Water-resource-rigid-constraint-based water-city-land-human-production-green coupling simulation method and system

Abstract

This invention discloses a water-city-land-human-industry-green space coupled simulation method and system under rigid water resource constraints. It relates to the field of water resource system analysis and comprehensive simulation technology, including constructing a six-element nonlinear coupling mechanism, building a distributed hydrological model coupling architecture, completing basic data processing and parameter library construction, realizing a multi-model bidirectional dynamic coupling mechanism, conducting data-driven and model verification, and performing watershed-scale multi-element collaborative evolution simulation analysis. This invention aims to solve the problems of fragmented multi-element structures, lack of feedback mechanisms, and insufficient nonlinear characterization capabilities in existing models. It also achieves distributed dynamic coupling simulation under a unified six-element framework, accurately characterizing nonlinear feedback and threshold response under rigid water resource constraints, improving the model's adaptability in complex scenarios, and providing scientific, reliable, and operable technical support for watershed water resource rigid constraint management decisions.

Description

Technical Field

[0001] This invention relates to the field of water resource system analysis and comprehensive simulation technology, and more specifically to a water-city-land-human-industry-green coupling simulation method and system under rigid water resource constraints. Background Technology

[0002] Currently, under the rigid water resource management requirement of "four waters and four determinations" (determining urban development, land use, population size, and production based on water availability) in the Yellow River Basin, the existing model system has revealed significant systemic limitations, making it difficult to support refined and dynamic decision-making for high-quality development of the basin.

[0003] First, mainstream research and applications remain fragmented, relying on single hydrological models, independent economic forecasting models, or isolated ecological assessment tools. At best, they can only establish local feedback relationships within binary or ternary subsystems such as "water-land," "water-production," and "water-human," failing to integrate the six major elements of "water-city-land-human-production-green space" into a unified coupling framework. This results in a lack of holistic characterization of the dynamic interaction mechanisms across the entire watershed water cycle, socio-economic activities, and ecosystem evolution. Second, existing models generally employ linear or weakly nonlinear assumptions, making it difficult to mathematically express the strong nonlinear feedback and threshold effects of rigid water resource constraints on urban expansion, industrial structure evolution, population spatial distribution, and ecological green space maintenance. When water scarcity exceeds a certain critical value, its cascading impacts on construction land expansion, food production layout, labor migration, and carbon sink function loss are severely underestimated in the models. Furthermore, the Yellow River Basin is in a complex environment where climate change (extreme precipitation and runoff, glacier and permafrost degradation) and human activities (inter-regional water transfer, energy and chemical industry agglomeration, and dramatic changes in land use) are deeply superimposed. Existing models have not formed an effective adaptation mechanism in terms of spatiotemporal resolution, scenario construction, and dynamic parameter updates. They are unable to capture the risk of sudden changes under extreme climate conditions, nor can they assess the immediate feedback of industrial transformation, urban renewal, or ecological restoration policies on water resource carrying capacity. Ultimately, the "four waters and four fixed points" water resource management policy lacks scientific, reliable, and operable support from concept to implementation.

[0004] Therefore, there is an urgent need to propose a watershed-scale simulation method that can achieve unified coupling of multiple elements such as water, city, land, people, industry, and green space, accurately characterize nonlinear feedback, and adapt to complex evolution scenarios. Summary of the Invention

[0005] In view of this, the purpose of this invention is to overcome the problems of fragmented multi-factor models, lack of feedback mechanisms, and insufficient nonlinear characterization capabilities in the existing technology, and to propose a water-city-land-human-industry-green coupling simulation method and system under rigid water resource constraints, so as to realize the dynamic collaborative evolution simulation of multi-factor systems at the watershed scale, and provide scientific, reliable and operable technical support for management decisions under rigid water resource constraints in watersheds.

