Regional capacity assessment method based on water resource availability 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 evaluation, and in particular, to a method for evaluating regional carrying capacity based on the occurrence characteristics of water resources.

Background Art

[0002] The occurrence characteristics of water resources refer to the nature and quantity of water resources owned by a specific area or region under natural conditions. These characteristics may affect the availability and water quality of water resources in that area, and have an important impact on the management and sustainable use of water resources. Based on the occurrence characteristics of water resources, the water resource carrying capacity of a region can be evaluated. The water resource carrying capacity refers to the ability of a region to support and meet the needs of human and natural systems for water resources under the natural conditions of that region. By analyzing the occurrence characteristics of water resources, it is possible to evaluate the needs balance of water resources in a region and whether there is a risk of overdevelopment or overuse. When evaluating the carrying capacity of a region based on the occurrence characteristics of water resources, several technical problems and challenges may be faced. For example, the evaluation of water resource carrying capacity often depends on mathematical models and computational models, and these models may contain uncertainties. The selection and assumptions of model parameters have an important impact on the evaluation results.

Summary of the Invention

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

Means for Solving the Problems

[0004] In order to achieve the above object, the technical solutions adopted by the present invention are as follows. A method for evaluating regional carrying capacity based on the occurrence characteristics of water resources, comprising the following steps. Step 1: Collect water resource data and spatial data, and determine the occurrence characteristics of water resources including the distribution of water areas, the availability of groundwater, and climate conditions in the evaluation area. Step 2: Based on the water resource data and the characteristics of water resource occurrence, construct a mathematical model to simulate the distribution and availability of water resources. Step 3: Regarding the parameters in the model, use probability distributions to represent uncertainties. Step 4: Use the Monte Carlo simulation method to evaluate the output results of the model through random sampling. Each time the simulation is executed, values are extracted from the distributions of the parameters and input data, the water resource model is run, and simulation results are generated. Step 5: Calculate the water resource carrying capacity of the region based on the results of the Monte Carlo simulation.

[0005] Furthermore, in the above Step 1, Collect water resource data including precipitation, river flow, groundwater level, and water quality. Precipitation data is obtained from the past records of weather observation stations. River flow data is monitored through hydrological observation stations. Groundwater level data is 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. Use remote sensing technology to obtain the distribution and characteristics of surface water areas. Determine the occurrence characteristics of water resources including the distribution of water areas, the availability of groundwater, and climate conditions. Here, the distribution of water areas is analyzed using GIS tools for remote sensing data to confirm the location, size, shape, and depth of the water areas. The availability of groundwater is derived from groundwater level data and hydrological model analysis. Climate conditions include evaluating the seasonal changes and long-term trends of the climate using meteorological data.

[0006] Furthermore, in the above Step 2, Include a hydrological model, a meteorological model, and a groundwater model. The hydrological model includes the following:

[0007]

Number

[0008] Here, P is the precipitation, ET is the evaporation and transpiration, representing the evaporation of water from the soil and water areas due to evaporation and plant transpiration, Q is the runoff, representing the inflow of water into rivers, lakes, and groundwater, and ΔS is the change in soil water content and groundwater level, The meteorological model includes the following,

[0009]

Number

[0010] Here, P represents the precipitation, R represents the runoff, that is, the amount of water flowing into rivers and lakes after precipitation, E represents the evaporation amount including soil evaporation and transpiration, and ΔS represents the change in soil moisture content including water infiltration or drainage, The groundwater model includes the following,

[0011]

Number

[0012] Here, ∂h / ∂t is the time change rate 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, representing the direction and velocity of groundwater flow, and Q is the recharge or discharge of groundwater including groundwater recharge and pumping.

[0013] Furthermore, the said step 3 includes the following. For each parameter in the model, use a probability distribution to represent its uncertainty, Initialize each parameter, For the selected probability distribution, determine its parameter, For the input data of the model, considering its uncertainty, generate the uncertainty value of the input data.

[0014] Using the probability distribution and the determined parameter values, sample a plurality of values from the distribution to generate a plurality of sets of uncertainty samples of the parameters and the input data, Using each parameter set and the input data sample, run a water resources model and obtain the corresponding simulation results, Analyze the simulation results and evaluate the variability of the model output considering the impact of uncertainty.

