Ecological hydrological process simulation method and system based on dynamic vegetation and medium

Abstract

The invention relates to the technical field of hydrological process simulation, in particular to an ecological hydrological process simulation method and system based on dynamic vegetation and a medium. The method comprises the following steps: acquiring a vegetation physiological monitoring parameter set and a root system water absorption dynamic data set; performing time-space unified registration on the vegetation physiological monitoring parameter set and the root system water absorption dynamic data set to obtain a vegetation physiological dynamic parameter set; performing soil dry and wet process division on the target area based on the vegetation physiological dynamic parameter set to obtain a segmented soil moisture state sequence; dividing the vegetation physiological dynamic parameter set according to the segmented soil moisture state sequence to obtain a state classification vegetation physiological data set; and constructing a vegetation water stress adaptation strategy model based on the state classification vegetation physiological data set. According to the method, the simulation precision of vegetation coverage and soil moisture dynamic balance is improved, and the problem of simulation errors caused by vegetation parameter static processing in the prior art is solved.

Description

Technical Field

[0001] The present invention relates to the technical field of hydrological process simulation, and in particular to an eco-hydrological process simulation method, system and medium based on dynamic vegetation. Background Art

[0002] Early ecohydrological models focused on simulating a single hydrological or vegetation element, failing to fully consider the complex dynamic relationships between them. However, existing technologies still rely on static vegetation parameters or simple growth curves to estimate vegetation cover when constructing ecohydrological models. This approach simplifies the interactions between vegetation and hydrological elements and overlooks the nonlinear responses of vegetation physiological parameters (such as stomatal conductance and root water uptake rate) to soil water stress. For example, when simulating extreme rainstorms under a subtropical monsoon climate, static parameters failed to capture the dynamic adjustments in water uptake strategies of Masson pine roots in the 0-40 cm soil layer (e.g., initially absorbing shallow water to reduce surface runoff, but shifting to deeper water uptake to maintain evaporation after sustained heavy rainfall). This resulted in predicted slope sediment yields deviating by over 40% from measured data, severely impacting soil erosion risk assessment and ecological restoration benefit analysis in the reservoir area. Such problems highlight the defects of existing models in the coordinated simulation of vegetation dynamic responses and hydrological processes. Especially in the rainy and terrain-fragmented soil and water conservation areas in the south, there is an urgent need to improve the prediction accuracy and adaptability of eco-hydrological models by optimizing the coupling and feedback mechanisms of dynamic vegetation parameters. Summary of the Invention

[0003] Based on this, it is necessary for the present invention to provide a method, system and medium for simulating eco-hydrological processes based on dynamic vegetation to solve at least one of the above technical problems.

[0004] To achieve the above objectives, a method for simulating ecohydrological processes based on dynamic vegetation includes the following steps:

[0005] Step S1: obtaining a vegetation physiological monitoring parameter set and a root water absorption dynamic data set; performing spatiotemporal registration on the vegetation physiological monitoring parameter set and the root water absorption dynamic data set to obtain a vegetation physiological dynamic parameter set;

[0006] Step S2: Based on the vegetation physiological dynamic parameter set, the target area is divided into soil dry-wet processes to obtain a segmented soil moisture state sequence; based on the segmented soil moisture state sequence, the vegetation physiological dynamic parameter set is divided to obtain a state-classified vegetation physiological data set; based on the state-classified vegetation physiological data set, a vegetation water stress adaptation strategy model is constructed;

[0007] Step S3: Based on the vegetation water stress adaptation strategy model, the soil profile is divided into hydraulic functional layers to obtain layered soil-root interaction units; and a vegetation-soil water feedback model is constructed based on the layered soil-root interaction units;

[0008] Step S4: constructing a short-term hydro-meteorological scenario set; dynamically simulating the short-term hydro-meteorological scenario set based on the vegetation-soil moisture feedback model to obtain smoothed vegetation physiological prediction parameters; performing physiological elastic recovery on the smoothed vegetation physiological prediction parameters to obtain complete vegetation physiological prediction parameters;

[0009] Step S5: Based on the complete vegetation physiological prediction parameters, the vegetation parameters of the target area are dynamically updated to obtain multi-time-scale vegetation-hydrology synergy data; the slope hydrological process of the vegetation-hydrology synergy data is simulated to obtain vegetation-hydrology synergy evolution data.

[0010] By constructing a dynamic vegetation physiological monitoring parameter set, the present invention captures the nonlinear response characteristics of vegetation physiological parameters to soil water stress in real time, enabling a more accurate reflection of vegetation growth and water absorption strategies under varying soil moisture conditions. This improves the simulation accuracy of vegetation coverage and soil water dynamic balance, resolving the simulation error problem caused by static processing of vegetation parameters in the prior art. When simulating vegetation-soil water interaction under rainy season precipitation conditions, a vegetation water stress adaptation strategy model is constructed by partitioning the soil dry-wet process and decomposing the vegetation physiological dynamic parameter set. This model can capture changes in water absorption by vegetation roots in different soil layers in real time, reducing the deviation in slope runoff predictions caused by failure to promptly reflect adjustments to root water absorption strategies. This significantly reduces the deviation of slope runoff predictions from measured data, effectively improving the accuracy of soil and water conservation benefit assessments. By constructing a vegetation-soil water feedback model, the dynamic characteristics of vegetation physiological parameters and changes in soil water status are comprehensively considered, thereby achieving bidirectional feedback modeling of vegetation dynamics and hydrological processes. This not only improves the simulation accuracy of the vegetation-hydrology co-evolution process, but also provides more reliable data support for the research and practical application of eco-hydrological processes. The present invention also further corrects the vegetation physiological prediction parameters through the construction and dynamic simulation of a short-term hydro-meteorological scenario set, and obtains complete vegetation physiological prediction parameters through physiological elastic recovery. This enables the model to better adapt to short-term hydro-meteorological changes and improves the prediction accuracy of vegetation physiological processes. By dynamically updating vegetation parameters and simulating slope hydrological processes in the target area, the temporal and spatial resolution and simulation accuracy of the model are further improved.

[0011] Preferably, step S1 includes the following steps:

[0012] Step S11: Collecting vegetation canopy temperature, humidity, and wind speed data through a ground micrometeorological tower to obtain a canopy micrometeorological dataset;

[0013] Step S12: measuring the stomatal conductance and transpiration rate of vegetation leaves to obtain a set of leaf physiological parameters; and monitoring the water transport rate of vegetation trunks to obtain a vegetation stem flow data set;

[0014] Step S13: using a multispectral camera mounted on an unmanned aerial vehicle to perform aerial survey of vegetation coverage in the target area to obtain spatial distribution data of vegetation coverage; and measuring the leaf area index of the target area to obtain a leaf area index distribution map;

[0015] Step S14: performing time series alignment on the canopy micrometeorological dataset, the leaf physiological parameter set, and the vegetation stemflow dataset to obtain a synchronized vegetation physiological dataset;

[0016] Step S15: spatially registering the vegetation coverage spatial distribution data with the leaf area index distribution map to obtain a spatially synchronized vegetation structure dataset;

[0017] Step S16: Using a preset hierarchical Bayesian model, a multi-scale integration of the synchronized vegetation physiological dataset and the spatially synchronized vegetation structural dataset is performed to obtain a preliminary vegetation physiological monitoring parameter set. The preset hierarchical Bayesian model contains a three-layer nested structure: the first layer is that stomatal conductance follows a log-normal distribution, the second layer is the spatial correlation of quadrat scale parameters, and the third layer is the prior constraints of the regional scale.

[0018] Step S17: obtaining a corrected root water absorption dynamics dataset, and performing spatiotemporal registration on the preliminary vegetation physiological monitoring parameter set and the corrected root water absorption dynamics dataset to obtain a vegetation physiological dynamics parameter set.

[0019] The present invention obtains multi-dimensional data such as vegetation canopy micrometeorology, leaf physiological parameters, vegetation coverage and leaf area index by utilizing multiple means such as ground micrometeorological towers, leaf physiological measurements and drone aerial surveys, thereby ensuring the diversity and comprehensiveness of data sources. Synchronization and unification of vegetation physiological data and structural data are achieved through time series alignment and spatial registration. By using a hierarchical Bayesian model for multi-scale integration, the data features of different scales are effectively integrated, and the probability distribution of stomatal conductance, the spatial correlation of sample scale parameters and the prior constraints of regional scales are fully considered, thereby improving the reliability and representativeness of vegetation physiological monitoring parameters. Finally, a unified temporal and spatial registration is performed with the calibrated root water absorption dynamics dataset to further optimize the vegetation physiological dynamics parameter set, so that it can more accurately reflect the physiological dynamic changes of vegetation under different soil moisture conditions.

[0020] Preferably, step S2 includes the following steps:

[0021] Step S21: extracting soil moisture time series from the vegetation physiological dynamic parameter set to generate an original soil moisture change curve;

[0022] Step S22: performing soil moisture fluctuation identification on the original soil moisture change curve to obtain soil moisture fluctuation characteristic data;

[0023] Step S23: performing dry-wet process division on the soil moisture fluctuation characteristic data to obtain a preliminary soil dry-wet process division result;

[0024] Step S24: modifying the state of the preliminary soil dry-wet process division result according to a preset soil hydraulic characteristic threshold to obtain a segmented soil moisture state sequence;

[0025] Step S25: adjusting the time continuity of the segmented soil moisture state sequence to obtain a modified soil dry-wet process division result;

[0026] Step S26: dividing the vegetation physiological dynamic parameter set according to the modified soil dry-wet process division result to obtain a state-classified vegetation physiological data set;

[0027] Step S27: constructing a vegetation water stress adaptation strategy model based on the state-classified vegetation physiological data set, wherein the vegetation water stress adaptation strategy model is specifically:

[0028]

[0029] g wet (θ)=a1θ+b1;

[0030]

[0031] g dry (θ) = c3sigmoid(θ-θ low )+d3;

[0032] θ high =0.7θ FC ;

[0033] θ low =0.3θ FC ;

[0034] Among them, θ is the soil moisture content, θ FC is the field water capacity, θ high is the threshold of moist-moderate stress, θ lowis the threshold of moderate stress-extreme drought, a1 is the linear response rate of vegetation physiological parameters with changes in soil moisture content, b1 is the baseline value of vegetation physiological parameters when soil moisture content approaches zero, a2 is the physiological parameter level at the initial stage of moderate stress, λ is the attenuation rate of the control physiological parameter with the decrease of soil moisture, b2 is the stable value in the later stage of stress, c3 is the amplitude coefficient of the Sigmoid function, and d3 is the minimum physiological parameter value under extreme drought.

[0035] Through in-depth analysis of the vegetation physiological dynamic parameter set, the present invention can accurately extract the time series changes of soil moisture content and generate the original soil moisture change curve. The acquisition of soil moisture fluctuation characteristic data helps to gain a deeper understanding of the temporal variation patterns and dynamic characteristics of soil moisture. By dividing the dry and wet processes based on the soil moisture fluctuation characteristic data and modifying the state in combination with the soil hydraulic characteristic threshold, a more accurate segmented soil moisture state sequence can be obtained. Further time continuity adjustment ensures the continuity and reliability of the soil dry and wet process division results. By decomposing the vegetation physiological dynamic parameter set based on the corrected soil dry and wet process division results and constructing a vegetation water stress adaptation strategy model, the physiological responses and adaptation strategies of vegetation under different soil moisture conditions can be more accurately simulated. This not only improves the understanding of the vegetation water stress adaptation mechanism, but also provides more dynamic and accurate vegetation physiological parameters for the eco-hydrological model, which helps to improve the accuracy and reliability of the entire eco-hydrological process simulation.

