A water and soil regulation method for a grid watershed in a black soil area based on freeze-thaw cycles

Abstract

The application discloses a kind of black soil area grid watershed water and soil regulation methods based on freeze-thaw cycle, comprising the following steps: S1: the configuration unit division of the to-be-configured watershed is carried out, the water supply-demand relationship between each configuration unit is determined, and a plurality of regional water and soil resource configuration grid units are generated, the spatial position between water and soil resource configuration grid units and the topological relationship of water and soil resource configuration grid units are confirmed;S2: typical soil sampling test is selected for the spatial position between water and soil resource configuration grid units grid, soil quality evaluation is carried out, and a typical soil database in black soil area is established;S3: a distributed hydrological model considering freeze-thaw cycle is constructed based on the typical soil database in black soil area, and calibration verification is carried out;The problem of considering the whole watershed multi-water source irrigation process from the perspective of gridding is solved.

Description

Technical Field

[0001] This invention belongs to the field of agricultural water management technology, specifically relating to a method for water and soil regulation in grid watersheds in black soil regions based on freeze-thaw cycles. Background Technology

[0002] As the world's largest agricultural economy, my country's agricultural water consumption reached 367.24 billion cubic meters in 2023. 3 The black soil region accounts for 62.3% of the country's total water consumption, but its effective utilization coefficient for irrigation water is only 0.576, highlighting the acute contradiction between inefficient water resource utilization and the demand for grain production capacity. As my country's most important commodity grain base, the black soil region undertakes about one-fifth of the country's grain production, and its water and soil resource protection is directly related to national food security. However, the unique hydrological characteristics of the cold black soil basin pose severe challenges: large freeze-thaw depth, abundant winter snow cover, and two flood seasons in spring and summer. Considering the heavy texture, high organic matter content, and significant swelling and shrinking characteristics of black soil, the water cycle process in the basin is complex and variable, making traditional management models difficult to adapt to its unique water and soil response mechanism.

[0003] As a complete hydrological unit, the watershed's trans-regional water cycle mechanism directly impacts sustainable agricultural development. The current watershed management model suffers from three imbalances: first, a spatial imbalance, where extensive scheduling ignores the dramatic fluctuations in surface water-groundwater interaction during the freeze-thaw cycle in the black soil region (such as the sudden increase in base flow caused by spring snowmelt); second, a process-based imbalance, failing to incorporate quantitative characterization of freeze-thaw physical processes into the regulation process; and third, an objective-based imbalance, overemphasizing agricultural economic output while lacking dynamic assessment of soil quality at the watershed scale, leading to a continuous decline in soil quality and a persistent accumulation of black soil degradation risks in the black soil region.

[0004] Current technologies struggle to achieve coordinated regulation of water and soil resources at the watershed scale while maintaining stable groundwater levels and ensuring soil quality. Furthermore, current technologies lack multi-scale coordination; watershed-level scheduling can only optimize water and soil resource allocation within the entire watershed and administrative divisions, failing to capture the correlation between water resource allocation and pollutant diffusion within a 500m×500m grid. This severely restricts the coordinated regulation capacity of the watershed's "water resources-black soil-food security" system. Therefore, it is urgent to consider the entire watershed's multi-source irrigation process from a gridded perspective. Summary of the Invention

[0005] To address the aforementioned technical problems, this invention proposes a method for water and soil regulation in a grid-based watershed in black soil regions based on freeze-thaw cycles, which solves the problem of considering the multi-source irrigation process of the entire watershed from a gridded perspective.

[0006] The technical solution of the present invention is as follows:

[0007] A method for soil and water regulation in a grid-based watershed in black soil regions based on freeze-thaw cycles includes the following steps:

[0008] S1: Divide the watershed to be configured into configuration units, determine the water supply and demand relationship between each configuration unit, generate several regional water and soil resource configuration grid units, and confirm the spatial location and topological relationship between the water and soil resource configuration grid units.

[0009] S2: Select typical soil sampling tests in the black soil region based on the spatial location between the grid units of the water and soil resource allocation grid, conduct soil quality evaluation, and establish a typical soil database of the black soil region.

[0010] S3: Construct a regional distributed hydrological model considering freeze-thaw cycles based on a typical soil database of the black soil region, and conduct calibration and verification.

[0011] S4: Obtain watershed monitoring data, and based on the watershed monitoring data and a distributed hydrological simulation model considering freeze-thaw cycles, obtain the regional water supply boundary conditions;

[0012] S5: Based on the grid cell topology of water and soil resource allocation, construct a distributed hydrological simulation model that considers the diffusion of non-point source pollution, and conduct calibration and verification.

[0013] S6: Obtain the relationship between grid water volume and non-point source pollution load based on the distributed hydrological simulation model that considers the diffusion of non-point source pollution;

[0014] S7: Construct a grid-watershed two-layer multi-objective multi-source grid optimization model, solve the model based on the watershed water resource allocation boundary conditions and the grid water volume-non-point source pollution load relationship, and obtain a preliminary grid-watershed water and soil resource allocation scheme;

[0015] S8: Construct a model verification system to verify the preliminary grid-watershed water and soil resource allocation scheme. If the verification fails, recalibrate and optimize the boundary conditions of the model and solve the model until it meets the feasibility requirements.

