A dynamic coupling method of moisture process based on black soil tillage layer-surface-atmosphere

Abstract

The present invention relates to the field of agricultural water management technology, and discloses a method for dynamically coupling water processes based on the black soil topsoil layer, surface layer, and atmosphere. The method comprises the following steps: S1: basic data collection; S2: soil type classification; S3: establishing a water movement process model at the micro-domain level; S4: establishing a water movement process model at the topsoil layer level, quantifying the water movement process at the topsoil layer using the soil water balance equation; S5: quantifying soil erosion and non-point source pollution emissions; S6: simulating water movement processes at the surface layer; S7: simulating water movement processes at the atmospheric scale; and S8: constructing a dynamic feedback relationship between coupled micro-domain, topsoil layer, surface layer, and atmosphere multi-scale water movement processes. This method, targeting different soil types in black soil regions, explores the feedback mechanism of water migration processes at the micro-domain and topsoil layer, surface layer, and atmosphere levels on soil migration and nitrogen and phosphorus emissions, and analyzes the response relationship between water movement and soil erosion and non-point source pollution emissions.

Description

Technical Field

[0001] The present invention relates to the field of agricultural water management, and in particular to a dynamic coupling method of moisture processes based on black soil plough layer-surface-atmosphere. Background Art

[0002] Due to long-term irrational development and utilization, the Black Soil Region is facing serious soil degradation problems, particularly soil erosion, nutrient loss, and improper water management. These problems not only threaten the sustainability of agricultural production in the region but also affect the stability of the ecological environment. With global climate change and intensified human activities, soil degradation in the Black Soil Region will become more complex and severe.

[0003] Existing agricultural production and water resource management technologies often struggle to balance efficient utilization and ecological protection in the complex environment of black soil regions. Traditional irrigation and fertilization methods often rely on empirical judgment and lack scientific quantitative analysis and dynamic monitoring, resulting in significant water waste and soil nutrient loss. Furthermore, soil erosion and non-point source pollution are increasingly prominent issues in large-scale agricultural production, especially under conditions of frequent and uneven rainfall events. Addressing these issues is urgent. Summary of the Invention

[0004] To overcome or alleviate one or more of the above technical problems, the present invention aims to provide a method for dynamically coupling water processes in the black soil topsoil layer, surface, and atmosphere. By conducting in-depth research on the water migration processes and their mutual feedback mechanisms among black soil micro-domains, the topsoil layer, the surface, and the atmosphere, this method explores green and efficient agricultural water resource management and governance technologies. This method not only helps agricultural producers optimize irrigation and fertilization strategies, improve water resource utilization efficiency, and reduce agricultural non-point source pollution, but also provides a scientific basis for promoting an ecological model for green and sustainable agricultural development.

[0005] The present invention provides the following technical solutions:

[0006] A method for dynamic coupling of moisture processes based on black soil plough layer, surface and atmosphere comprises the following steps:

[0007] S1: Basic data collection, including meteorological and hydrological data, irrigation data, environmental data and soil monitoring data;

[0008] S2: Based on the main soil types in the study area, the black soil is divided into four types: meadow soil, white pulp soil, black soil and dark brown soil;

[0009] S3: Establish a water movement process model at the micro-domain level. A microscopic water movement process model is established on the various soil types divided in step S2. The microscopic water movement equation involves the calculation of field water holding capacity, adsorbed water, and capillary water, which are quantified using experimental methods.

[0010] S4: Establish a model of water movement process at the tillage layer level, and quantify the water movement process in the tillage layer using the soil water balance equation;

[0011] S5: Quantify soil erosion and non-point source pollution emission processes;

[0012] S6: Simulate the water movement process at the surface level, using the SWAT model to simulate the water cycle process, soil erosion process and non-point source pollution process of various soil types, and obtain the contribution of different surface soil types to runoff, gravel and pollution;

[0013] S7: Simulate atmospheric-scale water movement;

[0014] S8: Construct a dynamic feedback relationship between the multi-scale water movement process of coupled micro-domains, tillage layer, surface and atmosphere, set up fixed-point monitoring systems for various soils, monitor soil moisture content and soil water potential change information, and realize all-round monitoring of environmental data of various soils through IoT sensing devices. The sensing data is collected and stored through the IoT data middle platform, and then handed over to the big data processing engine to use intelligent algorithms to connect to the SWAT model to analyze and simulate the water operation process. The simulation process considers the water balance equation at the atmospheric scale, and uses atmospheric-scale meteorological data as input and inputs it into the SWAT model, ultimately forming a water resource decision-making platform with the significance of guiding agricultural production; the water resource scheduling decision-making platform implements a multi-objective decision-making algorithm, whose goals include maximizing yield, minimizing soil erosion and minimizing non-point source pollution emissions; the water resource scheduling decision-making platform will pass the final scheduling information to the IoT interactive control device to complete the intelligent irrigation control of agriculture.

