Regional carrying capacity evaluation method based on water resource endowment characteristics

Abstract

To provide a regional capacity assessment method based on water resource availability characteristics.SOLUTION: A method comprises: a step 1 of collecting water resource data and spatial data to determine water resource availability characteristics including distribution of water area in an assessment area, availability of groundwater, and climate conditions; a step 2 of constructing a mathematical model for simulating the distribution and availability of water resources; a step 3 of representing uncertainty of parameters in the model using a probability distribution; a step 4 of using a Monte Carlo simulation method to evaluate the model output results through random sampling, extracting a value from the distribution of the parameters and input data each time the simulation is executed, and implementing the water resource model to generate a simulation result; and a step 5 of calculating a regional water resource capacity. This method first takes into account the regional water resource availability characteristics, which makes the assessment more realistic and helps the model to better reflect the actual situation.SELECTED DRAWING: Figure 1

Description

[Technical Field]

[0001] The present invention relates to the technical field of resource carrying capacity assessment, and in particular to a method for assessing regional carrying capacity based on the endowment characteristics of water resources. [Background technology]

[0002] Water resource endowment characteristics refer to the nature and quantity of water resources possessed by a particular district or region under natural conditions. These characteristics can affect the availability and quality of local water resources, significantly impacting the management and sustainable use of water resources. The carrying capacity of a region can be assessed based on its water resource endowment characteristics. Water resource carrying capacity refers to the region's ability to support and meet the water resource needs of humans and natural systems under the region's natural conditions. Analyzing water resource endowment characteristics can help assess the region's water resource balance and whether there is a risk of overexploitation or overuse. When assessing regional carrying capacity based on water resource endowment characteristics, several technical issues and challenges can be encountered. For example, assessment of water resource carrying capacity often relies on mathematical or computational models, which may contain uncertainties. The selection of model parameters and assumptions significantly impacts the assessment results. Summary of the Invention Problem that the invention aims to solve

[0003] In order to solve the above problems, the present invention provides a method for evaluating regional carrying capacity based on the endowment characteristics of water resources. [Means for solving the problem]

[0004] To achieve the above objectives, the technical solutions adopted by the present invention are as follows: A method for evaluating regional carrying capacity based on water resource endowment characteristics, comprising the following steps: Step 1: Collect water resource data and spatial data to determine the water resource endowment characteristics of the assessment area, including the distribution of water bodies, groundwater availability, and climatic conditions; Step 2: Based on the water resource data and water resource endowment characteristics, construct a mathematical model to simulate the distribution and availability of water resources; Step 3: For parameters in the model, use probability distributions to represent uncertainty; Step 4: Using the Monte Carlo simulation method, the model output results are evaluated through random sampling. Each time a simulation is run, values ​​are extracted from the distribution of parameters and input data, and the water resources model is run to generate simulation results. Step 5: Calculate the regional water resource carrying capacity based on the results of the Monte Carlo simulation.

[0005] Furthermore, in step 1, Collecting water resource data including precipitation, river flow, groundwater level, and water quality; Precipitation data are obtained from historical records of weather stations; River flow data is monitored through hydrological stations; Groundwater level data are obtained from groundwater level monitoring wells; Water quality data includes various water quality parameters such as dissolved oxygen, pH, and pollutant concentrations, and is collected by water quality monitoring stations; Using remote sensing techniques to obtain the distribution and characteristics of surface water bodies; Determine water resource endowment characteristics, including distribution of water bodies, groundwater availability, and climatic conditions; Here, the distribution of water bodies is analyzed using GIS tools to analyze remote sensing data and confirm the location, size, shape and depth of water bodies. Groundwater availability is derived from groundwater level data and hydrological model analysis; Climate conditions involve using meteorological data to assess seasonal changes and long-term trends in climate.

