Drainage basin hydrological toughness evaluation and regulation method based on Budyko framework

Abstract

The invention provides a drainage basin hydrological toughness evaluation and regulation method based on a Budyko framework, and belongs to the technical field of hydrological water resource management and environment monitoring. Firstly, an improved Budyko framework and a standardized rainfall index (SPI) are adopted to carry out hydrological toughness analysis calculation and mutation analysis research on the Weihe river basin; then, the driving force influencing the hydrological toughness of the watershed and the regulation and control mechanism of the driving force are analyzed by adopting CCM-Geoderator; and finally, constructing a natural scene without human activities through a VIC model, and illuminating the influence of human climate change on hydrological toughness. The new hydrological toughness evaluation method provided by the invention can comprehensively explore the space-time evolution characteristics and driving force of the hydrological toughness in the Weihe River basin, and provides effective support for improving the ecological restorability of the basin and promoting sustainable development.

Description

Technical Field

[0001] This invention relates to the field of hydrological and water resources management and environmental monitoring technology, specifically to a watershed hydrological resilience assessment method based on the Budyko-SPI-VIC framework and its driving mechanism analysis and regulation strategy generation system, which is particularly suitable for watershed hydrological system resilience analysis and sustainable management in arid and semi-arid regions. Background Technology

[0002] With the intensification of global climate change and human activities, river basin hydrological systems face increasingly frequent extreme weather events (such as droughts and floods) and human disturbances (such as land-use change and water conservancy project construction), leading to problems such as decreased hydrological system stability, imbalanced water resource allocation, and ecological degradation. These floods and droughts severely damage the structure and function of river basin hydrological systems, resulting in imbalanced water resource allocation, ecological degradation, and hindered socio-economic development. A systematic analysis of the resistance and resilience of river basin hydrological systems in the face of these external disturbances, while ensuring the sustainable development of river basin ecosystems, has become a crucial issue for policymakers.

[0003] Hydrological resilience, a key indicator for measuring a watershed system's ability to resist disturbances and maintain or restore its functions, is defined as the system's capacity to absorb disturbances and maintain or rapidly restore its hydrological functions. It is an important concept for assessing the hydrological system's response to climate change and human activities. In recent years, conducting hydrological resilience assessments, clarifying the driving factors of hydrological resilience, improving watershed ecological resilience, and promoting sustainable development have gradually become research hotspots.

[0004] Currently, while the concept of hydrological resilience is gaining increasing attention in the field, systematic analysis of its components, quantitative assessment methods, and their relationship with watershed characteristics remains insufficient. Assessments of hydrological resilience are largely based on water use efficiency, drought recovery time, and statistical methods, but these methods suffer from unclear mechanisms, strong data dependence, and difficulty in revealing nonlinear responses. In contrast, the Budyko framework provides a hydrological resilience analysis tool with clearly defined mechanisms and concise parameters, effectively addressing these issues.

[0005] Furthermore, existing research focuses primarily on the impact of climate mean changes, with insufficient research on the nonlinear threshold response mechanism of hydrological resilience under extreme events, and lacks quantitative methods for separating the contributions of human activities and climate change, resulting in a lack of scientific basis for watershed management strategies. Summary of the Invention

[0006] This invention aims to overcome the shortcomings of existing technologies and provide a watershed hydrological resilience assessment and attribution analysis method based on the Budyko framework. This method can accurately identify the spatiotemporal evolution characteristics of hydrological resilience, the threshold response mechanism under extreme climate, and the contribution rate of human activities and climate change, providing a scientific basis for watershed water resource management and ecological protection.

[0007] To achieve the above-mentioned technical features, the objective of this invention is as follows: a method for assessing and regulating watershed hydrological resilience based on the Budyko framework, comprising the following steps: S1, Data Preprocessing and Standardization: Screening monthly runoff data, precipitation data, temperature data, soil and wind data, and underlying surface data that have passed the quality inspection by relevant departments and meet the standards of the three-dimensional review; S2, Calculation of hydrological resilience index and dynamic deviation based on Budyko curve: Based on annual potential evapotranspiration, actual evapotranspiration and precipitation data, the hydrological resilience index and dynamic deviation index of the watershed are analyzed through the improved Budyko theoretical framework. S3, using statistical methods to analyze its spatiotemporal evolution characteristics: systematically analyzing the interannual variations of hydrological resilience and dynamic deviation using anomalies, cumulative anomalies, and the MK test method; S4, combined with the SPI index to identify threshold response under extreme climate: the standardized precipitation index SPI is used to further analyze the correlation between hydrological resilience and dynamic deviation, and to clearly present historical extreme wet and extreme drought events. S5, Using CCM and Geodetector to analyze driving factors: Analyze the driving elements and mechanisms of hydrological resilience through CCM and Geodetector's geographic factors and interactive detectors. S6. Using the VIC model to separate the contribution rates of climate change and human activities: The VIC model is used to reconstruct the evapotranspiration process under the natural scenario of "no human activity" to separate the impacts of climate change and human activities on hydrological resilience.

