Multi-target rasterization irrigation and drainage dynamic optimization method for drainage basin of black soil area in cold region

Abstract

The invention belongs to the technical field of agricultural water management, and relates to a cold region black soil area drainage basin multi-target rasterization irrigation and drainage dynamic optimization method, which comprises the following steps: carrying out configuration unit division on a to-be-configured drainage basin, determining a water supply and demand relationship among the units, and generating a plurality of drainage basin irrigation and drainage resource configuration grid units; determining spatial positions and topological relations among the units, and determining a grid irrigation and drainage spatial relation; establishing a black soil area typical soil database; constructing a drainage basin distributed freeze-thaw hydrological model and a crop model, and performing calibration verification; obtaining a drainage basin irrigation and drainage decision water resource configuration boundary condition and a water volume-yield function relationship; constructing and solving a drainage basin anti-corrosion emission reduction grid irrigation and drainage decision model, obtaining a drainage basin irrigation and drainage preliminary configuration scheme, constructing a drainage basin drainage simulation model, and determining drainage flow and time; and verifying the basin irrigation and drainage preliminary configuration scheme. The problem of considering the irrigation and drainage decision, emission reduction and corrosion resistance of the whole drainage basin from the perspective of rasterization is solved.

Description

Technical Field

[0001] The present invention belongs to the technical field of agricultural water management, and in particular relates to a multi-objective grid-based dynamic optimization method for irrigation and drainage in a cold black soil region watershed. Background Art

[0002] As the core unit of agricultural production, the efficiency of water and soil resource allocation in irrigation areas directly impacts sustainable agricultural development. For example, in black soil areas, the heavy clay texture and high organic matter content result in poor water infiltration and susceptibility to waterlogging. Traditional irrigation and drainage decision-making models often assume homogeneity, ignoring the spatial heterogeneity of soils, crops, and microtopography. This results in the coexistence of irrigation surpluses and water deficits in some areas.

[0003] Existing technology systems have significant limitations: First, insufficient model coordination leads to data lags between hydrological models (such as SWAT), crop models (such as DSSAT), and drainage models (such as DRAINMOD), making it difficult to respond in real time to dynamic climate scenarios (drought, normal, and flooded years). Second, a singular focus on maximizing water productivity often neglects the balance between irrigation and drainage flows and soil erosion. Third, insufficient scheduling precision exists. Traditional models only support extensive scheduling at the watershed level (10 km × 10 km), failing to address the specific requirements of waterlogging triggers and crop water requirements within a 200 m × 200 m grid. Due to the lack of a multi-process coupling mechanism for "hydrology, crops, and erosion," traditional methods struggle to simultaneously optimize multiple objectives, such as water productivity, minimizing irrigation and drainage flows, and controlling waterlogging rates. Innovative technologies are urgently needed to achieve efficient and ecologically coordinated regulation of water and soil resources. Summary of the Invention

[0004] In order to solve the above technical problems, the present invention proposes a multi-objective grid-based dynamic optimization method for irrigation and drainage in cold-region black soil areas, which solves the problem of considering the entire basin's irrigation and drainage decision-making, emission reduction and erosion resistance from a grid-based perspective.

[0005] The technical solutions of the present invention are as follows:

[0006] A multi-objective grid-based dynamic optimization method for irrigation and drainage in a cold black soil region watershed includes the following steps:

[0007] S1: Divide the watershed to be configured into configuration units, determine the water supply and demand relationship between each unit, and generate several watershed irrigation and drainage resource configuration grid units, confirm the spatial position and topological relationship between the units, and determine the grid irrigation and drainage spatial relationship;

[0008] S2: Determine the tillage method based on the grid spatial position, select typical soil samples from the black soil area for testing, and establish a typical soil database for the black soil area;

[0009] S3: Based on the typical soil database of the black soil region and the data from the basin meteorological stations, a distributed freeze-thaw hydrological model and a crop model were constructed for calibration and verification.

