A multi-process coupled soil multi-element biogeochemical cycle simulation method

Abstract

The application discloses a kind of multi-process coupling's soil multi-element biogeochemical cycle simulation method, comprising: S1, target area initialization and data input;S2, according to organic matter decomposition and mineralization, vegetation input and absorption, erosion and weathering input and dissolved leaching, simulate biogenic element cycle;S3, according to the input flux of metal, the flux of metal adsorption mediated by organic carbon, metal leaching migration, metal migration with soil erosion and metal vertical migration, simulate heavy metal element cycle;S4, according to the simulation process of S2 and S3 Dynamic update low-lying catchment and river network, recalculate water input flux;S5, according to the simulation process of S2-S4, dynamically output the reserve change of each element bank.The application does not depend on fixed area or resolution, but is driven by DEM, with good spatiotemporal scale adaptability, can be flexibly applied to soil process research under different landforms and climatic conditions.

Description

Technical Field

[0001] This application relates to the field of biogeochemical cycle research, specifically to a multi-process coupled soil multi-element biogeochemical cycle simulation method. Background Technology

[0002] Existing soil biogeochemical models primarily focus on simulating single elements or processes. For example, the CENTURY model simulates the dynamics of C, N, P, and S in soil and vegetation within natural or cultivated systems; its day-scale version, DayCent, provides a more detailed description of the C–N–P–S cycle and related greenhouse gas emissions. The Biome-BGC model mainly simulates the fluxes and storage of carbon, nitrogen, and water in terrestrial ecosystems. At the agricultural and watershed scales, the EPIC model integrates hydrological, crop growth, soil erosion, and nutrient cycling processes to simulate nitrogen and phosphorus inputs, outputs, and balances. The SWAT model couples hydrological cycling, sediment transport, and N and P cycles at the watershed scale for non-point source pollution assessment; however, its native version only supports point source inputs of heavy metals, requiring a dedicated module to handle heavy metal cycling. The RZWQM2 rhizosphere water quality model is a one-dimensional process model capable of simulating plant growth and the movement of water, nutrients (mainly nitrogen), and pesticides within the rhizosphere. Furthermore, the LEACHM model is a process-based model of the unsaturated soil zone, capable of simulating the transport, transformation, and uptake of water and solutes (including nutrients) by plants. While these models are widely used in academia and engineering, they often operate independently, focusing either on the C–N–P–S cycle or on the migration of single heavy metals, lacking a unified multi-element coupling framework. For example, while SWAT can handle pollutants such as phosphorus and nitrogen, it only addresses point-source heavy metal inputs; most models also fail to consider the feedback effects of slope or topographic changes on soil processes. Overall, existing models have shortcomings in multi-element coupling, spatial resolution, and topographic adaptability, thus requiring further research.

[0003] Application content

[0004] The purpose of this application is to provide a multi-process coupled method for simulating soil multi-element biogeochemical cycles, and the specific technical solution is as follows:

[0005] A multi-process coupled soil multi-element biogeochemical cycle simulation method includes: S1, target area initialization and data input; S2, simulating biogenic element cycle based on organic matter decomposition and mineralization, vegetation input and absorption, erosion and weathering input, and dissolved leaching; S3, simulating heavy metal element cycle based on metal input flux, organic carbon-mediated metal adsorption flux, metal leaching migration, metal migration with soil erosion, and metal vertical migration; S4, dynamically updating low-lying catchment points and river networks based on the simulation processes of S2 and S3, and then calculating water input flux; S5, dynamically outputting the storage changes of each element pool based on the simulation processes of S2-S4.

[0006] The initialization of the target area in S1 includes: calculating the slope and watershed confluence path based on the constructed rasterized spatial units.

[0007] Data input in S1 includes: inputting climate and hydrological data, inputting soil and bedrock properties, and inputting vegetation data; defining the feature library: organic / inorganic, C, N, P, and S, and heavy metals, Cd, Hg, and As.

