Moisture process dynamic coupling method based on black soil plough layer-earth surface-atmosphere

Abstract

The invention relates to the technical field of agricultural water management, and discloses a black soil plough layer-earth surface-atmosphere-based moisture process dynamic coupling method, which comprises the following steps of S1, collecting basic data; s2, soil types are divided; s3, establishing a micro-domain hierarchical moisture movement process model; s4, establishing a moisture movement process model of a plough layer level, and quantifying the moisture movement process of the plough layer by adopting a soil water balance equation; s5, quantifying the soil erosion and non-point source pollution discharge process; s6, simulating the moisture movement process of the surface level; s7, simulating an atmospheric scale moisture movement process; and S8, constructing a dynamic mutual feedback relation of the multi-scale moisture movement process of the coupled microdomain, the plough layer, the earth surface and the atmosphere. According to the method, for different soil types in the black soil area, a mutual feedback mechanism for soil migration and nitrogen and phosphorus emission in the water migration process based on micro-domain and plough layer-earth surface-atmosphere levels is explored, and the response relation between water movement and soil erosion and non-point source pollution emission is analyzed.

Description

Technical Field

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

[0002] Due to long-term irrational development and utilization, the black soil region is facing serious soil degradation problems, especially 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, the soil degradation problem in the black soil region will become more complex and serious.

[0003] Existing agricultural production and water resource management technologies often fail to balance the needs of efficient utilization and ecological protection when dealing with the complex environment of black soil areas. Traditional irrigation and fertilization methods usually rely on experience and judgment, lack scientific quantitative analysis and dynamic monitoring methods, resulting in serious waste of water resources and loss of soil nutrients. In addition, the problems of soil erosion and non-point source pollution are becoming increasingly prominent in large-scale agricultural production, especially under conditions of frequent and uneven rainfall events. Solving these problems is urgent. Summary of the invention

[0004] In order to overcome or alleviate one or more of the above technical problems, the purpose of the present invention is to provide a dynamic coupling method of moisture process based on black soil plough layer-surface-atmosphere, and explore green and efficient agricultural water resources management and governance technology by deeply studying the black soil micro-domain, plough layer, surface and atmospheric moisture migration process and its mutual feedback mechanism. The invented method can not only help agricultural producers optimize irrigation and fertilization strategies, improve water resource utilization efficiency, and reduce agricultural non-point source pollution, but also provide a scientific basis to promote the ecological model of green and sustainable development of agriculture.

[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-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: 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;

[0009] S3: Establish a water movement process model at the micro-domain level. Establish a microscopic water movement process model on the various types of soils 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 by experimental methods.

[0010] S4: Establish a model of water movement process at the tillage layer level, and the water movement process of the tillage layer is quantified 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, use the SWAT model to simulate the water cycle process, soil erosion process and non-point source pollution process of various types of soil, and obtain the contribution of different surface soil types to runoff, its gravel and pollution;

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

[0014] S8: Construct a dynamic feedback relationship between the multi-scale water movement process of coupled micro-domain-cultivated layer-surface-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 the meteorological data at the atmospheric scale 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, and its goals include maximizing yield, minimizing soil erosion, and minimizing non-point source pollution emissions; the water resource scheduling decision-making platform will issue 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 are sampled separately, and the original soil for the test is collected from various lands by the S-type sampling method. At least 3 soils are collected from each point for repeated tests;

[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 draw from the soil, mm; W f_i is the maximum water content of soil type i, also known as the field water holding capacity of soil type i, in mm.