[0010] S4: Obtain basin monitoring data, and obtain the boundary conditions of water resources allocation and the water-yield function relationship for basin irrigation and drainage decision-making based on the basin monitoring data, the basin's distributed freeze-thaw hydrology, and the crop model;

[0011] S5: Construct a watershed anti-erosion and emission reduction grid irrigation and drainage decision model, solve the model according to the water resources allocation boundary conditions and the water volume-yield function relationship of the watershed irrigation and drainage decision, and obtain a preliminary irrigation and drainage configuration plan for the watershed;

[0012] S6: Construct a drainage simulation model based on the preliminary irrigation and drainage configuration plan of the watershed to determine the drainage flow and time;

[0013] S7: Construct a model verification system and verify the preliminary configuration plan of the watershed irrigation and drainage based on the model verification system. If the verification fails, recalibrate and optimize the model boundary conditions and solve the model until it meets the feasibility requirements.

[0014] Preferably, S1 includes the following sub-steps:

[0015] S11: Vectorize the water system information of different levels in the basin and establish a water resources allocation database for the basin;

[0016] S12: Divide the watershed to be allocated into several identical grids and number them, identify the valid configuration units, generate watershed irrigation and drainage configuration grid units, and confirm the spatial positions and topological relationships between the irrigation and drainage configuration grid units.

[0017] Preferably, S2 includes the following sub-steps:

[0018] S21: Sampling monitoring points were selected for typical black soil and sandy soil in the basin to measure soil bulk density, organic carbon content, saturated hydraulic conductivity, electrical conductivity, calcium carbonate content, and pH;

[0019] S22: Monitoring points are arranged on both the transverse and along-slope according to different tillage methods in the basin to measure the slope direction, slope gradient, and soil erosion amount; and a typical soil database is constructed in the form required by the distributed hydrological model.

[0020] Preferably, S3 includes the following sub-steps:

[0021] S31: Build a distributed freeze-thaw hydrological simulation model for the watershed and obtain regional basic data, including meteorological and hydrological data, freeze-thaw snow data, digital elevation data, land use data, and soil type data;

[0022] S32: Analyze regional terrain based on digital elevation data to define the river network, divide it into sub-basins, and calculate sub-basin parameters;

[0023] S33: Divide the hydrological response units according to soil type data, land use data, and slope data, input meteorological and hydrological data into the distributed hydrological simulation model, and output the water volume converted from snowmelt water;

[0024] S34: Construct a crop growth simulation model, obtain watershed crop planting and growth data, wherein the watershed crop planting and growth data includes meteorological data, field management data, and soil data; input the meteorological data, field management data, and soil data into the crop growth simulation model.

[0025] S35: Parameter calibration and verification of distributed hydrological and crop simulation models using regional meteorological and hydrological data and crop yield data.

[0026] Preferably, in S5:

[0027] The constraints of the watershed anti-erosion and emission reduction grid irrigation and drainage decision-making model include dynamic water balance equation, total water resources constraint, dynamic response constraint of waterlogging, and dynamic threshold constraint of irrigation volume.

[0028] Preferably, S6 is specifically:

[0029] According to the topological relationship of the grid cells in the watershed irrigation and drainage configuration and the watershed water resources configuration database, the grid irrigation and drainage volume is determined, a grid drainage simulation model is constructed, and the drainage flow and time are output.

[0030] Preferably, the process of building the model verification system in S7 includes:

[0031] S71: Calculate the amount of soil erosion in the watershed and determine the threshold value of soil erosion in the watershed;

[0032] S72: Calculate the optimized watershed soil erosion amount based on the drainage flow and time determined in S6, the watershed slope aspect and slope and other soil erosion factors;

[0033] S73: Continue running the model until the watershed soil erosion threshold is met.

[0034] The beneficial effects of the multi-objective grid-based dynamic optimization method for irrigation and drainage in cold black soil watersheds of the present invention are as follows:

[0035] 1. By constructing a two-layer grid-watershed model framework, this approach organically integrates the hydrological cycle, crop water demand, and ecological response processes, achieving efficient and coordinated allocation of water and soil resources at different spatial scales. The model fully considers the soil and hydrological characteristics of black soil regions, significantly improving the systematic and coordinated nature of irrigation and drainage decision-making.

[0036] 2. Achieve adaptive meteorological scenarios and dynamic response to constraints. The system can automatically adjust water distribution strategies based on real-time environmental changes, significantly enhancing decision-making robustness under extreme climate conditions.