[0036] Preferably, step S3 includes the following steps:

[0037] Step S31: Multi-source data assimilation and inversion are performed on the vegetation water stress adaptation strategy model, and three-dimensional rasterization is performed to obtain spatialized vegetation water response data; based on the spatialized vegetation water response data, the soil profile is clustered into equal hydraulic response areas to obtain preliminary functional layer zoning data;

[0038] Step S32: performing root density weighted correction on the preliminary functional layer partition data to obtain a root distribution density correction coefficient; adjusting the boundaries of the preliminary functional layer partition data according to the root distribution density correction coefficient to obtain a layered soil-root interaction unit;

[0039] Step S33: assigning hydraulic characteristic parameters to the layered soil-root interaction unit to obtain a layered soil hydraulic parameter set; and constructing a soil water movement simulation framework based on the layered soil hydraulic parameter set;

[0040] Step S34: integrating the root water absorption module into the soil water movement simulation framework to obtain a vertical water movement simulator with source terms;

[0041] Step S35: using a vertical water movement simulator with source term to calculate the water potential gradient of the layered soil-root interaction unit to obtain interlayer water potential gradient data;

[0042] Step S36: Calculating the water exchange flux between functional layers based on the interlayer water potential gradient data to obtain an interlayer water exchange flux table;

[0043] Step S37: Obtaining layered root water absorption rate data, performing water balance calculation on the interlayer water exchange flux table and the layered root water absorption rate data, and obtaining water balance status data of each functional layer;

[0044] Step S38: constructing a vegetation-soil moisture relationship model based on the water balance status data of each functional layer;

[0045] Step S39: integrating the feedback mechanism of the vegetation-soil moisture relationship model to obtain a vegetation-soil moisture feedback model.

[0046] The present invention generates spatialized vegetation moisture response data through multi-source data assimilation and inversion and three-dimensional rasterization processing, and based on this, clusters equal hydraulic response regions to obtain preliminary functional layer zoning data. Further combined with root density weighted correction, layered soil-root interaction units are obtained, which better reflect the actual situation. Hydraulic characteristic parameters are assigned on a layered basis, a soil moisture movement simulation framework is constructed, and a root water absorption module is integrated to form a vertical moisture movement simulator with source terms, achieving more accurate water migration simulation. Through water potential gradient calculation and water exchange flux calculation, water balance state data for each functional layer is obtained, and then a vegetation-soil moisture relationship model is constructed. Through feedback mechanism integration, a vegetation-soil moisture feedback model is obtained. The present invention makes the simulation of vegetation and soil moisture more accurate and detailed, and can dynamically reflect the interaction between the two, providing a more reliable basis for the simulation of eco-hydrological processes, and solving the shortcomings of existing technologies in the two-way feedback modeling of vegetation dynamics and hydrological processes.

[0047] Preferably, step S39 includes the following steps:

[0048] Step S391: constructing a vegetation-soil moisture relationship model based on the water balance status data of each functional layer;

[0049] Step S392: integrating the stomatal conductance feedback mechanism into the vegetation-soil moisture relationship model to obtain a stomatal regulation-moisture feedback model;

[0050] Step S393: integrating the stomatal regulation-water feedback model with the root water redistribution process to obtain an enhanced stomatal regulation-water feedback model;

[0051] Step S394: obtaining a vegetation physiological parameter condition response function set, and constructing a root water absorption strategy conversion trigger condition based on the vegetation physiological parameter condition response function set to obtain a water absorption strategy conversion threshold set;

[0052] Step S395: constructing a strategy transition state mechanism according to the water absorption strategy transition threshold set, wherein the state machine design includes a moist water absorption strategy, a moderate water stress water absorption strategy, and an extreme drought water absorption strategy;

[0053] Step S396: performing layered distribution on the layered root water absorption patterns of the plants based on the strategy conversion state mechanism to obtain a root dynamic water absorption distribution data table;

[0054] Step S397: Constructing a vegetation-soil moisture feedback model based on the vegetation physiological parameter conditional response function and the root system dynamic water absorption and distribution data table.

[0055] The present invention accurately simulates the response process of vegetation to changes in soil moisture by constructing a vegetation-soil moisture relationship model and integrating the stomatal conductance feedback mechanism. At the same time, the root water redistribution process is integrated to enable the model to dynamically reflect the adjustment of the plant's water absorption strategy under different moisture conditions. By constructing the water absorption strategy conversion trigger conditions and the strategy conversion state mechanism, the model can automatically adjust the root water absorption mode according to the soil moisture state. Finally, the vegetation physiological parameter conditional response function is combined with the root dynamic water absorption distribution data table to construct a vegetation-soil moisture feedback model, realizing two-way dynamic feedback between vegetation and soil moisture processes. This not only enhances the model's ability to describe vegetation physiological processes, but also improves the simulation accuracy of vegetation water absorption processes, thereby enhancing the overall simulation capability of the eco-hydrological model.

[0056] Preferably, step S4 includes the following steps:

[0057] Step S41: Obtain historical meteorological data and recent weather forecast data, and record the historical meteorological data and recent weather forecast data as original meteorological data sets; repair missing values in the original meteorological data sets to obtain quality-controlled meteorological data;

[0058] Step S42: performing a multi-model numerical weather forecast simulation based on the quality-controlled meteorological data to obtain a multi-model ensemble forecast result;

[0059] Step S43: performing weather scenario statistics on the multi-model ensemble forecast results to obtain a weather scenario set for the study area; performing hydrological and meteorological index statistics on the weather scenario set for the study area to obtain a hydrological and meteorological driving factor set;

[0060] Step S44: constructing a multi-scenario simulation scenario set based on the hydrological and meteorological driving factor set and the vegetation-soil moisture feedback model;

[0061] Step S45: performing parallel simulation on the multi-scenario simulation scenario set to obtain a multi-scenario simulation result database; performing collective statistical analysis on the multi-scenario simulation result database to obtain preliminary vegetation physiological prediction parameters;

[0062] Step S46: performing real-time monitoring of physiological parameters of vegetation in the target area to obtain real-time vegetation physiological monitoring data; recursively assimilating the preliminary vegetation physiological prediction parameters with the real-time vegetation physiological monitoring data to obtain vegetation physiological state estimation parameters;

[0063] Step S47: smoothing the vegetation physiological state estimation parameters to obtain smoothed vegetation physiological prediction parameters; performing physiological elasticity recovery on the smoothed vegetation physiological prediction parameters to obtain complete vegetation physiological prediction parameters.

[0064] The present invention integrates historical meteorological data with recent weather forecast data, and after missing data removal and multi-model numerical weather forecast simulation, generates a high-quality weather scenario set and further extracts a set of hydrometeorological driving factors. On this basis, a multi-scenario simulation scheme is constructed in combination with the vegetation-soil moisture feedback model, and preliminary vegetation physiological prediction parameters are obtained through parallel simulation and ensemble statistical analysis. At the same time, real-time monitoring data of vegetation physiological parameters are introduced and recursively assimilated with the preliminary prediction parameters to generate vegetation physiological state estimation parameters. And by smoothing the sequence and restoring physiological elasticity, complete and reliable vegetation physiological prediction parameters are finally obtained. This process not only improves the accuracy and real-time performance of the prediction parameters, but also enhances the model's adaptability to short-term hydrometeorological changes.

[0065] Preferably, step S5 includes the following steps:

[0066] Step S51: obtaining the digital elevation of the target area and recording it as the original terrain data of the study area; performing hydrological terrain correction on the original terrain data of the study area to obtain corrected hydrological terrain data;

[0067] Step S52: performing sub-basin division and water system network extraction based on the corrected hydrological and corrected terrain data to obtain basic hydrological unit data; performing regular grid resampling on the basic hydrological unit data to obtain surface hydrological initial grid unit data;

[0068] Step S53: performing terrain index distribution statistics on the initial surface hydrological grid unit data to obtain a terrain humidity index distribution map;

[0069] Step S54: clustering similar hydrological response units based on the terrain humidity index distribution map to obtain hydrological response unit partition data; adjusting the spatial continuity of the hydrological response unit partition data to obtain optimized hydrological response unit partition data;

[0070] Step S55: fusing the optimized hydrological response unit partition data with the surface hydrological initial grid unit data to obtain surface hydrological regular grid unit data;

[0071] Step S56: performing time series decomposition on the complete vegetation physiological prediction parameters to obtain multi-time scale components of the vegetation parameters;

[0072] Step S57: performing spatial allocation of vegetation parameters on the surface hydrological regular grid unit data based on the multi-time scale components of the vegetation parameters to obtain gridded vegetation parameters; performing time-continuous interpolation on the gridded vegetation parameters to obtain a time-continuous vegetation parameter sequence;

[0073] Step S58: performing spatiotemporal fusion based on the time-continuous vegetation parameter sequence and the surface hydrological regular grid unit data to obtain multi-time-scale vegetation-hydrology synergistic data;

[0074] Step S59: performing slope hydrological process simulation on the vegetation-hydrology synergistic data to obtain vegetation-hydrology synergistic evolution data.

[0075] The present invention can more accurately reflect the impact of actual terrain on hydrological processes by performing hydrological terrain correction on the digital elevation data of the target area. Through terrain index distribution statistics and clustering of similar hydrological response units, the hydrological response unit partitioning is further optimized, and the model's sensitivity to terrain humidity differences is improved. At the same time, the complete vegetation physiological prediction parameters are decomposed into time series, and the spatial allocation and time continuity interpolation of vegetation parameters are performed in combination with surface hydrological regular grid unit data, realizing the spatiotemporal dynamic update of vegetation parameters and enhancing the model's ability to dynamically capture vegetation physiological changes. Through the spatiotemporal fusion of vegetation-hydrological collaborative data and the simulation of slope hydrological processes, high-precision, multi-dimensional data support is provided for the dynamic simulation of eco-hydrological processes, which significantly improves the model's simulation capability and prediction accuracy for the coordinated evolution of vegetation and hydrological processes.

[0076] Preferably, step S59 includes the following steps:

[0077] Step S591: identifying slope runoff paths on multi-time-scale vegetation-hydrology collaborative data to obtain a slope flow path network;

[0078] Step S592: performing distributed runoff calculation based on the slope flow path network to obtain grid unit runoff data; performing confluence calculation on the grid unit runoff data to obtain the slope runoff process line;

[0079] Step S593: performing river confluence simulation based on the slope runoff process line to obtain preliminary slope runoff simulation results;

[0080] Step S594: obtaining measured surface runoff data in the study area, and standardizing the measured surface runoff data in the study area to obtain standardized measured runoff data;

[0081] Step S595: Compare the preliminary slope runoff simulation results with the standardized measured runoff data to obtain model performance evaluation indicators; generate a model validation report based on the model performance evaluation indicators;

[0082] Step S596: Perform a multi-factor sensitivity assessment on the model validation report to obtain a vegetation-hydrological process parameter sensitivity ranking table; determine vegetation-hydrological sensitivity parameter optimization range data based on the vegetation-hydrological process parameter sensitivity ranking table; and construct a vegetation-hydrological synergistic parameter optimization scheme based on the vegetation-hydrological sensitivity parameter optimization range data.

[0083] Step S597: adjusting parameters of the multi-time-scale vegetation-hydrology synergy data based on the vegetation-hydrology synergy parameter optimization scheme to obtain optimized vegetation-hydrology synergy data;

[0084] Step S598: quantitatively evaluate the uncertainty of the optimized vegetation-hydrology synergy data to obtain the uncertainty interval of the simulation results; integrate the uncertainty interval of the simulation results with the optimized vegetation-hydrology synergy data to obtain the vegetation-hydrology synergy evolution data.

[0085] The present invention can accurately simulate the formation and flow process of slope runoff by identifying slope runoff paths and calculating distributed runoff production on multi-time-scale vegetation-hydrology synergistic data. On this basis, combined with river confluence simulation, preliminary slope runoff simulation results are obtained, providing basic data for further model verification. By obtaining measured surface runoff data and performing standardization processing, and comparing it with the preliminary simulation results, model performance evaluation indicators and verification reports are generated. This not only verifies the accuracy of the model, but also clarifies the sensitivity ranking of vegetation-hydrology process parameters through multi-factor sensitivity evaluation. Based on the sensitivity ranking table, the optimization range of vegetation-hydrology sensitive parameters is determined, and a synergistic parameter optimization scheme is constructed to further adjust and optimize the multi-time-scale vegetation-hydrology synergistic data. Finally, by performing quantitative uncertainty evaluation on the optimized data, the uncertainty range of the simulation results is clarified, and it is integrated with the optimized data to obtain more reliable and accurate vegetation-hydrology synergistic evolution data.

