A dynamic simulation system and method for coupling soil organic carbon and inorganic carbon

Abstract

This application provides a dynamic simulation system and method for coupled soil organic carbon and inorganic carbon. The system utilizes an internal observation data input layer, a mechanistic model layer with a coupled carbon model deployed in the system, a Bayesian inversion layer, and a posterior analysis and application layer. The coupled carbon model is used to describe the co-evolution process of organic carbon and inorganic carbon. Thus, the system provides an automated and reproducible modeling framework from observation → mechanistic constraints → posterior learning → future prediction → uncertainty quantification. It realizes a closed-loop process of parameter estimation, mechanism analysis, and scenario prediction of the coupled carbon model, thereby obtaining a carbon change prediction system for experimental sites with mechanistic interpretability and risk quantification capabilities, which can improve the prediction accuracy and decision interpretability of carbon change.

Description

Technical Field

[0001] This application relates to the field of soil science, and to, but is not limited to, a dynamic simulation system and method for coupled soil organic carbon and inorganic carbon. Background Technology

[0002] Current simulation methods for soil carbon cycle mainly fall into two categories: 1. Traditional empirical soil organic carbon (SOC) models, such as first-order decomposition and multi-library empirical models, divide SOC into several active libraries and simulate organic carbon changes with fixed or empirical decomposition rates. However, they have significant drawbacks, such as severe structural equifinality, meaning that different parameter combinations can produce almost identical fitting results, but the corresponding soil processes and regulatory mechanisms are completely different, resulting in models that can fit the data but lack explanatory power; and a lack of mechanistic basis, meaning they do not explicitly describe mineral stabilization, carbonate processes, dissolved inorganic carbon (DIC) balance, and Ca2+ balance. 2+ Key mechanisms such as supply cannot answer questions like which mechanism controls SOC stability; they cannot be used for scenario prediction, i.e., for factors such as temperature increase, acidification, moisture, and Ca. 2+ The response to changes in the environment, such as input, lacks a physical basis and is difficult to simulate long-term changes in the future.

[0003] 2. Traditional soil inorganic carbon (SIC) or carbonate process models: Most existing SIC models focus on the carbonate dissolution-precipitation process in calcareous soils, but they have shortcomings, such as: lack of coupling with SOC processes; and failure to consider the impact of particulate organic carbon (POC) / mineral-associated organic carbon (MAOC) decomposition on pH, DIC, and Ca2+. 2+ Feedback is lacking; it does not include MICP biological processes: it lacks quantification of microbially induced carbonate precipitation (MICP); it has weak scenario simulation capabilities: it usually cannot handle the interaction effects of management factors such as water, temperature, acidification, and nitrogen fertilizer; and it lacks a complete mechanism model-data constraint-uncertainty assessment chain.

[0004] In other words, existing models typically only achieve one of two things: mechanism construction or data fitting. The following three major challenges remain unresolved: 1. Structural equivalence: When the model contains a large number of carbon libraries / parameters, there are many different parameter combinations that can achieve similar fitting, which makes it impossible to uniquely identify the model structure from the data, making the mechanism inference unreliable.

[0005] 2. Unidentifiable parameters: Due to the strong nonlinearity of the model and the high correlation of parameters, especially in processes such as adsorption-desorption and pH-DIC coupling, traditional optimization methods cannot distinguish between the true and fitted values ​​of parameters, resulting in unstable estimation.

[0006] 3. Black box prediction: Lacking methods for analyzing the contribution of environmental driving factors, the predictions given by the model cannot answer questions such as: which parameters dominate SOC stability, which environmental factors cause SIC to decrease or increase, the magnitude of uncertainty in future scenarios, and what the sources are. Summary of the Invention

[0007] Based on the above problems, this application provides a dynamic simulation system and method for coupled soil organic carbon and inorganic carbon. It provides an automated and reproducible modeling framework from observation → mechanism constraint → posterior learning → future prediction → uncertainty quantification, realizing a closed loop of parameter estimation, mechanism analysis and scenario prediction of the dual-carbon coupling model. This results in a carbon change prediction system for experimental sites with mechanism interpretability and risk quantification capabilities, thereby improving the prediction accuracy and decision interpretability of carbon change.

[0008] The technical solution of this application embodiment is implemented as follows: In a first aspect, embodiments of this application provide a dynamic simulation system for the coupling of soil organic carbon and inorganic carbon. The system includes: an observation data input layer for acquiring soil core chemical parameter values, soil management data, and preset multi-scenario data at test sites with pH > 7; a mechanism model layer for analyzing the soil core chemical parameter values, soil management data, and preset multi-scenario data using an internal dual-carbon coupling model to obtain simulated annual variation curves of multiple functional carbon pool contents at the test sites; the dual-carbon coupling model is used to describe the co-evolution process of organic carbon and inorganic carbon; and a Bayesian inversion layer for analyzing the simulated annual variation curves of multiple functional carbon pool contents at the test sites. The study also included measured annual variation curves of multiple functional carbon pools at the experimental sites. The DREAMzs algorithm was used to sample the values ​​of multiple parameters to be estimated in the dual-carbon coupling model, resulting in a posterior distribution sample set for each parameter. The posterior analysis and application layer was used to audit the credibility of the posterior distribution sample set for each parameter and to analyze the environmental driving mechanism based on preset multi-scenario data. This yielded identifiable information and a parameter-environment response model. Uncertainty propagation was then performed based on the identifiable information and the parameter-environment response model, resulting in a carbon change prediction system for the experimental sites with mechanistic interpretability and risk quantification capabilities.

[0009] Secondly, embodiments of this application provide a method for dynamic simulation of soil organic carbon and inorganic carbon coupling. The method includes: acquiring soil core chemical parameter values, soil management data, and preset multi-scenario data at test sites with pH > 7; using a dual-carbon coupling model to analyze the soil core chemical parameter values, soil management data, and preset multi-scenario data to obtain simulated annual variation curves of multiple functional carbon pools at the test sites; the dual-carbon coupling model is used to describe the co-evolution process of organic carbon and inorganic carbon; and based on the simulated annual variation curves of multiple functional carbon pools at the test sites, and the simulated annual variation curves of multiple functional carbon pools at the test sites... The measured annual variation curve of the potential carbon pool content was obtained. The DREAMzs algorithm was used to sample the values ​​of multiple parameters to be estimated in the dual-carbon coupling model to obtain the posterior distribution sample set of each parameter. The credibility of the posterior distribution sample set of each parameter was audited, and the environmental driving mechanism was analyzed based on the preset multi-scenario data. The corresponding identifiable information and parameter-environment response model were obtained. Uncertainty propagation was performed based on the identifiable information and parameter-environment response model to obtain a carbon change prediction system with mechanistic interpretability and risk quantification capability for the experimental site.

