A distributed carbon storage prediction method under a changing environment

Abstract

The application discloses a distributed carbon storage prediction method under changing environment, comprising the following steps: S1. Real-time multi-source data is obtained, wherein the multi-source data comprises regional basic geographic information, meteorological data, land use / cover change data and carbon-related data; S2. A hydrological-ecological coupling model is constructed based on the obtained multi-source data; S3. Based on the constructed hydrological-ecological coupling model, a plurality of sets of changing environment scenarios are set, and the combined interference effect of human activities and natural changes on regional carbon cycle is evaluated. The hydrological-ecological coupling model constructed by the application realizes high-precision, dynamic and spatialized prediction of the carbon storage of the research region, effectively solves the problem of high-precision simulation and prediction of the carbon dynamics of the basin under the influence of climate change and human activities, and is helpful for researchers to scientifically formulate regional carbon sink management and carbon neutralization policies.

Description

Technical Field

[0001] This application relates to the field of carbon storage estimation technology, and in particular to a method for predicting distributed carbon storage under changing environments. Background Technology

[0002] With the intensification of global climate change and human activities, the carbon cycle process in ecosystems is undergoing significant changes, making accurate prediction of carbon storage crucial for addressing climate change and formulating carbon neutrality policies. However, existing carbon storage prediction methods are mostly based on static or semi-dynamic models, which are insufficient to accurately depict the highly spatially heterogeneous and complex carbon dynamics of terrestrial ecosystems.

[0003] At the watershed scale, carbon storage is influenced not only by natural factors such as vegetation type, soil properties, and climate conditions, but also by the combined effects of multiple human activities, including land use change, agricultural management practices, and water resource regulation. This results in a highly nonlinear and spatiotemporally heterogeneous carbon cycle. Traditional carbon storage estimation methods mainly include field surveys, remote sensing inversion, and ecosystem model simulations. Field surveys offer high accuracy but are costly, have limited coverage, and struggle to achieve long-term dynamic monitoring. Remote sensing methods offer high spatial resolution but are difficult to characterize carbon process mechanisms, particularly exhibiting significant uncertainties in estimating soil carbon and underground biomass. Among model methods, models such as Century, Biome-BGC, and TEM are widely used, but most have limited capabilities in handling complex terrain, hydrological processes, and interactions with human activities, and typically treat spatial units in a homogenized manner, failing to reflect the true spatial heterogeneity of the Earth's surface. Summary of the Invention

[0004] Therefore, it is necessary to provide a distributed carbon storage prediction method under changing environments that can effectively solve the problem of high-precision simulation and prediction of watershed carbon dynamics under the influence of climate change and human activities.

[0005] A method for predicting distributed carbon storage under changing environments includes the following steps:

[0006] S1. Acquire real-time multi-source data, including regional basic geographic information, meteorological data, land use / cover change data, and carbon-related data;

[0007] S2. Based on the acquired multi-source data, construct a hydrological-ecological coupling model;

[0008] S3. Based on the constructed hydrological-ecological coupling model, multiple sets of changing environmental scenarios are set up to assess the combined interference effects of human activities and natural changes on the regional carbon cycle.

[0009] This invention provides a distributed carbon storage prediction method under changing environments. It acquires real-time multi-source data, including regional basic geographic information, meteorological data, land use / cover change data, and carbon-related data. Based on this data, a hydrological-ecological coupling model is constructed. Then, based on this model, multiple environmental scenarios are set to assess the combined interference effects of human activities and natural changes on the regional carbon cycle. Therefore, this invention achieves high-precision, dynamic, and spatial prediction of carbon storage in the study area through the constructed hydrological-ecological coupling model. It effectively solves the problem of high-precision simulation and prediction of watershed carbon dynamics under the influence of climate change and human activities, and helps researchers scientifically formulate regional carbon sink management and carbon neutrality policies. Attached Figure Description

[0010] To more clearly illustrate the technical solutions in the embodiments of this application, the accompanying drawings used in the embodiments will be briefly introduced below. Obviously, the accompanying drawings described below are only some embodiments of this application. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0011] Figure 1 This is a flowchart of a distributed carbon storage prediction method under changing environments, as shown in one embodiment.

[0012] Figure 2 This is a watershed-scale map of soil carbon density, plant carbon density, and litter carbon density in one embodiment.

[0013] Figure 3 This is a map showing soil carbon density, plant carbon density, litter carbon density, and total carbon density at the patch scale in one embodiment.

[0014] Figure 4 This is a graph showing the trend of soil carbon variation in one embodiment;

[0015] Figure 5 This is a model carbon density verification diagram in one embodiment;

[0016] Figure 6 This is a graph showing the spatial heterogeneity analysis results of total carbon density in one embodiment. Detailed Implementation

[0017] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only a part of the embodiments of the present invention, and not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those of ordinary skill in the art without creative effort are within the scope of protection of the present invention.

[0018] It should be noted that all directional indications (such as up, down, left, right, front, back, etc.) in the embodiments of the present invention are only used to explain the relative positional relationship and movement of each component in a certain specific posture (as shown in the figure). If the specific posture changes, the directional indication will also change accordingly.

[0019] Furthermore, the use of terms such as "first" and "second" in this invention is for descriptive purposes only and should not be construed as indicating or implying their relative importance or implicitly specifying the number of technical features indicated. Therefore, a feature defined with "first" or "second" may explicitly or implicitly include at least one of those features. Additionally, the term "and / or" throughout the text includes three solutions; taking A and / or B as an example, it includes technical solution A, technical solution B, and a technical solution that simultaneously satisfies A and B. Furthermore, the technical solutions of various embodiments can be combined with each other, but this must be based on the ability of a person skilled in the art to implement them. When the combination of technical solutions is contradictory or impossible to implement, it should be considered that such a combination of technical solutions does not exist and is not within the scope of protection claimed by this invention.

[0020] like Figure 1 As shown, this invention provides a method for predicting distributed carbon storage under changing environments, which includes the following steps:

[0021] S1. Acquire real-time multi-source data, including regional basic geographic information, meteorological data, land use / cover change data, and carbon-related data;

[0022] S2. Based on the acquired multi-source data, construct a hydrological-ecological coupling model;

[0023] S3. Based on the constructed hydrological-ecological coupling model, multiple sets of changing environmental scenarios are set up to assess the combined interference effects of human activities and natural changes on the regional carbon cycle.

[0024] This invention provides a distributed carbon storage prediction method under changing environments. It acquires real-time multi-source data, including regional basic geographic information, meteorological data, land use / cover change data, and carbon-related data. Based on this data, a hydrological-ecological coupling model is constructed. Then, based on this model, multiple environmental scenarios are set to assess the combined interference effects of human activities and natural changes on the regional carbon cycle. Therefore, this invention achieves high-precision, dynamic, and spatial prediction of carbon storage in the study area through the constructed hydrological-ecological coupling model. It effectively solves the problem of high-precision simulation and prediction of watershed carbon dynamics under the influence of climate change and human activities, and helps researchers scientifically formulate regional carbon sink management and carbon neutrality policies.

[0025] Specifically, in the data preparation and preprocessing stage, it is necessary to systematically collect and organize multi-source data. In step S1, the regional basic geographic information includes spatial data such as digital elevation models (DEM), soil type distribution maps, and vegetation type maps to characterize the spatial heterogeneity of topography, soil, and vegetation; meteorological data includes long-term meteorological observation data (such as temperature, precipitation, radiation, etc.) and future climate change scenario data (such as the CMIP6 dataset) to reflect the disturbance process of human activities on the ecosystem; land use / cover change data includes land use maps based on remote sensing image interpretation and national spatial planning data; carbon-related data includes carbon flux data, soil carbon storage survey data, and forest inventory data to be used for subsequent model calibration and verification to ensure the reliability of simulation results.