[0015] Furthermore, in step 5 above, Aggregate a series of simulation results obtained from Monte Carlo simulations, including estimated values of water resource carrying capacity, to form a dataset containing the results of multiple simulations, Define criteria and indicators regarding the sustainable use of water resources, including sustainable water consumption, water quality standards, and ecosystem needs, Perform a statistical analysis on the set of simulation results to calculate the probability distribution of the water resource carrying capacity, Based on the analysis of the sustainable water resource use criteria and the probability distribution, determine the boundary values of the water resource carrying capacity, Considering uncertainty, determine the range of uncertainty of the water resource carrying capacity.

Advantages of the Invention

[0016] Compared with the existing technologies, the technological progress achieved by the present invention is as follows. In this method, first, when constructing a water resources model, considering the endowment characteristics of regional water resources, including the distribution of water areas, the availability of groundwater, and climate conditions, etc., making the evaluation more realistic and enabling the model to better reflect the actual situation. In this method, the parameters and assumptions are clearly defined, which helps to reduce the uncertainty of the model because the parameters and assumptions of the model are no longer implicit but are carefully considered and determined.

[0017] Through Monte Carlo simulation and probability distribution analysis, this method can generate a probability distribution by comprehensively considering the uncertainties of model parameters and input data, assisting decision-makers in better understanding the range of uncertainties in evaluation results. This method clearly defines the boundary values of the carrying capacity of water resources, including sustainable water consumption, water quality standards, and ecosystem needs, clearly defines the sustainability of water resources, and helps to provide clear guidelines for decision-makers. In this method, the occurrence characteristics of water resources, model parameters and assumptions, uncertainty analysis, and sustainable water resource utilization criteria are comprehensively considered. Such comprehensive consideration contributes to a more comprehensive evaluation of the carrying capacity of water resources.

[0018] The clear definition and uncertainty analysis of this method help to make more targeted decisions to ensure the sustainable management of water resources, and decision-makers can have a deeper understanding of the sustainability boundaries and potential risks.

[0019] Overall, this method helps to improve the reliability and feasibility of water resource carrying capacity evaluation, while reducing the impact of model uncertainties and enabling decision-makers to formulate sustainable water resource management strategies.

Brief Description of the Drawings

[0020] The drawings are used for a further understanding of the present invention, form a part of this specification, and are used in conjunction with the embodiments of the present invention for the purpose of explaining the present invention, but do not constitute a limitation of the present invention. In the drawings,

Figure 1

Modes for Carrying Out the Invention

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

[0022] As shown in FIG. 1, the present invention discloses a method for evaluating regional carrying capacity based on the occurrence characteristics of water resources, and includes the following steps. Step 1: Data collection and determination of water resource occurrence characteristics Collect various data related to water resources, including precipitation, river flow, groundwater level, water quality, etc., obtain spatial data using remote sensing technology and geographic information system (GIS), and determine the occurrence characteristics of water resources in the evaluation area, including the distribution of water areas, groundwater availability, climate conditions, etc.

[0023] Step 2: Construction of water resource model Based on the data and water resource occurrence characteristics, construct a mathematical model including a hydrological model, a meteorological model, a groundwater model, etc. to simulate the distribution and availability of water resources, clearly define the parameters and assumptions in the model, and these parameters and assumptions will be analyzed for uncertainty in subsequent steps.

[0024] Step 3: Analysis of uncertainty of model parameters The parameters in the model are expressed with probability distributions, and the distribution of parameters can be determined based on historical data or expert judgment. The uncertainty of the input data of the model, such as precipitation and temperature, is also considered.

[0025] Step 4: Monte Carlo simulation Use the Monte Carlo simulation method to evaluate the output of the model through random sampling. In this method, to consider the uncertainty of parameters and input data, a large number of simulation results can be generated under uncertainty. Each time the simulation is executed, values are extracted from the distributions of parameters and input data, the water resource model is executed, and simulation results are generated.

[0026] Step 5: Evaluation of carrying capacity Based on the results of Monte Carlo simulation, the water resource carrying capacity of the region, including sustainable water consumption, water quality requirements, ecosystem needs, etc., was calculated, and through the statistical analysis of multiple simulation results, the probability distribution of the carrying capacity to consider uncertainty was calculated.