[0010] The beneficial effects of the technical solutions provided in this application include at least the following: The soil organic carbon and inorganic carbon coupled dynamic simulation system and method provided in this application embodiment includes the following layers: An observation data input layer is used to acquire soil core chemical parameter values, soil management data, and preset multi-scenario data for test sites with pH > 7; a mechanism model layer is used to analyze the soil core chemical parameter values, soil management data, and preset multi-scenario data using an internal dual-carbon coupling model to obtain simulated annual variation curves of multiple functional carbon pool contents at the test sites; the dual-carbon coupling model is used to describe the co-evolution process of organic carbon and inorganic carbon; and a Bayesian inversion layer is used to obtain the simulated annual variation curves of multiple functional carbon pool contents at the test sites based on the simulated annual variation curves of multiple functional carbon pool contents, and... The measured annual variation curves of multiple functional carbon pools at the experimental site were used to sample the values ​​of multiple parameters to be estimated in the dual-carbon coupling model using the DREAMzs algorithm, resulting in a posterior distribution sample set for each parameter. The posterior analysis and application layer was used to audit the credibility of the posterior distribution sample set for each parameter and to analyze the environmental driving mechanism based on preset multi-scenario data, thereby obtaining identifiable information and a parameter-environment response model. Uncertainty propagation was then performed based on the identifiable information and the parameter-environment response model, resulting in a carbon change prediction system for the experimental site with mechanistic interpretability and risk quantification capabilities. In this way, the dynamic simulation system for coupled soil organic and inorganic carbon, through its internal observation data input layer, mechanism model layer, Bayesian inversion layer, and posterior analysis and application layer, provides an automated and reproducible modeling framework from observation → mechanism constraint → posterior learning → future prediction → uncertainty quantification. It realizes a closed-loop process for parameter estimation, mechanism analysis, and scenario prediction of the dual-carbon coupled model, thereby obtaining a carbon change prediction system for the experimental sites with mechanism interpretability and risk quantification capabilities, which can improve the prediction accuracy and decision interpretability of carbon change.

[0011] It should be understood that the above general description and the following detailed description are exemplary and explanatory only, and are not intended to limit the technical solutions provided in the embodiments of this application. Attached Figure Description

[0012] To more clearly illustrate the technical solutions in the embodiments of this application, the accompanying drawings used in the description of 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, wherein: Figure 1 This is a schematic diagram of the composition of a dynamic simulation system for coupled soil organic carbon and inorganic carbon, provided in an embodiment of this application. Figure 2 A structural framework diagram of a coupled dynamic simulation system for soil organic carbon and inorganic carbon provided in this application embodiment; Figure 3 A data processing flowchart for a coupled dynamic simulation system of soil organic carbon and inorganic carbon provided in the embodiments of this application; Figure 4 This is a schematic flowchart of a dynamic simulation method for coupled soil organic carbon and inorganic carbon provided in an embodiment of this application. Detailed Implementation

[0013] To make the objectives, technical solutions, and advantages of the embodiments of this application clearer, the technical solutions of the embodiments of this application will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of this application, not all embodiments. The following embodiments are used to illustrate this application, but are not intended to limit the scope of this application. Based on the embodiments in this application, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of this application.

[0014] In the following description, references are made to “some embodiments,” which describe a subset of all possible embodiments. However, it is understood that “some embodiments” may be the same subset or different subsets of all possible embodiments and may be combined with each other without conflict.

[0015] It should be noted that the terms "first, second, and third" used in the embodiments of this application are merely to distinguish similar objects and do not represent a specific ordering of objects. It is understood that "first, second, and third" can be interchanged in a specific order or sequence where permitted, so that the embodiments of this application described herein can be implemented in an order other than that illustrated or described herein.

[0016] It will be understood by those skilled in the art that, unless otherwise defined, all terms used herein (including technical and scientific terms) have the same meaning as commonly understood by one of ordinary skill in the art to which the embodiments of this application pertain. It should also be understood that terms such as those defined in general dictionaries should be understood to have a meaning consistent with their meaning in the context of the prior art, and should not be interpreted in an idealized or overly formal sense unless specifically defined as herein.

[0017] Example 1: See Figure 1 The diagram shown is a schematic representation of the composition of a dynamic simulation system for coupled soil organic carbon and inorganic carbon provided in this application embodiment; the dynamic simulation system 100 for coupled soil organic carbon and inorganic carbon is combined with... Figure 1 The following explanation is provided: The observation data input layer 110 is used to obtain soil core chemical parameter values, soil management data, and preset multi-scenario data for test sites with pH>7.

[0018] Mechanism model layer 120 is used to simulate soil core chemical parameter values, soil management data and preset multi-scenario data using the internal dual-carbon coupling model to obtain simulated curves of annual changes in the content of multiple functional carbon pools at the test sites; the dual-carbon coupling model is used to describe the co-evolution process of organic carbon and inorganic carbon.

[0019] Bayesian inversion layer 130 is used to sample the values ​​of multiple parameters to be estimated in the dual-carbon coupling model based on the simulated curves of annual changes in the content of multiple functional carbon pools at the test sites and the measured curves of annual changes in the content of multiple functional carbon pools at the test sites, using the DREAMzs algorithm, to obtain the posterior distribution sample set of each parameter to be estimated.

[0020] The posterior analysis and application layer 140 is used to audit the credibility of the posterior distribution sample set of each parameter among multiple parameters to be estimated, and to analyze the environmental driving mechanism based on preset multi-scenario data, thereby obtaining identifiable information and parameter-environment response model. Based on the identifiable information and parameter-environment response model, uncertainty is transferred to obtain a carbon change prediction system for the test point with mechanistic interpretability and risk quantification capability.

[0021] In some embodiments, test sites with pH > 7 characterize inorganic carbon as a predominantly carbonate system under calcareous soil conditions with pH > 7, which is suitable for characterizing the SiC-DIC-Ca system. 2+ The coupling transformation process, where the soil pH at the experimental site is >7, places the soil system in a calcareous environment where inorganic carbon mainly exists as a carbonate system, thus enabling the characterization of SiC, DIC, and Ca. 2+ The dynamic process of dissolution-precipitation between SOC and the coupling mechanism of CO2 generated by SOC decomposition to SiC.

[0022] Here, the test site with pH>7 can be considered as a soil environment point corresponding to a typical calcareous dryland.

[0023] In some embodiments, the core chemical parameters of the soil at the test site include at least: total annual soil carbon input, particulate organic carbon (POC) content at the current time, mineral associated organic carbon (MAOC) content at the current time, soil pH, and exogenous calcium. 2+ Annual input volume.

[0024] Soil management data at the pilot sites should include at least: annual fertilization data, annual irrigation data, and annual climate data.

[0025] The pre-set multi-scenario data for the test sites should include at least: soil moisture, temperature, acidification, and calcium. 2+ The input is data for the driving factors.

[0026] In some embodiments, the mechanistic model layer 120 is used to analyze soil core chemical parameter values, soil management data, and preset multi-scenario data using an internally included dual-carbon coupling model to obtain simulated annual variation curves of multiple functional carbon pool contents at the experimental site. Here, the mechanistic model layer 120 can also first integrate the soil core chemical parameter values, soil management data, and preset multi-scenario data according to time series information to obtain the annual time series data sequence of the experimental site, and then input the annual time series data sequence of the experimental site into the dual-carbon coupling model for analysis to obtain simulated annual variation curves of multiple functional carbon pool contents at the experimental site.

[0027] Here, the two-carbon coupling model can include the following ordinary differential equation (ODE): 1. POC decomposition → MAOC stabilization; 2. Desorption and redistribution of MAOC; 3. Soil organic carbon (SOC) decomposes to produce CO2; 4. Acid-base balance of dissolved inorganic carbon (DIC); 5. Dissolution and precipitation of CaCO3; 6. Microbially Induced Carbonate Precipitation (MICP) enhances bioalkalization; 7. Moisture-temperature-pH response; 8.Ca 2+ Input and loss process.