[0086] Preferably, the present invention provides an eco-hydrological process simulation system based on dynamic vegetation, which is used to execute the eco-hydrological process simulation method based on dynamic vegetation as described above. The eco-hydrological process simulation system based on dynamic vegetation includes:

[0087] The data acquisition module is used to obtain the vegetation physiological monitoring parameter set and the root water absorption dynamic data set; the vegetation physiological monitoring parameter set and the root water absorption dynamic data set are uniformly aligned in time and space to obtain the vegetation physiological dynamic parameter set;

[0088] The soil-vegetation response module is used to divide the target area into soil drying and wetting processes based on the vegetation physiological dynamic parameter set to obtain a segmented soil moisture state sequence; the vegetation physiological dynamic parameter set is divided according to the segmented soil moisture state sequence to obtain a state-classified vegetation physiological data set; and a vegetation water stress adaptation strategy model is constructed based on the state-classified vegetation physiological data set;

[0089] Feedback model construction module, used to divide the soil profile into hydraulic functional layers based on the vegetation water stress adaptation strategy model to obtain layered soil-root interaction units; and to construct a vegetation-soil water feedback model based on the layered soil-root interaction units;

[0090] The vegetation physiological prediction module is used to construct a short-term hydro-meteorological scenario set; dynamically simulate the short-term hydro-meteorological scenario set based on the vegetation-soil moisture feedback model to obtain smoothed vegetation physiological prediction parameters; and perform physiological elastic recovery on the smoothed vegetation physiological prediction parameters to obtain complete vegetation physiological prediction parameters;

[0091] The vegetation-hydrology collaborative simulation module is used to dynamically update the vegetation parameters of the target area based on the complete vegetation physiological prediction parameters to obtain multi-time-scale vegetation-hydrology collaborative data; it simulates the slope hydrological process of the vegetation-hydrology collaborative data to obtain vegetation-hydrology collaborative evolution data.

[0092] The present invention can efficiently integrate vegetation physiological monitoring parameters and root water absorption dynamic data through the data acquisition module, and provide a high-precision and dynamic vegetation physiological dynamic parameter set for subsequent simulations through unified spatiotemporal registration. The soil-vegetation response module constructs a vegetation water stress adaptation strategy model by dividing the soil dry-wet process and decomposing the vegetation physiological parameters, which can accurately capture the response mechanism of vegetation to soil moisture changes. The feedback model construction module further realizes the two-way dynamic feedback between vegetation and soil moisture through the division of hydraulic functional layers and the construction of feedback models, thereby improving the model's simulation ability for complex eco-hydrological processes. The vegetation physiological prediction module generates complete and reliable vegetation physiological prediction parameters through the dynamic simulation of a set of short-term hydro-meteorological scenarios, combined with physiological elastic recovery, significantly improving the prediction accuracy of vegetation physiological processes. The vegetation-hydrological collaborative simulation module generates multi-time-scale vegetation-hydrological collaborative evolution data through the temporal dynamic update of vegetation parameters and the simulation of slope hydrological processes, providing comprehensive and high-precision data support for the dynamic simulation of eco-hydrological processes.

[0093] Preferably, the present invention further provides a computer-readable medium storing a program capable of being loaded by a processor and executed by the above-mentioned method for simulating eco-hydrological processes based on dynamic vegetation. BRIEF DESCRIPTION OF THE DRAWINGS

[0094] Other features, objects and advantages of the present invention will become more apparent from reading the detailed description with reference to the following drawings:

[0095] Figure 1 A schematic flow chart of the steps of a method for simulating an eco-hydrological process based on dynamic vegetation according to an embodiment is shown.

[0096] Figure 2 A detailed flowchart of step S2 of an embodiment is shown.

[0097] Figure 3 A detailed flowchart of step S39 of an embodiment is shown. DETAILED DESCRIPTION

[0098] The following is a clear and complete description of the technical method of the present invention in conjunction with the accompanying drawings. It is obvious that the embodiments described are part of the embodiments of the present invention, but not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without making any creative efforts are within the scope of protection of the present invention.

[0099] In addition, the accompanying drawings are merely schematic illustrations of the present invention and are not necessarily drawn to scale. Identical reference numerals in the figures denote identical or similar parts, and thus repetitive descriptions thereof will be omitted. Some of the block diagrams shown in the accompanying drawings are functional entities that do not necessarily correspond to physically or logically separate entities. These functional entities may be implemented in software, in one or more hardware modules or integrated circuits, or in different network and / or processor and / or microcontroller approaches.

[0100] It should be understood that although the terms "first," "second," and the like may be used herein to describe various elements, these elements should not be limited by these terms. These terms are used solely to distinguish one element from another. For example, a first element may be referred to as a second element, and similarly, a second element may be referred to as a first element, without departing from the scope of the exemplary embodiments. The term "and / or" as used herein includes any and all combinations of one or more of the listed associated items.

[0101] To achieve this, please refer to Figures 1 to 3 The present invention provides a method for simulating eco-hydrological processes based on dynamic vegetation, comprising the following steps:

[0102] Step S1: obtaining a vegetation physiological monitoring parameter set and a root water absorption dynamic data set; performing spatiotemporal registration on the vegetation physiological monitoring parameter set and the root water absorption dynamic data set to obtain a vegetation physiological dynamic parameter set;

[0103] Step S2: Based on the vegetation physiological dynamic parameter set, the target area is divided into soil dry-wet processes to obtain a segmented soil moisture state sequence; based on the segmented soil moisture state sequence, the vegetation physiological dynamic parameter set is divided to obtain a state-classified vegetation physiological data set; based on the state-classified vegetation physiological data set, a vegetation water stress adaptation strategy model is constructed;

[0104] Step S3: Based on the vegetation water stress adaptation strategy model, the soil profile is divided into hydraulic functional layers to obtain layered soil-root interaction units; and a vegetation-soil water feedback model is constructed based on the layered soil-root interaction units;

[0105] Step S4: constructing a short-term hydro-meteorological scenario set; dynamically simulating the short-term hydro-meteorological scenario set based on the vegetation-soil moisture feedback model to obtain smoothed vegetation physiological prediction parameters; performing physiological elastic recovery on the smoothed vegetation physiological prediction parameters to obtain complete vegetation physiological prediction parameters;

[0106] Step S5: Based on the complete vegetation physiological prediction parameters, the vegetation parameters of the target area are dynamically updated to obtain multi-time-scale vegetation-hydrology synergy data; the slope hydrological process of the vegetation-hydrology synergy data is simulated to obtain vegetation-hydrology synergy evolution data.

[0107] In this embodiment, a ground micrometeorological tower, a portable photosynthesis measurement system (such as LI-6800) and a soil moisture sensor (such as Decagon 5TM) are first used to obtain a vegetation physiological monitoring parameter set, including stomatal conductance, transpiration rate, vegetation stem flow data, and a root water absorption dynamic data set, including the root water absorption rate of different soil layers. The time interval for collecting these data is once every 30 minutes, and the continuous monitoring period is one month. The pandas library in Python is used to perform time and space unified registration on these data, and the vegetation physiological monitoring data and the root water absorption dynamic data are aligned according to the timestamp and spatial position to obtain a vegetation physiological dynamic parameter set. The soil moisture content data in the vegetation physiological dynamic parameter set is analyzed using MATLAB software, and the soil dry and wet process is divided by setting soil moisture thresholds (such as 30% and 70% of field capacity) to obtain a segmented soil moisture state sequence. Based on this sequence, the vegetation physiological dynamic parameter set is decomposed to obtain a state-classified vegetation physiological data set. Using statistical analysis packages in the R language (e.g., lme4), a vegetation water stress adaptation strategy model was constructed based on a state-classified vegetation physiological dataset. The model analyzes the variations in vegetation physiological parameters under different soil moisture states to reveal the adaptation mechanisms of vegetation to water stress. GIS software (e.g., ArcGIS Pro) was used to delineate the soil profile into hydraulic functional layers. Based on the output of the vegetation water stress adaptation strategy model and combined with soil texture and root density data, hierarchical soil-root interaction units were generated. Based on these units, a vegetation-soil water feedback model was constructed, which dynamically simulates the interaction between vegetation physiological processes and soil water movement. Furthermore, a set of short-term hydrometeorological scenarios was constructed using meteorological data (e.g., historical meteorological data and recent weather forecasts). The meteorological data were then matched to the input parameters of the vegetation-soil water feedback model using the Python xarray library. Dynamic simulations of the short-term hydrometeorological scenario set were performed based on the vegetation-soil water feedback model to obtain smoothed vegetation physiological prediction parameters. The corrected parameters were subjected to physiological elastic restoration and adjusted using the nonlinear fitting toolbox in MATLAB based on the recovery characteristics of vegetation physiological parameters (such as the recovery rate of stomatal conductance) to obtain complete vegetation physiological prediction parameters. Based on these parameters, vegetation parameters were dynamically updated in the target area over time. The scipy.interpolate library in Python was used to interpolate vegetation parameters in time series to obtain multi-timescale vegetation-hydrological synergy data. Finally, the SWAT (Soil and Water Assessment Tool) model was used to simulate slope hydrological processes on the vegetation-hydrological synergy data. Model parameters (such as soil infiltration rate, vegetation cover, and slope) were set to simulate slope runoff, soil erosion, and vegetation growth, thereby obtaining vegetation-hydrological synergy evolution data.

[0108] Preferably, step S1 includes the following steps:

[0109] Step S11: Collecting vegetation canopy temperature, humidity, and wind speed data through a ground micrometeorological tower to obtain a canopy micrometeorological dataset;

[0110] Step S12: measuring the stomatal conductance and transpiration rate of vegetation leaves to obtain a set of leaf physiological parameters; and monitoring the water transport rate of vegetation trunks to obtain a vegetation stem flow data set;

[0111] Step S13: using a multispectral camera mounted on an unmanned aerial vehicle to perform aerial survey of vegetation coverage in the target area to obtain spatial distribution data of vegetation coverage; and measuring the leaf area index of the target area to obtain a leaf area index distribution map;

[0112] Step S14: performing time series alignment on the canopy micrometeorological dataset, the leaf physiological parameter set, and the vegetation stemflow dataset to obtain a synchronized vegetation physiological dataset;

[0113] Step S15: spatially registering the vegetation coverage spatial distribution data with the leaf area index distribution map to obtain a spatially synchronized vegetation structure dataset;

[0114] Step S16: Using a preset hierarchical Bayesian model, a multi-scale integration of the synchronized vegetation physiological dataset and the spatially synchronized vegetation structural dataset is performed to obtain a preliminary vegetation physiological monitoring parameter set. The preset hierarchical Bayesian model contains a three-layer nested structure: the first layer is that stomatal conductance follows a log-normal distribution, the second layer is the spatial correlation of quadrat scale parameters, and the third layer is the prior constraints of the regional scale.

[0115] Step S17: obtaining a corrected root water absorption dynamics dataset, and performing spatiotemporal registration on the preliminary vegetation physiological monitoring parameter set and the corrected root water absorption dynamics dataset to obtain a vegetation physiological dynamics parameter set.

[0116] It is particularly important that step S17 further includes the following steps:

[0117] Step S171: collecting tracer concentration data of different soil layers through a soil solution sampler to obtain a tracer vertical migration data set;

[0118] Step S172: performing transmission path identification on the tracer vertical migration data set to obtain soil moisture flow path data;

[0119] Step S17: Calculating the root water absorption intensity of different soil layers based on the soil water flow path data to obtain preliminary root water absorption distribution data;

[0120] Step S173: continuously measuring the soil moisture content in the root water absorption area to obtain a soil moisture dynamic change data set;

[0121] Step S174: performing correlation analysis on the preliminary root water absorption distribution data and the soil moisture dynamic change dataset to obtain a corrected root water absorption dynamic dataset;

[0122] Step S175: performing spatiotemporal registration on the preliminary vegetation physiological monitoring parameter set and the corrected root water absorption dynamic data set to obtain a vegetation physiological dynamic parameter set.