[0027] The Monte Carlo simulation method can effectively handle the uncertainty of the model, simulate a large number of different scenarios, and fully consider the impact of changes in parameters and input data on the load-bearing capacity evaluation, thereby improving the accuracy of the evaluation results and providing more comprehensive information for decision-makers to formulate sustainable water resource management policies.

[0028] Specifically, Step 1 includes the following. In Step 1, the data collection and determination of the occurrence characteristics of water resources can be carried out in the following ways. Data collection: Precipitation data can be obtained from the past records of meteorological observation stations, and the data includes date and location information. River flow data can be monitored through hydrological observation stations, and the data includes time and location information. Groundwater level data can be 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 concentration, and is collected by water quality monitoring stations. Remote sensing technology and GIS can obtain the distribution and characteristics of surface water areas including spatial data such as lakes, reservoirs, and wetlands.

[0029] Determination of the occurrence characteristics of water resources: The distribution of water areas can be analyzed using GIS tools for remote sensing data to confirm the location, size, shape, and depth of water areas. The availability of groundwater can be derived from groundwater level data and hydrological model analysis. Regarding climate conditions, meteorological data such as temperature, humidity, and wind speed can be used to evaluate seasonal changes and long-term trends in climate. By collecting and integrating these data, a database of the occurrence characteristics of water resources can be constructed and used for subsequent model construction and carrying capacity evaluation. These data are also used for parameter setting and input conditions in the model for model simulation and uncertainty analysis.

[0030] Specifically, step 2 includes the following. In step 2, the construction of the hydrological model can use the hydrological balance equation and is used to simulate the distribution and availability of water resources.

[0031]

Number

[0032] Here, P is precipitation, ET is evaporation and transpiration, representing the evaporation of water from soil and water areas due to evaporation and plant transpiration, Q is the outflow, representing the inflow of water into rivers, lakes, and groundwater, and ΔS is the change in soil water content and groundwater level.

[0033] When constructing a hydrological model, it is necessary to consider the specific conditions of the region and the occurrence characteristics of water resources. The setting of model parameters and assumptions may include the following aspects. Regional characteristics: Consider the geographical and climatic characteristics of the region, such as soil type, vegetation cover, precipitation pattern, etc. Hydrological parameters: Determine hydrological parameters in the model, such as evaporation coefficient, outflow coefficient, soil water retention capacity, etc. These parameters usually need to be estimated based on actual observation data and literature. Model time step: Select the model time step, such as day, month, year, etc., to match the time scale of precipitation and outflow data. Type of model: Select an appropriate hydrological model according to specific needs. For example, 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: Use the data collected in Step 1, including precipitation data, meteorological data, groundwater level data, land use data, etc., as the input to the model.

[0034] The construction of the hydrological model can be realized through hydrological software tools, which usually provide functions for model construction and parameter estimation. Once the model is constructed, it can be used to simulate the distribution and availability of water resources, providing a basis for subsequent uncertainty analysis, in which the uncertainty of parameters and input data is considered to generate simulation results under different scenarios.

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

[0036]

Number

[0037] Here, P represents precipitation, R represents runoff, that is, the amount of water flowing 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.

[0038] The implementation steps for constructing the meteorological model are as follows: Data collection: Obtain past precipitation data (P) and meteorological data including temperature, humidity, wind speed, etc. Parameter setting: Determine the parameters in the model including soil type, evaporation coefficient (E), runoff coefficient (R), etc. These parameters need to be estimated based on actual observation data and literature. Model construction: Construct a mathematical model of the water balance equation, including substituting parameters and input data into the equation. Simulation execution: Generate time series data of precipitation, evaporation, and runoff by performing simulations using past meteorological data and the model. Uncertainty analysis: In subsequent steps, perform uncertainty analysis and generate the probability distribution of simulation results by considering the uncertainty of parameters and input data.

[0039] To better understand the relationships among precipitation, evaporation, and runoff and consider uncertainty, the construction of this meteorological model can simulate the distribution and availability of water resources. The simulation results are used for subsequent carrying capacity evaluations in formulating sustainable water resource management policies.

