Irrigation water quantity and canal system water use efficiency estimation method, device and equipment

Abstract

The application relates to the field of agricultural water resource management, in particular to an irrigation water consumption and canal system water use efficiency estimation method, device and equipment, wherein the method comprises the following steps: screening extreme meteorological and hydrological data meeting an extreme rainfall scenario condition from historical meteorological data and hydrological data, combining net exchange flux at a deep soil boundary under the extreme rainfall scenario, calculating label data representing water storage and discharge capacity of an underground water variation zone, constructing a time and space variable water supply degree estimation model based on nonlinear influence and spatial heterogeneity of the extreme rainfall scenario on soil moisture dynamics and combining a machine learning algorithm, and training the water supply degree estimation model by using a data set carrying the label data; and based on similar characteristics of soil moisture dynamics under the extreme rainfall scenario and an irrigation scenario, the trained water supply degree estimation model is migrated to the irrigation scenario to estimate the irrigation water consumption and the canal system water use efficiency. Thus, the problem of how to improve the parameter accuracy in an arid region is solved.

Description

TECHNICAL FIELD

[0001] The present application relates to the field of agricultural water resource management, and in particular to an irrigation water consumption and canal system water use efficiency estimation method, device and equipment. BACKGROUND

[0002] With the rapid growth of population and the intensification of climate change, the demand for agricultural irrigation water is expected to increase, especially in arid irrigation areas where rainfall usually cannot meet the needs of crop growth. Therefore, accurate estimation of irrigation water has important significance for precise management of agricultural water, optimization of agricultural water resources allocation and guarantee of food security.

[0003] Traditional monitoring methods such as water meter measurement and manual investigation have problems such as non-public data, poor spatial and temporal accuracy, time-consuming and labor-intensive, and are difficult to accurately reflect the irrigation water consumption of farmland in a large area. Therefore, many indirect estimation methods have emerged, which can be mainly divided into three categories: (1) integrating artificially designed irrigation modules into hydrological models, agricultural models or land surface models, which are usually estimated by setting irrigation thresholds or satisfying soil water deficit; (2) using remote sensing technology to estimate by combining evapotranspiration, soil water or other hydrological factors with water balance principle; (3) using the sensitivity of soil water to external water input and the similarity between rainfall and irrigation scenarios to calibrate the model during rainfall and apply it to irrigation scenarios. These methods have provided technical support for quantifying farmland irrigation to varying degrees.

[0004] However, each method has limitations, especially in arid irrigation areas where in-situ monitoring data are relatively scarce. First, observation data in arid areas are scarce, especially soil moisture observations sensitive to irrigation activities. The scarce data are usually difficult to support model requirements. Second, remote sensing can provide continuous spatial and temporal observations, but its observation depth is limited to the surface, thereby oversimplifying or even ignoring the water movement in the deep soil, which is particularly important in areas with shallow groundwater depth. Finally, the rainfall in arid areas is much lower than the irrigation amount, and the parameter calibration based on typical rainfall scenarios can easily lead to serious underestimation. Although the similarity between rainfall and irrigation is theoretically feasible, in arid and semi-arid areas, the significant difference between the two can introduce uncertainty. SUMMARY

[0005] The present application provides an irrigation water consumption and canal system water use efficiency estimation method, device and equipment to solve the problems of traditional monitoring methods such as observation data scarcity, observation depth limited to the surface, and parameter calibration based on typical rainfall scenarios leading to serious underestimation of parameters in arid areas.

[0006] The first embodiment of the present application provides a method for estimating irrigation water consumption and canal water utilization efficiency, comprising the following steps: obtaining historical meteorological data, hydrological data, vegetation data, topographic data, soil texture data, and land use data of the target irrigation area; screening extreme meteorological and hydrological data that meet the conditions of extreme rainfall scenarios from the historical meteorological and hydrological data, and calculating and determining the various hydrological elements required to establish a water balance equation under extreme rainfall scenarios; calculating the number of tags that characterize the water storage and discharge capacity of the groundwater fluctuation zone based on the extreme meteorological and hydrological data and the net exchange flux at the deep soil boundary under extreme rainfall scenarios. A dataset was generated based on labeled data, extreme meteorological and hydrological data, vegetation data, terrain data, soil texture data, and land use data. Based on the nonlinear impact of extreme rainfall scenarios on soil moisture dynamics and spatial heterogeneity, a spatiotemporal variable water supply estimation model was constructed in combination with a machine learning algorithm, and the water supply estimation model was trained using the dataset. Based on the similar characteristics of soil moisture dynamics under extreme rainfall scenarios and irrigation scenarios, the trained water supply estimation model was transferred to the irrigation scenario, and the irrigation water consumption of the target irrigation area was estimated. The water utilization efficiency of the canal system was estimated based on the irrigation water consumption and water diversion volume of the target irrigation area.

[0007] Optionally, the historical meteorological data and hydrological data and the extreme meteorological and hydrological data include at least one of the rainfall in the target irrigation area, potential evapotranspiration, actual evapotranspiration, soil moisture content, groundwater depth and water diversion amount in the irrigation area; the vegetation data include the leaf area index of the target irrigation area; the terrain data include at least one of the elevation and slope of the target irrigation area; and the soil texture data include at least one of the proportions of sand, clay and silt in the soil of the target irrigation area.

[0008] Optionally, after obtaining the historical meteorological data, hydrological data, vegetation data, terrain data, soil texture data and land use data of the target irrigation area, it also includes: resampling and reprojecting the historical meteorological data, hydrological data, vegetation data, terrain data, soil texture data and land use data to obtain resampled and reprojected data, wherein all resampled and reprojected data are located in the same target coordinate system and have the same spatiotemporal resolution.

[0009] Optionally, calculating and determining various hydrological elements required to establish a water balance equation under an extreme rainfall scenario includes: establishing a soil water balance equation under an extreme rainfall scenario; inputting extreme meteorological and hydrological data into the soil water balance equation under an extreme rainfall scenario, wherein the soil water balance equation under an extreme rainfall scenario outputs a net exchange flux at a soil boundary under an extreme rainfall scenario, wherein the calculation formula of the soil water balance equation under an extreme rainfall scenario is:

[0010] ,

[0011] ,

[0012] wherein, represents the regional soil water storage; represents the regional soil water storage change amount in the calculation period; P represents the rainfall in the extreme rainfall scenario; ET represents the actual evapotranspiration; n represents the number of soil layers; represents the average volumetric water content of each layer of soil; represents the depth of each layer of soil; NEF represents the net exchange flux at the soil boundary, a positive value by default indicates that the flux is downward, and a negative value indicates that the flux is upward, and the groundwater fluctuation equation is used estimate NEF , represents the water supply degree, represents the groundwater depth change amount.

[0013] Optionally, the water supply degree is calculated by the groundwater fluctuation equation, and the calculation formula is:

[0014]

[0015] wherein, represents the water supply degree, which characterizes the water storage and discharge capacity of the groundwater fluctuation zone, and the meanings of other variables are the same as those described above.

[0016] Optionally, based on the similar characteristics of soil moisture dynamics in the extreme rainfall scenario and the irrigation scenario, the trained water supply degree estimation model is migrated to the irrigation scenario, and the irrigation water consumption of the target irrigation area is estimated, including: inputting historical meteorological data, hydrological data, vegetation data, topographic data, soil texture data and land use data into the water supply degree estimation model, and the water supply degree estimation model outputs the spatiotemporal dynamic distribution of the water supply degree of the target irrigation area in the target period; calculate the net exchange flux at the deep soil boundary point by point and hour by hour according to the spatiotemporal dynamic distribution of the water supply degree and the groundwater depth distribution; determine the spatiotemporal distribution of the net exchange flux in the target irrigation area in the target period according to the net exchange flux calculated point by point and hour by hour; estimate the total water entering the soil according to the spatiotemporal distribution of the net exchange flux, and obtain the irrigation water consumption according to the total water entering the soil and the rainfall of the target irrigation area.