[0026] Specifically, model calibration and validation driven by multi-source data are carried out in four stages: independent spatiotemporal data partitioning, multi-objective collaborative calibration, independent cross-validation, and comprehensive evaluation of multiple indicators. First, long-term observation data are divided into independent subsets according to proportions (e.g., 70% for calibration and 30% for validation) to ensure no temporal overlap and no spatial redundancy, thus avoiding data leakage. Second, multi-objective collaborative calibration is implemented, using runoff data to calibrate key hydrological parameters, while simultaneously optimizing ecological process parameters based on observation data such as gross primary productivity (GPP) and evapotranspiration (ET), achieving bidirectional constraints and collaborative calibration of hydrological and carbon cycle processes. Subsequently, spatiotemporal cross-validation is adopted, using data from independent time periods or regions not involved in calibration to conduct extrapolation tests and evaluate model stability and generalization ability. Finally, indicators such as Nash efficiency coefficient (NSE), Kling-Gupta efficiency coefficient (KGE), and root mean square error (RMSE) are selected to quantitatively evaluate the model's simulation accuracy and overall performance on key variables such as runoff, evapotranspiration, productivity, and carbon storage from dimensions such as goodness of fit, bias, and correlation.

[0027] Specifically, step S2 includes:

[0028] S21. Core model selection and spatial data preprocessing for the hydro-ecological coupling model;

[0029] S22. Definition of Hydro-Ecological Coupled Model Structure and Classification of Parameter Systems;

[0030] S23. Parameter sensitivity analysis and key parameter calibration;

[0031] S24. Multi-objective verification and reliability assessment of the hydrological-ecological coupling model.

[0032] Specifically, step S21 includes:

[0033] S211. To meet the simulation requirements of hydrological-ecological coupling processes under changing environments, the RHESSys model (i.e., regional hydrological-ecological simulation system) is selected as the core model.

[0034] Specifically, the RHESSys model, as the core model of the distributed hydrological-ecological coupling model, can simultaneously simulate hydrological processes (runoff, evapotranspiration, soil moisture content, etc.) and ecological processes (vegetation growth, GPP, NPP, etc.). It can effectively respond to multi-factor driving forces under changing environments (such as temperature, precipitation changes, and land cover adjustment), which meets the core requirement of "hydrological-ecological coupling". Compared with other coupling models (such as SWAT-EPIC and MIKE-SHE), the RHESSys model is more flexible in the simulation of multiple ecological processes at the small and medium watershed scale, and supports spatially heterogeneous data input, adapting to the dynamic simulation needs under changing environments.

[0035] The specific operating modes of the RHESSys model in this solution include:

[0036] 1. Distributed mode: The simulated watershed is discretized into several independent spatial units. Each spatial unit performs calculations independently based on its own topography, vegetation, soil and other attribute parameters, without interfering with each other. This mode can accurately adapt to the simulation of spatial heterogeneity under complex terrain conditions and improve the accuracy of complex watershed simulation.

[0037] 2. Semi-distributed mode: adopts a hierarchical discretization strategy, first dividing the entire watershed into several sub-watersheds. The sub-watersheds are treated as a whole with uniform attributes and are subject to unified parameter settings and calculations. This approach balances simulation efficiency and spatial representativeness and is suitable for rapid simulation scenarios at the watershed scale.

[0038] 3. Time step setting: Supports flexible configuration of multi-scale time steps. It can be set to daily, hourly or minute-level steps according to the simulation accuracy requirements to meet the needs of refined simulation of hydrological and ecological processes at different time scales.

[0039] 4. Process coupling method: A flexible coupling design is adopted, which can realize the complete or partial coupling of hydrological and ecological processes. The complete coupling can simulate the mutual feedback mechanism between the two, while the partial coupling can selectively retain the core coupling process according to the research focus, thereby improving the applicability of the model.

[0040] Specifically, the hydrological-ecological coupling model of the present invention includes the following:

[0041] 1. Infiltration process

[0042] The calculation principles for runoff processes and vegetation transpiration are as follows, where the retention and infiltration processes are calculated using the Philip infiltration equation, involving the following formulas:

[0043]

[0044]

[0045]

[0046]

[0047]

[0048] In the above formula: This refers to the infiltration rate, expressed in m³ / d. Rainfall intensity, expressed in m / d; and These represent the precipitation duration and water accumulation time, respectively, in days (d); Z represents the saturation layer depth, in meters (m). , and These are the saturated hydraulic conductivity, the saturated hydraulic conductivity at the wetting peak, and the surface hydraulic conductivity, respectively, in m / d. Indicates water absorption, unit is 1. ; m is the rate at which the permeability coefficient decreases with depth. Intake pressure, unit: meters (m). Porosity; It is the initial moisture content of the soil.

[0049] 2. Evaporation process

[0050] Eagleson's computational method was used to estimate the potential capillary rise in the unsaturated zone, and evapotranspiration and vegetation transpiration were calculated by combining the Penman-Monteith equation and the Jarvis model.

[0051]

[0052] In the above formula: The current amount of reserves held back, in meters (m). and These are the duration of intraday precipitation and the theoretical sunshine duration, respectively, in seconds (s). The average daily water vapor deficit is expressed in Pa. Canopy conductance, in m / s; Conductivity without pores, in m / s; and Leaf canopy conductance on the sunny and shady sides, respectively, in m / s.

[0053] 3. Photosynthesis and the carbon cycle

[0054] In the hydro-ecological coupling model, the entry of carbon into the ecosystem and its cycling process involves multiple stages. First, carbon dioxide is converted into plant biomass through photosynthesis, then enters the soil through plant death and decomposition, and finally returns to the atmosphere or is converted into soil organic matter through microbial activity. The following are the main calculated processes of carbon in the ecosystem through photosynthesis, respiration, phenology, distribution, and death. The carbon cycle process is shown in the figure.

[0055] Photosynthesis and respiration were modeled using the Farquhar model and the Ryan model, respectively.

[0056]

[0057] In the above formula: The net assimilation rate is expressed in units of LAI; gs is porosity, and pa is atmospheric pressure. It refers to the atmospheric carbon dioxide concentration. The average daytime temperature is given, lnc is the calculated leaf nitrogen concentration, and irad is the net incident radiation per unit leaf area index, calculated using the following formula:

[0058]

[0059] The Farquhar model calculates the assimilation rate based on the leaf area index (LAI). Within a diurnal time step, this is used to determine the total daily photosynthetic output of the canopy. The model uses average absorbed photosynthetically active radiation (PAR), average stomatal conductance, and daytime temperature to calculate the average assimilation rate. This rate is then scaled by day length and LAI to obtain the total daily photosynthetic output of the canopy. To accurately reflect the nonlinear characteristics of leaf responses under direct sunlight and shade, the model calculates the assimilation rate for both separately, thus deriving the total daily canopy photosynthetic output of the stratified structure. ;

[0060]

[0061] LAI sunlit and LAI shaded These refer to the proportions of sunlight and shade in the leaf area index, respectively.

[0062] The calculation of respiration is similar to that of the BGC model, simulating it as a function of nitrogen concentration and air temperature. Simultaneously, the model calculates growth respiration and subtracts it from the carbon allocated to different parts of the vegetation.

[0063] 4. Carbon distribution and phenology

[0064] The phenological calculation process is similar to the BIOME-BGC model, which dynamically simulates the carbon allocation process in plants through the distribution of net photosynthesis. Each day, net photosynthesis is allocated to different tissues (such as leaves, roots, and stems), and unallocated carbon is stored and released during leaf shedding. For deciduous vegetation, the model ensures sufficient carbon reserves for spring growth while defining the turnover process of leaves and fine roots. Turnover carbon from leaves and fine roots is gradually transferred to litter banks and further participates in litter decomposition. The leaf and fine root turnover process in autumn follows a linear decreasing trend, reflecting the dynamic transfer of carbon from plants to soil.

[0065] During the allocation process, carbon produced by net photosynthesis needs to be distributed among roots, stems, and leaves. The BIOME-BGC model uses a fixed proportion of carbon allocation, while the RHESSys model introduces a variable allocation strategy that considers the effects of soil moisture and nutrient stress. When water or nutrients are insufficient, more carbon is preferentially allocated to the roots, with the remainder then allocated to the leaves and stems in a fixed proportion. For trees, the carbon allocated to the stem is further divided into coarse roots and trunk, as well as living and dead wood, in fixed proportions. Once the proportion allocated to each plant component is determined, the total amount of carbon allocated to the leaves can be calculated using the following formula:

[0066] Carbon allocated to the blades that day:

[0067]

[0068] The amount of carbon stored for the next leaf growth period:

[0069]

[0070] In the above formula: Total carbon available for allocation, nlc is the proportion allocated daily. It is the proportion allocated to the blades.

