A simulation system for urban spatial growth coupled with water resources and environmental load

Abstract

The present invention discloses a system for simulating urban space growth coupled with water resource environmental carrying capacity, including a water resource carrying capacity dynamic assessment module, a water ecological sensitive area identification module and an urban land use change simulation module, the modules can respectively realize the assessment and prediction of the maximum scale of urban space during the planning period; identify the water ecological security pattern and determine the spatial regions that need to be avoided for urban space growth; and predict the characteristics of urban space layout under different water ecological sensitive area protection methods and urban space growth scale conditions. The system as a whole can predict the changing trends of urban population, industry and construction land by simulating the coupling relationship between water resource environmental carrying capacity, water ecological sensitive area protection characteristics and urban social and economic development and urban land expansion, and is used to assist in the delineation of urban development boundaries and the formulation of relevant urban space growth control policies, providing a new idea for the coupled simulation of the relationship between the urban artificial environment and the natural environment.

Description

Technical Field

[0001] The present invention relates to the technical field of urban and rural planning, and in particular to a town space growth simulation system coupled with water resource environment carrying capacity. Background Art

[0002] Water is the source of life, an irreplaceable basic natural resource and a strategic economic resource. “Building cities based on water and taking actions based on water” is an important basis for urban social and economic development and spatial growth.

[0003] The carrying capacity of water resources and environment has an impact on the scale structure and spatial layout of urban construction land. Current research either evaluates the population and industrial development level that the water resources and environment can carry from the perspective of environmental and resource management, thereby determining the upper limit of the scale of new urban construction land; or from the perspective of ecological security pattern, by identifying important water ecological spaces such as water sources, rivers, lakes, and wetlands, delineating protection areas and ecological red lines, and determining the ecological bottom line that urban development and construction need to avoid. However, in fact, under different water resources and environmental carrying levels, there are also differences in the water ecological sensitive spaces that need to be protected. When formulating urban spatial growth management policies, it is necessary to comprehensively evaluate and analyze the scale constraint and spatial constraint of the water resources and environment.

[0004] Therefore, there is an urgent need for an urban spatial growth simulation system that can couple the water resources and environmental carrying capacity, which can simulate the comprehensive impact of the water resources and environmental carrying capacity on the scale structure and spatial layout of urban spatial growth, as well as the coupling and feedback relationship between the urban system and the water resources and environmental system. Summary of the invention

[0005] In response to the above-mentioned problems, the present invention aims to provide a town spatial growth simulation system coupled with water resources and environmental carrying capacity. The system simulates the town spatial growth scenarios under different town development and water resources and environmental protection conditions by coupling the interaction and feedback between water resources supply, water environment protection, water ecological security and town population, industry and land use scale and spatial layout. The system is used to assist in the demarcation of town development boundaries and the formulation of relevant urban spatial growth control policies.

[0006] In order to achieve the above-mentioned purpose, the technical solution adopted by the present invention is as follows: a town space growth simulation system coupled with water resources and environmental bearing, characterized in that the simulation system comprises:

[0007] A dynamic assessment module for water resource carrying capacity, which is used to assess the maximum size of urban space within the forecast planning period;

[0008] A module for identifying water ecological sensitive areas, which is used to identify water ecological security patterns and determine the spatial areas that need to be avoided for urban spatial growth;

[0009] The urban land use change simulation module is used to predict the urban spatial layout characteristics under different water ecological sensitive area protection methods and urban spatial growth scale conditions.