[0029] According to some embodiments, in step S4, the water movement process of the tillage layer is quantified using the soil water balance equation, wherein the soil water content is calculated using the following formula:

[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 irrigation water required for the topsoil crops, mm; P is the rainfall, mm; ET a is the evapotranspiration, including soil surface evaporation (mm2) and plant transpiration (mm2), 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 process of soil erosion and water movement in multiple groups of black soil, quantify different soil moisture content, soil bulk density, runoff rate, and soil erosion rate by experimental simulation, and calculate the 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 crop c in soil type i, m 3 / s;L ic is the distance that the runoff passes, which is 0.5 m in the experiment; t is the time it takes for the runoff of soil type i crop c to pass through distance L, s; 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 is 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: The movement of water at the tillage layer scale will inevitably lead to the emission of non-point source pollutants. According to 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. 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 of crop c from soil type i, g / m 3 ;cN ic is the nitrogen content of soil type i crop c, g / s; the random forest model was used to fit the functional relationship between rainfall and nitrogen loss, irrigation water and nitrogen loss under different rainfall intensities, and the relationship between nitrogen loss and water movement was obtained.

[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 process in step S7 includes the following calculation process:

[0048] The atmospheric water cycle guides the water movement process at the micro-domain-cultivated land-surface scale. 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 indicates 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 formula (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 at the top of the atmosphere, kPa, respectively; dp is the change in air pressure; 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 formula (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 formula (16), u is the wind speed vector, m / s;

[0059]

[0060] In formula 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 water vapor in clouds 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 of 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] The minimum soil erosion target calculation formula is:

[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 of soil type i and crop c and irrigation and rainfall, 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 crop c of soil type i, 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 method for dynamic coupling of water processes based on the black soil plough layer-surface-atmosphere provided by the present invention is aimed at different soil types in the black soil area. The mutual feedback mechanism of soil migration and nitrogen and phosphorus emissions based on the water migration process at the micro-domain and plough layer-surface-atmosphere levels is used to analyze the response relationship between water movement and soil erosion and non-point source pollution emissions, and a relatively scientific quantitative analysis and dynamic monitoring are carried out.

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

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

[0077] The present invention is described in detail below in conjunction with the embodiments, but it should be understood that the embodiments are only used to exemplify the present invention and do not constitute any limitation on the protection scope of the present invention. All reasonable changes and combinations included in the scope of the invention spirit of the present invention fall within the protection scope of the present invention.

[0078] The present invention is further described below.

[0079] Example 1

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

[0081] S1: Basic data collection, basic data include: meteorological and hydrological data, including effective rainfall, irrigation water utilization coefficient, crop water demand; irrigation data, including irrigation area and head loss per unit area along the way, head loss per unit pipe length, calculated length of pipe section, 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 coefficient, 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 water movement process model at the micro-domain level. A microscopic water movement process model is established 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 by experimental methods. The experimental process includes the following steps:

[0084] S31: First, four types of soil were sampled. The soil samples were collected in May, and the soil blocks were collected from 30 cm thick soil layers. The original soil was collected from the four types of land using the S-type sampling method. Three soil samples were collected from each point for repeated tests.

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

[0086] Field water holding capacity determination: usually obtained by on-site measurement. First, fully irrigate the soil, then wait for a period of time (usually 24-48 hours) to allow gravity water to drain. The moisture content measured at this time is the field water holding 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 reabsorb 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 = adsorption 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 to ensure 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 draw from the soil, mm; W f_i is the maximum water content of soil type i, also known as the field water holding capacity of soil type i, in mm.

[0098] S4: Establish a water movement process model at the tillage layer level. The water movement process of the tillage layer is quantified using the soil water balance equation, where the soil water content is quantified using the method in step S3, and 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 irrigation water required for the topsoil crops, mm; P is the rainfall, mm; ET a is the evapotranspiration, including soil surface evaporation (mm2) and plant transpiration (mm2), mm; L is the infiltration water of soil type i; R i is the runoff loss for soil type i, mm.

[0103] S5: As the moisture in the tillage layer moves, soil erosion and non-point source pollution emission processes will occur. It is necessary to quantify the soil erosion and non-point source pollution emission processes, including 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 of the four types of black soil. This embodiment uses experimental simulation to quantify the relevant process.

