A method for quantifying a blue-green water resource composite risk of a river basin

Abstract

The application discloses a kind of basin blue-green water resource composite risk quantification method, belong to hydrology water resources field, specific steps include: to the multi-source geography and hydrology meteorological basic data of basin is standardized pre-processing, constructs and calibrates and verifies SWAT hydrology model;Set up multiple groups of land use change and climate change combination scene to carry out hydrology simulation, obtain the scarcity index and vulnerability index of sub-basin scale blue water, green water;Fitting optimization optimal edge distribution function, construct two-dimensional Copula joint distribution model to analyze the nonlinear dependence structure between index, finally calculated and obtained the spatiotemporal distribution quantification evaluation result of basin blue-green water resource composite risk.The application can accurately quantify the composite risk of blue-green water coupling system, break through the limitation of single element independent evaluation, provide reliable scientific support for watershed water resources management and risk prevention and control.

Description

Technical Field

[0001] This invention relates to the field of hydrology and water resources technology, and in particular to a method for quantifying the combined risks of blue and green water resources in a watershed. Background Technology

[0002] Existing watershed water resources assessment technologies typically use the SWAT distributed hydrological model as the core, combined with hydrological statistical analysis methods, to conduct water quantity simulation and scarcity assessment of blue water and green water under the superimposed scenarios of climate change and land use change. Copula functions, as a conventional tool for multivariate joint probability analysis, are widely used to characterize the dependency structure and calculate the joint probability of traditional hydrological variables such as runoff and drought, and to analyze the probabilistic correlation characteristics between hydrological elements.

[0003] However, the aforementioned existing technologies have inherent limitations that are difficult to overcome. Risk assessments conducted separately for blue water and green water as independent elements cannot fully characterize the inherent nonlinear synergistic relationship between the two in the hydrological cycle, which can easily lead to insufficient risk assessment and identification. Furthermore, the application of Copula functions to the dependency analysis between variables such as precipitation, runoff, and drought index does not cover the coupled analysis dimension of the scarcity and vulnerability of blue and green water. It is difficult to quantitatively analyze the complex risk mechanism of blue and green water in terms of resource scarcity and system anti-interference ability, and it is difficult to fully support the actual needs of watershed blue and green water collaborative management and precise risk prevention and control. Summary of the Invention

[0004] The purpose of this invention is to provide a method for quantifying the combined risks of blue and green water resources in a watershed, thereby solving the aforementioned technical problems.

[0005] To achieve the above objectives, this invention provides a method for quantifying the combined risks of blue and green water resources in a watershed, comprising the following steps: S1. Based on the basic geographical and hydrological meteorological data of the watershed, the input dataset of the SWAT hydrological model is obtained through standardized preprocessing with unified temporal and spatial scales and data formats. S2. Based on the input dataset, a reliable watershed hydrological simulation model is obtained by constructing a SWAT hydrological model of the watershed and conducting parameter sensitivity analysis and calibration verification. S3. Based on the watershed hydrological simulation model, by setting multiple sets of combined land use change and climate change scenarios and performing hydrological simulation calculations for each scenario, the blue water scarcity index, green water scarcity index, blue water vulnerability index and green water vulnerability index at the sub-watershed scale under each scenario are obtained. S4. Based on time series data of four types of indices, the optimal marginal distribution function is selected by fitting and a two-dimensional Copula joint distribution model is constructed to obtain the nonlinear dependency structure of the scarcity of blue water and green water and the vulnerability of blue water and green water. S5. Based on the two-dimensional Copula joint distribution model, by calculating the joint probability index under different risk levels, the spatiotemporal distribution quantitative assessment results of the composite risk of blue and green water resources in the basin are obtained.

[0006] Preferably, the specific steps of S1 are as follows: based on the collected digital elevation model, land use data, soil data, daily meteorological data, monthly hydrological data, water resource utilization data, and future scenario data of the CMIP6 global climate model, an input dataset conforming to the SWAT hydrological model input specifications is obtained through preprocessing operations of unified projection coordinate system, spatial resolution, and time step.