[0071] To address the uncertainties inherent in the carbon allocation strategy model, this model introduces a user-substitutable carbon allocation method that reflects the adjustment of allocation strategies by vegetation in response to changes in average canopy light levels during growth. This method calculates the proportion of assimilated carbon allocated to leaves using the following formula:

[0072]

[0073] For trees, the carbon distribution between the roots and the woody parts (including the coarse roots and the trunk) is determined by calculating the carbon content of the roots and the woody parts. The proportion of it tends to a constant.

[0074]

[0075] In the above formula and These are species-specific empirical constants.

[0076] S212. Use the GIS2RHESSys dedicated tool to complete the standardization processing of multi-source data;

[0077] Step S212 specifically includes: (1) Data format unification: convert all spatial data into vector (shp format) or raster (tif format) supported by ArcGIS to ensure consistent spatial coordinate system (such as WGS84 coordinate system) and avoid projection deviation; (2) Sub-basin division: use GIS2RHESSys tool to perform terrain analysis based on DEM data, extract water system, slope and aspect of watershed, divide into sub-basins, determine the spatial boundary, area and terrain parameters of each sub-basin, and define the location of watershed outlet to lay the foundation for distributed simulation; (3) Spatial parameter assignment: use GIS2RHESSys tool to associate data such as land cover, soil and vegetation type with sub-basin / raster unit to realize the visual assignment of spatial parameters, generate spatial input files (such as .hdr, .gis and other formats) that can be recognized by RHESSys model, and ensure that spatial heterogeneity is reflected in the model.

[0078] In one embodiment, the GIS2RHESSys tool processes spatial data according to a standardized process, the specific steps of which are as follows:

[0079] 1. Input data preparation: Collect core spatial data of the study area, including digital elevation model (DEM), land use map, soil map, vegetation map, etc. The data format adopts the industry-standard GeoTIFF or ArcGIS format to ensure that the data accuracy matches the research needs.

[0080] 2. Data format conversion: The collected raster spatial data is standardized and converted into ASCII or binary format that can be recognized and read by the RHESSys model to ensure data compatibility.

[0081] 3. Spatial unit division: Based on DEM data, hierarchical spatial units such as watershed boundaries, slopes, and channels are generated through the built-in algorithm of the tool to construct a spatial topology structure that meets the needs of model simulation;

[0082] 4. Parameter spatialization: Extract attribute information from data such as soil, vegetation, and land use, and accurately assign various attributes to each spatial unit through spatial matching algorithms to achieve spatial correlation of parameters.

[0083] 5. Model Input File Generation: Based on the above processing results, the complete set of input files required for the RHESSys model to run is automatically generated, mainly including the world file describing the comprehensive information of the underlying surface in the study area, the flow table characterizing river connectivity, and the climate input file, providing complete data support for the subsequent simulation operation of the model.

[0084] Specifically, step S22 includes:

[0085] S221. Based on the structural characteristics of the RHESSys model, the parameters are divided into structural parameters and non-structural parameters. Through classification and group simplification, the model parameterization is completed, taking into account both the accuracy of model simulation and calibration efficiency, and adapting to the dynamic simulation needs under changing environments. Among them, structural parameters determine the overall framework and physical mechanism of the model, while non-structural parameters are divided according to the control of hydrological / ecological processes.

[0086] Parameter classification and definition:

[0087] Structural parameters, primarily used to define the basic framework for model operation and determine the model's simulation logic, do not change dynamically during the simulation process. They mainly include:

[0088] Model operation parameters: such as simulation time step (day / month), water cycle simulation module selection (e.g., whether to consider interflow and groundwater runoff), and ecological process coupling mode (e.g., switching on / off the feedback mechanism between vegetation growth and hydrological processes).

[0089] Soil hydraulic model parameters, such as soil texture parameters (sand, silt, and clay content), soil hydraulic conductivity (saturated / unsaturated), field capacity, wilting water content, and porosity, directly affect the simulation of hydrological processes such as soil moisture content, infiltration, and evapotranspiration.

[0090] Vegetation structure parameters, such as vegetation type parameters (distinguishing between trees / shrubs / herbs), vegetation canopy parameters (leaf area index LAI, canopy interception), and vegetation root distribution parameters (root depth, root water absorption rate), are associated with the coupling of ecological and hydrological processes.

[0091] Unstructured parameters, which are related to the specific control process simulated by the model, can be dynamically adjusted according to driving factors (such as climate and land use) under changing environments. They are classified according to the control process as follows:

[0092] Hydrological process control parameters, such as runoff coefficient, evapotranspiration coefficient, infiltration coefficient, and groundwater recharge coefficient, control the proportion of precipitation converted into runoff, evapotranspiration, and groundwater runoff;

[0093] Ecological process control parameters: such as vegetation growth rate, GPP calculation parameters (photosynthetically active radiation utilization rate, leaf area index threshold), vegetation litter decomposition coefficient, etc., to control ecological processes such as vegetation growth and material cycling.

[0094] Environmental change response parameters, such as land use change adjustment coefficient and climate fluctuation response coefficient, are used to adapt to the dynamic adjustment of parameters under changing environments (such as the adaptation adjustment of vegetation parameters and hydrological parameters after farmland is converted into forest land).

[0095] Unified Parameter Settings and Grouping: To simplify the subsequent calibration process and reduce the complexity of parameter calibration, parameters with strong correlation and low spatial heterogeneity are uniformly set and grouped.

[0096] Unified settings: Parameters of the same soil type and vegetation type are assigned uniform values ​​(such as field capacity and saturated hydraulic conductivity of the same soil texture are given uniform initial values) to avoid parameter redundancy; the basic parameters of model operation (such as time step and simulation module selection) are set uniformly to ensure the consistency of model operation.

[0097] Parameter grouping: The parameters are grouped into “hydrological process group”, “ecological process group”, “topographic parameter group”, and “soil parameter group”. Core parameters are selected for each group as the focus of calibration, while default values ​​or empirical values ​​are used for non-core parameters to reduce the number of calibration parameters and improve calibration efficiency. At the same time, the physical meaning and adjustment range of each group of parameters are clearly defined to avoid parameter adjustments exceeding reasonable physical boundaries.

[0098] Initial parameter assignment: Based on actual data from the study area, empirical values ​​from relevant literature, and default values ​​from the model, initial values ​​for all parameters were assigned. For parameters lacking measured data (such as vegetation photosynthetic parameters), empirical values ​​from similar areas were used as substitutes, and subsequent optimization and adjustment were carried out through sensitivity analysis and calibration.

[0099] Specifically, step S23 includes:

[0100] S231. By using sensitivity analysis, key parameters that significantly affect the simulation results of the hydrological-ecological coupling model are identified, and parameters with low sensitivity are eliminated to reduce the workload of model calibration. At the same time, the key parameters are adjusted using multipliers to determine the reasonable adjustment range of the parameters, providing a basis for subsequent model calibration.

[0101] Among them, the sensitivity analysis method selection is as follows: local sensitivity analysis method (such as single parameter analysis method) is adopted. Combined with the parameter characteristics of the RHESSys model, other parameters are kept unchanged, and only the value of a single parameter is changed. The influence of the parameter change on the simulation results (runoff, evapotranspiration, GPP) is analyzed, the sensitivity coefficient (such as elasticity coefficient) is calculated, and the sensitivity level of the parameter (high, medium, low) is determined.

[0102] Key parameter screening: Based on the sensitivity analysis results, key parameters with high sensitivity were screened out, with a focus on parameters that have a significant impact on the hydrological-ecological coupling process, such as soil saturated hydraulic conductivity, leaf area index, evapotranspiration coefficient, runoff coefficient, and GPP photosynthetic utilization rate; parameters with low sensitivity (such as some vegetation litter parameters) were removed, and the initial values ​​were kept unchanged in subsequent calibration processes to simplify the calibration procedure.

[0103] Key parameter multiplier adjustment: For the selected key parameters, set several multipliers (such as 0.5, 0.7, 0.9, 1.0, 1.1, 1.3, 1.5, etc.), adjust the parameter values ​​by multipliers (e.g., initial parameter value × multiplier = adjusted value), simulate the model output results under different parameter combinations; record the simulation results corresponding to each multiplier, analyze the impact of parameter adjustment on simulation accuracy, determine the reasonable adjustment range of each key parameter (e.g., multiplier 0.7~1.3), provide a clear basis for parameter adjustment for subsequent calibration, and avoid parameter adjustment exceeding the physical reasonable range.