[0037] 3. Establish a dynamic constraint system, using linkage control based on soil erosion thresholds and waterlogging triggers to create a priority protection mechanism for key ecologically vulnerable areas. This system effectively balances agricultural production with ecological conservation needs and promotes the sustainable use of water and soil resources.

[0038] 4. Grid-based modeling technology breaks through the bottleneck of extensive regulation in traditional watershed management. Through high-resolution spatial analysis, it accurately identifies differences in soil and water responses within micro-topographic units, significantly improving the targeting and adaptability of irrigation and drainage measures.

[0039] 5. While ensuring stable grain production, the model systematically reduces soil erosion, achieving synergistic gains in economic and ecological benefits. This model provides a comprehensive solution for cold-region black soil watersheds that combines technological innovation with engineering practicality. BRIEF DESCRIPTION OF THE DRAWINGS

[0040] In order to more clearly explain the purpose, design ideas and innovation of the multi-objective grid-based dynamic optimization method for irrigation and drainage in cold-region black soil watersheds proposed in the present invention, the present invention will be described in detail below with reference to the accompanying drawings and appendixes.

[0041] Figure 1 Flowchart of the present invention. DETAILED DESCRIPTION

[0042] The present invention will be described in detail below with reference to the accompanying drawings and specific embodiments.

[0043] The specific embodiments of the present invention are described below to facilitate understanding of the present invention by those skilled in the art. However, it should be clear that the present invention is not limited to the scope of the specific embodiments. For those skilled in the art, as long as various changes are within the spirit and scope of the present invention as defined and determined by the appended claims, these changes are obvious, and all inventions and creations utilizing the concepts of the present invention are protected.

[0044] like Figure 1 As shown, a multi-objective grid-based dynamic optimization method for irrigation and drainage in a cold black soil watershed includes the following steps:

[0045] S1: Divide the watershed to be configured into configuration units, determine the water supply and demand relationship between each unit, and generate several watershed irrigation and drainage resource configuration grid units, confirm the spatial position and topological relationship between the units, and determine the grid irrigation and drainage spatial relationship;

[0046] S2: Determine the tillage method based on the grid spatial position, select typical soil samples from the black soil area for testing, and establish a typical soil database for the black soil area;

[0047] S3: Based on the typical soil database of the black soil region and the data from the basin meteorological stations, a distributed freeze-thaw hydrological model and a crop model were constructed for calibration and verification.

[0048] S4: Obtain basin monitoring data, and obtain the boundary conditions of water resources allocation and the water-yield function relationship for basin irrigation and drainage decision-making based on the basin monitoring data, the basin's distributed freeze-thaw hydrology, and the crop model;

[0049] S5: Construct a watershed anti-erosion and emission reduction grid irrigation and drainage decision model, solve the model according to the water resources allocation boundary conditions and the water volume-yield function relationship of the watershed irrigation and drainage decision, and obtain a preliminary irrigation and drainage configuration plan for the watershed;

[0050] S6: Construct a drainage simulation model based on the preliminary irrigation and drainage configuration plan of the watershed to determine the drainage flow and time;

[0051] S7: Construct a model verification system and verify the preliminary configuration plan of the watershed irrigation and drainage based on the model verification system. If the verification fails, recalibrate and optimize the model boundary conditions and solve the model until it meets the feasibility requirements.

[0052] Preferably, S1 includes the following sub-steps:

[0053] S11: Vectorize the water system information of different levels in the basin and establish a water resources allocation database for the basin;

[0054] S12: Divide the watershed to be allocated into several identical grids and number them, identify the valid configuration units, generate watershed irrigation and drainage configuration grid units, and confirm the spatial positions and topological relationships between the irrigation and drainage configuration grid units.

[0055] Preferably, S2 includes the following sub-steps:

[0056] S21: Sampling monitoring points were selected for typical black soil and sandy soil in the basin to measure soil bulk density, organic carbon content, saturated hydraulic conductivity, electrical conductivity, calcium carbonate content, and pH;

[0057] S22: Monitoring points are arranged on both the transverse and along-slope according to different tillage methods in the basin to measure the slope direction, slope gradient, and soil erosion amount; and a typical soil database is constructed in the form required by the distributed hydrological model.