[0016] Preferably, S1 includes the following sub-steps:

[0017] S11: Vectorize the information of different levels of water system in the basin and establish a water resources allocation database for the basin;

[0018] S12: Divide the watershed to be allocated into several identical grids and number them, identify the effective allocation units, generate regional water and soil resource allocation grid units, and confirm the spatial location and topological relationship between water and soil resource allocation units.

[0019] Preferably, S2 includes the following sub-steps:

[0020] S21: Sampling and monitoring points were selected for three typical soils in the black soil region: black soil, albic soil, and black calcareous soil. The bulk density, organic carbon content, saturated hydraulic conductivity, electrical conductivity, calcium carbonate content, and pH of the three soils were measured.

[0021] S22: Using the coefficient of variation method, five typical representative soils of each of the three soil types were formed, and a typical soil database was constructed according to the form required by the distributed hydrological model.

[0022] Preferably, S3 includes the following sub-steps:

[0023] S31: Construct a distributed hydrological simulation model for the region that takes into account the freeze-thaw cycle, and obtain basic regional data, including meteorological and hydrological data, freeze-thaw transformation data, digital elevation data, land use data, and soil type data.

[0024] S32: Define the river network based on the analysis of regional topography using digital elevation data, divide it into sub-basins, and calculate the parameters of each sub-basin;

[0025] S33: Based on soil type data, land use data, and slope data, divide the hydrological response units, input meteorological and hydrological data and freeze-thaw conversion data into the regional distributed hydrological simulation model, calculate the flow rate of snowmelt water within the hydrological response unit, and obtain the watershed water resources.

[0026] S34: Perform parameter calibration and adjustment of the regional distributed hydrological simulation model by combining regional meteorological and hydrological data.

[0027] Preferably, S5 specifically includes:

[0028] Based on the topological relationship of the grid units for regional water and soil resource allocation and the watershed water resource allocation database, the grid pollution load is identified, and a grid non-point source pollution diffusion model is constructed.

[0029] Preferably, S6 includes the following sub-steps:

[0030] S61: Obtain long-term hydrological observation data of the watershed, input the watershed observation data into the watershed distributed hydrological simulation model, and obtain the boundary conditions of the watershed surface water available water volume.

[0031] S62: Run the hydrological simulation model that considers the diffusion of non-point source pollution multiple times to obtain the grid-water non-point source pollution load relationship.

[0032] Preferably, in S7:

[0033] The grid scale constraints of the grid-watershed two-layer multi-objective multi-water source grid optimization model include total water resources constraints, groundwater constraints, surface water constraints, total planting area constraints of the zone, and food demand constraints.

[0034] The watershed-scale constraints of the grid-watershed two-layer multi-objective multi-source grid optimization model include total water resources constraints, groundwater constraints, surface water constraints, and sub-watershed water resources constraints.

[0035] Preferably, the process of constructing the model verification system described in S8 includes:

[0036] S81: Construct a non-point source pollution load assessment system and determine the grid non-point source pollution load threshold;

[0037] S82: Construct a groundwater simulation model and determine the groundwater level threshold of the watershed by running the groundwater simulation model;

[0038] S83: Construct a sustainable evaluation index system for soil quality and determine soil quality thresholds;

[0039] S84: A model verification system is constructed based on grid-based non-point source pollution load threshold, watershed groundwater level threshold, and watershed soil quality threshold.

[0040] Preferably, the indicators in the non-point source pollution load assessment system of S81 include: total nitrogen emissions and total phosphorus emissions;

[0041] The sustainable indicators in the soil quality sustainable evaluation index system of S83 include: soil erosion modulus, soil organic carbon storage, soil nutrient balance index, soil heavy metal pollution index, and land crop production potential.

[0042] The beneficial effects of the present invention on the grid-based water and soil regulation method for black soil regions based on freeze-thaw cycles are as follows:

[0043] 1. This invention establishes a grid-watershed two-layer multi-objective multi-source grid optimization model and develops freeze-thaw-hydrology-pollution coupled simulation and dynamic feedback control technology.

[0044] 2. The model of this invention innovatively and synergistically considers the heat transfer and water phase change mechanism of the freeze-thaw cycle, the diffusion process of non-point source pollution, and the soil characteristics of the black soil region, comprehensively depicting the dynamic response process of watershed water and soil resources.

[0045] 3. This invention realizes the optimal water and soil resource allocation scheme under multi-objective collaboration by constructing a grid-watershed two-layer multi-objective multi-source grid optimization model.

[0046] 4. This invention, from the perspective of gridded precise control, for the first time embeds the freeze-thaw process and the non-point source pollution diffusion function into a "grid-watershed" two-layer optimization framework, and dynamically adjusts the water resource allocation strategy through a simulation-optimization cyclic feedback mechanism.