[0017] Optionally, the total water entering the soil is estimated according to the spatiotemporal distribution of the net exchange flux, including: establishing a soil water balance equation under a non-extreme rainfall scenario; input the spatiotemporal distribution of the net exchange flux into the soil water balance equation under the non-extreme rainfall scenario, and the soil water balance equation under the non-extreme rainfall scenario outputs the total water entering the soil, wherein the calculation formula of the soil water balance equation under the non-extreme rainfall scenario is:

[0018]

[0019] in, I It represents the amount of irrigation water flowing from the final channel to the field. The meanings of other variables are the same as those of the above variables.

[0020] Optionally, the canal water utilization efficiency is estimated based on the irrigation water consumption and water diversion volume of the target irrigation area, including: performing temporal and spatial data processing on the irrigation water consumption of the target irrigation area to obtain irrigation water consumption with consistent temporal and spatial resolution, and performing time series accumulation on the annual scale on the irrigation water consumption with consistent temporal and spatial resolution to obtain irrigation water consumption at the annual scale and irrigation area scale; and estimating the canal water utilization efficiency based on the irrigation water consumption at the annual scale and irrigation area scale and the water diversion volume of the target irrigation area.

[0021] The second embodiment of the present application provides an irrigation water consumption and canal water utilization efficiency estimation device, including: an acquisition module for acquiring historical meteorological data, hydrological data, vegetation data, terrain data, soil texture data and land use data of the target irrigation area; a determination module for screening extreme meteorological and hydrological data that meet the conditions of extreme rainfall scenarios from the historical meteorological data and hydrological data, and calculating and determining the various hydrological elements required to establish a water balance equation under extreme rainfall scenarios; a calculation module for calculating a label representing the water storage and discharge capacity of the groundwater fluctuation zone based on the extreme meteorological and hydrological data and the net exchange flux at the deep soil boundary under extreme rainfall scenarios Data, which generates a dataset based on labeled data, extreme meteorological and hydrological data, vegetation data, terrain data, soil texture data, and land use data; a modeling module, which is used to build a spatiotemporally variable water supply estimation model based on the nonlinear impact and spatial heterogeneity of extreme rainfall scenarios on soil moisture dynamics, combined with machine learning algorithms, and train the water supply estimation model using the dataset; an estimation module, which is used to migrate the trained water supply estimation model to the irrigation scenario based on the similar characteristics of soil moisture dynamics under extreme rainfall scenarios and irrigation scenarios, and estimate the irrigation water consumption of the target irrigation area, and estimate the canal water utilization efficiency based on the irrigation water consumption and water diversion volume of the target irrigation area.

[0022] The third aspect of the present application provides an electronic device, comprising: a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the program to implement the irrigation water consumption and canal water utilization efficiency estimation method of the first aspect.

[0023] Therefore, this application has the following beneficial effects:

[0024] The present embodiment of the present invention screens extreme meteorological and hydrological data that meet the conditions of extreme rainfall scenarios from historical meteorological and hydrological data, vegetation and topographic data, and soil and land data to determine soil water storage and variation, and calculates label data that characterizes the water storage and discharge capacity of the groundwater variation zone. A dataset is generated from the above data, and a spatiotemporal variable water supply estimation model is constructed under irrigation scenarios. The water supply estimation model is trained using the dataset, and finally the trained water supply estimation model is used to estimate the irrigation water consumption of the target irrigation area. The two methods are used to estimate the water use efficiency of the canal system. This overcomes the estimation bias caused by the serious mismatch between rainfall and irrigation volume in arid irrigation areas in existing studies, accurately describes the spatiotemporal variation characteristics of water supply and the net water exchange flux at the deep soil boundary, and realizes high spatiotemporal resolution irrigation water consumption estimation in large-scale areas, thereby improving water resource utilization efficiency, alleviating water resource shortage problems, and providing technical support for the sustainable development of agriculture. Thus, the present invention solves the problems of traditional monitoring methods such as scarce observation data, observation depth limited to the surface, and parameter calibration based on typical rainfall scenarios that easily leads to serious underestimation of parameters in arid areas.

[0025] Additional aspects and advantages of the present application will be given in part in the description below, and in part will become apparent from the description below, or will be learned through practice of the present application. BRIEF DESCRIPTION OF THE DRAWINGS

[0026] The above and / or additional aspects and advantages of the present application will become apparent and easily understood from the following description of the embodiments in conjunction with the accompanying drawings, in which:

[0027] Figure 1 A flow chart of a method for estimating irrigation water consumption and canal water utilization efficiency according to an embodiment of the present application;

[0028] Figure 2 This is a flow chart of a method for estimating irrigation water consumption and canal water utilization efficiency in arid areas based on extreme rainfall scenarios according to one embodiment of the present application;

[0029] Figure 3 This is a schematic diagram of model accuracy results in an extreme rainfall scenario provided according to one embodiment of the present application;

[0030] Figure 4 This is a schematic diagram of the spatiotemporal distribution of deep soil net exchange flux according to one embodiment of the present application;

[0031] Figure 5 A schematic diagram of the spatiotemporal distribution of irrigation water consumption according to one embodiment of the present application;

[0032] Figure 6 A schematic diagram of a canal water efficiency estimation result provided according to one embodiment of the present application;

[0033] Figure 7 This is a block diagram of an example of an apparatus for estimating irrigation water consumption and canal water utilization efficiency according to an embodiment of the present application;

[0034] Figure 8 A schematic diagram of the structure of an electronic device provided according to an embodiment of the present application. DETAILED DESCRIPTION

[0035] The following describes in detail embodiments of the present application. Examples of the embodiments are shown in the accompanying drawings, wherein the same or similar reference numerals throughout represent the same or similar elements or elements having the same or similar functions. The embodiments described below with reference to the accompanying drawings are exemplary and are intended to be used to explain the present application, and should not be construed as limiting the present application.

[0036] The following describes the irrigation water consumption and canal water utilization efficiency estimation method, device and equipment of the embodiment of the present application with reference to the accompanying drawings. In response to the problems mentioned in the above background technology, such as the scarcity of observation data of traditional monitoring methods, the observation depth is limited to the surface, and the parameter calibration based on typical rainfall scenarios easily leads to serious underestimation of parameters in arid areas, the present application provides an irrigation water consumption and canal water utilization efficiency estimation method. In this method, by screening extreme meteorological and hydrological data that meet the conditions of extreme rainfall scenarios from historical meteorological and hydrological data, vegetation topography data and soil and land data, the soil water storage capacity and change are determined, and the label data that characterizes the water storage and discharge capacity of the groundwater variation zone is calculated. The above data generate a data set to construct an irrigation system. A spatiotemporal variable water supply estimation model was developed under different scenarios. The dataset was used to train the water supply estimation model. Finally, the trained water supply estimation model was used to estimate the irrigation water consumption of the target irrigation area. The two were used to estimate the water use efficiency of the canal system. This overcomes the estimation bias caused by the serious mismatch between rainfall and irrigation volume in arid irrigation areas in existing studies. It accurately describes the spatiotemporal variation characteristics of water supply and the net water exchange flux at the deep soil boundary, and achieves high spatiotemporal resolution irrigation water consumption estimation in large-scale areas, thereby improving water resource utilization efficiency, alleviating water resource shortages, and providing technical support for the sustainable development of agriculture. This solves the problems of traditional monitoring methods such as scarce observation data, observation depth limited to the surface, and parameter calibration based on typical rainfall scenarios, which easily leads to serious underestimation of parameters in arid areas.