[0008] Preferably, the data preprocessing and unification steps in step S1 are as follows: S101. For the missing evapotranspiration data, a meteorological forcing field for the VIC model is constructed using daily precipitation, maximum temperature, minimum temperature, and wind speed data. Combined with soil and vegetation cover databases, a VIC hydrological model is established. Daily meteorological data is used as model input to generate daily hydrological output. Then, monthly runoff data is used for model calibration and validation. Finally, the results are aggregated into annual-scale data to meet the needs of Budyko analysis. S102 employs a genetic algorithm to optimize parameters and improve the accuracy of simulation results. The genetic algorithm uses the Nash efficiency coefficient as the objective function to optimize parameters. This algorithm is an optimization algorithm based on the principles of natural selection and genetics, simulating the process of biological evolution, and finding the optimal or near-optimal solution to the problem through iteration.

[0009] Preferably, in step S2, during the calculation of the hydrological resilience index and dynamic deviation based on the Budyko curve, the hydrological resilience index... e The calculation formula is: ; In the formula, Indicates hydrological elasticity, The system is considered to possess hydrological resilience at that time, and the larger the value, the greater the hydrological resilience of the system. The drought index, The evaporation index is defined as 'max', where 'max' represents the maximum value and 'min' represents the minimum value. express Subtract the residual of the theoretical value B of the Budyko standard curve.

[0010] Preferably, step S3 specifically includes: S301, Comparison of Anomaly and Cumulative Anomaly. Anomaly reflects the degree of deviation of data over a certain period; cumulative anomaly provides a direct observation of the trend of the sequence. By observing significant fluctuations in the curve, one can determine the long-term trend and persistence of meteorological factors, and even estimate the approximate time of abrupt changes. Small fluctuations in the curve are helpful in analyzing short-term changes in anomaly values. The cumulative anomaly formula is detailed below: ; In the formula, For the first Cumulative anomaly over the years; For the first Time series of years; This represents the average value of the time series samples. S302, Comparison of Mann-Kendall (MK) Trend and Mutation Tests: The Mann-Kendall (MK) trend test and Mann-Kendall (MK) mutation test are introduced, with the specific formulas as follows: ; ; ; ; In the formula, It is the MK statistic; It is a symbolic function; It is a statistical trend test evaluation value; It is a statistic The variance; and It is the first in the sample and The value, , This indicates the number of times the array appears repeatedly. yes The number of data points in the repeating group; It is the number of times any repeated value appears in the data sequence; It refers to the number of data points; ; ; ; In the formula, For statistical purposes; Representing time series middle The cumulative number; For statistics The mean; For statistics The variance; It follows a standard normal distribution. Reverse order of time series Repeat the above steps to obtain the reversed sequence value. , ;like and If two curves intersect at a point within the confidence sub-region, then the time corresponding to the intersection is the time when the mutation begins.

[0011] Preferably, in step S4, the system supports dynamic assessment and management recommendations for watershed hydrological resilience at multiple time scales and spatial resolutions. According to the SPI classification standard, SPI>2 represents an extreme wet event, SPI>1.5 represents a wet event, SPI<-1.5 represents a drought event, and SPI<-2 represents an extreme drought event.

[0012] Preferably, step S5 specifically includes: S501 divides influencing factors into two categories: "short-term factors" (such as hydrological and meteorological elements like precipitation and temperature) that fluctuate significantly over time, and "long-term factors" (such as underlying surface characteristics like land use types) that change slowly over time.