[0047] 5. The three-level dynamic constraint system built into the model of this invention, namely "grid-pollution load, grid-soil quality, and watershed-groundwater level", can effectively ensure the feasibility and ecological safety of the scheme, and give priority to the protection of ecologically fragile areas such as groundwater funnel areas.

[0048] 6. The model of this invention is applicable to cold black soil watersheds and has unique adaptability to the bimodal runoff characteristics during the freeze-thaw period and the properties of black soil. It provides watershed managers with a scientific decision support tool, helps to formulate water and soil resource management strategies that take into account both food security and black soil protection, and promotes sustainable agricultural development. Attached Figure Description

[0049] To more clearly illustrate the purpose, design concept, and innovation of the proposed method for soil and water regulation in black soil regions based on freeze-thaw cycles, the invention will be described in detail below with reference to the accompanying drawings and tables.

[0050] Figure 1 This is a flowchart of the present invention. Detailed Implementation

[0051] The present invention will now be described in detail with reference to the accompanying drawings and specific embodiments.

[0052] The specific embodiments of the present invention are described below to enable those skilled in the art to understand the present invention. However, it should be understood that the present invention is not limited to the scope of the specific embodiments. For those skilled in the art, various changes are obvious as long as they are within the spirit and scope of the present invention as defined and determined by the appended claims. All inventions utilizing the concept of the present invention are protected.

[0053] A method for soil and water regulation in a grid-based watershed in black soil regions based on freeze-thaw cycles includes the following steps:

[0054] S1: Divide the watershed to be configured into configuration units, determine the water supply and demand relationship between each configuration unit, generate several regional water and soil resource configuration grid units, and confirm the spatial location and topological relationship between the water and soil resource configuration grid units.

[0055] S2: Select typical soil sampling tests in the black soil region based on the spatial location between the grid units of the water and soil resource allocation grid, conduct soil quality evaluation, and establish a typical soil database of the black soil region.

[0056] S3: Construct a regional distributed hydrological model considering freeze-thaw cycles based on a typical soil database of the black soil region, and conduct calibration and verification.

[0057] S4: Obtain watershed monitoring data, and based on the watershed monitoring data and a distributed hydrological simulation model considering freeze-thaw cycles, obtain the regional water supply boundary conditions;

[0058] S5: Based on the grid cell topology of water and soil resource allocation, construct a distributed hydrological simulation model that considers the diffusion of non-point source pollution, and conduct calibration and verification.

[0059] S6: Obtain the relationship between grid water volume and non-point source pollution load based on the distributed hydrological simulation model that considers the diffusion of non-point source pollution;

[0060] S7: Construct a grid-watershed two-layer multi-objective multi-source grid optimization model, solve the model based on the watershed water resource allocation boundary conditions and the grid water volume-non-point source pollution load relationship, and obtain a preliminary grid-watershed water and soil resource allocation scheme;

[0061] The proposed implementation plan establishes a model verification system to verify the preliminary grid-watershed water and soil resource allocation scheme. If the verification fails, the boundary conditions of the optimized model are recalibrated and the model is solved until it meets the feasibility requirements.

[0062] S1 of this implementation plan includes the following sub-steps:

[0063] S11: Vectorize the information of different levels of water system in the basin and establish a water resources allocation database for the basin;

[0064] S12: Divide the watershed to be allocated into several identical grids and number them, identify the effective allocation units, generate regional water and soil resource allocation grid units, and confirm the spatial location and topological relationship between water and soil resource allocation units.

[0065] S2 of this implementation plan includes the following sub-steps:

[0066] S21: Sampling and monitoring points were selected for three typical soils in the black soil region: black soil, albic soil, and black calcareous soil. The bulk density, organic carbon content, saturated hydraulic conductivity, electrical conductivity, calcium carbonate content, and pH of the three soils were measured.

[0067] S22: Using the coefficient of variation method, five typical representative soils of each of the three soil types were formed, and a typical soil database was constructed according to the form required by the distributed hydrological model.

[0068] S3 of this implementation plan includes the following sub-steps:

[0069] S31: Construct a distributed hydrological simulation model for the region that takes into account the freeze-thaw cycle, and obtain basic regional data, including meteorological and hydrological data, freeze-thaw transformation data, digital elevation data, land use data, and soil type data.

[0070] S32: Define the river network based on the analysis of regional topography using digital elevation data, divide it into sub-basins, and calculate the parameters of each sub-basin;

[0071] S33: Based on soil type data, land use data, and slope data, divide the hydrological response units, input meteorological and hydrological data and freeze-thaw conversion data into the regional distributed hydrological simulation model, calculate the flow rate of snowmelt water within the hydrological response unit, and obtain the watershed water resources.

[0072] S34: Perform parameter calibration and adjustment of the regional distributed hydrological simulation model by combining regional meteorological and hydrological data.