[0037] Specifically, Figure 1 This is a flow chart of a method for estimating irrigation water consumption and canal water utilization efficiency provided in an embodiment of the present application.

[0038] like Figure 1 As shown, the method for estimating irrigation water consumption and canal water use efficiency includes the following steps:

[0039] In step S101 , historical meteorological data, hydrological data, vegetation data, topographic data, soil texture data, and land use data of a target irrigation area are obtained.

[0040] Among them, the historical meteorological and hydrological data, vegetation topography data and soil and land data of the target irrigation area can be obtained from the databases of the meteorological bureau, hydrological station and other institutions in the region.

[0041] It is understandable that the embodiments of the present application can obtain historical meteorological and hydrological data, vegetation and topographic data, and soil and land data of the target irrigation area by consulting the databases of the meteorological bureau, hydrological station, and other institutions in the region.

[0042] In an embodiment of the present application, the historical meteorological data and hydrological data and the extreme meteorological and hydrological data all include at least one of the rainfall in the target irrigation area, potential evapotranspiration, actual evapotranspiration, soil moisture content, groundwater depth and irrigation area water diversion volume; the vegetation data includes the leaf area index of the target irrigation area; the terrain data includes at least one of the elevation and slope of the target irrigation area; and the soil texture data includes at least one of the proportions of sand, clay and silt in the soil of the target irrigation area.

[0043] Among them, rainfall refers to the accumulated precipitation in a certain period; potential evapotranspiration refers to the maximum amount of water that can theoretically be removed from the surface through evaporation and plant transpiration in the absence of water restrictions; actual evapotranspiration refers to the amount of water actually lost from the soil and plant systems through evaporation and transpiration; soil moisture content refers to the water content in the soil; groundwater depth refers to the distance from the groundwater surface to the ground; irrigation water diversion refers to the amount of water introduced from the water source into the irrigation system; leaf area index refers to half of the total leaf area per unit surface area, which is used to measure vegetation density; the elevation of the target irrigation area refers to the height of the ground in the target irrigation area relative to the reference plane (such as sea level); the slope of the target irrigation area refers to the angle of inclination of the surface in the target irrigation area; the proportion of sand, clay and silt in the soil reflects the distribution of soil particle size and determines the texture of the soil; land use data describes the different uses of land, such as cultivated land, forest, construction land, etc.

[0044] It can be understood that historical meteorological and hydrological data and extreme meteorological and hydrological data may include rainfall in the target irrigation area, potential evapotranspiration, actual evapotranspiration, soil moisture content, groundwater depth and irrigation area water diversion; vegetation topography data include leaf area index of the target irrigation area, elevation and slope of the target irrigation area, or one or more of them; soil and land data include the proportion of sand, clay and silt in the soil of the target irrigation area, and one or more of the land use data; the meteorological and hydrological conditions of the target irrigation area are determined by the above quantities.

[0045] In the embodiment of the present application, after obtaining the historical meteorological data, hydrological data, vegetation data, terrain data, soil texture data and land use data of the target irrigation area, the historical meteorological data, hydrological data, vegetation data, terrain data, soil texture data and land use data are all resampled and reprojected to obtain resampled and reprojected data, wherein all the resampled and reprojected data are located in the same target coordinate system and have the same spatio-temporal resolution.

[0046] The reprojecting is a process of converting data from one coordinate system to another coordinate system, and the resampling is a process of changing the spatial resolution of grid data; the processed data are located in the same target coordinate system and have the same spatio-temporal resolution, which is beneficial to comparison and comprehensive analysis.

[0047] It can be understood that, after obtaining the historical meteorological and hydrological data, vegetation and terrain data and soil and land data of the target irrigation area, the obtained data need to be resampled and reprojected to convert the above data into a group of data having the same spatio-temporal resolution in the same target coordinate system, so as to ensure the consistency of data of different dimensions and facilitate subsequent processing and analysis of the data.

[0048] It is worth mentioning that the station data in the data need to be additionally interpolated, and the station data refers to the data collected through various monitoring stations. These stations are usually in fixed positions, and there may be unmeasured areas between the stations. The interpolation processing can estimate the data values of these unmeasured areas, thereby filling the spatial blank.

[0049] In step S102, extreme meteorological and hydrological data meeting the extreme rainfall scenario condition are selected from the historical meteorological data and hydrological data, and each hydrological element required for establishing a water balance equation under the extreme rainfall scenario is calculated and determined.

[0050] The extreme meteorological and hydrological data meeting the extreme rainfall scenario condition are selected from the historical meteorological and hydrological data by determining the determination standard of extreme rainfall through a statistical method, determining the potential extreme rainfall event by comparing the rainfall amount with the determination standard, avoiding the interference of irrigation on the water dynamic through a spatial consistency screening condition, eliminating the local rainfall in a small area, analyzing the spatial distribution data in the target irrigation area, selecting the rainfall event widely covering the whole area, and finally obtaining the extreme rainfall scenario meeting the condition; the method for determining the regional soil water storage and the change amount under the extreme rainfall scenario will be described in detail below, and will not be described herein again.

[0051] It can be understood that the embodiment of the present application filters historical meteorological and hydrological data through statistical methods and spatial consistency screening conditions, and selects rainfall events that widely cover the entire region and have rainfall exceeding the judgment criteria, that is, extreme meteorological and hydrological data of extreme rainfall scene conditions, and determines the regional soil water storage and change under extreme rainfall scenarios based on these data.

[0052] In an embodiment of the present application, the calculation and determination of the hydrological elements required to establish a water balance equation under an extreme rainfall scenario includes: establishing a soil water balance equation under the extreme rainfall scenario; inputting the extreme meteorological and hydrological data into the soil water balance equation under the extreme rainfall scenario, wherein the soil water balance equation under the extreme rainfall scenario outputs a net exchange flux at the soil boundary under the extreme rainfall scenario, wherein the calculation formula of the soil water balance equation under the extreme rainfall scenario is:

[0053] ,

[0054] ,

[0055] in, Indicates regional soil water storage; Indicates the change in regional soil water storage during the calculation period; P represents the rainfall amount under extreme rainfall scenarios; ET represents actual evapotranspiration; n Indicates the number of soil layers; It represents the average volumetric moisture content of each soil layer; Indicates the depth of each soil layer; NEF Represents the net exchange flux at the soil boundary. By default, a positive value indicates a downward flux, and a negative value indicates an upward flux. The groundwater wave equation is used. estimate NEF , Indicates the water supply degree, Indicates the change in groundwater depth.

[0056] It can be understood that through the above formula, the change in regional soil water storage within a period can be calculated from the rainfall in extreme rainfall scenarios, the actual evapotranspiration, and the net exchange flux at the soil boundary; the regional soil water storage can be calculated by the average volumetric moisture content of each soil layer and the depth of each soil layer.

[0057] In step S103, label data representing the water storage and discharge capacity of the groundwater fluctuation zone is calculated based on extreme meteorological and hydrological data and the net exchange flux at the deep soil boundary under extreme rainfall scenarios, and a data set is generated based on the label data, extreme meteorological and hydrological data, vegetation data, terrain data, soil texture data, and land use data.

[0058] The calculation of the label data will be described in detail below and will not be repeated here.

[0059] It can be understood that the embodiments of the present application can obtain extreme meteorological and hydrological data and regional soil water storage and changes under extreme rainfall scenarios, and calculate label data characterizing the water storage and discharge capacity of the groundwater fluctuation zone based on the above data. A data set is generated by label data, extreme meteorological and hydrological data, vegetation terrain data and soil land data for model training.