[0006] To achieve the above objectives, the present invention adopts the following technical solution: A coupled simulation method for water-city-land-human-industry-green space under rigid water resource constraints includes: S1. Construct a nonlinear coupling mechanism of six elements: water, city, land, people, industry, and green. Establish a two-way feedback link between water resource elements and urban scale, land use, population distribution, industrial structure, and ecosystem. Use water resource quantity and water quality elements as mandatory variables to drive the evolution of other elements. Changes in other elements will have a reverse effect on the water system through water demand and pollution load. S2. Construct the overall architecture of the coupled simulation model. The coupled simulation model adopts a distributed hydrological model coupling architecture. The base model is divided into multiple hydrological response units based on different hydrological regions. Each response unit is simulated independently and works collaboratively through an information sharing mechanism. The coupled simulation model integrates four core modules: hydrological cycle process module, material cycle process module, biological process module, and human process expression module. S3. Complete the basic preparations for building the coupled simulation model, including spatial discretization of watershed geospatial data based on the geographic information platform, and building a localized parameter database. S4. Implement a multi-model coupling mechanism, establish a two-way dynamic feedback system between the natural water cycle and the socio-economic system, set the data exchange step size in daily / monthly units, and form a dynamic closed-loop simulation of the six elements of water-city-land-people-industry-green. S5. Conduct data-driven and model verification, integrate multi-source spatiotemporal data as input to the coupled simulation model, and calibrate the parameters of the coupled simulation model through historical sequence data to ensure the rationality and stability of the coupled simulation model structure and simulation results. S6. Based on the completed coupled simulation model, conduct dynamic collaborative evolution simulation analysis of the multi-element system of water-city-land-people-industry-green at the watershed scale.

[0007] Optionally, the hydrological cycle process module is used to simulate the rainfall-runoff process in the watershed, reveal the watershed water cycle pattern, and is divided into slope runoff generation and river runoff simulation. Based on the natural water cycle and social water cycle process, it considers the influence of dams and water redistribution processes within the watershed, and realizes the distributed simulation of watershed runoff.

[0008] Optionally, the material cycle process module is used to simulate the migration and transformation processes of pollutants, focusing on the migration of urban point source pollution and agricultural non-point source pollution, while also considering the purification effect of natural processes on pollutants. It embeds nitrogen cycle equations and pollutant migration equations, where the nitrogen cycle equations simulate the migration and transformation patterns of dissolved nitrogen and adsorbed nitrogen, respectively. Optionally, the biological process module is used to simulate the growth, succession, and feedback mechanisms of biological communities in water bodies and their surrounding ecosystems, characterizing the ecosystem's response to changes in water resource conditions and its reverse regulatory effect on the water system. Optionally, the human process expression module is used to simulate the dynamic disturbance and feedback mechanisms of human activities on the water resource system and the ecological environment, characterizing typical water resource behavior processes and quantifying the policy adjustment effects, constructing the coupling relationship between population growth, economic expansion, and water demand. Optionally, the spatial discretization of the geospatial data specifically involves: performing depression filling, flow direction calculation, and flow accumulation analysis based on the Digital Elevation Model (DEM); extracting the river network system according to a preset catchment area threshold; and dividing sub-basins and basin boundaries; through overlay analysis of land use, soil type, and slope data, removing fragmented patches, and further subdividing the sub-basins into Hydrological Response Units (HRUs) as the basic spatial units for model calculation. Optionally, the construction of the localized parameter database specifically involves: using SPAW software to calculate and input the hydrophysical parameters of each soil layer based on measured soil texture data; constructing a user-defined weather generator based on the distribution characteristics of meteorological stations in the study area, calculating statistical parameters, and generating a meteorological index table for simulating or correcting meteorological driving data. Optionally, the implementation of the multi-model coupling mechanism is as follows: a distributed hydrological model is used as the physical basis to simulate rainfall-runoff, evapotranspiration and groundwater processes; an integrated system dynamics model is used to simulate the evolution of population and industrial variables; a biological process module is introduced to assess ecosystem service functions; at the end of each simulation step, water resources and water environment capacity are output as constraints, and water use quotas and pollution discharge intensity are adjusted accordingly. The updated water demand is fed back by modifying the model's water use file, thereby realizing the dynamic closed-loop simulation of the six elements of "water-city-land-people-industry-green".