[0010] Furthermore, the water resources carrying capacity dynamic assessment module includes:

[0011] The water resources supply submodule is used to simulate and quantify the supply capacity of various conventional and unconventional water resources in the city area. The variables included are: annual water supply, water supply from other water sources, water supply from external water transfers, and total local water resources;

[0012] The water resource demand submodule is used to simulate and quantify the water resource demand of towns and villages. The variables it contains are: ecological water consumption, agricultural irrigation water consumption, rural domestic water consumption, urban domestic water consumption, total industrial water consumption, urban per capita domestic water consumption, rural per capita domestic water consumption, and agricultural irrigation water consumption per hectare;

[0013] The water pollution feedback submodule is used to simulate and quantify the feedback process of improving environmental water quality and reducing pollutant emissions under the pressure of water pollution. The variables included are: water pollution pressure, annual total sewage discharge, agricultural wastewater discharge, industrial wastewater discharge, regional GDP, annual water demand, surface runoff pollution pressure, urban construction land area, urbanization rate, urban domestic sewage discharge, and total population;

[0014] The water balance feedback submodule is used to simulate and quantify the feedback process of improving water resource utilization efficiency under the pressure of water resource supply. The variables include: water resource supply-demand ratio, annual water demand, annual water supply, water supply from other water sources, total population, regional GDP, and urbanization rate.

[0015] The urban development submodule is used to simulate and quantify the impact of urban development on the level of water supply and demand and water pollution pressure. The variables included are: annual water demand, rural population, urban population, total population, population growth rate, water supply and demand ratio, GDP growth rate, regional gross domestic product, water consumption per 10,000 yuan of GDP, urbanization rate growth rate, urbanization rate, water pollution pressure, and urban construction land area.

[0016] Furthermore, the water ecological sensitive area identification module includes:

[0017] The water ecological security pattern construction submodule is used to form a spatial pattern composed of local, point and spatial relationships that play a key role in maintaining ecological security;

[0018] The water ecological sensitive area identification submodule is used to extract important spatial regions for protecting the health of the water ecological environment.

[0019] Furthermore, the data required by the water ecological sensitive area identification module includes:

[0020] 1) Vector map of the study area boundary;

[0021] 2) Vector maps of river systems, roads and railways;

[0022] 3) 100m resolution DEM raster data;

[0023] 4) 100m resolution annual normalized difference vegetation index (NDVI) raster data;

[0024] 5) 100m resolution land use type raster data;

[0025] 6) Study regional urban master plans and statistical yearbook data.

[0026] Furthermore, the processing steps of the water ecological sensitive area identification module include establishing a spatial data layer of the water ecological source, evaluating the resistance surface to obtain a resistance surface data layer, extracting the water ecological corridor to obtain a water ecological corridor data layer, and dividing the importance level of the water ecological security pattern, which specifically include the following:

[0027] 1) Identify the spatial scope of water ecological source areas and establish spatial data layers of water ecological source areas;

[0028] 2) Evaluate the resistance surface and obtain the resistance surface data layer through multiple data types on the surface;

[0029] 3) Extract water ecological corridors and delineate water ecological corridors within 100-300m of the river channel and on both sides according to the resistance value of each section of the river;

[0030] 4) Divide the importance level of water ecological security pattern, divide the resistance surface data layer using the natural breakpoint method, and identify the spatial areas that need to be avoided for urban spatial growth.

[0031] Furthermore, the urban land use change simulation module includes:

[0032] Urban land use change simulation model submodule is used to establish an urban land use change simulation model based on the historical land use change law;

[0033] The urban land use change scenario simulation submodule is used to simulate and predict urban land use changes under different water resource environmental protection and development and utilization scenarios, and generate urban spatial growth simulation results coupled with water resource environmental carrying capacity.

[0034] Furthermore, the data required by the urban land use change simulation module include:

[0035] 1) 100m resolution historical land use raster data, 2 historical years data (Y1, Y2), with an interval of more than 5 years; land use types are divided into urban construction land, water wetlands and other land;

[0036] 2) Vector maps of river systems, roads and railways;

[0037] 3) 100m resolution DEM raster data;

[0038] 4) Average annual rainfall raster data for the same year as the land use raster data;

[0039] 5) Resident population and GDP statistics for the same year as the land use raster data;

[0040] 6) Vector maps of ecological corridors, urban main centers, secondary centers, and district centers;

[0041] 7) Vector maps of flood storage and detention areas, ecological protection red lines, basic farmland protection red lines, and urban development boundaries.

