A simulation method and device for soil salinization response to water saving

Abstract

The application provides a soil salinization response simulation method and device, the method comprises the following steps: obtaining the soil type, terrain grid data and meteorological data of a target area; inputting the terrain grid data into a first processing model to obtain a sub-basin division result; inputting the meteorological data and soil type into the first processing model according to the sub-basin division result to obtain sub-basin daily precipitation data and sub-basin evapotranspiration; inputting the sub-basin daily precipitation data and sub-basin evapotranspiration into a second processing model to obtain soil salt content and crop yield in combination with sub-basin irrigation; obtaining soil salinization prevention and control measures according to the soil salt content and crop yield; wherein the first processing model is obtained by calibrating a preset processing module according to the difference between the monthly runoff of a basin and the measured calibration data. The application can improve the accuracy, applicability and operability of soil salinization prevention and control.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of soil data processing, in particular to a simulation method and device for water-saving response of soil salinization. BACKGROUND

[0002] Salinization is a key factor limiting the sustainable development of agricultural irrigation areas, especially in arid and semiarid irrigation areas. The existing water and salt monitoring and crop prediction methods mostly rely on single models, such as soil and water assessment models or crop water productivity models, which are difficult to simultaneously consider the hydrological process at the basin scale and the crop growth response at the field scale, resulting in a lack of quantitative and systematic prediction means in water resource allocation and salinization prevention and control. SUMMARY

[0003] The technical problem to be solved by the present application is to provide a simulation method and device for water-saving response of soil salinization. The accuracy, applicability and operability of soil salinization prevention and control can be improved.

[0004] To solve the above technical problems, the technical solutions of the present application are as follows:

[0005] A simulation method for water-saving response of soil salinization, comprising:

[0006] obtaining soil type, terrain grid data and meteorological data of a target area;

[0007] inputting the terrain grid data into a first processing model to obtain a sub-basin division result;

[0008] According to the sub-basin division result, inputting the meteorological data and soil type into the first processing model to obtain sub-basin daily precipitation data and sub-basin evapotranspiration;

[0009] inputting the sub-basin daily precipitation data and sub-basin evapotranspiration into a second processing model, combining sub-basin irrigation amount to obtain soil salt content and crop yield;

[0010] According to the soil salt content and crop yield, obtaining soil salinization prevention and control measures;

[0011] The first processing model is obtained by calibrating a preset processing module according to the difference between the monthly runoff of the basin and the measured calibration data.

[0012] Optionally, the soil type of the target area is obtained, comprising:

[0013] obtaining soil grid data of the target area;

[0014] matching the soil grid data with soil classification standards to obtain soil type; the soil type includes clay type, silt type, sandy type and sandy loam type.

[0015] Optionally, the terrain grid data is input into a first processing model to obtain a sub-basin division result, including:

[0016] The terrain grid data is subjected to depression filling to obtain target terrain grid data;

[0017] According to the target terrain grid data, flow direction determination is performed to obtain a flow direction matrix;

[0018] According to the flow direction matrix, flow accumulation is performed to determine a basin boundary to obtain a sub-basin division result.

[0019] Optionally, according to the sub-basin division result, the meteorological data and soil type are input into the first processing model to obtain sub-basin daily precipitation data and sub-basin evapotranspiration, including:

[0020] The meteorological data is distributed to the sub-basins through geometric division to obtain sub-basin daily precipitation data;

[0021] According to the meteorological data and soil type of the sub-basins, sub-basin evapotranspiration is obtained.

[0022] Optionally, according to the difference between the basin monthly runoff and the measured calibration data, a preset processing model is calibrated to obtain a first processing model, including:

[0023] The basin monthly runoff is obtained;

[0024] According to the dispersion degree between the basin monthly runoff and the measured calibration data, a determination coefficient is obtained;

[0025] According to the determination coefficient, the preset processing model is calibrated to obtain the first processing model.

[0026] Optionally, the sub-basin daily precipitation data and the sub-basin evapotranspiration are input into a second processing model in combination with sub-basin irrigation amount to obtain soil salt content and crop yield, including:

[0027] Using the second processing model, according to the sub-basin daily precipitation data and the sub-basin evapotranspiration, and the sub-basin irrigation amount, irrigation water salt concentration, and soil volume, soil salt content is obtained;

[0028] According to the soil salt content, the sub-basin daily precipitation data, the sub-basin evapotranspiration, and the sub-basin irrigation amount, crop yield is obtained.

[0029] Optionally, according to the soil salt content and the crop yield, soil salinization prevention and control measures are obtained, including:

[0030] The change curve among the soil salt content, crop yield and water saving degree is constructed, the water saving threshold is determined based on the change curve inflection point, and the soil salinization prevention and control measure is obtained.

[0031] Embodiments of the present application also provide a simulation device for water saving response of soil salinization, comprising:

[0032] The acquisition module is configured to acquire the soil type, the terrain grid data and the meteorological data of the target region.

[0033] The processing module is configured to input the terrain grid data into a first processing model to obtain a sub-basin division result; input the meteorological data and the soil type into the first processing model according to the sub-basin division result to obtain sub-basin daily precipitation data and sub-basin evapotranspiration; input the sub-basin daily precipitation data and the sub-basin evapotranspiration into a second processing model to obtain the soil salt content and the crop yield in combination with sub-basin irrigation; and obtain the soil salinization prevention and control measure according to the soil salt content and the crop yield; wherein the first processing model is obtained by calibrating a preset processing model according to the difference between the basin monthly runoff and the measured calibration data.

[0034] Embodiments of the present application also provide a computing device, comprising: one or more processors; a storage device configured to store one or more programs, when the one or more programs are executed by the one or more processors, the one or more processors implement the simulation method for water saving response of soil salinization according to the present application.