[0013] S502, Time-based analysis of the causal relationship between hydrological resilience and short-term time series variables: examining two time series... X and YThe causal relationship between them was established by constructing time series using hysteresis coordinate embedding technology. X and Y State space reconstruction - shadow manifold M X and M Y Then investigate M Y Can the points in the map be accurately mapped to? M X The corresponding state; the specific formula is as follows: ; ; ; ; In the formula, Minimum is The maximum is ; For the embedded dimension; For the time lag period, From the first lag coordinate To the last lagging coordinate ,exist Dimensional number of points; Based on the strength of causal relationship, CCM is divided into three levels: strong causal relationship, r>0.7; moderate causal relationship, r is 0.4-0.7; weak causal relationship, r<0.4; the statistical significance of causal relationship between variables is determined at the 95% significance level. S502, Spatial analysis of the causal relationship between hydrological resilience and long-term variables: through factor influence. q The magnitude of land use change can be used to explain its impact on hydrological resilience. ; In the formula, For the total number of sub-basins, As a representative impact factor, As a factor The number of floors, , Indicates the spatial distribution of runoff. Determinant right The extent of the impact For the entire research area The overall variance; and They represent factors respectively The Number and factors of sub-basins in the layer The On the layer The variance; The value range is 0 to 1. The larger the value, the better. right The greater the impact; By analyzing the changes in the explanatory power of the explained variable after the superposition of two influencing factors, these combined effects can be categorized into the following scenarios: ; In the formula, A and B These are two factors that influence the explained variable; and Calculate for the interaction detector A and B Factors Value; Symbol express A and B The superposition effect, through comparison and and The value determines the form of their interaction.

[0014] Preferably, in step S501, the short-time factor includes hydrological and meteorological elements; Long-term factors include underlying surface features.

[0015] Preferably, step S6 specifically includes: S601, VIC model construction of natural scenario reconstruction: Based on the mutation analysis in S2, the baseline period and the validation period are determined, a five-year sliding window is applied to determine the hydrological resilience baseline period, and the assumption of no human activity is reconstructed. S602, Contribution Rate Assessment: Comparative analysis of the contribution rates of the impact on the two periods, with the specific formula as follows: ; ; ; ; In the formula, This represents the total change in hydrological resilience during the actual process of change. Hydrological resilience for the baseline period; This refers to the hydrological resilience during periods of change due to climate change and human activities in actual processes. The change in hydrological resilience caused by climate change; This indicates the hydrological resilience during the period of change reconstructed by the model; The amount of hydrological resilience change caused by human activities; The contribution rate of climate change to changes in hydrological resilience; This represents the contribution rate of human activities to changes in hydrological resilience.

[0016] The present invention has the following beneficial effects: 1. Enhanced Systemic Modeling: This invention integrates multiple methods such as the Budyko framework, VIC model, CCM, and geographic detectors to achieve full-process analysis from assessment to attribution; effectively compensates for the problems of non-steady-state regions, data scarcity, and insufficient analysis of hydrological systems in response to extreme climate conditions, and significantly improves the depth of research in the field of hydrological resilience.

[0017] 2. Nonlinear identification: For the first time, the threshold response of hydrological resilience under extreme climate conditions was identified in arid and semi-arid watersheds.

[0018] 3. Quantitative attribution: Clarify the contribution rates of human activities and climate change to hydrological resilience changes, supporting precise management; 4. Significant Application Significance: This invention is particularly suitable for arid and semi-arid regions where data is scarce, providing technical support for research on the nonlinear threshold response mechanism of watershed hydrological resilience under extreme events, and possesses strong potential for widespread application. Attached Figure Description The present invention will be further described below with reference to the accompanying drawings and embodiments.

[0019] Figure 1 This is a schematic diagram of the technical route of the method of the present invention.

[0020] Figure 2 This is a schematic diagram illustrating the principle of hydrological resilience calculation under the Budyko framework of this invention.

[0021] Figure 3 This illustrates the distribution patterns of hydrological resilience and related indicators in different sub-basins in the embodiments of this invention.

[0022] Figure 4 This describes the data distribution characteristics of hydrological resilience and related indicators in an embodiment of the present invention.

[0023] Figure 5 This describes the interdecadal spatial distribution characteristics of hydrological resilience and dynamic deviation in an embodiment of the present invention.

[0024] Figure 6 The results of the analysis of hydrological resilience and dynamic deviation anomalies and cumulative anomalies in the embodiments of the present invention are shown.

[0025] Figure 7 The results of the MK mutation test on hydrological resilience and dynamic deviation in the embodiments of the present invention are shown.