[0028] In other words, the dual-carbon coupling model integrates key processes of the co-evolution of SOC and solid soil inorganic carbon (SIC), including the transformation between POC and MAOC, soil CO2 release and dissolved inorganic carbon (DIC), carbonate dissolution-precipitation, and the influence of MIP, moisture, temperature, pH, and Ca. 2+ By adjusting environmental factors such as inputs, long-term dynamic simulations and sensitivity analyses of soil dual carbon pools can be achieved under different climate change and agricultural management scenarios, thus providing tool support for soil health assessment, carbon sequestration potential estimation, and agricultural management decision-making at experimental sites.

[0029] In some embodiments, this dual-carbon coupling model is used to simulate five main carbon-related state variables, namely: POC (unit: g C kg). -1 MAOC (unit: g C kg) -1 SiC (unit: g C kg) -1 DIC (unit: mmol / kg) -1 ), Ca 2+ (Units: mmol c kg) -1 Here, Ca 2+ This refers to soluble / exchangeable calcium in the soil. The processes corresponding to the five main carbon-related state variables involved can all be solved by time-progression using ordinary differential equations, and can be obtained through numerical integration of deSolve::ode.

[0030] In some embodiments, the dual-carbon coupling model includes at least: a first mass conservation equation for particulate organic carbon (POC) and mineral-bound organic carbon (MAOC), and a characterization equation for inorganic carbon (including SiC and DIC) and calcium ions (Ca). 2+ The second mass conservation equation relating the mass balance and transformation relationship between them; The first mass conservation equation (the mass conservation equations for POC and MAOC) includes at least the following: Formula (1); Formula (2); Formula (3); Formula (4); The second mass conservation equation includes at least the following: Formula (5); Formula (6); Formula (7); Formula (8); Formula (9); Formula (10); in, The state variables of the POC at the test site; For the test site at the current time Total annual soil carbon input; The decomposition rate of POC is a constant. The POC for the test site at the current time content The flux of organic matter adsorbed onto the carbonate surface at the test site The rate at which MAOC desorbs from the carbonate surface; MAOC at the test site at the current time The content; For the state variables of MAOC at the test point; The proportion of POC decomposed to form MAOC, i.e., a stabilization proportion; The decomposition rate of MAOC is a constant. The adsorption rate constant is denoted by . The saturation function of carbonate surface activity at the test site; is the half-saturation constant of the surface activity response function of carbonate minerals; SIC at the test site at the current time The content; The state variables of solid inorganic carbon (SIC) produced at the test site after microbial-induced carbonate precipitation (MICP) are shown in units of 1000 m³ / s. ); The SiC mass conversion factor corresponding to the unit dissolved inorganic carbon DIC carbon flux ( = (i.e., 1 mmol C = 0.012 C). The actual carbonate precipitation rate at the test point after considering MICP; The dissolution rate of calcium carbonate (CaCO3) at the test site; The state variable of DIC generated at the test site after microbial-induced carbonate precipitation (MICP) (unit: ); The test site was located at the soil soluble / exchangeable Ca2+ level after microbial induced carbonate precipitation (MICP). 2+ State variables (unit: ); For the test site, the external Ca 2+ Annual input volume; The maximum kinetic enhancement factor of MICP, ≥1; The functional expression for microbial-induced carbonate precipitation (MICP) at the test site; Baseline carbonate precipitation rate (in mmol C kg) -1 a -1 ); Carbonate precipitation rate (unit: mmol / Ckg) -1 a -1 ) The carbonate saturation index (dimensionless) is determined by DIC species and effective pH. For translation ReLU function; For the water in the sedimentation process The response function takes the maximum value (dimensionless) at moderate moisture content. The microbial biomass at the test site, such as microbial biomass carbon, is expressed in g C kg. -1 ; The microbial half-saturation constant is... = At that time, the microbial factor was 0.5 g C kg -1 ; , , The background of the soil Soil temperature (Unit: °C) and soil moisture The response function.

[0031] It should be noted that, It is the adsorption rate constant, specifically the adsorption rate constant of organic carbon adsorbing onto the mineral surface to form MAOC, that is, it is used to describe the rate at which organic carbon adsorbs onto the mineral surface to form MAOC per unit time. It can be used to characterize the enhancing effect of SIC on increasing the surface area of ​​minerals and their adsorption capacity for organic carbon; A saturated characteristic parameter that can be used to characterize the effect of soil inorganic carbon content on the surface area of ​​carbonate minerals and their organic carbon adsorption capacity; It is a model state variable whose value is calculated by the dynamic equation of soil inorganic carbon, and the initial SIC observation data of the test site is used as the initial condition; microbial factors represent soil microbial biomass carbon and are used to characterize the regulatory effect of microbial activity on the microbial induced carbonate precipitation (MICP) process.

[0032] It should be noted that MIPs have an amplifying effect on the carbonate precipitation rate; that is, after considering MIPs, As shown in formula (8) above.

[0033] at the same time, When =0, ; →1, Enlarged to .

[0034] Here, soil moisture It can be expressed as volumetric water content or relative water content. , , All are dimensionless functions between 0 and 1.

[0035] In some embodiments, to characterize the impact of MIP on soil inorganic carbon dynamics, the two-carbon coupling model uses a microbial enhancement function, i.e. It simultaneously affects the effective pH and carbonate precipitation rate, thereby enhancing the coupling process of SOC-SIC.

[0036] In the above formulas (5) to (10), Simultaneously consumes DIC and Ca 2+ And convert it to SIC; Then perform the reverse process, releasing DIC and Ca from SIC. 2+ MICP improves With magnification These two pathways together enhance the coupling of SOC-SIC and the formation of solid-phase carbonates.

[0037] here, The pH response function is an sigmoid logic function, which can be expressed as: Formula (11); in, The pH response slope parameter (dimensionless). Background pH; The optimal pH (dimensionless) for the MIP process.

[0038] Furthermore, in order to reflect the local alkalization caused by MIP, an effective pH is introduced, namely... : Formula (12); in, This represents the maximum pH increase that MIP can cause under given soil and environmental conditions. Used for calculating DIC species distribution and saturation index (dimensionless).

[0039] In some embodiments, That is, the moisture response function, which can be represented by a bell-shaped function with the optimum moisture content as the peak value: Formula (13); in, Soil moisture; This represents the optimal soil moisture content for the MICP process.

[0040] In addition, temperature response function It can be expressed using the following formula: Formula (14); in, Q10 coefficient for microbial induced carbonate precipitation (MICP) related processes, representing the factorial change in reaction rate for every 10°C increase in temperature; Reference temperature (unit: °C); This represents the current ambient temperature.

[0041] In conclusion, A dimensionless amplification factor between 0 and 1, which is used when microbial activity is high, pH is moderate, and temperature and moisture are close to their optimal range. Under conditions of extreme microbial deficiency, strong acid / strong alkali, or extreme dry / extreme wetness, In other words, when (When MICP is off or the environment is unsuitable) ;when and At that time, the local pH was raised to .

[0042] It should be noted that the environmental driving data changes over time as follows: 1. Soil moisture Different baseline values ​​and seasonal amplitudes are set according to the scenario (dry / wet), and interannual fluctuations are simulated using a sine function.