[0006] Furthermore, in step 2, It includes hydrological, meteorological and groundwater models. The hydrological model includes:

[0007]

number

[0008] where P is precipitation, ET is evaporation and transpiration, which represent the loss of water from soil and water bodies due to evaporation and plant transpiration, Q is runoff, which represents the inflow of water into rivers, lakes, and groundwater, and ΔS is the change in soil water content and groundwater level. Weather models include:

[0009]

number

[0010] where P is the amount of precipitation, R is the amount of runoff, i.e., the amount of water that flows into rivers and lakes after precipitation, E is the amount of evaporation, including soil evaporation and transpiration, and ΔS is the change in soil moisture content, including water infiltration or drainage. The groundwater model includes:

[0011]

number

[0012] where ∂h / ∂t is the time rate of change of the groundwater level, h is the groundwater level, S is the specific storage coefficient, and ∇ 2 h is the gradient of the groundwater level, which represents the direction and velocity of groundwater flow, and Q is the groundwater recharge or discharge, including the groundwater recharge rate and pumping rate.

[0013] Furthermore, step 3 includes the following: For each parameter in the model, a probability distribution is used to represent its uncertainty, Initialize each parameter, For the selected probability distribution, determine its parameters; The uncertainty of the input data of the model is taken into account to generate an uncertainty value for the input data.

[0014] Using the probability distribution and the determined parameter values, sampling a plurality of values ​​from the distribution to generate a plurality of sets of uncertainty samples for the parameters and input data; Using each parameter set and input data sample, run the water resources model and obtain the corresponding simulation results; The simulation results are analyzed to assess the variability of the model output, taking into account the effects of uncertainty.

[0015] Furthermore, in step 5, aggregating a set of simulation results from the Monte Carlo simulation, including estimates of water resource carrying capacity, to form a data set containing results from multiple simulations; Define criteria and indicators for sustainable water resource use, including sustainable water consumption, water quality standards and ecosystem needs; Perform statistical analysis on the set of simulation results to calculate the probability distribution of water resource carrying capacity; Determine the boundary value of water resource carrying capacity based on the analysis of sustainable water resource utilization criteria and probability distribution; Taking into account uncertainties, determine the uncertainty range of water resource carrying capacity. [Effects of the Invention]

[0016] Compared to existing technologies, the technical advances achieved by the present invention are: This method first takes into account the characteristics of regional water resources endowments, including the distribution of water bodies, groundwater availability, climatic conditions, etc., making the assessment more realistic and enabling the model to better reflect actual conditions. When building a water resources model, this method clearly defines the parameters and assumptions, helping to reduce model uncertainty, because the model parameters and assumptions are no longer implicit but are carefully considered and determined.

[0017] Through Monte Carlo simulation and probability distribution analysis, this method can generate probability distributions by comprehensively considering the uncertainties in model parameters and input data, helping decision makers better understand the range of uncertainty in the assessment results. This method clearly defines the boundary values ​​of water resource carrying capacity, including sustainable water consumption, water quality standards, and ecosystem needs, clearly defines the sustainability of water resources, and helps provide clear guidance to decision makers. This method comprehensively considers the water resource endowment characteristics, model parameters and assumptions, uncertainty analysis, and sustainable water resource use criteria. Such comprehensive consideration contributes to a more comprehensive assessment of water resource carrying capacity.

[0018] The method's clear definitions and uncertainty analysis can help make more targeted decisions to ensure the sustainable management of water resources, providing decision makers with a better understanding of sustainability boundaries and potential risks.

[0019] Overall, this method helps to improve the reliability and feasibility of water resource carrying capacity assessment, while reducing the impact of model uncertainties and enabling decision makers to develop sustainable water resource management strategies. [Brief explanation of the drawings]

[0020] The drawings are included to provide a further understanding of the invention, constitute a part of this specification, and are used in conjunction with the examples of the invention for the purpose of explaining the invention and are not to be construed as limiting the invention. In the drawings: [Figure 1] 1 is a flowchart of the present invention. DETAILED DESCRIPTION OF THE INVENTION

[0021] The following specific embodiments can be combined with each other, and the same or similar concepts or processes may not be described again in some embodiments.Hereinafter, embodiments of the present invention will be described with reference to the drawings.

[0022] As shown in FIG. 1, the present invention discloses a regional carrying capacity assessment method based on water resource endowment characteristics, which includes the following steps: Step 1: Data collection and characterization of water resources Various data on water resources, including precipitation, river flow, groundwater level, water quality, etc., will be collected, and spatial data will be obtained using remote sensing technology and geographic information systems (GIS) to determine the water resource endowment characteristics of the water resources in the assessment area, including the distribution of water bodies, groundwater availability, climatic conditions, etc.