[0104] Sensitivity analysis results verification: Verify the sensitivity analysis results to ensure that the selected key parameters do indeed have a significant impact on the simulation results, and eliminate falsely identified sensitive parameters; at the same time, compile a sensitivity analysis report, clarify the sensitivity ranking of each key parameter, and provide a reference for the priority of subsequent calibration (parameters with high sensitivity should be calibrated first).

[0105] In one embodiment, step S231 includes:

[0106] S2311. Screen the parameters to be analyzed and determine their reasonable value range and distribution characteristics;

[0107] S2312. Multiple parameter combinations are generated using Morris screening, Sobol global sensitivity analysis, or Latin hypercube sampling. The hydrological-ecological coupling model is run independently for each parameter combination, and key output results of carbon storage are recorded.

[0108] S2313. Calculate sensitivity indices, such as the Morris screening method to calculate the average effect (μ) and standard deviation (σ), and the Sobol method to calculate the first-order and total-order sensitivity indices, in order to quantify the strength of the parameter's influence and the interaction.

[0109] S2314. Sort the parameters according to their sensitivity indices to identify the key parameters that have the most significant impact on the model output, which will be used for subsequent model calibration and uncertainty control.

[0110] In one embodiment, the model calibration steps specifically include: preparing a calibration dataset, sorting out the core calibration parameters of the model, determining the initial range through literature review and experimental data, adopting an optimization algorithm, setting an objective function, iteratively adjusting the parameters, selecting the parameter combination with the smallest objective function as the optimal parameters, and verifying its rationality.

[0111] Specifically, step S24 includes:

[0112] S241. The hydrological-ecological coupling model is calibrated and validated in stages using multi-source data. By adjusting key parameters, the deviation between the simulation results of the hydrological-ecological coupling model and the corresponding measured data is controlled within a reasonable range, ensuring that the hydrological-ecological coupling model can accurately simulate the hydrological-ecological coupling process under changing environments.

[0113] The calibration and validation data are divided into two periods: calibration and validation (usually based on time proportions, such as 70% for calibration and 30% for validation). This ensures that the data in both periods are representative and cover different scenarios under changing conditions (such as above-average / below-average precipitation, land use adjustment phase), thus avoiding bias in calibration results due to limited data.

[0114] Calibration process (focusing on optimizing key parameters):

[0115] Calibration objective: Based on the measured data (runoff, evapotranspiration, GPP) during the calibration period, adjust several key parameters (within the adjustment range determined by sensitivity analysis) to achieve the best fit between the model simulation results and the measured data.

[0116] Calibration order: Calibrate according to parameter sensitivity priority, first calibrate parameters with high sensitivity (such as soil hydraulic parameters and vegetation canopy parameters), then calibrate parameters with medium sensitivity (such as runoff coefficient and evapotranspiration coefficient), and finally calibrate key parameters with low sensitivity.

[0117] Calibration metrics: Multiple evaluation metrics are used to quantify the fitting accuracy. These metrics include at least the coefficient of determination (R²), Nash efficiency coefficient (NSE), and relative error (RE).

[0118] Calibration method: Use a trial-and-error method combined with an automatic calibration tool (such as the calibration module that comes with the RHESSys model) to gradually adjust the parameter values, compare the deviation between the simulation results and the measured data, until all calibration indicators reach the qualified standard, and record the parameter combination at this time (parameter values ​​after calibration).

[0119] Validation process (verifying model stability):

[0120] Validation method: Using the calibrated parameter combination, the model was run to simulate the hydrological and ecological processes during the validation period, and the simulation results (runoff, evapotranspiration, GPP, etc.) were output.

[0121] Validation and evaluation: Using the same evaluation indicators (R², NSE, RE) as the calibration period, the fit between the simulation results and the measured data during the validation period is compared. If the validation indicators meet the qualified standards, it indicates that the model calibration effect is good, with good stability and applicability, and can accurately simulate the hydrological-ecological coupling process under changing environments. If the qualified standards are not met, the model is returned to the calibration stage, the key parameters are adjusted, and the calibration and validation are repeated until the requirements are met.

[0122] Multi-source data cross-validation: Cross-validation is performed by combining multi-source data such as runoff, evapotranspiration, and GPP to avoid model bias caused by single-data calibration (e.g., calibration using only runoff may lead to insufficient accuracy in ecological process simulation); ensuring that the model meets the qualification standards in both hydrological and ecological process simulations, and realizing the reliability of hydrological-ecological coupled simulation.

[0123] In summary, during the model construction and parameter localization phases, this invention uses the RHESSys model as its core to construct a hydrological-ecological coupled model framework under changing environments. It introduces a complete carbon cycle module to precisely simulate the dynamic changes in plant carbon (including aboveground and belowground biomass), soil carbon (organic and inorganic carbon), and litter carbon. Using acquired multi-source data, key eco-hydrological parameters in the model (such as maximum carboxylation rate, litter decomposition coefficient, and soil carbon turnover time) are calibrated and validated. Iterative optimization improves the model's accuracy in simulating regional carbon processes, ensuring that the model accurately reflects the ecosystem characteristics of the study area.

[0124] Furthermore, in the scenario setting phase of environmental change, the core objective is to comprehensively assess the combined interference effects of human activities and natural changes on the carbon cycle in the study area by designing multi-dimensional and comprehensive scenarios.

[0125] The core orientation of the scenario setting in this invention revolves around "carbon cycle response," focusing on three key driving factors: climate change, land use change, and water resource regulation. It quantifies the impact of each factor and its combination on regional carbon storage (vegetation carbon storage and soil carbon storage), and ultimately assesses the combined interference effects of human activities on the regional carbon cycle.

[0126] This invention sets up three main categories of scenarios: climate change scenarios, land use change scenarios, and water resource regulation scenarios. It defines the core influencing factors of each scenario and clarifies the relationships between them (e.g., water resource regulation scenarios can indirectly affect climate change and the effect of land use change on carbon storage, forming a compound interference), providing a clear framework for subsequent step-by-step design.

[0127] Specifically, the multiple environmental change scenarios include climate change scenario, land use change scenario, and water resource regulation scenario. Step S3 includes:

[0128] S31. Based on the climate characteristics of the study area, design differentiated climate change scenarios, and analyze the dynamic response of carbon storage (vegetation and soil) in the study area under different CO2 emissions using a hydro-ecological coupling model, so as to clarify the direct driving role of climate factors on carbon cycle.

[0129] S32. Based on the current land use status, development plans and policy orientation of the study area, design diversified land use change scenarios, and use a hydro-ecological coupling model to assess the direct impact of human land management decisions (urbanization, returning farmland to forest, low-carbon development) on the carbon storage of the study area, and clarify the core disturbance effects of human activities;

[0130] S33. Taking changes in hydrological processes as the core, design diverse water resource regulation scenarios, and use a hydro-ecological coupling model to assess the indirect impacts of human water resource management activities on vegetation growth and soil carbon dynamics, thereby improving the assessment system for the combined interference effects of human activities on the carbon cycle.

[0131] Specifically, step S31 includes:

[0132] S311. Select representative emission scenarios under the CMIP6 framework. The representative emission scenarios include three core scenarios: SSP1-2.6, SSP2-4.5, and SSP5-8.5, taking into account low, medium, and high emission intensities and covering the main possible trends of future climate change.

[0133] Specifically, SSP1-2.6 (low emission scenario): corresponds to the "sustainable development path", where CO2 emissions gradually decrease, suitable for analyzing the evolution of carbon storage under a low-carbon climate background; SSP2-4.5 (medium emission scenario): corresponds to the "intermediate path", where CO2 emissions steadily increase and then stabilize, serving as the benchmark scenario for analyzing carbon storage response; SSP5-8.5 (high emission scenario): corresponds to the "fossil energy intensive development path", where CO2 emissions continue to grow rapidly, and this scenario is used to analyze the changing trends and potential risks of carbon storage under extreme climates (high temperature, abnormal precipitation) and high CO2 concentrations.