[0009] A water-city-land-human-industry-green coupling simulation system under rigid water resource constraints, comprising: The coupling mechanism construction module is used to establish a two-way feedback link between the six elements of water, city, land, people, industry and green, so as to realize the nonlinear coupling of the six elements; The model architecture building module is used to build a distributed hydrological model coupling architecture, integrating four core modules: hydrological cycle process module, material cycle process module, biological process module, and human process expression module, dividing hydrological response units and configuring information sharing mechanisms; The basic data processing module is used to complete the spatial discretization of watershed geospatial data and build a localized parameter database, providing basic spatial units and parameter support for model calculations. The coupling mechanism implementation module is used to establish a two-way dynamic feedback system between the natural water cycle and the socio-economic system, set the data exchange step size, and realize the dynamic closed-loop simulation of the six elements. The data-driven verification module is used to integrate multi-source spatiotemporal data into the model, calibrate model parameters using historical sequence data, and verify the model's rationality and stability. The simulation analysis module is used to conduct dynamic collaborative evolution simulation and analysis of the multi-element system of water, city, land, people, industry and green space at the watershed scale, based on the calibrated coupled simulation model.

[0010] As can be seen from the above technical solution, compared with the prior art, the present invention discloses a water-city-land-human-industry-green coupling simulation method and system under rigid water resource constraints, which has the following beneficial effects: 1. A distributed dynamic coupling simulation of the six elements of water, city, land, people, industry, and green space was achieved under a unified framework; 2. It can characterize the nonlinear feedback and threshold response features of multi-factor systems under rigid water resource constraints; 3. Improved the model's adaptability and stability under scenarios where climate change and human activities overlap; 4. It provides a repeatable simulation tool for the evolution of complex systems at the watershed scale. Attached Figure Description

[0011] To more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are only embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on the provided drawings without creative effort.

[0012] Figure 1 This is a schematic diagram of the method flow provided by the present invention. Detailed Implementation

[0013] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0014] This invention discloses a water-city-land-human-industry-green coupling simulation method under rigid water resource constraints, such as... Figure 1 As shown, it includes: S1. Construct a nonlinear coupling mechanism of six elements: water, city, land, people, industry, and green. Establish a two-way feedback link between water resource elements and urban scale, land use, population distribution, industrial structure, and ecosystem. Use water resource quantity and water quality elements as mandatory variables to drive the evolution of other elements. Changes in other elements will have a reverse effect on the water system through water demand and pollution load. S2. The overall architecture of the coupled simulation model is constructed. This coupled simulation model adopts a distributed hydrological model coupled architecture to simulate the dynamic changes and mutual influences between various modules. Its base model is divided into multiple hydrological response units based on different hydrological regions. Each response unit independently simulates hydrological cycle processes, material cycle processes, etc., and collaborates with other nodes through an information sharing mechanism to adapt to the analytical needs of different spatial and temporal scales. The coupled simulation model integrates four core modules: hydrological cycle process module, material cycle process module, biological process module, and human process expression module. Hydrological Cycle Process Module: This module is a crucial component of the hydrological model. Primarily based on water volume estimation coupled with human and socio-economic activities, it simulates the rainfall-runoff process within a watershed, revealing the water cycle's patterns. It is divided into slope runoff generation and channel runoff simulations. During model research, this module mainly relies on the traditional hydrological model's water cycle module, using the natural water cycle's evaporation, precipitation, surface water, and groundwater processes as a link, and the social water cycle's water intake and drainage processes as a link. It considers the impact of dams and the redistribution of water within the watershed, achieving distributed simulation of watershed runoff. Through distributed simulation, it accurately reflects the water resource supply and demand relationship in different regions, providing key hydrological driving data for other modules. Its embedded equations include, but are not limited to: a. Water balance equation: By unit i As the control volume, within the time step Δt, the water conservation equation is:

[0015] In the formula, : Controlling changes in total body water retention; Precipitation inflow; Evaporation rate; , These refer to surface runoff and lateral inflows and outflows of rivers, respectively. , : These are respectively groundwater lateral recharge and discharge; Human inputs that can be included, such as water returned from external sources or water transferred from elsewhere; Net loss due to human error.