[0060] In the embodiment of the present application, the water supply degree is calculated by the groundwater wave equation, and the calculation formula is:

[0061]

[0062] in, It represents the water supply degree, which characterizes the water storage and discharge capacity of the groundwater fluctuation zone. The meanings of other variables are the same as those in step S102.

[0063] It can be understood that the embodiments of the present application can calculate label data by changing the regional soil water storage capacity within a time period, the rainfall in extreme rainfall scenarios, the actual evaporation and the change in groundwater depth, which can be used to train machine learning models.

[0064] In step S104, based on the nonlinear impact of extreme rainfall scenarios on soil moisture dynamics and spatial heterogeneity, a spatiotemporally variable water supply estimation model is constructed in combination with a machine learning algorithm, and the water supply estimation model is trained using the dataset.

[0065] Among them, the similar characteristics of soil moisture dynamics under extreme rainfall scenarios and irrigation scenarios refer to the fact that extreme rainfall scenarios and irrigation are both large-scale water input events that occur in a short period of time for the land, and the two have similar characteristics on soil moisture dynamics; the impact of extreme rainfall scenarios on soil moisture dynamics is nonlinear. For example, when the soil is not yet saturated, a small amount of precipitation may be quickly absorbed by the soil. When the soil is close to saturation, further precipitation will be more converted into runoff or infiltration; the spatial heterogeneity of extreme rainfall scenarios on soil moisture dynamics refers to the fact that factors such as soil type, vegetation cover, and topography in different locations will lead to significant differences in soil moisture responses.

[0066] It can be understood that the spatiotemporally variable water supply estimation model under the irrigation scenario of the embodiment of the present application is constructed based on the similar characteristics of soil moisture dynamics under extreme rainfall scenarios and irrigation scenarios, the nonlinear impact of extreme rainfall scenarios on soil moisture dynamics and spatial heterogeneity, and the model is trained using a generated data set including multi-source data variables, which fully considers the complexity of soil moisture changes and rainfall scenarios.

[0067] In the embodiment of the present application, based on the similar characteristics of soil moisture dynamics in extreme rainfall scenarios and irrigation scenarios, the trained water supply degree estimation model is migrated to the irrigation scenario, and the irrigation water consumption of the target irrigation area is estimated, including: inputting the historical meteorological data, hydrological data, vegetation data, terrain data, soil texture data and land use data into the water supply degree estimation model, and the water supply degree estimation model outputs the spatio-temporal dynamic distribution of the water supply degree of the target irrigation area in the target period; according to the spatio-temporal dynamic distribution of the water supply degree and the groundwater depth distribution, the net exchange flux at the boundary of the deep soil is calculated point by point and hour by hour, and the spatio-temporal distribution of the net exchange flux in the target irrigation area in the target period is determined according to the net exchange flux calculated point by point and hour by hour; the total water entering the soil is estimated according to the spatio-temporal distribution of the net exchange flux, and the irrigation water consumption is obtained according to the total water entering the soil and the rainfall of the target irrigation area.

[0068] The spatio-temporal dynamic distribution of the water supply degree refers to the change of the ability of the soil to allow water to pass at different times and spatial positions in the target period; the point-by-point and hour-by-hour calculation can capture the subtle changes in time and space; the net exchange flux at the boundary of the deep soil refers to the net inflow or outflow between the water in the soil and the groundwater at the junction of the soil and the groundwater; the total water entering the soil is estimated according to the spatio-temporal distribution of the net exchange flux.

[0069] It can be understood that, by using the trained water supply degree estimation model, inputting historical meteorological and hydrological data, vegetation and terrain data, and soil and land data, the spatio-temporal dynamic distribution of the water supply degree of the target irrigation area in the target period can be obtained, the net exchange flux at the boundary of the deep soil can be calculated in combination with the groundwater depth distribution, the point-by-point and hour-by-hour calculation can capture the subtle changes in time and space, and the spatio-temporal distribution of the net exchange flux in the target irrigation area in the target period can be determined according to the net exchange flux, so that the total water entering the soil can be estimated according to the spatio-temporal distribution of the net exchange flux, and finally the irrigation water consumption is determined together with the precipitation.

[0070] In the embodiment of the present application, the total water entering the soil is estimated according to the spatio-temporal distribution of the net exchange flux, including: establishing a soil water balance equation under a non-extreme rainfall scenario; inputting the spatio-temporal distribution of the net exchange flux into the soil water balance equation under the non-extreme rainfall scenario, and the soil water balance equation under the non-extreme rainfall scenario outputs the total water entering the soil, wherein the calculation formula of the soil water balance equation under the non-extreme rainfall scenario is:

[0071]

[0072] wherein,I Indicates the amount of irrigation water flowing from the final channel to the field. The meanings of the other variables are the same as those in step S102.

[0073] It can be understood that the embodiments of the present application can substitute the rainfall under extreme rainfall scenarios, actual evapotranspiration, net exchange flux at the deep soil boundary and net exchange flux at the soil boundary into the formula for calculation to obtain an irrigation water consumption distribution consistent with the spatiotemporal resolution of the input data.

[0074] In step S105, based on the similar characteristics of soil moisture dynamics in extreme rainfall scenarios and irrigation scenarios, the trained water supply estimation model is migrated to the irrigation scenario, and the irrigation water consumption of the target irrigation area is estimated. The canal water utilization efficiency is estimated based on the irrigation water consumption and water diversion volume of the target irrigation area.

[0075] Among them, canal water utilization efficiency refers to the utilization efficiency achieved by the water transported through the canal in the irrigation system when it reaches the field and is effectively utilized by crops.

[0076] It can be understood that the embodiment of the present application can estimate the irrigation water consumption of the target irrigation area through the trained water supply estimation model, and obtain the canal water utilization efficiency by estimating the water diversion amount. The estimation method will be described in detail below and will not be repeated here.

[0077] In an embodiment of the present application, the canal water utilization efficiency is estimated based on the irrigation water consumption and water diversion volume of the target irrigation area, including: performing temporal and spatial data processing on the irrigation water consumption of the target irrigation area to obtain irrigation water consumption with consistent temporal and spatial resolution, and performing time series accumulation on the annual scale on the irrigation water consumption with consistent temporal and spatial resolution to obtain irrigation water consumption at the annual scale and irrigation area scale; estimating the canal water utilization efficiency based on the irrigation water consumption at the annual scale and irrigation area scale and the water diversion volume of the target irrigation area.

[0078] Among them, the irrigation water consumption of the target irrigation area is processed in time and space to obtain irrigation water consumption with consistent temporal and spatial resolution in order to ensure the consistency of the data in time and space and facilitate analysis and calculation; the method of estimating the irrigation water consumption at the annual scale and irrigation area scale and the water diversion amount of the target irrigation area is to calculate the ratio between the irrigation water consumption based on the annual scale and irrigation area scale and the irrigation area water diversion amount obtained in step S101, so as to accurately estimate the water utilization efficiency of the canal system.

[0079] It can be understood that the embodiment of the present application first processes the data in time and space to obtain the irrigation water volume with consistent temporal and spatial resolution, ensuring the consistency of the data in time and space, and then performs time series accumulation on the annual scale for the irrigation water volume with consistent temporal and spatial resolution to obtain the irrigation water volume at the annual scale and the irrigation district scale, ensuring accurate characterization of the long-term irrigation water dynamics; finally, the canal water utilization efficiency is estimated based on the irrigation water volume at the annual scale and the irrigation district scale and the water diversion volume of the target irrigation district, and by conducting year-by-year comparative analysis of the irrigation water volume and water diversion volume in different years, the dynamic change trend of the canal water utilization coefficient in a multi-year time series is revealed.