[0134] S312. Based on CMIP6 climate model data, extract key climate parameters of the study area under three emission scenarios, quantify and assign values ​​to ensure that the parameters can be input into the hydro-ecological coupling model.

[0135] Specifically, referring to the standard output of the CMIP6 model, specific values ​​of CO2 concentration for different key periods are set according to the scenario to ensure that the parameters are accurate and can be used for model input. Temperature and precipitation change parameters under each scenario are set simultaneously to ensure that climate parameters match CO2 emission intensity.

[0136] S313. Using a hydro-ecological coupling model, the spatiotemporal variation patterns of vegetation carbon storage and soil carbon storage in the study area were compared under three different emission scenarios.

[0137] Specifically, through hydrological-ecological coupling model simulation, the study compares the spatiotemporal variation patterns of vegetation carbon storage (GPP and NPP correlation indicators) and soil carbon storage (soil organic carbon content) in the study area under three different emission scenarios, analyzes the impact of increased CO2 concentration on vegetation photosynthesis and soil organic matter decomposition, clarifies the direct driving mechanism of climate change on carbon cycle, and identifies sensitive areas and trends of carbon storage under different emission scenarios.

[0138] Specifically, step S32 includes:

[0139] S321. Select representative land use change scenarios, including rapid urbanization expansion scenario, large-scale conversion of farmland to forest scenario, and low-carbon economy-oriented scenario.

[0140] Specifically, setting up a rapid urbanization expansion scenario includes: based on the urbanization development plan of the study area, setting the future annual urbanization growth rate, simulating the continuous expansion of urban land and the occupation of land types such as cultivated land, forest land, and grassland; at the same time, setting the land use structure within urban land (such as the proportion of building land and green space) to analyze the depletion of vegetation carbon storage and the disturbance effect of urban expansion on soil carbon storage.

[0141] Setting up a large-scale farmland-to-forest scenario specifically includes: combining the ecological protection plan of the study area, setting the area of ​​farmland to be converted to forest, clarifying the vegetation type after conversion, setting the vegetation restoration cycle, simulating the gradual vegetation restoration process, and using this to analyze the effect of ecological restoration measures on carbon storage and quantify the carbon sink value of the farmland-to-forest policy.

[0142] Setting up low-carbon economy-oriented scenarios specifically includes: combining the concept of low-carbon development to set up land use structure optimization schemes, including: strictly controlling disorderly urban expansion, increasing ecological land, promoting low-carbon agriculture, and constructing ecological corridors (connecting scattered ecological land), to analyze the optimization effect of low-carbon land management models on the carbon cycle and provide practical reference for regional carbon peaking and carbon neutrality.

[0143] S322. Quantify and spatially assign values ​​to land use change scenario parameters;

[0144] Specifically, regarding spatial parameters, ArcGIS and GIS2RHESSys tools are used to visualize and assign values ​​to the spatial distribution of land use types under various scenarios, generate a spatial layer of land use change, clarify the conversion range and conversion ratio of different land use types, and ensure that the spatial parameters are consistent with the model simulation scale.

[0145] For the associated parameters, corresponding vegetation parameters and soil parameters (such as leaf area index and soil organic carbon content for forest land, and vegetation cover and soil carbon density for urban land) are set for different land use types to ensure that the parameter system of the land use change and hydro-ecological coupling model are connected to achieve accurate simulation of carbon storage.

[0146] S323. Using a hydro-ecological coupled model, the total changes and spatial distribution differences of carbon storage in the study area were compared under three land use change scenarios.

[0147] Specifically, through hydrological-ecological coupling model simulation, the total changes and spatial distribution differences of regional carbon storage under three land use change scenarios are compared. The impact of different land management decisions on vegetation carbon sinks and soil carbon sequestration is quantified, key areas of carbon storage change (such as the core area of ​​returning farmland to forest and sensitive areas of urban expansion) are identified, and the direct interference mechanism of human land use activities on the carbon cycle is clarified.

[0148] Specifically, step S33 includes:

[0149] S331. Design representative water resource regulation scenarios, including reservoir scheduling rule change scenarios and irrigation area adjustment scenarios;

[0150] Specifically, the scenarios for changing reservoir scheduling rules include: designing two differentiated scheduling rules based on the current distribution of reservoirs in the study area: ① Conventional scheduling scenario (baseline scenario), which follows the traditional principle of prioritizing water supply and power generation, keeping the reservoir discharge flow stable; ② Ecological priority scheduling scenario, which adjusts the scheduling rules to prioritize downstream ecological water demand, simulates the changes in downstream watershed hydrological processes (runoff, soil moisture content) by reservoir scheduling, and then analyzes the indirect effects of changes in hydrological processes on vegetation growth (such as vegetation cover, GPP) and soil carbon dynamics (such as the impact of soil moisture content on organic matter decomposition).

[0151] The specific scenarios for adjusting irrigation area include: based on the current irrigation status of the study area, three irrigation adjustment scenarios are designed: ① Irrigation area expansion scenario; ② Irrigation area reduction scenario (mainly reducing inefficiently irrigated farmland); ③ Precision irrigation scenario (maintaining the existing irrigation area, optimizing irrigation methods, improving irrigation efficiency, and reducing water waste). By simulating changes in soil moisture content and groundwater depth under different irrigation scenarios, the indirect impact of changes in water conditions on vegetation growth and soil carbon sequestration is analyzed, and the indirect interference of water resource regulation on the carbon cycle is quantified.

[0152] S332. Quantify and integrate water resource regulation scenario parameters with models;

[0153] Specifically, step S322 includes:

[0154] S3221. Set reservoir scheduling parameters (discharge flow, scheduling period) and irrigation parameters (irrigated area, irrigation quota, irrigation period) for each scenario, and combine them with water resource data of the study area to ensure that the parameters conform to the actual water resource regulation capacity;

[0155] S3222. Integrate water resource regulation parameters with the hydrological-ecological coupling model to clarify the feedback relationship between hydrological processes (runoff, soil moisture content) and ecological processes (vegetation growth, soil carbon dynamics), ensuring that the model can accurately simulate the indirect impact of water resource regulation on carbon storage and avoid the disconnect between hydrological and ecological processes.

[0156] S333. Through hydrological-ecological coupling model simulation, the variation patterns of hydrological processes, vegetation growth, and soil carbon storage in the study area under different water resource regulation scenarios are compared.

[0157] By using a hydro-ecological coupling model to simulate and compare changes in regional hydrological processes, vegetation growth, and soil carbon storage under different water resource regulation scenarios, this study clarifies the indirect driving mechanism of hydrological process changes on the carbon cycle. Combining the simulation results of the above-mentioned climate change and land use change scenarios, this study comprehensively assesses the combined interference effects of climate change, land use change, and water resource regulation, thereby gaining a comprehensive understanding of the impact pathways and intensity of human activities on the regional carbon cycle.

[0158] In one embodiment, step S3 further includes:

[0159] S34. Based on the calibrated hydrological-ecological coupling model, run multiple scenario combinations in batches to realize the spatiotemporal dynamic simulation of carbon storage;

[0160] S35. Perform visualization processing and uncertainty analysis on the simulation results to generate intuitive and reliable visualization results, providing support for subsequent analysis.

[0161] Specifically, based on the calibrated hydrological-ecological coupling model, the spatiotemporal evolution of carbon storage under different scenario combinations is simulated, focusing on capturing the spatial distribution differences and temporal dynamic changes of carbon storage. Step S34 specifically includes:

[0162] S341. Simulation scheme setup: Input the preprocessed basic data into the calibrated hydro-ecological coupled model. For each scenario combination, set a uniform simulation time step (such as annual step) and simulation range, and clarify the core output indicators of the simulation (such as vegetation carbon storage, soil carbon storage, and total carbon storage) to ensure that the simulation conditions of different scenarios are comparable (only change the scenario variables, and keep the other parameters consistent).

[0163] S342. Spatiotemporal Evolution Simulation Operation: The calibrated hydrological-ecological coupled model is initiated for parallel simulation across multiple scenarios. During the simulation, the model's operational status is monitored in real time to identify and address issues such as data anomalies and parameter drift, ensuring the stability of the simulation process. The core of the simulation is to recreate the spatial distribution patterns of carbon storage (e.g., differences in carbon storage across different regions and vegetation types) and its dynamic temporal evolution (e.g., annual increases and decreases in carbon storage, long-term trends) under different scenarios, with a focus on capturing the carbon storage change characteristics at key nodes (e.g., years of abrupt climate change, critical periods of human intervention).