[0015] According to some embodiments, the method of quantification in step S3 comprises the following steps:

[0016] S31: First, various soils were sampled separately. The original soil for the test was collected from various sites using the S-type sampling method. At least three soil samples were collected from each site for repeated testing.

[0017] S32: Test to determine field water holding capacity, adsorbed water and capillary water;

[0018] The adsorbed water content is calculated as follows:

[0019] Adsorbed water content (%) = (Wwet - Wdry) / Wdry × 100% (1)

[0020] W a_i = Absorbed water content (%) × soil thickness (mm) (2)

[0021] In formulas 1 and 2, i is the soil type index, i = 1...4; Wwet is the wet weight of the soil after adsorption of water, g; Wdry is the dry weight of the soil, g; W a_i is the amount of adsorbed water of soil type i, mm;

[0022] The capillary water content is calculated as follows:

[0023] Capillary water content (%) = (Wzwet - Wdry) / Wdry × 100% - adsorbed water content (%) (3)

[0024] W c_i = Capillary water content (%) × soil thickness (mm) (4)

[0025] In Equations 3 and 4, Wzwet is the weight of wet soil after gravity water removal, g; Wdry is the dry weight of soil, g; W c_i is the capillary water content of soil type i, mm;

[0026] S33: Construct a microscopic water movement process model. There is a certain relationship between field water holding capacity, adsorbed water, capillary water, soil moisture content, and crop absorbable water. The formula is as follows:

[0027] W E_i =W f_i -W L_i (5)

[0028] Where W L_i is the minimum water content of soil type i, mm; W E_i is the effective water content of soil type i, which is also the amount of water that crops can extract from the soil, mm; W f_i is the maximum water content of soil type i, also known as the field capacity of soil type i, in mm.

[0029] According to some embodiments, in step S4, the water movement process of the plough layer is quantified using the soil water balance equation, wherein the soil moisture content is calculated as follows:

[0030] W L_i =W c_i +W a_i (6)

[0031] W E_i =IQ+P-ET a -L i -R i (7)

[0032] In formula 6, WL_i is the minimum water content of soil type i, mm; W c_i is the capillary water content of soil type i, mm; W a_i is the amount of adsorbed water of soil type i, mm;

[0033] In formula 7, IQ is the amount of irrigation water required for topsoil crops, mm; P is the rainfall, mm; ET a is evapotranspiration, including soil surface evaporation (mm) and plant transpiration (mm), mm; L is the infiltration water of soil type i; R i is the runoff loss for soil type i, mm.

[0034] According to some embodiments, step S5 comprises the following steps:

[0035] S51: Establish the mechanism of soil erosion and water movement in multiple groups of black soil. Use experimental simulation to quantify different soil moisture contents, soil bulk density, runoff rate, and soil erosion rate. Calculate runoff rate and soil erosion rate. The formula is as follows:

[0036] V ic =L / t ic (8)

[0037]

[0038] D ic =M ic / T (10)

[0039] In formulas 8, 9, and 10, c is the crop type, c = 1...3; V ic is the runoff velocity of soil type i and crop c, m 3 / s;L ic is the distance that runoff passes, which is 0.5 m in the experiment; t is the time it takes for soil type i crop c to pass the runoff distance L, in seconds; R ic is the cumulative runoff of soil type i and crop c, L; R n_ic is the runoff per unit time of soil type i and crop c, the unit time is 4 minutes, and N is 15; D ic is soil type i crop c soil erosion rate, g / min; M ic is the mass of eroded sediment for soil type i and crop c, g; T is the time for collecting eroded sediment, min, which was 2 min in the experiment;

[0040] The random forest model was used to fit the functional relationship between rainfall and soil erosion, irrigation water and soil erosion under different rainfall intensities, and the relationship between soil erosion and water movement was obtained.

[0041] S52: Water movement at the tillage level will inevitably lead to the emission of non-point source pollutants. Based on the experimental simulation method, the relationship between water and pollutant migration is quantified. The nitrogen content in the collected runoff water is measured to obtain the pollutant emission rate. The calculation formula is as follows:

[0042] N ic =V ic ·cN ic (11)

[0043] In formula 11, N ic is the nitrogen loss rate for crop c from soil type i, g / m 3 ;cN ic is the nitrogen content of soil type i and crop c, g / s; the random forest model was used to fit the functional relationship between rainfall and nitrogen loss, and between irrigation water and nitrogen loss under different rainfall intensities to obtain the relationship between nitrogen loss and water movement.

[0044] According to some embodiments, the surface water movement equation of step S6 is as follows:

[0045] S id =S id-1 +(P day -Q sur -ET a -W ise -Q gw ) (12)

[0046] In formula 12, S id is the soil water storage of soil type i at growth stage d, mm; S id-1 is the soil water storage of soil type i during the growing period d-1, mm; P day Daily precipitation, mm; Q sur is the surface runoff, mm; W ise is the surface runoff of soil type i, mm; Q gw is the base flow from shallow groundwater to the river, mm.