[0105] Four types of soil were selected for the experiment, and three crops, rice, corn and soybean, were planted respectively. The soil samples were collected in May, and no crops had been planted on the cultivated land yet. The soil blocks were collected from the 30cm thick plow layer to retain the original structure of the soil. After the soil blocks were collected, the original soil was collected in the experimental site by 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 100kg / hm 2 、200kg / hm 2 、300kg / hm 2 , 400kg / hm 2 There were four fertilization gradients, 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 for 12 h to prevent evaporation of soil moisture.

[0107] The experiment started from the time when the slope flow was generated, and the rainfall duration was 60 minutes. During the rainfall process, key information such as slope flow, fall, rill occurrence and gully wall collapse time were recorded. From the beginning of slope flow, runoff sediment samples were collected every 2 minutes. The runoff velocity was measured by dye tracer method (KMnO4), and the time for water flow to pass through the slope direction 0.5m was recorded. The flow velocity was measured continuously until the end of the rainfall. When measuring the flow velocity, select the part with obvious water flow on the slope, and after the rill is formed, measure the flow velocity in the rill.

[0108] After the test, record the slope erosion conditions. If rills are formed, use a ruler to measure and record the specific location of the rills on the slope, as well as the length, width and depth of the rills. Immediately weigh all runoff sediment samples to calculate the runoff rate and soil erosion rate.

[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 crop c in soil type i, m 3 / s;L ic is the distance that the runoff passes, which is 0.5 m in the experiment; t is the time it takes for the runoff of soil type i crop c to pass through distance L, s; 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 is 2 min in the experiment.

[0114] 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.

[0115] S52: The movement of water at the tillage level will inevitably lead to the emission of non-point source pollutants. This step quantifies the relationship between water and pollutant migration based on the experimental simulation method. According to the experimental method of S51, the nitrogen content in the collected runoff water is measured to obtain the pollutant emission rate. The calculation formula is as follows:

[0116] N ic =V ic ·cN ic (11)

[0117] In formula 11, N ic is the nitrogen loss rate of 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, 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 the soil moisture content information 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 output of the plow layer and the non-point source pollution emission coefficient can be used to calculate the long-term fixed-point emissions, and the calculated long-term soil erosion and non-point source pollution 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, 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 water movement process at the atmospheric scale. The atmospheric water cycle guides the water movement process at the micro-domain-cultivated land-surface scale. 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 indicates 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 formula (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 at the top of the atmosphere, kPa, respectively; dp is the change in air pressure; 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 formula (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 formula (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 water vapor in clouds 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 multi-scale water movement process of coupled micro-domain-cultivated layer-surface-atmosphere, 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 the meteorological data at the atmospheric scale 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, and its goals include maximizing yield, minimizing soil erosion, and minimizing non-point source pollution emissions.

[0139] (1) Maximum production 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 of 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 crop c of soil type i, 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 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 only preferred implementations of the present invention, and the protection scope of the present invention is not limited to the above embodiments. All technical solutions under the concept of the present invention belong to the protection scope of the present invention. It should be pointed out that for ordinary technicians in this technical field, improvements and modifications without departing from the principle of the present invention should also be regarded as the protection scope 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: 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; S3: Establish a water movement process model at the micro-domain level. Establish a microscopic water movement process model on the various types of soils 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 by experimental methods. S4: Establish a model of water movement process at the tillage layer level, and the water movement process of the tillage layer is quantified 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, use the SWAT model to simulate the water cycle process, soil erosion process and non-point source pollution process of various types of soil, and obtain the contribution of different surface soil types to runoff, its gravel and pollution; S7: Simulate atmospheric scale water movement processes; S8: Construct a dynamic feedback relationship between the multi-scale water movement process of coupled micro-domain-cultivated layer-surface-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 the meteorological data at the atmospheric scale 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, and its goals include maximizing yield, minimizing soil erosion, and minimizing non-point source pollution emissions; the water resource scheduling decision-making platform will issue the final scheduling information to the IoT interactive control device to complete the intelligent irrigation control of agriculture.