[0008] S2 includes the following when simulating the cycle of student source elements:

[0009] S2.1, Organic-inorganic transformation is calculated using microbially driven decomposition and mineralization fluxes, expressed as:

[0010] ,

[0011] in, The decomposition rate constant is Organic matter content, This is a temperature correction factor. It is a slope inhibition factor;

[0012] S2.2 The role of vegetation is mainly manifested in the input of vegetation litter. and plant absorption Two parts, the expression is:

[0013] ,

[0014] ,

[0015] in, The annual turnover or litter ratio indicates how much of a unit of aboveground biomass becomes litter each year; Aboveground biomass; This is a correction factor for the impact of slope on litter retention or transport; The absorption coefficient; It serves as a reservoir of usable inorganic nutrients in the soil; Effective root depth indicates the depth from which a plant can absorb nutrients;

[0016] S2.3, The expression for erosion loss and bedrock weathering process is as follows:

[0017] ,

[0018] ,

[0019] in, The elemental flux carried away by soil erosion, The soil quality of the grid or plot that has been eroded. The element enrichment factor describes the enrichment or loss of elements in the suspended phase compared to the element concentration in the parent material during erosion. This refers to the mass of the element within this soil unit. This represents the total soil mass within the soil unit. The amount of elements carried by new soil or dissolved substances produced and introduced into the soil by bedrock weathering. For weathering rate, The mass concentration of this element in the parent rock and it needs to be compared with... Conversions should be performed consistently using the same units;

[0020] S2.4, Leaching and Runoff Outflow Process: The flux of dissolved inorganic nutrients leached into water bodies by runoff (from soil ponds to water input) is expressed as follows:

[0022] ,

[0023] in, For soil inorganic pool, For runoff volume, The leaching coefficient is denoted as .

[0024] S3 includes the following when simulating the cycling of heavy metal elements:

[0025] S3.1 The expression for the sum of the input fluxes of metals is:

[0026] ,

[0027] in, For total input, For wet settling input, Metal input due to baseflow / groundwater upwelling It was introduced by bedrock weathering;

[0028] S3.2. The adsorption saturation effect of organic carbon-mediated metal adsorption flux is described using Michaelis–Menten type kinetics, and the expression is:

[0029] ,

[0030] in, For adsorption flux, For the maximum adsorption rate, The concentration of dissolved metals, It is the half-saturation constant;

[0031] S3.3. The Hill equation is used to characterize the synergistic / threshold effect of dissolved metals in leaching / migration. The expression is:

[0032] ,

[0033] in, This refers to the mass flux of metal leaching. This refers to the concentration of dissolved metals. This is the half-saturation constant, corresponding to the characteristic concentration of the Hill curve; The Hill index determines the steepness of the response; This refers to the runoff volume, used to convert concentration into mass.

[0034] S3.4 The nonlinear relationship between the amount of metal loss due to erosion and the soil erosion rate describes the nonlinear amplification or attenuation of element loss with increasing erosion intensity. Its expression is:

[0035] ,

[0036] in, This refers to the mass of metal that is carried away by corrosion. As a scale factor, The quality of soil erosion, It is a power exponent, commonly used or ;

[0037] S3.5. The vertical migration process of metals was investigated by establishing a one-dimensional diffusion equation and numerically solving it using the explicit finite difference method to calculate the evolution of metal concentration over time in different soil layers; among which...

[0038] One-dimensional vertical diffusion controls the migration of metals in a soil profile, and its expression is as follows:

[0039] ,

[0040] in, For a certain depth Time Metal concentration; The effective diffusion coefficient represents the "effective" vertical migration rate under the combined effects of soil porosity and adsorption-desorption, and can be estimated empirically or obtained through inversion. Using depth coordinates, downward is the accepted positive direction; The time step unit is used; this expression can be discretized into a display format to solve for numerical solutions:

[0041] .

[0042] The expression for the water input flux in S4 is:

[0043] ,

[0044] in, The summation is the annual input per unit area or a catchment unit; the summation object is usually all leaching and particle / solute transport contributions in the upstream or within the watershed / grid.

[0045] The advantages of this application lie in its independence from fixed regions or resolutions. Driven by a DEM (Digital Elevation Model), it exhibits excellent spatiotemporal scale adaptability and can be flexibly applied to soil process studies under diverse geomorphological and climatic conditions. Within a unified simulation framework spanning multiple factors and processes, this method provides a reliable computational tool for watershed management, agricultural production, ecosystem evolution, and soil pollution risk assessment. It not only overcomes the shortcomings of existing models in multi-factor coupling, topographic response, and vertical migration simulation but also validates its scientific rationality and application prospects through comparison with independent data and existing research.

[0046] Instruction manual illustrations

[0047] Figure 1 This is a schematic diagram of the method flow of the present invention.