[0035] Embodiments of the present application also provide a computer readable storage medium, the computer readable storage medium stores a program, when the program is executed by a processor, the simulation method for water saving response of soil salinization according to the present application is implemented.

[0036] The above technical solutions of the present application have at least the following technical effects:

[0037] The simulation method of soil salinization responding to water saving according to the application, by obtaining the soil type, terrain grid data and meteorological data of the target area; inputting the terrain grid data into the first processing model to obtain the sub-basin division result; according to the sub-basin division result, inputting the meteorological data and soil type into the first processing model to obtain the sub-basin daily precipitation data and sub-basin evapotranspiration; inputting the sub-basin daily precipitation data and sub-basin evapotranspiration into the second processing model, combined with the sub-basin irrigation amount, to obtain the soil salt content and crop yield; according to the soil salt content and crop yield, obtaining the soil salinization prevention and control measures; wherein the first processing model is obtained by calibrating the preset processing module according to the difference between the monthly runoff of the basin and the measured calibration data. The second processing model is based on the sub-basin daily precipitation data and sub-basin evapotranspiration output by the first processing model to couple and predict the soil salt content and crop yield, which can improve the accuracy, applicability and operability of soil salinization prevention and control. BRIEF DESCRIPTION OF DRAWINGS

[0038] Figure 1 is the overall schematic diagram of the simulation method of soil salinization responding to water saving according to the application;

[0039] Figure 2 is the flowchart of the simulation method of soil salinization responding to water saving according to the application;

[0040] Figure 3 is the coupling deduction diagram of soil salt content and crop yield responding to water saving of the simulation method of soil salinization responding to water saving according to the application;

[0041] Figure 4 is the calibration result diagram of soil moisture of the simulation method of soil salinization responding to water saving according to the application;

[0042] Figure 5 is the calibration result diagram of soil salt content of the simulation method of soil salinization responding to water saving according to the application;

[0043] Figure 6 is the verification result diagram of soil moisture of the simulation method of soil salinization responding to water saving according to the application;

[0044] Figure 7 is the verification result diagram of soil salt content of the simulation method of soil salinization responding to water saving according to the application;

[0045] Figure 8 is the prediction result diagram of salt content under different water saving degrees of the simulation method of soil salinization responding to water saving according to the application;

[0046] Figure 9is a schematic diagram of a prediction result of yield under different water-saving degrees of a simulation method of response of soil salinization to water saving according to the present application;

[0047] Figure 10 is a schematic diagram of a simulation device of response of soil salinization to water saving according to the present application; DETAILED DESCRIPTION

[0048] Exemplary embodiments of the present application will be described in greater detail below with reference to the accompanying drawings. Although exemplary embodiments of the present application are shown in the drawings, it is understood that the present application can be implemented in various forms and should not be limited by the embodiments set forth herein. Rather, these embodiments are provided so that the present application can be more thoroughly understood and so that the scope of the present application can be completely conveyed to those skilled in the art.

[0049] As Figure 1 shown, an embodiment of the present application proposes a simulation method of response of soil salinization to water saving, comprising:

[0050] Step S1, obtaining soil type, terrain raster data and meteorological data of a target area;

[0051] Step S2, inputting the terrain raster data into a first processing model to obtain sub-basin division results;

[0052] Step S3, according to the sub-basin division results, inputting the meteorological data and soil type into the first processing model to obtain sub-basin daily precipitation data and sub-basin evapotranspiration;

[0053] Step S4, inputting the sub-basin daily precipitation data and sub-basin evapotranspiration into a second processing model to obtain soil salt content and crop yield in combination with sub-basin irrigation amount;

[0054] Step S5, obtaining soil salinization prevention and control measures according to the soil salt content and crop yield;

[0055] The first processing model is obtained by calibrating a preset processing module according to the difference between the monthly runoff of the basin and the measured calibration data.

[0056] In this embodiment, as Figure 1As shown in the simulation method of the response of soil salinization to water saving, first, relevant data of a target region is acquired, land use types are acquired through a public land use data set, the accuracy of the land use data set is 30*30m, the land use types are divided into 8 types, i.e., farmland, forest land, grassland, shrubbery, wetland, water area, construction land and bare land; acquired meteorological data includes precipitation, maximum air temperature, minimum air temperature, sunshine duration, relative humidity, wind speed, monthly average maximum and minimum temperature and standard deviation, monthly average rainfall and standard deviation, dew point temperature, monthly average wind speed, average solar radiation, etc., solar radiation is calculated through the sunshine duration by using an empirical formula; soil types and terrain raster data also need to be acquired;

[0057] Then, the terrain raster data is processed by using a first processing model to calculate river network and flow direction data, and a sub-basin division result is obtained; the first processing model is obtained by calibrating a preset processing model according to the difference between the monthly runoff of a basin and the measured calibration data;

[0058] Then, according to the sub-basin division result, the meteorological data is processed by using the first processing model to obtain sub-basin daily precipitation data and sub-basin evapotranspiration;

[0059] Then, the sub-basin daily precipitation data and the sub-basin evapotranspiration are processed by using a second processing model, and combined with sub-basin irrigation amount, soil salt content and crop yield are obtained;

[0060] Finally, according to the soil salt content and the crop yield, a change curve is drawn to obtain soil salinization prevention and control measures.

[0061] The scheme of the present application realizes quantitative prediction of soil water and salt dynamics and crop yield under different irrigation scenarios through the organic combination of basin hydrological process simulation and field crop model, solves the simulation problem of large-scale soil salinization response to water saving, and determines the water saving threshold of salinization prevention and control.