[0043] 2. Background pH: This refers to linear acidification at a fixed rate per year under high nitrogen application scenarios.

[0044] 3. Temperature T: The warming scenario is superimposed on the regional annual average temperature by a fixed annual increase.

[0045] 4.Ca 2+ External input: High Ca 2+ The scenario adds a constant flux to the baseline input.

[0046] In some embodiments, time t can be used as the independent variable, and the numerical integration method provided by the R language package deSolve (such as Runge-Kutta) can be called to perform time-step solution; wherein, the time step can be set as needed, and the default value in this application is 0.5 years.

[0047] In some embodiments, regarding The time function expression is used in this application to simulate the regulatory effects of different management measures, such as fertilization, irrigation, straw return to the field, and dissolved organic carbon input, on SOC-SIC coupling. The dual-carbon coupling model in this application uses exogenous Ca2+. 2+ The input and the exogenous organic matter input define a consistent time function expression to drive the state variable Ca. 2+ The dynamic changes of POC and DIC.

[0048] In some embodiments, the time variable is set as (unit: a or d, depending on the simulation scale), and the dual-carbon coupling model allows three types of input modes: pulse, constant, and stepwise.

[0049] Among them, the exogenous Ca at the test site 2+ Annual input for: Formula (15); Formula (16); Formula (17); Formula (18); in, , as well as They are pulsed Ca 2+ The function corresponding to the annual input (simulating a one-time application of lime and gypsum), and the continuous Ca 2+ The function corresponding to the annual input (simulating the Ca of irrigation water or seepage solution) 2+ Supply) and phased Ca 2+ The function corresponding to the annual input (simulating fertilization in stages or irrigation in phases). For a single application of Ca 2+ quantity; A time variable measured in years; For application time; This is the Dirac impulse function, used to represent instantaneous input; For average Ca 2+ Input flux; The time corresponding to the maximum input; For periodicity; For the first Secondary Ca 2+ The intensity of investment; For the first Enter the time starting from the next step; It is the Heaviside step function; The number of inputs is for each stage.

[0050] In some embodiments, when formula (1) above distinguishes between exogenous organic matter input and the intrinsic term of the two-carbon coupling model, it can be described as follows: The intrinsic term in the dual-carbon coupling model includes processes such as POC decomposition and adsorption-desorption transfer to MAOC.

[0051] in, The function representing the exogenous organic carbon input into the POC carbon pool per unit time is the exogenous organic carbon input flux into the POC carbon pool per unit time, which includes crop residues, root input, and organic carbon input formed by the application of organic fertilizers.

[0052] This includes straw return to the field, root input, and application of compost / organic fertilizer, which can be represented as: Formula (19); Formula (20); Formula (21); Formula (22); in, For inputting the content of POC; , as well as These are: pulsed organic matter input function (straw or one-time organic fertilizer application), exponential decay input function (simulating the staged decomposition of straw into POC, where a lot of field organic matter, especially straw, does not enter POC instantaneously but is released slowly), and periodic input function (simulating the periodic root source input caused by root growth-death). Enter the total amount; A time variable measured in years; For application time; The amount of carbon in the mineralizable organic matter after input; is the C release rate constant of straw / organic fertilizer; For a step function, make it only when release; The maximum intensity input to the root system; The period is usually one year, P=1a; This is the period of maximum root growth within the year; Positive values ​​are truncated, while negative values ​​are 0.

[0053] In some embodiments, the dual-carbon coupling model of this application allows for the simultaneous activation of multiple input modes, such as: Corn stalk return to the field + lime application + seasonal irrigation (Ca 2+ enter : Formula (23); Formula (24).

[0054] Furthermore, all input functions support scenario simulation (such as high Ca). 2+ Low Ca 2+(e.g., enhanced root input, low straw input) can be used to analyze the sensitivity and threshold effect of the SOC-SIC coupling mechanism within the dual-carbon coupling model.

[0055] In some embodiments, the mechanistic model layer 120 obtains simulated curves of annual variations in the content of multiple functional carbon pools at the test sites, i.e., each carbon pool, such as: POC, MAOC, SOC (POC+MAOC), SIC, DIC, Ca. 2+ Curve showing changes over time. Correspondingly, see the reference [link / reference]. Figure 2 The diagram shown illustrates the structural framework of a dynamic simulation system for coupled soil organic carbon and inorganic carbon.

[0056] In some embodiments, the Bayesian inversion layer 130 is specifically used to determine the simulation error of each functional carbon pool at multiple time points within the year based on the simulated annual variation curves of multiple functional carbon pool contents at the test site and the measured annual variation curves of multiple functional carbon pool contents at the test site; under the condition that the simulation errors corresponding to the test sites are all independent and simultaneously follow a Gaussian distribution, the likelihood function corresponding to the measured annual variation curve of each functional carbon pool at the test site is generated; the likelihood function is logarithmically transformed to obtain a log-likelihood function with each of the multiple estimated parameters as independent variables; the DREAMzs algorithm is used to obtain the posterior distribution sample set of each of the multiple estimated parameters by random sampling based on the log-likelihood function with each of the multiple estimated parameters as independent variables and prior knowledge.

[0057] In some embodiments, in order to constrain the dual-carbon coupling model, the dual-carbon coupling model is represented by the SOC-SIC coupling model in the following embodiments of this application. The key parameters of the SOC-SIC coupling model, such as decomposition rate, carbonate precipitation / dissolution coefficient, pH response coefficient, MICP module parameters, etc., are represented by the Bayesian parameter inversion framework based on the differential evolution adaptive Metropolis algorithm (DREAMzs), forming a complete chain of "observation data → posterior distribution → scenario simulation → uncertainty propagation".

[0058] In some embodiments, the output of the SOC-SIC coupling model is: Formula (25); in, The observation time point; The parameter vector to be inverted (length) ); The variables such as SOC, SIC, DIC, or CO2 flux obtained from the SOC-SIC coupled model simulation are read from the annual variation simulation curves of multiple functional carbon pool contents. Here, in the SOC-SIC coupled model of this application, CO2 flux is calculated from the organic carbon decomposition process, for example, derived from the decomposition terms of POC and MAOC, and is used to characterize the CO2 released during the organic carbon mineralization process.

[0059] Among them, the observation data is denoted as (Unit: g C kg) -1 mmol c kg -1 or mg; CO2: C m -2 d -1 (This depends on the specific indicators.)

[0060] In some embodiments, the Bayesian inversion layer 130 is used to sample the values ​​of multiple parameters to be estimated in the dual-carbon coupling model using the DREAMzs algorithm, based on the simulated annual variation curves of multiple functional carbon pool contents at the experimental sites and the measured annual variation curves of multiple functional carbon pool contents at the experimental sites, to obtain the posterior distribution sample set of each parameter to be estimated in the SOC-SIC coupling model. ;in, This represents the parameter vector to be inverted, including several key parameters in the two-carbon coupled model, such as: , , , , , Model parameters and related parameters in the MICP module.