[0073] S5 of this implementation plan is specifically as follows:

[0074] Based on the topological relationship of the grid units for regional water and soil resource allocation and the watershed water resource allocation database, the grid pollution load is identified, and a grid non-point source pollution diffusion model is constructed.

[0075] S6 of this implementation plan includes the following sub-steps:

[0076] S61: Obtain long-term hydrological observation data of the watershed, input the watershed observation data into the watershed distributed hydrological simulation model, and obtain the boundary conditions of the watershed surface water available water volume.

[0077] S62: Run the hydrological simulation model that considers the diffusion of non-point source pollution multiple times to obtain the grid-water non-point source pollution load relationship.

[0078] In S7 of this implementation plan:

[0079] The grid scale constraints of the grid-watershed two-layer multi-objective multi-water source grid optimization model include total water resources constraints, groundwater constraints, surface water constraints, total planting area constraints of the zone, and food demand constraints.

[0080] The watershed-scale constraints of the grid-watershed two-layer multi-objective multi-source grid optimization model include total water resources constraints, groundwater constraints, surface water constraints, and sub-watershed water resources constraints.

[0081] The process of constructing the model verification system described in S8 of this implementation scheme includes:

[0082] S81: Construct a non-point source pollution load assessment system and determine the grid non-point source pollution load threshold;

[0083] S82: Construct a groundwater simulation model and determine the groundwater level threshold of the watershed by running the groundwater simulation model;

[0084] S83: Construct a sustainable evaluation index system for soil quality and determine soil quality thresholds;

[0085] S84: A model verification system is constructed based on grid-based non-point source pollution load threshold, watershed groundwater level threshold, and watershed soil quality threshold.

[0086] The indicators in the non-point source pollution load assessment system of S81 in this implementation plan include: total nitrogen emissions and total phosphorus emissions;

[0087] The sustainable indicators in the soil quality sustainable evaluation index system of S83 include: soil erosion modulus, soil organic carbon storage, soil nutrient balance index, soil heavy metal pollution index, and land crop production potential.

[0088] When this implementation plan is implemented,

[0089] Step 1: Divide the watershed to be configured into configuration units, determine the water supply and demand relationship between each unit, and generate several regional water and soil resource configuration grid units, confirming the spatial location and topological relationship between the units;

[0090] Specifically, using geographic information processing software such as ArcGIS, the watershed's water system information at different levels is vectorized. Using ArcGIS and similar software, and considering simulation accuracy requirements, the study area is divided into 500m × 500m grid units. The rows and columns of these grid units are numbered, and valid units within the area are identified, generating watershed water resource allocation raster units. Based on the watershed water system database, the watershed's scope and catchment / discharge areas are determined, down to the water supply relationships of different water system levels. The surface water supply range is established. Based on the surface water supply range and the spatial coordinates of groundwater wells in the watershed, the water supply targets and quantities are determined, the water supply and demand relationship is established, and the spatial location and topological relationships between each unit are clarified.

[0091] Step 2: Select typical soil samples from the black soil region for the grid location, conduct soil quality evaluation, and establish a database of typical soils in the black soil region;

[0092] Specifically, sampling and monitoring points were selected for three typical soils in the black soil region: black soil, albic soil, and black calcareous soil. The bulk density, organic carbon content, saturated hydraulic conductivity, electrical conductivity, calcium carbonate content, and pH of the three soils were measured. The coefficient of variation method was used to sort the soils into five grades (A, B, C, D, E) of typical representative soils for each of the three soils. A typical soil database was constructed according to the form required by the distributed hydrological model.

[0093] The processing flow for the coefficient of variation method is as follows:

[0094] The coefficient of variation β of the i-th indicator is:

[0095] β=δ i / x i

[0096] In the formula, δi x is the standard deviation of the i-th indicator; i Let be the average of the i-th indicator.

[0097] The weights of each evaluation indicator are as follows:

[0098]

[0099] In the formula, ω i β represents the weight of the i-th indicator; i Let be the coefficient of variation of the i-th indicator.

[0100] Step 3: Collect basic data of the watershed, construct a distributed hydrological simulation model of the watershed using SWAT-FT (an improved SWAT model that considers the freeze-thaw process), calibrate and standardize the parameters of the hydrological model that considers the freeze-thaw process, verify it using multi-year measured data from watershed hydrological stations, and perform localization of the model.

[0101] Specifically, meteorological and hydrological data, digital elevation data, land use data, and soil type data of the irrigation area are collected. The topography of the irrigation area is analyzed using digital elevation data to define the river network, divide it into sub-basins, calculate sub-basin parameters, and, based on soil type data, land use data, and slope data, hydrological response units are divided. Meteorological data is input, and the flow rate within each hydrological response unit, considering snowmelt water, is calculated to obtain the watershed water resources. The SWAT-FT model indirectly reflects changes in hydraulic conductivity related to changes in water phase by altering effective soil porosity (water-holding capacity).