[0062] In an optional embodiment of the present application, in step S1, the soil types of the target region are acquired, including:

[0063] Step S11, acquiring soil raster data of the target region;

[0064] Step S12, matching the soil raster data with soil classification standards to obtain soil types; the soil types include clay type, silt type, sandy type and sandy loam type.

[0065] In this embodiment, first, the soil grid data of the target area is extracted from the public soil database, and then the actual soil type names in the target area are collected and sorted into a list;Key physicochemical property data such as particle composition (sand, silt, clay content), organic matter content, bulk density, pH value, etc. are extracted from the soil grid data, and according to the soil particle composition, refer to the soil texture classification standard, match the soil texture of the target area with the texture classification field (such as clay, silt, sandy, sandy loam, etc.), and get the soil type;For example: if the particle size of less than 0.002mm in the soil accounts for more than 40%, it can be matched to clay type;If the particle size of 0.002-0.05mm accounts for more than 50% and the clay is weak, it is matched to silt;If the sand content of 0.05-2mm in the soil is more than 70%, it corresponds to sandy;When the sand content is 50%-70%, and the silt and clay content is moderate, it has certain water and fertilizer retention capacity and air permeability, and it is matched to sandy loam.

[0066] In an optional embodiment of the present application, in step S2, the terrain grid data is input into a first processing model to obtain a sub-basin division result, including:

[0067] Step S21, fill the depression in the terrain grid data to obtain target terrain grid data;

[0068] Step S22, according to the target terrain grid data, determine the flow direction to obtain the flow direction matrix;

[0069] Step S23, according to the flow direction matrix, accumulate the flow, determine the basin boundary, and obtain the sub-basin division result.

[0070] In this embodiment, the 90m resolution terrain grid data of the target area is obtained from the basic geographic information database, and the depression in the terrain grid data is first identified and filled to avoid interruption of the water flow path. The elevation of the depression unit is raised to the elevation of the adjacent higher unit by calculating the elevation difference between each grid unit and its adjacent unit. The expression of depression filling is:

[0071]

[0072] Where D represents the terrain grid data matrix, D' represents the target terrain grid data matrix after filling, i represents the row index, and j represents the column index.

[0073] Based on the filled terrain grid data, i.e., the target terrain grid data, the water flow direction of each grid unit is determined. The slope of each grid unit and its eight adjacent units is calculated, and the water flow direction points to the adjacent unit with the largest slope;The calculation formula of the slope is:

[0074]

[0075] wherein SL represents the slope, i represents the row index, j represents the column index, k represents one of the 8 neighborhood cells, x represents the planar horizontal coordinate of the grid cell, y represents the planar vertical coordinate of the grid cell, represents the filled terrain grid data matrix.

[0076] The direction code corresponding to the neighborhood cell with the maximum slope is taken as the flow direction matrix.

[0077] Then, according to the flow direction matrix, the upstream catchment area of each grid cell, i.e., the flow accumulation, is calculated.

[0078] Again, based on the flow accumulation matrix, the grid cells with flow accumulation values greater than a threshold value are identified as the watershed outlets, and the entire watershed boundary is determined by backtracking; the judgment condition is:

[0079]

[0080] wherein WS represents the watershed boundary, represents the flow accumulation matrix, and AR represents the minimum catchment area threshold.

[0081] Finally, within the main watershed boundary, according to the tributaries and the terrain characteristics, the main watershed is divided into multiple sub-watersheds by finding the local extreme points (i.e., the confluence nodes) on the flow accumulation curve.

[0082] In an optional embodiment of the present application, in step S3, according to the sub-watershed division result, the meteorological data and the soil type are input into a first processing model to obtain the sub-watershed daily precipitation data and the sub-watershed evapotranspiration, including:

[0083] Step S31: The meteorological data is distributed to the sub-watersheds through geometric division to obtain the sub-watershed daily precipitation data.

[0084] Step S32: According to the meteorological data and the soil type of the sub-watersheds, the sub-watershed evapotranspiration is obtained; the meteorological data includes the average air temperature, the wind speed at 2 meters height, the saturated water vapor pressure, the actual water vapor pressure, the wet and dry table constant, the net radiation flux, and the soil heat flux of the sub-watersheds.

[0085] In the embodiment, first, daily precipitation data of multiple weather stations in a sub-basin is collected from a weather observation basic database, then a set of discrete points of weather stations on a plane is divided into multiple polygonal regions by vertical bisectors, each polygonal region contains only one discrete point, and the distance from any position in the polygonal region to the point is less than the distance to other discrete points, so as to divide the influence range of the monitoring stations in the sub-basin, convert the discrete monitoring data into continuous spatial data, obtain daily precipitation data of each coordinate point in the sub-basin, and effectively analyze the spatial distribution characteristics of the degree of soil salinization and ecological indexes, thereby providing a basis for quantitative evaluation of the influence of water-saving measures on the regional environment.

[0086] The sub-basin evapotranspiration is obtained by comprehensively considering the weather elements and soil types of the sub-basin and combining the energy balance principle, and the calculation formula of the sub-basin evapotranspiration is as follows:

[0087]

[0088] wherein ET represents the evapotranspiration, Rn represents the net radiation flux, G represents the soil heat flux, Ps represents the slope of the saturated water vapor pressure curve, T represents the average air temperature, Ces represents the wet / dry constant, V2 represents the wind speed at a height of 2 meters, es represents the saturated water vapor pressure, e represents the actual water vapor pressure.

[0089] In an optional embodiment of the present application, the preset processing model is calibrated according to the difference between the monthly runoff of the basin and the measured calibration data, to obtain a first processing model, which comprises the following steps:

[0090] In step S33, the monthly runoff of the basin is obtained.