[0061] In some embodiments, the Bayesian inversion layer 130 is specifically used to determine the simulation error of each functional carbon at multiple time points within the year based on the simulated curves of the annual variation of multiple functional carbon pool contents at the test point and the measured curves of the annual variation of multiple functional carbon pool contents at the test point; under the condition that the simulation errors corresponding to the test points are all independent and simultaneously follow a Gaussian distribution, the likelihood function corresponding to the measured curve of the annual variation of each functional carbon pool at the test point is generated; the likelihood function is logarithmically transformed to obtain a log-likelihood function with each of the multiple estimated parameters as independent variables; the DREAMzs algorithm is used to obtain the posterior distribution sample set of each of the multiple estimated parameters by random sampling based on the log-likelihood function with each of the multiple estimated parameters as independent variables and prior knowledge.

[0062] The likelihood function represents the simulated error corresponding to the observed experimental points. It is assumed to be an independent and identically distributed Gaussian error, with variances that may vary depending on the variables. Therefore, the likelihood function is: Formula (26); in, For the first One observation (observation data); These are the simulated values ​​output by the SOC-SIC coupled model; The variance of the observation error is expressed in units consistent with the corresponding variable, for example: SOC / SIC: DIC: CO2.

[0063] In some embodiments, its log-likelihood can be used in the implementation: Formula (27).

[0064] Here, if a certain type of data (such as CO2 peak values) exhibits significant heteroscedasticity, it can be used... The proportional error model provided in this application has reserved a corresponding interface for the SOC-SIC coupling model.

[0065] Here, each parameter to be inverted is assigned a weakly-informative successive prior to avoid overly restricting the parameter space. The parameters involved are listed in Tables 1, 2, and 3 below.

[0066] Table 1 SOC-MAOC process parameters Table 2. Parameters of carbonate kinetics (precipitation / dissolution) Table 3 Module Parameters of MICP In some embodiments, the Bayesian inversion algorithm can employ multi-chain (typically 3-5 chains) parallel differential evolution MCMC. Each chain carries a genome, and information exchange between chains is maintained through differential mutation, improving the exploration efficiency of high-dimensional parameter spaces. Each sampling step executes: Formula (28); in, Candidate parameters; This is the difference coefficient of variation (typically ranging from 0.5 to 1.0, adaptively adjusted). and For other chain indices selected randomly; This is a small disturbance (usually normal noise).

[0067] Its corresponding acceptance probability is: .

[0068] In some embodiments, the posterior analysis and application layer 140 includes: The parameter identifiability diagnostic sublayer 1401 is used to perform credibility audits on the posterior distribution sample set to obtain identifiability information.

[0069] The identifiable information includes at least: parameters that can be reliably identified and their posterior statistical characteristics, and parameters of unidentifiable problems and their causes.

[0070] The environmental factor interpretation sublayer 1402 is used to perform multiple linear regression analysis on reliably identified parameters and their posterior statistical characteristics, as well as the pre-set multi-scenario data of the test points, to obtain a parameter-environment response model for quantifying the driving relationship and influence intensity of the pre-set multi-scenario data on parameter values.

[0071] Uncertainty prediction sublayer 1403 is used to transfer and integrate uncertainty based on parameter-environment response models, reliably identified parameters and their posterior statistical characteristics, through Monte Carlo simulation, to obtain a carbon change prediction system for test sites with mechanistic interpretability and risk quantification capability.

[0072] In some embodiments, credibility auditing includes at least: posterior uncertainty analysis, linear correlation analysis between parameters, and global collinearity diagnosis.

[0073] In some embodiments, to ensure the stability and reliability of the posterior distribution, this software uses the following three standards: 1. Gelman-Rubin diagnosis ( That is, all parameters must satisfy: If some parameters This indicates that the chain has not yet been mixed or has multiple peaks, requiring longer iterations.

[0074] 2. Effective Sample Size (ESS) This ensures that the posterior distribution is sufficiently stable for subsequent scenario simulation and uncertainty analysis.

[0075] 3. Parameter identifiability check, i.e., checking the posterior covariance matrix. If represents the correlation coefficient between parameter i and parameter j in the posterior covariance matrix. ,and If there are strongly correlated parameter pairs, it indicates a structural equivalence problem; if a parameter's posterior is almost equal to its prior (i.e., posterior ≈ prior), it is considered an unidentifiable parameter. Unidentifiable parameters in the SOC-SIC coupled model are not included in the scenario simulation or require additional constraints (e.g., fixed literature values).

[0076] In some embodiments, all posterior sampling is used to: generate scenario simulation results of parameter uncertainty propagation; estimate the 95% uncertainty intervals of carbon pool changes (ΔSOC, ΔSIC) and key fluxes (precipitation / dissolution) under different climate management scenarios; and calculate the vulnerability index and factor contribution.

[0077] like Figure 3 The diagram shown is a data flow chart of the coupled dynamic simulation system for soil organic carbon and inorganic carbon provided in this application, which forms a closed-loop structure of observation → inversion → prediction → uncertainty; wherein: 301. Observational Data Inputs: Includes SOC / SIC / DIC time series, CO2 emissions and isotopic fluxes, and basic soil physicochemical properties such as pH and Ca. 2+ , water content, temperature, etc. All observation data are input into the mechanistic model layer after being uniformly formatted.

[0078] 302. Mechanism Modeling Layer (Process-based Modeling): This layer uses the core ODE model of the SDCC-Simulator, including POC-MAOC kinetics, DIC equilibrium, carbonate precipitation / dissolution processes, the MICP module, and external inputs, such as: Ca 2+ Organic matter, etc. The model is based on a given set of parameters. Generate a set of simulated values .

[0079] 303. Bayesian Inference Layer: Using the DREAM (ZS) multi-chain MCMC framework, simulated and observed values ​​are connected through a likelihood function to estimate the posterior distribution of parameters. The inversion process includes prior setting, chain mixing, and convergence monitoring, such as Gelman-Rubin. Effective sample size, i.e., ESS>500, structural equivalence diagnosis, etc.

[0080] 304. Scenario Simulation Layer: This layer incorporates posterior parameters, such as: Input the data into the model one by one, considering different climate scenarios (e.g., warming, moisture changes, pH fluctuations) and management scenarios (e.g., fertilization, straw return to the field, Ca). 2+ Multiple simulations were performed using the input (MICP intensity changes) to obtain the full distribution prediction of carbon pool and carbon flux.

[0081] 305. Uncertainty Output Layer: The full parameter distribution of the scenario simulation output is used to calculate: 1. 95% confidence intervals of SOC / SIC changes (ΔSOC, ΔSIC); 2. Uncertainty ranges of PC formation / dissolution fluxes and MICP fluxes; 3. Multi-factor contributions (structural equation ΔR). 2 1. Disturbance / sensitivity analysis); 4. Vulnerability index; 5. Generate visualization results such as probability distribution, scenario bands, and contribution matrix.

[0082] This data flow structure forms an automated and reproducible modeling framework that goes from observation to mechanism constraints, posterior learning, future prediction, and uncertainty quantification. It realizes a closed loop for the entire process of parameter estimation, mechanism analysis, and scenario prediction of the core ODE model of SDCC-Simulator.

[0083] Based on the above description, the soil organic carbon and inorganic carbon coupled dynamic simulation system provided in this application achieves, for the first time, a closed-loop system encompassing functional carbon pool observation → mechanistic modeling → Bayesian inversion → parameter identifiability → environmental factor analysis → uncertainty prediction. This is the first software system in the field to realize this framework in the context of soil dual-carbon coupling. This closed-loop system can solve three core problems that have long plagued the field of soil carbon cycle modeling: ① structural equivalence; ② parameter unidentifiability; ③ prediction black box. Correspondingly, it has the following innovative points: 1. Functional carbon pool-driven three-library model + MICP explicit mechanism, namely: (1) For the first time, the POC-MAOC time series from long-term positioning experiments was directly used to constrain a three-library model containing MOCL-MOCP adsorption-desorption kinetics.