[0040] In Step 2, the construction of the groundwater model is for simulating the distribution and availability of groundwater and can be expressed as follows using the groundwater flow equation.

[0041]

Equation

[0042] Here, ∂h / ∂t is the temporal change rate 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, representing the direction and velocity of groundwater flow, and Q is the recharge or discharge of groundwater including groundwater recharge and pumping volume.

[0043] The implementation steps of the groundwater model are as follows. Data collection: Obtain historical data on the groundwater level and groundwater recharge data for use in initializing and calibrating the model. Parameter setting: Determine the parameters in the model including the specific yield (S) and groundwater recharge / pumping volume (Q). These parameters need to be estimated based on actual observed data. Model construction: Construct a mathematical model of the groundwater flow equation and substitute the parameters and input data into the equation. Simulation execution: To generate the simulation results of the groundwater level over time, perform a simulation using the past groundwater level data and the model. Uncertainty analysis: In the subsequent steps, conduct an uncertainty analysis and generate the probability distribution of the simulation results by considering the uncertainties of the parameters and input data.

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

[0045] Specifically, Step 3 includes the following. In Step 3, the purpose of performing the uncertainty analysis of the model parameters is to introduce probability distributions to the model parameters and input data in order to consider uncertainties, and the uncertainties of these parameters and input data can be represented using probability distributions as follows.

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

[0047] Parameter estimation: Use existing data or literature to estimate or initialize each parameter, and these values are used as one of the parameters of the probability distribution, such as the mean (μ).

[0048] Determination of distribution parameters: For the selected probability distribution, determine parameters such as the standard deviation (σ) and other distribution characteristics so as to be able to explain the range of uncertainty and the shape of the distribution.

[0049] Uncertainty analysis of input data: Regarding the input data of the model such as precipitation and temperature, its uncertainty is also considered. By collecting past meteorological data, the variability of the data can be considered, or uncertainty can be introduced using simulation methods. For example, Monte Carlo simulation or probability distributions can be used to generate values of the uncertainty of the input data.

[0050] Sampling of parameters and input data: Using the selected probability distribution and the determined parameter values, sample multiple values from these distributions to generate multiple sets of uncertainty samples of parameters and input data, which can be realized using a random number generator or statistical software.

[0051] Simulation of the model: Use each parameter set and input data sample to run the water resources model, obtain the corresponding simulation results, generate a set of multiple simulation results, and reflect the influence of the uncertainty of parameters and input data on the model output.

[0052] Analysis of uncertainty: Analyze the set of simulation results, consider the influence of uncertainty, for example, evaluate the variability of the model output under different combinations of parameters, and include the calculation of statistical indicators of the simulation results such as the mean, standard deviation, and quantiles.

[0053] By using such a method for uncertainty analysis of parameters, the range of uncertainty in the model results can be more fully understood, providing more reliable simulation results for subsequent evaluation of water resource carrying capacity and assisting decision-makers to better understand potential risks and uncertainties in order to support the formulation of water resource management policies.

[0054] Specifically, step 4 includes the following. The Monte Carlo simulation in step 4 is used to evaluate the output of the model through random sampling, taking into account the uncertainty of parameters and input data, and the specific implementation procedure is as follows. Sampling of parameters and input data: Random samples are extracted for each model parameter and input data from the probability distribution in step 3, which can be realized using a random number generator and sampled based on the distribution probability of the parameters.

[0055] Model simulation: Using each set of parameters and input data samples, the water resource model is run to obtain the corresponding simulation results. Each time the simulation is run, the model generates output results including an estimated value of the water resource carrying capacity.

[0056] Repeated simulation: Steps 1 and 2 are repeated. Usually, a large number of simulations are run to ensure that uncertainty is fully considered. Monte Carlo simulation usually involves running thousands of simulations.

[0057] Aggregation of results: Each time the simulation is run, the estimated value of the water resource carrying capacity is recorded, generating a set that includes the results of multiple simulations.

[0058] Analysis of uncertainty: Analyze the set of simulation results, calculate statistical indicators such as the mean, standard deviation, and quantiles, to understand the range of uncertainty in water resource carrying capacity, and these statistical indicators can be used to evaluate the impact and risk of uncertainty.