[0164] S343. Preliminary verification of simulation results: After the simulation is completed, extract the basic carbon storage data under each scenario, compare the rationality of the simulation results under different scenarios, check for outliers (such as negative carbon storage, sudden abnormal increases or decreases, etc.), and combine the actual situation of the study area (such as the carbon storage in the ecological restoration area should show an increasing trend, and the carbon storage in extreme climate years should fluctuate), to conduct preliminary screening and correction of the simulation results to ensure that the simulation results conform to objective laws.

[0165] Specifically, the visualization results include at least a spatial distribution map of carbon reserves, a long-term time series dynamic change curve of carbon reserves, and an uncertainty analysis diagram of the carbon reserves simulation results. The spatial distribution map of carbon reserves shows the spatial distribution of carbon reserves in the study area under specific time and scenario, presented in map form, with different colors or shades representing different carbon reserve levels. The long-term time series dynamic change curve of carbon reserves shows the trend of carbon reserves changing over time, which can be annual, seasonal, or daily changes in carbon reserves. The uncertainty analysis diagram includes a confidence interval diagram and a sensitivity analysis diagram, used to show the uncertainty of the model prediction. Specifically, the confidence interval diagram shows the confidence interval of the predicted value, reflecting the reliability of the prediction, and the sensitivity analysis diagram shows the sensitivity of the model output to changes in input parameters, helping to identify which parameters have the greatest impact on the model results.

[0166] Specifically, step S35 includes:

[0167] S351. Generation of high-resolution carbon storage spatial distribution maps: Based on the spatialized carbon storage data output by simulation, GIS technology is used to draw high-resolution carbon storage spatial distribution maps for different scenarios and time points (the spatial resolution is consistent with the basic data and can be refined according to needs), clarifying the location and range of the spatial distribution of high-value and low-value carbon storage areas, marking the area proportion of different carbon storage levels, and intuitively presenting the spatial heterogeneity of carbon storage.

[0168] S352. Generation of dynamic change curves for long-term series: Extract total carbon storage, vegetation carbon storage, and soil carbon storage data for long-term series (e.g., each year within the simulation period) under various scenarios. Use data visualization tools (e.g., Origin, Matlab) to plot dynamic change curves, label key parameters such as trend slope, peak value, and trough value of the curves, and clearly present the time evolution trend of carbon storage under different scenarios (e.g., stable growth, fluctuating decline, first increase and then stabilization, etc.). Compare the magnitude and rate of difference in carbon storage changes under different scenarios.

[0169] S353. Uncertainty Analysis: For the sources of uncertainty in the simulation results (such as parameter errors, data errors, and model structure errors), a systematic uncertainty analysis is conducted. The specific methods for this uncertainty analysis include:

[0170] 1) Calculate the confidence interval, repeat the simulation multiple times (e.g., 1000 times), and calculate the confidence interval of carbon storage at different confidence levels (e.g., 95%) to reflect the reliability of the simulation results;

[0171] 2) Conduct sensitivity analysis. Using the single-variable control method, change the core parameters of the model (such as NPP calculation parameters and soil carbon turnover coefficient) and input data (such as precipitation and land use change rate) one by one, analyze the degree of influence of each variable on the carbon storage simulation results, draw a sensitivity analysis diagram, and identify the variable with the most significant impact on carbon storage changes.

[0172] 3) Based on the model validation results, quantify the simulation error, clarify the scope and main sources of uncertainty, and provide a reference for subsequent interpretation and application of results.

[0173] Specifically, after the simulation is completed, the simulation results are visualized and uncertainty analyzed to generate intuitive and reliable results, providing support for subsequent analysis. Step S3 also includes:

[0174] S36. Using the contribution rate decomposition method, quantitatively distinguish the relative contributions of human activities and natural changes to changes in carbon storage, in order to identify the dominant driving factors and their mechanisms of action.

[0175] Specifically, step S36 includes:

[0176] S361. Focusing on the core objective of carbon storage change, define the spatiotemporal boundaries of the analysis, select structural decomposition analysis (SDA) or exponential decomposition analysis (EDA) techniques based on data conditions, and use scenario comparison methods to assist in verification to ensure the reliability of the decomposition results.

[0177] S362. Organize and standardize data related to carbon storage, meteorology, land use, and human activities. Apply the selected method to calculate the contribution and contribution rate of each driving factor, and cross-validate to ensure the accuracy of the results.

[0178] S363. Interpret the decomposition results, distinguish between dominant and secondary driving factors, clarify the degree of influence (promotion or inhibition) of each factor on carbon storage changes, and interpret the differences in contribution in conjunction with the characteristics of the study area to lay the foundation for subsequent analysis.

[0179] In one embodiment, the contribution of each independent variable to the change in the dependent variable can be quantified by calculating the proportion of each variable's partial regression sum of squares in the total sum of squares of the dependent variable. The partial regression sum of squares of a particular independent variable is determined by comparing the sum of squares of the residuals of the complete model including all independent variables and the simplified model after removing one independent variable. The specific calculation method is as follows:

[0180]

[0181]

[0182]

[0183]

[0184] In the formula: and Let represent the i-th dependent variable and the i-th independent variable, respectively, and ∠ is the total intercept estimated by the least squares method. These are the regression coefficients in the least squares estimation, and b is the residual. It is the correlation coefficient, and SST is the total variation of the dependent variable. Represent the independent variable The partial regression sum of squares, Representing variables The sum of squared residuals of the model, Represent the independent variable The degree of contribution to the total variance.

[0185] Furthermore, step S36 also includes:

[0186] S364. Based on the results of contribution rate decomposition and sensitivity analysis, screen the dominant factors with the highest contribution rates, clarify the differences in dominant factors in different scenarios and regions, and eliminate the influence of accidental factors.

[0187] S365. Based on the carbon cycle pattern, analyze the specific pathways through which the dominant factors affect changes in carbon reserves.

[0188] S366. By combining historical data and field surveys to verify the rationality of the mechanism, summarize the synergistic or antagonistic relationships among the driving factors, form a complete driving mechanism system, and provide support for carbon management.

[0189] It should be noted that the carbon cycle model is one of the core components of the hydro-ecological coupling model. The former focuses on carbon budget and storage, while the latter provides a complete simulation framework that includes water, energy, carbon and nutrient cycles. The carbon cycle process depends on the water availability provided by the hydrological module, and its products affect the hydrological process.

[0190] In this embodiment, taking the Yi-Luo River as an example, the carbon density values ​​of each carbon pool in the Yi-Luo River basin from 2000 to 2023 were predicted based on the input scenario (e.g., Figure 2 As shown), soil carbon density, plant carbon density, litter carbon density, and total carbon density at the patch scale (e.g.) Figure 3 a- Figure 3 (as shown in d).

[0191] Furthermore, Figure 4 a is a spatial distribution map of soil carbon. Figure 4 b is a diagram showing the spatial variation trend of soil carbon. Figure 4 c is a graph showing the significance of spatial variation trends in soil carbon. Figure 4 d represents the regression analysis plot. Taking soil carbon density as an example, the trend analysis results of soil carbon density in the Irrawaddy River Basin from 2000 to 2023 show (e.g.) Figure 4 As shown in d), soil carbon density exhibits a significant positive correlation with the year, with a slope of 0.099 and a rate of change of approximately 0.099 kg·m⁻²·yr⁻¹. Furthermore, the slope analysis result shows p < 0.005, indicating a significant upward trend in soil carbon density during the study period. The LOWESS estimate also indicates an upward trend in soil carbon density in the Irrawaddy River Basin during the study period, and the LOWESS estimation curve is nearly linear, suggesting a near-linear interannual growth trend in soil carbon density. In summary, soil carbon density in the Irrawaddy River Basin shows a clear upward trend during the study period. It should be noted that LOWESS (Locally Weighted Regression Scatter Smoothing) is a non-parametric regression method used to describe the trend of the relationship between two variables. It does not presuppose a specific functional form. In this invention, the LOWESS estimation curve is used to demonstrate the trend of soil carbon density over time.