[0047] According to some embodiments, the simulation of atmospheric-scale water movement in step S7 includes the following calculation process:

[0048] The atmospheric water cycle guides the water movement process at the micro-domain, farmland, and surface scales. Its calculation formula is as follows:

[0049]

[0050] In formula 13, W is the water vapor content in the atmosphere (specific humidity), that is, the total mass of water vapor in the atmospheric column per unit area, kg / m 2 ; is the rate of change of water vapor content with time; Water vapor flux divergence, which represents the horizontal and vertical transport of water vapor; Q q is the water vapor flux vector, kg / (m 2 ·s); E is evaporation or transpiration, which represents the input of water vapor from the surface to the atmosphere, kg / m 2 / s; P is precipitation, which indicates the output of water vapor from the atmosphere to the surface, kg / m 2 / s;

[0051]

[0052] In Equation 14, q is the specific humidity, the mass of water vapor per unit mass of air, kg / kg; psfc and psfc are the air pressures at the surface and top of the atmosphere, kPa, respectively; dp is the change in air pressure; and g is the acceleration due to gravity, m / s 2 ;

[0053] Time rate of change of water vapor content Describes the change in the total amount of water vapor in the atmospheric column per unit time; it can usually be calculated from a continuous time series of water vapor content data

[0054]

[0055] In Equation 15, Δt is the time interval;

[0056] The water vapor flux divergence term represents the horizontal and vertical transport of water vapor; the water vapor flux Q q The calculation involves the product of wind speed and specific humidity:

[0057] Q q =q·u (16)

[0058] In Equation 16, u is the wind speed vector, m / s;

[0059]

[0060] In Equation 17, u, v, and w are the components of wind speed in the x, y, and z directions, respectively;

[0061] P=C·(e s -e a )·W Y (18)

[0062] In Equation 18, C is the efficiency of converting cloud water vapor into precipitation; e s is the saturated water vapor pressure, kPa; e a is the actual water vapor pressure, kPa; W Y is the cloud water content, g / m 3 .

[0063] According to some embodiments, the multi-objective decision-making algorithm in step S8 includes the following calculation steps:

[0064] Calculation formula for maximum output target:

[0065] maxYA ic =a ic IQ ic +b ic F c +c ic (19)

[0066] In equation 19, YA ic is the yield per unit area of ​​crop c in soil type i, kg / hm 2 IQ ic is the irrigation water quantity for crop c of soil type i, i.e., the decision variable, m 3 / hm 2 ; F c Fertilizer application rate for crop c, kg / hm 2 ;a ic 、b ic and c ic is the fitting parameter of soil type i and crop c;

[0067] Calculation formula for minimum soil erosion target:

[0068]

[0069] In formula 20, SE ic is the soil erosion modulus of soil type i and crop c, e ic 、f ic 、g ic 、h ic 、j ic are the fitting parameters between soil erosion amount and irrigation and rainfall for soil type i and crop c, respectively;

[0070] Calculation formula for minimum target of non-point source pollution:

[0071]

[0072] In formula 21, SN ic is the non-point source pollution output of soil type i and crop c, k ic 、o ic 、r ic are the fitting parameters between non-point source pollution and irrigation for soil type i and crop c, respectively.

[0073] Compared with the prior art, the present invention has the following beneficial effects:

[0074] (1) The present invention provides a dynamic coupling method for water processes based on the black soil topsoil-surface-atmosphere. For different soil types in black soil areas, the method uses the mutual feedback mechanism of water migration processes based on micro-domains and the topsoil-surface-atmosphere levels on soil migration and nitrogen and phosphorus emissions to analyze the response relationship between water movement and soil erosion and non-point source pollution emissions, and conducts relatively scientific quantitative analysis and dynamic monitoring.

[0075] (2) Based on the micro-domain and tillage-surface-atmosphere scale transformation relationships of different soils, quantitatively characterize the threshold value of the relationship between water and environmental effects of various black soil types;

[0076] (3) Quantify the water driving factors between the micro-domain, tillage layer, surface and atmosphere levels step by step, and reveal the mutual feedback effect of meteorological element changes on the dynamic coupling mechanism. DETAILED DESCRIPTION

[0077] The present invention is described in detail below with reference to the embodiments. However, it should be understood that the embodiments are merely illustrative of the present invention and do not limit the scope of protection of the present invention. All reasonable variations and combinations within the scope of the present invention fall within the scope of protection of the present invention.

[0078] The present invention will be further described below.

[0079] Example 1

[0080] This embodiment provides a method for dynamically coupling moisture processes based on black soil plough layer, surface, and atmosphere, comprising the following steps:

[0081] S1: Basic data collection, basic data include: meteorological and hydrological data, including effective rainfall, irrigation water utilization coefficient, and crop water demand; irrigation data, including irrigation area and head loss per unit area along the way, head loss per unit pipe length, calculated pipe section length, pipe roughness, pipe section flow, pipe section flow, pump station efficiency, and loss along the way; environmental data, including surface water supply, groundwater supply, runoff, aquifer permeability, and evapotranspiration; soil monitoring data, including 0.1-1m soil temperature and soil moisture content data.