[0059] The core idea of Monte Carlo simulation is to capture the uncertainty of parameters and input data through the execution of a large number of simulations, thereby providing information on the distribution of water resource carrying capacity and providing a more comprehensive understanding so that decision-makers can formulate more reliable policies and management strategies in the face of uncertainty.

[0060] Specifically, step 5 includes the following. The evaluation of carrying capacity in step 5 is based on the results of Monte Carlo simulation. By calculating the regional water resource carrying capacity, certainty is considered, and the implementation steps are as follows.

[0061] 5.1 Aggregation of simulation results: Aggregate a series of simulation results obtained from Monte Carlo simulation including estimated values of water resource carrying capacity to form a dataset containing the results of multiple simulations.

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

[0063]

Equation

[0064] Among them, SWW is the sustainable water consumption, that is, the maximum sustainable water consumption, AR is the available water resources, usually represented as the total amount of water resources in a normal year, ER is the amount of water used for ecosystem maintenance. This is the amount of water resources required to maintain the balance of the ecosystem, R is the amount of water resources secured or allocated for other uses, and T is the period considered, usually on an annual basis.

[0065] The calculation of sustainable water consumption includes determining the available water resources in the determined area, and the amount of water held or allocated to the ecosystem and the amount of water already allocated to other regions. By doing so, it is possible to ensure that the sustainable water consumption does not exceed the amount obtained by subtracting the ecological needs and other allocated uses from the available water resources.

[0066] 5.2.2 Water Quality Standards: To ensure the compliance of water quality, water quality standards including various water quality parameters are determined. The formulation of water quality standards aims to ensure the compliance of water resources to meet various uses such as drinking water, industrial water, and agricultural water. Water quality standards are based on the limit values of various water quality parameters including dissolved oxygen, pH, pollutant concentration, etc. 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] Formulate limit values: For each water quality parameter, formulate the corresponding limit values. The limit values are based on regulations, international standards, health standards, etc. For example, in the case of drinking water, the limit values may be more stringent, while in the case of industrial water, higher concentrations may be permitted.

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

[0070] Basis for Regulation: Water quality standards should be formulated based on regulatory grounds to ensure their legality and enforceability.

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

[0072] This process is to ensure that the water quality of water bodies meets the criteria for sustainable use to satisfy various needs and maintain ecological balance. Specific limit values and criteria vary depending on geographical location, regulations, uses, and specific environmental conditions.

[0073] 5.2.3 Needs of Ecosystems: Consider the water resources necessary to maintain ecosystems in order to maintain the balance of ecosystems. Measurement and Monitoring of Ecological Needs: First, it is necessary to determine and measure the water resources required for ecosystems, including the water requirements of various ecosystems such as wetlands, rivers, lakes, and aquatic ecosystems.

[0074] Construction of Ecological Needs Model: To quantify the needs of ecosystems, an ecological needs model is constructed, and the model can estimate the water resource needs of different ecosystems based on ecological knowledge, field surveys, and monitoring data. Formulation of Ecological Needs Standards: Formulate water resource management policies and standards to ensure that the needs of ecosystems are duly considered in the allocation of water resources, and these standards can include the requirements of different ecosystem types and different periods.

[0075] The specific ecological needs vary depending on factors such as geographical location, ecosystem type, and water body type. In actual situations, multiple indicators and models may be required to represent the ecological needs.

[0076] In water resource management, it is important to ensure an appropriate supply to maintain the needs of ecosystems, which helps support the ecological balance and protection of biodiversity and prevent overexploitation of water resources and damage to ecosystems.

[0077] 5.3 Analysis of Probability Distribution: Perform statistical analysis on the set of simulation results to calculate the probability distribution of water resource carrying capacity, including calculating statistical indicators such as the mean, standard deviation, and quantiles (such as the 25th percentile and 75th percentile).

[0078] The general steps to calculate these statistical indicators are as follows.

[0079] Collection of simulation results: First, a dataset containing the results of multiple simulations is required. This is usually generated by Monte Carlo simulation, and each time the simulation is run, an estimated value of the water resource carrying capacity is generated.