[0023] Step 2: Building a water resources model Based on the data and water resource endowment characteristics, mathematical models, including hydrological models, meteorological models, and groundwater models, are constructed to simulate the distribution and availability of water resources. Parameters and assumptions are clearly defined in the models, and the uncertainties of these parameters and assumptions are analyzed in subsequent steps.

[0024] Step 3: Analyze the uncertainty of the model parameters The uncertainty of the model parameters is expressed using a probability distribution, and the parameter distribution can be determined based on historical data or expert judgment. The uncertainty of the model's input data, such as precipitation and temperature, is also taken into account.

[0025] Step 4: Monte Carlo simulation The Monte Carlo simulation method is used to evaluate the model output through random sampling. This method can generate a large number of simulation results under uncertainty to take into account the uncertainty of parameters and input data. Each time a simulation is run, values ​​are extracted from the distribution of parameters and input data, and the water resources model is run to generate simulation results.

[0026] Step 5: Assess carrying capacity Based on the results of Monte Carlo simulations, the carrying capacity of regional water resources was calculated, including sustainable water consumption, water quality requirements, and ecosystem needs, and through statistical analysis of multiple simulation results, the probability distribution of carrying capacity was calculated to take into account uncertainty.

[0027] The Monte Carlo simulation method can effectively handle model uncertainties, and by simulating a large number of different scenarios and fully considering the effects of parameter and input data changes on the load-bearing assessment, the accuracy of the assessment results can be improved and decision makers can be provided with more comprehensive information for formulating sustainable water resource management policies.

[0028] Specifically, Step 1 includes: In step 1, data collection and determination of water resource endowment characteristics can be done in the following ways: Data collection: Precipitation data can be obtained from historical weather station records, and includes date and location information. River flow data can be monitored through hydrological stations, and the data includes time and location information. Groundwater level data is obtained from groundwater level monitoring wells, and the data includes time and location information. Water quality data includes various water quality parameters such as dissolved oxygen, pH, and pollutant concentrations, and is collected by water quality monitoring stations. Remote sensing techniques and GIS can capture the distribution and characteristics of surface water bodies, including spatial data on lakes, reservoirs, wetlands, etc.

[0029] Determining water resource endowment characteristics: The distribution of water bodies can be determined by analyzing remote sensing data using GIS tools to ascertain the location, size, shape and depth of water bodies, and groundwater availability can be derived from groundwater level data and hydrological model analysis. Climate conditions use meteorological data such as temperature, humidity, and wind speed to assess seasonal changes and long-term trends in the climate. By collecting and integrating these data, a database of water resource endowment characteristics can be built and used for subsequent model construction and carrying capacity assessment. These data are also used to set parameters and input conditions in the model for model simulation and uncertainty analysis.

[0030] Specifically, Step 2 includes: In step 2, the construction of a hydrological model can be used to simulate the distribution and availability of water resources using hydrological balance equations.

[0031]

number

[0032] where P is precipitation, ET is evaporation and transpiration, which represents the loss of water from soil and water bodies due to evaporation and plant transpiration, Q is runoff, which represents the inflow of water into rivers, lakes, and groundwater, and ΔS is the change in soil water content and groundwater level.

[0033] The construction of a hydrological model requires consideration of the specific conditions and water resource endowments of the area, and the setting of model parameters and assumptions may include the following aspects: Area characteristics: Considering the geographical and climatic characteristics of the area, such as soil type, vegetation cover, and precipitation patterns; Hydrological parameters: Determine the hydrological parameters such as evaporation coefficient, runoff coefficient, soil water holding capacity in the model, which usually need to be estimated based on actual observation data or literature. Model time step: Select the model time step, such as days, months, or years, to match the time scale of your precipitation and runoff data. Model type: Choose the appropriate hydrological model depending on your specific needs, e.g., a simple water balance model or a more complex hydrological model such as the Soil and Water Assessment Tool (SWAT) or the Hydrologic Engineering Center- Hydrologic Modeling System (HEC-HMS). Data Input: The data collected in step 1, including precipitation data, meteorological data, groundwater level data, land use data, etc., are used as inputs to the model.

