Underground water supply amount estimation method and device and computer equipment

Abstract

The invention relates to a groundwater replenishment amount estimation method and device and computer equipment. The method comprises the following steps: acquiring multi-source data of a target area; based on the actual scene evapotranspiration data, the natural scene evapotranspiration data, the natural scene multilayer soil water content data and the reference crop evapotranspiration data, determining actual scene multilayer soil water content data of the target area; inputting the multi-layer soil moisture content data of the actual scene into the moisture vertical motion model between two adjacent layers of soil to obtain the soil moisture vertical motion of the target area in the actual scene; the moisture vertical motion model is generated according to a soil water balance equation, the natural scene evapotranspiration data, the natural scene multilayer soil moisture content data and the soil parameter data of the target area; and determining the groundwater supply amount of the target area according to the soil water amount balance equation and the soil moisture vertical movement in the actual scene. By adopting the method, the groundwater replenishment amount can be accurately obtained.

Description

Technical Field

[0001] The present application relates to the field of water conservancy technology, and in particular to a method, device, computer equipment, computer-readable storage medium, and computer program product for estimating groundwater recharge. Background Art

[0002] Available freshwater resources are scarce, and agriculture consumes a significant amount of water. The effective utilization rate of irrigation water needs to be further improved. Many regions rely heavily on groundwater for irrigation, leading to overexploitation. Therefore, timely and accurate measurement of groundwater recharge and assessment of the impact of irrigation on groundwater recharge are crucial for the coordinated management of surface and groundwater resources and guiding the sustainable development of agriculture.

[0003] Therefore, there is an urgent need for a groundwater recharge estimation method that can accurately obtain groundwater recharge. Summary of the Invention

[0004] Based on this, it is necessary to provide a groundwater recharge estimation method, device, computer equipment, computer-readable storage medium and computer program product that can accurately obtain groundwater recharge to address the above technical problems.

[0005] In a first aspect, the present application provides a method for estimating groundwater recharge, comprising:

[0006] Acquire multi-source data for the target area; the multi-source data includes actual scene evapotranspiration data based on remote sensing observations, natural scene evapotranspiration data based on land surface models, multi-layer soil moisture data for natural scenes based on land surface models, and reference crop evapotranspiration data based on meteorological observations;

[0007] Determining the actual scene multi-layer soil moisture data of the target area based on the actual scene evapotranspiration data, the natural scene evapotranspiration data, the natural scene multi-layer soil moisture content data, and the reference crop evapotranspiration data;

[0008] Inputting the actual scene multi-layer soil moisture content data into a water vertical movement model between two adjacent soil layers to obtain the soil water vertical movement in the actual scene of the target area; the water vertical movement model is generated based on the soil water balance equation, the natural scene evapotranspiration data, the natural scene multi-layer soil moisture content data, and the soil parameter data of the target area;

[0009] The groundwater recharge of the target area is determined based on the soil water balance equation and the vertical movement of soil moisture in the actual scenario.

[0010] In one embodiment, determining the actual scene multi-layer soil moisture data of the target area based on the actual scene evapotranspiration data, the natural scene evapotranspiration data, the natural scene multi-layer soil moisture data, and the reference crop evapotranspiration data includes:

[0011] performing systematic error correction on the actual scene evapotranspiration data according to the actual scene evapotranspiration data and the natural scene evapotranspiration data of the non-irrigated pixels to obtain corrected actual scene evapotranspiration data;

[0012] The actual scene multi-layer soil moisture content data of the target area is determined based on the corrected actual scene evapotranspiration data, the natural scene evapotranspiration data, the natural scene multi-layer soil moisture content data and the reference crop evapotranspiration data.

[0013] In one embodiment, determining the actual scene multi-layer soil moisture data of the target area based on the corrected actual scene evapotranspiration data, the natural scene evapotranspiration data, the natural scene multi-layer soil moisture data, and the reference crop evapotranspiration data includes:

[0014] Calculating a natural scene evapotranspiration ratio based on the natural scene evapotranspiration data and the reference crop evapotranspiration data;

[0015] For each layer of soil, a linear fitting parameter corresponding to the layer of soil is obtained by fitting according to a preset linear relationship between the moisture content of the layer of soil and the evapotranspiration ratio, the evapotranspiration ratio of the natural scene, and the moisture content data of multiple layers of soil in the natural scene corresponding to the layer of soil;

[0016] Calculating an actual scene evapotranspiration ratio based on the corrected actual scene evapotranspiration data and the reference crop evapotranspiration data;

[0017] For each layer of soil, the actual scene multi-layer soil moisture content data corresponding to the layer of soil is calculated based on the preset linear relationship between the moisture content of the layer of soil and the evapotranspiration ratio, the actual scene evapotranspiration ratio, the natural scene evapotranspiration ratio, the natural scene multi-layer soil moisture content data corresponding to the layer of soil, and the linear fitting parameters corresponding to the layer of soil.