[0084] (2) Explicitly construct the DIC equilibrium + CaCO3 dissolution / precipitation mechanism and couple it with the SOC process.

[0085] (3) The MICP module is introduced for the first time, including: ① microbial enhancement of local pH upregulation; ② kinetic amplification of carbonate precipitation rate; ③ stable biological pump for SOC→SIC formed by bio-chemical process interaction.

[0086] (4) The model structure breaks through the traditional SOC single library / dual library mode and realizes true physical-chemical-biological triple coupling.

[0087] 2. DREAM-MCMC achieves parameter inversion and identifiability diagnosis: (1) The DREAMzs algorithm was used to achieve stable and global sampling for the first time in the SOC-SIC coupled model.

[0088] (2) Simultaneous inversion is possible: , , , , The response parameters, MICP enhancement parameters, etc., are shown in Table 4: Table 4 shows the module parameters of the MICP module for the inversion parameters. (3) Obtain the posterior distribution → solve the problem of unidentifiable parameters → identify the most critical control factors.

[0089] (4) Obtain the posterior mean, CI interval, Gelman-Rubin diagnosis, parameter sensitivity, and parameter correlation matrix for each parameter, providing quantifiable credibility for subsequent scenario simulation.

[0090] 3. For the first time, a factor contribution analysis system for the SOC-SIC coupling process was established, i.e., a complete factor attribution framework was constructed: moisture, pH, and Ca. 2+ The contribution rates of supply, temperature, and MICP biological processes can be calculated, and the contribution of each factor to ΔSOC, ΔSIC, ΔCO2, and carbonate precipitation rate can be calculated using scenario simulation and sensitivity surface analysis.

[0091] 4. Establish a vulnerability assessment system for future changes in dual carbon pools, based on posterior arithmetic and Monte Carlo methods: Vulnerability = (P97.5 - P2.5) / Median, to quantify the sensitivity of future soil carbon pools to management and climate change. This indicator can be widely used for: soil health assessment, carbon sequestration risk assessment, and identification of climate change sensitive areas.

[0092] 5. Fully automated process, multi-scenario combination, directly applicable to scientific research; users only need to provide: ①Long-term experimental input.

[0093] ② By selecting a scenario, the following can be completed automatically: parameter inversion, scenario simulation, plotting, report table generation, and output of graphics.

[0094] The operating environment of the coupled dynamic simulation system 100 for soil organic carbon and inorganic carbon includes: 1. Hardware environment: (1) CPU: A general PC or workstation that supports a 64-bit instruction set (≥4 cores recommended). (2) Memory: ≥8GB (16GB or more recommended); (3) Storage space: The program and data files require approximately ≥500MB.

[0095] 2. Software environment: (1) Operating system: Windows 10 and above / macOS / Linux (supports R language runtime environment); (2) Software dependencies: R language (version 4.2 or above is recommended); R packages: deSolve, dplyr, tidyr, purrr, ggplot2, etc.

[0096] In other words, the soil organic carbon and inorganic carbon coupled dynamic simulation system 100 provided in this application can be implemented in the form of an R language program, and its main functions include: 1. Soil dual carbon pool kinetic simulation: (1) Describe POC, MAOC, SIC, DIC, and soil soluble Ca in the form of ODE. 2+ The temporal changes; (2) Built-in processes such as POC input, POC→MAOC stabilization, SOC mineralization, carbonate dissolution-precipitation, and MICP enhancement; (3) It can support continuous simulation on a time scale of 0-50 years.

[0097] 2. Scenario combination design and batch simulation: Based on typical soil management scenarios in arid regions, it has five built-in combinable factors, automatically generates all scenario combinations, runs the model in batches, and outputs the results. (1) Soil moisture (W): Dry / Wet; (2) Ca 2+ Exogenous input: Low calcium 2+ High Calcium 2+ ); (3) MICP activity: Off (No MICP) / On (MICP); (4) Nitrogen fertilizer application: No nitrogen (No N) / High nitrogen (High N, accompanied by acidification); (5) Climate warming: Baseline temperature (BaselineT) / warming scenario (Warming); 3. Key Processes and Indicator Calculation (1) Output the time series of each carbon pool: POC, MAOC, SOC (POC+MAOC), SIC, DIC, Ca 2+ ; (2) Output of key process fluxes: total CO2 production, CO2 emission into the atmosphere, carbonate precipitation, and dissolution; (3) Calculate the temporal changes of environmental variables such as effective pH, soil moisture W, and soil temperature; (4) Calculate key indicators for each scenario: ΔPOC, ΔMAOC, ΔSOC, ΔSIC, ΔDIC, cumulative CO2 emissions, long-term average pH_eff, etc.

[0098] 4. Results visualization and scenario comparison: Automatic plotting for all scenarios. (1) Curves showing the change of each carbon pool over time (displayed by scenario); (2) Scenario comparison diagram of SOC and SIC dynamics; (3) Scenario comparison diagram of SOC and SIC dynamics; (4) Bar chart comparing indicators such as ΔSIC, ΔSOC, and cumulative CO2 between scenarios.

[0099] The coupled dynamic simulation system for soil organic carbon and inorganic carbon (100) is designed for typical calcareous dryland soils and proposes the following key technical approaches: (1) Construct a dual-carbon coupling model system with realistic mechanism, namely, explicitly simulate POC→MAOC stabilization, SOC mineralization, DIC equilibrium, carbonate dissolution / precipitation, MIP process, moisture-temperature-acidification-Ca 2+ Input response enables a unified dynamic framework for SOC and SIC.

[0100] (2) Use long-term positioning test data to perform parameter inversion, combine long-sequence SOC, POC and MAOC measurement data, and constrain model parameters based on Bayesian parameter inversion to solve the problem of parameter unidentification.

[0101] (3) Constructing an analysis system for the contribution of environmental control factors: Through sensitivity analysis, scenario decomposition, and key factor attribution, quantitatively analyze the impact of climate change, fertilization, water, and Ca on the changes in SOC and SIC. 2+ The relative contribution of supply.

[0102] (4) Construct a simulation platform for quantifiable prediction of uncertainty, that is, through posterior distribution-Monte Carlo-scenario comparison, realize the uncertainty range, vulnerability index, and parameter contribution decomposition of future scenarios, and solve the black box prediction problem. The system ultimately realizes the closed-loop chain of: functional carbon pool observation → mechanism model → Bayesian inversion → identifiability diagnosis → environmental factor analysis → uncertainty prediction.

[0103] The dynamic simulation system for the coupling of soil organic carbon and inorganic carbon provided in this application is a scenario simulation system for the coupling process of soil organic carbon and inorganic carbon (Soil Dual Carbon Coupling Simulator, SDCC Simulator), and its current version is SDCC SimulatorV1.0.