[0016] b. Surface runoff calculation:

[0017] In the formula, Surface runoff, mm; To account for the rainfall depth after correction for social water cycle drainage, mm; Rain depth caused by precipitation, in mm; The rainfall depth, in mm, is caused by the drainage volume at an unknown drainage outlet location. The maximum possible infiltration rate is in mm; Number of curves; Initial damage, mm.

[0018]

[0019] In the formula, For the first Surface runoff entering the main river channel, mm; For the first Surface runoff generated in the Tianzi River Basin, mm; For the first Surface runoff retained over time, mm; surlag The lag factor; This refers to the convergence time.

[0020] c. Evaporation and transpiration calculations: Evaporation is the primary mode of water consumption within the watershed. The model categorizes evaporation into canopy evaporation, evapotranspiration, snow sublimation, and soil water evaporation. Potential evapotranspiration is calculated using the Penman-Monteith formula.

[0021] In the formula, Latent heat of vaporization, MJ / kg; Evaporation rate, mm / d; The slope of the pressure curve is expressed in kPa / °C. Net radiation, MJ / m 2 d; Soil heat flux, MJ / m 2 d; The humidity constant is kPa / °C. These are conversion factors; Atmospheric pressure, kPa; air density, kg / m³ 3 ; The saturated vapor pressure is kPa. The actual water vapor pressure is expressed in kPa. For vegetation canopy impedance; It is the aerodynamic impedance.

[0022] d. Groundwater movement: Shallow groundwater is primarily recharged through infiltration. The main modes of water loss include recharge from main river channels, soil water recharge, deep groundwater infiltration, and artificial water extraction. The water balance formula can be used to redistribute groundwater storage, as shown below:

[0023] In the formula, For the first Shallow groundwater storage capacity, mm; For the first -1 day shallow groundwater storage, mm; For the first Soil infiltration recharge, mm; The base flow rate into the main river channel, in mm; The amount of water entering the soil layer, in mm; The amount of shallow groundwater extracted to participate in the social water cycle, in mm; The material cycling process module primarily simulates the continuous changes of various substances other than water bodies, focusing on the migration of pollutants such as urban point source pollution and agricultural non-point source pollution, while also considering the purification effects of natural processes (such as sedimentation, degradation, and biological uptake) on pollutants. Different substances have different properties, and the basic equations describing their cycling processes vary greatly; some substances even have fundamental equations that are difficult to describe. This section briefly introduces the material migration and transformation equations and the nitrogen cycle equation.

[0024] a. Nitrogen cycle equation: Nitrogen in HRUs migrates and transforms primarily in two forms: dissolved nitrogen and adsorbed nitrogen. Nutrients enter the main channel via runoff and interflow and migrate downstream. Dissolved nitrogen load is calculated by simulating different loss pathways, including surface runoff, lateral flow, and seepage. Adsorbed nitrogen load is calculated using a transport function of organic nitrogen lost with soil, expressed as follows:

[0025] In the formula: Organic nitrogen loss (kg / hm) 2 ), ρ represents the concentration of organic nitrogen in the top 10 mm soil layer (kg / t), m represents soil loss (t), and ρ represents the area of ​​the corresponding hydrological unit (hm²). 2 ), The nitrogen enrichment coefficient is the ratio of the concentration of organic nitrogen lost from the soil to the concentration of organic nitrogen in the soil surface.

[0026] b. Pollutant migration equations

[0027] In the formula: The concentration of substances within a single water body; The concentration of substances flowing into the water body of this unit; , These represent the flow rate entering and leaving the unit, respectively. This represents the water volume of the unit. For the source and drain terms of this unit, it represents the changes in a certain substance per unit volume of water caused by various processes (such as biodegradation, sedimentation, etc.). The amount of change per unit of time. When an item is added, it is taken as a positive sign and called a source item; when an item is decreased, it is taken as a negative sign and called a missing item.