[0058] Preferably, S3 includes the following sub-steps:

[0059] S31: Build a distributed freeze-thaw hydrological simulation model for the watershed and obtain regional basic data, including meteorological and hydrological data, digital elevation data, land use data, and soil type data;

[0060] S32: Analyze regional terrain based on digital elevation data to define the river network, divide it into sub-basins, and calculate sub-basin parameters;

[0061] S33: Divide the hydrological response units according to soil type data, land use data, and slope data, input meteorological and hydrological data into the distributed hydrological simulation model, and output the water volume converted from snowmelt water;

[0062] S34: Construct a crop growth simulation model, obtain watershed crop planting and growth data, wherein the watershed crop planting and growth data includes meteorological data, field management data, and soil data; input the meteorological data, field management data, and soil data into the crop growth simulation model.

[0063] S35: Parameter calibration and verification of distributed hydrological and crop simulation models using regional meteorological and hydrological data and crop yield data.

[0064] Preferably, in S5:

[0065] The constraints of the watershed anti-erosion and emission reduction grid irrigation and drainage decision-making model include dynamic water balance equation, total water resources constraint, dynamic response constraint of waterlogging, and dynamic threshold constraint of irrigation volume.

[0066] Preferably, S6 is specifically:

[0067] According to the topological relationship of the grid cells in the watershed irrigation and drainage configuration and the watershed water resources configuration database, the grid irrigation and drainage volume is determined, a grid drainage simulation model is constructed, and the drainage flow and time are output.

[0068] Preferably, the process of building the model verification system in S7 includes:

[0069] S71: Calculate the amount of soil erosion in the watershed and determine the threshold value of soil erosion in the watershed;

[0070] S72: Calculate the optimized watershed soil erosion amount based on the drainage flow and time determined in S6, the watershed slope aspect and slope and other soil erosion factors;

[0071] S73: Continue running the model until the watershed soil erosion threshold is met.

[0072] When this implementation plan is implemented,

[0073] Step S1: Divide the watershed to be configured into configuration units, determine the water supply and demand relationship between each unit, and generate several watershed irrigation and drainage resource configuration grid units, confirm the spatial position and topological relationship between the units, and determine the grid irrigation and drainage spatial relationship;

[0074] Specifically, ArcGIS and other geographic information processing software were used to vectorize the water system information of different levels in the basin. Using ArcGIS and other geographic information processing software, the study area was divided into 250m×250m grid cells, based on the simulation accuracy requirements. The rows and columns of the grid cells were numbered, valid units within the area were identified, and grid cells for irrigation and drainage resource allocation in the basin were generated. Based on the basin water system database, the basin scope and the scope of the receiving and discharge area were determined, and the water supply and drainage relationship of the different levels of water systems was determined. The surface water supply and drainage scope was established. Based on the surface water supply scope and the spatial coordinates of the groundwater wells in the basin, the water supply objects and water supply quantities were determined, the irrigation and drainage relationship was determined, and the spatial location and topological relationship between each unit was clarified.

[0075] Step S2: Determine the tillage method according to the grid spatial position, select typical soil samples from the black soil area for testing, and establish a typical soil database for the black soil area;

[0076] Specifically, sampling monitoring points were selected for the typical black soil and sandy soil in the basin, and the bulk density, organic carbon content, saturated hydraulic conductivity, electrical conductivity, calcium carbonate content, and pH of the three soil types were measured; monitoring points were arranged on the cross slopes and along the slopes according to different tillage methods in the basin, and the slope direction, slope gradient, and soil erosion amount were measured; and soil data were collected and organized to construct a typical soil database in the form required by the distributed hydrological model.

[0077] Step S3: Based on the typical soil database of the black soil area and the data of the basin meteorological station, the SWAT-FT model is used to construct a basin-distributed freeze-thaw hydrological model, and the DSSAT model is used to construct a basin-distributed crop model, and calibration verification is performed;

[0078] Specifically, we collected basin-wide meteorological and hydrological data, digital elevation data, land use data, and soil type data. We used digital elevation data to analyze the irrigation area's topography, define river networks, divide sub-basins, and calculate sub-basin parameters. We then divided hydrological response units based on soil type, land use, and slope data, and used meteorological data as input to run the basin's distributed hydrological model.