[0007] Preferably, the specific steps of S2 are as follows: Based on the input dataset, the SUFI-2 algorithm is used on the SWAT-CUP platform to carry out global sensitivity analysis and parameter calibration optimization of hydrological parameters, and the model applicability is verified by three indicators: coefficient of determination, Nash efficiency coefficient and percentage deviation, so as to obtain a reliable watershed hydrological simulation model. Among them, the coefficient of determination The calculation formula is: ; In the formula, For the first Runoff observations at time [time] For the first Simulated runoff values ​​at time [time]. This represents the average of the observed runoff values. The number of samples; Nash efficiency coefficient The calculation formula is: ; Percentage deviation The calculation formula is: .

[0008] Preferably, the specific steps of S3 are as follows: Based on the watershed hydrological simulation model, by selecting key years, matching the corresponding historical climate periods with the two types of future climate scenarios SSP245 and SSP585, a total of 12 sets of land use and climate combination simulation scenarios are set up, and hydrological simulation calculations are carried out for each scenario to obtain the spatiotemporal distribution results of blue water scarcity index, green water scarcity index, blue water vulnerability index and green water vulnerability index at the sub-watershed scale under each scenario.

[0009] Preferably, in step S4, based on the time series data of the blue water scarcity index, green water scarcity index, blue water vulnerability index, and green water vulnerability index, the unknown parameters of various candidate marginal distribution functions are estimated by the maximum likelihood method, and the fitting effect is evaluated by the Akaike information criterion (AIC) and the Bayesian information criterion (BIC), and the optimal marginal distribution function corresponding to each index is selected.

[0010] Preferably, before constructing the two-dimensional Copula joint distribution model, based on the time series data of the four types of indices, the significant correlation test results between the blue water and green water scarcity indices and between the blue water and green water vulnerability indices are obtained by calculating the Kendall rank correlation coefficient and the Spearman rank correlation coefficient.

[0011] Preferably, in S4, based on the optimal marginal distribution function corresponding to each type of index, four types of two-dimensional Copula functions, namely Gaussian, Clayton, Frank, and Gumbel, are selected to perform joint distribution fitting on the two sets of variables, namely blue water scarcity-green water scarcity and blue water vulnerability-green water vulnerability. The unknown parameters of each Copula function are estimated by the maximum likelihood method to obtain multiple sets of candidate Copula joint distribution models.

[0012] Preferably, based on multiple candidate Copula joint distribution models, the root mean square error of each model is calculated. With Nash efficiency coefficient By combining the Akaike information criterion and the Bayesian information criterion, the Copula function with the best fit for each criterion is selected to obtain the optimal two-dimensional Copula joint distribution model for the corresponding variable pair.

[0013] Preferably, the specific steps of S5 are as follows: based on the optimal two-dimensional Copula joint distribution model, by calculating the joint cumulative probability, joint transcendence probability and conditional probability corresponding to different scarcity levels and different vulnerability levels, the joint occurrence probability results of the blue-green water coupling system under different risk levels are obtained.

[0014] Preferably, based on the joint occurrence probability results of the blue-green water coupling system, spatial visualization and multi-scenario comparative analysis at the sub-basin scale are used to obtain the spatial distribution map and spatiotemporal evolution characteristics of the blue-green water composite risk in the basin under different climate change and land use change scenarios, providing scientific support for the optimal allocation of water resources and risk management in the basin.

[0015] Therefore, the present invention employs the above-mentioned method for quantifying the combined risk of blue and green water resources in a watershed, which has the following beneficial effects: 1. This invention combines the SWAT distributed hydrological model with a two-dimensional Copula function to construct a complete technical process from hydrological process simulation to composite risk quantification. It overcomes the shortcomings of existing blue-green water risk assessment methods that combine hydrological process simulation with probabilistic statistical analysis, and improves the ability to quantify the collaborative risk of blue-green water. It relies on the SWAT model to reconstruct the physical mechanism of the watershed hydrological cycle, ensuring the spatial distribution accuracy of blue-green water index calculations, and utilizes the Copula function to characterize the nonlinear dependencies between blue-green water indices, achieving quantitative analysis of the composite risk of the blue-green water coupled system. This provides a new technical approach for watershed water resource risk assessment.

[0016] 2. A joint distribution model for blue water and green water is constructed from the two core dimensions of scarcity and vulnerability, overcoming the shortcomings of existing technologies that assess blue and green water independently and conduct joint analysis only on traditional hydrological variables. By quantitatively calculating the joint transcendence probability and conditional probability at different levels, the model can systematically reveal the synergistic risk characteristics of blue and green water in terms of resource scarcity and system resilience, comprehensively characterizing the composite risk status of the watershed water resources system. This avoids the one-sidedness of results caused by single-factor and single-dimensional assessments, and improves the comprehensiveness and accuracy of risk assessment.