[0028] Biological Processes Module: The ecological process expression module is used to simulate the growth, succession, and feedback mechanisms of biological communities in aquatic bodies and their surrounding ecosystems. It focuses on characterizing the ecosystem's response to changes in water resource conditions and its reverse regulatory effects on the water system. Furthermore, it quantifies the evolutionary patterns of ecosystems in water purification, biodiversity maintenance, and enhancement of ecosystem services. The module's embedded formulas include, but are not limited to:

[0029] a. Exponential growth model (Malthus)

[0030] b. Vegetation dynamics equation (Logistic growth model)

[0031] In the formula It is vegetation biomass. It is the potential growth rate. It is the maximum carrying capacity of biomass.

[0032] c. The Lotka-Volterra model with two competing indices:

[0033] In the formula: H 1,t , H 2,t IndicatorsH 1. H 2 pairs t The function, r 1. r 2 are indicators H 1. H a growth rate of 2 K 1. K 2 are indicators H 1. H 2 constraints, α As an indicator H 2 pairs H A competition intensity coefficient of 1 β As an indicator H 1 pair H The competition intensity coefficient is 2.

[0034] d. Habitat suitability weighted model:

[0035] In the formula Let represent the influence functions of water resources, aquatic vegetation coverage, and water quality index on habitat suitability, respectively. These are weighting coefficients, which can be determined through experience or statistical fitting.

[0036] e. Integrated Ecosystem Service Functions

[0037] In the formula, the parameterization function is used to quantify the relationship between ecosystem service functions and vegetation biomass. Water resources Soil quality The relationship between them, with different weighting coefficients, can be constructed based on specific ecosystem services and fitted with parameters using statistical regression.

[0038] Human Process Expression Module: This module simulates the dynamic disturbances and feedback mechanisms of human activities on water resource systems and the ecological environment. It focuses on characterizing typical water resource behaviors such as agricultural irrigation, industrial production, urban domestic water use, and wastewater discharge and treatment / reuse, and quantifies the regulatory effects of policy implementation on water conservation management and ecological restoration. By constructing a coupling relationship between population growth, economic expansion, and water demand, this module characterizes the pressure and response of human activities on water resource systems, revealing the evolution of human-water relationships at the watershed scale and their feedback impact on the ecosystem. Embedded formulas within the module include, but are not limited to: a. Population projection equation: This model treats the population growth rate as a constant, and estimates the population in the forecast year based on the population size in the base year using methods of statistical population growth trends. The calculation formula is as follows:

[0039] In the formula, To predict the population size for the target year; Population size in the baseline year; This represents the annual population growth rate. The number of years is the prediction.

[0040] b. Expression of the growth rate equation:

[0041] In the formula: H This refers to the quantity of population or other specific socio-economic indicators at time t. s t , r t They are respectively t time H t The growth rate and relative growth rate.

[0042] c. Agricultural water use equation:

[0043] In the formula: For agricultural water consumption, This is the irrigation efficiency coefficient. For irrigated area, This refers to the water requirements of crops.

[0044] d. Industrial water use equation:

[0045] In the formula, For industrial water consumption, m 3 ; Industrial output value, in ten thousand yuan; and Reuse rates for the baseline year and the projected year, respectively, are %; and Water consumption per 10,000 yuan of output value for the baseline year and the projected year, respectively, in m 3 / ten thousand yuan; n is the predicted year; The industrial technology progress coefficient is generally taken as 0.02-0.05.

[0046] e. Domestic water consumption equation:

[0047] In the formula, For the region Domestic water consumption, m 3 / Year; For the region Urban domestic water consumption, m 3 / Year; For the region Rural domestic water consumption, m 3 Year; For the region Total urban population, in people; For the region Total rural population, in people; For the region Total number of livestock in rural areas; For the region Urban per capita domestic water consumption quota, liters / person·day; For the region Rural per capita domestic water consumption quota, liters / person·day; For the region Average daily water consumption quota for livestock, liters per head per day.