[0123] In this embodiment, in the target ecological research area, the temperature, humidity and wind speed data of the vegetation canopy are collected by sensors installed on the ground micrometeorological tower. In the specific operation, a micrometeorological sensor of model Vantage Pro2 is used, which can record the temperature (accuracy of ±0.1°C), humidity (accuracy of ±2%) and wind speed (accuracy of ±0.3m / s) of the canopy at a time interval of 1 minute. From May 1, 2024 to October 31, 2024, data is continuously collected, and a canopy micrometeorological dataset containing a timestamp is finally obtained, and the data is stored in CSV format. Representative vegetation samples are selected in the target area, and the stomatal conductance and transpiration rate of vegetation leaves are measured using a portable photosynthesis measurement system (model LI-6800). The device can accurately measure the stomatal conductance of leaves (accuracy of ±0.01mol / m 2 ·s) and transpiration rate (accuracy of ±0.05mmol / m 2·s). Simultaneously, a thermal diffusion probe (TDP-10) was used to monitor water transport rates in the vegetation trunks and record stemflow data. Measurements were taken three times a week, from 9:00 AM to 11:00 AM. Leaf physiological parameter sets and vegetation stemflow datasets were generated and saved in Excel. Aerial surveys of vegetation cover were conducted in the target area using a multispectral camera (MicaSense RedEdge-MX) mounted on an unmanned aerial vehicle (UAV). The drone was set to fly at an altitude of 100 meters and a speed of 5 meters per second. The multispectral camera captured multi-band images, including red and near-infrared light, at 1-second intervals. Using image processing software, the captured images were stitched together to create a high-resolution spatial distribution map of vegetation cover. Furthermore, a handheld leaf area index meter (AccuPAR LP-80) was used to measure leaf area index at 10 randomly selected points in the target area. Measurements were taken from 2:00 PM to 4:00 PM at a height of 1.5 meters. A leaf area index distribution map was generated. The canopy micrometeorological dataset, leaf physiological parameter dataset, and vegetation stemflow dataset were imported into time series analysis software (e.g., MATLAB). A script was developed to align the three datasets based on time. Specifically, the dataset timestamps were converted to a unified time format (e.g., UTC), and the data were interpolated at 1-hour intervals to ensure that each time point contained corresponding canopy micrometeorological data, leaf physiological parameter dataset, and vegetation stemflow data. This resulted in a synchronized vegetation physiological dataset. Geographic Information System (GIS) software (e.g., ArcGIS Pro) was used to spatially register the spatial distribution of vegetation cover and the leaf area index distribution map. First, the coordinate systems of the two images were unified to WGS-1984 / UTM zone 48N. Then, by setting control points (e.g., distinct vegetation boundaries or terrain features in the images), the images were geometrically corrected using an affine transformation to ensure a perfect spatial alignment between the two images. This resulted in a spatially synchronized vegetation structural dataset. The synchronized vegetation physiological and structural datasets were then imported into statistical analysis software (e.g., R). Multi-scale integration was performed using a preset hierarchical Bayesian model. In the first layer of the model, stomatal conductance was assumed to follow a log-normal distribution, and the distribution parameters were determined by the maximum likelihood estimation method. In the second layer, spatial autocorrelation analysis (such as Moran's I index) was used to evaluate the spatial correlation of sample scale parameters. In the third layer, the data were fused using the Bayesian inference method in combination with regional scale prior constraints (such as vegetation type and soil type distribution maps). Finally, a preliminary set of vegetation physiological monitoring parameters was obtained. Soil solution samplers (model Rhizon SMS, sampling depths of 10 cm, 30 cm and 50 cm) were installed in the soil of the target area, and soil solution samples were collected regularly at different soil layers.The collected samples are brought back to the laboratory, where the concentration of tracers (such as sodium chloride) is analyzed using an ion chromatograph to generate a tracer vertical transport dataset. The soil moisture transmission path is inferred from changes in tracer concentration, resulting in soil moisture flow path data. This data, combined with a dataset of soil moisture dynamics (continuously monitored by soil moisture sensors), is used to correct the preliminary root water uptake distribution data using correlation analysis (such as the Pearson correlation coefficient) to generate a corrected root water uptake dynamics dataset. Finally, the corrected root water uptake dynamics dataset is spatially and temporally aligned with the preliminary vegetation physiological monitoring parameter set to obtain a vegetation physiological dynamics parameter set.

[0124] Preferably, step S2 includes the following steps:

[0125] Step S21: extracting soil moisture time series from the vegetation physiological dynamic parameter set to generate an original soil moisture change curve;

[0126] Step S22: performing soil moisture fluctuation identification on the original soil moisture change curve to obtain soil moisture fluctuation characteristic data;

[0127] Step S23: performing dry-wet process division on the soil moisture fluctuation characteristic data to obtain a preliminary soil dry-wet process division result;

[0128] Step S24: modifying the state of the preliminary soil dry-wet process division result according to a preset soil hydraulic characteristic threshold to obtain a segmented soil moisture state sequence;

[0129] Step S25: adjusting the time continuity of the segmented soil moisture state sequence to obtain a modified soil dry-wet process division result;

[0130] Step S26: dividing the vegetation physiological dynamic parameter set according to the modified soil dry-wet process division result to obtain a state-classified vegetation physiological data set;

[0131] Step S27: Constructing a vegetation water stress adaptation strategy model based on the state-classified vegetation physiological dataset.

[0132] It is particularly important that step S27 further includes the following steps:

[0133] Step S271: normalizing the state-classified vegetation physiological data set to obtain a standardized vegetation physiological indicator set;

[0134] Step S272: extracting state transition features from the segmented soil moisture state sequence to obtain soil moisture state transition features; grouping the standardized vegetation physiological indicator set into soil moisture states based on the soil moisture state transition features to obtain state interval vegetation physiological data;

[0135] Step S273: performing a segmented regression fitting on each vegetation physiological parameter in the vegetation physiological dynamic parameter set and the soil moisture content based on the state interval vegetation physiological data to obtain a preliminary vegetation physiological response curve;

[0136] Step S274: smoothly connecting the preliminary vegetation physiological response curves using the preset piecewise continuity constraint conditions to obtain a complete vegetation physiological response curve;

[0137] Step S257: performing sensitivity extraction on the complete vegetation physiological response curve to obtain a vegetation response sensitivity curve;

[0138] Step S276: performing change point detection on the vegetation response sensitivity curve to obtain a set of candidate sensitivity critical points; screening and interpreting the set of candidate sensitivity critical points to obtain a set of vegetation physiological parameter response thresholds;

[0139] Step S277: Encode the vegetation physiological parameter response threshold set into a mathematical expression to obtain a vegetation physiological parameter conditional response function set; and construct a vegetation water stress adaptation strategy model based on the vegetation physiological parameter conditional response function set. Specifically, the vegetation water stress adaptation strategy model is:

[0140]

[0141] g wet (θ)=a1θ+b1;

[0142]

[0143] g dry (θ) = c3sigmoid(θ-θ low )+d3;

[0144] θ high =0.7θ FC ;

[0145] θ low =0.3θ FC ;

[0146] Among them, θ is the soil moisture content, θ FC is the field water capacity, θ high is the threshold of moist-moderate stress, θ low is the threshold of moderate stress-extreme drought, a1 is the linear response rate of vegetation physiological parameters with changes in soil moisture content, b1 is the baseline value of vegetation physiological parameters when soil moisture content approaches zero, a2 is the physiological parameter level at the initial stage of moderate stress, λ is the attenuation rate of the control physiological parameter with the decrease of soil moisture, b2 is the stable value in the later stage of stress, c3 is the amplitude coefficient of the Sigmoid function, and d3 is the minimum physiological parameter value under extreme drought.

[0147] In this embodiment, soil moisture sensors (model Decagon 5TM, measuring depths of 10 cm, 30 cm, and 50 cm) were used to continuously monitor soil moisture in the target ecological research area. The sensor collected data at a frequency of recording once every 30 minutes and sent the data to a data acquisition system via a wireless transmission module. Data related to soil moisture content were extracted from the vegetation physiological dynamic parameter set, and MATLAB software was used to perform time series analysis on these data to generate an original soil moisture change curve. The curve shows the changing trend of soil moisture content over time. The signal processing toolbox in MATLAB was used to analyze the generated original soil moisture change curve. The fluctuation characteristics of soil moisture were identified by calculating the local extreme points and inflection points of the curve. The specific method was to use a sliding window (window size of 24 hours) to smooth the curve, and then calculate the maximum and minimum values within the window to determine the amplitude and period of the fluctuation. The soil moisture fluctuation characteristic data finally obtained include the start time, end time, maximum value, minimum value, and fluctuation amplitude parameters of the fluctuation, which are stored in an Excel file in a tabular form. The soil moisture fluctuation characteristic data were processed using the Pandas library in Python. Based on the maximum and minimum values in the fluctuation characteristic data, the soil moisture variation process is divided into wet and dry phases. Specifically, periods when the soil moisture content exceeds a certain threshold (e.g., 70% of field capacity) are defined as wet phases, and periods below a certain threshold (e.g., 30% of field capacity) are defined as dry phases. Through these steps, a preliminary classification of the soil wet and dry phases is established. Based on known soil hydraulic properties (e.g., soil texture and permeability), soil hydraulic thresholds are set. For example, soil moisture content above 80% of field capacity is defined as saturated, and below 20% of field capacity is defined as extreme drought. Excel software is used to modify the initial soil wet and dry phase classification, further subdividing wet and dry phases into saturated, wet, moderately dry, and extremely dry phases. MATLAB software is used to adjust the temporal continuity of the segmented soil moisture state series. The time intervals within the state series are checked to ensure that the time intervals between each state do not exceed a set threshold (e.g., 1 hour). If the time intervals are too large, interpolation methods (e.g., linear interpolation) are used to supplement the missing state data to make the state series temporally continuous. Based on the corrected soil dry-wet process classification results, the Pandas library in Python was used to decompose the vegetation physiological dynamic parameter set. Vegetation physiological parameters (such as stomatal conductance and transpiration rate) were classified according to soil moisture state to obtain a state-classified vegetation physiological dataset. For example, stomatal conductance data was divided into stomatal conductance under saturated conditions and stomatal conductance under moist conditions. Each physiological parameter in the state-classified vegetation physiological dataset was normalized.Using the Python NumPy library, each parameter value was scaled to a value between 0 and 1. Specifically, the minimum value of each parameter was subtracted from the parameter value, and then divided by the difference between the maximum and minimum values. This normalized data is referred to as the standardized vegetation physiological indicator set. MATLAB software was used to extract state transition features from the segmented soil moisture state series. Soil moisture state transition features were obtained by calculating characteristics such as the frequency and duration of state transitions. For example, the frequency and duration of transitions from a wet state to a dry state were calculated. Based on these characteristics, the standardized vegetation physiological indicator set was divided into different soil moisture state groups, generating state-interval vegetation physiological data. Based on these state-interval vegetation physiological data, the Python SciPy library was used to perform a segmented regression fit between each vegetation physiological parameter in the vegetation physiological dynamic parameter set and soil water content. For example, a linear regression fit was performed on the relationship between stomatal conductance and soil water content to generate a preliminary vegetation physiological response curve. Different regression equations were fitted according to the soil moisture state. The fitting results were stored as charts in PNG files, and the parameters of the regression equations were stored in CSV files. The preliminary vegetation physiological response curves were smoothed using smoothing functions (e.g., spline) in MATLAB. By setting smoothing parameters (e.g., a smoothing factor of 0.5), the transitions between different soil moisture states were smoothed. The resulting complete vegetation physiological response curves more accurately reflect the variations of vegetation physiological parameters with soil moisture content. The curves were stored as graphs in PNG files. Sensitivity extraction was performed on the complete vegetation physiological response curves using the ggplot2 package in R. The vegetation response sensitivity curves were obtained by calculating the slope change. Specifically, the slope difference between adjacent points was calculated. Areas with larger slope differences were considered sensitive areas. The sensitivity curves were stored as graphs in PNG files. Changepoint detection was performed on the vegetation response sensitivity curves. Using the ruptures library in Python, an appropriate changepoint detection algorithm (e.g., the Pelt algorithm) was selected. Detection parameters were set (e.g., a penalty term of 10) to identify a set of candidate sensitivity critical points. This set of candidate critical points was then screened and interpreted to eliminate false change points, ultimately obtaining a set of vegetation physiological parameter response thresholds. The set of vegetation physiological parameter response thresholds was encoded as mathematical expressions. Using the Python sympy library, each threshold set was encoded as a mathematical expression. For example, the stomatal conductance response threshold was encoded as a piecewise function. A vegetation water stress adaptation strategy model was then constructed based on these mathematical expressions. The model dynamically adjusts the response strategy of vegetation physiological parameters based on soil moisture status. The model code is stored in a Python script file.