[0042] Furthermore, the data processing of the urban land use change simulation module includes the following steps:

[0043] 1) Naming the data layer and assigning grid values ​​according to the data material category to obtain the driving factor layer of urban land use change;

[0044] 2) Calculate the spatial autocorrelation factor (Autocov), the formula is as follows:

[0045]

[0046] In the formula, y j is the land use status of grid j, assigned values ​​of 1 and 0; W ij is the spatial weight between grids i and j, which is determined by the inverse distance weighted method. The specific calculation method is:

[0047]

[0048] Where D ij is the Euclidean distance between grids i and j;

[0049] 3) Multicollinearity test of driving factors;

[0050] 4) Reclassify historical land use raster data;

[0051] 5) Run the R language program;

[0052] 6) Output simulation results.

[0053] Furthermore, in step 3), the kappa coefficient and variance inflation factor are applied to exclude factors with multicollinearity; when the kappa coefficient is less than 100 and the VIF is less than 10, the factor passes the multicollinearity test.

[0054] Furthermore, in step 4), the water wetland is assigned a value of 3, the urban construction land is assigned a value of 2, and other land types are assigned a value of 1.

[0055] The beneficial effect of the present invention is that the system as a whole can predict the changing trends of urban population, industry and construction land by simulating the coupling relationship between water resource environmental carrying capacity, protection characteristics of water ecological sensitive areas and urban social and economic development and urban land expansion, and can be used to assist in the demarcation of urban development boundaries and the formulation of relevant urban spatial growth control policies, providing a new idea for the coupled simulation of the relationship between urban artificial environment and natural environment. BRIEF DESCRIPTION OF THE DRAWINGS

[0056] Figure 1 It is a logical framework diagram of the urban space growth simulation system of the present invention.

[0057] Figure 2 Graph showing the result of resistance surface evaluation according to an embodiment of the present invention.

[0058] Figure 3 This is a diagram of an example of extracting water ecological corridor results in an embodiment of the present invention.

[0059] Figure 4 This is a case diagram of the importance level of the water ecological security pattern and the areas that need to be avoided for urban space growth in an embodiment of the present invention.

[0060] Figure 5 It is a diagram of the driving factor layer case data of an embodiment of the present invention.

[0061] Figure 6 This is an example diagram of simulation results of an embodiment of the present invention. DETAILED DESCRIPTION

[0062] In order to enable those skilled in the art to better understand the technical solution of the present invention, the technical solution of the present invention is further described below in conjunction with the accompanying drawings and embodiments.

[0063] See attached Figure 1-6 The urban spatial growth simulation system coupled with water resource environmental carrying capacity is shown in the figure. The simulation system includes three system modules: a water resource carrying capacity dynamic assessment module, a water ecological sensitive area identification module and an urban land use change simulation module, among which:

[0064] Module 1: Dynamic assessment module for water resources carrying capacity, which can assess the maximum scale of urban space during the forecast planning period based on the local water resources conditions and characteristics of the city, as well as future urban socio-economic development goals, water resources management goals and other factors.

[0065] The water resources carrying capacity dynamic assessment module consists of five parts, namely, water resources supply submodule, water resources demand submodule, water pollution feedback submodule, water balance feedback submodule, and urban development submodule, among which:

[0066] The water resources supply submodule is used to simulate and quantify the supply capacity of various conventional and unconventional water resources in the city area. The variables included are: annual water supply (WS), water supply from other sources (QTS), external water supply (OWS), and total local water resources (NWS);

[0067] The water resource demand submodule is used to simulate and quantify the water resource demand of towns and villages. The variables it contains are: ecological water consumption (ECOWR), agricultural irrigation water consumption (AGRWR), rural domestic water consumption (RURWR), urban domestic water consumption (URBWR), total industrial water consumption (INDWR), urban per capita domestic water consumption (UWPP), rural per capita domestic water consumption (RWPP), and agricultural irrigation water consumption per hectare (AGRWP);