[0034] Hydrological modeling can be achieved through hydrological software tools, which typically provide model construction and parameter estimation capabilities. Once constructed, the model can be used to simulate the distribution and availability of water resources, providing the basis for subsequent uncertainty analysis, which considers parameter and input data uncertainties to generate simulation results under different scenarios.

[0035] In step 2, a meteorological model is constructed to simulate the impact of meteorological conditions such as precipitation and evaporation on water resources, which can be expressed as a water balance model.

[0036]

number

[0037] where P is precipitation, R is runoff, i.e., the amount of water that flows into rivers and lakes after precipitation, E is evaporation, which includes soil evaporation and transpiration, and ΔS is the change in soil moisture content, which includes water infiltration or drainage.

[0038] The implementation steps for building a meteorological model are as follows: Data collection: Obtain past precipitation data (P) and meteorological data including temperature, humidity, wind speed, etc. Parameter setting: The parameters in the model, including the type of soil, evaporation coefficient (E) and runoff coefficient (R), etc., must be determined. These parameters must be estimated based on actual observation data and literature. Model construction: involves constructing the water balance equation into a mathematical model and substituting parameters and input data into the equation; Running simulations: Running simulations using historical meteorological data and models to generate time series data for precipitation, evaporation, and runoff. Uncertainty analysis: The next step is to perform an uncertainty analysis and generate a probability distribution for the simulation results by taking into account the uncertainties in the parameters and input data.

[0039] To better understand the relationship between precipitation, evaporation and runoff and to take into account uncertainties, this meteorological model construction can simulate the distribution and availability of water resources, and the simulation results will be used in subsequent carrying capacity assessments to formulate sustainable water resource management policies.

[0040] In step 2, the groundwater model is constructed to simulate the distribution and availability of groundwater, which can be expressed using the groundwater flow equation as follows:

[0041]

number

[0042] where ∂h / ∂t is the time rate of change of the groundwater level, h is the groundwater level, S is the specific storage coefficient, and ∇ 2 h is the gradient of the groundwater level, which represents the direction and velocity of groundwater flow, and Q is the groundwater recharge or discharge, including the groundwater recharge rate and pumping rate.

[0043] The steps for implementing the groundwater model are as follows: Data collection: Obtain historical data on groundwater levels and groundwater recharge data for use in initializing and calibrating the model; Parameter setting: The parameters in the model, including the water storage coefficient (S) and groundwater recharge / pumping rate (Q), must be determined and estimated based on actual observation data. Model construction: The groundwater flow equation is constructed into a mathematical model, and parameters and input data are substituted into the equation. Run the simulation: Run a simulation using historical groundwater level data and the model to generate simulated results of groundwater levels over time. Uncertainty analysis: The next step is to perform an uncertainty analysis and generate a probability distribution for the simulation results by taking into account the uncertainties in the parameters and input data.

[0044] The construction of a groundwater model can simulate groundwater flow and changes, consider the impact of groundwater storage capacity and recharge / pumping on water resources, take into account uncertainties, and the simulation results can be used for subsequent carrying capacity assessment to formulate sustainable water resource management policies.

[0045] Specifically, Step 3 includes: In Step 3, the purpose of performing uncertainty analysis of the model parameters is to introduce probability distributions into the model parameters and input data to take into account the uncertainty, and the uncertainty of these parameters and input data can be displayed using probability distributions as follows:

[0046] Selection of parameter uncertainty distributions: For each parameter in the model, an appropriate probability distribution (e.g., normal, uniform, exponential, etc.) should be selected to represent its uncertainty based on prior knowledge, historical data, and expert opinion, and the selection of these distributions should be based on the nature of the parameter and the available information.

[0047] Parameter estimation: Using existing data or literature sources, each parameter is estimated or initialized, and these values ​​are used as one of the parameters of the probability distribution, for example, the mean (μ).

[0048] Determining the distribution parameters: For a selected probability distribution, determine parameters such as the standard deviation (σ) and other distribution characteristics that describe the range of uncertainty and the shape of the distribution.