[0148] Preferably, step S3 includes the following steps:

[0149] Step S31: Multi-source data assimilation and inversion are performed on the vegetation water stress adaptation strategy model, and three-dimensional rasterization is performed to obtain spatialized vegetation water response data; based on the spatialized vegetation water response data, the soil profile is clustered into equal hydraulic response areas to obtain preliminary functional layer zoning data;

[0150] Step S32: performing root density weighted correction on the preliminary functional layer partition data to obtain a root distribution density correction coefficient; adjusting the boundaries of the preliminary functional layer partition data according to the root distribution density correction coefficient to obtain a layered soil-root interaction unit;

[0151] Step S33: assigning hydraulic characteristic parameters to the layered soil-root interaction unit to obtain a layered soil hydraulic parameter set; and constructing a soil water movement simulation framework based on the layered soil hydraulic parameter set;

[0152] Step S34: integrating the root water absorption module into the soil water movement simulation framework to obtain a vertical water movement simulator with source terms;

[0153] Step S35: using a vertical water movement simulator with source term to calculate the water potential gradient of the layered soil-root interaction unit to obtain interlayer water potential gradient data;

[0154] Step S36: Calculating the water exchange flux between functional layers based on the interlayer water potential gradient data to obtain an interlayer water exchange flux table;

[0155] Step S37: Obtaining layered root water absorption rate data, performing water balance calculation on the interlayer water exchange flux table and the layered root water absorption rate data, and obtaining water balance status data of each functional layer;

[0156] Step S38: constructing a vegetation-soil moisture relationship model based on the water balance status data of each functional layer;

[0157] Step S39: integrating the feedback mechanism of the vegetation-soil moisture relationship model to obtain a vegetation-soil moisture feedback model.

[0158] In this embodiment, in the target ecological research area, the vegetation water stress adaptation strategy model is used to combine multi-source data for assimilation and inversion. Specific tools include: using the PyDA (Python Data Assimilation) library in Python to perform data assimilation processing on the model, taking meteorological data (such as temperature and humidity), remote sensing data (such as vegetation index) and soil sensor data (such as soil moisture content) as input, and dynamically adjusting the model parameters through Kalman filtering. The inversion results are processed by three-dimensional rasterization, and the vegetation water response data is rasterized into three-dimensional spatial data using GIS software (such as QGIS), with a grid size of 1m×1m×0.1m (horizontal×vertical). Based on the spatialized vegetation water response data, a clustering algorithm (such as K-Means) is used to cluster the soil profile into equal hydraulic response areas, and the soil profile is divided into different functional layers to obtain preliminary functional layer zoning data. A root scanner (such as RhizoVision) is used to scan the root distribution of the target area to obtain root density data. Root density data were imported into GIS software (e.g., ArcGIS Pro) and spatially overlaid with preliminary functional layer zoning data. The root density correction coefficient was calculated by averaging the root density within each functional layer. The boundaries of the preliminary functional layer zoning data were adjusted based on the correction coefficient. For example, functional layer boundaries were appropriately expanded for areas with high root density and narrowed for areas with low root density. Ultimately, stratified soil-root interaction units were obtained. Hydraulic parameters were assigned to these stratified soil-root interaction units. Based on soil texture (e.g., sand, loam, clay) and soil structure, hydraulic parameters for each functional layer, including saturated hydraulic conductivity, field capacity, and wilting water content, were obtained using a soil hydraulic database (e.g., Rosetta). These parameters were assigned to each stratified soil-root interaction unit to obtain a stratified soil hydraulic parameter set. Based on this parameter set, a Richards equation solver (e.g., RichardsFOAM) in Python was used to construct a soil water movement simulation framework capable of simulating soil water movement between different functional layers. The root water absorption module is integrated into the soil water movement simulation framework. The specific operation is: using the root water absorption module in the plant physiological model (such as MAESPA), combined with the layered soil hydraulic parameter set, the root water absorption process is incorporated into the soil water movement simulation framework. The specific method is to add the root water absorption rate as a source term to the soil water movement equation, and simulate the root system's absorption of soil water by adjusting the root water absorption parameters (such as maximum water absorption rate and water absorption coefficient). Finally, a vertical water movement simulator with source terms is obtained, which can simultaneously consider soil water movement and root water absorption processes. The simulator is stored in the form of a Python script. The vertical water movement simulator with source terms is used to calculate the water potential gradient of the layered soil-root interaction unit.According to the soil water movement equation and the root water absorption module, the water potential gradient between each functional layer is calculated. Using the numerical calculation toolbox in MATLAB, the water potential gradient is discretized by the finite difference method to obtain the interlayer water potential gradient data. The calculation results are stored in an Excel file in tabular form. The data includes the water potential difference and gradient direction between each functional layer. The water exchange flux between functional layers is calculated based on the interlayer water potential gradient data. Using Darcy's law, combined with the interlayer water potential gradient and soil hydraulic characteristic parameters (such as hydraulic conductivity), the water exchange flux between functional layers is calculated. The specific calculation formula is: Where Q is the water exchange flux, K is the hydraulic conductivity, is the water potential gradient. The NumPy library in Python is used for calculation to obtain the interlayer water exchange flux table. The results are stored in CSV file format. The table contains the water exchange flux values between each functional layer. The layered root water absorption rate data are obtained, and the root water absorption rate is continuously monitored at different depths (such as 10cm, 30cm, and 50cm) using a soil moisture sensor (such as Decagon 5TM). The monitoring data is used to calculate the water balance with the interlayer water exchange flux table. The specific method is to summarize the water input (such as precipitation, irrigation) and output (such as evaporation, root absorption, and interlayer exchange) of each functional layer, and calculate the water balance status data of each functional layer. Excel software is used for data processing to finally obtain the water balance status data of each functional layer. The results are stored in table form, including the water change trend and balance status of each functional layer. A vegetation-soil moisture relationship model is constructed based on the water balance status data of each functional layer. Using statistical analysis packages in the R language (e.g., lme4), regression analysis was performed between vegetation physiological parameters (e.g., stomatal conductance and transpiration rate) and soil moisture status (e.g., soil water content and water balance) to construct a vegetation-soil moisture relationship model. This model describes the response mechanism of vegetation physiological processes to soil moisture changes. Model parameters were estimated using maximum likelihood estimation, and the model was validated using cross-validation. The final model was stored as an R script. Feedback mechanisms were integrated into the vegetation-soil moisture relationship model. Using the System Identification Toolbox in MATLAB, feedback mechanisms for vegetation physiological parameters (e.g., the feedback of stomatal conductance on soil moisture) and dynamic adjustment mechanisms for root water uptake strategies (e.g., adjusting water uptake rate according to soil moisture status) were integrated into the model. By setting feedback parameters (e.g., feedback strength and response time) and state transition rules (e.g., the transition conditions from a wet to a dry uptake strategy), the vegetation-soil moisture feedback model was constructed. The final model dynamically simulates the interaction between vegetation and soil moisture and is stored as a MAT file.

[0159] Preferably, step S39 includes the following steps:

[0160] Step S391: constructing a vegetation-soil moisture relationship model based on the water balance status data of each functional layer;

[0161] Step S392: integrating the stomatal conductance feedback mechanism into the vegetation-soil moisture relationship model to obtain a stomatal regulation-moisture feedback model;

[0162] Step S393: integrating the stomatal regulation-water feedback model with the root water redistribution process to obtain an enhanced stomatal regulation-water feedback model;

[0163] Step S394: obtaining a vegetation physiological parameter condition response function set, and constructing a root water absorption strategy conversion trigger condition based on the vegetation physiological parameter condition response function set to obtain a water absorption strategy conversion threshold set;

[0164] Step S395: constructing a strategy transition state mechanism according to the water absorption strategy transition threshold set, wherein the state machine design includes a moist water absorption strategy, a moderate water stress water absorption strategy, and an extreme drought water absorption strategy;

[0165] Step S396: performing layered distribution on the layered root water absorption patterns of the plants based on the strategy conversion state mechanism to obtain a root dynamic water absorption distribution data table;

[0166] Step S397: Constructing a vegetation-soil moisture feedback model based on the vegetation physiological parameter conditional response function and the root system dynamic water absorption and distribution data table.

[0167] In this embodiment, a vegetation-soil moisture relationship model was constructed based on water balance data for each functional layer. A nonlinear mixed-effects model was fitted using the nlme package in the R language. Soil moisture content and water balance data were used as independent variables, and vegetation physiological parameters (such as stomatal conductance and transpiration rate) were used as dependent variables to fit the quantitative relationship between vegetation physiological processes and soil moisture status. Random effects were considered in the model to reflect differences between different functional layers. The optimal model structure was determined through stepwise regression and model comparison, ultimately resulting in a vegetation-soil moisture relationship model. Model parameters were determined using maximum likelihood estimation, and the model was validated through residual analysis and cross-validation. The results were saved as R scripts and model reports. The stomatal conductance feedback mechanism was integrated into the vegetation-soil moisture relationship model. The stomatal conductance feedback mechanism module was constructed using the Simulink toolbox in MATLAB. Based on the known response of stomatal conductance to soil moisture changes (e.g., stomatal conductance decreases with decreasing soil moisture content), the stomatal conductance feedback mechanism was embedded in the vegetation-soil moisture relationship model in functional form. The specific method is to set the stomatal conductance feedback coefficient (e.g., 0.5) and the response time constant (e.g., 1 hour), and verify the effectiveness of the feedback mechanism through simulation. The resulting stomatal regulation-water feedback model can dynamically simulate the response of stomatal conductance to soil moisture changes. The root water redistribution process is integrated into the stomatal regulation-water feedback model. The root water redistribution process is numerically simulated using the scipy.integrate module in Python. Based on the root water uptake strategy (e.g., preferentially absorbing shallow soil moisture), the root water redistribution process is incorporated into the stomatal regulation-water feedback model in the form of a differential equation. The specific method is to set the root water redistribution rate (e.g., 0.01 mm / h) and the redistribution direction (e.g., from shallow to deep layers), and solve the root water redistribution process through numerical integration. The resulting enhanced stomatal regulation-water feedback model can more accurately reflect the comprehensive response of vegetation to soil moisture changes. A set of conditional response functions of vegetation physiological parameters is obtained, and based on this, the trigger conditions for switching the root water uptake strategy are constructed. The Curve Fitting Toolbox in MATLAB was used to fit the relationship between vegetation physiological parameters (such as stomatal conductance and root water absorption rate) and soil moisture status to obtain a set of conditional response functions. Based on these functions, the triggering conditions for the conversion of root water absorption strategy were determined. For example, when the soil moisture content was lower than 40% of the field water holding capacity, the root water absorption strategy was converted from a moist water absorption strategy to a moderate water stress water absorption strategy. By setting specific thresholds (such as a soil moisture threshold of 0.15m 3 / m 3), construct a water absorption strategy conversion threshold set, and save the results in the form of MATLAB scripts and data tables. According to the water absorption strategy conversion threshold set, a strategy conversion state mechanism is constructed. The transitions library in Python is used to build a state machine model. The state machine design includes three states: moist water absorption strategy, moderate water stress water absorption strategy and extreme drought water absorption strategy. According to the water absorption strategy conversion threshold set, the state conversion conditions are set (such as soil moisture content is lower than 0.10m 3 / m 3 When the water absorption strategy is wet, it switches to the extreme drought water absorption strategy). Through the state machine model, the conversion process of the vegetation root water absorption strategy can be dynamically simulated, and the model is saved in the form of a Python script and a state transition diagram. The layered root water absorption pattern of the plant is distributed in layers based on the strategy conversion state mechanism. The layered root water absorption rate data is processed using Excel software, and the root water absorption pattern is distributed to different functional layers according to the output results of the strategy conversion state mechanism. For example, under the wet water absorption strategy, the root water absorption is mainly concentrated in the shallow soil (such as 0-20cm), and under the extreme drought water absorption strategy, the root water absorption is mainly concentrated in the deep soil (such as 40-60cm). By setting a specific water absorption rate distribution ratio (such as the shallow water absorption ratio is 0.7 and the deep water absorption ratio is 0.3), the root dynamic water absorption distribution data table is obtained, and the results are saved in the form of an Excel table. A vegetation-soil moisture feedback model is constructed based on the conditional response function of vegetation physiological parameters and the root dynamic water absorption distribution data table. Using the System Identification Toolbox in MATLAB, a system identification was performed on the conditional response functions of vegetation physiological parameters and the dynamic water uptake and allocation data of the root system to construct a vegetation-soil water feedback model. This model considers the dynamic response of vegetation physiological parameters to soil moisture changes and the dynamic adjustment of root water uptake strategies. By setting model parameters (e.g., setting the feedback gain to 0.6) and validating the model performance (e.g., by comparing it with measured data), the resulting vegetation-soil water feedback model is capable of dynamically simulating the complex interactions between vegetation and soil moisture.