[0017] 3. By setting up multiple combined scenarios of land use change and climate change, covering the historical baseline period and several key future time nodes, the contribution of different driving factors to the combined risks of blue-green water can be quantified. Simultaneously, based on the output of risk spatial distribution results at the sub-basin scale, the spatial differences and spatiotemporal evolution patterns of risks within the basin can be clearly presented, providing technical support for differentiated water resource management, adaptive regulation, and optimal allocation within the basin.

[0018] 4. A multi-index verification mechanism is implemented in each stage of hydrological model calibration, marginal distribution fitting, and Copula function optimization: In the hydrological simulation stage, the coefficient of determination, Nash efficiency coefficient, and percentage deviation are used to verify the model's applicability; in the marginal distribution and Copula modeling stages, indicators such as AIC, BIC, and root mean square error are used to select the optimal model. Through multi-level precision control throughout the entire process, the uncertainty in risk quantification is reduced, ensuring the reliability and stability of the assessment results.

[0019] The technical solution of the present invention will be further described in detail below with reference to the accompanying drawings and embodiments. Attached Figure Description

[0020] Figure 1 A flowchart of a method for quantifying the combined risks of blue and green water resources in a watershed, provided by this invention; Figure 2 The blue-green water scarcity coupling probability distribution diagram for the baseline period of 1995-2023 in the blue-green water joint probability distribution diagram provided in the embodiments of the present invention; Figure 3 The blue-green water scarcity coupling probability distribution diagram for the SSP245 scenario from 2024 to 2050 in the blue-green water joint probability distribution diagram provided in the embodiments of the present invention; Figure 4 The blue-green water scarcity coupling probability distribution diagram for the SSP585 scenario from 2024 to 2050 in the blue-green water joint probability distribution diagram provided in the embodiments of the present invention; Figure 5 The blue-green water vulnerability coupling probability distribution diagram for the baseline period of 1995-2023 in the blue-green water joint probability distribution diagram provided in the embodiments of the present invention; Figure 6 The blue-green water vulnerability coupling probability distribution diagram for SSP245 scenario from 2024 to 2050 in the blue-green water joint probability distribution diagram provided in the embodiments of the present invention; Figure 7 The blue-green water joint probability distribution map of SSP585 scenario from 2024 to 2050 is provided in the embodiments of the present invention. Detailed Implementation

[0021] To make the objectives, technical solutions, and advantages of the embodiments of the present invention clearer, the embodiments of the present invention will be further described in detail below with reference to the accompanying drawings and examples. It should be understood that the specific embodiments described herein are merely illustrative of the embodiments of the present invention and are not intended to limit the embodiments of the present invention. All other embodiments obtained by those skilled in the art based on the embodiments of the present invention without creative effort are within the scope of protection of this application. Examples of the embodiments are shown in the accompanying drawings, wherein the same or similar reference numerals denote the same or similar elements or elements having the same or similar functions throughout.

[0022] It should be noted that the terms "comprising" and "having," and any variations thereof, are intended to cover non-exclusive inclusion, such as a process, method, system, product, or server that includes a series of steps or units, not necessarily limited to those steps or units explicitly listed, but may include other steps or units not explicitly listed or inherent to such process, method, product, or device.

[0023] The following is in conjunction with the appendix Figures 1-7 The embodiments of the present invention will be described in detail below.

[0024] Existing technologies have significant limitations in practical applications: First, current watershed water resource risk assessments often evaluate blue water and green water as independent hydrological elements, severing the nonlinear correlation and synergistic risk characteristics that naturally exist between the two in the hydrological cycle. The single-dimensional, single-element evaluation results cannot fully and accurately reflect the true risk status of the water resource system. Second, the joint probability analysis of Copula functions is currently mostly limited to traditional hydrological variables such as runoff and drought, and has not yet been extended to the dual-dimensional coupling analysis of the scarcity and vulnerability of blue and green water. It is difficult to quantitatively analyze the compound risk occurrence mechanism of the blue-green water coupled system, and cannot fully support the practical application needs of watershed blue-green water collaborative management and precise risk prevention and control.