[0026] Figure 8This illustrates the interannual variation of the SPI index and the functional relationship between hydrological resilience and dynamic deviation based on the SPI index in an embodiment of the present invention.

[0027] Figure 9 This is a schematic diagram illustrating the influence relationship between short-term variables and hydrological resilience in an embodiment of the present invention.

[0028] Figure 10 This is an example of the driving analysis of hydrological resilience based on long-term time-series variables using a geographic factor detector in an embodiment of the present invention.

[0029] Figure 11 This is an example of the driving analysis of hydrological resilience based on long-term time-series variables of a geographic interactive detector in an embodiment of the present invention.

[0030] Figure 12 This is a comparison of hydrological resilience processes under different scenarios in the embodiments of the present invention.

[0031] Figure 13 This is a quantitative result of the impact of climate change and human activities on hydrological resilience in an embodiment of the present invention. Detailed Implementation

[0032] The present invention will be further described in detail below through specific embodiments. These embodiments are intended to enable those skilled in the art to gain a more comprehensive understanding of the present invention, but do not limit the invention in any way.

[0033] Example 1: See Figure 1 This invention provides a method for assessing and regulating watershed hydrological resilience based on the Budyko framework, comprising the following steps: S1. Data Preparation and Preprocessing: Collect multi-year precipitation, temperature, wind speed, runoff, evapotranspiration, land use, NDVI, and other data within the watershed, and perform spatial interpolation and unified temporal scale processing. For missing evapotranspiration data, construct a VIC model meteorological forcing field using daily precipitation, maximum temperature, minimum temperature, and wind speed data, and establish a VIC hydrological model by combining soil and vegetation cover databases.

[0034] S2, Hydrological Resilience Calculation: Based on the Budyko framework, the hydrological resilience index is calculated by combining potential evapotranspiration (PET), actual evapotranspiration (AET), and precipitation (P) data. e The dynamic deviation d is given by the following formula: ; In the formula, Indicates hydrological elasticity, The system is considered to possess hydrological resilience at that time, and the larger the value, the greater the hydrological resilience of the system. The drought index, The evaporation index is defined as 'max', where 'max' represents the maximum value and 'min' represents the minimum value. express Subtract the residual of the theoretical value B of the Budyko standard curve.

[0035] S3, Spatiotemporal Evolution Analysis: The spatiotemporal variation characteristics of hydrological resilience are analyzed using methods such as the Mann-Kendall trend test, Sen's slope estimation, anomaly and cumulative anomaly method, and abrupt change detection.

[0036] S4, Extreme Climate Threshold Identification: Combining the Standardized Precipitation Index (SPI), according to the SPI classification standard, SPI>2 represents an extreme wet event, SPI>1.5 represents a wet event, SPI<-1.5 represents a drought event, and SPI<-2 represents an extreme drought event.

[0037] Nonlinear thresholds for hydrological resilience and dynamic deviation under extreme drought and wet events are identified, and critical points are determined by Gaussian function fitting.

[0038] S5, Driving Factor Analysis: Cross-converging mapping (CCM) was used to analyze the causal relationship between short-term time series variables (meteorology, hydrology, NDVI) and hydrological resilience; geographic detectors (factor detectors and interaction detectors) were used to analyze the impact of land use type and its transformation on hydrological resilience. S6, Identification of the impacts of climate change and human activities: The VIC model is used to reconstruct hydrological processes under natural scenarios and separate the contribution rates of climate change and human activities to changes in hydrological resilience.

[0039] S7, Regulation Strategy Generation: Based on resilience assessment and attribution results, propose targeted watershed management recommendations, such as upstream ecological restoration, midstream and downstream water-saving irrigation, and urban green infrastructure construction.

[0040] Example 2: Taking the Wei River basin as an example, the technical solution of this invention will be further explained with reference to the accompanying drawings and examples: S1. Data Preprocessing and Unification: Acquire daily meteorological data (CN05.1), runoff data (Hydrological Yearbook), land use data (RESDC), NDVI data (NOAA) from 1982 to 2018, and unify them into a 0.25°×0.25° grid using ArcGIS 10.2, aggregating them at the annual scale. See Table 1 below for specific data.