[0082] S2: According to the main soil types contained in the black soil in the study area, the black soil is divided into four types: meadow soil, white pulp soil, black soil and dark brown soil (hereinafter referred to as the four types of soil);

[0083] S3: Establish a micro-level water movement process model. A microscopic water movement process model is established based on the four soil types divided in step S2. The microscopic water movement equation involves the calculation of field water holding capacity, adsorbed water, and capillary water, which are quantified using experimental methods. The experimental process includes the following steps:

[0084] S31: First, four soil samples were collected. The soil samples were collected in May, and the soil blocks were collected from the topsoil layer with a thickness of 30 cm. Using the S-type sampling method, the original soil of the four plots was collected. Three soil samples were collected at each location for repeated testing.

[0085] S32: Test to determine field water holding capacity, adsorbed water and capillary water;

[0086] Field Capacity: This is usually measured on-site. First, the soil is fully irrigated, and then the soil is allowed to drain by gravity for a period of time (usually 24-48 hours). The moisture content measured at this time is the field capacity.

[0087] Determination of adsorbed water: Dry the soil sample in an oven at 105°C to constant weight, ensuring that all capillary water and free water have been completely evaporated, leaving only adsorbed water and soil particles. Record the dry weight of the soil at this time (Wdry). Place the dried soil sample in a high humidity environment (usually an environment with a relative humidity of about 98% or close to 100%) to allow the soil sample to re-absorb moisture from the air until equilibrium is reached. Once the soil sample reaches adsorption equilibrium, weigh the soil sample again (Wwet), and the moisture at this time is mainly adsorbed water. The formula for calculating the adsorbed water content is as follows:

[0088] Adsorbed water content (%) = (Wwet - Wdry) / Wdry × 100% (1)

[0089] W a_i = adsorbed water content (%) × soil thickness (mm) (2)

[0090] In formulas 1 and 2, i is the soil type index, i = 1...4; Wwet is the wet weight of the soil after adsorption of water, g; Wdry is the dry weight of the soil, g; W a_i is the amount of adsorbed water of soil type i, mm.

[0091] Capillary water determination: Soak the soil sample completely in water so that the soil pores are filled with water and reach a saturated state. The time is 24 hours, and make sure that all pores are filled with water. Place the saturated soil sample in a drainable container, place the sample on a porous plate or filter paper, and let the gravity water drain under the action of gravity. Let the sample stand for 24-48 hours with free drainage. After the gravity water is removed, weigh the weight of the wet soil sample at this time (Wzwet). Place the soil sample in an oven and dry it at 105°C to constant weight, and record the dry weight of the soil after drying (Wdry). The formula for calculating capillary water content is as follows:

[0092] Capillary water content (%) = (Wzwet - Wdry) / Wdry × 100% - adsorbed water content (%) (3)

[0093] W c_i = Capillary water content (%) × soil thickness (mm) (4)

[0094] In Equations 3 and 4, Wzwet is the weight of wet soil after gravity water removal, g; Wdry is the dry weight of soil, g; W c_i is the capillary water content of soil type i, mm.

[0095] S33: Construct a microscopic water movement process model. There is a certain relationship between field water holding capacity, adsorbed water, capillary water, soil moisture content, and crop absorbable water. The formula is as follows:

[0096] W E_i =W f_i -W L_i (5)

[0097] In formula 5, W L_i is the minimum water content of soil type i, mm; W E_i is the effective water content of soil type i, which is also the amount of water that crops can extract from the soil, mm; W f_i is the maximum water content of soil type i, also known as the field capacity of soil type i, in mm.

[0098] S4: Establish a model for the water movement process at the tillage layer level. The water movement process in the tillage layer is quantified using the soil water balance equation, where the soil moisture content is quantified using the method in step S3. The calculation formula is as follows:

[0099] W L_i =W c_i +W a_i (6)

[0100] W E_i =IQ+P-ET a -L i -R i (7)

[0101] In formula 6, W L_i is the minimum water content of soil type i, mm; W c_i is the capillary water content of soil type i, mm; W a_i is the amount of adsorbed water of soil type i, mm.

[0102] In formula 7, IQ is the amount of irrigation water required for topsoil crops, mm; P is the rainfall, mm; ET a is evapotranspiration, including soil surface evaporation (mm) and plant transpiration (mm), mm; L is the infiltration water of soil type i; R i is the runoff loss for soil type i, mm.

[0103] S5: As moisture in the tillage layer moves, soil erosion and non-point source pollution emissions will occur. Quantifying these processes requires the following steps:

[0104] S51: Soil erosion is one of the main reasons for the degradation of black soil. Therefore, it is necessary to improve the mechanism of soil erosion and water movement in the four types of black soil. This embodiment uses experimental simulation to quantify the relevant processes.