[0102] Partial differential equation for soil temperature change:

[0103]

[0104] Where T is the soil / snow temperature, in °C; t is the time step, in days; x is the distance from the surface to a certain depth, in cm; and k is the soil thermal conductivity, in J / cm². -1 day -1 ℃ -1 C is the soil heat capacity, in J / cm³. -3 ℃ -1 ;s is latent heat, J cm -3 day -1 .

[0105] By interpolating soil layers defined by the SWAT-FT model, the temperature, water content, and ice content values ​​at a specified soil depth are obtained. The interpolation formula is as follows:

[0106]

[0107] Among them, nd spec For specified soil depth, in cm; HT ndThe soil hydrothermal property value at this depth; HT ly and HT ly+1 The soil hydrothermal properties of the original SWAT soil layers ly and ly+1 are respectively; solnd ly and solnd ly+1 Let ly and ly+1 be the depths of the soil layers from the surface to their bottom.

[0108] The constructed SWAT-FT model was calibrated and validated using SWAT-CUP software. Parameter calibration and adjustment were performed using multi-year hydrological data from the watershed. The SUFI-2 algorithm was used for iterative calculations to determine the optimal parameter values. Based on these optimal values, SWAT-FT parameters were adjusted and substituted into the model for simulation validation. The Nash coefficient (ENS) and correlation coefficient (R²) were selected as indicators to evaluate the model's simulation accuracy. The formulas for calculating these indicators are as follows:

[0109]

[0110] In the formula, Q m The measured runoff value is in meters. 3 / s;Q m The measured runoff value is in meters. 3 / s;Q avg The measured average value of runoff is m. 3 / s; n is the number of measured values.

[0111] When ENS>0.5 and R2>0.6, the model can be used for watershed SWAT simulation.

[0112] Step 4: Based on the topological relationship of the grid cells for water and soil resource allocation in the region, construct a distributed hydrological simulation model (SWAT-NP) that considers the diffusion of non-point source pollution, and perform measurement and verification; obtain the grid water volume-non-point source pollution load relationship based on the distributed hydrological simulation model that considers the diffusion of non-point source pollution.

[0113] Specifically, in the SWAT model, soil nitrogen exists primarily in three forms: organic nitrogen and two inorganic nitrogen forms: nitrate nitrogen and ammonium nitrogen. Organic nitrogen in the soil mainly originates from the decomposition of plant residues and humus; while nitrate nitrogen and ammonium nitrogen mainly originate from the input of agricultural fertilizers. During nitrogen entry into the soil, biological processes such as mineralization of organic nitrogen, nitrification and ammonia volatilization of ammonium nitrogen, denitrification of nitrate nitrogen, and crop uptake occur. The transport pathways of nitrate nitrogen to drainage ditches are mainly through surface runoff, lateral runoff, and groundwater runoff; organic nitrogen is adsorbed on the surface of sediment particles and transported with runoff. To calculate the amount of nitrate nitrogen migrating with water bodies, the concentration of nitrate nitrogen in the soil water must first be calculated. Multiplying this concentration by the total amount of water flowing through each waterway yields the total amount of nitrate nitrogen lost from the soil.

[0114] The concentration of nitrate nitrogen in soil water is as follows:

[0115]

[0116] In the formula, Nitrate nitrogen ion concentration in soil water, kg / mm 3 ; Nitrate nitrogen ion content in soil, kg / km² 2 Q represents the amount of water in the soil, in mm; θ e Porosity; SAT ly This represents the soil saturation moisture content.

[0117] The formula for calculating the nitrate nitrogen content of seepage loss is as follows:

[0118]

[0119] In the formula: The content of nitrate nitrogen lost through seepage, in kg / km² 2 ; Nitrate nitrogen ion concentration in soil water, kg / mm 3 Q perc,ly For seepage, mm.

[0120] In the SWAT model, phosphorus in soil exists primarily in three chemical forms: organic phosphorus, inorganic phosphorus, and dissolved phosphorus. Organic phosphorus mainly originates from humus; inorganic phosphorus is insoluble in soil water; and dissolved phosphorus mainly comes from fertilizer input and is generally absorbed and utilized by crops. Compared to nitrogen, phosphorus has lower reactivity and less soluble phosphorus, so it is less lost through runoff. Phosphorus transport to drainage ditches is mainly through soil erosion and removal from the soil by runoff.

[0121] The migration of dissolved phosphorus in soil is mainly achieved through diffusion, which refers to the migration of solutes at a small scale (1-2 mm) due to concentration gradients. Because dissolved phosphorus is not very active, very little phosphorus in dissolved form is carried away from the top layer (10 m) of soil by surface runoff. The dissolved phosphorus transported by surface runoff is as follows:

[0122]

[0123] In the formula, P surf Dissolved phosphorus lost through surface runoff, kg / km 2 ;P solution,surf Dissolved phosphorus in soil (top 10 mm), kg / km 2 ;ρ b Soil solute density, mg / m³ 3 h surf The depth of the topsoil layer is measured in mm; kd,surf Soil phosphorus partition coefficient is the ratio of the concentration of dissolved phosphorus in the topsoil (10 mm) to the concentration of dissolved phosphorus in surface runoff.