[0091] In step S34, a coefficient of determination is obtained according to the dispersion degree between the monthly runoff of the basin and the measured calibration data.

[0092] In step S35, the preset processing model is calibrated according to the coefficient of determination, to obtain a first processing model.

[0093] In the embodiment, the monthly runoff is obtained by the first processing model, and the calculation formula of the monthly runoff is as follows:

[0094]

[0095] wherein, Rm represents the monthly runoff, Rd represents the daily runoff, C represents the precipitation-runoff conversion coefficient, Pd represents the daily precipitation data of the sub-basin, ET is the evapotranspiration amount.

[0096] Specifically, the first processing model uses the monthly runoff data of the first year to the sixth year of a hydrological station, the third year to the fourth year as the calibration year, and the fifth year to the sixth year as the verification year. The first processing model runs from the second year to the sixth year, the preheating period is two years, and the monthly output data is output; the coefficient of determination is used As a core index for measuring the goodness of fit of the first processing model, by comparing the dispersion degree between the predicted value and the measured value of the model, the explanation ability of the model to the variation of the observation data is quantitatively evaluated. The coefficient of determination The value range is between 0 and 1, the closer to 1, the higher the degree of agreement between the predicted value and the actual observation value of the model, and the stronger the explanation ability of the model to the data; on the contrary, if it tends to 0, it means that the prediction effect of the model is poor, and there may be problems such as missing key variables or unreasonable model structure; specifically, the calculation formula of the coefficient of determination is:

[0097]

[0098] Among them, is the coefficient of determination, is the measured value, is the simulation value, is the mean of the measured value, is the mean of the simulation value.

[0099] Through stage-by-stage calibration and multi-index verification, the numerical simulation accuracy of the first processing model is ensured to meet the requirements.

[0100] In an optional embodiment of the present application, in step S4, the daily precipitation data and evapotranspiration of the sub-basin are input into the second processing model, and the soil salt content and crop yield are obtained by combining the irrigation amount of the sub-basin, including:

[0101] Step S41, using the second processing model, according to the daily precipitation data and evapotranspiration of the sub-basin, and the irrigation amount, irrigation water salt concentration, and soil volume of the sub-basin, the soil salt content is obtained;

[0102] Step S42, according to the soil salt content, daily precipitation data, evapotranspiration and irrigation amount of the sub-basin, the crop yield is obtained.

[0103] In this embodiment, the dynamic change of soil salt is affected by many factors such as precipitation, evapotranspiration and irrigation amount, and the calculation of soil salt needs to consider precipitation, evapotranspiration, irrigation amount, initial soil salt content, irrigation water salt concentration and other factors, and the calculation formula of soil salt content is:

[0104]

[0105] wherein S is the soil salt content, is the initial soil salt content, is the irrigation water salt concentration, I is the irrigation amount, and V is the soil volume, is the daily precipitation data of the sub-basin, and ET is the evapotranspiration, is the current average soil salt concentration.

[0106] The crop yield has a linear relationship with the precipitation, the irrigation amount, the evapotranspiration, and the soil salt content, a weight coefficient is introduced to quantify the influence of each parameter, and a multiple linear regression model expression is constructed; meanwhile, considering that there may be interactions between the factors, an interaction term is added for correction; the calculation formula of the crop yield is:

[0107]

[0108] wherein Y is the crop yield, is the daily precipitation data of the sub-basin, ET is the evapotranspiration, S is the soil salt content, and I is the irrigation amount, is the constant term, is the regression coefficient of the daily precipitation data of the sub-basin, is the regression coefficient of the irrigation amount, is the regression coefficient of the evapotranspiration, is the regression coefficient of the soil salt content, is the interaction coefficient of the daily precipitation data of the sub-basin and the irrigation amount, is the interaction coefficient of the daily precipitation data of the sub-basin and the evapotranspiration, is the interaction coefficient of the daily precipitation data of the sub-basin and the soil salt content, is the interaction coefficient of the irrigation amount and the evapotranspiration, is the interaction coefficient of the irrigation amount and the soil salt content, is the interaction coefficient of the evapotranspiration and the soil salt content, is the random error term.

[0109] In an optional embodiment of the present application, in step S5, the soil salinization prevention and control measures are obtained according to the soil salt content and the crop yield, and the soil salinization prevention and control measures comprise:

[0110] In step S51, a change curve among the soil salt content, the crop yield, and the water saving degree is constructed, a water saving threshold is determined based on the change curve inflection point, and the soil salinization prevention and control measures are obtained.

[0111] In this embodiment, based on the actual irrigation amount, four water-saving scenarios are set, water-saving 10%, 15%, 20%, 30%, such as corn flooding irrigation original irrigation amount 450mm, water-saving 10% after 405mm, according to the original irrigation times equal proportion distribution single irrigation amount. For each sub-basin point, input the rainfall and ET data output by the first processing model into the second processing model, adjust the irrigation parameters according to different water-saving scenarios, simulate the soil salt content in 0-20cm soil and crop yield during the crop growth period. Calculate the average soil salt content (such as 3.2g / kg when water-saving 30%) and yield average (such as 6.8t / hm 2 ) of all points under each scenario, and exclude outliers (deviation > 20%).