[0025] Based on the above analysis, specific embodiments of the present invention are given, including: This embodiment provides a method for quantifying the combined risk of blue and green water resources in a watershed based on the SWAT model and Copula function. Taking a southern watershed as the research object, it integrates tools such as ArcGIS, SWAT, SWAT-CUP, and Python for implementation. The specific steps are as follows: S1. Data Collection and Standardization Preprocessing: The system collects watershed digital elevation models (DEMs), land use data, soil data, meteorological data, hydrological data, water resource utilization data, and future climate scenario data as basic data. Among them, meteorological data includes daily-scale observation data of precipitation, maximum temperature, minimum temperature, relative humidity, and wind speed; hydrological data includes monthly-scale runoff observation data; and water resource utilization data is used to calculate blue-green water evaluation indicators. Future land use data adopts existing research results, and future climate data adopts simulation results from five global climate models in the CMIP6 program, specifically CanESM5, GFDL-CM4, IPSL-CM6A-LR, MPI-ESM1-2-HR, and MRI-ESM2-0, covering two typical scenarios, SSP245 and SSP585.

[0026] A unified preprocessing operation is performed on the above multi-source basic data, including unifying the projected coordinate system, spatial resolution, and time step, to form an input dataset that conforms to the input specifications of the SWAT hydrological model.

[0027] S2 and SWAT hydrological model construction and calibration verification: Based on the input dataset obtained from S1, a SWAT distributed hydrological model for a southern watershed was constructed. The SWAT-CUP software was used to conduct a global sensitivity analysis on the parameters related to the watershed hydrological process, identify the sensitive parameters that have a significant impact on the simulation results, and use the SUFI-2 algorithm to calibrate and optimize the sensitive parameters to determine the optimal parameter combination for the model.

[0028] Using the coefficient of determination Nash efficiency coefficient and percentage deviation Three indicators are used to validate the model simulation results, evaluate the model's applicability and reliability, and obtain a reliable watershed hydrological simulation model; the calculation formulas for each indicator are as follows: (1) Coefficient of determination : ; In the formula, For the first Runoff observations at time [time] For the first Simulated runoff values ​​at time [time]. This represents the average of the observed runoff values. This represents the number of samples.

[0029] (2) Nash efficiency coefficient : .

[0030] (3) Percentage deviation : .

[0031] S3. Multi-scenario settings and simulation of blue-green water indexes: Based on the watershed hydrological simulation model obtained from S2, the years 2000, 2010, 2030, 2050, 2070, and 2100 were selected as key years. Coupled with different land use patterns and climate periods, a total of 12 simulation scenarios were set up, as detailed in the table below: Table 1. Climate, Land Use, and Land Cover LULC Scenario Settings:

[0032] Using historical periods as the baseline scenario, corresponding land use and climate data are input for each scenario and hydrological simulations are carried out to obtain the spatiotemporal variation results of water resources in the basin under each scenario. The amount of blue and green water resources, blue water scarcity index (BWSI), green water scarcity index (GWSI), blue water vulnerability index (BWVI), and green water vulnerability index (GWVI) of each sub-basin are calculated, and a spatial distribution map of blue and green water related indicators in a certain southern watershed is drawn.

[0033] S4. Marginal distribution fitting and construction of a two-dimensional Copula joint distribution model: Based on the four types of exponential time series data obtained from S3, correlation tests, marginal distribution fitting, Copula function fitting, and optimal model selection were carried out sequentially. The specific process is as follows: (1) Correlation analysis: Kendall rank correlation coefficient and Spearman rank correlation coefficient were used to analyze the correlation between blue water scarcity index and green water scarcity index, and between blue water vulnerability index and green water vulnerability index, respectively, to test the significance of the correlation between variables and to provide a basis for the construction of Copula function.

[0034] (2) Selection of optimal marginal distribution: Marginal distribution functions were fitted for the blue water scarcity index, green water scarcity index, blue water vulnerability index and green water vulnerability index respectively; the unknown parameters of each candidate marginal distribution function were estimated by the maximum likelihood method; the fitting effect of each distribution function was evaluated by the Akaike information criterion (AIC) and the Bayesian information criterion (BIC); and the distribution function with the best fitting effect was selected as the marginal distribution function of the corresponding index, so as to provide a basis for Copula joint modeling.