[0124] The constructed SWAT-NP model was calibrated and verified using SWAT-CUP software. Parameter calibration and verification were performed using multi-year hydrological data from the watershed. The calibration and verification process is as described above.

[0125] Step 5: Run SWAT-FT and SWAT-NP based on the watershed monitoring data, and obtain the boundary conditions of the configuration unit through the running results;

[0126] Specifically, based on the watershed monitoring data, the watershed SWAT-FT model is run to obtain the available surface water volume of the watershed, which is used for the boundary conditions of the available surface water volume in the optimization model in step 6 and the source-sink term input in the MODFLOW simulation in step 7. The SWAT-NP model is run to obtain the grid water volume-non-point source pollution load relationship, which is used for the output condition input in the optimization model in step 6.

[0127] Step 6: Construct a grid-scale model considering the non-point source pollution diffusion process during grid-based agricultural production, and a watershed-scale model considering the freeze-thaw water cycle process during watershed-based agricultural production. Construct a grid-watershed multi-objective multi-water source optimization model, solve the multi-objective optimization model, and form a preliminary grid-watershed water resource allocation scheme.

[0128] Specifically, a grid-watershed multi-objective, multi-source optimization model is constructed.

[0129] Grid scale:

[0130] (1) Economic benefit objective function

[0131]

[0132] In the formula, i represents the crop type, i = 1, 2, 3 representing corn, soybean, and rice respectively; j represents the irrigation water source, j = 1, 2 representing groundwater and surface water respectively; k represents the grid cell; t represents the crop growth period in days; YI i This represents the yield per unit area of ​​crop i, in kg / hm². 2 ;P i C represents the unit price of crop i, in yuan / kg; i This represents the planting cost of crop i, expressed in yuan / hm². 2 CSW represents the surface water price, in yuan / m³. 3 CGW represents the groundwater price, in yuan / m³. 3 Q ijkt A ijk Q is the decision variable. ijkt The amount of irrigation water for crop i and water source j in unit k is represented by m. 3 Aijk Let hm be the irrigated area of ​​crop i and water source j unit k. 2 .

[0133] (2) Objective function of water use efficiency

[0134]

[0135] In the formula, For the water productivity of crops, kg / m 3 .

[0136] (3) Objective function for non-point source pollution emissions

[0137]

[0138] In the formula, Total nitrogen and total phosphorus emissions from the grid, in kg; TN j (Q ijkt The relationship between total nitrogen discharge and water volume in water source j is expressed as a function (obtained through multiple runs of SWAT-NP fitting); TP j (Q ijkt The relationship between total phosphorus emissions and water volume in water source j is a function (obtained by multiple runs of SWAT-NP fitting).

[0139] (4) Soil quality objective function

[0140]

[0141] m=K×R×L×S×P×C

[0142] In the formula, t represents the total amount of soil erosion; m represents the soil erosion modulus, t / km². -2 a -1 K represents soil erodibility factor; R represents rainfall erosion factor; L represents slope length factor; S represents slope factor; C represents vegetation cover factor; P represents soil and water conservation measures factor. All of the above factors are dimensionless.

[0143] Constraints:

[0144] (1) Constraints on total water resources

[0145]

[0146] In the formula, IR ijt The amount of irrigation water used by crop i at time t, m 3 / hm 2 ;TA i The total amount of agricultural water available at time t, in m 3 .

[0147] (2) Groundwater constraints

[0148]

[0149] In the formula, GW kt m represents the total amount of agriculturally available groundwater in time unit k within a sub-basin (feedback from watershed-level decision variables, requiring time-scale processing). 3 .

[0150] (3) Surface water constraints

[0151]

[0152] In the formula, SW kt m represents the total amount of agriculturally available surface water in time unit k within a sub-basin (feedback from watershed-level decision variables, requiring time-scale processing). 3 .

[0153] (4) Total planting area constraint of each zone

[0154]

[0155] In the formula, hm represents the lower limit of the total planting area of ​​unit k. 2 ; hm represents the upper limit of the total planting area in time unit k, t. 2 .

[0156] (5) Food demand constraints

[0157]

[0158] In the formula, This represents the lower limit of food demand for crop i in unit k, in kg; This represents the upper limit of food demand for crop i in unit k, expressed in kg.

[0159] After running the grid scaling model multiple times, the fitted Y(SW) i +GW i (Sub-basin irrigation water volume - economic function), with the function as the link, two-layer model iteration.

[0160] The watershed-scale model is as follows:

[0161] (1) Economic benefit objective function

[0162]

[0163] In the formula, i is the sub-basin number, i = 1, ..., n, representing the sub-basins divided by SWAT; M1 is the surface water management fee, yuan / m³.3 M2 represents groundwater management fees, in yuan / m³. 3 ;SW i For the irrigation water volume of surface water in sub-basin i, m 3 ;GW i For the irrigation water volume of groundwater in sub-basin i, m 3 .