[0105] Four types of soil were selected for the experiment, and rice, corn and soybean were planted respectively. The soil samples were collected in May, and crops had not yet been planted on the cultivated land. The soil blocks were collected from the 30cm thick plow layer to preserve the original structure of the soil. After the soil blocks were collected, the original experimental soil was collected in the experimental site using the S-type sampling method. The soil water content of different soil layers was measured by the drying method, and the soil bulk density of different soils was determined by the ring knife method. The plot was set to 100cm×35cm, and the ditch irrigation scenarios with rainfall intensities of 40mm / h, 60mm / h, 80mm / h, 100mm / h and 120mm / h (sprinkler irrigation) and a flow rate of 1.0L / min were set, and the flow rate of 100kg / hm 2 , 200kg / hm 2 、300kg / hm 2 , 400kg / hm 2 Four fertilization gradients were used, the fertilizer was nitrogen fertilizer, the soil slope was 5°, and each experimental treatment was repeated 5 times to avoid experimental contingency.

[0106] Before starting the experiment, the soil was moistened to saturation, and then covered with plastic sheeting and left to stand for 12 h to prevent evaporation of soil moisture.

[0107] The experiment began with the start of slope runoff, and the rainfall duration was 60 minutes. During the rainfall process, key information such as slope runoff, drop-offs, gully occurrence, and gully wall collapse was recorded. Runoff sediment samples were collected every 2 minutes from the start of slope runoff. Runoff velocity was measured using a dye tracer method (KMnO4). The time it took for the water to pass 0.5 meters in the direction of the slope was recorded, and the flow velocity was continuously measured until the rainfall ended. When measuring the flow velocity, an area on the slope with obvious water flow was selected. After the gully was formed, the water flow velocity in the gully was measured.

[0108] After the test, record the slope erosion conditions. If rills are present, measure their exact location on the slope, along with their length, width, and depth, using a ruler. Immediately weigh all runoff sediment samples for use in calculating runoff and soil erosion rates.

[0109] The runoff rate and soil erosion rate are calculated using the following formula:

[0110] Vic =L / t ic (8)

[0111]

[0112] D ic =M ic / T (10)

[0113] In formulas 8, 9, and 10, c is the crop type, c = 1...3; V ic is the runoff velocity of soil type i and crop c, m 3 / s;L ic is the distance that runoff passes, which is 0.5 m in the experiment; t is the time it takes for soil type i crop c to pass the runoff distance L, in seconds; R ic is the cumulative runoff of soil type i and crop c, L; R n_ic is the runoff per unit time of soil type i and crop c, the unit time is 4 minutes, and N is 15; D ic is soil type i crop c soil erosion rate, g / min; M ic is the mass of eroded sediment for soil type i and crop c, g; T is the eroded sediment collection time, min, which was 2 min in the experiment.

[0114] The random forest model was used to fit the functional relationship between rainfall and soil erosion, and between irrigation water and soil erosion under different rainfall intensities, and the relationship between soil erosion and water movement was obtained.

[0115] S52: Water movement at the tillage level inevitably leads to the emission of non-point source pollutants. This step quantifies the relationship between water and pollutant migration using experimental simulation methods. Based on the experimental method of S51, the nitrogen content of the collected runoff water is measured to obtain the pollutant emission rate, which is calculated as follows:

[0116] N ic =V ic ·cN ic (11)

[0117] In formula 11, N ic is the nitrogen loss rate for crop c from soil type i, g / m 3 ;cN ic is the nitrogen content of soil type i, crop c, g / s.

[0118] The random forest model was used to fit the functional relationship between rainfall and nitrogen loss, and between irrigation water and nitrogen loss under different rainfall intensities, and the relationship between nitrogen loss and water movement was obtained.

[0119] S6: Simulate the water movement process at the surface level. Since the surface level area is large, its water movement process and soil erosion and non-point source pollution emissions cannot be simulated experimentally, so the SWAT model is used for simulation. The micro-domain feeds back information related to the soil moisture content of the plow layer, which is used to calculate the water movement of the plow layer, soil erosion, and non-point source pollution. The soil erosion amount and non-point source pollution emission coefficient output by the plow layer can be used to calculate the long-term fixed-point emission amount. The calculated long-term soil erosion and non-point source pollution amount are used to calibrate the localized parameters of the SWAT model. The SWAT model is used to simulate the water cycle process, soil erosion process and non-point source pollution process of four types of soil to obtain the contribution of different surface soil types to runoff, its gravel and pollution.

[0120] The surface water movement equation is as follows:

[0121] S id =S id-1 +(P day -Q sur -ET a -W ise -Q gw ) (12)

[0122] In formula 12, S id is the soil water storage of soil type i at growth stage d, mm; S id-1 is the soil water storage of soil type i during the growing period d-1, mm; P day Daily precipitation, mm; Q sur is the surface runoff, mm; W ise is the surface runoff of soil type i, mm; Q gw is the base flow from shallow groundwater to the river, mm.