[0068] The water pollution feedback submodule is used to simulate and quantify the feedback process of improving environmental water quality and reducing pollutant emissions under the pressure of water pollution. The variables included are: water pollution pressure (WP), annual total sewage discharge (TOTWP), agricultural wastewater discharge (AGRWW), industrial wastewater discharge (INDWW), gross regional product (GDP), annual water demand (WR), surface runoff pollution pressure (DBJLWP), urban construction land area (UBA), urbanization rate (UR), urban domestic sewage discharge (URBWW), and total population (POP);

[0069] The water balance feedback submodule is used to simulate and quantify the feedback process of improving water resource utilization efficiency under the pressure of water resource supply. The variables include: water resource supply-demand ratio (WSWR), annual water demand (WR), annual water supply (WS), water supply from other sources (QTSR), total population (POP), gross regional product (GDP), and urbanization rate (UR);

[0070] The urban development submodule simulates and quantifies the impact of urban development on the level of water supply and demand and water pollution pressure. The variables included are: annual water demand (WR), rural population (RPOP), urban population (UPOP), total population (POP), population increase rate (POPR), water supply and demand ratio (WSWR), GDP increase rate (GDPR), gross regional product (GDP), water consumption per 10,000 yuan of GDP (INDWP), urbanization rate growth rate (URR), urbanization rate (UR), water pollution pressure (WP), and urban construction land area (CUBA).

[0071] Table 1 below is a list of variables in the water resources carrying capacity dynamic assessment module:

[0072]

[0073]

[0074] Table 1 Variables of the dynamic assessment module of water resources carrying capacity

[0075] The system equations of the dynamic assessment module of water resources carrying capacity using case data are set up in detail as follows:

[0076] 1) INITIALTIME = 2010 (simulation start time)

[0077] Units:Year

[0078] 2) FINALTIME = 2025 (simulation end time)

[0079] Units:Year

[0080] 3) SAVEPER = 1 (result storage time interval)

[0081] Units:Year

[0082] 4)TIME STEP=1 (simulation time step)

[0083] Units:Year

[0084] 5) AGR = INTEG (CAGR, 353.2)

[0085] Units: Thousand hectares

[0086] 6) AGRWP = 0.357

[0087] Units: 10,000 tons / hectare

[0088] 7) AGRWR = AGRWP * AGR * 1000

[0089] Units: 10,000 tons

[0090] 8) AGRWW=AGRWR*AGRWWR

[0091] Units: 100 million cubic meters

[0092] 9) AGRWWR = 0.3

[0093] Units: **undefined**

[0094] 10) AREA = 11917

[0095] Units: Square Kilometers

[0096] 11) CAGR = AGRLAND Table (Time)

[0097] Units: Thousand hectares

[0098] 12)AGRLANDTable([(2000,-30)-(2025,2),(2000,1),(2001,1.1),(2002,0.1),(2003,-0.3),(2004,-0.7),(2005,1.8),(2006,-5.6),(2007 ,-0.3),(2008,-1.3),(2009,-0.4),(2010,-3),(2011,-6.6),(2012,- 1),(2013,-28.1),(2014,0),(2015,0),(2016,-2.3),(2017,0),(2018, -1.9),(2025,-0.5)],(2000,1),(2001,1.1),(2002,0.1),(2003,-0.3),(2004,-0.7),(2005,1.8),(2006,-5.6),(2007,-0.3),(2008,-1.3),(2009,-0.4),(2010,-3),(2011,-6.6),(2012,-1),(2013,-28.1),(2014,0),(2015,0),(2016,-2.3),(2017,0),(2018,-1.9),(2025,-0.5))