[0041] Table 1 Statistical data from 1982 to 2018

[0042] S2. Calculation of Hydrological Resilience Index and Dynamic Bias Based on Budyko Curves: Using annual potential evapotranspiration, actual evapotranspiration, and precipitation data as input, an improved Budyko theoretical framework was applied to analyze the hydrological distribution process in the Weihe River Basin. Through the Budyko framework, the distribution relationship of precipitation between runoff and evapotranspiration was quantitatively assessed. Based on the evaporation index and aridity index in the Budyko curves, the resilience and bias indices of the basin's hydrological system were calculated (see Appendix). Figure 3 .

[0043] To satisfy the multi-year steady-state constraints of the Budyko framework, a 5-year sliding motion was used for calculation. A hydrological resilience index e greater than 1 indicates that the watershed possesses hydrological resilience characteristics, while a value less than 1 indicates that the watershed does not.

[0044] This study reveals the spatial distribution patterns of hydrological resilience in the Weihe River Basin, showcasing its interdecadal variations in the form of sub-basins; it also reveals the changing patterns of hydrological resilience, conducting in-depth analysis of dynamic deviation indicators representing extreme climate responses, and showcasing their interdecadal variations in the form of sub-basins. (See...) Figure 5 .

[0045] Dynamic deviations can be used to analyze the sensitivity of a watershed's hydrological system to climate change. Taking the Wei River basin as an example, the basin as a whole (excluding the Linjiacunzi basin) exhibited high dynamic deviations from 1982 to 1990, indicating a strong sensitivity of the watershed's hydrological system to climate change during this period. From 1991 to 2000, dynamic deviations were generally lower, indicating improved stability of the hydrological system. However, from 2001 to 2010 and from 2011 to 2018, dynamic deviations showed varying degrees of increase, indicating an increased vulnerability of the watershed to external disturbances.

[0046] S3 employs statistical methods to analyze its spatiotemporal evolution characteristics: the interannual variations of hydrological resilience and dynamic deviation are systematically analyzed using anomalies, cumulative anomalies, and MK trend and abrupt change tests.

[0047] Analysis of the Wei River Basin reveals a significant overall decline in hydrological resilience, although the Linjiacun and Zhangjiashanzi basins show only a slight decrease. Dynamic deviation, on the whole, did not show significant changes, with the Beidao and Zhangjiashanzi basins exhibiting a decreasing trend, while the others showed an increasing trend. These findings reveal the evolution of the Wei River Basin's hydrological system's response to environmental changes and its adaptive capacity.

[0048] Table 2. Statistical results of hydrological resilience and dynamic deviation trends based on the MK trend test and Sen's slope.

[0049] Depend on Figure 6-7 It can be seen that the overall hydrological resilience of the Weihe River Basin shows a significant downward trend, while the dynamic deviation shows a non-significant downward trend. Significant abrupt changes in hydrological resilience were observed in the sub-basins of Beidao, Linjiacun, Xianyang, and Huaxian, whose cumulative anomaly curves and MK abrupt change test results were consistent. The abrupt change years were 2003, 1990, 2003, and 2001, respectively. The abrupt change points for the Zhangjiashan and Zhuangtou sub-basins were 1992 and 2001, respectively. The years of abrupt changes in dynamic deviation did not coincide with those of hydrological resilience, indicating that the abrupt changes in hydrological resilience were not caused by dynamic deviation. The significant difference between the years of abrupt changes in dynamic deviation and those in hydrological resilience suggests that the changes in hydrological resilience were not caused by dynamic deviation. This finding reveals that the two indicators may be driven by different mechanisms, suggesting that the resilience characteristics of the basin's hydrological system may be dominated by other factors.

[0050] S4. Identifying Threshold Responses under Extreme Climate Conditions Using the SPI Index: Climate change, as a key perturbation factor in hydrological resilience, plays a crucial role in the evolution of hydrological resilience. The standardized precipitation index (SPI) is used to further analyze the correlation between hydrological resilience and dynamic bias, clearly revealing historical extreme wet and extreme drought events. According to the SPI classification standard, SPI > 2 indicates an extreme wet event, SPI > 1.5 indicates a wet event, SPI < -1.5 indicates a drought event, and SPI < -2 represents an extreme drought event. Figure 8 The extreme wet event of 2003 and the extreme drought event of 1997 are clearly presented.

[0051] Dynamic deviation is defined as the disturbance caused by extreme weather events to a watershed system; therefore, in-depth analysis of extreme events is crucial for understanding the response of hydrological systems. When exploring the relationship between hydrological resilience and dynamic deviation based on the SPI index, a clear functional relationship was found between the two under extreme events.