[0192] Spatially, soil carbon storage in the Yi-Luo River basin exhibits a decreasing pattern from west to east and from upstream to downstream (e.g., ...). Figure 4 (as shown in a). According to Sen-MK analysis (e.g., ... Figure 4As shown in b), during the study period, soil carbon storage in almost the entire watershed increased slowly, and the slope test in more than 95% of the entire watershed reached the significance level (p<0.05) (as shown in b). Figure 4 (As shown in c). It should be noted that Sen-MK analysis is a combination of Sen slope estimation and Mann-Kendall trend test. Sen slope estimation is used to calculate the median change trend of time series data, reflecting the magnitude and direction of variable change (e.g., upward or downward). The Mann-Kendall trend test is used to determine whether the time series has a monotonic trend, and the significance level (e.g., p-value) is used to determine whether the trend is significant. In this invention, Sen-MK analysis is used to assess the spatial variation trend of soil carbon storage.

[0193] This invention selects 50 sets of carbon density data (including soil carbon density and plant carbon density) covering the Irrawaddy River Basin and surrounding areas from the carbon density dataset to validate the carbon density simulation results of the RHESSys model. The validation results are as follows: Figure 5 As shown, the NSE and R² for soil carbon density are 0.65 and 0.66, respectively, while the NSE and R² for plant carbon density are 0.64 and 0.68, respectively. The results indicate that the RHESSys model has good applicability in carbon density simulation, and its simulation results meet the requirements of this invention.

[0194] Furthermore, in one embodiment, taking the SSP2-4.5 scenario combination as an example, total carbon density (Y) was selected as the dependent variable, and leaf area index (LAI) (X1), evapotranspiration (X2), precipitation (X3), maximum temperature (X4), minimum temperature (X5), NPP (X6), and GPP (X7) were selected as driving factors affecting its spatial heterogeneity for analysis. The explanatory power of single factors in 2000, 2006, 2012, 2018, and 2023 is shown in Table 1.

[0195] Table 1 Explanatory power of single-factor probes

[0196]

[0197] It should be noted that the q-values ​​in Table 1, representing the single-factor explanatory power, are derived from the geospatial survey method and range from 0 to 1. They indicate the degree to which a certain independent variable (such as LAI, temperature, etc.) explains the spatial distribution of the dependent variable (such as total carbon density). A larger q-value indicates a stronger influence of the factor on the spatial distribution of carbon reserves. The p-values ​​in Table 1, representing the significance level, are used to determine whether the explanatory power of the factor is statistically significant. If p < 0.05, it indicates that the factor's influence on the spatial distribution of carbon reserves is significant and not due to random error. To better explain the above parameters, for example, in Table 1, "LAI(X1)" has q = 0.18 and p < 0.05, indicating that the leaf area index has a significant explanatory power for the spatial distribution of carbon reserves.

[0198] Furthermore, Figure 6 (a-1)~ Figure 6 (a-5) are single-factor probing analysis charts for 2000, 2006, 2012, 2018, and 2023, respectively. Figure 6 (b-1)~ Figure 6 (b-5) are interaction analysis diagrams for 2000, 2006, 2012, 2018, and 2023, respectively. Figure 6 (c-1)~ Figure 6 (c-5) are ecological detection analysis maps for 2000, 2006, 2012, 2018, and 2023, respectively. Among them, the three analysis methods—single-factor detection analysis, interaction analysis, and ecological detection analysis—are all based on the Geodetector model to analyze the driving mechanism of spatial differentiation of carbon storage, but they have different focuses:

[0199] (1) Single-factor probing analysis can assess the explanatory power (i.e., q-value) of a single factor on the spatial distribution of carbon reserves. It can identify which factors (such as temperature, precipitation, LAI, etc.) have a significant impact on the spatial differentiation of carbon reserves and compare the intensity of their impact. The results are output as the q-value and significance p-value of each factor;

[0200] (2) Interaction analysis can assess whether the explanatory power of two factors acting together on the spatial differentiation of carbon reserves is greater than the sum of their individual effects. It can be used to determine whether there is a synergistic enhancement or an independent relationship between the factors, and to reveal the multi-factor composite driving mechanism. The result output is the q value of the interaction, which is compared with the q value of the single factor to determine the interaction type (such as dual-factor enhancement, nonlinear enhancement, etc.).

[0201] (3) Ecological detection analysis can determine whether there are significant differences in the impact of two factors on the spatial differentiation of carbon storage, identify whether the differences in the impact mechanism between factors are statistically significant, and thus distinguish the independence or substitutability of different factors in the driving process. The output is the determination result of whether there are significant differences between each pair of factors.

[0202] Analysis based on geographic detectors shows (e.g.) Figure 6 As shown in Figure a), the spatial differentiation mechanism of total carbon storage has shifted from being ecologically dominant to being temperature-ecologically synergistic. The explanatory power of temperature has significantly increased, reaching a combined contribution of 0.45 in 2023, indicating that maximum and minimum temperatures, by regulating the rate of organic matter decomposition and vegetation growth, have become the core driving factors of the spatial pattern of carbon pool. Meanwhile, the explanatory power of leaf area index (LAI) showed a stepwise increase (0.14→0.19), reflecting the continuous promoting effect of vegetation cover on carbon storage, while the explanatory power of evapotranspiration and precipitation showed an initial increase followed by a decrease (0.12→0.17→0.13), revealing the complex regulatory characteristics of meteorological elements on total carbon distribution. Furthermore, the explanatory power of vegetation carbon storage-related indices NPP and GPP showed an inverted V-shaped evolution (0.04→0.14→0.09) and an asymmetric change (0.05→0.19→0.16), respectively, suggesting that the driving force of vegetation carbon sink capacity and photosynthetic-respiratory balance on carbon storage has a time-dependent decay and metabolic lag effect.

[0203] Interaction detection analysis shows (e.g.) Figure 6 As shown in b), the interaction strength among the factors significantly increased over time, with the interactions between the highest and lowest temperatures and leaf area index (LAI) being the most significant, their q-values ​​increasing from 0.2154 and 0.2089 in 2000 to 0.3146 and 0.3099 in 2023, respectively. The interactions among variables in 2023 exhibited greater complexity, showing enhanced multi-factor synergy, which may be closely related to climate change and ecosystem succession processes in the study area. Ecological observation results (such as...) Figure 6 As shown in c), the significance of the independent variables' impact on total carbon fluctuated during the study period. The differences in the impacts of precipitation on maximum and minimum temperatures were significant throughout the entire period, forming a stable core driving framework, indicating that their impact on total carbon is long-term independent. The difference in the impact of precipitation on GPP remained significant from 2000 to 2018, but disappeared in 2023. The network of differences in the impact of evapotranspiration expanded in 2023, showing significant differences with precipitation, maximum temperature, and minimum temperature, while in previous years there were only brief significant differences with maximum temperature (2006–2018). The difference between NPP and GPP was significant throughout the entire period; while the difference in the impact of LAI remained limited to maximum and minimum temperatures (2006–2023).

[0204] In summary, this invention addresses the core issues of insufficient accuracy and weak predictive capabilities in watershed-scale carbon storage dynamic simulation under the dual disturbances of climate change and human activities. It constructs a distributed hydrological-ecological coupled model under changing environments. Its core value lies in achieving high-precision simulation and scientific prediction of watershed carbon dynamics, providing solid technical support and data reference for refined management of regional carbon sink resources, implementation of carbon neutrality goals, and formulation of related policies, thereby contributing to the enhancement of regional ecosystem carbon sink functions and sustainable development.

[0205] At the level of precise characterization of carbon processes, this invention innovatively introduces the RHESSys hydrological-ecological coupling mechanism, breaking the limitation of traditional carbon models being disconnected from hydrological processes, and realizing the synergistic simulation of water transport and vegetation carbon assimilation processes. By simultaneously calculating dynamic changes in soil moisture and carbon fluxes (including key processes such as vegetation photosynthetic carbon fixation and soil respiration carbon release) within the model framework, the driving effect of water conditions on carbon cycle processes is accurately captured, effectively restoring the evolution characteristics of carbon storage at different spatiotemporal scales, improving the realism and accuracy of the model's carbon dynamic simulation, and overcoming the shortcomings of traditional models that only focus on a single carbon process and ignore hydrological-ecological coupling effects.