[0035] (3) Copula joint distribution fitting: Based on the optimal marginal distributions of each index obtained by optimization, a two-dimensional Copula joint distribution function is constructed. The expression of the joint distribution function is: ; in, For variables and The joint distribution function; , Variables , The marginal distribution function, This refers to the Copula function.

[0036] Four two-dimensional Copula functions—Gaussian, Clayton, Frank, and Gumbel—were selected to fit the joint distribution of the two sets of variables, namely “blue water scarcity-green water scarcity” and “blue water vulnerability-green water vulnerability.” The unknown parameters of each Copula function were estimated using the maximum likelihood method, resulting in multiple candidate Copula joint distribution models.

[0037] (4) Selection of the optimal Copula model: Calculate the root mean square error of each candidate Copula function. With Nash efficiency coefficient A comprehensive evaluation is conducted using four indicators: AIC, BIC, RMSE, and NSE. The option with the lowest AIC, BIC, and RMSE values ​​is selected. The Copula function with the value closest to 1 is used as the optimal two-dimensional Copula joint distribution model for the corresponding variable pair, resulting in the nonlinear dependency structure of the scarcity and vulnerability of blue and green water.

[0038] S5. Joint probability calculation and quantitative assessment of the combined risk of blue-green water: Based on the optimal two-dimensional Copula joint distribution model obtained from S4, the joint cumulative probability, joint transcendence probability, and conditional probability of blue water and green water under different scarcity levels and different vulnerability levels are calculated, so as to obtain the joint occurrence probability of the blue-green water coupling system under different risk levels.

[0039] Based on the joint probability calculation results, the combined risks of blue-green water in each sub-basin of the basin are quantitatively evaluated, and a spatial distribution map of the combined risks of blue-green water is drawn by combining spatial analysis methods. Furthermore, the calculation results under different climate change and land use change scenarios are compared to analyze the spatiotemporal evolution characteristics of the combined risks of blue-green water, so as to provide a scientific basis for the optimal allocation of water resources and risk management in the basin.

[0040] In summary, this invention achieves high-precision spatiotemporal simulation of the scarcity and vulnerability indicators of blue-green water under changing environments through the SWAT distributed hydrological model. It combines a two-dimensional Copula function to characterize the nonlinear dependence between these two types of indicators, quantitatively calculates the joint risk probability at multiple levels, and outputs the spatial distribution and evolution patterns. This not only overcomes the limitations of traditional single-factor independent assessment but also expands the application dimensions of the Copula function in the field of blue-green water risk assessment. It can systematically analyze the complex risk mechanism of the blue-green water coupled system, providing reliable technical support and quantitative basis for watershed water resource adaptive management and risk prevention.

[0041] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and not to limit them. Although the present invention has been described in detail with reference to preferred embodiments, those skilled in the art should understand that modifications or equivalent substitutions can still be made to the technical solutions of the present invention, and these modifications or equivalent substitutions cannot cause the modified technical solutions to deviate from the spirit and scope of the technical solutions of the present invention.

Claims

1. A method for quantifying the combined risk of blue and green water resources in a watershed, characterized in that, Includes the following steps: S1. Based on the basic geographical and hydrological meteorological data of the watershed, the input dataset of the SWAT hydrological model is obtained through standardized preprocessing with unified temporal and spatial scales and data formats. S2. Based on the input dataset, a reliable watershed hydrological simulation model is obtained by constructing a SWAT hydrological model of the watershed and conducting parameter sensitivity analysis and calibration verification. S3. Based on the watershed hydrological simulation model, by setting multiple sets of combined land use change and climate change scenarios and performing hydrological simulation calculations for each scenario, the blue water scarcity index, green water scarcity index, blue water vulnerability index and green water vulnerability index at the sub-watershed scale under each scenario are obtained. S4. Based on time series data of four types of indices, the optimal marginal distribution function is selected by fitting and a two-dimensional Copula joint distribution model is constructed to obtain the nonlinear dependency structure of the scarcity of blue water and green water and the vulnerability of blue water and green water. S5. Based on the two-dimensional Copula joint distribution model, by calculating the joint probability index under different risk levels, the spatiotemporal distribution quantitative assessment results of the composite risk of blue and green water resources in the basin are obtained.