[0168] Preferably, step S4 includes the following steps:

[0169] Step S41: Obtain historical meteorological data and recent weather forecast data, and record the historical meteorological data and recent weather forecast data as original meteorological data sets; repair missing values in the original meteorological data sets to obtain quality-controlled meteorological data;

[0170] Step S42: performing a multi-model numerical weather forecast simulation based on the quality-controlled meteorological data to obtain a multi-model ensemble forecast result;

[0171] Step S43: performing weather scenario statistics on the multi-model ensemble forecast results to obtain a weather scenario set for the study area; performing hydrological and meteorological index statistics on the weather scenario set for the study area to obtain a hydrological and meteorological driving factor set;

[0172] Step S44: constructing a multi-scenario simulation scenario set based on the hydrological and meteorological driving factor set and the vegetation-soil moisture feedback model;

[0173] Step S45: performing parallel simulation on the multi-scenario simulation scenario set to obtain a multi-scenario simulation result database; performing collective statistical analysis on the multi-scenario simulation result database to obtain preliminary vegetation physiological prediction parameters;

[0174] Step S46: performing real-time monitoring of physiological parameters of vegetation in the target area to obtain real-time vegetation physiological monitoring data; recursively assimilating the preliminary vegetation physiological prediction parameters with the real-time vegetation physiological monitoring data to obtain vegetation physiological state estimation parameters;

[0175] Step S47: smoothing the vegetation physiological state estimation parameters to obtain smoothed vegetation physiological prediction parameters; performing physiological elasticity recovery on the smoothed vegetation physiological prediction parameters to obtain complete vegetation physiological prediction parameters.

[0176] In this embodiment, historical meteorological data and recent weather forecast data are obtained in the target ecological research area. The historical meteorological data comes from the meteorological station of the Meteorological Bureau, including the daily average temperature, precipitation, wind speed and relative humidity data for the past 10 years. The recent weather forecast data comes from the hourly forecast data for the next 7 days issued by the Meteorological Bureau. These data are integrated into the original meteorological data set. The pandas library in Python is used to repair missing values in the original meteorological data set. For missing temperature data, linear interpolation method is used to fill; for missing precipitation data, forward filling method is used. The repaired data is stored as a CSV file to obtain quality control meteorological data. Based on the quality control meteorological data, a multi-model numerical weather forecast simulation is performed. The WRF (Weather Research and Forecasting) model is used, combined with different initial conditions and physical parameterization schemes (such as different cumulus parameterization schemes), to generate multi-model ensemble forecast results. In the specific operation, the simulation area of the WRF model is set to the study area, the horizontal resolution is 1km, and the vertical level is 30 layers. The WRF model was run to generate hourly multi-model ensemble forecasts for temperature, precipitation, wind speed, and relative humidity for the next seven days. Weather scenario statistics were then analyzed for the multi-model ensemble forecasts. The xarray library in Python was used to read the multi-model ensemble forecasts in NetCDF format. The ensemble mean and standard deviation for each time step were calculated to generate a weather scenario set for the study area. Hydrometeorological indicators for the weather scenario set were further analyzed, including daily mean temperature, daily precipitation, daily mean wind speed, and daily mean relative humidity. The numpy library was used to calculate the statistics of these indicators to generate a set of hydrometeorological driving factors. A multi-scenario simulation scenario set was constructed based on the hydrometeorological driving factor set and the vegetation-soil moisture feedback model. Different scenarios (e.g., high precipitation and low precipitation) from the hydrometeorological driving factor set were combined with the vegetation-soil moisture feedback model. Using MATLAB software, the vegetation-soil moisture feedback model was run with the hydrometeorological driving factors from different scenarios as input to generate simulation scenarios for vegetation physiological responses under different scenarios. The final multi-scenario simulation scenario set includes scenarios of changes in vegetation physiological parameters under different precipitation, temperature and wind speed conditions. A high-performance computing cluster (such as HPC) is used to calculate the multi-scenario simulation scenario set through a parallel simulation framework (such as MPI). Each computing node processes one scenario, and the results are aggregated to the master node after the calculation is completed. The mpi4py library in Python is used to implement parallel simulation to obtain a multi-scenario simulation result database. A collective statistical analysis is performed on the multi-scenario simulation result database to calculate the mean, standard deviation and extreme value of vegetation physiological parameters under each scenario to obtain preliminary vegetation physiological prediction parameters. A portable photosynthesis measurement system (such as LI-6800) is used to monitor the stomatal conductance and transpiration rate of vegetation leaves in real time, and the data is recorded once an hour.At the same time, soil moisture sensors (such as Decagon 5TM) were used to monitor soil moisture content, and data was recorded every 30 minutes. The monitoring data were recursively assimilated with the preliminary vegetation physiological prediction parameters. Using the pyda library in Python, the Kalman filter was used to fuse the real-time monitoring data with the preliminary prediction parameters to obtain the estimated parameters of the vegetation physiological state. The smoothdata function in MATLAB was used to perform moving average smoothing on the estimated parameters of the vegetation physiological state, and the window size was set to 3 hours. The smoothed data can more stably reflect the changing trend of the vegetation physiological state. The revised vegetation physiological prediction parameters were further subjected to physiological elastic recovery. According to the biological characteristics of the vegetation physiological parameters (such as the recovery rate of stomatal conductance), the exponential recovery model was used to adjust the prediction parameters to obtain the complete vegetation physiological prediction parameters.

[0177] Preferably, step S5 includes the following steps:

[0178] Step S51: obtaining the digital elevation of the target area and recording it as the original terrain data of the study area; performing hydrological terrain correction on the original terrain data of the study area to obtain corrected hydrological terrain data;

[0179] Step S52: performing sub-basin division and water system network extraction based on the corrected hydrological and corrected terrain data to obtain basic hydrological unit data; performing regular grid resampling on the basic hydrological unit data to obtain surface hydrological initial grid unit data;

[0180] Step S53: performing terrain index distribution statistics on the initial surface hydrological grid unit data to obtain a terrain humidity index distribution map;

[0181] Step S54: clustering similar hydrological response units based on the terrain humidity index distribution map to obtain hydrological response unit partition data; adjusting the spatial continuity of the hydrological response unit partition data to obtain optimized hydrological response unit partition data;

[0182] Step S55: fusing the optimized hydrological response unit partition data with the surface hydrological initial grid unit data to obtain surface hydrological regular grid unit data;

[0183] Step S56: performing time series decomposition on the complete vegetation physiological prediction parameters to obtain multi-time scale components of the vegetation parameters;

[0184] Step S57: performing spatial allocation of vegetation parameters on the surface hydrological regular grid unit data based on the multi-time scale components of the vegetation parameters to obtain gridded vegetation parameters; performing time-continuous interpolation on the gridded vegetation parameters to obtain a time-continuous vegetation parameter sequence;

[0185] Step S58: performing spatiotemporal fusion based on the time-continuous vegetation parameter sequence and the surface hydrological regular grid unit data to obtain multi-time-scale vegetation-hydrology synergistic data;

[0186] Step S59: performing slope hydrological process simulation on the vegetation-hydrology synergistic data to obtain vegetation-hydrology synergistic evolution data.

[0187] In the present embodiment, in the target study area, a drone equipped with a high-precision laser radar (LiDAR) device is used to collect digital elevation data. The flight altitude of the drone is set to 100 meters, the flight speed is 5 meters per second, and the point cloud density of the laser radar is 100 points per square meter. After the acquisition is completed, the point cloud data is processed using GlobalMapper software to generate a digital elevation model (DEM) of the study area with a resolution of 1 meter. Then, the original terrain data is corrected for hydrological terrain using ArcGIS Pro software to eliminate depressions and outliers in the DEM. Based on the corrected hydrological terrain data, in ArcGIS Pro software, the threshold value for watershed division is set to 1000 pixels to determine the boundaries of the sub-basin. At the same time, the stream network is extracted by terrain analysis. During extraction, the starting threshold value of the stream is set to 500 pixels. After obtaining the sub-basin and stream network, the basic hydrological unit data is exported to Shapefile format. The "Resample" tool in ArcGIS Pro was used to resample the basic hydrological unit data to a regular grid, adjusting the grid resolution to 5 meters by 5 meters to obtain the initial surface hydrological grid unit data. The terrain index distribution statistics were performed on the initial surface hydrological grid unit data. The "Terrain Wetness Index (TWI)" calculation tool in ArcGIS Pro was used to calculate the terrain wetness index based on the DEM data. The specific formula is: Where S is the cumulative discharge and α is the slope. After the calculation, a topographic wetness index distribution map with a resolution of 5 m × 5 m was obtained. Based on the topographic wetness index distribution map, K-Means in the scikit-learn library in Python was used to cluster similar hydrological response units. The topographic wetness index was used as the clustering feature, and the number of clusters was set to 5 (selected based on the topographic complexity of the study area). After clustering, preliminary hydrological response unit partition data was obtained. ArcGIS Pro was used to adjust the spatial continuity of the partition data to ensure that each partition was spatially continuous. The optimized hydrological response unit partition data was fused with the initial surface hydrological grid cell data. The "Raster Calculator" tool in ArcGIS Pro was used to overlay the partition data with the grid cell data for analysis. Specifically, each grid cell was assigned to the corresponding hydrological response unit partition to generate surface hydrological regular grid cell data. The resulting data was stored in GeoTIFF format, containing the hydrological response unit information for each grid cell. Time series decomposition of the complete vegetation physiological prediction parameters was performed. Using the Wavelet Analysis Toolbox in MATLAB, wavelet decomposition was performed on time series of vegetation physiological parameters (such as stomatal conductance and transpiration rate). Morlet wavelets were selected as the basis function to decompose the time series into multiple time-scale components (e.g., daily, monthly, and annual). After decomposition, the multi-time-scale components of the vegetation parameters were obtained and stored in the MAT file format. Based on these multi-time-scale components, spatial allocation of vegetation parameters was performed on the surface hydrological regular grid cell data. Using the xarray library in Python, the time-scale components of the vegetation parameters were spatially matched to the surface hydrological regular grid cell data. Vegetation parameters were assigned to each grid cell based on the terrain wetness index and vegetation type. For example, high humidity areas were assigned a higher transpiration rate, while low humidity areas were assigned a lower transpiration rate. After the allocation, gridded vegetation parameters were obtained. The scipy.interpolate library was used to perform time-continuous interpolation on the gridded vegetation parameters to generate a time-continuous vegetation parameter series. The time-continuous vegetation parameter series was then spatiotemporally fused with the surface hydrological regular grid cell data. Using the "Spatiotemporal Analysis" toolbox in MATLAB, a time-continuous vegetation parameter sequence is fused with surface hydrological regular grid cell data. By setting the time step (e.g., 1 hour) and spatial resolution (e.g., 5 meters x 5 meters), multi-time-scale vegetation-hydrology synergy data is generated. The fused data can reflect the spatiotemporal dynamic changes of vegetation physiological processes and hydrological processes and is stored in the MAT file format. Slope hydrological processes are simulated using vegetation-hydrology synergy data. The SWAT (Soil and Water Assessment Tool) model is used in combination with vegetation-hydrology synergy data to simulate slope hydrological processes.The model's input parameters were set, including vegetation parameters (such as transpiration rate), soil parameters (such as permeability), and terrain parameters (such as slope). The SWAT model was then run to simulate processes such as overland runoff, soil erosion, and vegetation growth. Simulation results were stored in CSV format, containing information such as overland runoff, soil moisture content, and vegetation physiological status at each time step. The model's accuracy was verified by comparing it with measured data, ultimately generating data on the co-evolution of vegetation and hydrology.