[0079] The SWAT-FT model indirectly reflects the changes in hydraulic conductivity associated with changes in water phase by changing the effective soil porosity (water holding capacity).

[0080] The partial differential equation for soil temperature change:

[0081]

[0082] Where T is the soil / snow temperature, °C; t is the time step, days; x is the distance from the surface to a depth, cm; k is the soil thermal conductivity, J cm -1 day -1 ℃ -1 ; C is the soil heat capacity, J cm -3 ℃ -1 ; s is latent heat, J cm -3 day -1 .

[0083] The soil layers defined by the SWAT-FT model are interpolated to obtain the temperature, water content, and ice content at a specified soil depth. The interpolation formula is as follows:

[0084]

[0085] Among them, nd spec is the specified soil depth, cm; HT nd is the soil water and heat property value at this depth; HT ly and HT ly+1 are the soil water and heat property values ​​of the original soil layers ly and ly+1 of SWAT; solnd ly and Solnd ly+1 is the depth of soil layers ly and ly+1 from the surface to their bottom.

[0086] The constructed SWAT model was calibrated using SWAT-CUP software, and the parameters were calibrated and calibrated using the multi-year hydrological data of the irrigation area. The SUFI-2 algorithm was used for iterative calculation to determine the optimal parameter values. The SWAT model was adjusted according to the optimal parameter values ​​and substituted into the model for simulation verification. The Nash coefficient (E NS ) and correlation coefficient (R 2 ) is used as an indicator to evaluate the simulation accuracy of the model. The indicator calculation formula is as follows:

[0087]

[0088] Where Q m is the measured value of runoff, m 3 / s;Q m is the measured value of runoff, m 3 / s;Q avg is the measured average value of runoff, m 3 / s; n is the number of measured values.

[0089] When ENS>0.5, R 2 When it is >0.6, the model can be used for basin surface water simulation.

[0090] Specifically, watershed crop planting data collected includes meteorological data, field management data, and soil data. Meteorological data includes: maximum temperature, minimum temperature, precipitation, and solar radiation. Field management data includes crop variety data, sowing date, water and fertilizer management, planting density, and planting depth. Soil data includes soil pH, soil organic matter content, soil bulk density, soil texture, and soil moisture content. These meteorological data, field management data, and soil data are input into the DSSAT model. Using DSSAT-GLUE, the model's genetic parameters are continuously adjusted to approach measured values. The crop model parameters are calibrated to localized parameters, transforming the model into a localized crop model.

[0091] S4: Obtain basin monitoring data, and obtain the boundary conditions of water resources allocation and the water-yield function relationship for basin irrigation and drainage decision-making based on the basin monitoring data, basin distributed hydrology, and crop models;

[0092] Specifically, the basin SWAT model is run according to the basin monitoring data to obtain the basin's surface water availability for the surface water availability boundary conditions of the decision-making model in S5, and the DSSAT model is run to obtain the grid water volume-yield function relationship for the yield condition input of the decision-making model in S5.

[0093] S5: Construct a watershed anti-erosion and emission reduction grid irrigation and drainage decision model, solve the model according to the water resources allocation boundary conditions and the water volume-yield function relationship of the watershed irrigation and drainage decision, and obtain a preliminary irrigation and drainage configuration plan for the watershed;

[0094] Specifically, a watershed anti-erosion and emission reduction grid irrigation and drainage decision-making model is constructed.

[0095] The model focuses on efficient water conservation, emission reduction and erosion resistance in the basin, and comprehensively considers water productivity, irrigation and drainage flow, and inundation time of dry crops. It also considers constraints such as the total agricultural water use in the basin, surface water availability and groundwater extraction capacity. The decision variables are the irrigation and drainage volume in different grids j. The limited available water is allocated to different grids, and based on the water balance equation, a refined water use plan is output on a daily scale.