[0052] In extreme climate events, there is a clear critical point between hydrological resilience and dynamic deviation: -0.081 under extreme drought conditions and 0.227 under extreme wet conditions. Before this critical point, hydrological resilience increases with increasing dynamic deviation; after exceeding the critical point, hydrological resilience decreases with increasing dynamic deviation.

[0053] S5. Using CCM and Geodetector to analyze driving factors: Considering the differences in the temporal variation characteristics of different variables, this invention categorizes influencing factors into two types: "short-term factors" with significant temporal fluctuations (such as hydrometeorological elements like precipitation and temperature) and "long-term factors" with slow temporal changes (such as underlying surface characteristics like land use types). The Cross-Convergence Mapping (CCM) method is used to explore the causal relationship between short-term factors and hydrological resilience. Geographic factors and interactive detectors in the Geodetector are applied to analyze the influence of long-term factors on hydrological resilience.

[0054] Based on the strength of the causal relationship, it was divided into three levels: strong causal relationship (r>0.7), moderate causal relationship (r=0.4-0.7), and weak causal relationship (r<0.4). The study used a 95% significance level to determine the statistical significance of the causal relationship between variables. Figure 9 This study visually demonstrates the causal pattern of short-term variables on hydrological resilience, clearly showing the intensity of the influence of different variables on hydrological resilience. All short-term variables exhibit significant causal relationships with hydrological resilience, but the intensity and direction of these influences differ markedly. Meteorological factors generally show a strong causal relationship, while the causal relationships between hydrological and underlying surface factors on hydrological resilience are relatively weak.

[0055] The influence of land use type on hydrological resilience was analyzed using a geographic factor detector, and the strength of this relationship was quantified by the q-value. Four basic land types (arable land, forest land, grassland, and built-up land) and eight major transformation types (such as grassland-arable land, arable land-built-up land, etc.) were selected as key driving factors. Figure 10 The study demonstrates the strength of the impact of these land use types and their transformations on hydrological resilience; a larger q value indicates a stronger explanatory power for changes in hydrological resilience.

[0056] When land use generally has a strong explanatory power for hydrological resilience, the conversion between forest and grassland has a weaker explanatory power; conversely, when its overall explanatory power is weak, the conversion between forest and cultivated land, and the explanatory power of forest land for hydrological resilience, are relatively prominent. This indicates that land type conversion involving forest land has a more significant impact on hydrological resilience.

[0057] To further elucidate the spatial variation mechanism of hydrological resilience, a geographic interaction detector was used to detect whether two factors work independently. Figure 10 Table 3 shows the explanatory power and manifestation of the interaction between the two factors. The interaction between land use types in the Weihe River Basin mainly exhibits a two-factor enhancement effect, meaning that the combined impact of the two land use types on hydrological resilience is greater than the simple superposition of their individual effects. Land use involving forest land types has a more prominent explanatory power for hydrological resilience.

[0058] Table 3 Results of interaction mode detection in 2010

[0059] S6 uses the VIC model to separate the contribution rates of climate change and human activities: The Weihe River basin experienced a sudden change in runoff in 1994, coinciding with the implementation of comprehensive small watershed management in the 1990s and the promotion of the "Grain for Green" policy in 2000. After 1990, human activities in the basin significantly increased, shifting towards ecological governance, while interventions in the 1980s were relatively weak. Therefore, 1982-1989 was used as the baseline period, consistent with the VIC model calibration and validation period. Simultaneously, to meet multi-year steady-state conditions, a five-year sliding window method was applied; therefore, 1986-1989 was designated as the natural baseline period for hydrological resilience, and 1990-2018 as the period of change under the influence of human activities. The reconstruction of hydrological resilience under the natural scenario during the change period is based on the assumption of "no human activity." A VIC model for 1990-2018 was constructed using natural land use type data from 1980, constructing the evapotranspiration process under the natural scenario. By comparing actual and natural scenario evapotranspiration and combining watershed rainfall data, hydrological resilience under the two backgrounds was calculated based on the Budyko framework.

[0060] Table 4 presents the attribution analysis results of the hydrological resilience evolution in the Weihe River Basin. Except for the Beiluo River, where hydrological resilience changes are controlled by climate change, human activities have a significant impact on hydrological resilience changes in other areas. Human activities are a crucial factor that cannot be ignored in the process of hydrological resilience evolution.