[0048] f. Equations for wastewater treatment and reuse:

[0049] In the formula: For recycled water volume, For the sake of reuse efficiency, This refers to the amount of wastewater discharged.

[0050] S3. Complete the basic preparations for building the coupled simulation model, including spatial discretization of watershed geospatial data based on the geographic information platform, and building a localized parameter database. Specifically, this includes: performing depression filling, flow direction calculation, and flow accumulation analysis based on the digital elevation model (DEM), extracting the river network system according to the preset catchment area threshold, and dividing the sub-basins and basin boundaries; subsequently, through the overlay analysis of land use, soil type, and slope data, removing fragmented patches, and further subdividing the sub-basins into hydrological response units (HRUs), which are used as the basic spatial units for model calculation.

[0051] During the localization parameter database construction phase, to improve the model's adaptability to specific watersheds, SPAW software was used to calculate and input key hydrophysical parameters such as saturated hydraulic conductivity, field capacity, and wilting coefficient for each soil layer based on measured soil texture data. Simultaneously, a user-defined weather generator was constructed based on the distribution characteristics of meteorological stations in the study area. A meteorological index table was generated by calculating statistical parameters such as monthly average rainfall, standard deviation of temperature, dew point temperature, solar radiation, and wind speed, which was used to simulate or correct meteorological driving data.

[0052] S4. Implement a multi-model coupling mechanism, establish a two-way dynamic feedback system between the natural water cycle and the socio-economic system, set the data exchange step size in daily / monthly units, and form a dynamic closed-loop simulation of the six elements of water-city-land-people-industry-green. In the implementation phase of the multi-model coupling mechanism, a two-way dynamic feedback system between the natural water cycle and the socio-economic system is established. This mechanism uses a distributed hydrological model as the physical foundation to simulate rainfall-runoff, evapotranspiration, and groundwater processes. It integrates a system dynamics model to simulate the evolution of population and industrial variables and introduces a biological process module to assess ecosystem service functions. The system sets data exchange steps on a daily or monthly basis: at the end of each simulation step, the model outputs water resources and water environmental capacity as constraints; the model adjusts water use quotas and pollution discharge intensity accordingly and feeds back the updated water demand by modifying the model's water intake and discharge files, thereby achieving a dynamic closed-loop simulation of the six elements of "water-city-land-people-industry-green".

[0053] S5. Conduct data-driven and model verification, integrate multi-source spatiotemporal data as input to the coupled simulation model, and calibrate the parameters of the coupled simulation model through historical sequence data to ensure the rationality and stability of the coupled simulation model structure and simulation results. The model input integrates multi-source spatiotemporal data, including remote sensing land use data, meteorological grid data, socioeconomic statistics data, and water intake and consumption monitoring data; the model parameters are calibrated using historical sequence data to ensure the rationality and stability of the model structure and simulation results.

[0054] S6. Based on the completed coupled simulation model, conduct dynamic collaborative evolution simulation analysis of the multi-element system of water-city-land-people-industry-green at the watershed scale.

[0055] Specifically, taking the Yellow River Basin as the implementation area, this invention will be implemented according to the following steps: 1. Construct a distributed hydrological model to simulate the evolution of water resources and water quality in the study area.

[0056] 2. It nests human activities and socio-economic modules, as well as hydrological response units and administrative units, and uses the hydrological cycle as a carrier to simulate the evolution of cities, populations and industries.

[0057] 3. Construct an ecological water demand and carrying capacity constraint module, and achieve model coupling by using water resource carrying capacity as a rigid constraint condition.

[0058] 4. Run the model under different climate and socio-economic scenarios to obtain the results of multi-factor system co-evolution.

