according to claim 1, wherein the improved atmospheric pollutant diffusion model construction process specifically comprises the following sub-steps:
1-a. first obtaining weather related data,
replacing a discharge source, namely replacing a rough underlying surface carried by WRF-Chem with a fine underlying surface;
1-b, then selecting different grid division modes according to the complexity of the calculation domain boundary of the region, and selecting a mode of combining a tetrahedron and a pyramid;
after the mesh partition mode is determined, the boundary condition is determined by the following formula:
in the formula, z0、
Is the standard reference altitude and the average wind speed at the standard reference altitude; z, z,
Is the average wind speed at any altitude and at any altitude; alpha is a ground roughness index;
1-c, on the basis of finishing the step 1-b, constructing an atmospheric pollutant diffusion simulation model according to basic data, and firstly constructing a WRF pretreatment system, wherein the WRF pretreatment system consists of Geogrid, Ungrib and Metgrid; determining a mode area in Geogrid, interpolating static terrain data to grid points, extracting meteorological element fields from GRIB format data in Ungrib, and horizontally interpolating the extracted meteorological element fields to grid points determined by Geogrid from Metgrid;
constructing a WRF-Chem model after the preprocessing system is set, and firstly configuring namelist and iput parameters in the WRF-Chem, wherein the namelist and iput parameters mainly comprise setting simulated time control, output time interval, the number of files contained in each file, simulated integral time step length, vertical direction termination grid values, vertical layer times of input data and the number of soil layers of the input data; the mode selects a three-layer nested area simulation scheme, the longitude and latitude of the center are 32.022N and 118.7825E, the projection mode is Lambert projection, and the two standard latitudes are respectively 30 degrees N and 60 degrees N; the number of outer layer grids is 59, the horizontal resolution is 4500km, the number of inner layer grids is 61, the horizontal resolution is 0.5km, and the mode ceiling is 50 hPa; the physical parameter scheme adopts a Lin micro physical scheme, a Grel I-3 cloud convection parameterization scheme, a Dudhia short wave radiation scheme, an RRTM long wave radiation scheme, a YSU boundary layer scheme and a Noah land surface process mode; the chemical parameter scheme adopts a RADM2 gas-phase chemical reaction mechanism and a MOSAIC aerosol mechanism; linking an output file of the metagrid to an operation path of a WRF-Chem model, executing real.exe to realize WRF-Chem model initialization, and operating wrf.exe to start model calculation after initialization is completed;
1-d, setting the simulation time in the model in the step 1-c to be 24 hours and the time interval to be hours, calculating to obtain the distribution characteristics of the atmospheric pollutants, and verifying the result;
the verification formula adopts average deviation, normalized average error and root mean square error, and the specific formula is as follows:
in the above expression, CmIs an analog value, C0Is a monitoring value, and n is the number of samples; the average deviation degree of the NMB simulation forecast value and the monitored value is reflected, the average error of the simulation value and the monitored value is reflected by the NME, and the deviation degree of the simulation value and the monitored value is reflected by the RMSE.
4. The urban ecological environment gas-soil-water coupling simulation forecasting method according to claim 1, wherein the urban water environment virtual sensing monitoring and dynamic prediction model building process specifically comprises the following substeps:
2-a, completing water environment basic data collection; analyzing and extracting characteristic parameters of a river water system by using high-resolution remote sensing to serve as a basic base map and characteristic parameters of model construction; an ecological environment fusion data platform is utilized to butt against elements such as weather, hydrology, hydrodynamic force, water quality, soil water content and the like, real-time and forecast information is carried out, and a support model is constructed and simulated and calculated;
2-b, constructing a river network hydrodynamic-water quality model based on the step 2-a, wherein the motion of the water body in the river channel in the research area has obvious one-dimensional characteristics, the river network is described by adopting a 1D model, and St.Vennant equation set is solved and applied for description:
the continuous equation:
equation of motion:
in the formula: q is the flow (m 3/s); h is the section water depth (m); s0 is the source term; a is the cross-sectional area (m 2); b is the total width (m) of the river surface; vx is the flow velocity component (m/s) of the side inflow in the water flow direction; k is the flow modulus:
g is the acceleration of gravity; α is a momentum correction coefficient:
the basic equation of the one-dimensional river channel unsteady flow is a group of quasi-linear partial differential equations, and a numerical calculation method is a finite difference method;
the non-point source pollution generation comprises two processes of dry-period accumulation and rain-period scouring, wherein the pollutant accumulation and scouring processes are described by using an exponential equation and are respectively shown as follows:
m＝Bmax(1-e-KBt) (8)
in the formula: m is the actual accumulated amount of pollutants, the variable amount of the actual accumulated amount of pollutants is equal to the runoff pollutant load, and the unit is kg & ha & lt-1 & gt; b ismaxThe unit is kg & ha-1 for the maximum accumulated amount of pollutants; kBThe rate of contaminant accumulation is given in day-1; c1And C2Respectively representing a non-point source pollution scouring coefficient and a scouring index;