[0096] Objective function:

[0097] (1) Maximizing water productivity in the watershed: The objective function is to maximize the total water productivity of each grid, that is, the ratio of the yield of different crops in the grid to the amount of irrigation water. The expression is as follows:

[0098]

[0099] Where, f 1 is the water productivity of crops, kg / m 3 ; i is crop type; j is grid unit; k is irrigation time; YI i (Q ijk) represents the water production function of crop i (obtained by DSSAT simulation); A ijk is the irrigated area of ​​crop i unit j at time k, ha; Q ijk is the decision variable, which represents the amount of irrigation water for crop i unit j at time k, m 3 ;H jk is the number of days of waterlogging damage, which is 1 when the waterlogging damage condition is met, otherwise it is 0, and λ is the yield reduction due to waterlogging damage, kg.

[0100] (2) Minimization of irrigation and drainage flow: To reduce soil erosion and reduce the amount of irrigation and drainage used by each grid in the basin, the objective function is to minimize the irrigation and drainage flow of each grid. The expression is as follows:

[0101]

[0102] Where, f 2 is the sum of irrigation and drainage flow in the basin, m 3 ;D ijk is the decision variable, representing the drainage volume of crop i unit j at time k, m 3 ; α, β are the irrigation and drainage weight coefficients, where α=1, β=0 in drought years, α=0.5, β=0.5 in normal years, and α=0, β=1 in wet years.

[0103] (3) Minimization of annual waterlogging damage ratio: The objective function is to minimize the annual waterlogging damage ratio of each grid, that is, the ratio of the number of waterlogged days to the total number of days. The expression is as follows:

[0104]

[0105] Where, f 3 is the watershed waterlogging damage ratio, T i is the total number of days in the growth period of crop i.

[0106] Constraints:

[0107] (1) Dynamic water balance equation (embedded waterlogging triggering mechanism). That is, the water volume of each grid field is in a dynamic balance state.

[0108]

[0109] Where, is the initial and final value of soil water output in unit j at time k, m 3 ; R jk is the rainfall in unit j at time k, m 3 ET ijk is the crop evaporation of crop i unit j time k, m3; G jk is the groundwater exchange volume in unit j at time k, where mining is positive and recharge is negative, m 3 ; When H jk=1 (waterlogging occurs), drainage volume D ijk Forcefully increase the pressure by η times to accelerate water discharge.

[0110] (2) Constraints on total water resources. That is, the water consumption of each grid during the growth period of different crops is within the total available water volume of the basin.

[0111]

[0112] Where W surface is the surface water supply, m 3 ;W ground is the available groundwater supply, m3.

[0113] (3) Dynamic response constraint of waterlogging. That is, drainage is carried out after waterlogging occurs, judging by soil moisture content.

[0114]

[0115] Where θ jk is the soil moisture content at unit j at time k, θ sat is the saturated moisture content of the soil, and H is the depth of the soil water layer.

[0116] (4) Dynamic threshold constraints on irrigation volume: restricting irrigation when the soil is close to saturation to prevent waterlogging.

[0117]

[0118] Where, is the maximum irrigation water volume for crop i unit j at time k, m 3 .

[0119] S6: Based on the results of the multi-objective optimization model and the preliminary irrigation and drainage configuration plan of the watershed, DRAINMOD is used to construct a watershed drainage simulation model to determine the drainage flow and time;

[0120] Specifically, grid cells are configured for the irrigation and drainage resources in the basin, and a drainage simulation model is established. The model is as follows:

[0121] During the calculation period, the surface water balance calculation equation of the DRAINMOD model can be expressed as:

[0122] P+I=F+△S+R

[0123] Where P is rainfall, cm; I is irrigation water, cm; F is surface infiltration, cm; △S is the change in surface water storage, cm; R is surface runoff, cm.

[0124] In the same calculation period, the water balance calculation equation in the soil profile is:

[0125] △V=D+ET+Ds-F

[0126] Where △V is the change in soil moisture, cm; D is the lateral drainage volume of the concealed pipe, cm; ET is the evaporation and transpiration, cm; and Ds is the deep seepage volume, cm.

[0127] S7: Construct a model verification system, calculate the amount of soil erosion in the watershed based on the CSLE model, determine the threshold of soil erosion in the watershed, and verify the preliminary configuration plan of irrigation and drainage in the watershed. If the verification fails, recalibrate the optimization model boundary conditions and solve the model until it meets the feasibility requirements.