[0112] With water-saving degree (10%, 15%, 20%, 30%) as the horizontal coordinate, the average soil salt content and crop yield as the vertical coordinate, draw the change curve, fit the trend equation, and extract the response law; for example: the soil salt content slowly increases (5%-10% increase) when water-saving 10%-20%, and significantly increases (increase > 15%) when water-saving 30%. The crop yield slightly decreases (3%-8% decrease) when water-saving 10%-20%, and significantly decreases (decrease > 12%) when water-saving 30%. Based on the inflection point of the curve, the reasonable water-saving threshold (such as 20%) is determined, at this time the salt content increase is ≤10%, and the yield decrease is ≤8%, so as to consider water-saving effect and ecological safety. The embodiment can quantitatively reveal the response law of soil salinization and crop yield to water-saving in the irrigation area, provide a scientific basis for formulating reasonable water-saving measures, and avoid ecological risks caused by blind water-saving.

[0113] The following will illustrate the specific implementation process of the above method of the present application by means of specific examples:

[0114] Step 1, using the target area 90m resolution terrain raster data (DEM), the terrain raster data is preprocessed.

[0115] Step 2, the land use data adopts GlobeLand30 land use data set (raster data), the precision is 30x30m. The land type is divided into 8 kinds, farmland, forest land, grassland, shrubbery, wetland, water area, construction land and bare land, which is reclassified according to the data requirement built-in the first processing model.

[0116] Step 3, the establishment of soil type database. The soil type database adopts world soil database HWSD (raster data), which is cropped according to the target area, and the specific steps are spatial analysis-extraction analysis-mask extraction, the soil data is reclassified, the soil name is corresponding to the built-in classification field of the first processing model, and is arranged into an index table according to the requirement.

[0117] Step 4, weather data processing. Four weather stations were selected, and daily weather data (text data) from the first year to the sixth year were used. Weather data included precipitation, maximum temperature, minimum temperature, sunshine hours, relative humidity, wind speed, etc. Solar radiation was calculated using an empirical formula based on sunshine hours. The main parameters required by the weather generator included monthly average maximum and minimum temperature and standard deviation, monthly average rainfall and standard deviation, dew point temperature, monthly average wind speed, and average solar radiation.

[0118] Step 5, establishment of a preset processing model: (1) hydrological terrain input: input the terrain raster data prepared in step 1, calculate the river network and flow direction data. (2) sub-basin division: manually set the location of the hydrological station and the outlet point of the basin, generally choose the river intersection point, monitoring section, and irrigation area outlet. Based on the outlet point and river network, automatically generate the basin boundary and sub-basin, a total of 53 sub-basins are divided; (3) import the reclassified land use and soil type data, and link them with the soil type index table of land use. The sub-basins are divided into three levels, and the appropriate area proportion threshold range is set. (4) weather data input: the required data are rainfall, temperature, relative humidity, wind speed, and solar radiation (data prepared in step 4). (5) agricultural management: add irrigation parameters to the sub-basins, set the irrigation method to irrigation in April, May, June, July, and September, with irrigation amounts of 240 mm, 25 mm, 25 mm, 25 mm, and 240 mm, respectively, and the water source is canal water. (6) simulation control parameters: simulation start and end time: first year to sixth year, output monthly scale sub-basin rainfall and ET.

[0119] Step 6, calibration of the first processing model. Parameter uncertainty considers all sources of uncertainty, such as driving variables such as rainfall, air temperature, and soil properties. The conceptual model, parameters, and monitoring data are used to judge the accuracy of the model by comparing the differences between the monitoring data and the final "best" simulation. This paper uses the monthly average flow data of a certain hydrological station from the first year to the sixth year. The third year to the fourth year of the model are the calibration years, and the fifth year to the sixth year are the verification years. The model is set to run from the second year to the sixth year, with a preheating period of two years. The output data is monthly scale, the calibrated parameters and corresponding types and values are selected, all calibration parameters are modified in turn, the model is run, and the output data is compared with the measured value. The Nash-Sutcliffe coefficient (E) and RMSE (root mean square error) are used as the evaluation indicators of the model. After calibration and verification, the daily rainfall and evapotranspiration data of the sub-basins in the fifth and sixth years are obtained.

[0120] ​Step 7, input the rainfall and reference crop evapotranspiration data output by the first processing model into the second processing model corresponding to each sub-basin point, and the modeling steps are as follows: (1) In the second processing model, set the corn growth period from May 1 to September 27 each year, and the wheat growth period from September 25 each year to July 6 next year. (2) In the second processing model, set the irrigation system of corn as two irrigation methods of drip irrigation and flooding irrigation. Drip irrigation is irrigated 9 times after sowing, 20 days, 36 days, 56 days, 66 days, 74 days, 82 days, 89 days, 99 days, and 112 days after sowing, each time 22 mm, a total of 198 mm, and flooding irrigation is irrigated 3 times, 20 days, 54 days, and 88 days after sowing, each time 150 mm, a total of 450 mm. The wheat irrigation system is set to be irrigated 214 days, 234 days, 250 days, 269 days, and 278 days after sowing, each time 8 mm. (3) The field management is set to be no fertilizer restriction, and the weed coverage is 15%. (4) The soil profile is selected according to the soil texture of each point, the thickness is set to 2m, and other parameters are set to default values according to the selection of soil texture. (5) The groundwater level is set to 1.7m, and the groundwater level salinity is determined according to the simulation value in reverse. (6) The simulation time starts from the initial sampling time to the end of the growth period. (7) The initial conditions are set, the initial water content of 0-20cm soil layer is set to 70% of the field water holding capacity, and the soil salinity is the measured value. (8) Run the model and compare the output value with the measured value to calibrate and verify the model.