[0049] Input data uncertainty analysis: Uncertainty is also taken into account for model input data such as precipitation and temperature. The variability of the data can be taken into account by collecting historical weather data, or uncertainty can be introduced using simulation techniques, for example, using Monte Carlo simulation or probability distributions to generate values ​​for the uncertainty of the input data.

[0050] Parameter and input data sampling: Using the selected probability distributions and determined parameter values, multiple values ​​are sampled from these distributions to generate multiple sets of uncertainty samples for the parameters and input data, which can be achieved using a random number generator or statistical software.

[0051] Model simulation: The water resources model is run using each parameter set and input data sample to obtain corresponding simulation results, generating a set of multiple simulation results that reflect the effects of parameter and input data uncertainties on the model output.

[0052] Uncertainty analysis: Analyzing a set of simulation results and considering the effects of uncertainty can include, for example, assessing the variability of model outputs under different parameter combinations and calculating statistical measures of the simulation results, such as means, standard deviations, and quantiles.

[0053] The use of such parameter uncertainty analysis methods allows for a more complete understanding of the uncertainty range of model results, providing more reliable simulation results for subsequent water resource carrying capacity assessments and helping decision makers better understand the potential risks and uncertainties to support the formulation of water resource management policies.

[0054] Specifically, step 4 includes: Monte Carlo simulation in step 4 is used to evaluate the model output through random sampling, taking into account the uncertainty of parameters and input data. The specific implementation steps are as follows: Parameter and input data sampling: From the probability distribution in step 3, draw random samples for each model parameter and input data. This can be achieved using a random number generator, and sampling can be based on the distribution probability of the parameters.

[0055] Model simulation: Using each parameter set and input data sample, the water resources model is run to obtain corresponding simulation results, and after each simulation run, the model generates output results including an estimate of the water resources carrying capacity.

[0056] Simulation repeat: Steps 1 and 2 are repeated, and to ensure that uncertainties are fully considered, a large number of simulations are typically run, with Monte Carlo simulations typically running thousands of simulations.

[0057] Aggregating results: Each time a simulation is run, an estimate of the water resource carrying capacity is recorded and a set of results for multiple simulations is generated.

[0058] Uncertainty analysis: The set of simulation results can be analyzed and statistical indicators such as mean, standard deviation, and quantiles can be calculated to understand the range of uncertainty in water resource carrying capacity, and these statistical indicators can be used to assess the impact and risk of uncertainty.

[0059] The core idea of ​​Monte Carlo simulation is to capture uncertainties in parameters and input data through a large number of simulation runs, which provides information on the distribution of carrying capacity of water resources and provides a more comprehensive understanding that allows decision makers to develop more reliable policies and management strategies in the face of uncertainty.

[0060] Specifically, Step 5 includes: The carrying capacity assessment in step 5 is based on the results of Monte Carlo simulation, which takes into account the certainty of calculating the regional water resource carrying capacity. The implementation steps are as follows:

[0061] 5.1 Summary of simulation results: A set of simulation results from the Monte Carlo simulations, including estimates of water resource carrying capacity, are aggregated to form a dataset containing results from multiple simulations.

[0062] 5.2 Criteria for sustainable use of water resources: The criteria and indicators that define sustainable water resource use include the following aspects: 5.2.1 Sustainable Water Withdrawal: Determine the maximum sustainable water consumption of regional water resources to meet various needs. Sustainable water withdrawal is an important indicator used to determine the maximum sustainable water consumption of regional water resources to meet various needs such as drinking water, agriculture, and industry. Sustainable water withdrawal (SWW) is estimated using the following formula:

[0063]

number

[0064] Among them, SWW is the sustainable water consumption, i.e., the maximum sustainable water consumption, AR is the available water resource, usually expressed as the total annual amount of water resource, ER is the amount of water used for ecosystem maintenance, which is the amount of water resource required to maintain the balance of the ecosystem, R is the amount of water resource reserved or allocated for other uses, and T is the period under consideration, usually in years.

[0065] Calculating sustainable water consumption involves determining the available water resources of a decision area, the amount of water to be retained or allocated to ecosystems, and the amount of water already allocated to other areas, so that sustainable water consumption does not exceed the available water resources minus ecosystem needs and other allocated uses.