2. The method for quantifying the combined risk of blue and green water resources in a watershed according to claim 1, characterized in that: The specific steps of S1 are as follows: Based on the collected digital elevation model, land use data, soil data, daily meteorological data, monthly hydrological data, water resource utilization data, and future scenario data of the CMIP6 global climate model, the input dataset conforming to the SWAT hydrological model input specifications is obtained through preprocessing operations of unified projection coordinate system, spatial resolution, and time step.

3. The method for quantifying the combined risk of blue and green water resources in a watershed according to claim 2, characterized in that: The specific steps of S2 are as follows: Based on the input dataset, the SUFI-2 algorithm is used on the SWAT-CUP platform to carry out global sensitivity analysis and parameter calibration optimization of hydrological parameters, and the model applicability is verified by three indicators: coefficient of determination, Nash efficiency coefficient and percentage deviation, so as to obtain a reliable watershed hydrological simulation model. Among them, the coefficient of determination The calculation formula is: ； In the formula, For the first Runoff observations at time [time] For the first Simulated runoff values ​​at time [time]. This represents the average of the observed runoff values. The number of samples; Nash efficiency coefficient The calculation formula is: ； Percentage deviation The calculation formula is: 。 4. The method for quantifying the combined risk of blue and green water resources in a watershed according to claim 3, characterized in that: The specific steps of S3 are as follows: Based on the watershed hydrological simulation model, by selecting key years, matching the corresponding historical climate periods with the two types of future climate scenarios SSP245 and SSP585, a total of 12 land use and climate combination simulation scenarios are set up, and hydrological simulation calculations are carried out for each scenario to obtain the spatiotemporal distribution results of blue water scarcity index, green water scarcity index, blue water vulnerability index and green water vulnerability index at the sub-watershed scale under each scenario.

5. The method for quantifying the combined risk of blue and green water resources in a watershed according to claim 4, characterized in that: In the specific steps of S4, based on the time series data of the blue water scarcity index, green water scarcity index, blue water vulnerability index and green water vulnerability index, the unknown parameters of various candidate marginal distribution functions are estimated by the maximum likelihood method, and the fitting effect is evaluated by the Akaike information criterion (AIC) and the Bayesian information criterion (BIC), and the optimal marginal distribution function corresponding to each index is selected.

6. The method for quantifying the combined risk of blue and green water resources in a watershed according to claim 5, characterized in that: Before constructing the two-dimensional Copula joint distribution model, based on the time series data of the four types of indices, the significant correlation test results between the blue water and green water scarcity indices and between the blue water and green water vulnerability indices were obtained by calculating the Kendall rank correlation coefficient and the Spearman rank correlation coefficient.

7. The method for quantifying the combined risk of blue-green water resources in a watershed according to claim 6, characterized in that: In S4, based on the optimal marginal distribution functions corresponding to various indices, four types of two-dimensional Copula functions, namely Gaussian, Clayton, Frank, and Gumbel, are selected to perform joint distribution fitting on the two sets of variables, namely blue water scarcity-green water scarcity and blue water vulnerability-green water vulnerability. The unknown parameters of each Copula function are estimated by the maximum likelihood method to obtain multiple sets of candidate Copula joint distribution models.

8. The method for quantifying the combined risk of blue-green water resources in a watershed according to claim 7, characterized in that: Based on multiple candidate Copula joint distribution models, the root mean square error of each model is calculated. With Nash efficiency coefficient By combining the Akaike information criterion and the Bayesian information criterion, the Copula function with the best fit for each variable pair is selected, and the optimal two-dimensional Copula joint distribution model for the corresponding variable pair is obtained.

9. The method for quantifying the combined risk of blue and green water resources in a watershed according to claim 8, characterized in that: The specific steps of S5 are as follows: Based on the optimal two-dimensional Copula joint distribution model, the joint cumulative probability, joint transcendence probability and conditional probability corresponding to different scarcity levels and different vulnerability levels are calculated to obtain the joint occurrence probability results of the blue-green water coupling system under different risk levels.

10. The method for quantifying the combined risk of blue and green water resources in a watershed according to claim 9, characterized in that: Based on the joint occurrence probability results of the blue-green water coupling system, spatial distribution maps and spatiotemporal evolution characteristics of the blue-green water composite risk in the basin under different climate change and land use change scenarios are obtained through spatial visualization and multi-scenario comparative analysis at the sub-basin scale, providing scientific support for the optimal allocation of water resources and risk management in the basin.

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