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

[0081]

Number

[0082] Here, N is the number of times the simulation is run, and Xi is the estimated value of the water resource carrying capacity in the i-th simulation.

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

[0084]

Number

[0085] Percentiles: Percentiles represent the values at specific percentage positions of the simulation results. The 25th and 75th percentiles are used to represent the 50% range in the middle of the simulation results. In the calculation of percentiles, it is necessary to sort the simulation results in ascending order to determine the values at the corresponding percentile positions.

[0086] 25th Percentile: Among all the simulation results, 25% of the results are below the 25th percentile.

[0087] 75th Percentile: Among all the simulation results, 75% of the results are below the 75th percentile.

[0088] These statistical indicators are helpful for understanding the central tendency, uncertainty range, and distribution shape of water resource carrying capacity. The mean and standard deviation provide information about the average level and variability of water resource carrying capacity, while percentiles represent the values at different distribution percentile positions and are useful for understanding the range of carrying capacity under different situations.

[0089] 5.4 Determination of Carrying Capacity Boundaries: Based on the analysis of sustainable water resource utilization criteria and probability distributions, determine the boundary values of water resource carrying capacity, which may be the sustainable water consumption, the boundary values of water quality standards, or the amount of water required to meet the needs of the ecosystem, etc. The implementation method is as follows.

[0090] Boundary of Sustainable Water Consumption: Based on the definition of sustainable water consumption and the results of probability distribution analysis, determine the upper limit or threshold of sustainable water use, which may be a specific percentile value, such as the 95th percentile representing the upper limit of sustainable water consumption.

[0091] Boundary of Water Quality Standards: Regarding water quality standards, based on the results of regulations and probability distribution analysis, determine the compliance boundaries of various water quality parameters, including specific percentile values of various water quality parameters, to ensure that the water quality is within the allowable range.

[0092] Boundary of ecosystem needs: Regarding 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 specific ecosystem needs.

[0093] Comprehensive consideration: By considering multiple boundary values, various factors are ensured to be comprehensively considered in the water resource carrying capacity. This includes integrating sustainable water consumption, water quality standards, and ecosystem needs to determine the final comprehensive boundary of water carrying capacity.

[0094] The determination of these boundary values is important for the water resource carrying capacity, providing guidelines for decision-makers on water resource management and allocation methods. To formulate these boundary values, it is necessary to comprehensively consider regulatory, scientific, environmental, and social factors to ensure sustainable management and utilization of water resources.

[0095] 5.5 Consideration of uncertainty: By considering uncertainty, the range of uncertainty of the water resource carrying capacity is determined. This can be achieved by calculating the confidence interval or quantiles of the distribution to understand the degree of uncertainty in the carrying capacity assessment. The implementation steps are as follows.

[0096] Probability distribution: Use the dataset of simulation results, which includes estimated values of water resource carrying capacity from multiple simulation runs.

[0097] Calculation of confidence interval: Regarding the determination of the range of uncertainty of the water resource carrying capacity, a confidence interval can be calculated. The confidence interval is a statistical range that includes the possibility of the true parameter value. The confidence level of the confidence interval is represented using a confidence level (e.g., 95% confidence level).

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

[0099]

Number

[0100] Calculation of Quantiles: Quantiles represent the values at specific quantile positions in a distribution. The quantiles used to represent uncertainty include the 25th and 75th quantiles to consider the middle 50% range of the distribution. The calculation of quantiles usually involves sorting the simulation results and then determining the values at specific quantile positions. For example, the 25th quantile represents that 25% of all simulation results are less than or equal to the 25th quantile value, and the 75th quantile represents that 75% of all simulation results are less than or equal to the 75th quantile value.

[0101] By calculating confidence intervals and quantiles, the range and distribution of the uncertainty of the estimated value of water resource carrying capacity can be understood, which helps decision-makers better understand the evaluation results and take appropriate actions to address uncertainty.

[0102] 5.6 Formulation of Policies and Recommendations: Based on the results of the carrying capacity assessment, provide decision-makers with policy recommendations, including formulating water resource allocation strategies, proposing recommendations for water resource protection, and adjusting water consumption allocations, etc., to ensure the sustainable management of water resources.