[0121] As shown in Figure 3 , in this embodiment, the second processing model after calibration and verification is used to predict and deduce the changes of soil salinization and crop growth under different water-saving situations for each point. Four water-saving situations of 10%, 15%, 20%, and 30% are set, and the rainfall and evapotranspiration of each sub-basin corresponding to each point are input into the second processing model according to the above steps. The parameters in the model are set, the 0-20cm soil salinity content during the whole growth period of the crop is predicted by the second processing model, the average soil salinity content during the whole growth period is calculated, and the crop yield is obtained by the second processing model. The 0-20cm soil salinity of all representative points in the irrigation area and the crop yield of each point are obtained, and the average value is taken. Different water-saving degrees are set in turn to obtain the soil salinity and yield of each point, and the average value is taken. The change curves of soil salinity and yield under different water-saving degrees are established to obtain the response law of soil salinity and crop yield to water-saving.

[0122] As shown in Figure 4 , Figure 5 , Figure 6 , Figure 7 , the calibration results of the soil water content and soil salinity of the model are shown in All are greater than 0.9, the maximum RMSE (root mean square error) is 4.73%, and the salt rate is determined and verified All are greater than 0.9, the maximum RMSE (root mean square error) is 4.73%, and the salt rate is determined and verified

[0123] As shown in Table 1, the error between the simulated and measured values of crop yield is within 6.5%, and the simulation effect is good.

[0124] Table 1 Simulated and measured values of crop yield

[0125]

[0126] As shown in Table 1, the error between the simulated and measured values of crop yield is within 6.5%, and the simulation effect is good. Figure 8 As shown in Table 1, the error between the simulated and measured values of crop yield is within 6.5%, and the simulation effect is good.

[0127] As shown in Table 1, the error between the simulated and measured values of crop yield is within 6.5%, and the simulation effect is good. Figure 9 As shown in Table 1, the error between the simulated and measured values of crop yield is within 6.5%, and the simulation effect is good.

[0128] The scheme of the present application has the following advantages:

[0129] (1) The present application realizes the spatialization processing of meteorological driving data by obtaining the precipitation and potential evapotranspiration at the sub-basin scale. In traditional research, meteorological data is often limited to a limited number of observation stations, which cannot reflect the spatial differences in large-scale basins, resulting in large deviations in the simulation results. The present application combines digital elevation model, land use and soil type data to divide the sub-basins and simulate the hydrological process in the study area, and then converts the point meteorological data into spatialized daily driving data. This method not only improves the applicability and accuracy of the model, but also enhances the popularization value of the model in large-scale irrigation water resources management.

[0130] (2) The present application introduces the soil water and salt data and crop yield observation data collected on site into the calibration and verification process of the model, realizing the deep coupling of the model and the measured data. Compared with the model which simply relies on literature parameters or empirical values, this method is closer to the actual situation of the study area, and can effectively reduce the error caused by unreasonable parameter setting. In the salinization area, the distribution of soil salt has significant spatial and temporal variability, and the response of crops to salt also has great difference. The present application realizes the synchronous calibration and verification of soil water and salt dynamics and crop yield by using the measured data at different growth stages, thereby significantly improving the reliability and scientificity of the prediction results of the model.

[0131] (3) The irrigation scenario library is constructed, and soil water and salt evolution and crop yield change under different water-saving levels can be simulated flexibly. The prior art often simulates only under a single irrigation mode, and it is difficult to compare the influence of different irrigation systems on soil water and salt distribution and crop yield. The present application sets up conventional irrigation, drip irrigation, flooding irrigation and different water-saving ratios (such as water-saving 10%, 20% and 30%) scenarios, forming a rich irrigation management scheme library. The scenario library can not only simulate the growth performance of crops under different irrigation strategies, but also quantify the dynamic changes of water and salt transport process, providing decision support for regional agricultural water resources allocation and optimization management.

[0132] (4) The present application can provide a water-saving threshold for saltification prevention and control, thereby providing scientific support for regional water resources regulation and optimization of farming system. In the irrigation area with serious salinization, simply pursuing water-saving often leads to salt accumulation, thereby threatening crop yield and soil quality. The present application reveals the nonlinear response relationship between soil water and salt and crop yield under different water-saving levels by combining simulation and measured data, and can provide a threshold reference for balancing the contradiction between water-saving and salt prevention for managers. This achievement has important significance for guiding the irrigation area to develop a reasonable irrigation system and give consideration to water-saving and saline land improvement.

[0133] (5) The present application has advantages in data requirements. Traditional water-salt and crop response simulation often relies on large-scale long-term field experiments and high-frequency monitoring data, resulting in high data acquisition cost and limiting the popularization and application of the technology. The present application couples the first processing model and the second processing model to maximize the use of existing spatial data (such as terrain grid, land use, soil data) and conventional meteorological data, supplemented by limited field investigation and a small amount of water-salt and yield monitoring point data, so that high-precision simulation and prediction can be realized. This "moderate data and reliable results" feature greatly reduces the threshold for model application, so that the technology can be successfully developed and applied in regions with limited data basis, providing convenience for the popularization of the technology in arid and semi-arid areas. In summary, the present application has significant advantages in meteorological driving data spatialization, measured data fusion rate setting, scenario library construction, water-saving threshold identification and moderate data requirements. The present application not only improves the prediction accuracy and applicability of the model, but also provides a systematic and operable technical path for water-saving irrigation and saltification prevention and control in irrigation areas, and has broad application prospect and popularization value.

[0134] As shown in Figure 10 , the embodiment of the present application also provides a simulation device 100 for water-saving response of soil salinization, comprising:

[0135] An acquisition module 101 is configured to acquire soil type, terrain grid data and meteorological data of a target region.