[0059] A water-city-land-human-industry-green coupling simulation system under rigid water resource constraints, comprising: The coupling mechanism construction module is used to establish a two-way feedback link between the six elements of water, city, land, people, industry and green, so as to realize the nonlinear coupling of the six elements; The model architecture building module is used to build a distributed hydrological model coupling architecture, integrating four core modules: hydrological cycle process module, material cycle process module, biological process module, and human process expression module, dividing hydrological response units and configuring information sharing mechanisms; The basic data processing module is used to complete the spatial discretization of watershed geospatial data and build a localized parameter database, providing basic spatial units and parameter support for model calculations. The coupling mechanism implementation module is used to establish a two-way dynamic feedback system between the natural water cycle and the socio-economic system, set the data exchange step size, and realize the dynamic closed-loop simulation of the six elements. The data-driven verification module is used to integrate multi-source spatiotemporal data into the model, calibrate model parameters using historical sequence data, and verify the model's rationality and stability. The simulation analysis module is used to conduct dynamic collaborative evolution simulation and analysis of the multi-element system of water, city, land, people, industry and green space at the watershed scale, based on the calibrated coupled simulation model.

[0060] The various embodiments in this specification are described in a progressive manner, with each embodiment focusing on its differences from other embodiments. Similar or identical parts between embodiments can be referred to interchangeably. For the apparatus disclosed in the embodiments, since they correspond to the methods disclosed in the embodiments, the description is relatively simple; relevant parts can be referred to the method section.

[0061] The above description of the disclosed embodiments enables those skilled in the art to make or use the invention. Various modifications to these embodiments will be readily apparent to those skilled in the art, and the general principles defined herein may be implemented in other embodiments without departing from the spirit or scope of the invention. Therefore, the invention is not to be limited to the embodiments shown herein, but is to be accorded the widest scope consistent with the principles and novel features disclosed herein.

Claims

1. A coupled simulation method for water-city-land-human-industry-green space under rigid water resource constraints, characterized in that, include: S1. Construct a nonlinear coupling mechanism of six elements: water, city, land, people, industry, and green. Establish a two-way feedback link between water resource elements and urban scale, land use, population distribution, industrial structure, and ecosystem. Use water resource quantity and water quality elements as mandatory variables to drive the evolution of other elements. Changes in other elements will have a reverse effect on the water system through water demand and pollution load. S2. Construct the overall architecture of the coupled simulation model. The coupled simulation model adopts a distributed hydrological model coupling architecture. The base model is divided into multiple hydrological response units based on different hydrological regions. Each response unit is simulated independently and works collaboratively through an information sharing mechanism. The coupled simulation model integrates four core modules: hydrological cycle process module, material cycle process module, biological process module, and human process expression module. S3. Complete the basic preparations for building the coupled simulation model, including spatial discretization of watershed geospatial data based on the geographic information platform, and building a localized parameter database. S4. Implement a multi-model coupling mechanism, establish a two-way dynamic feedback system between the natural water cycle and the socio-economic system, set the data exchange step size in daily / monthly units, and form a dynamic closed-loop simulation of the six elements of water-city-land-people-industry-green. S5. Conduct data-driven and model verification, integrate multi-source spatiotemporal data as input to the coupled simulation model, and calibrate the parameters of the coupled simulation model through historical sequence data to ensure the rationality and stability of the coupled simulation model structure and simulation results. S6. Based on the completed coupled simulation model, conduct dynamic collaborative evolution simulation analysis of the multi-element system of water-city-land-people-industry-green at the watershed scale.

2. The water-city-land-human-industry-green coupling simulation method under rigid water resource constraints according to claim 1, characterized in that, The hydrological cycle process module is used to simulate the rainfall-runoff process in the watershed, reveal the watershed water cycle pattern, and is divided into slope runoff generation and river runoff simulation. Based on the natural water cycle and social water cycle process, it considers the influence of dams and water redistribution processes within the watershed, and realizes the distributed simulation of watershed runoff.