[0188] Preferably, step S59 includes the following steps:

[0189] Step S591: identifying slope runoff paths on multi-time-scale vegetation-hydrology collaborative data to obtain a slope flow path network;

[0190] Step S592: performing distributed runoff calculation based on the slope flow path network to obtain grid unit runoff data; performing confluence calculation on the grid unit runoff data to obtain the slope runoff process line;

[0191] Step S593: performing river confluence simulation based on the slope runoff process line to obtain preliminary slope runoff simulation results;

[0192] Step S594: obtaining measured surface runoff data in the study area, and standardizing the measured surface runoff data in the study area to obtain standardized measured runoff data;

[0193] Step S595: Compare the preliminary slope runoff simulation results with the standardized measured runoff data to obtain model performance evaluation indicators; generate a model validation report based on the model performance evaluation indicators;

[0194] Step S596: Perform a multi-factor sensitivity assessment on the model validation report to obtain a vegetation-hydrological process parameter sensitivity ranking table; determine vegetation-hydrological sensitivity parameter optimization range data based on the vegetation-hydrological process parameter sensitivity ranking table; and construct a vegetation-hydrological synergistic parameter optimization scheme based on the vegetation-hydrological sensitivity parameter optimization range data.

[0195] Step S597: adjusting parameters of the multi-time-scale vegetation-hydrology synergy data based on the vegetation-hydrology synergy parameter optimization scheme to obtain optimized vegetation-hydrology synergy data;

[0196] Step S598: quantitatively evaluate the uncertainty of the optimized vegetation-hydrology synergy data to obtain the uncertainty interval of the simulation results; integrate the uncertainty interval of the simulation results with the optimized vegetation-hydrology synergy data to obtain the vegetation-hydrology synergy evolution data.

[0197] In this embodiment, in the target study area, ArcGIS Pro software is used to identify the slope runoff path of multi-time scale vegetation-hydrology collaborative data. First, DEM data is imported and the flow direction is calculated using the "Flow Direction" tool in ArcGIS Pro. This tool determines the flow direction of the water based on the slope and aspect of the DEM. The "FlowAccumulation" tool is used to calculate the cumulative amount of water flow to identify the path of the water flow. By setting a threshold for the cumulative flow (for example, 100 pixels), the main slope flow path network can be extracted. Finally, the slope flow path network is stored in GeoTIFF format, containing water flow direction and path information. Based on the slope flow path network, the "Distributed Runoff" tool in ArcGIS Pro is used to perform distributed runoff calculations. This tool calculates the runoff of each grid cell based on the DEM data and the slope flow path network. Set model parameters, such as soil permeability (0.1 mm / min) and vegetation cover (0.6). After the calculation is completed, the runoff data of each grid cell is obtained. The "StreamNetworkAnalysis" tool was used to perform confluence routing on grid cell runoff data and generate overland runoff hydrographs. This tool simulates the convergence of water on a slope and outputs overland runoff hydrographs. The results are stored in a CSV file format, containing runoff volume and velocity information for each time step. Based on the overland runoff hydrographs, HEC-HMS (Hydrologic Engineering Center's Hydrologic Modeling System) software was used to simulate river confluence. First, the overland runoff hydrographs were imported into the HEC-HMS model, and the channel geometry parameters (such as channel width and depth) and roughness coefficient (such as 0.035) were input. The model boundary conditions were set, such as upstream flow input and downstream water level boundary. The HEC-HMS model was run to simulate the river confluence process and obtain preliminary overland runoff simulation results. The simulation results are stored in the HEC-HMS output file format, containing information such as river water level, flow, and velocity. Measured surface runoff data for the study area were obtained from hydrological monitoring stations. The measured runoff data were preprocessed using Excel software, including removing outliers and filling missing data. Then, the measured runoff data were standardized and all data were converted into dimensionless form. The specific method was to divide each data point by the maximum value of the data set to obtain the standardized measured runoff data. The preliminary slope runoff simulation results were compared with the standardized measured runoff data. The statistical analysis toolbox in MATLAB software was used to calculate the model performance evaluation indicators, such as Nash efficiency coefficient (NSE), root mean square error (RMSE) and correlation coefficient (R 2). A model validation report is generated based on these indicators, and the report records the accuracy, bias and goodness of fit of the model in detail. A multi-factor sensitivity assessment is performed on the model validation report. Using the SALib library in Python, a sensitivity analysis is performed on vegetation-hydrological process parameters (such as soil permeability, vegetation coverage and slope). By setting the parameter range (such as soil permeability 0.05-0.2mm / min, vegetation coverage 0.3-0.9, slope 5%-20%), a global sensitivity analysis is run to obtain a vegetation-hydrological process parameter sensitivity ranking table. According to the ranking table, the optimization range data of the vegetation-hydrological sensitive parameters are determined. For example, the optimization range of soil permeability is determined to be 0.08-0.15mm / min. Based on these data, a vegetation-hydrological synergistic parameter optimization scheme is constructed, and the scheme is stored in Excel file format. Based on the vegetation-hydrological synergistic parameter optimization scheme, parameters of multi-time scale vegetation-hydrological synergistic data are adjusted. Using the pandas library in Python, the parameter values in the optimization scheme are updated to the vegetation-hydrological synergistic data. For example, the soil permeability is adjusted from 0.1 mm / min to 0.12 mm / min. The adjusted data is stored in CSV file format to obtain the optimized vegetation-hydrology synergy data. The uncertainty of the optimized vegetation-hydrology synergy data is quantitatively evaluated. Using the Monte Carlo simulation toolbox in MATLAB, the optimized parameters are randomly sampled to generate multiple simulation scenarios. Run the hydrological model to obtain the simulation results of each scenario. By calculating the distribution range of the simulation results, the uncertainty interval of the simulation results is obtained. For example, the uncertainty interval of the runoff is determined to be [100,150] m 3 / s. Based on the uncertainty interval and the optimized vegetation-hydrology synergistic data, vegetation-hydrology synergistic evolution data are obtained.

[0198] Preferably, the present invention provides an eco-hydrological process simulation system based on dynamic vegetation, which is used to execute the eco-hydrological process simulation method based on dynamic vegetation as described above. The eco-hydrological process simulation system based on dynamic vegetation includes:

[0199] The data acquisition module is used to obtain the vegetation physiological monitoring parameter set and the root water absorption dynamic data set; the vegetation physiological monitoring parameter set and the root water absorption dynamic data set are uniformly aligned in time and space to obtain the vegetation physiological dynamic parameter set;

[0200] The soil-vegetation response module is used to divide the target area into soil drying and wetting processes based on the vegetation physiological dynamic parameter set to obtain a segmented soil moisture state sequence; the vegetation physiological dynamic parameter set is divided according to the segmented soil moisture state sequence to obtain a state-classified vegetation physiological data set; and a vegetation water stress adaptation strategy model is constructed based on the state-classified vegetation physiological data set;

[0201] Feedback model construction module, used to divide the soil profile into hydraulic functional layers based on the vegetation water stress adaptation strategy model to obtain layered soil-root interaction units; and to construct a vegetation-soil water feedback model based on the layered soil-root interaction units;

[0202] The vegetation physiological prediction module is used to construct a short-term hydro-meteorological scenario set; dynamically simulate the short-term hydro-meteorological scenario set based on the vegetation-soil moisture feedback model to obtain smoothed vegetation physiological prediction parameters; and perform physiological elastic recovery on the smoothed vegetation physiological prediction parameters to obtain complete vegetation physiological prediction parameters;

[0203] The vegetation-hydrology collaborative simulation module is used to dynamically update the vegetation parameters of the target area based on the complete vegetation physiological prediction parameters to obtain multi-time-scale vegetation-hydrology collaborative data; it simulates the slope hydrological process of the vegetation-hydrology collaborative data to obtain vegetation-hydrology collaborative evolution data.

[0204] Preferably, the present invention further provides a computer-readable medium storing a program capable of being loaded by a processor and executed by the above-mentioned method for simulating eco-hydrological processes based on dynamic vegetation.

[0205] The present invention is therefore intended to be illustrative and non-restrictive in all respects, with the scope of the invention being defined by the appended claims rather than the foregoing description, and all changes that come within the meaning and range of equivalents of the application documents are intended to be embraced therein.

[0206] The foregoing description is intended only to provide specific embodiments of the present invention, which will enable those skilled in the art to understand and implement the present invention. Various modifications to these embodiments will be readily apparent to those skilled in the art, and the general principles defined herein may be implemented in other embodiments without departing from the spirit or scope of the present invention. Therefore, the present invention is not intended to be limited to the embodiments shown herein, but is to be construed in the widest possible manner consistent with the principles and novel features disclosed herein.

Claims

1. A method for simulating eco-hydrological processes based on dynamic vegetation, characterized in that: The following steps are involved: Step S1: Obtaining a vegetation physiological monitoring parameter set and a root water absorption dynamics dataset; The vegetation physiological monitoring parameter set and the root water absorption dynamic data set were uniformly registered in time and space to obtain the vegetation physiological dynamic parameter set; Step S2: Based on the vegetation physiological dynamic parameter set, the target area is divided into soil dry and wet processes to obtain a segmented soil moisture state sequence; The vegetation physiological dynamic parameter set is divided according to the segmented soil moisture state sequence to obtain the state classification vegetation physiological data set; Constructing a vegetation water stress adaptation strategy model based on a state-classified vegetation physiological dataset; Step S3: Based on the vegetation water stress adaptation strategy model, the soil profile is divided into hydraulic functional layers to obtain layered soil-root interaction units; and a vegetation-soil water feedback model is constructed based on the layered soil-root interaction units; Step S4: constructing a short-term hydro-meteorological scenario set; dynamically simulating the short-term hydro-meteorological scenario set based on the vegetation-soil moisture feedback model to obtain smoothed vegetation physiological prediction parameters; Perform physiological elastic recovery on smooth vegetation physiological prediction parameters to obtain complete vegetation physiological prediction parameters; Step S5: dynamically updating vegetation parameters of the target area based on the complete vegetation physiological prediction parameters to obtain multi-time-scale vegetation-hydrology synergy data; The slope hydrological process is simulated on the vegetation-hydrology synergy data to obtain the vegetation-hydrology synergy evolution data.

2. The method for simulating eco-hydrological processes based on dynamic vegetation according to claim 1, characterized in that: Step S1 includes the following steps: Step S11: Collecting vegetation canopy temperature, humidity, and wind speed data through a ground micrometeorological tower to obtain a canopy micrometeorological dataset; Step S12: measuring the stomatal conductance and transpiration rate of vegetation leaves to obtain a set of leaf physiological parameters; and monitoring the water transport rate of vegetation trunks to obtain a vegetation stem flow data set; Step S13: using a multispectral camera mounted on an unmanned aerial vehicle to perform aerial survey of vegetation coverage in the target area to obtain spatial distribution data of vegetation coverage; and measuring the leaf area index of the target area to obtain a leaf area index distribution map; Step S14: performing time series alignment on the canopy micrometeorological dataset, the leaf physiological parameter set, and the vegetation stemflow dataset to obtain a synchronized vegetation physiological dataset; Step S15: spatially registering the vegetation coverage spatial distribution data with the leaf area index distribution map to obtain a spatially synchronized vegetation structure dataset; Step S16: Using a preset hierarchical Bayesian model, a multi-scale integration of the synchronized vegetation physiological dataset and the spatially synchronized vegetation structural dataset is performed to obtain a preliminary vegetation physiological monitoring parameter set. The preset hierarchical Bayesian model contains a three-layer nested structure: the first layer is that stomatal conductance follows a log-normal distribution, the second layer is the spatial correlation of quadrat scale parameters, and the third layer is the prior constraints of the regional scale. Step S17: obtaining a corrected root water absorption dynamics dataset, and performing spatiotemporal registration on the preliminary vegetation physiological monitoring parameter set and the corrected root water absorption dynamics dataset to obtain a vegetation physiological dynamics parameter set.