[0080] According to the method for estimating irrigation water consumption and canal water utilization efficiency proposed in the embodiment of the present application, extreme meteorological and hydrological data that meet the conditions of extreme rainfall scenarios are screened from historical meteorological and hydrological data, vegetation topographic data, and soil and land data to determine the soil water storage capacity and change, and calculate label data that characterizes the water storage and discharge capacity of the groundwater variation zone. A data set is generated from the above data, and a spatiotemporal variable water supply estimation model is constructed under the irrigation scenario. The data set is used to train the water supply estimation model. Finally, the trained water supply estimation model is used to estimate the irrigation water consumption of the target irrigation area, and the canal water utilization efficiency is estimated from the two. This overcomes the estimation bias caused by the serious mismatch between rainfall and irrigation volume in drought irrigation areas in existing studies, accurately describes the spatiotemporal variation characteristics of water supply and net water exchange flux at the deep soil boundary, and realizes irrigation water consumption estimation with high spatiotemporal resolution in large-scale areas, thereby improving water resource utilization efficiency, alleviating water resource shortage problems, and providing technical support for the sustainable development of agriculture.

[0081] The following is a further description of the method for estimating irrigation water consumption and canal water utilization efficiency through a specific embodiment. Figure 2 As shown, the specific process includes the following steps:

[0082] Step S201, obtaining meteorological and hydrological data, vegetation data, topographic data, soil texture data, and land use data of the target irrigation area, specifically includes the following sub-steps:

[0083] Step S201-1: Acquire meteorological and hydrological data, vegetation data, topographic data, soil texture data, and land use data for the target irrigation area. The meteorological and hydrological data include rainfall, potential evapotranspiration, actual evapotranspiration, soil moisture content, groundwater depth, and irrigation water diversion for the target irrigation area. Vegetation data includes the leaf area index for the target irrigation area. Topographic data includes the elevation and slope of the target irrigation area. Soil texture data includes the proportions of sand, clay, and silt in the soil of the target irrigation area. In this embodiment, rainfall data and groundwater depth data are site data, and the remaining data are remote sensing data.

[0084] Step S201-2, the above data is processed by resampling and re-projection to obtain grid data with the same spatial resolution in the same target coordinate system. Among them, the rainfall grid data is obtained by radial basis function interpolation in advance, and the groundwater depth grid data is obtained by Kriging function interpolation in advance.

[0085] Step S202, the rainfall data obtained by step S201 is screened based on statistical analysis and meteorological indicators to obtain extreme rainfall scenarios. The spatial and temporal variable water supply degree estimation model integrating machine learning algorithm is constructed by using the similar characteristics of soil moisture dynamics under extreme rainfall scenarios and irrigation scenarios, considering the nonlinear influence and spatial heterogeneity of extreme rainfall scenarios on soil moisture dynamics. The specific steps include the following sub-steps:

[0086] Step S202-1, screening of extreme rainfall scenarios:

[0087] Based on the rainfall data collected in step S201-1, the daily rainfall sequence of the target irrigation area in the study period is extracted and arranged in descending order of rainfall. The statistical method is used to determine the extreme rainfall criterion, and the 95% quantile in the sub-sequence with rainfall not equal to 0 is set as the extreme rainfall threshold. On this basis, if the rainfall of the target irrigation area on a certain day exceeds the threshold, it is marked as a potential extreme rainfall event. In order to ensure that no irrigation activities occur during the rainfall event, a spatial consistency screening condition is further introduced to maximize the disturbance of irrigation on water dynamics. Among all potential extreme rainfall events, local rainfall in small areas is removed, and by analyzing the spatial distribution data in the target irrigation area, rainfall events widely covering the entire area are selected, i.e. all monitoring points in the entire irrigation area record rainfall. Finally, the extreme rainfall scenarios that meet the conditions are obtained.

[0088] Step S203, acquisition of machine learning labels:

[0089] According to the principle of water balance, the soil water balance equation under the extreme rainfall scenario screened in step S202-1 is established, which can be expressed as:

[0090] (1)

[0091] (2)

[0092] wherein, represents the regional soil water storage capacity; represents the change of regional soil water storage capacity in the calculation period; P represents the rainfall under the extreme rainfall scenario; ET represents the actual evapotranspiration; n represents the number of soil layers; represents the average volumetric water content of each soil layer; Indicates the depth of each soil layer; NEF Represents the net exchange flux at the soil boundary. By default, a positive value indicates a downward flux, and a negative value indicates an upward flux. For ease of calculation, the groundwater wave equation is used to estimate NEF , as shown in formula (3).

[0093] (3)

[0094] in, Indicates water supply degree; Indicates the change in groundwater depth.

[0095] The label of water supply degree is estimated by the soil water balance equation, as shown in formula (4):

[0096] (4)

[0097] Based on the evapotranspiration, rainfall, soil water storage, and groundwater depth data under the selected extreme rainfall scenarios, the water supply value of the corresponding pixel is estimated as the label of the model.

[0098] Step S204: Constructing a water supply degree estimation model based on machine learning:

[0099] The water supply estimation model is constructed based on the random forest algorithm. The training input data includes multi-source data variables. These variables fully consider the complexity of soil moisture changes and rainfall scenarios, including actual evapotranspiration, potential evapotranspiration, soil water storage changes, surface soil moisture content, deep soil moisture content, leaf area index, groundwater depth changes, elevation data, slope, land use type identification, and soil texture (the proportion of sand, clay, and silt). All input data are standardized before entering the model to ensure consistency of data of different dimensions. Water supply The dataset is divided into training set, test set and validation set in a ratio of 6:2:2. By training the random forest model and continuously optimizing the hyperparameters, the optimal parameter combination is finally found, so that the model can extract implicit nonlinear relationships from complex multi-source data. After the model is trained, it is evaluated on the test set and validation set. , mean square error (MAE) and root mean square error (RMSE) are used to evaluate the accuracy and generalization ability of the model. Figure 3 As shown, it shows that the model effectively captures the complex nonlinear relationship between water supply degree and its influencing factors, and realizes the spatiotemporal dynamic inversion of water supply degree parameters.

[0100] Step S205, based on the high spatiotemporal resolution water supply estimation model trained in step S202, estimates the net water exchange flux in the deep soil layer. Combined with the soil water balance equation, the total amount of water entering the soil is accurately estimated. Regional rainfall is deducted to quantify irrigation water consumption. This specifically includes the following sub-steps:

[0101] Step S205-1: Application of water supply model:

[0102] All actual evapotranspiration, potential evapotranspiration, changes in soil water storage, surface soil moisture content, deep soil moisture content, leaf area index, changes in groundwater depth, elevation data, slope, land use type identification, and soil texture data during the study period are used as input data for the model established in step S202-3. The spatiotemporal dynamic distribution of the water supply degree of the target irrigation area during the study period is calculated using the trained spatiotemporal variable water supply degree estimation model, thereby solving the limitation of the single calibration of the water supply degree parameter in existing research.

[0103] Step S205-2: Estimation of net exchange flux:

[0104] According to formula (3), the water supply degree distribution obtained in step S205-1 is multiplied by the groundwater depth distribution obtained in step S201-2 to obtain the spatiotemporal distribution of the net exchange flux of the target irrigation area during the study period, as follows: Figure 4 The results show that the net exchange flux estimated in this example is consistent with the actual situation of the irrigation area where the example is located, which helps to clarify the sustainable use of groundwater and the utilization of irrigation return water.