[0164] (2) Fairness objective function

[0165]

[0166] In the formula, For the fairness of water resource allocation (Theil coefficient).

[0167] Constraints:

[0168] (1) Constraints on total water resources

[0169]

[0170] In the formula, TW represents the total water resources of the basin, and m 3 .

[0171] (2) Constraints on total surface water resources

[0172]

[0173] In the formula, TSW represents the total surface water resources of the basin, in m 3 .

[0174] (3) Constraints on groundwater resource extraction

[0175]

[0176] In the formula, TGW represents the total groundwater resources in the basin, m 3 .

[0177] (4) Water resource constraints in sub-basins

[0178]

[0179] In the formula, ASW i For the total surface water resources of sub-basin i, m 3 AGW i For the total groundwater resources of sub-basin i, m 3 .

[0180] The watershed-scale decision variables are the boundary conditions of the grid-scale model. The two-layer model is used for iterative optimization. After multiple iterations and the model is stable, the watershed water resource use scheme and the grid-scale water and soil resource regulation scheme are output.

[0181] Step 7: Based on the solution results of the multi-objective optimization model, construct a groundwater simulation model using MODFLOW, run the simulation model, determine the groundwater level threshold in the irrigation area, establish a sustainable evaluation index system for soil quality, determine the soil quality threshold, construct a model verification system based on the groundwater level and soil quality threshold, input the preliminary water resource allocation plan into the verification system, verify the feasibility of the model, if the verification fails, recalibrate the boundary conditions of the optimization model and solve the model, and iterate and feedback.

[0182] Specifically, a groundwater simulation model is established for each watershed water resource allocation unit, as follows:

[0183]

[0184] In the formula, Ω represents the area under study for seepage; h is the hydraulic head of the aquifer, in meters; B is the elevation of the aquifer floor, in meters; K x ,K y ,K z ε is the permeability coefficient of the aquifer in the x, y, z directions, in m / d; μ is the gravity specific yield of the aquifer; ε is the vertical recharge and discharge intensity of the aquifer, in m / d; ε < 0 indicates outflow, ε > 0 indicates inflow, including source and sink terms such as SWAT output seepage recharge intensity, groundwater evaporation, and industrial and agricultural extraction; h0 is the initial water level of the aquifer, in m; q is the unit width discharge at the Class II boundary, in m. 2 / d, inflow is positive, outflow is negative, and the impermeable boundary is 0; Γ1 is the flow boundary; Γ22 is the bottom plate of the shallow aquifer, the impermeable boundary; The direction of the outward normal on the boundary; The permeability coefficient is the coefficient along the outer normal direction on the boundary.

[0185] The elements in the groundwater dynamic equation of the water resource allocation unit are determined (the infiltration recharge intensity in the source and sink terms is input from the SWAT model in step 3, and the irrigation infiltration is input from the optimization model results in step 6). The groundwater level is simulated using MODFLOW groundwater numerical simulation. At the watershed scale, the watershed over-extraction level and the water level at which soil salinization occurs are determined, serving as ecological groundwater level control thresholds to verify the rationality of the large-scale system water allocation results. At the grid scale, based on the preliminary configuration results of the optimization model, a soil quality sustainability evaluation index system is established, using soil erosion modulus, soil organic carbon storage, soil nutrient balance index, soil heavy metal pollution index, and land crop production potential as indicators. A non-point source pollution emission index system is also established, using total nitrogen and total phosphorus emissions as indicators. The grid soil quality sustainability and non-point source pollution emissions are assessed, thereby effectively evaluating the soil quality and non-point source pollution emissions after the implementation of the water allocation scheme. Based on the verification standards at each level, the gridded watershed water resource allocation results are input into the three-level water and soil verification system to verify the sustainability of the optimized control model results. If the verification fails, the boundary conditions of the optimization system are recalibrated and the model is solved. The feedback loop forms a "simulation-optimization" feedback control technology to correct the watershed water resource allocation results and form a multi-source water resource utilization plan for the watershed.