2-c, calibrating and verifying the model based on the step 2-b, and simulating; selecting historical water level observation data to finish initial parameter calibration work of the model, respectively selecting a flood season high water level and a flood season low water level to carry out a prototype observation experiment, calibrating and verifying the model, wherein a Nash-Sutcliffe coefficient NSE and a coefficient R2 are adopted in the calibration verification to evaluate the effectiveness of the model, the Nash-Sutcliffe coefficient is used for representing the order of magnitude approximation degree of a simulated calculation value series and an actual measurement series, and the coefficient R2 is used for representing the shape matching degree of the simulated calculation value series and the actual measurement series, and the main formula is as follows:
in the formula: NSE is a Nash-Sutcliffe coefficient for simulating a field flood process; y isi obsThe ith data of the measured sequence; y isi simCalculating the ith data of the sequence; y isi meanThe measured sequence mean value is obtained; n is the total number of the measured data;
in the formula：R2Determining coefficients for simulating a flood process of a field; y isobsActual measurement sequence data;
the measured sequence mean value is obtained; y issimTo calculate sequence data;
calculating a sequence mean value;
2-d, simulating and researching the migration and diffusion of water flow pollutants according to the step 2-c, selecting sensory indexes and typical water quality indexes of the water body as analysis objects based on real-time monitoring data of the cross section and the water quality of a discharge port of a river channel, establishing a relation between turbidity and transparency of the water body, simulating the migration and diffusion process of the pollutants in the water body, analyzing the influence of convection, diffusion and dispersion physical processes and related parameters such as roughness and degradation coefficient on the migration and diffusion process of the pollutants, completing model calibration verification based on the internet of things perception data and combining historical water quality data, and analyzing and researching the two-dimensional diffusion evolution process of the pollutants and the time concentration change process of the pollutants of interest points.
5. The urban ecological environment gas-soil-water coupling simulation forecasting method according to claim 1, wherein the urban water environment virtual sensing monitoring and dynamic prediction model is carried out according to a certain standard in the construction process, the standard at least comprising: river section naming standard, point naming standard, hydraulic engineering naming standard, river reach naming standard, embankment naming standard, road naming standard, polygonal naming standard and 2D interval naming standard.
6. The urban ecological environment gas-soil-water coupling simulation forecasting method according to claim 1, wherein the urban multi-scale soil element monitoring simulation model building process specifically comprises the following sub-steps:
acquiring main research data, acquiring earth surface spectrum, unmanned aerial vehicle imaging data and remote sensing image data in a research area, taking a digital photo for extracting vegetation information, and acquiring a soil sample; besides laying soil sampling points, laying ground control points at road intersections to carry out geometric fine correction on the remote sensing images;
3-b, constructing a partial least squares regression model to simulate soil moisture content based on the step 3-a;
3-c, based on the simulation prediction result obtained in the step 3-b, determining a coefficient by using a verification set, a root mean square error of the verification set and a ratio of a standard deviation of an actually measured value to a standard deviation of a predicted value, wherein a specific formula is as follows:
RPD＝SD/RMSEp (14)
μdand
respectively are an actual measured value and a predicted value,
the average value of the measured values is shown as a, the number of samples in the modeling set is shown as b, the number of samples in the verification set is shown as SD, and the standard deviation of the measured values of the samples in the verification set is shown as SD.
7. The urban ecological environment gas-soil-water coupling simulation forecasting method according to claim 1, wherein the model in step three is based on refined underlying surface data, urban river foundation data and dynamic time sequence monitoring data, and based on an atmospheric pollutant diffusion model, a soil property estimation model and an urban water environment model, the atmosphere, the water environment and the soil model are coupled in series on the basis of the principles of dry-wet settlement, soil water infiltration, water evaporation and the like, wherein the dry-wet settlement formula is as follows:
qj sedimentation ═ a (Cj wet × Vj wet + Cj dry × Vj dry) × (18)
The method comprises the following steps of determining the deposition flux density of a dry deposition j element and a wet deposition j element in the atmosphere, determining the deposition flux density of the dry deposition j element in the atmosphere, determining the wet deposition flux density of the wet deposition j element in the atmosphere, determining the wet deposition volume of the Vj element in the test container, determining the dry deposition flux density of the Cj element in the atmosphere, determining the dry deposition weight of the dry deposition j element in the test container Vj element in the atmosphere, determining the conversion constant A, and finally analyzing and predicting the transfer process of pollutants in the multi-media by using a coupling result.
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