[0061] Table 4. Attribution Analysis Results of Hydrological Resilience Evolution in the Weihe River Basin

[0062] In summary, this invention provides a method and system for assessing and regulating watershed hydrological resilience based on the Budyko framework, effectively providing a comprehensive assessment of the spatiotemporal evolution of watershed hydrological resilience, extreme climate threshold response, driving factor identification, and quantitative analysis of contribution rates. This invention innovatively proposes a threshold effect for extreme climate events, which is not commonly found in previous studies. The assessment method of this invention provides technical support for monitoring watershed hydrological resilience, helps ensure the sustainable development of watershed ecosystems, and provides new ideas and methods for systematically analyzing the resistance and resilience of watershed hydrological systems in the face of these external disturbances.

[0063] Although the preferred embodiments of the present invention have been described above in conjunction with the accompanying drawings, the present invention is not limited to the specific embodiments described above. The specific embodiments described above are merely illustrative and not restrictive. Those skilled in the art can make many specific modifications under the guidance of the present invention without departing from the spirit of the invention and the scope of protection of the claims, and these modifications all fall within the scope of protection of the present invention.

Claims

1. A method for assessing and regulating watershed hydrological resilience based on the Budyko framework, characterized in that, Includes the following steps: S1, Data Preprocessing and Standardization: Screening monthly runoff data, precipitation data, temperature data, soil and wind data, and underlying surface data that have passed the quality inspection by relevant departments and meet the standards of the three-dimensional review; S2, Calculation of hydrological resilience index and dynamic deviation based on Budyko curve: Based on annual potential evapotranspiration, actual evapotranspiration and precipitation data, the hydrological resilience index and dynamic deviation index of the watershed are analyzed through the improved Budyko theoretical framework. S3, using statistical methods to analyze its spatiotemporal evolution characteristics: systematically analyzing the interannual variations of hydrological resilience and dynamic deviation using anomalies, cumulative anomalies, and the MK test method; S4, combined with the SPI index to identify threshold response under extreme climate: the standardized precipitation index SPI is used to further analyze the correlation between hydrological resilience and dynamic deviation, and to clearly present historical extreme wet and extreme drought events. S5, Using CCM and Geodetector to analyze driving factors: Analyze the driving elements and mechanisms of hydrological resilience through CCM and Geodetector's geographic factors and interactive detectors. S6. Using the VIC model to separate the contribution rates of climate change and human activities: The VIC model is used to reconstruct the evapotranspiration process under the natural scenario of "no human activity" to separate the impacts of climate change and human activities on hydrological resilience.

2. The method for assessing and regulating watershed hydrological resilience based on the Budyko framework according to claim 1, characterized in that, The data preprocessing and unification steps in step S1 are as follows: S101. For the missing evapotranspiration data, the meteorological forcing field of the VIC model is constructed using daily precipitation, maximum temperature, minimum temperature and wind speed data. Combined with the soil and vegetation cover database, the VIC hydrological model is established. Daily meteorological data is used as model input to generate daily hydrological output. Then, monthly runoff data is used for model calibration and validation. Finally, the results are aggregated into annual data to meet the needs of Budyko analysis. S102, using a genetic algorithm to optimize parameters and improve the accuracy of simulation results: A genetic algorithm is used to optimize parameters with the Nash efficiency coefficient as the objective function.

3. The method for assessing and regulating watershed hydrological resilience based on the Budyko framework according to claim 1, characterized in that, In step S2, the hydrological resilience index and dynamic deviation are calculated based on the Budyko curve. e The calculation formula is: ； In the formula, Indicates hydrological elasticity, The system is considered to possess hydrological resilience at that time, and the larger the value, the greater the hydrological resilience of the system. The drought index, The evaporation index is defined as 'max', where 'max' represents the maximum value and 'min' represents the minimum value. express Subtract the residual of the theoretical value B of the Budyko standard curve.