Claims

1. A method for water and soil regulation in a grid watershed in a black soil area based on freeze-thaw cycles, characterized in that, Includes the following steps: S1: Divide the watershed to be configured into configuration units, determine the water supply and demand relationship between each configuration unit, generate several regional water and soil resource configuration grid units, and confirm the spatial location and topological relationship between the water and soil resource configuration grid units. S2: Select typical soil sampling tests in the black soil region based on the spatial location between the grid units of the water and soil resource allocation grid, conduct soil quality evaluation, and establish a typical soil database of the black soil region. S3: Construct a regional distributed hydrological model considering freeze-thaw cycles based on a typical soil database of the black soil region, and conduct calibration and verification. S4: Obtain watershed monitoring data, and based on the watershed monitoring data and a distributed hydrological simulation model considering freeze-thaw cycles, obtain the regional water supply boundary conditions; S5: Based on the grid cell topology of water and soil resource allocation, construct a distributed hydrological simulation model that considers the diffusion of non-point source pollution, and conduct calibration and verification. S6: Obtain the relationship between grid water volume and non-point source pollution load based on the distributed hydrological simulation model that considers the diffusion of non-point source pollution; S7: Construct a grid-watershed two-layer multi-objective multi-source grid optimization model, solve the model based on the watershed water resource allocation boundary conditions and the grid water volume-non-point source pollution load relationship, and obtain a preliminary grid-watershed water and soil resource allocation scheme; S8: Construct a model validation system, and validate the preliminary configuration scheme of grid-watershed water and soil resources based on the model validation system. If the validation fails, recalibrate and optimize the boundary conditions of the model and solve the model until it meets the feasibility requirements. S3 includes the following sub-steps: S31: Construct a distributed hydrological simulation model for the region that takes into account the freeze-thaw cycle, and obtain basic regional data, including meteorological and hydrological data, freeze-thaw transformation data, digital elevation data, land use data, and soil type data. S32: Define the river network based on the analysis of regional topography using digital elevation data, divide it into sub-basins, and calculate the parameters of each sub-basin; S33: Based on soil type data, land use data, and slope data, divide the hydrological response units, input meteorological and hydrological data and freeze-thaw conversion data into the regional distributed hydrological simulation model, calculate the flow rate of snowmelt water within the hydrological response unit, and obtain the watershed water resources. S34: Perform parameter calibration and adjustment of the regional distributed hydrological simulation model by combining regional meteorological and hydrological data.

2. The freeze-thaw cycle based water-soil regulation method for grid watershed of black soil region according to claim 1, characterized in that, S1 includes the following sub-steps: S11: Vectorize the information of different levels of water system in the basin and establish a water resources allocation database for the basin; S12: Divide the watershed to be allocated into several identical grids and number them, identify the effective allocation units, generate regional water and soil resource allocation grid units, and confirm the spatial location and topological relationship between water and soil resource allocation units.

3. The freeze-thaw cycle based water-soil regulation method for grid watershed of black soil region according to claim 1, characterized in that, S2 includes the following sub-steps: S21: Sampling and monitoring points were selected for three typical soils in the black soil region: black soil, albic soil, and black calcareous soil. The bulk density, organic carbon content, saturated hydraulic conductivity, electrical conductivity, calcium carbonate content, and pH of the three soils were measured. S22: Using the coefficient of variation method, five typical representative soils of each of the three soil types were formed, and a typical soil database was constructed according to the form required by the distributed hydrological model.

4. The freeze-thaw cycle based water-soil regulation method for grid watershed of black soil region according to claim 1, characterized in that, Specifically, S5 is: Based on the topological relationship of the grid units for regional water and soil resource allocation and the watershed water resource allocation database, the grid pollution load is identified, and a grid non-point source pollution diffusion model is constructed.

5. The freeze-thaw cycle based water-soil regulation method for grid watershed of black soil region according to claim 1, characterized in that, S6 includes the following sub-steps: S61: Obtain long-term hydrological observation data of the watershed, input the watershed observation data into the watershed distributed hydrological simulation model, and obtain the boundary conditions of the watershed surface water available water volume. S62: Run the hydrological simulation model that considers the diffusion of non-point source pollution multiple times to obtain the grid-water non-point source pollution load relationship.

6. The freeze-thaw cycle based water-soil regulation method for grid watershed of black soil region according to claim 1, characterized in that, In S7: The grid scale constraints of the grid-watershed two-layer multi-objective multi-water source grid optimization model include total water resources constraints, groundwater constraints, surface water constraints, total planting area constraints of the zone, and food demand constraints. The watershed-scale constraints of the grid-watershed dual-layer multi-objective multi-source grid optimization model include total water resources constraints, groundwater constraints, surface water constraints, and sub-watershed water resources constraints.

7. The freeze-thaw cycle based water-soil regulation method for grid watershed of black soil region according to claim 1, characterized in that, The process of constructing the model verification system in S8 includes: S81: Construct a non-point source pollution load assessment system and determine the grid non-point source pollution load threshold; S82: Construct a groundwater simulation model and determine the groundwater level threshold of the watershed by running the groundwater simulation model; S83: Construct a sustainable evaluation index system for soil quality and determine soil quality thresholds; S84: A model verification system is constructed based on grid-based non-point source pollution load threshold, watershed groundwater level threshold, and watershed soil quality threshold.

8. The freeze-thaw cycle based water-soil regulation method for grid watershed of black soil region according to claim 7, characterized in that, The indicators in the non-point source pollution load assessment system of S81 include: total nitrogen emissions and total phosphorus emissions; The sustainable indicators in the soil quality sustainable evaluation index system of S83 include: soil erosion modulus, soil organic carbon storage, soil nutrient balance index, soil heavy metal pollution index, and land crop production potential.

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