[0128] Specifically, the soil erosion modulus is calculated using the Chinese Soil Loss Equation (CSLE), whose basic structure is as follows:

[0129] A=RKLSBET

[0130] Where A is the soil erosion modulus, t km -2 a -1 ; R represents rainfall erosion factor; K represents soil erodibility factor; L represents slope length factor, dimensionless; S represents slope factor, dimensionless; B represents vegetation cover factor; P represents soil and water conservation measures factor.

[0131] (1) Rainfall erosivity R

[0132] The rainfall erosivity factor (REF) is the potential for soil erosion caused by rainfall. It reflects the combined effects of raindrops impacting and separating soil particles and runoff from rainfall on soil erosion. The erosion caused by rainfall varies greatly. To quantify this variation, the following formula is used to analyze rainfall:

[0133]

[0134] α=21.586β -7.1891

[0135] Where k is the number of days with erosive rainfall (12 mm) in half a month; P i is the erosive rainfall amount of the i-th period in the half-month period. d12 is the daily average rainfall of ≥12 mm, P y12 The annual average rainfall is ≥12 mm daily.

[0136] (2) Soil erodibility factor K

[0137] Soil erodibility is an important indicator that reflects the sensitivity of soil to separation, impact, and transport by rainfall erosion. The calculation formula for soil erodibility factor is as follows:

[0138] K=0.1317×[2.1×10 -4(12-OM)M 1.14 +3.25(s-2)+2.5(p-3)] / 100

[0139] Where OM is the organic matter content; M is the product of the dominant particle size composition = (0.002-0.1 mm particle size content) * (silt content + sand content); s is the grade; p is the soil permeability grade; and 0.1317 is the conversion coefficient.

[0140] (3) Terrain factor SL

[0141] The topographic factor is the main topographic characteristic element reflecting the regional landform. It is the ratio of the amount of soil loss on the slope to the amount of soil loss generated on a slope with a slope length of 22.13m and a slope gradient of 5.13°, with other conditions being the same. The topographic factor is calculated as follows:

[0142]

[0143] m=β / (1+β)

[0144]

[0145] Where S is the slope factor, dimensionless; L is the slope length factor, dimensionless; θ is the slope, in degrees; λ is the slope length projection, in meters; m is the slope length index; and β is the ratio of rill erosion to interrill erosion.

[0146] (4) Biological measures factor B

[0147] The biological measure factor is the ratio of soil loss on land covered with vegetation to soil loss on cleared, fallow land under the same conditions. Because the study site is cultivated land with no non-crop vegetation cover, the B value is 1.

[0148] (5) Engineering measures factor E

[0149] The engineering measure factor is the ratio of soil loss on land with a certain engineering measure to soil loss on land without engineering measures under the same conditions. Because the study site is cultivated land with no engineering measures, the E value is 1.

[0150] (6) Tillage measure factor T

[0151] The tillage practice factor refers to the ratio of soil loss on land that has adopted a certain tillage practice to soil loss on traditionally cultivated land under the same conditions. There are two sub-factors of the T factor, one of which is T due to crop differences. v , the other is T due to the difference in ridge direction r .

[0152] Based on the drainage flow and time determined by S6, soil erosion factors such as the basin slope and slope, the optimized soil erosion amount in the basin is calculated; the gridded basin irrigation and drainage resource allocation results are input into the verification system to verify the sustainability of the optimization control model results. If the verification fails, the optimization system boundary conditions are recalibrated and the model is solved, and feedback is looped to form a "simulation-optimization" loop feedback control technology to correct the basin water resource allocation results and form a basin irrigation and drainage resource allocation plan.