[0048] Figure 2 The following are the long-term evolution of the major organic and inorganic nutrient pools in the top 30 cm soil simulated in this application: (a) Spatial distribution of average soil organic carbon (SOC) storage from 1901 to 2020; (b) Total change of SOC pool over 120 years; (c) Temporal trend of global annual average values ​​of the three organic nutrient pools: soluble organic nitrogen (DON), organic phosphorus (POP), and organic sulfur (Sorg); (d) Temporal trend of global annual average values ​​of the three inorganic nutrient pools: inorganic nitrogen (DIN), inorganic phosphorus (Pi), and inorganic sulfur (Sinorg).

[0049] Figure 3The temporal and vertical evolution characteristics of global topsoil heavy metal reserves from 1901 to 2020 are shown in (a, c) and (b, d) respectively. The spatial distribution of the annual average rate of change of Cd and Hg reserves is shown in (b, d) respectively.

[0050] Figure 4 This is a scatter plot showing the density of SOC storage in topsoil (0-30 cm) compared with the simulation results of this application and the SOC storage observed by WOSIS. Specific Implementation

[0051] To make the objectives, technical solutions, and advantages of this application clearer, the application will be further described in detail below with reference to specific embodiments and accompanying drawings. It should be understood that these descriptions are merely exemplary and not intended to limit the scope of this application. Furthermore, descriptions of well-known structures and technologies are omitted in the following description to avoid unnecessarily obscuring the concepts of this application.

[0052] like Figure 1 As shown:

[0053] A multi-process coupled method for simulating soil multi-element biogeochemical cycles, comprising:

[0054] S1. Target Area Initialization and Data Input. Target area initialization includes calculating slope and watershed runoff paths based on the constructed rasterized spatial units. Data input includes: inputting climate and hydrological data (rainfall, temperature, runoff, groundwater recharge); inputting soil and bedrock properties (initial thickness, texture, parent rock weathering rate, initial element concentration); inputting vegetation data (litter amount, root depth, absorption coefficient, etc.); defining the element library: organic / inorganic C, N, P, S, and heavy metals such as Cd, Hg, and As.

[0055] S2. Simulate the cycle of biogenic elements based on organic matter decomposition and mineralization, vegetation input and absorption, erosion and weathering input, and dissolved leaching. The simulation of the biogenic element cycle includes:

[0056] S2.1, Organic-inorganic transformation is calculated using microbially driven decomposition and mineralization fluxes, expressed as:

[0057] ,

[0058] in, The decomposition rate constant is Organic matter content, This is a temperature correction factor. It is a slope inhibition factor;

[0059] S2.2 The role of vegetation is mainly manifested in the input of vegetation litter. and plant absorption Two parts, the expression is:

[0060] ,

[0061] ,

[0062] in, The annual turnover or litter ratio indicates how much of a unit of aboveground biomass becomes litter each year; Aboveground biomass; This is a correction factor for the impact of slope on litter retention or transport; The absorption coefficient; It serves as a reservoir of usable inorganic nutrients in the soil; Effective root depth indicates the depth from which a plant can absorb nutrients;

[0063] S2.3, The expression for erosion loss and bedrock weathering process is as follows:

[0064] ,

[0065] ,

[0066] in, The elemental flux carried away by soil erosion, The soil quality of the grid or plot that has been eroded. The element enrichment factor describes the enrichment or loss of elements in the suspended phase compared to the element concentration in the parent material during erosion. This refers to the mass of the element within this soil unit. This represents the total soil mass within the soil unit. The amount of elements carried by new soil or dissolved substances produced and introduced into the soil by bedrock weathering. For weathering rate, The mass concentration of this element in the parent rock and it needs to be compared with... Conversions should be performed consistently using the same units;

[0067] S2.4, Leaching and Runoff Outflow Process: The flux of dissolved inorganic nutrients leached into water bodies by runoff (from soil ponds to water input) is expressed as follows:

[0068] ,

[0069] in, For soil inorganic pool, For runoff volume, The leaching coefficient is denoted as .

[0070] S3. Simulate heavy metal element cycling based on metal input flux, organic carbon-mediated metal adsorption flux, metal leaching migration, metal migration via soil erosion, and vertical metal migration. The simulation of heavy metal element cycling includes:

[0071] S3.1 The expression for the sum of the input fluxes of metals is:

[0072] ,

[0073] in, For total input, For wet settling input, Metal input due to baseflow / groundwater upwelling It was introduced by bedrock weathering;

[0074] S3.2. The adsorption saturation effect of organic carbon-mediated metal adsorption flux is described using Michaelis–Menten type kinetics, and the expression is:

[0075] ,

[0076] in, For adsorption flux, For the maximum adsorption rate, The concentration of dissolved metals, It is the half-saturation constant;

[0077] S3.3. The Hill equation is used to characterize the synergistic / threshold effect of dissolved metals in leaching / migration. The expression is:

[0078] ,

[0079] in, This refers to the mass flux of metal leaching. This refers to the concentration of dissolved metals. This is the half-saturation constant, corresponding to the characteristic concentration of the Hill curve; The Hill index determines the steepness of the response; This refers to the runoff volume, used to convert concentration into mass.