[0066] 5.2.2 Water Quality Standards: To ensure acceptable water quality, water quality standards are established, including various water quality parameters. The formulation of water quality standards aims to ensure the acceptability of water resources to meet various uses, such as drinking water, industrial water, and agricultural water. Water quality standards are based on limit values ​​for various water quality parameters, including dissolved oxygen, pH, and pollutant concentrations. The method for formulating water quality standards is as follows:

[0067] Determine the water quality parameters that need to be controlled: First, determine the water quality parameters that need to be controlled and monitored.

[0068] Establish limit values: For each water quality parameter, establish corresponding limit values. The limit values ​​are based on regulations, international standards, health standards, etc. For example, the limit values ​​may be stricter for drinking water, while higher concentrations may be allowed for industrial water.

[0069] Monitoring and evaluation: Establishing a water quality monitoring system and regularly monitoring the concentration of each water quality parameter will help ensure that the water quality is within the specified range and meets the standard requirements.

[0070] Regulatory basis: Water quality standards should be developed on a regulatory basis to ensure their legitimacy and enforceability.

[0071] Adjustment and Revision: Water quality standards are periodically evaluated and revised to reflect new scientific research, technological advances, and environmental needs.

[0072] This process is intended to ensure that the water quality of a body of water meets the standards for sustainable use to meet various needs and maintain ecological balance. Specific limits and standards vary depending on geographic location, regulations, uses, and specific environmental conditions.

[0073] 5.2.3 Ecosystem needs: Consider the water resources required to sustain the ecosystem in order to maintain ecological balance. Measuring and monitoring ecological needs: First, the water resources required by ecosystems must be determined and measured, including the water requirements of various ecosystems such as wetlands, rivers, lakes and aquatic ecosystems.

[0074] Building an ecological needs model: To quantify the needs of ecosystems, an ecosystem needs model is built, which can estimate the water resource needs of different ecosystems based on ecological knowledge, field surveys and monitoring data. Develop ecological needs criteria: Develop water resource management policies and criteria to ensure that ecosystem needs are taken into account in the allocation of water resources; these criteria can include requirements for different ecosystem types and for different time periods.

[0075] Specific ecosystem needs vary depending on factors such as geographic location, ecosystem type, and water body type, and real-world situations may require multiple indicators and models to represent ecosystem needs.

[0076] In water resources management, it is important to ensure adequate supplies to sustain ecosystem needs, which in turn supports ecological balance and biodiversity conservation and helps prevent overexploitation of water resources and damage to ecosystems.

[0077] 5.3 Analysis of Probability Distributions: Statistical analysis is performed on the set of simulation results to calculate the probability distribution of water resource carrying capacity, including calculation of statistical indicators such as the mean, standard deviation, and quantiles (e.g., 25th and 75th quantiles).

[0078] The general steps for calculating these statistical indices are as follows:

[0079] Collecting Simulation Results: First, a dataset containing the results of multiple simulations is required, typically generated by Monte Carlo simulation, with each run producing an estimate of the carrying capacity of the water resource.

[0080] Average (mean value): The average is the average value of the simulation results and is used to represent the central tendency of water resource carrying capacity, and the average is calculated as follows:

[0081]

number

[0082] where N is the number of simulation runs, and Xi is the estimated water resource carrying capacity in the i-th simulation.

[0083] Standard deviation: The standard deviation measures the variability of the simulation results and is used to represent the uncertainty of the water resource carrying capacity, and is calculated as follows:

[0084]

number

[0085] Percentiles: Quantiles represent values ​​at specific percentiles of the simulation results. The 25th and 75th quantiles are used to represent the middle 50% range of the simulation results. Calculating quantiles requires sorting the simulation results in ascending order to determine the value of the corresponding quantile position.

[0086] 25th quantile: Of all simulation results, 25% of the results are below the 25th quantile.

[0087] 75th quantile: Of all simulation results, 75% of the results are below the 75th quantile.

[0088] These statistical indicators help to understand the central tendency, uncertainty range and distribution shape of water resource carrying capacity; the mean and standard deviation provide information on the average level and variability of water resource carrying capacity; and the quantiles represent values ​​at different distribution quantile positions, which helps to understand the range of carrying capacity under different circumstances.