[0136] The processing module 102 is configured to input the terrain grid data into a first processing model to obtain a sub-basin division result; input the meteorological data and the soil type into the first processing model according to the sub-basin division result to obtain sub-basin daily precipitation data and sub-basin evapotranspiration; input the sub-basin daily precipitation data and the sub-basin evapotranspiration into a second processing model to obtain soil salinity and crop yield in combination with sub-basin irrigation; and obtain soil salinization prevention and control measures according to the soil salinity and the crop yield. The first processing model is obtained by calibrating a preset processing module according to a difference between a basin monthly runoff and measured calibration data.

[0137] Optionally, the soil type of the target region is obtained, including:

[0138] The soil grid data of the target region is obtained.

[0139] The soil grid data is matched with a soil classification standard to obtain the soil type. The soil type includes clay type, silt type, sandy type and sandy loam type.

[0140] Optionally, the terrain grid data is input into the first processing model to obtain the sub-basin division result, including:

[0141] The terrain grid data is subjected to depression filling to obtain target terrain grid data.

[0142] Flow direction determination is performed according to the target terrain grid data to obtain a flow direction matrix.

[0143] Flow accumulation is performed according to the flow direction matrix to determine a basin boundary to obtain the sub-basin division result.

[0144] Optionally, the meteorological data and the soil type are input into the first processing model according to the sub-basin division result to obtain the sub-basin daily precipitation data and the sub-basin evapotranspiration, including:

[0145] The meteorological data is distributed to the sub-basins through geometric division to obtain the sub-basin daily precipitation data.

[0146] The sub-basin evapotranspiration is obtained according to the meteorological data and the soil type of the sub-basins.

[0147] Optionally, the first processing model is calibrated according to a difference between the basin monthly runoff and the measured calibration data, including:

[0148] The basin monthly runoff is obtained.

[0149] A determination coefficient is obtained according to a dispersion degree between the basin monthly runoff and the measured calibration data.

[0150] According to the decision coefficient, a preset processing model is calibrated to obtain a first processing model.

[0151] Optionally, the sub-basin daily precipitation data and the sub-basin evapotranspiration are input into a second processing model to obtain soil salt content and crop yield in combination with sub-basin irrigation amount, including:

[0152] According to the sub-basin daily precipitation data and the sub-basin evapotranspiration, and the sub-basin irrigation amount, irrigation water salt concentration and soil volume, the soil salt content is obtained by using the second processing model.

[0153] According to the soil salt content, the sub-basin daily precipitation data, the sub-basin evapotranspiration and the sub-basin irrigation amount, the crop yield is obtained.

[0154] Optionally, according to the soil salt content and the crop yield, soil salinization prevention and control measures are obtained, including:

[0155] A change curve between the soil salt content, the crop yield and the water saving degree is constructed, a water saving threshold is determined based on an inflection point of the change curve, and the soil salinization prevention and control measures are obtained.

[0156] It should be noted that all the implementation manners in the above method embodiments are applicable to the embodiments of the device and can achieve the same technical effects.

[0157] Embodiments of the present application also provide a computing device, including: one or more processors; a storage device for storing one or more programs, when the one or more programs are executed by the one or more processors, the one or more processors implement the soil salinization response to water saving simulation method described in the present application. All implementation manners in the above method embodiments are applicable to the embodiments of the computing device and can achieve the same technical effects.

[0158] Embodiments of the present application also provide a computer readable storage medium, the computer readable storage medium stores a program, the program is executed by a processor to implement the soil salinization response to water saving simulation method described in the present application. All implementation manners in the above method embodiments are applicable to the embodiments of the computer readable storage medium and can achieve the same technical effects.

[0159] Those skilled in the art can realize that the units and algorithm steps of the examples described in combination with the embodiments disclosed herein can be realized by electronic hardware or a combination of computer software and electronic hardware. Whether the functions are realized in hardware or software depends on the specific application and design constraints of the technical solution. Professionals can use different methods to implement the described functions for each specific application, but such implementation should not be considered beyond the scope of the present application.

[0160] Those skilled in the art can clearly understand that, for the convenience and brevity of description, the specific working processes of the above-described system, device and unit can refer to the corresponding processes in the foregoing method embodiments, which will not be repeated here.

[0161] In the embodiments provided by the present application, it should be understood that the disclosed apparatus and method can be implemented in other manners. For example, the described apparatus embodiments are merely schematic, and the division of units is merely a logical function division, and there can be another division manner in actual implementation. For example, a plurality of units or components can be combined or integrated into another system, or some features can be ignored or not executed. In addition, the displayed or discussed mutual couplings or direct couplings or communication connections can be indirect couplings or communication connections through some interfaces, devices or units, and can be electrical, mechanical or in other forms.

[0162] The units described as separate components can or can not be physically separate, and the components displayed as units can or can not be physical units, i.e., can be located in one place, or can be distributed on a plurality of network units. Some or all of the units can be selected according to actual needs to achieve the purposes of the embodiments.

[0163] In addition, each functional unit in the various embodiments of the present application can be integrated in one processing unit, or each unit can exist physically, or two or more units can be integrated in one unit.

[0164] If the functions are implemented in the form of software function units and sold or used as independent products, they can be stored in a computer readable storage medium. Based on this understanding, the technical solutions of the present application essentially or the part that makes a contribution to the prior art or part of the technical solutions can be embodied in the form of a software product. The computer software product is stored in a storage medium, and includes several instructions for causing a computer device (which can be a personal computer, a server, or a network device, etc.) to execute all or part of the steps of the methods in the various embodiments of the present application. The foregoing storage medium includes various media that can store program codes, such as U disk, mobile hard disk, ROM, RAM, magnetic disk or optical disk, etc.