[0018] In one embodiment, the method further comprises:

[0019] According to the soil water balance equation, constructing the water balance residual of the top soil layer and the water balance residual of the middle soil layer;

[0020] constructing a loss function of a target neural network according to the water balance residual of the top layer of soil and the water balance residual of the middle layer of soil;

[0021] Based on the natural scene evapotranspiration data, the natural scene multi-layer soil moisture data and the soil parameter data, the target neural network is trained to obtain a vertical water movement model between two adjacent soil layers.

[0022] In one embodiment, determining the groundwater recharge of the target area based on the soil water balance equation and the vertical movement of soil moisture in the actual scenario includes:

[0023] Calculating a change in soil moisture content in the actual scenario of the bottom soil according to the actual scenario multi-layer soil moisture content data of the bottom soil and a preset time step;

[0024] Calculating the actual scenario recharge of the target area based on the soil water balance equation, the vertical movement of soil moisture in the actual scenario, the actual scenario soil moisture content change of the underlying soil, and the soil thickness of the underlying soil;

[0025] The groundwater recharge amount of the target area is determined based on the actual scenario recharge amount.

[0026] In one embodiment, determining the groundwater recharge amount of the target area based on the actual scenario recharge amount includes:

[0027] Calculate the vertical movement of soil moisture in the natural scene based on the soil water balance equation, the multi-layer soil moisture content data of the natural scene, precipitation, surface runoff, and the evapotranspiration data of the natural scene;

[0028] Calculating a change in the natural scene soil moisture content of the bottom soil according to the natural scene multi-layer soil moisture content data of the bottom soil and a preset time step;

[0029] Calculating the natural scene recharge of the target area based on the soil water balance equation, the vertical movement of soil water in the natural scene, the change in soil water content in the natural scene of the underlying soil, and the soil thickness of the underlying soil;

[0030] The difference between the actual scene supply amount and the natural scene supply amount is used as the irrigation supply amount of the target area;

[0031] The groundwater recharge amount of the target area is determined based on the irrigation recharge amount.

[0032] In a second aspect, the present application further provides a groundwater recharge estimation device, comprising:

[0033] An acquisition module is used to acquire multi-source data of the target area; the multi-source data includes actual scene evapotranspiration data based on remote sensing observations, natural scene evapotranspiration data based on land surface models, multi-layer soil moisture data of natural scenes based on land surface models, and reference crop evapotranspiration data based on meteorological observations;

[0034] a first determining module, configured to determine the actual scene multi-layer soil moisture content data of the target area based on the actual scene evapotranspiration data, the natural scene evapotranspiration data, the natural scene multi-layer soil moisture content data, and the reference crop evapotranspiration data;

[0035] a second determination module configured to input the actual scenario multi-layer soil moisture content data into a water vertical movement model between two adjacent soil layers to obtain the soil water vertical movement in the actual scenario of the target area; the water vertical movement model is generated based on the soil water balance equation, the natural scenario evapotranspiration data, the natural scenario multi-layer soil moisture content data, and the soil parameter data of the target area;

[0036] The third determination module is used to determine the groundwater recharge of the target area based on the soil water balance equation and the vertical movement of soil moisture in the actual scenario.

[0037] In a third aspect, the present application further provides a computer device comprising a memory and a processor, wherein the memory stores a computer program, and the processor implements the steps described in the first aspect when executing the computer program.

[0038] In a fourth aspect, the present application further provides a computer-readable storage medium having a computer program stored thereon, which implements the steps described in the first aspect when executed by a processor.

[0039] In a fifth aspect, the present application also provides a computer program product, comprising a computer program, which implements the steps described in the first aspect when executed by a processor.

[0040] The above-mentioned groundwater recharge estimation method, apparatus, computer equipment, computer-readable storage medium and computer program product obtain multi-source data of the target area; the multi-source data includes actual scene evapotranspiration data based on remote sensing observations, natural scene evapotranspiration data based on a land surface model, natural scene multi-layer soil moisture data based on a land surface model and reference crop evapotranspiration data based on meteorological observations; based on the actual scene evapotranspiration data, the natural scene evapotranspiration data, the natural scene multi-layer soil moisture data and the reference crop evapotranspiration data, the actual scene multi-layer soil moisture data of the target area is determined; the actual scene multi-layer soil moisture data is input into a water vertical movement model between two adjacent soil layers to obtain the actual scene soil moisture vertical movement of the target area; the water vertical movement model is generated based on the soil water balance equation, the natural scene evapotranspiration data, the natural scene multi-layer soil moisture data and soil parameter data of the target area; based on the soil water balance equation and the actual scene soil moisture vertical movement, the groundwater recharge of the target area is determined. In this way, based on the actual scene evapotranspiration data based on remote sensing observations, the natural scene evapotranspiration data based on the land surface model and the natural scene multi-layer soil moisture data, as well as the reference crop evapotranspiration data based on meteorological observations, the actual scene multi-layer soil moisture data is determined, and input into the water vertical movement model generated according to the soil water balance equation, the natural scene evapotranspiration data, the natural scene multi-layer soil moisture data and the soil parameter data, to obtain the vertical movement of soil moisture in the actual scene. Combined with the soil water balance equation, the groundwater recharge in the target area is determined, taking into account the influence of irrigation and other environmental factors in the actual scene, in accordance with the actual situation, and able to accurately obtain the groundwater recharge. BRIEF DESCRIPTION OF THE DRAWINGS