[0105] Step S206: Estimation of irrigation water consumption:

[0106] According to the principle of water balance, the soil water balance equation under general circumstances can be expressed as:

[0107] (5)

[0108] in, I represents the amount of irrigation water flowing from the final channel to the field. The other variables have the same meanings as in formula (1). Substituting the rainfall and evapotranspiration obtained in step S201-2, the net exchange flux obtained in step S205-2, and the soil moisture content obtained in step S201-2 with the change in soil water storage obtained in formula (2) into formula (5), we obtain an irrigation water distribution consistent with the temporal and spatial resolution of the input data, as follows: Figure 5 The results can help clarify the irrigation dynamics and irrigation water use within the year, and provide guidance for agricultural water management and optimized water resource allocation in irrigation areas.

[0109] Step S207 uses the irrigation water consumption estimated in step S205 and the target irrigation area water diversion volume obtained in step S201 to accurately calculate the canal water utilization efficiency through ratio analysis, so as to effectively evaluate the water resource management efficiency of the irrigation system at the regional scale. Specifically, it includes the following sub-steps:

[0110] Step S207-1: Data post-processing:

[0111] The irrigation water consumption estimated in step S206 is processed temporally and spatially. First, irrigation water consumption with consistent temporal and spatial resolution is accumulated on an annual scale to ensure accurate depiction of long-term irrigation water dynamics. Second, the spatially distributed irrigation water consumption is spatially aggregated within the region and aggregated to a specific regional point scale, providing a more macroscopic regional-scale irrigation water consumption estimate for subsequent analysis.

[0112] Step S207-2: Estimation of canal water utilization coefficient:

[0113] Based on the irrigation water consumption at the annual and irrigation district scales, the ratio between it and the irrigation district water diversion volume obtained in step S201-1 is calculated to accurately estimate the canal water utilization efficiency. This ratio reflects the water loss from the canal head to the fields in the irrigation district water distribution system. By comparing and analyzing the irrigation water consumption and diversion volume in different years year by year, the dynamic change trend of the canal water utilization coefficient over a multi-year time series is revealed, such as Figure 6 shown.

[0114] In summary, firstly, the embodiment of the present application utilizes the similarity between extreme rainfall events and irrigation processes in terms of water dynamics to construct a deep soil water exchange estimation model, and successfully migrates it for application in irrigation scenarios. This innovative method effectively solves the problem of serious mismatch between rainfall and irrigation in arid areas, and provides a new solution for areas lacking irrigation data. Secondly, the present embodiment fully considers the water exchange process between saturated and unsaturated zones in deep soil, especially the return and reuse of groundwater recharge, discharge, and groundwater evaporation, which enhances the comprehensive understanding of regional hydrological processes. At the same time, the present embodiment combines multi-source remote sensing data and hydro-meteorological factors to fully explore the complex nonlinear relationship between these data and irrigation water consumption. By training the model with machine learning technology, it can estimate the spatiotemporal distribution of irrigation water consumption with high precision and continuously, significantly improving the analysis accuracy of canal water utilization efficiency in arid areas. Its powerful nonlinear fitting ability and data migration ability make the model highly versatile in different regions and conditions. This embodiment has important practical value and can provide strong technical support and decision-making basis for water resource management, agricultural irrigation planning and sustainable utilization of groundwater resources in arid areas. It also provides reliable basic data support for the subsequent establishment of distributed hydrological models in irrigation areas, thereby further improving the scientificity and effectiveness of water resource management in arid areas.

[0115] Next, the irrigation water consumption and canal water utilization efficiency estimation device proposed in accordance with the embodiment of the present application will be described with reference to the accompanying drawings.

[0116] Figure 7 3 is a block diagram of an apparatus for estimating irrigation water consumption and canal water utilization efficiency according to an embodiment of the present application.

[0117] like Figure 7 As shown, the irrigation water consumption and canal water utilization efficiency estimation device 10 includes: an acquisition module 301 , a determination module 302 , a calculation module 303 , a modeling module 304 and an estimation module 305 .

[0118] Among them, the acquisition module 301 is used to obtain historical meteorological data, hydrological data, vegetation data, topographic data, soil texture data and land use data of the target irrigation area; the determination module 302 is used to screen extreme meteorological and hydrological data that meet the conditions of extreme rainfall scenarios from the historical meteorological data and hydrological data, and calculate and determine the various hydrological elements required to establish the water balance equation under the extreme rainfall scenario; the calculation module 303 is used to calculate the label data representing the water storage and discharge capacity of the groundwater fluctuation zone based on the extreme meteorological and hydrological data and the net exchange flux at the deep soil boundary under the extreme rainfall scenario, and calculate the label data representing the water storage and discharge capacity of the groundwater fluctuation zone based on the label data, the extreme meteorological and hydrological data The data set is generated based on the vegetation data, the terrain data, the soil texture data and the land use data; the modeling module 304 is used to construct a spatiotemporally variable water supply estimation model based on the nonlinear impact of extreme rainfall scenarios on soil moisture dynamics and spatial heterogeneity, combined with a machine learning algorithm, and train the water supply estimation model using the data set; the estimation module 305 is used to migrate the trained water supply estimation model to the irrigation scenario based on the similar characteristics of soil moisture dynamics under extreme rainfall scenarios and irrigation scenarios, and estimate the irrigation water consumption of the target irrigation area, and estimate the canal water utilization efficiency based on the irrigation water consumption and water diversion volume of the target irrigation area.

[0119] In an embodiment of the present application, the historical meteorological data and hydrological data and the extreme meteorological and hydrological data all include at least one of the rainfall in the target irrigation area, potential evapotranspiration, actual evapotranspiration, soil moisture content, groundwater depth and irrigation area water diversion volume; the vegetation data includes the leaf area index of the target irrigation area; the terrain data includes at least one of the elevation and slope of the target irrigation area; and the soil texture data includes at least one of the proportions of sand, clay and silt in the soil of the target irrigation area.

[0120] In an embodiment of the present application, a processing module is also included, and the processing module is further used to: after obtaining the historical meteorological data, hydrological data, vegetation data, terrain data, soil texture data and land use data of the target irrigation area, it also includes: resampling and reprojecting the historical meteorological data, hydrological data, vegetation data, terrain data, soil texture data and land use data to obtain resampled and reprojected data, wherein all resampled and reprojected data are located in the same target coordinate system and have the same spatiotemporal resolution.

[0121] In the embodiment of the present application, the determination module 302 is further configured to: establish a soil water balance equation under the extreme rainfall scenario; input the extreme meteorological and hydrological data into the soil water balance equation under the extreme rainfall scenario, and the soil water balance equation under the extreme rainfall scenario outputs the net exchange flux at the soil boundary under the extreme rainfall scenario, wherein the calculation formula of the soil water balance equation under the extreme rainfall scenario is:

[0122] ,

[0123] ,

[0124] wherein, represents the regional soil water storage; represents the change of the regional soil water storage in the calculation period; P represents the rainfall in the extreme rainfall scenario; ET represents the actual evapotranspiration; n represents the number of soil layers; represents the average volumetric water content of each layer of soil; represents the depth of each layer of soil; NEF represents the net exchange flux at the soil boundary, a positive value by default indicates that the flux is downward, and a negative value indicates that the flux is upward, and the groundwater fluctuation equation estimating NEF , represents the water supply degree, represents the change of the groundwater depth.

[0125] In the embodiment of the present application, the water supply degree is calculated by the groundwater fluctuation equation, and the calculation formula is:

[0126]

[0127] wherein, represents the water supply degree, which characterizes the water storage and discharge capacity of the groundwater fluctuation zone, and the meanings of other variables are the same as the meanings of the above variables.