[0089] 5.4 Determination of carrying capacity boundaries: Based on the sustainable water resource utilization criteria and the analysis of probability distribution, the boundary value of water resource carrying capacity is determined, which may be the sustainable water consumption amount, the boundary value of water quality criteria, or the amount of water required to meet the needs of the ecosystem, etc. The implementation method is as follows:

[0090] Sustainable water consumption boundaries: Based on the definition of sustainable water consumption and the results of the probability distribution analysis, determine an upper limit or threshold for sustainable water use, which may be a specific quantile value, for example, the 95th quantile, which represents the upper limit of sustainable water consumption.

[0091] Water quality standard boundaries: For water quality standards, based on the results of regulations and probability distribution analysis, the acceptability boundaries for various water quality parameters, including specific quantile values ​​of various water quality parameters, are determined to ensure that the water quality is within acceptable limits.

[0092] Boundaries of ecosystem needs: For ecosystem needs, based on the results of the ecosystem needs model and probability distribution analysis, the amount of water required to maintain ecosystem balance is determined, which can be the threshold for a particular ecosystem need.

[0093] Overall considerations: Considering multiple boundary values ​​will ensure that water resource carrying capacity takes into account a comprehensive range of factors, including integrating sustainable water consumption, water quality standards, and ecosystem needs to determine a final, comprehensive boundary for water carrying capacity.

[0094] The determination of these boundary values ​​is important for the carrying capacity of water resources and provides guidance to decision makers on how to manage and allocate water resources. The development of these boundary values ​​requires a comprehensive consideration of regulatory, scientific, environmental and social factors to ensure sustainable water resource management and use.

[0095] 5.5 Considering Uncertainties: Taking uncertainty into account, determine the uncertainty range of water resource carrying capacity, which can be calculated by calculating the confidence interval or quantile of the distribution, to understand the degree of uncertainty in the carrying capacity assessment, and the implementation steps are as follows:

[0096] Probability distribution: A dataset of simulation results is used, which includes estimates of water resource carrying capacity from multiple simulation runs.

[0097] Confidence interval calculation: To determine the range of uncertainty in the carrying capacity of a water resource, a confidence interval can be calculated, which is a statistical range that includes the likelihood of the true parameter value. A confidence level is used to express the confidence level of the confidence interval (e.g., a 95% confidence level).

[0098] The calculation of a 95% confidence interval is usually based on the mean and standard deviation, and for a normal distribution, the 95% confidence interval can be calculated as follows:

[0099]

number

[0100] Calculating quantiles: Quantiles represent values ​​at specific quantile locations in a distribution. Quantiles used to represent uncertainty include the 25th and 75th quantiles to account for the middle 50% range of the distribution. Calculating quantiles typically involves sorting simulation results and then determining the value at a specific quantile location. For example, the 25th quantile represents 25% of all simulation results that are at or below the 25th quantile, and the 75th quantile represents 75% of all simulation results that are at or below the 75th quantile.

[0101] Calculating confidence intervals and quantiles allows understanding the range and distribution of uncertainty in water resource carrying capacity estimates, helping decision makers better understand the assessment results and take appropriate actions to address the uncertainties.

[0102] 5.6 Formulation of policies and recommendations: Based on the results of the carrying capacity assessment, policy recommendations will be provided to decision makers, including formulating water resource allocation strategies, proposing recommendations on water resource protection, and adjusting water consumption quotas to ensure sustainable management of water resources.

[0103] The results of the carrying capacity assessment provide insights into regional water management, allowing decision makers to better understand the limits of sustainable use and potential risks while taking into account uncertainties. This will help them formulate policies to ensure the sustainable management and use of water resources.

[0104] Finally, it should be noted that the above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. Although the present invention has been described in detail with reference to the above embodiments, those skilled in the art may modify the technical solutions described in the above embodiments or replace some of the technical features. Any modifications, substitutions and improvements made within the spirit and principle of the present invention shall be included in the scope of the claims of the present invention.