[0103] The results of the carrying capacity assessment provide insights into regional water management, enabling decision-makers to better understand the limits of sustainable use and potential risks while considering uncertainty. This helps in formulating policies for ensuring the sustainable management and utilization 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 foregoing embodiments, those skilled in the art can modify the technical solutions described in the foregoing embodiments or replace some of the technical features. Any modifications, substitutions, and improvements made within the scope of the spirit and principle of the present invention shall be included in the scope of the claims of the present invention.

Claims

1. Step 1 of collecting water resource data and spatial data and determining the water resource endowment characteristics including the distribution of water areas, the availability of groundwater, and the climate conditions in the evaluation area; Step 2 of constructing a mathematical model to simulate the distribution and availability of water resources based on the water resource data and the water resource endowment characteristics; Step 3 of using a probability distribution to represent the uncertainty about the parameters in the model; Step 4 of using the Monte Carlo simulation method to evaluate the output results of the model through random sampling. Each time the simulation is executed, values are extracted from the distributions of the parameters and the input data, the water resource model is executed, and simulation results are generated; A method for evaluating the regional carrying capacity based on the water resource endowment characteristics, characterized by including Step 5 of calculating the regional water resource carrying capacity based on the results of the Monte Carlo simulation.

2. In the said Step 1, collecting water resource data including precipitation, river flow, groundwater level, and water quality, the precipitation data is obtained from the past records of meteorological observation stations, the river flow data is monitored through hydrological observation stations, the groundwater level data is obtained from groundwater level monitoring wells, the 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 technology to obtain the distribution and characteristics of surface water areas, determining the water resource endowment characteristics including the distribution of water areas, the availability of groundwater, and the climate conditions, the distribution of water areas is analyzed using GIS tools for remote sensing data to confirm the location, size, shape, and depth of the water areas, the availability of groundwater is derived from the groundwater level data and hydrological model analysis, the climate conditions include evaluating the seasonal changes and long-term trends of the climate using meteorological data. The method for evaluating the regional carrying capacity based on the water resource endowment characteristics according to Claim 1 is characterized by this.

3. In the said Step 2, including a hydrological model, a meteorological model, and a groundwater model, the hydrological model includes the following, 【Number 1】 Here, P is precipitation, ET is evaporation and transpiration, representing the evaporation of water from the soil and water areas due to evaporation and plant transpiration, Q is the outflow, representing the inflow of water into rivers, lakes, and groundwater, and ΔS is the change in soil water volume and groundwater level. the meteorological model includes the following, 【Number 2】 Here, P represents precipitation, R represents runoff, i.e., the amount of water flowing 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 the following: [Number 3] Here, ∂h / ∂t is the time change rate 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, representing the direction and velocity of the groundwater flow, and Q is the recharge or discharge of groundwater including the recharge amount and pumping amount of groundwater. The regional carrying capacity evaluation method based on the occurrence characteristics of water resources according to claim 2, characterized in that

4. In step 3 above: For each parameter in the model, its uncertainty is represented using a probability distribution. Each parameter is initialized. For the selected probability distribution, the parameter is determined. For the input data of the model, considering its uncertainty, an uncertainty value of the input data is generated. Using the probability distribution and the determined parameter values, multiple values are sampled from the distribution to generate multiple sets of parameter and input data uncertainty samples. Using each set of parameter and input data samples, the water resources model is executed to obtain the corresponding simulation results. The method for evaluating regional carrying capacity based on the occurrence characteristics of water resources according to claim 3, characterized in that it includes analyzing the simulation results and evaluating the variability of the model output considering the influence of uncertainty.

5. In step 5 above: A series of simulation results obtained from Monte Carlo simulation including the estimated value of water resources carrying capacity are aggregated to form a data set including the results of multiple simulations. Criteria and indicators regarding the sustainable use of water resources including sustainable water consumption, water quality standards, and ecosystem needs are defined. Statistical analysis is performed on the set of simulation results to calculate the probability distribution of water resources carrying capacity. Based on the analysis of sustainable water resources use criteria and the probability distribution, the boundary value of water resources carrying capacity is determined. The method for evaluating regional carrying capacity based on the occurrence characteristics of water resources according to claim 4, characterized in that it includes determining the range of uncertainty of water resources carrying capacity considering uncertainty.

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