[0123] S7: Simulate the atmospheric water movement process. The atmospheric water cycle guides the water movement process at the micro-domain, farmland, and surface scales. The calculation formula is as follows:

[0124]

[0125] In formula 13, W is the water vapor content in the atmosphere (specific humidity), that is, the total mass of water vapor in the atmospheric column per unit area, kg / m 2 ; is the rate of change of water vapor content with time; Water vapor flux divergence, which represents the horizontal and vertical transport of water vapor; Q q is the water vapor flux vector, kg / (m 2 ·s); E is evaporation or transpiration, which represents the input of water vapor from the surface to the atmosphere, kg / m 2 / s; P is precipitation, which indicates the output of water vapor from the atmosphere to the surface, kg / m2 / s.

[0126]

[0127] In Equation 14, q is the specific humidity, the mass of water vapor per unit mass of air, kg / kg; psfc and psfc are the air pressures at the surface and top of the atmosphere, kPa, respectively; dp is the change in air pressure; and g is the acceleration due to gravity, m / s 2 .

[0128] Time rate of change of water vapor content Describes the change in the total amount of water vapor in the atmospheric column per unit time. It can usually be calculated using a continuous time series of water vapor content data.

[0129]

[0130] In Equation 15, Δt is the time interval.

[0131] The water vapor flux divergence term represents the horizontal and vertical transport of water vapor. q The calculation involves the product of wind speed and specific humidity:

[0132] Q q =q·u (16)

[0133] In Equation 16, u is the wind speed vector, m / s.

[0134]

[0135] In Equation 17, u, v, and w are the components of wind speed in the x, y, and z directions, respectively.

[0136] P=C·(e s -e a )·W Y (18)

[0137] In Equation 18, C is the efficiency of converting cloud water vapor into precipitation; e s is the saturated water vapor pressure, kPa; e a is the actual water vapor pressure, kPa; W Y is the cloud water content, g / m 3 .

[0138] S8: Construct a dynamic feedback relationship between the coupled micro-domain-cultivated layer-surface-atmosphere multi-scale water movement process, set up fixed-point monitoring systems for four types of soil, monitor soil moisture content and soil water potential change information, and realize all-round monitoring of environmental data of the four types of soil through IoT sensing devices. The sensing data is collected and stored through the IoT data middle platform, and then handed over to the big data processing engine to use intelligent algorithms to connect to the SWAT model to analyze and simulate the water operation process. The simulation process considers the water balance equation at the atmospheric scale, and uses atmospheric-scale meteorological data as input and inputs it into the SWAT model, eventually forming a water resource decision-making platform with the significance of guiding agricultural production. The water resource decision-making platform implements a multi-objective decision-making algorithm, whose goals include maximizing yield, minimizing soil erosion, and minimizing non-point source pollution emissions.

[0139] (1) Maximum output target

[0140] maxYA ic =a ic IQ ic +b ic F c +c ic (19)

[0141] In equation 19, YA ic is the yield per unit area of ​​crop c in soil type i, kg / hm 2 IQ ic is the irrigation water quantity for crop c of soil type i, i.e., the decision variable, m 3 / hm 2 ; F c Fertilizer application rate for crop c, kg / hm 2 ;a ic 、b ic and c ic are the fitting parameters for soil type i and crop c.

[0142] (2) The soil erosion minimum target related parameters are output by the water resources decision-making platform.

[0143]

[0144] In formula 20, SE ic is the soil erosion modulus of soil type i and crop c, e ic 、f ic 、g ic 、h ic 、j ic are the fitting parameters between soil erosion of soil type i and crop c and irrigation and rainfall, respectively.

[0145] (3) The parameters related to the minimum target of non-point source pollution are output by the water resources decision-making platform.

[0146]

[0147] In formula 21, SN ic is the non-point source pollution output of soil type i and crop c, k ic 、o ic 、r ic are the fitting parameters between non-point source pollution and irrigation for soil type i and crop c, respectively.

[0148] The scheduling decision-making platform will pass the final scheduling information to the IoT interactive control equipment to complete the intelligent irrigation control of agriculture.

[0149] The above embodiments are merely preferred embodiments of the present invention, and the scope of protection of the present invention is not limited to the above embodiments. All technical solutions that fall within the scope of protection of the present invention are within the scope of protection of the present invention. It should be noted that improvements and modifications that can be made by a person skilled in the art without departing from the principles of the present invention are also considered to be within the scope of protection of the present invention.