[0080] S3.4 The nonlinear relationship between the amount of metal loss due to erosion and the soil erosion rate describes the nonlinear amplification or attenuation of element loss with increasing erosion intensity. Its expression is:

[0081] ,

[0082] in, This refers to the mass of metal that is carried away by corrosion. As a scale factor, The quality of soil erosion, It is a power exponent, commonly used or ;

[0083] S3.5. The vertical migration process of metals was investigated by establishing a one-dimensional diffusion equation and numerically solving it using the explicit finite difference method to calculate the evolution of metal concentration over time in different soil layers; among which...

[0084] One-dimensional vertical diffusion controls the migration of metals in a soil profile, and its expression is as follows:

[0085] ,

[0086] in, For a certain depth Time Metal concentration; The effective diffusion coefficient represents the "effective" vertical migration rate under the combined effects of soil porosity and adsorption-desorption, and can be estimated empirically or obtained through inversion. Using depth coordinates, downward is the accepted positive direction; The time step unit is used; this expression can be discretized into a display format to solve for numerical solutions:

[0087] .

[0088] S4. Based on the simulation process in S2 and S3, dynamically update the low-lying catchment points and river network, and then calculate the water input flux. The expression for the water input flux is:

[0089] ,

[0090] in, The summation is the annual input per unit area or a catchment unit; the summation object is usually all leaching and particle / solute transport contributions in the upstream or within the watershed / grid.

[0091] S5. Based on the simulation process in S2-S4, dynamically output the changes in reserves of each element library. Output flux results (erosion, weathering, deposition, leaching); output dynamic distribution map of metal concentration in soil profiles with depth; output time-series data and spatial distribution map.

[0092] To make this application easier to understand, practical application examples are provided below.

[0093] In one embodiment, a global land grid (0.5° × 0.5° resolution) is selected as the study area. A digital elevation model (DEM) is used to calculate the slope and runoff paths of each grid cell, and climate datasets (rainfall, temperature, runoff, etc.) and soil property data (initial SOC storage, texture, thickness, etc.) are overlaid. Bedrock chemical composition is used to provide weathering input, while rainfall and wet deposition data drive the input fluxes of nitrogen, phosphorus, sulfur, and heavy metals. Vegetation input is obtained through litter quantity and vegetation type parameterization, and plant uptake is determined by root depth and uptake coefficient.

[0094] During the simulation, the biogenic element (C, N, P, S) pool is updated through organic matter decomposition and mineralization (step 2.1), vegetation input and absorption (step 2.2), erosion and weathering input (step 2.3), and dissolved leaching (step 2.4). Heavy metals (Cd, Hg, As) are dynamically calculated through input (step 3.1), organic matter-mediated adsorption (step 3.2), leaching migration (step 3.3), erosion loss (step 3.4), and vertical migration simulated by a one-dimensional diffusion equation (step 3.5). For each time step, elemental mass balance is ensured through conservation constraints. The water input flux is calculated in step 4.

[0095] To verify the feasibility of this application, long-term simulation experiments were conducted from 1901 to 2020. The results show that the multi-year average storage of topsoil organic carbon is approximately 977 Pg, consistent with the estimated range of existing datasets (577–1171 Pg). Under global warming conditions of approximately 1.0–1.2 °C, the simulated total SOC loss over 120 years is 375 Pg, highly consistent with the estimates from independent studies (approximately 343 ± 67 Pg), demonstrating that this invention can reliably characterize the soil carbon cycle on a global scale.

[0096] Regarding nutrient cycling, simulation results show that the organic nitrogen, phosphorus, and sulfur pools have generally increased over the past century, while the inorganic nitrogen, phosphorus, and sulfur pools have exhibited a temporal pattern of first decreasing and then increasing. This reflects that this method can effectively reveal the dynamic differences in organic-inorganic transformation and leaching migration among different elements.