[0104] The soil organic carbon and inorganic carbon coupled dynamic simulation system 100 provided in this application is applicable to at least the following fields: soil science, ecology, geobiogeochemistry; agricultural carbon sink assessment and soil health evaluation; farmland management simulation under climate change scenarios; regional carbon budget and climate-adaptive agricultural management simulation; visualization demonstration of soil carbon cycle processes in scientific research and teaching; and university graduate courses and scientific computing teaching.

[0105] Meanwhile, the output of the soil organic carbon and inorganic carbon coupled dynamic simulation system 100, namely the output graphics, can be used directly as the basis for graphics in scientific research papers, project reports and teaching presentations by adopting a unified theme, clear legends and unit labels.

[0106] Here, relevant information regarding the practical application of the soil organic carbon and inorganic carbon coupled dynamic simulation system 100 is as follows: 1. Development language and tools: The development language is R language; the auxiliary tools are RStudio software or other R language integrated development environments.

[0107] 2. Program size (approximately), of which core functions and modules: approximately 500 lines of R language code; which may include: model equations, scenario construction, indicator statistics, plotting, etc.

[0108] 3. Development method: (1) The developers proposed a theoretical framework for soil dual carbon cycle based on previous experiments and literature research.

[0109] (2) Complete model building, debugging and scenario testing in the R language environment.

[0110] (3) Conduct a scientific and reasonable test on the results, and gradually improve the parameter settings and drawing templates.

[0111] Furthermore, the test results of this coupled dynamic simulation system 100 for soil organic carbon and inorganic carbon in practical applications are as follows: (1) The model stability test was completed under multiple sets of different parameter combinations and scenarios, and no numerical divergence or abnormal interruption was found.

[0112] (2) The simulation results are generally consistent with existing research and experience on typical arid soils. For example, high Ca 2+ Input and adequate moisture promote soil inorganic carbon enhancement, while continuous acidification and warming increase CO2 emissions and weaken soil inorganic carbon.

[0113] Correspondingly, the application prospects of the coupled dynamic simulation system 100 for soil organic carbon and inorganic carbon include at least the following: (1) It can be used as a basic tool for soil dual carbon process simulation and scenario analysis in scientific research projects.

[0114] (2) It can be further packaged into an R language package or graphical interface software, and connected with the measured data to carry out model calibration and uncertainty assessment.

[0115] (3) It can be used for postgraduate teaching to help students understand the synergistic stabilization mechanism of soil organic carbon and soil inorganic carbon and their impact on soil health and climate change.

[0116] Based on the foregoing embodiments, this application further provides a method for coupled dynamic simulation of soil organic carbon and inorganic carbon, see [link to relevant documentation]. Figure 4 The diagram shown is a flowchart illustrating a dynamic simulation method for coupled soil organic carbon and inorganic carbon provided in an embodiment of this application. The method includes: Step 401: Obtain soil core chemical parameter values, soil management data, and preset multi-scenario data for test sites with pH>7.

[0117] Step 402: Using the dual-carbon coupling model, analyze the core chemical parameters of the soil, soil management data, and preset multi-scenario data to obtain the annual variation simulation curves of multiple functional carbon pools at the test site; the dual-carbon coupling model is used to describe the co-evolution process of organic carbon and inorganic carbon.

[0118] Step 403: Based on the simulated annual variation curves of multiple functional carbon pool contents at the test sites and the measured annual variation curves of multiple functional carbon pool contents at the test sites, the DREAMzs algorithm is used to sample the values ​​of multiple parameters to be estimated in the dual-carbon coupling model to obtain the posterior distribution sample set of each parameter to be estimated.

[0119] Step 404: Conduct a credibility audit on the posterior distribution sample set of each of the multiple parameters to be estimated, and analyze the environmental driving mechanism based on the preset multi-scenario data to obtain identifiable information and parameter-environment response model. Based on the identifiable information and parameter-environment response model, perform uncertainty propagation to obtain a carbon change prediction system for the test site with mechanistic interpretability and risk quantification capability.

[0120] It should be noted that the descriptions of the above method embodiments are similar to those of the above system embodiments, and have similar beneficial effects. For technical details not disclosed in the method embodiments of this application, please refer to the descriptions of the system embodiments of this application for understanding.

[0121] It should be understood that the phrase "one embodiment" or "an embodiment" throughout the specification means that a specific feature, structure, or characteristic related to the embodiment is included in at least one embodiment of this application. Therefore, "in one embodiment" or "in an embodiment" appearing throughout the specification does not necessarily refer to the same embodiment. Furthermore, these specific features, structures, or characteristics can be combined in any suitable manner in one or more embodiments. It should be understood that in the various embodiments of this application, the sequence numbers of the above-described processes do not imply a sequential order of execution; the execution order of each process should be determined by its function and internal logic, and should not constitute any limitation on the implementation process of the embodiments of this application. The sequence numbers of the above-described embodiments are merely descriptive and do not represent the superiority or inferiority of the embodiments.

[0122] It should be noted that, in this document, the terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such a process, method, article, or apparatus. Unless otherwise specified, an element defined by the phrase "comprising one..." does not exclude the presence of other identical elements in the process, method, article, or apparatus that includes that element.

[0123] In the several embodiments provided in this application, it should be understood that the disclosed devices and methods can be implemented in other ways. The device embodiments described above are merely illustrative. For example, the division of units is only a logical functional division, and in actual implementation, there may be other division methods, such as: multiple units or components can be combined, or integrated into another system, or some features can be ignored or not executed. In addition, the coupling, direct coupling, or communication connection between the various components shown or discussed can be through some interfaces, and the indirect coupling or communication connection between devices or units can be electrical, mechanical, or other forms.

[0124] The units described above as separate components may or may not be physically separate. The components shown as units may or may not be physical units. They may be located in one place or distributed across multiple network units. Some or all of the units may be selected to achieve the purpose of the embodiments of this application, depending on actual needs.

[0125] In addition, each functional unit in the various embodiments of this application can be integrated into one processing unit, or each unit can be a separate unit, or two or more units can be integrated into one unit; the integrated unit can be implemented in hardware or in the form of hardware plus software functional units.

[0126] Alternatively, if the integrated units described above are implemented as software functional modules and sold or used as independent products, they can also be stored in a computer-readable storage medium. Based on this understanding, the technical solutions of the embodiments of this application, or the parts that contribute to related technologies, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions to cause the device automatic test line to execute all or part of the methods described in the various embodiments of this application. The aforementioned storage medium includes various media capable of storing program code, such as mobile storage devices, ROMs, magnetic disks, or optical disks.

[0127] The methods disclosed in the several system embodiments provided in this application can be arbitrarily combined without conflict to obtain new system embodiments.

[0128] The features disclosed in the several systems provided in this application can be arbitrarily combined without conflict to obtain new system embodiments.

[0129] The above description is merely an embodiment of this application, but the scope of protection of this application is not limited thereto. Any variations or substitutions that can be easily conceived by those skilled in the art within the scope of the technology disclosed in this application should be included within the scope of protection of this application. Therefore, the scope of protection of this application should be determined by the scope of the claims.