Claims

1. A dynamic coupling method of moisture process based on black soil plough layer-surface-atmosphere, characterized in that: The steps include: S1: Basic data collection, including meteorological and hydrological data, irrigation data, environmental data and soil monitoring data; S2: Based on the main soil types in the study area, the black soil is divided into four types: meadow soil, white pulp soil, black soil and dark brown soil; S3: Establish a microscopic water movement process model at the micro-domain level. This model is established on the various soil types divided in step S2. The microscopic water movement equation involves the calculation of field water holding capacity, adsorbed water, and capillary water, which are quantified using experimental methods. S4: Establish a model of water movement process at the tillage layer level, and quantify the water movement process in the tillage layer using the soil water balance equation; S5: Quantify soil erosion and non-point source pollution emission processes; S6: Simulate the water movement process at the surface level, using the SWAT model to simulate the water cycle process, soil erosion process and non-point source pollution process of various soil types, and obtain the contribution of different surface soil types to runoff, gravel and pollution; S7: Simulate atmospheric-scale water movement; S8: Construct a dynamic feedback relationship between the multi-scale water movement process of coupled micro-domains, tillage layer, surface and atmosphere, set up fixed-point monitoring systems for various soils, monitor soil moisture content and soil water potential change information, and realize all-round monitoring of environmental data of various soils through IoT sensing devices. The sensing data is collected and stored through the IoT data middle platform, and then handed over to the big data processing engine to use intelligent algorithms to connect to the SWAT model to analyze and simulate the water operation process. The simulation process considers the water balance equation at the atmospheric scale, and uses atmospheric-scale meteorological data as input and inputs it into the SWAT model, ultimately forming a water resource decision-making platform with the significance of guiding agricultural production; the water resource scheduling decision-making platform implements a multi-objective decision-making algorithm, whose goals include maximizing yield, minimizing soil erosion and minimizing non-point source pollution emissions; the water resource scheduling decision-making platform will pass the final scheduling information to the IoT interactive control device to complete the intelligent irrigation control of agriculture.

2. The method of dynamic coupling of moisture processes based on black soil plough layer, surface and atmosphere according to claim 1, characterized in that: The quantification method in step S3 includes the following steps: S31: First, various soils were sampled separately. The original soil for the test was collected from various sites using the S-type sampling method. At least three soil samples were collected from each site for repeated testing. S32: Test to determine field water holding capacity, adsorbed water and capillary water; The adsorbed water content is calculated as follows: Adsorbed water content (%) = (Wwet-Wdry) / Wdry×100% (1)W a_i = adsorbed water content (%) × soil thickness (mm) (2) In formulas 1 and 2, i is the soil type index, i = 1...4; Wwet is the wet weight of the soil after adsorption of water, g; Wdry is the dry weight of the soil, g; W a_i is the amount of adsorbed water of soil type i, mm; The capillary water content is calculated as follows: Capillary water content (%) = (Wwet-Wdry) / Wdry×100%-adsorbed water content (%) (3)W c_i = capillary water content (%) × soil thickness (mm) (4) In formulas 3 and 4, Wzwet is the weight of wet soil after gravity water removal, g; Wdry is the dry weight of soil, g; W c_i is the capillary water content of soil type i, mm; S33: Construct a microscopic water movement process model. There is a certain relationship between field water holding capacity, adsorbed water, capillary water, soil moisture content, and crop absorbable water. The formula is as follows: IN E_i =In f_i -IN L_i (5) Where W L_i is the minimum water content of soil type i, mm; W E_i is the effective water content of soil type i, which is also the amount of water that crops can extract from the soil, mm; W f_i is the maximum water content of soil type i, also known as the field capacity of soil type i, in mm.

3. The method for dynamic coupling of moisture processes based on black soil plough layer, surface and atmosphere according to claim 2, characterized in that: In step S4, the water movement process of the plough layer is quantified using the soil water balance equation, where the soil moisture content is calculated as follows: IN L_i =In c_i +W a_i (6) W E_i =IQ+P-ET a -L i -R i (7) In formula 6, W L_i is the minimum water content of soil type i, mm; W c_i is the capillary water content of soil type i, mm; W a_i is the amount of adsorbed water of soil type i, mm; In formula 7, IQ is the amount of irrigation water required for topsoil crops, mm; P is the rainfall, mm; ET a is evapotranspiration, including soil surface evaporation (mm) and plant transpiration (mm), mm; L i is the infiltration water volume of soil type i; R i is the runoff loss for soil type i, mm.

4. The method of dynamic coupling of moisture process based on black soil plough layer, surface and atmosphere according to claim 3, characterized in that: Step S5 includes the following steps: S51: Establish the mechanism of soil erosion and water movement in multiple groups of black soil. Use experimental simulation to quantify different soil moisture contents, soil bulk density, runoff rate, and soil erosion rate. Calculate runoff rate and soil erosion rate. The formula is as follows: D ic =M ic / T (10) In formulas 9 and 10, R ic is the cumulative runoff of soil type i and crop c, L; R n_ic is the runoff per unit time of soil type i and crop c, the unit time is 4 minutes, and N is 15; D ic is soil type i crop c soil erosion rate, g / min; M ic is the mass of eroded sediment for soil type i and crop c, g; T is the time for collecting eroded sediment, min, which was 2 min in the experiment; The random forest model was used to fit the functional relationship between rainfall and soil erosion, and between irrigation water and soil erosion under different rainfall intensities, to obtain the relationship between soil erosion and water movement; S52: Water movement at the tillage level will inevitably lead to the emission of non-point source pollutants. Based on the experimental simulation method, the relationship between water and pollutant migration is quantified, the nitrogen content in the collected runoff water is measured, and the pollutant emission rate is obtained.