[0097] Regarding heavy metal cycling, simulation results show that Cd and Hg exhibit an accumulation trend in global soils, with average annual increases of +721 mg·ha⁻¹·yr⁻¹ and +320 mg·ha⁻¹·yr⁻¹, respectively. Spatially, high accumulation areas are distributed in tropical regions such as Southeast Asia, Central Africa, and northern South America. Vertically, Cd and Hg are significantly enriched in the surface layer, while the changes are smaller in the middle and lower layers, indicating that this method can effectively capture the differentiation characteristics of heavy metals in soil profiles.

[0098] Therefore, the method proposed in this invention can simultaneously simulate multi-element cycles such as C–N–P–S and Cd / Hg / As within a unified framework. The results are consistent with observations and independent studies, proving the scientific rationality and application value of the method.

[0099] The model's simulation results for soil organic carbon (SOC) storage are consistent with authoritative global datasets and existing research findings. The simulated total surface SOC shows moderate spatial consistency with the WOSIS dataset (correlation coefficient R²≈0.39) and is highly consistent with recent global SOC storage estimation intervals (e.g., ...). Figure 2 and 4 (As shown). In long-term simulations from 1901 to 2020, the total SOC loss obtained by this method was approximately 375 Pg, which is consistent with the independent estimate based on SoilGrids (343±67 Pg C loss under a 1 ℃ heating condition), verifying the reliability of the model on a global scale (e.g., Figure 2 (As shown).

[0100] In addition to the carbon cycle, the temporal evolution of nutrient pools such as nitrogen, phosphorus, and sulfur has also been systematically characterized. Simulations show that organic nitrogen (DON), organic phosphorus (POP), and organic sulfur (Sorg) have generally shown an upward trend over the past century, with average annual growth rates of 1.19%, 0.24%, and 0.40%, respectively; while inorganic nitrogen (DIN), inorganic phosphorus (Pi), and inorganic sulfur (Sinorg) declined before the 1920s and then continued to rise, with average annual growth rates of 0.41%, 0.08%, and 0.19%, respectively (e.g., ...). Figure 2 (As shown). These differentiated trends reflect the comprehensive simulation capabilities of the method of this invention in processes such as organic-inorganic transformation, leaching migration, and plant action, and can reveal the dominant driving mechanisms of different element cycling processes in the context of climate change and land use evolution.

[0101] This invention, for the first time, achieves two-dimensional spatial distribution simulation and vertical profile migration simulation of heavy metals within the same framework. The results show that the global soil stock of both Cd and Hg exhibits an accumulating trend, with annual average rates of change of +721 mg·ha⁻¹·yr⁻¹ and +320 mg·ha⁻¹·yr⁻¹, respectively. High accumulation areas are mainly distributed in tropical regions such as Southeast Asia, Central Africa, and northern South America, reflecting the responsiveness of this method to rainfall leaching and vegetation input (e.g., Figure 3 (As shown). In terms of vertical distribution, the simulation results show that Cd and Hg are significantly enriched in the topsoil, with Hg showing a more prominent near-surface accumulation signal, while the storage in the middle and lower layers changes less, indicating that the model can capture the differentiation characteristics of heavy metals in the soil profile.

[0102] This method, which does not rely on a fixed region or resolution but is driven by a DEM, exhibits good spatiotemporal scale adaptability and can be flexibly applied to soil process studies under different geomorphological and climatic conditions. Within a unified simulation framework spanning multiple elements and processes, this method provides a reliable computational tool for watershed management, agricultural production, ecosystem evolution, and soil pollution risk assessment.

[0103] In summary, this invention not only overcomes the shortcomings of existing models in multi-factor coupling, terrain response, and vertical migration simulation, but also verifies its scientific rationality and application prospects through comparison with independent data and existing research.

Claims

1. A method for simulating multi-process coupled soil multi-element biogeochemical cycles, characterized in that, include: S1. Initialization and data input of the target area; S2. Simulate the cycle of biological elements based on organic matter decomposition and mineralization, vegetation input and absorption, erosion and weathering input, and dissolved leaching. S3. Simulate the heavy metal element cycle based on the metal input flux, organic carbon-mediated metal adsorption flux, metal leaching migration, metal migration with soil erosion, and metal vertical migration. S4. Dynamically update the low-lying catchment points and river network based on the simulation process of S2 and S3, and then calculate the water input flux. S5. Based on the simulation process described in S2-S4, dynamically output the changes in the reserves of each element database.