Claims

1. A dynamic simulation system for coupled soil organic carbon and inorganic carbon, characterized in that, The system includes: The observation data input layer is used to obtain soil core chemical parameter values, soil management data, and preset multi-scenario data for test sites with pH>7; The mechanism model layer is used to analyze soil core chemical parameter values, soil management data, and preset multi-scenario data using the internal dual-carbon coupling model to obtain simulated curves of annual changes in the content of multiple functional carbon pools at the test sites; the dual-carbon coupling model is used to describe the co-evolution process of organic carbon and inorganic carbon. The Bayesian inversion layer is used to sample the values ​​of multiple parameters to be estimated in the dual-carbon coupling model based on the simulated curves of the annual variation of the contents of multiple functional carbon pools at the test sites and the measured curves of the annual variation of the contents of multiple functional carbon pools at the test sites, using the DREAMzs algorithm, to obtain the posterior distribution sample set of each parameter to be estimated. The posterior analysis and application layer is used to audit the credibility of the posterior distribution sample set of each parameter among multiple parameters to be estimated, and to analyze the environmental driving mechanism based on preset multi-scenario data, thereby obtaining identifiable information and parameter-environment response models. Based on the identifiable information and parameter-environment response models, uncertainty is transferred to obtain a carbon change prediction system for the test site with mechanistic interpretability and risk quantification capabilities.

2. The system according to claim 1, characterized in that, The core chemical parameters of the soil at the test site should include at least: the total annual soil carbon input, the content of particulate organic carbon (POC) at the current time, and the mineral-bound organic carbon. At the current time, the content, soil pH, and exogenous Ca content 2+ Annual input volume; Soil management data at the pilot sites should include at least: annual fertilization data, annual irrigation data, and annual climate data; The preset multi-scenario data of the test point at least includes: soil moisture, temperature, acidification, and Ca 2+ The input is data that drives the factors.

3. The system according to claim 1 or 2, characterized in that, The dual-carbon coupling model includes at least: the first mass conservation equation for particulate organic carbon (POC) and mineral-bound organic carbon (MAOC), and a characterization equation for inorganic carbon (SIC) and calcium ions (Ca). 2+ The second mass conservation equation relating the mass balance and transformation relationship between them; The first mass conservation equation includes at least the following: The second mass conservation equation includes at least the following: in, The state variables of the POC at the test site; For the test site at the current time Total annual soil carbon input; The decomposition rate of POC; The POC for the test site at the current time The content; The flux of organic matter adsorbed onto the carbonate surface at the test site The rate at which MAOC desorbs from the carbonate surface; MAOC at the test site at the current time The content; For the test point State variables; The proportion of POC decomposed to form MAOC; The decomposition rate of MAOC; The adsorption rate constant is denoted by . The saturation function of carbonate surface activity at the test site; is the half-saturation constant of the surface activity response function of carbonate minerals; SIC at the test site at the current time The content; The state variable is the solid inorganic carbon SiC produced at the test site after microbial-induced carbonate precipitation (MICP). The SiC mass conversion factor is the SIC mass conversion factor corresponding to a unit dissolved inorganic carbon (DIC) carbon flux. The actual carbonate precipitation rate at the test point after considering MICP; The dissolution rate of calcium carbonate (CaCO3) at the test site; The state variable of DIC generated at the test site after microbial-induced carbonate precipitation (MICP); The test site was located at the soil soluble / exchangeable Ca2+ level after microbial induced carbonate precipitation (MICP). 2+ State variables; For the test site, the external Ca 2+ Annual input volume; The maximum dynamic enhancement factor of MICP; The functional expression for microbial-induced carbonate precipitation (MICP) at the test site; The baseline carbonate precipitation rate; For carbonate precipitation rate The carbonate saturation index; For translation ReLU function; For the water in the sedimentation process Response function; The microbial biomass at the test site; It is the microbial half-saturation constant; , , The background of the soil Soil temperature and soil moisture The response function.

4. The system according to claim 3, characterized in that, in, This is the pH response slope parameter; The background pH at the test site; The optimal pH for the MICP process; Soil moisture at the test site; This represents the optimal soil moisture content for the MICP process.

5. The system according to claim 2, characterized in that, Exogenous Ca 2+ The annual input is: in, For the test site, the external Ca 2+ Annual input volume; , as well as They are pulsed Ca 2+ The function corresponding to the annual input, the continuous Ca 2+ The function corresponding to the annual input and the phased Ca 2+ The function corresponding to the annual input volume; For a single application of Ca 2+ quantity; A time variable measured in years; For application time; This is the Dirac impulse function, used to represent instantaneous input; For average Ca 2+ Input flux; The time corresponding to the maximum input; For periodicity; For the first Secondary Ca 2+ The intensity of investment; For the first Enter the time starting from the next step; It is the Heaviside step function; The number of times to input in stages.

6. The system according to claim 1, characterized in that, The Bayesian inversion layer is specifically used to determine the simulation error of each functional carbon at multiple time points within the year based on the simulated curves of the annual changes in the content of multiple functional carbon pools at the test site and the measured curves of the annual changes in the content of multiple functional carbon pools at the test site. Given that the simulation errors corresponding to the test points are all independent and simultaneously follow a Gaussian distribution, the likelihood functions corresponding to the measured annual variation curves of each functional carbon pool at the test points are generated. Logarithmic transformation of the likelihood function yields a log-likelihood function with each of the estimated parameters as independent variables. The DREAMzs algorithm is used to obtain the posterior distribution sample set of each of the multiple estimated parameters by random sampling, based on the log-likelihood function of each estimated parameter as an independent variable and prior knowledge.

7. The system according to claim 1, characterized in that, The posterior analysis and application layer includes: The parameter identifiability diagnostic sublayer is used to audit the credibility of the posterior distribution sample set to obtain identifiable information; wherein, the identifiable information includes at least: reliably identifiable parameters and their posterior statistical characteristics, parameters of unidentifiable problems and their causes; The environmental factor interpretation sublayer is used to perform multiple linear regression analysis on reliably identified parameters and their posterior statistical characteristics, as well as the pre-set multi-scenario data of the test points, to obtain a parameter-environment response model for quantifying the driving relationship and influence intensity of the pre-set multi-scenario data on parameter values. The uncertainty prediction sublayer is used to transfer and integrate uncertainty based on the parameter-environment response model, reliably identified parameters and their posterior statistical characteristics, through Monte Carlo simulation, to obtain a carbon change prediction system for the test sites with mechanistic interpretability and risk quantification capability.

8. A method for coupled dynamic simulation of soil organic carbon and inorganic carbon, characterized in that, The method includes: Obtain soil core chemical parameter values, soil management data, and preset multi-scenario data for test sites with pH > 7; Using a dual-carbon coupling model, we analyzed soil core chemical parameters, soil management data, and pre-set multi-scenario data to obtain simulated curves of annual changes in the content of multiple functional carbon pools at the experimental sites. The dual-carbon coupling model is used to describe the co-evolution process of organic carbon and inorganic carbon. Based on the simulated annual variation curves of multiple functional carbon pool contents at the test sites, as well as the measured annual variation curves of multiple functional carbon pool contents at the test sites, the DREAMzs algorithm is used to sample the values ​​of multiple parameters to be estimated in the dual-carbon coupling model, and the posterior distribution sample set of each parameter to be estimated is obtained. The credibility of the posterior distribution sample set of each of the multiple parameters to be estimated is audited, and the environmental driving mechanism is analyzed based on the preset multi-scenario data. The corresponding identifiable information and parameter-environment response model are obtained. Uncertainty is transferred based on the identifiable information and parameter-environment response model, resulting in a carbon change prediction system with mechanistic interpretability and risk quantification capability for the test site.

Images