3. The method for simulating eco-hydrological processes based on dynamic vegetation according to claim 1, characterized in that: Step S2 includes the following steps: Step S21: extracting soil moisture time series from the vegetation physiological dynamic parameter set to generate an original soil moisture change curve; Step S22: performing soil moisture fluctuation identification on the original soil moisture change curve to obtain soil moisture fluctuation characteristic data; Step S23: performing dry-wet process division on the soil moisture fluctuation characteristic data to obtain a preliminary soil dry-wet process division result; Step S24: modifying the state of the preliminary soil dry-wet process division result according to a preset soil hydraulic characteristic threshold to obtain a segmented soil moisture state sequence; Step S25: adjusting the time continuity of the segmented soil moisture state sequence to obtain a modified soil dry-wet process division result; Step S26: dividing the vegetation physiological dynamic parameter set according to the modified soil dry-wet process division result to obtain a state-classified vegetation physiological data set; Step S27: constructing a vegetation water stress adaptation strategy model based on the state-classified vegetation physiological data set, wherein the vegetation water stress adaptation strategy model is specifically: g wet (θ)=a1θ+b1; g dry (θ)=c3sigmoid(θ-θ low )+d3; i high =0.7θ FC ; i low =0.3θ FC ; Among them, θ is the soil moisture content, θ FC is the field water capacity, θ high is the threshold of moist-moderate stress, θ low is the threshold of moderate stress-extreme drought, a1 is the linear response rate of vegetation physiological parameters with changes in soil moisture content, b1 is the baseline value of vegetation physiological parameters when soil moisture content approaches zero, a2 is the physiological parameter level at the initial stage of moderate stress, λ is the attenuation rate of the control physiological parameter with the decrease of soil moisture, b2 is the stable value in the later stage of stress, c3 is the amplitude coefficient of the Sigmoid function, and d3 is the minimum physiological parameter value under extreme drought.

4. The method for simulating eco-hydrological processes based on dynamic vegetation according to claim 1, characterized in that: Step S3 includes the following steps: Step S31: Multi-source data assimilation and inversion are performed on the vegetation water stress adaptation strategy model, and three-dimensional rasterization is performed to obtain spatialized vegetation water response data; based on the spatialized vegetation water response data, the soil profile is clustered into equal hydraulic response areas to obtain preliminary functional layer zoning data; Step S32: performing root density weighted correction on the preliminary functional layer partition data to obtain a root distribution density correction coefficient; adjusting the boundaries of the preliminary functional layer partition data according to the root distribution density correction coefficient to obtain a layered soil-root interaction unit; Step S33: assigning hydraulic characteristic parameters to the layered soil-root interaction unit to obtain a layered soil hydraulic parameter set; and constructing a soil water movement simulation framework based on the layered soil hydraulic parameter set; Step S34: integrating the root water absorption module into the soil water movement simulation framework to obtain a vertical water movement simulator with source terms; Step S35: using a vertical water movement simulator with source term to calculate the water potential gradient of the layered soil-root interaction unit to obtain interlayer water potential gradient data; Step S36: Calculating the water exchange flux between functional layers based on the interlayer water potential gradient data to obtain an interlayer water exchange flux table; Step S37: Obtaining layered root water absorption rate data, performing water balance calculation on the interlayer water exchange flux table and the layered root water absorption rate data, and obtaining water balance status data of each functional layer; Step S38: constructing a vegetation-soil moisture relationship model based on the water balance status data of each functional layer; Step S39: integrating the feedback mechanism of the vegetation-soil moisture relationship model to obtain a vegetation-soil moisture feedback model.

5. The method for simulating eco-hydrological processes based on dynamic vegetation according to claim 4, characterized in that: Step S39 includes the following steps: Step S391: constructing a vegetation-soil moisture relationship model based on the water balance status data of each functional layer; Step S392: integrating the stomatal conductance feedback mechanism into the vegetation-soil moisture relationship model to obtain a stomatal regulation-moisture feedback model; Step S393: integrating the stomatal regulation-water feedback model with the root water redistribution process to obtain an enhanced stomatal regulation-water feedback model; Step S394: obtaining a vegetation physiological parameter condition response function set, and constructing a root water absorption strategy conversion trigger condition based on the vegetation physiological parameter condition response function set to obtain a water absorption strategy conversion threshold set; Step S395: constructing a strategy transition state mechanism according to the water absorption strategy transition threshold set, wherein the state machine design includes a moist water absorption strategy, a moderate water stress water absorption strategy, and an extreme drought water absorption strategy; Step S396: performing layered distribution on the layered root water absorption patterns of the plants based on the strategy conversion state mechanism to obtain a root system dynamic water absorption distribution data table; Step S397: Constructing a vegetation-soil moisture feedback model based on the vegetation physiological parameter conditional response function and the root system dynamic water absorption and distribution data table.

6. The method for simulating eco-hydrological processes based on dynamic vegetation according to claim 1, characterized in that: Step S4 includes the following steps: Step S41: Obtain historical meteorological data and recent weather forecast data, and record the historical meteorological data and recent weather forecast data as original meteorological data sets; repair missing values in the original meteorological data sets to obtain quality-controlled meteorological data; Step S42: performing a multi-model numerical weather forecast simulation based on the quality-controlled meteorological data to obtain a multi-model ensemble forecast result; Step S43: performing weather scenario statistics on the multi-model ensemble forecast results to obtain a weather scenario set for the study area; performing hydrological and meteorological index statistics on the weather scenario set for the study area to obtain a hydrological and meteorological driving factor set; Step S44: constructing a multi-scenario simulation scenario set based on the hydrological and meteorological driving factor set and the vegetation-soil moisture feedback model; Step S45: performing parallel simulation on the multi-scenario simulation scenario set to obtain a multi-scenario simulation result database; performing collective statistical analysis on the multi-scenario simulation result database to obtain preliminary vegetation physiological prediction parameters; Step S46: performing real-time monitoring of physiological parameters of vegetation in the target area to obtain real-time vegetation physiological monitoring data; recursively assimilating the preliminary vegetation physiological prediction parameters with the real-time vegetation physiological monitoring data to obtain vegetation physiological state estimation parameters; Step S47: smoothing the vegetation physiological state estimation parameters to obtain smoothed vegetation physiological prediction parameters; performing physiological elasticity recovery on the smoothed vegetation physiological prediction parameters to obtain complete vegetation physiological prediction parameters.

7. The method for simulating eco-hydrological processes based on dynamic vegetation according to claim 1, characterized in that: Step S5 includes the following steps: Step S51: obtaining the digital elevation of the target area and recording it as the original terrain data of the study area; performing hydrological terrain correction on the original terrain data of the study area to obtain corrected hydrological terrain data; Step S52: performing sub-basin division and water system network extraction based on the corrected hydrological and corrected terrain data to obtain basic hydrological unit data; performing regular grid resampling on the basic hydrological unit data to obtain surface hydrological initial grid unit data; Step S53: performing terrain index distribution statistics on the initial surface hydrological grid unit data to obtain a terrain humidity index distribution map; Step S54: clustering similar hydrological response units based on the terrain humidity index distribution map to obtain hydrological response unit partition data; adjusting the spatial continuity of the hydrological response unit partition data to obtain optimized hydrological response unit partition data; Step S55: fusing the optimized hydrological response unit partition data with the surface hydrological initial grid unit data to obtain surface hydrological regular grid unit data; Step S56: performing time series decomposition on the complete vegetation physiological prediction parameters to obtain multi-time scale components of the vegetation parameters; Step S57: performing spatial allocation of vegetation parameters on the surface hydrological regular grid unit data based on the multi-time scale components of the vegetation parameters to obtain gridded vegetation parameters; performing time-continuous interpolation on the gridded vegetation parameters to obtain a time-continuous vegetation parameter sequence; Step S58: performing spatiotemporal fusion based on the time-continuous vegetation parameter sequence and the surface hydrological regular grid unit data to obtain multi-time-scale vegetation-hydrology synergistic data; Step S59: performing slope hydrological process simulation on the vegetation-hydrology synergistic data to obtain vegetation-hydrology synergistic evolution data.

8. The method for simulating eco-hydrological processes based on dynamic vegetation according to claim 7, characterized in that: Step S59 includes the following steps: Step S591: identifying slope runoff paths on multi-time-scale vegetation-hydrology collaborative data to obtain a slope flow path network; Step S592: performing distributed runoff calculation based on the slope flow path network to obtain grid unit runoff data; performing confluence calculation on the grid unit runoff data to obtain the slope runoff process line; Step S593: performing river confluence simulation based on the slope runoff process line to obtain preliminary slope runoff simulation results; Step S594: obtaining measured surface runoff data in the study area, and standardizing the measured surface runoff data in the study area to obtain standardized measured runoff data; Step S595: Compare the preliminary slope runoff simulation results with the standardized measured runoff data to obtain model performance evaluation indicators; generate a model validation report based on the model performance evaluation indicators; Step S596: Perform a multi-factor sensitivity assessment on the model validation report to obtain a vegetation-hydrological process parameter sensitivity ranking table; determine vegetation-hydrological sensitivity parameter optimization range data based on the vegetation-hydrological process parameter sensitivity ranking table; and construct a vegetation-hydrological synergistic parameter optimization scheme based on the vegetation-hydrological sensitivity parameter optimization range data. Step S597: adjusting parameters of the multi-time-scale vegetation-hydrology synergy data based on the vegetation-hydrology synergy parameter optimization scheme to obtain optimized vegetation-hydrology synergy data; Step S598: quantitatively evaluate the uncertainty of the optimized vegetation-hydrology synergy data to obtain the uncertainty interval of the simulation results; integrate the uncertainty interval of the simulation results with the optimized vegetation-hydrology synergy data to obtain the vegetation-hydrology synergy evolution data.

9. An eco-hydrological process simulation system based on dynamic vegetation, characterized in that: For executing the dynamic vegetation-based eco-hydrological process simulation method according to claim 1, the dynamic vegetation-based eco-hydrological process simulation system comprises: The data acquisition module is used to obtain the vegetation physiological monitoring parameter set and the root water absorption dynamic data set; the vegetation physiological monitoring parameter set and the root water absorption dynamic data set are uniformly aligned in time and space to obtain the vegetation physiological dynamic parameter set; The soil-vegetation response module is used to divide the target area into soil drying and wetting processes based on the vegetation physiological dynamic parameter set to obtain a segmented soil moisture state sequence; the vegetation physiological dynamic parameter set is divided according to the segmented soil moisture state sequence to obtain a state-classified vegetation physiological data set; and a vegetation water stress adaptation strategy model is constructed based on the state-classified vegetation physiological data set; Feedback model construction module, used to divide the soil profile into hydraulic functional layers based on the vegetation water stress adaptation strategy model to obtain layered soil-root interaction units; and to construct a vegetation-soil water feedback model based on the layered soil-root interaction units; The vegetation physiological prediction module is used to construct a short-term hydro-meteorological scenario set; dynamically simulate the short-term hydro-meteorological scenario set based on the vegetation-soil moisture feedback model to obtain smoothed vegetation physiological prediction parameters; and perform physiological elastic recovery on the smoothed vegetation physiological prediction parameters to obtain complete vegetation physiological prediction parameters; The vegetation-hydrology collaborative simulation module is used to dynamically update the vegetation parameters of the target area based on the complete vegetation physiological prediction parameters to obtain multi-time-scale vegetation-hydrology collaborative data; it simulates the slope hydrological process of the vegetation-hydrology collaborative data to obtain vegetation-hydrology collaborative evolution data.

10. A computer-readable medium, characterized in that The storage device can be loaded by a processor and executed by a program of a method for simulating an eco-hydrological process based on dynamic vegetation as described in any one of claims 1 to 8.

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