4. The method for watershed hydrological resilience assessment and regulation based on the Budyko framework according to claim 1, characterized in that, Step S3 specifically includes: S301, Comparison of Anomaly and Cumulative Anomaly: Anomaly reflects the degree of deviation of data over a certain period; cumulative anomaly allows for a direct observation of the trend of the series. The formula for cumulative anomaly is detailed below: ； In the formula, For the first Cumulative anomaly over the years; For the first Time series of years; This represents the average value of the time series samples. S302, Comparison of Mann-Kendall (MK) Trend and Mutation Tests: The Mann-Kendall (MK) trend test and Mann-Kendall (MK) mutation test are introduced, with the specific formulas as follows: ； ； ； ； In the formula, It is the MK statistic; It is a symbolic function; It is a statistical trend test evaluation value; It is a statistic The variance; and It is the first in the sample and The value, , This indicates the number of times the array appears repeatedly. yes The number of data points in the repeating group; It is the number of times any repeated value appears in the data sequence; It refers to the number of data points; ； ； ； In the formula, For statistical purposes; Representing time series middle The cumulative number; For statistics The mean; For statistics The variance; It follows a standard normal distribution. Reverse order of time series Repeat the above steps to obtain the reversed sequence value. , ;like and If two curves intersect at a point within the confidence sub-region, then the time corresponding to the intersection is the time when the mutation begins.

5. The method for watershed hydrological resilience assessment and regulation based on the Budyko framework according to claim 1, characterized in that, In step S4, the system supports dynamic assessment and management recommendations for watershed hydrological resilience at multiple time scales and spatial resolutions. According to the SPI classification standard, SPI>2 represents an extreme wet event, SPI>1.5 represents a wet event, SPI<-1.5 represents a drought event, and SPI<-2 represents an extreme drought event.

6. The method for assessing and regulating watershed hydrological resilience based on the Budyko framework according to claim 1, characterized in that, Step S5 specifically includes: S501 divides the influencing factors into two categories: short-term factors with significant time fluctuations and long-term factors with slow time changes. S502, Time-based analysis of the causal relationship between hydrological resilience and short-term time series variables: examining two time series... X and Y The causal relationship between them was established by constructing time series using hysteresis coordinate embedding technology. X and Y State space reconstruction - shadow manifold M X and M Y Then investigate M Y Can the points in the map be accurately mapped to? M X The corresponding state; the specific formula is as follows: ； ； ； ； In the formula, Minimum is The maximum is ; For the embedded dimension; For the time lag period, From the first lag coordinate To the last lagging coordinate ,exist Dimensional number of points; Based on the strength of causal relationship, CCM is divided into three levels: strong causal relationship, r>0.7; moderate causal relationship, r is 0.4-0.7; weak causal relationship, r<0.4; the statistical significance of causal relationship between variables is determined at the 95% significance level. S502, Spatial analysis of the causal relationship between hydrological resilience and long-term variables: through factor influence. q The magnitude of land use change can be used to explain its impact on hydrological resilience. ； In the formula, For the total number of sub-basins, As a representative impact factor, As a factor The number of floors, , Indicates the spatial distribution of runoff. Determinant right The extent of the impact For the entire research area The overall variance; and They represent factors respectively The Number and factors of sub-basins in the layer The On the layer The variance; The value range is 0 to 1. The larger the value, the better. right The greater the impact; By analyzing the changes in the explanatory power of the explained variable after the superposition of two influencing factors, these combined effects can be categorized into the following scenarios: ； In the formula, A and B These are two factors that influence the explained variable; and Calculate for the interaction detector A and B Factors Value; Symbol express A and B The superposition effect, through comparison and and The value determines the form of their interaction.

7. The method for watershed hydrological resilience assessment and regulation based on the Budyko framework according to claim 1, characterized in that, In step S501, the short-term factors include hydrological and meteorological elements; Long-term factors include underlying surface features.

8. The method for watershed hydrological resilience assessment and regulation based on the Budyko framework according to claim 1, characterized in that, Step S6 specifically includes: S601, VIC model construction of natural scenario reconstruction: Based on the mutation analysis in S2, the baseline period and the validation period are determined, a five-year sliding window is applied to determine the hydrological resilience baseline period, and the assumption of no human activity is reconstructed. S602, Contribution Rate Assessment: Comparative analysis of the contribution rates of the impact on the two periods, with the specific formula as follows: ； ； ； ； In the formula, This represents the total change in hydrological resilience during the actual process of change. Hydrological resilience for the baseline period; This refers to the hydrological resilience during periods of change due to climate change and human activities in actual processes. The change in hydrological resilience caused by climate change; This indicates the hydrological resilience during the period of change reconstructed by the model; The amount of hydrological resilience change caused by human activities; The contribution rate of climate change to changes in hydrological resilience; This represents the contribution rate of human activities to changes in hydrological resilience.

Images