[0128] In the embodiment of the present application, the estimation module 305 is further configured to: input the historical meteorological data, hydrological data, vegetation data, terrain data, soil texture data and land use data into the water supply degree estimation model, and the water supply degree estimation model outputs the spatiotemporal dynamic distribution of the water supply degree of the target irrigation area in the target period; calculate the net exchange flux at the deep soil boundary point by point and hour by hour according to the spatiotemporal dynamic distribution of the water supply degree and the groundwater depth distribution, and determine the spatiotemporal distribution of the net exchange flux in the target irrigation area in the target period according to the net exchange flux calculated point by point and hour by hour; estimate the total water entering the soil according to the spatiotemporal distribution of the net exchange flux, and obtain the irrigation water quantity according to the total water entering the soil and the rainfall of the target irrigation area.

[0129] In an embodiment of the present application, estimating the total amount of water entering the soil based on the spatiotemporal distribution of the net exchange flux includes: establishing a soil water balance equation under a non-extreme rainfall scenario; inputting the spatiotemporal distribution of the net exchange flux into the soil water balance equation under the non-extreme rainfall scenario, and outputting the total amount of water entering the soil from the soil water balance equation under the non-extreme rainfall scenario, wherein the calculation formula of the soil water balance equation under the non-extreme rainfall scenario is:

[0130]

[0131] in, I It represents the amount of irrigation water flowing from the final channel to the field. The meanings of other variables are the same as those of the above variables.

[0132] In an embodiment of the present application, the estimation module 305 is further used to: perform temporal and spatial data processing on the irrigation water consumption of the target irrigation area to obtain irrigation water consumption with consistent temporal and spatial resolution, and perform time series accumulation on the annual scale on the irrigation water consumption with consistent temporal and spatial resolution to obtain irrigation water consumption at the annual scale and the irrigation area scale; and estimate the canal water utilization efficiency based on the irrigation water consumption at the annual scale and the irrigation area scale and the water diversion volume of the target irrigation area.

[0133] It should be noted that the above explanations of the embodiment of the method for estimating irrigation water consumption and canal water utilization efficiency are also applicable to the device for estimating irrigation water consumption and canal water utilization efficiency of this embodiment, and will not be repeated here.

[0134] According to the irrigation water consumption and canal water utilization efficiency estimation device proposed in the embodiment of the present application, through the coordinated action of the acquisition module, the determination module, the calculation module, the modeling module and the estimation module, it is possible to screen extreme meteorological and hydrological data that meet the conditions of extreme rainfall scenarios from historical meteorological and hydrological data, vegetation and topographic data and soil and land data, determine the soil water storage capacity and change, and calculate label data representing the water storage and discharge capacity of the groundwater variation zone. A data set is generated from the above data, a spatiotemporal variable water supply estimation model under the irrigation scenario is constructed, and the data set is used to train the water supply estimation model. Finally, the trained water supply estimation model is used to estimate the irrigation water consumption of the target irrigation area, and the canal water utilization efficiency is estimated from the two. This overcomes the estimation bias caused by the serious mismatch between rainfall and irrigation volume in drought irrigation areas in existing studies, accurately describes the spatiotemporal variation characteristics of water supply and net water exchange flux at the deep soil boundary, and realizes irrigation water consumption estimation with high spatiotemporal resolution in large-scale areas, thereby improving water resource utilization efficiency, alleviating water resource shortage problems, and providing technical support for the sustainable development of agriculture.

[0135] Figure 8 This is a schematic diagram of the structure of an electronic device provided in an embodiment of the present application. The electronic device may include:

[0136] Memory 401 , processor 402 , and computer programs stored in the memory 401 and executable on the processor 402 .

[0137] When the processor 402 executes the program, the irrigation water consumption and canal water utilization efficiency estimation method provided in the above embodiment is implemented.

[0138] Furthermore, the electronic device further includes:

[0139] The communication interface 403 is used for communication between the memory 401 and the processor 402 .

[0140] The memory 401 is used to store computer programs that can be run on the processor 402 .

[0141] The memory 401 may include a high-speed RAM (Random Access Memory) memory, and may also include a non-volatile memory, such as at least one disk memory.

[0142] If the memory 401, processor 402, and communication interface 403 are implemented independently, the communication interface 403, memory 401, and processor 402 can be connected to each other via a bus and communicate with each other. The bus can be an ISA (Industry Standard Architecture) bus, a PCI (Peripheral Component Interconnect) bus, or an EISA (Extended Industry Standard Architecture) bus. The bus can be divided into an address bus, a data bus, a control bus, etc. For ease of representation, Figure 8 Only one thick line is used in the diagram, but this does not mean that there is only one bus or one type of bus.

[0143] Optionally, in a specific implementation, if the memory 401, the processor 402 and the communication interface 403 are integrated on a chip, the memory 401, the processor 402 and the communication interface 403 can communicate with each other through an internal interface.

[0144] The processor 402 may be a CPU (Central Processing Unit), or an ASIC (Application Specific Integrated Circuit), or one or more integrated circuits configured to implement the embodiments of the present application.

[0145] In the description of this specification, the description with reference to the terms "one embodiment", "some embodiments", "example", "specific example", or "some examples" means that the specific features, structures, materials or characteristics described in conjunction with the embodiment or example are included in at least one embodiment or example of the present application. In this specification, the schematic representations of the above terms do not necessarily refer to the same embodiment or example. Moreover, the specific features, structures, materials or characteristics described can be combined in any one or N embodiments or examples in a suitable manner. In addition, those skilled in the art can combine and combine different embodiments or examples described in this specification and features of different embodiments or examples without contradiction.

[0146] Furthermore, the terms "first" and "second" are used for descriptive purposes only and should not be understood to indicate or imply relative importance or implicitly specify the number of technical features indicated. Thus, a feature specified as "first" or "second" may explicitly or implicitly include at least one such feature. In the description of this application, "N" means at least two, for example, two, three, etc., unless otherwise specifically defined.

[0147] Any process or method description in a flowchart or otherwise described herein may be understood to represent a module, fragment or portion of code comprising one or N executable instructions for implementing a custom logical function or process step, and the scope of the preferred embodiments of the present application includes alternative implementations in which functions may be performed in a different order than shown or discussed, including performing functions in a substantially simultaneous manner or in a reverse order depending on the functions involved, which should be understood by those skilled in the art to which the embodiments of the present application pertain.

[0148] It should be understood that various parts of the present application can be implemented using hardware, software, firmware, or a combination thereof. In the above-described embodiments, the steps or methods can be implemented using software or firmware stored in a memory and executed by a suitable instruction execution system. For example, if implemented using hardware, as in another embodiment, any one of the following technologies known in the art or a combination thereof can be used to implement the method: a discrete logic circuit having logic gate circuits for implementing logic functions on data signals, an application-specific integrated circuit having suitable combinational logic gate circuits, a programmable gate array, a field programmable gate array, etc.

[0149] A person skilled in the art may understand that all or part of the steps carried out in the method for implementing the above-mentioned embodiment may be completed by instructing the relevant hardware through a program, and the above-mentioned program may be stored in a computer-readable storage medium, which, when executed, includes one of the steps of the method embodiment or a combination thereof.

[0150] Although the embodiments of the present application have been shown and described above, it is understood that the above-described embodiments are exemplary and are not to be construed as limiting the present application, and that changes, modifications, substitutions and variations can be made by those skilled in the art without departing from the scope of the present application.