3. The water-city-land-human-industry-green coupling simulation method under rigid water resource constraints according to claim 1, characterized in that, The material cycling process module is used to simulate the migration and transformation of pollutants, focusing on the migration of urban point source pollution and agricultural non-point source pollution, while also considering the purification effect of natural processes on pollutants. It incorporates nitrogen cycle equations and pollutant migration equations, where the nitrogen cycle equations simulate the migration and transformation of dissolved nitrogen and adsorbed nitrogen, respectively.

4. The water-city-land-human-industry-green coupling simulation method under rigid water resource constraints according to claim 1, characterized in that, The biological process module is used to simulate the growth, succession and feedback mechanisms of biological communities in water bodies and their surrounding ecosystems, and to characterize the ecosystem's response to changes in water resource conditions and its reverse regulation of the water system.

5. The water-city-land-human-industry-green coupling simulation method under rigid water resource constraints according to claim 1, characterized in that, The human process expression module is used to simulate the dynamic disturbance and feedback mechanism of human activities on water resource systems and the ecological environment, characterize typical water resource behavior processes and quantify the policy adjustment effect, and construct the coupling relationship between population growth, economic expansion and water demand.

6. The water-city-land-human-industry-green coupling simulation method under rigid water resource constraints according to claim 1, characterized in that, The spatial discretization of the geospatial data specifically involves: performing depression filling, flow direction calculation, and flow accumulation analysis based on the digital elevation model (DEM); extracting the river network system based on a preset catchment area threshold; dividing sub-basins and basin boundaries; and further subdividing the sub-basins into hydrological response units (HRUs) through overlay analysis of land use, soil type, and slope data to remove fragmented patches and serve as the basic spatial units for model calculation.

7. The water-city-land-human-industry-green coupling simulation method under rigid water resource constraints according to claim 1, characterized in that, The construction of the localized parameter database specifically involves: using SPAW software to calculate and input the hydrophysical parameters of each soil layer based on measured soil texture data; and constructing a user-defined weather generator based on the distribution characteristics of meteorological stations in the study area to calculate statistical parameters and generate a meteorological index table for simulating or correcting meteorological driving data.

8. The water-city-land-human-industry-green coupling simulation method under rigid water resource constraints according to claim 1, characterized in that, The multi-model coupling mechanism is implemented as follows: a distributed hydrological model is used as the physical basis to simulate rainfall-runoff, evapotranspiration and groundwater processes; an integrated system dynamics model is used to simulate the evolution of population and industrial variables; and a biological process module is introduced to assess ecosystem service functions. At the end of each simulation step, water resources and water environment capacity are output as constraints, and water use quotas and pollution discharge intensity are adjusted accordingly. The updated water demand is fed back by modifying the model's water use file, thereby realizing the dynamic closed-loop simulation of the six elements of "water-city-land-people-industry-green".

9. A water-city-land-human-industry-green coupling simulation system under rigid water resource constraints, characterized in that, A method for implementing the water-city-land-human-industry-green coupling simulation method under rigid water resource constraints as described in any one of claims 1-8 includes: The coupling mechanism construction module is used to establish a two-way feedback link between the six elements of water, city, land, people, industry and green, so as to realize the nonlinear coupling of the six elements; The model architecture building module is used to build a distributed hydrological model coupling architecture, integrating four core modules: hydrological cycle process module, material cycle process module, biological process module, and human process expression module, dividing hydrological response units and configuring information sharing mechanisms; The basic data processing module is used to complete the spatial discretization of watershed geospatial data and build a localized parameter database, providing basic spatial units and parameter support for model calculations. The coupling mechanism implementation module is used to establish a two-way dynamic feedback system between the natural water cycle and the socio-economic system, set the data exchange step size, and realize the dynamic closed-loop simulation of the six elements. The data-driven verification module is used to integrate multi-source spatiotemporal data into the model, calibrate model parameters using historical sequence data, and verify the model's rationality and stability. The simulation analysis module is used to conduct dynamic collaborative evolution simulation and analysis of the multi-element system of water, city, land, people, industry and green space at the watershed scale, based on the calibrated coupled simulation model.

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