[0099] Units: Thousand hectares

[0100] 13) CGDP=GDP*GDPR

[0101] Units: 100 million yuan

[0102] 14) CPOP = POP * POPR

[0103] Units: 10,000 people

[0104] 15) CQTS = QTS * QTSR

[0105] Units: 10,000 tons

[0106] 16) CUBA = UBA * UBAR

[0107] Units: Square Kilometers

[0108] 17) CUR = UR * URR

[0109] Units: **undefined**

[0110] 18) DBJLWP = UBA / AREA

[0111] Units: **undefined**

[0112] 19)ECOWR=ECOWRTable(Time)*10000

[0113] Units: 10,000 tons

[0114] 20)ECOWRTable([(2010,0)-(2025,10)],(2010,1.22),(2011,1.1),(2012,1.4),(20 13,1.9),(2014,2.1),(2015,2.9),(2016,4.1),(2017,5.2),(2018,5.6),(2025,10))

[0115] Units: 100 million cubic meters

[0116] 21) GDP = INTEG (CGDP, 9224)

[0117] Units: 100 million yuan

[0118] 22) GDPR=IF THEN ELSE(WP<0.14:AND:WSWR>1,0.3779-1.944e-05*GDP,

[0119] 0.3779-1.944e-05*GDP*reduction factor)

[0120] Units: **undefined**

[0121] 23)INDWP=10.44-0.0002896*GDP-5.16504*SIND

[0122] Units: 10,000 tons / 10,000 yuan

[0123] 24) INDWR = GDP * INDWP

[0124] Units: 10,000 tons

[0125] 25)INDWTable([(2010,0)-(2025,0.5)],(2010,0.41),(2011,0.412),(2012,0.375),(2013,0. 346),(2014,0.352),(2015,0.358),(2016,0.328),(2017,0.329),(2018,0.32),(2025,0.28))

[0126] Units: 10,000 tons

[0127] 26)INDWWR=INDWTable(Time)

[0128] Units: **undefined**

[0129] 27)INDWW=INDWR*INDWWR

[0130] Units: 100 million cubic meters

[0131] 28) NWS = 100000

[0132] Units: 10,000 tons

[0133] 29) OWS = WDSTable (Time) * 10000

[0134] Units: 10,000 tons

[0135] 30)POP=INTEG(CPOP,1299.29)

[0136] Units: 10,000 people

[0137] 31)POPR=IF THEN ELSE(WP<0.14:AND:WSWR>1,0.0271,0.0271*reduction factor)

[0138] Units: **undefined**

[0139] 32)QTS=INTEG(CQTS,3900)

[0140] Units: 10,000 tons

[0141] 33)QTSR=IF THEN ELSE(WSWR>1,QTSRTable(Time),QTSRTable(Time)*1.1)

[0142] Units: %

[0143] 34)QTSRTable=

[0144] [(2009,0)-(2025,3)],(2009,1.6),(2010,0.3077),(2011,2.2276),(2012,0.1945),(2025,0.1945)

[0145] Units: %

[0146] 35) RPOP=POP-UPOP

[0147] Units: 10,000 people

[0148] 36) RURWR=RPOP*RWPP

[0149] Units: 10,000 tons

[0150] 37) RWPP = 74 * 365 / 1000

[0151] Units: tons / (person*year)

[0152] 38)TOTWP=AGRWW+URBWW*(1-URBWWP)+INDWW

[0153] Units: 10,000 tons

[0154] 39)UBA=INTEG(CUBA,686.71)

[0155] Units: Square Kilometers

[0156] 40)UBAPP=(UBA*1e+06) / (UPOP*10000)

[0157] Units: Square meters / person

[0158] 41)UBAR=0.05

[0159] Units: **undefined**

[0160] 42)UPOP=POP*UR / 100

[0161] Units: 10,000 people

[0162] 43)UR=INTEG(CUR,79.55)