Claims

1. A multi-objective grid-based dynamic optimization method for irrigation and drainage in cold black soil watersheds, characterized by: The following steps are involved: S1: Divide the watershed to be configured into configuration units, determine the water supply and demand relationship between each unit, and generate multiple watershed irrigation and drainage resource configuration grid units, confirm the spatial position and topological relationship between the units, and determine the grid irrigation and drainage spatial relationship; S2: Determine the tillage method based on the grid spatial position, select typical soil samples from the black soil area for testing, and establish a typical soil database for the black soil area; S3: Based on the typical soil database of the black soil region and the data from the basin meteorological stations, a distributed freeze-thaw hydrological model and a crop model were constructed for calibration and verification. S4: Obtain basin monitoring data, and obtain the boundary conditions of water resources allocation and the water-yield function relationship for basin irrigation and drainage decision-making based on the basin monitoring data, the basin's distributed freeze-thaw hydrology, and the crop model; S5: Construct a watershed anti-erosion and emission reduction grid irrigation and drainage decision model, solve the model according to the water resources allocation boundary conditions and the water volume-yield function relationship of the watershed irrigation and drainage decision, and obtain a preliminary irrigation and drainage configuration plan for the watershed; S6: Construct a drainage simulation model based on the preliminary irrigation and drainage configuration plan of the watershed to determine the drainage flow and time; S7: Construct a model verification system and verify the preliminary configuration plan of the watershed irrigation and drainage based on the model verification system. If the verification fails, recalibrate and optimize the model boundary conditions and solve the model until it meets the feasibility requirements.

2. The multi-objective grid-based dynamic optimization method for irrigation and drainage in cold black soil watersheds according to claim 1 is characterized in that: S1 includes the following sub-steps: S11: Vectorize the water system information of different levels 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 valid configuration units, generate watershed irrigation and drainage configuration grid units, and confirm the spatial positions and topological relationships between the irrigation and drainage configuration grid units.

3. The multi-objective grid-based dynamic optimization method for irrigation and drainage in cold black soil watersheds according to claim 1 is characterized in that: S2 includes the following sub-steps: S21: Sampling monitoring points were selected for typical black soil and sandy soil in the basin to measure soil bulk density, organic carbon content, saturated hydraulic conductivity, electrical conductivity, calcium carbonate content, and pH; S22: Monitoring points are arranged on both the transverse and along-slope according to different tillage methods in the basin to measure the slope direction, slope gradient, and soil erosion amount; and a typical soil database is constructed in the form required by the distributed hydrological model.

4. The multi-objective grid-based dynamic optimization method for irrigation and drainage in cold black soil watersheds according to claim 1 is characterized in that: S3 includes the following sub-steps: S31: Build a distributed freeze-thaw hydrological simulation model for the watershed and obtain regional basic data, including meteorological and hydrological data, freeze-thaw snow data, digital elevation data, land use data, and soil type data; S32: Analyze regional terrain based on digital elevation data to define the river network, divide it into sub-basins, and calculate sub-basin parameters; S33: Divide the hydrological response units according to soil type data, land use data, and slope data, input meteorological and hydrological data into the distributed hydrological simulation model, and output the water volume converted from snowmelt water; S34: constructing a crop growth simulation model, obtaining watershed crop planting and growth data, wherein the watershed crop planting and growth data includes meteorological data, field management data, and soil data; and inputting the meteorological data, field management data, and soil data into the crop growth simulation model; S35: Parameter calibration and verification of distributed hydrological and crop simulation models using regional meteorological and hydrological data and crop yield data.

5. The multi-objective grid-based dynamic optimization method for irrigation and drainage in cold black soil watersheds according to claim 1 is characterized in that: In S5, the constraints of the watershed anti-erosion and emission reduction grid irrigation and drainage decision-making model include the dynamic water balance equation, the total water resources constraint, the dynamic response constraint of waterlogging, and the dynamic threshold constraint of irrigation volume.

6. The multi-objective grid-based dynamic optimization method for irrigation and drainage in cold black soil watersheds according to claim 1 is characterized in that: S6 is specifically as follows: according to the topological relationship of the grid units in the watershed irrigation and drainage configuration and the watershed water resources configuration database, the grid irrigation and drainage volume is clarified, a grid drainage simulation model is constructed, and the drainage flow and time are output.

7. The multi-objective grid-based dynamic optimization method for irrigation and drainage in cold black soil watersheds according to claim 1 is characterized in that: The process of building a model verification system described in S7 includes: S71: Calculate the amount of soil erosion in the watershed and determine the threshold value of soil erosion in the watershed; S72: Calculate the optimized watershed soil erosion amount based on the drainage flow and time, watershed slope and slope soil erosion factor determined in S6; S73: Continue running the model until the watershed soil erosion threshold is met.

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