[0206] At the scenario analysis and application support level, this invention constructs a multi-scenario coupled analysis framework for climate change and land use change. It innovatively integrates RCPs (Representative Concentration Pathways) and SSP-RCP (Shared Socioeconomic Pathways-Typical Concentration Pathways) combined scenarios, systematically quantifying the independent impacts and comprehensive contributions of climate change and land use change on watershed carbon storage under different scenarios, and clarifying the intensity and mechanism of various driving factors. This design not only improves the technical methods for assessing ecosystem carbon sinks under changing environments, but also provides scientific scenario simulation and decision-making basis for formulating differentiated carbon sink protection strategies, optimizing land use structure, and addressing climate change in regions, further expanding the practical application value of the model.

[0207] In summary, this distributed carbon storage prediction model effectively addresses the key issues of low accuracy in watershed carbon dynamics simulation and difficulty in quantifying driving factors under changing environments by coupling hydrological-ecological mechanisms and strengthening multi-scenario coupling analysis. It achieves the integration of carbon storage simulation, prediction, and driving mechanism analysis, providing important technical support and methodological reference for regional carbon sink management and carbon neutrality policy formulation.

[0208] The above are merely preferred embodiments of the present invention and do not limit the scope of the patent. Any equivalent structural transformations made using the contents of the specification and drawings of the present invention under the inventive concept of the present invention, or direct / indirect applications in other related technical fields, are included within the scope of patent protection of the present invention.

Claims

1. A method for predicting distributed carbon storage under changing environments, characterized in that, Includes the following steps: S1. Acquire real-time multi-source data, including regional basic geographic information, meteorological data, land use / cover change data, and carbon-related data; S2. Based on the acquired multi-source data, construct a hydrological-ecological coupling model; S3. Based on the constructed hydrological-ecological coupling model, multiple sets of changing environmental scenarios are set up to assess the combined interference effects of human activities and natural changes on the regional carbon cycle.

2. The method of claim 1, wherein, Step S2 includes: S21. Core model selection and spatial data preprocessing of the hydro-ecological coupling model; S22. Definition of the structure and classification of the parameter system of the hydro-ecological coupling model; S23. Parameter sensitivity analysis and key parameter calibration; S24. Multi-objective verification and reliability assessment of the hydrological-ecological coupling model.

3. The distributed carbon storage prediction method under changing environments according to claim 2, characterized in that, Step S21 includes: S211. To meet the simulation requirements of hydrological-ecological coupling processes under changing environments, the RHESSys model is selected as the core model. S212. The standardization process of the multi-source data is completed using the GIS2RHESSys dedicated tool; Step S22 includes: S221. Based on the structural characteristics of the RHESSys model, the parameters are divided into structural parameters and non-structural parameters. Through classification and grouping simplification, the model parameterization is completed. Among them, the structural parameters determine the overall framework and physical mechanism of the model, and the non-structural parameters are divided according to the control of hydrological / ecological processes. Step S23 includes: S231. By using sensitivity analysis, key parameters that significantly affect the simulation results of the hydrological-ecological coupling model are identified, and parameters with low sensitivity are eliminated to reduce the workload of model calibration. At the same time, the key parameters are adjusted using multipliers to determine the reasonable adjustment range of the parameters, providing a basis for subsequent model calibration. Step S24 includes: S241. The hydrological-ecological coupling model is calibrated and verified in stages using the multi-source data. By adjusting key parameters, the deviation between the simulation results of the hydrological-ecological coupling model and the corresponding measured data is controlled within a reasonable range, ensuring that the hydrological-ecological coupling model can accurately simulate the hydrological-ecological coupling process under changing environments.

4. The distributed carbon storage prediction method under changing environments according to claim 3, characterized in that, Step S231 includes: S2311. Screen the parameters to be analyzed and determine their reasonable value range and distribution characteristics; S2312. Multiple parameter combinations are generated using Morris screening, Sobol global sensitivity analysis, or Latin hypercube sampling. The hydrological-ecological coupling model is run independently for each parameter combination, and key output results of carbon storage are recorded. S2313. Calculate sensitivity indices to quantify the strength of parameter influence and interaction; S2314. Sort the parameters according to their sensitivity indices to identify the key parameters that have the most significant impact on the model output, which will be used for subsequent model calibration and uncertainty control.

5. The distributed carbon storage prediction method under changing environments according to claim 4, characterized in that, The specific steps of model calibration include: preparing a calibration dataset, sorting out the core calibration parameters of the model, determining the initial range through literature review and experimental data, adopting an optimization algorithm and setting an objective function, iteratively adjusting the parameters, selecting the parameter combination with the smallest objective function as the optimal parameters and verifying its rationality.

6. The distributed carbon storage prediction method under changing environments according to claim 1, characterized in that, The multiple sets of changing environmental scenarios include climate change scenarios, land use change scenarios, and water resource regulation scenarios. Step S3 includes: S31. Based on the climate characteristics of the study area, design differentiated climate change scenarios, and analyze the dynamic response law of carbon storage in the study area under different CO2 emissions using the hydro-ecological coupling model. S32. Based on the current land use status, development plans and policy orientation of the study area, design diverse land use change scenarios, and use the hydro-ecological coupling model to assess the direct impact of human land management decisions on carbon storage in the study area. S33. Taking changes in hydrological processes as the core, design diverse water resource regulation scenarios, and evaluate the indirect impacts of human water resource management activities on vegetation growth and soil carbon dynamics through the aforementioned hydrological-ecological coupling model.

7. The distributed carbon storage prediction method under changing environments according to claim 6, characterized in that, Step S31 includes: S311. Select representative emission scenarios under the CMIP6 framework, including three core scenarios: SSP1-2.6, SSP2-4.5, and SSP5-8.

5. S312. Based on CMIP6 climate model data, extract key climate parameters of the study area under three emission scenarios, quantify and assign values ​​to ensure that the parameters can be input into the hydro-ecological coupling model. S313. Using the aforementioned hydrological-ecological coupling model, the spatiotemporal variation patterns of vegetation carbon storage and soil carbon storage in the study area under three different emission scenarios were compared. Step S32 includes: S321. Select representative land use change scenarios, including rapid urbanization expansion scenario, large-scale conversion of farmland to forest scenario, and low-carbon economy-oriented scenario; S322. Quantify and spatially assign values ​​to land use change scenario parameters; S323. Using the aforementioned hydro-ecological coupling model, compare the total changes and spatial distribution differences of carbon storage in the study area under three land use change scenarios; Step S33 includes: S331. Design representative water resource regulation scenarios, including reservoir scheduling rule change scenarios and irrigation area adjustment scenarios; S332. Quantify and integrate water resource regulation scenario parameters with models; S333. Through simulation using the aforementioned hydrological-ecological coupling model, the changing patterns of hydrological processes, vegetation growth, and soil carbon storage in the study area under different water resource regulation scenarios are compared.

8. The distributed carbon storage prediction method under changing environments according to claim 6, characterized in that, Step S3 further includes: S34. Based on the calibrated hydrological-ecological coupling model, run multiple scenario combinations in batches to realize the spatiotemporal dynamic simulation of carbon storage; S35. Perform visualization processing and uncertainty analysis on the simulation results to generate intuitive and reliable visualization results, providing support for subsequent analysis.

9. The distributed carbon storage prediction method under changing environments according to claim 8, characterized in that, Step S3 further includes: S36. Using the contribution rate decomposition method, quantitatively distinguish the relative contributions of human activities and natural changes to changes in carbon storage, in order to identify the dominant driving factors and their mechanisms of action.

10. The distributed carbon storage prediction method under changing environments according to claim 9, characterized in that, Step S36 includes: S361. Focusing on the core objective of carbon storage change, define the spatiotemporal boundaries of the analysis, select structural decomposition analysis (SDA) or exponential decomposition analysis (EDA) techniques based on data conditions, and use scenario comparison method to assist in verification; S362. Organize and standardize data related to carbon storage, meteorology, land use and human activities, and calculate the contribution and contribution rate of each driving factor using the selected method; S363. Interpret the decomposition results, distinguish between dominant and secondary driving factors, clarify the degree of influence of each factor on carbon storage changes, and interpret the differences in contribution in conjunction with the characteristics of the study area, laying the foundation for subsequent analysis.

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