5. The method for dynamic coupling of moisture processes based on black soil plough layer, surface and atmosphere according to claim 4, characterized in that: The surface water movement equation in step S6 is as follows: S id =S id-1 +(P day -Q sur -AND a -W ise -Q gw ) (12) In formula 12, S id is the soil water storage of soil type i at growth stage d, mm; S id-1 is the soil water storage of soil type i during the growing period d-1, mm; P day Daily precipitation, mm; Q sur is the surface runoff, mm; W ise is the surface runoff of soil type i, mm; Q gw is the base flow from shallow groundwater to the river, mm.

6. The method for dynamic coupling of moisture processes based on black soil plough layer, surface and atmosphere according to claim 5, characterized in that: The simulation of atmospheric-scale water movement in step S7 includes the following calculations: The atmospheric water cycle guides the water movement process at the micro-domain, farmland, and surface scales. Its calculation formula is as follows: In formula 13, W is the total water vapor content in the atmosphere, that is, the total mass of water vapor per unit area of ​​the atmospheric column, kg / m 2 ; is the rate of change of water vapor content over time, kg / (m 2 s); Water vapor flux divergence, which represents the horizontal and vertical transport of water vapor; Q q is the water vapor flux vector, kg / (m 2 ·s); E is evaporation or transpiration, which represents the input of water vapor from the surface to the atmosphere, kg / (m 2 ·s); P is precipitation, which represents the output of water vapor from the atmosphere to the surface, kg / (m 2 s); In Equation 14, q is the specific humidity, the mass of water vapor per unit mass of air, kg / kg; psfc and ptop are the air pressures at the surface and top of the atmosphere, kPa, respectively; dp is the change in air pressure; and g is the acceleration due to gravity, m / s 2 ; Time rate of change of water vapor content Describes the change in the total amount of water vapor in the atmospheric column per unit time; calculated using continuous time series water vapor content data In Equation 15, Δt is the time interval; The water vapor flux divergence term represents the horizontal and vertical transport of water vapor; the water vapor flux Q q The calculation involves the product of wind speed and specific humidity: Q q =q·U (16) In Equation 16, U is the wind speed vector, m / s; In Equation 17, u, v, and w are the components of wind speed in the x, y, and z directions, respectively; P=C·(e s -e a )·W Y (18) In Equation 18, C is the efficiency of converting cloud water vapor into precipitation; e s is the saturated water vapor pressure, kPa; e a is the actual water vapor pressure, kPa; W Y is the cloud water content, g / m 3 .

7. The method for dynamic coupling of moisture processes based on black soil plough layer, surface and atmosphere according to claim 6, characterized in that: The multi-objective decision-making algorithm in step S8 includes the following calculation steps: Calculation formula for maximum output target: maxYA ic =a ic IQ ic +b ic F c +m ic (19) In equation 19, YA ic is the yield per unit area of ​​crop c in soil type i, kg / hm 2 IQ ic is the irrigation water quantity for crop c of soil type i, i.e., the decision variable, m 3 / hm 2 ; F c Fertilizer application rate for crop c, kg / hm 2 ;a ic 、b ic and m ic is the fitting parameter of soil type i and crop c; Calculation formula for minimum soil erosion target: In formula 20, SE ic is the soil erosion modulus of soil type i and crop c, e ic 、f ic 、g ic 、h ic 、j ic are the fitting parameters between soil erosion amount and irrigation and rainfall for soil type i and crop c, respectively; Calculation formula for minimum target of non-point source pollution: In formula 21, SN ic is the non-point source pollution output of soil type i and crop c, k ic 、o ic 、r ic are the fitting parameters between non-point source pollution and irrigation for soil type i and crop c, respectively.

8. The method for dynamic coupling of moisture processes based on black soil plough layer, surface and atmosphere according to any one of claims 1 to 7, characterized in that: The meteorological and hydrological data in step S1 include effective rainfall, irrigation water utilization coefficient and crop water requirement; the irrigation data include irrigation area and head loss per unit area along the way, head loss per unit pipe length, calculated pipe section length, pipe roughness, pipe section flow, pipe section flow, pump station efficiency and loss along the way; the environmental data include surface water supply, groundwater supply, runoff, aquifer permeability coefficient and evaporation; the monitored soil data include 0.1-1m soil temperature and soil moisture content data.

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