2. The method for dynamic coupling of moisture process based on black soil plough layer-surface-atmosphere according to claim 1 is characterized in that: The quantification method in step S3 includes the following steps: S31: First, various soils are sampled separately, and the original soil for the test is collected from various lands by the S-type sampling method. At least 3 soils are collected from each point for repeated tests; 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 = adsorption 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 (%) = (Wzwet-Wdry) / Wdry×100%-adsorbed water content (%) (3) W c_i = capillary water content (%) × soil thickness (mm) (4) 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; 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 draw from the soil, mm; W f_i is the maximum water content of soil type i, also known as the field water holding capacity of soil type i, in mm.

3. The method for dynamic coupling of moisture process based on black soil plough layer-surface-atmosphere according to claim 2 is characterized in that: In step S4, the water movement process of the tillage layer is quantified using the soil water balance equation, where the soil water 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 irrigation water required for the topsoil crops, mm; P is the rainfall, mm; ET a is the evapotranspiration, including soil surface evaporation (mm2) and plant transpiration (mm2), mm; L is the infiltration water of soil type i; R i is the runoff loss for soil type i, mm.

4. The method for dynamic coupling of moisture process based on black soil plough layer-surface-atmosphere according to claim 3 is characterized in that: Step S5 includes the following steps: S51: Establish the mechanism process of soil erosion and water movement in multiple groups of black soil, quantify different soil moisture content, soil bulk density, runoff rate, and soil erosion rate by experimental simulation, and calculate the runoff rate and soil erosion rate. The formula is as follows: V ic =L / t ic (8) D ic =M ic / T (10) In formulas 8, 9, and 10, c is the crop type, c = 1...3; V ic is the runoff velocity of crop c in soil type i, m 3 / s;L ic is the distance that the runoff passes, which is 0.5 m in the experiment; t is the time it takes for the runoff of soil type i crop c to pass through distance L, s; 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 is 2 min in the experiment; 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. S52: The movement of water at the tillage layer scale will inevitably lead to the emission of non-point source pollutants. According to 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. The calculation formula is as follows: N ic =V ic ·cN ic (11) In formula 11, N ic is the nitrogen loss rate of crop c from soil type i, g / m 3 ;cN ic is the nitrogen content of soil type i crop c, g / s; the random forest model was used to fit the functional relationship between rainfall and nitrogen loss, irrigation water and nitrogen loss under different rainfall intensities, and the relationship between nitrogen loss and water movement was obtained.

5. The method for dynamic coupling of moisture process based on black soil plough layer-surface-atmosphere according to claim 4 is 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 process based on black soil plough layer-surface-atmosphere according to claim 5 is characterized in that: The simulation of atmospheric scale water movement process in step S7 includes the following calculation process: The atmospheric water cycle guides the water movement process at the micro-domain-cultivated land-surface scale. Its calculation formula is as follows: 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 over time; ▽·Q q 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 indicates 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; In formula (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 at the top of the atmosphere, kPa, respectively; dp is the change in air pressure; 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; it can usually be calculated from a continuous time series of water vapor content data In formula (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 formula (16), u is the wind speed vector, m / s; In formula 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 water vapor in clouds 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 process based on black soil plough layer-surface-atmosphere according to claim 6 is characterized in that: The multi-objective decision-making algorithm in step S8 includes the following calculation steps: Calculation formula for maximum output target: max YA ic =a ic IQ ic +b ic F c +c ic (19) In equation 19, YA ic is the yield per unit area of ​​crop c of 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; The minimum soil erosion target calculation formula is: 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; Calculation formula for minimum target of non-point source pollution: In formula 21, SN ic is the non-point source pollution output of crop c of soil type i, 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 process based on black soil plough layer-surface-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 and evaporation; the monitored soil data include 0.1-1m soil temperature and soil moisture content data.

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