Claims

1. A method for estimating irrigation water consumption and canal water utilization efficiency, characterized in that: The following steps are involved: Obtain historical meteorological data, hydrological data, vegetation data, topographic data, soil texture data, and land use data for the target irrigation area; screening extreme meteorological and hydrological data that meet the conditions of extreme rainfall scenarios from the historical meteorological and hydrological data, and calculating and determining various hydrological elements required to establish a water balance equation under the extreme rainfall scenario; Calculating label data representing the water storage and discharge capacity of the groundwater fluctuation zone based on the extreme meteorological and hydrological data and the net exchange flux at the deep soil boundary under the extreme rainfall scenario, and generating a dataset based on the label data, the extreme meteorological and hydrological data, the vegetation data, the topographic data, the soil texture data, and the land use data; Based on the nonlinear impact and spatial heterogeneity of extreme rainfall scenarios on soil moisture dynamics, a spatiotemporally variable water supply estimation model is constructed in combination with a machine learning algorithm, and the water supply estimation model is trained using the dataset; Based on the similar characteristics of soil moisture dynamics under extreme rainfall scenarios and irrigation scenarios, the trained water supply estimation model is transferred to the irrigation scenario, and the irrigation water consumption of the target irrigation area is estimated. The water utilization efficiency of the canal system is estimated based on the irrigation water consumption and water diversion volume of the target irrigation area.

2. The method for estimating irrigation water consumption and canal water utilization efficiency according to claim 1, characterized in that: The historical meteorological data and hydrological data and the extreme meteorological and hydrological data include at least one of the rainfall in the target irrigation area, potential evapotranspiration, actual evapotranspiration, soil moisture content, groundwater depth and water diversion amount in the irrigation area; the vegetation data includes the leaf area index of the target irrigation area; the terrain data includes at least one of the elevation and slope of the target irrigation area; and the soil texture data includes at least one of the proportions of sand, clay and silt in the soil of the target irrigation area.

3. The method for estimating irrigation water consumption and canal water utilization efficiency according to claim 1 or 2, characterized in that: After obtaining historical meteorological data, hydrological data, vegetation data, topographic data, soil texture data, and land use data for the target irrigation area, it also includes: The historical meteorological data, hydrological data, vegetation data, terrain data, soil texture data and land use data are all resampled and reprojected to obtain resampled and reprojected data, wherein all the resampled and reprojected data are in the same target coordinate system and have the same spatiotemporal resolution.

4. The method for estimating irrigation water consumption and canal water utilization efficiency according to claim 1, wherein: The calculations determine the hydrological elements required to establish the water balance equation under extreme rainfall scenarios, including: Establishing a soil water balance equation under the extreme rainfall scenario; The extreme meteorological and hydrological data are input into the soil water balance equation under the extreme rainfall scenario. The soil water balance equation under the extreme rainfall scenario outputs the net exchange flux at the soil boundary under the extreme rainfall scenario. The calculation formula of the soil water balance equation under the extreme rainfall scenario is: ， ， in, Indicates regional soil water storage; Indicates the change in regional soil water storage during the calculation period; P represents the rainfall amount under extreme rainfall scenarios; ET represents actual evapotranspiration; n Indicates the number of soil layers; It represents the average volumetric moisture content of each soil layer; Indicates the depth of each soil layer; NEF Represents the net exchange flux at the soil boundary. By default, a positive value indicates a downward flux, and a negative value indicates an upward flux. The groundwater wave equation is used. estimate NEF , Indicates the water supply degree, Indicates the change in groundwater depth.

5. The method for estimating irrigation water consumption and canal water utilization efficiency according to claim 4, characterized in that: The water supply degree is calculated by the groundwater wave equation, and the calculation formula is: in, It indicates the water supply degree and characterizes the water storage and discharge capacity of the groundwater fluctuation zone.

6. The method for estimating irrigation water consumption and canal water utilization efficiency according to claim 1, characterized in that: Based on the similar characteristics of soil moisture dynamics under extreme rainfall scenarios and irrigation scenarios, the trained water supply estimation model is transferred to the irrigation scenario, and the irrigation water consumption of the target irrigation area is estimated, including: Inputting the historical meteorological data, hydrological data, vegetation data, topographic data, soil texture data, and land use data into the water supply estimation model, the water supply estimation model outputting the spatiotemporal dynamic distribution of the water supply of the target irrigation area within the target period; Calculating the net exchange flux at the deep soil boundary point by point and hour by hour based on the spatiotemporal dynamic distribution of the water supply degree and the groundwater depth distribution, and determining the spatiotemporal distribution of the net exchange flux in the target irrigation area within the target period based on the net exchange flux calculated point by hour and hour; The total amount of water entering the soil is estimated according to the spatiotemporal distribution of the net exchange flux, and the irrigation water consumption is obtained according to the total amount of water entering the soil and the rainfall in the target irrigation area.

7. The method for estimating irrigation water consumption and canal water utilization efficiency according to claim 6, characterized in that: The estimating the total amount of water entering the soil according to the spatiotemporal distribution of the net exchange flux comprises: Establish soil water balance equations under non-extreme rainfall scenarios; The spatiotemporal distribution of the net exchange flux is input into the soil water balance equation under the non-extreme rainfall scenario, and the soil water balance equation under the non-extreme rainfall scenario outputs the total amount of water entering the soil. The calculation formula of the soil water balance equation under the non-extreme rainfall scenario is: in, I It represents the amount of irrigation water flowing from the final channel to the field; P represents the rainfall amount under extreme rainfall scenarios; ET represents actual evapotranspiration; NEF Represents the net exchange flux at the soil boundary. By default, positive values ​​indicate downward flux and negative values ​​indicate upward flux.

8. The method for estimating irrigation water consumption and canal water utilization efficiency according to claim 1, wherein: The estimating of canal water utilization efficiency based on the irrigation water consumption and water diversion volume of the target irrigation area includes: Performing temporal and spatial data processing on the irrigation water volume of the target irrigation area to obtain an irrigation water volume with consistent temporal and spatial resolution, and performing time series accumulation on the irrigation water volume with consistent temporal and spatial resolution on an annual scale to obtain an irrigation water volume at an annual scale and an irrigation area scale; The canal water utilization efficiency is estimated based on the irrigation water consumption at the annual scale and the irrigation district scale and the water diversion volume of the target irrigation district.

9. A device for estimating irrigation water consumption and canal water utilization efficiency, characterized in that: include: An acquisition module is used to obtain historical meteorological data, hydrological data, vegetation data, topographic data, soil texture data, and land use data of the target irrigation area; a determination module, configured to select extreme meteorological and hydrological data that meet the conditions of an extreme rainfall scenario from the historical meteorological and hydrological data, and calculate and determine the hydrological elements required to establish a water balance equation under the extreme rainfall scenario; a calculation module, configured to calculate label data representing the water storage and discharge capacity of the groundwater fluctuation zone based on the extreme meteorological and hydrological data and the net exchange flux at the deep soil boundary under the extreme rainfall scenario, and to generate a data set based on the label data, the extreme meteorological and hydrological data, the vegetation data, the terrain data, the soil texture data, and the land use data; a modeling module for constructing a spatiotemporally variable water supply estimation model based on the nonlinear effects and spatial heterogeneity of extreme rainfall scenarios on soil moisture dynamics and combining a machine learning algorithm, and training the water supply estimation model using the dataset; The estimation module is used to migrate the trained water supply estimation model to the irrigation scenario based on the similar characteristics of soil moisture dynamics in extreme rainfall scenarios and irrigation scenarios, and estimate the irrigation water consumption of the target irrigation area, and estimate the canal water utilization efficiency based on the irrigation water consumption and water diversion volume of the target irrigation area.

10. An electronic device, characterized in that: include: A memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the program to implement the method for estimating irrigation water consumption and canal water utilization efficiency according to any one of claims 1 to 8.

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