[0163] Units: %

[0164] 44)URBWR=UPOP*UWPP

[0165] Units: 10,000 tons

[0166] 45)URBWW=URBWR*(1-URBWWR)

[0167] Units: 100 million cubic meters

[0168] 46)URBWWP=MIN((31.35+0.0005279*GDP+0.8042*UR) / 100,1)

[0169] Units: **undefined**

[0170] 47)URBWWR=0.8

[0171] Units: **undefined**

[0172] 48)URR=IF THEN ELSE(WP<0.14:AND:WSWR>1,0.0071,0.0071*reduction factor)

[0173] Units: **undefined**

[0174] 49) UWPP = 114 * 365 / 1000

[0175] Units: tons / person / year

[0176] 50)WDSTable([(2000,0)-(2025,20)],(2000,5.2),(2010,8.06),(2011,7.96),(2012,4.39),(2013

[0177] ,5.45),(2014,9.5),(2015,8.5),(2016,10.8),(2018,14.3),(2025,20))

[0178] Units: 10,000 tons

[0179] 51)WP=(TOTWP / WR)*0.5+DBJLWP*0.5

[0180] Units: **undefined**

[0181] 52)WR=AGRWR+RURWR+URBWR+INDWR+ECOWR

[0182] Units: 10,000 tons

[0183] 53) WS=OWS+NWS+QTS

[0184] Units: 100 million cubic meters

[0185] 54) WSWR = WS / WR

[0186] Units: **undefined**

[0187] After inputting data and running the dynamic assessment module of water resources carrying capacity, the annual urban construction land area is obtained, and a land demand matrix is ​​established, named demand.txt, with the format shown in Table 2. Columns 1 to 4 are respectively the forecast year, other land area, urban construction land area, water area and wetland area. Among them, the urban construction land value is the urban construction land (UBA) value output by submodule 1, and the water area and wetland values ​​can be set according to the simulation scenario.

[0188] 1 2 3 2000 1490007 227437 287191 2001 1477151 238512 288972 2002 1464295 249588 290752 2003 1451440 260662 292533 2004 1438584 271738 294313 2005 1425728 282813 296094 2006 1412872 293888 297875 2007 1400016 304964 299655 2008 1387161 316038 301436 2009 1374305 327114 303216 2010 1361449 338189 304997 2011 1348593 349264 306778 2012 1335737 360340 308558 2013 1322882 371414 310339 2014 1310026 382490 312119 2015 1297170 393565 313900 2016 1284314 404640 315681 2017 1271458 415716 317461 2018 1258603 426790 319242

[0189] Table 2 Example of land demand matrix

[0190] Module 2: Water Ecological Sensitive Area Identification Module, which includes:

[0191] The water ecological security pattern construction submodule is used to form a spatial pattern composed of local, point and spatial relationships that play a key role in maintaining ecological security;

[0192] The water ecological sensitive area identification submodule is used to extract important spatial regions for protecting the health of the water ecological environment.

[0193] The data materials required for the water ecological sensitive area identification module include:

[0194] 1) Vector map of the study area boundary;

[0195] 2) Vector maps of river systems, roads and railways;

[0196] 3) 100m resolution DEM raster data;

[0197] 4) 100m resolution annual normalized difference vegetation index (NDVI) raster data;

[0198] 5) 100m resolution land use type raster data;

[0199] 6) Study regional urban master plans and statistical yearbook data.

[0200] This module 2 contains 3 steps, as follows:

[0201] Step 1: Identify the spatial scope of water ecological sources according to the table below. The identified objects include water resource protection sources, hydrological regulation sources, biological habitat sources and cultural protection sources (see Table 3). Create a spatial data layer (shapefile format) of water ecological sources in the ArcGIS platform.