2. The method for simulating multi-process coupled soil multi-element biogeochemical cycles as described in claim 1, characterized in that, The initialization of the target area in S1 includes: calculating the slope and the watershed confluence path based on the constructed rasterized spatial units.

3. The method for simulating multi-process coupled soil multi-element biogeochemical cycles as described in claim 2, characterized in that, The data input in S1 includes: Input climate and hydrological data, soil and bedrock properties, and vegetation data; define the feature library: organic / inorganic, C, N, P, and S, and heavy metals, Cd, Hg, and As.

4. The method for simulating multi-process coupled soil multi-element biogeochemical cycles as described in claim 3, characterized in that, The simulation of the student source element cycle in S2 includes: S2.1, Organic-inorganic transformation is calculated using microbially driven decomposition and mineralization fluxes, expressed as: ， in, The decomposition rate constant is Organic matter content, This is a temperature correction factor. It is a slope inhibition factor; S2.2 The role of vegetation is mainly manifested in the input of vegetation litter. and plant absorption Two parts, the expression is: ， ， in, The annual turnover or litter ratio indicates how much of a unit of aboveground biomass becomes litter each year; Aboveground biomass; This is a correction factor for the impact of slope on litter retention or transport; The absorption coefficient; It serves as a reservoir of usable inorganic nutrients in the soil; Effective root depth indicates the depth from which a plant can absorb nutrients; S2.3, The expression for erosion loss and bedrock weathering process is as follows: ， ， in, The elemental flux carried away by soil erosion, The soil quality of the grid or plot that has been eroded. The element enrichment factor describes the enrichment or loss of elements in the suspended phase compared to the element concentration in the parent material during erosion. This refers to the mass of the element within this soil unit. This represents the total soil mass within the soil unit. The amount of elements carried by new soil or dissolved substances produced and introduced into the soil by bedrock weathering. For weathering rate, The mass concentration of this element in the parent rock and it needs to be compared with... Conversions should be performed consistently using the same units; S2.4, Leaching and Runoff Outflow Process: The flux of dissolved inorganic nutrients leached into water bodies by runoff (from soil ponds to water input) is expressed as follows: ， in, For soil inorganic pool, For runoff volume, The leaching coefficient is denoted as .

5. The method for simulating multi-process coupled soil multi-element biogeochemical cycles as described in claim 4, characterized in that, The simulation of heavy metal element cycling in S3 includes: S3.1 The expression for the sum of the input fluxes of metals is: ， in, For total input, For wet settling input, Metal input due to baseflow / groundwater upwelling It was introduced by bedrock weathering; S3.

2. The adsorption saturation effect of organic carbon-mediated metal adsorption flux is described using Michaelis–Menten type kinetics, and the expression is: ， in, For adsorption flux, For the maximum adsorption rate, The concentration of dissolved metals, It is the half-saturation constant; S3.

3. The Hill equation is used to characterize the synergistic / threshold effect of dissolved metals in leaching / migration. The expression is: ， in, This represents the mass flux of metal leaching. This refers to the concentration of dissolved metals. This is the half-saturation constant, corresponding to the characteristic concentration of the Hill curve; The Hill index determines the steepness of the response; This refers to the runoff volume, used to convert concentration into mass. S3.4 The nonlinear relationship between the amount of metal loss due to erosion and the soil erosion rate describes the nonlinear amplification or attenuation of element loss with increasing erosion intensity. Its expression is: ， in, This refers to the mass of metal that is carried away by corrosion. As a scale factor, The quality of soil erosion, It is a power exponent, commonly used or ; S3.

5. The vertical migration process of metals was investigated by establishing a one-dimensional diffusion equation and numerically solving it using the explicit finite difference method to calculate the evolution of metal concentration over time in different soil layers; among which... One-dimensional vertical diffusion controls the migration of metals in a soil profile, and its expression is as follows: ， in, For a certain depth Time Metal concentration; The effective diffusion coefficient represents the "effective" vertical migration rate under the combined effects of soil porosity and adsorption-desorption, and can be estimated empirically or obtained through inversion. Using depth coordinates, downward is the accepted positive direction; The time step unit is used; this expression can be discretized into a display format to solve for numerical solutions: 。 6. The method for simulating multi-process coupled soil multi-element biogeochemical cycles as described in claim 5, characterized in that, The expression for the water input flux in S4 is: ， in, The summation is the annual input per unit area or a catchment unit; the summation object is usually all leaching and particle / solute transport contributions in the upstream or within the watershed / grid.

Images