Claims

1. A method for evaluating regional carrying capacity based on water resource endowment characteristics, implemented by a computer, comprising: Step 1: collecting water resource data and spatial data to determine water resource endowment characteristics, including distribution of water bodies, groundwater availability, and climatic conditions, in the evaluation area; Step 2: constructing a water resources model for simulating the distribution and availability of water resources based on the water resources data and the water resource endowment characteristics determined in step 1; Step 3: expressing uncertainty about the parameters in the water resources model constructed in step 2 using probability distributions; Step 4: Evaluating the output results of the water resources model constructed in step 2 through random sampling using Monte Carlo simulation, and each time a simulation is run, values ​​are extracted from the distribution of parameters and input data, and the water resources model is run to simulate the distribution and availability of water resources, thereby generating simulation results; A method for evaluating regional carrying capacity based on water resource endowment characteristics, comprising step 5 of calculating regional water resource carrying capacity based on the results of Monte Carlo simulation.

2. In the step 1, Collecting water resource data including precipitation, river flow, groundwater level, and water quality; Precipitation data are obtained from historical records of weather stations; River flow data is monitored through hydrological stations; Groundwater level data are obtained from groundwater level monitoring wells; the water quality data includes dissolved oxygen, pH, and contaminant concentrations and is collected by a water quality monitoring station; Using remote sensing techniques to obtain the distribution and characteristics of surface water bodies; Determine water resource endowment characteristics, including distribution of water bodies, groundwater availability, and climatic conditions; The distribution of water bodies is analyzed using GIS tools to analyze remote sensing data and identify the location, size, shape and depth of water bodies. Groundwater availability is derived from groundwater level data and hydrological model analysis; The method for evaluating regional carrying capacity based on water resource endowment characteristics as described in claim 1, characterized in that the climatic conditions include using meteorological data to evaluate seasonal changes and long-term trends in the climate.

3. The water resources model constructed in step 2 includes a hydrological model, a meteorological model, and a groundwater model; The hydrological model includes: [Equation 1] where P is precipitation, ET is evaporation and transpiration, which represent the loss of water from soil and water bodies due to evaporation and plant transpiration, Q is runoff, which represents the inflow of water into rivers, lakes, and groundwater, and ΔS is the change in soil water content and groundwater level. Weather models include: [Equation 2] where P represents precipitation, R represents runoff, i.e., the amount of water that flows into rivers and lakes after precipitation, E represents evaporation, including soil evaporation and transpiration, and ΔS represents the change in soil moisture content, including water infiltration or drainage. The groundwater model includes: [Equation 3] where ∂h / ∂t is the time rate of change of the groundwater level, h is the groundwater level, S is the specific water storage coefficient, and ∇ ２ 3. The method for evaluating regional carrying capacity based on water resource endowment characteristics as described in claim 2, wherein h is the gradient of the groundwater level, representing the direction and speed of groundwater flow, and Q is the groundwater recharge or discharge, including the groundwater recharge rate and pumping rate.

4. In step 3, expressing the uncertainty of each parameter in the water resources model constructed in step 2 using a probability distribution; Initialize each parameter, For the selected probability distribution, determine its parameters; generating input data uncertainty values ​​for input data of the water resources model; Using a probability distribution and determined parameter values, sample multiple values ​​from the distribution to , generating multiple sets of parameter and input data uncertainty samples; Executing the water resources model using each parameter set and input data sample to obtain corresponding simulation results; 4. The method for evaluating regional carrying capacity based on water resource endowment characteristics according to claim 3, further comprising analyzing the simulation results and evaluating the variability of the output results of the water resources model.

5. In step 5, aggregating a set of simulation results from the Monte Carlo simulation, including estimates of water resource carrying capacity, to form a data set containing results from multiple simulations; Define criteria and indicators for sustainable water resource use, including sustainable water consumption, water quality standards and ecosystem needs; Perform statistical analysis on the set of simulation results to calculate the probability distribution of water resource carrying capacity; Determine the boundary value of water resource carrying capacity based on the analysis of sustainable water resource utilization criteria and probability distribution; The method for evaluating regional carrying capacity based on water resource endowment characteristics according to claim 4, further comprising determining an uncertainty range for water resource carrying capacity.

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