[0202]

[0203] Table 3 Identification objects of water ecological source areas

[0204] Step 2: Evaluate the resistance surface. Collect the surface elevation model (DEM) data, surface cover type data, vegetation cover index (NDVI) data, and vector maps of roads and railways in the study area, and assign values ​​and weighted calculations based on the resistance value evaluation indicators in Table 4 on the ArcGIS platform. Each indicator is a raster format layer. After weighted calculation using the ArcGIS raster calculator tool, the resistance surface data layer (raster format) is obtained. Example of resistance surface evaluation results Figure 2 shown.

[0205]

[0206] Table 4 Resistance value evaluation index, value assignment and weight

[0207] Step 3: Extract water ecological corridors. In ArcGIS, use the vector layer of the river system to calculate the sum of the resistance surface grid values ​​across each section of the river to obtain the resistance value of each section of the river. The higher the value, the smaller the spatial resistance, and the more conducive it is to form an ecological corridor between the source areas. According to the principle that there is at least one ecological corridor between two water ecological sources, the river network line is determined, and then the river channel and the range of about 100-300m on both sides are delineated as water ecological corridors. Example of the results of extracting water ecological corridors Figure 3 shown.

[0208] Step 4: Divide the importance level of water ecological security pattern. In ArcGIS platform, the natural breakpoint method is used to divide the resistance surface data layer into four layers: low security level, lower security level, higher security level, and high security level. Then, the water ecological source map layer, the water ecological corridor layer and the high security level layer are superimposed, and together they are used as the spatial areas that need to be avoided for urban spatial growth identified in module 2, and output as mask.shp file. Examples of water ecological security pattern importance level and urban spatial growth need to avoid areas Figure 4 shown.

[0209] Module 3: Urban land use change simulation module includes:

[0210] Urban land use change simulation model submodule is used to establish an urban land use change simulation model based on the historical land use change law;

[0211] The urban land use change scenario simulation submodule is used to simulate and predict urban land use changes under different water resource environmental protection and development and utilization scenarios, and generate urban spatial growth simulation results coupled with water resource environmental carrying capacity.

[0212] The data materials required for this module 3 include the following:

[0213] 1) 100m resolution historical land use raster data, 2 historical years data (Y1, Y2), with an interval of more than 5 years; land use types are divided into urban construction land, water wetlands and other land;

[0214] 2) Vector maps of river systems, roads and railways;

[0215] 3) 100m resolution DEM raster data;

[0216] 4) Average annual rainfall raster data for the same year as the land use raster data;

[0217] 5) Resident population and GDP statistics for the same year as the land use raster data;

[0218] 6) Vector maps of ecological corridors, urban main centers, secondary centers, and district centers;

[0219] 7) Vector maps of flood storage and detention areas, ecological protection red lines, basic farmland protection red lines, and urban development boundaries.

[0220] The above data processing includes the following steps:

[0221] 1) According to Table 5, the data layer is named and the grid is assigned, and the driving factor layer of land use change in 22 towns is obtained. The case data of the driving factor layer is as follows Figure 5 shown.

[0222]

[0223]

[0224] Table 5 Driving factor layer of urban land use change

[0225] 2) Calculate the spatial autocorrelation factor (Autocov), the formula is as follows:

[0226]

[0227] In the formula, y jis the land use status of grid j, assigned values ​​of 1 and 0; W ij is the spatial weight between grids i and j, which is determined by the inverse distance weighted method. The specific calculation method is:

[0228]

[0229] Where D ij is the Euclidean distance between grids i and j (meters).

[0230] 3) Multicollinearity test of driving factors: Kappa coefficient and variance inflation factor (VIF) are used to exclude factors with multicollinearity. When the kappa coefficient is less than 100 and the VIF is less than 10, the factor passes the multicollinearity test.

[0231] 4) Reclassify the historical land use raster data, assigning a value of 3 to water wetlands, 2 to urban construction land, and 1 to other land use types.

[0232] 5) Run the R language program. The code is as follows:

[0233]

[0234]

[0235]

[0236] Table 6 below shows the parameters and meanings of the urban land use change simulation module:

[0237]

[0238] Table 6

[0239] 6) Output simulation results and save them as tiff format files. Figure 6 The following are examples of simulation results.