[0165] Moreover, it is pointed out that in the device and method of the present application, obviously, the components or steps can be decomposed and / or recombined. These decompositions and / or recombination should be considered as equivalent solutions of the present application. Also, the steps of performing the above series of processes can naturally be executed in time sequence according to the order of description, but do not need to be necessarily executed in time sequence. Some steps can be executed in parallel or independently of each other. It can be understood by those skilled in the art that all or any steps or components of the method and device of the present application can be implemented in hardware, firmware, software or a combination thereof in any computing device (including processors, storage media, etc.) or network of computing devices, using the basic programming skills of those skilled in the art upon reading the description of the present application.

[0166] Therefore, the object of the present application can also be achieved by running a program or a set of programs on any computing device. The computing device can be a commonly known general-purpose device. Therefore, the object of the present application can also be achieved only by providing a program product containing program code for implementing the method or device. That is, such a program product also constitutes the present application, and a storage medium storing such a program product also constitutes the present application. Obviously, the storage medium can be any commonly known storage medium or any storage medium developed in the future. It is also pointed out that in the device and method of the present application, obviously, the components or steps can be decomposed and / or recombined. These decompositions and / or recombination should be considered as equivalent solutions of the present application. Also, the steps of performing the above series of processes can naturally be executed in time sequence according to the order of description, but do not need to be necessarily executed in time sequence. Some steps can be executed in parallel or independently of each other.

[0167] The above is the preferred embodiment of the present application. It should be pointed out that for those skilled in the art, several improvements and refinements can be made without departing from the principles of the present application, and these improvements and refinements should also be considered as the protection scope of the present application.

Claims

1. A method for simulating the water-saving response of soil salinization, characterized in that, include: Acquire soil type, topographic raster data, and meteorological data for the target area; The terrain raster data is input into the first processing model to obtain the sub-basin division results; Based on the sub-basin division results, the meteorological data and soil type are input into the first processing model to obtain the daily precipitation data and sub-basin evapotranspiration of the sub-basin. The daily precipitation data and evapotranspiration of the sub-basin are input into the second processing model, and combined with the irrigation amount of the sub-basin, the soil salinity and crop yield are obtained. Based on the soil salinity and crop yield, soil salinization control measures are derived. The first processing model is obtained by calibrating a preset processing module based on the difference between the monthly runoff volume of the watershed and the measured calibration data.

2. The method for simulating the water-saving response of soil salinization according to claim 1, characterized in that, Obtain the soil type of the target area, including: Acquire soil raster data for the target area; The soil raster data is matched with soil classification standards to obtain soil types; the soil types include clay, silt, sandy, and sandy loam.

3. The method for simulating the water-saving response of soil salinization according to claim 1, characterized in that, The terrain raster data is input into the first processing model to obtain the sub-basin division results, including: The terrain raster data is filled with depressions to obtain the target terrain raster data; Based on the target terrain raster data, flow direction is determined to obtain a flow direction matrix; Based on the flow direction matrix, flow accumulation is performed to determine the watershed boundary and obtain the sub-watershed division results.

4. The method for simulating the water-saving response of soil salinization according to claim 1, characterized in that, Based on the sub-basin division results, the meteorological data and soil type are input into the first processing model to obtain daily precipitation data and sub-basin evapotranspiration, including: Meteorological data is distributed to sub-basins through geometric division to obtain daily precipitation data for each sub-basin; The evapotranspiration of the sub-basin is obtained based on meteorological data and soil type.

5. The method for simulating the water-saving response of soil salinization according to claim 1, characterized in that, Based on the difference between the monthly runoff volume of the watershed and the measured calibration data, the preset processing model was calibrated to obtain the first processing model, which includes: Obtain monthly runoff in the watershed; The coefficient of determination is obtained based on the degree of dispersion between the monthly runoff volume of the watershed and the measured calibration data; Based on the determination coefficients, the preset processing model is calibrated to obtain the first processing model.

6. The method for simulating the water-saving response of soil salinization according to claim 1, characterized in that, The daily precipitation data and evapotranspiration of the sub-basin are input into the second processing model, and combined with the irrigation amount of the sub-basin, the soil salinity and crop yield are obtained, including: Using the second processing model, the soil salinity is obtained based on the daily precipitation data and evapotranspiration of the sub-basin, as well as the irrigation amount, irrigation water salt concentration, and soil volume of the sub-basin. Crop yield is obtained based on the soil salinity, daily precipitation data of the sub-basin, evapotranspiration of the sub-basin, and irrigation amount of the sub-basin.

7. The method for simulating the water-saving response of soil salinization according to claim 1, characterized in that, Based on the soil salinity and crop yield, soil salinization control measures are derived, including: A curve is constructed to show the relationship between soil salinity, crop yield, and water-saving level. Based on the inflection point of the curve, a water-saving threshold is determined, and soil salinization control measures are obtained.

8. A device for simulating the water-saving response of soil salinization, characterized in that, include: The acquisition module is used to acquire soil type, terrain raster data, and meteorological data for the target area; The processing module inputs the topographic raster data into a first processing model to obtain sub-basin division results; based on the sub-basin division results, it inputs the meteorological data and soil type into the first processing model to obtain daily precipitation data and evapotranspiration of the sub-basin; it inputs the daily precipitation data and evapotranspiration of the sub-basin into a second processing model, and combines them with the irrigation amount of the sub-basin to obtain soil salinity and crop yield; based on the soil salinity and crop yield, it obtains soil salinization control measures; wherein, the first processing model is obtained by calibrating the preset processing module based on the difference between the monthly runoff of the watershed and the measured calibration data.

9. A computing device, characterized in that, include: One or more processors; A storage device for storing one or more programs, which, when executed by one or more processors, cause the one or more processors to implement the method as described in any one of claims 1 to 7.

10. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores a program that, when executed by a processor, implements the method as described in any one of claims 1 to 7.

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