[0240] The principle of the present invention is: by coupling the interaction and feedback between water resources supply, water environment protection, water ecological security and urban population, industry and land use scale and spatial layout, the urban spatial growth scenarios under different urban development and water resources environment protection conditions are simulated; the changing trends of urban population, industry and construction land are predicted to assist in the demarcation of urban development boundaries and the formulation of relevant urban spatial growth control policies.

[0241] The above shows and describes the basic principle, main features and advantages of the present invention. Without departing from the spirit and scope of the present invention, the present invention may have various changes and improvements, which fall within the scope of the present invention claimed.

Claims

1. A town space growth simulation system coupled with water resources and environmental load, characterized in that: The simulation system includes a water resources carrying capacity dynamic assessment module, a water ecological sensitive area identification module and an urban land use change simulation module, among which: A dynamic assessment module for water resource carrying capacity, which is used to assess the maximum size of urban space within the forecast planning period; A module for identifying water ecological sensitive areas, which is used to identify water ecological security patterns and determine the spatial areas that need to be avoided for urban spatial growth; Urban land use change simulation module, which is used to predict the spatial layout characteristics of urban land under different water ecological sensitive area protection methods and urban space growth scale conditions; The urban land use change simulation module includes: Urban land use change simulation model submodule is used to establish an urban land use change simulation model based on the historical land use change law; The urban land use change scenario simulation submodule is used to simulate and predict the urban land use change under different water resource environmental protection and development and utilization scenarios, and generate the urban space growth simulation results coupled with the water resource environmental carrying capacity; The urban land use change simulation module includes the following processing steps: 1) Name the data layer and assign grid values ​​according to the data material category to obtain the driving factor layer of urban land use change; 2) Calculate the spatial autocorrelation factor (Autocov), the formula is as follows: In the formula, y j is the land use status of grid j, assigned values ​​of 1 and 0; W ij is the spatial weight between grids i and j, which is determined by the inverse distance weighted method. The specific calculation method is: Where D ij is the Euclidean distance between grids i and j; 3) Multicollinearity test of driving factors; 4) Reclassify historical land use raster data; 5) Run the R language program; 6) Output simulation results; The water resources carrying capacity dynamic assessment module includes: The water resources supply submodule is used to simulate and quantify the supply capacity of various conventional and unconventional water resources in the city area; Water resource demand submodule, used to simulate and quantify water resource demand in towns and villages; The water pollution feedback submodule is used to simulate and quantify the feedback process of improving environmental water quality and reducing pollutant emissions under water pollution pressure; The water balance feedback submodule is used to simulate and quantify the feedback process of improving water resource utilization efficiency under water resource supply pressure. Urban development submodule, which is used to simulate and quantify the impact of urban development on water supply and demand levels and water pollution pressure; The water ecological security pattern construction submodule of the water ecological sensitive area identification module is used to form a spatial pattern composed of local, point and spatial relationships that play a key role in maintaining ecological security; The water ecological sensitive area identification submodule is used to extract important spatial regions for protecting the health of the water ecological environment.

2. The simulation system according to claim 1, characterized in that: The processing steps of the water ecological sensitive area identification module include establishing a spatial data layer of the water ecological source, evaluating the resistance surface to obtain a resistance surface data layer, extracting the water ecological corridor to obtain a water ecological corridor data layer, and dividing the importance level of the water ecological security pattern.

3. The simulation system according to claim 1, characterized in that: In step 3), the kappa coefficient and variance inflation factor are used to exclude factors with multicollinearity; when the kappa coefficient is less than 100 and the VIF is less than 10, the factor passes the multicollinearity test.

4. The simulation system according to claim 3, characterized in that: In step 4), water wetlands are assigned a value of 3, urban construction land is assigned a value of 2, and other land types are assigned a value of 1.

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