[0041] In order to more clearly illustrate the technical solutions in the embodiments of the present application or related technologies, the following briefly introduces the drawings required for use in the embodiments of the present application or related technical descriptions. Obviously, the drawings described below are only some embodiments of the present application. For ordinary technicians in this field, other related drawings can be obtained based on these drawings without paying any creative work.

[0042] FIG1 is a schematic flow chart of a method for estimating groundwater recharge in one embodiment;

[0043] FIG2 is a flowchart illustrating steps for determining actual scenario multi-layer soil moisture data for a target area based on actual scenario evapotranspiration data, natural scenario evapotranspiration data, natural scenario multi-layer soil moisture data, and reference crop evapotranspiration data, in one embodiment;

[0044] FIG3 is a flowchart illustrating steps for determining actual scene multi-layer soil moisture data for a target area based on calibrated actual scene evapotranspiration data, natural scene evapotranspiration data, natural scene multi-layer soil moisture data, and reference crop evapotranspiration data, in one embodiment;

[0045] FIG4 is a flow chart showing steps further included in the method for estimating groundwater recharge in one embodiment;

[0046] FIG5 is a flowchart illustrating steps for determining groundwater recharge in a target area based on a soil water balance equation and vertical movement of soil moisture in an actual scenario, according to one embodiment;

[0047] FIG6 is a flow chart illustrating the steps of determining the groundwater recharge amount in a target area based on the actual recharge amount in an embodiment;

[0048] FIG7 is a schematic diagram showing the linear fitting relationship between the irrigation water consumption calculated by the model and the irrigation water intake / water consumption in the water resources bulletin for the target area A and the five sub-areas bf included in A in one embodiment;

[0049] FIG8A is a schematic diagram showing a trend comparison of irrigation water withdrawals from 2003 to 2022 for multiple sub-regions 01-16 included in target region A according to a statistical bulletin and irrigation water withdrawals calculated by the model;

[0050] FIG8B is a schematic diagram showing a trend comparison of irrigation water withdrawals from 2003 to 2022 for multiple sub-regions 17-31 included in target region A according to statistical bulletins and irrigation water withdrawals calculated by the model, in one embodiment;

[0051] FIG9 is a block diagram of a groundwater recharge estimation device according to one embodiment;

[0052] FIG10 is a diagram showing the internal structure of a computer device in one embodiment. DETAILED DESCRIPTION

[0053] In order to make the purpose, technical solutions and advantages of this application more clear, the following further describes this application in detail with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain this application and are not intended to limit this application.

[0054] In one embodiment, as shown in FIG1 , a method for estimating groundwater recharge is provided. This embodiment uses the method applied to a terminal as an example for illustration. It is understandable that the method can also be applied to a server, and can also be applied to a system including a terminal and a server, and is implemented through the interaction between the terminal and the server. Among them, the terminal can be, but is not limited to, various personal computers, laptops, smart phones, tablet computers, Internet of Things devices and portable wearable devices. The Internet of Things devices can be smart speakers, smart TVs, smart air conditioners, smart car devices, projection devices, etc. Portable wearable devices can be smart watches, smart bracelets, head-mounted devices, etc. The head-mounted devices can be virtual reality (VR) devices, augmented reality (AR) devices, smart glasses, etc. The server can be an independent physical server, or a server cluster or distributed system composed of multiple physical servers, or a cloud server that provides cloud computing services. In this embodiment, the method includes the following steps:

[0055] Step 101: Acquire multi-source data of a target area.

[0056] Among them, multi-source data include actual scene evapotranspiration data based on remote sensing observations, natural scene evapotranspiration data based on land surface models, natural scene multi-layer soil moisture data based on land surface models, and reference crop evapotranspiration data based on meteorological observations.

[0057] In an embodiment of the present application, a terminal can obtain actual scene evapotranspiration data for a target area through remote sensing observations. The terminal can also obtain natural scene evapotranspiration data and natural scene multi-layer soil moisture data for the target area through a land surface model. The terminal can also obtain reference crop evapotranspiration data for the target area through meteorological observations.

[0058] The target area is the area where groundwater recharge estimation is to be performed. The target area can be a watershed or a field. The target area can be areas with different climatic conditions, such as arid and humid areas. The target area can be areas with different irrigation conditions, such as sprinkler irrigation, flood irrigation, and drip irrigation. The target area can be areas with different multiple cropping indices, such as single-season and double-season areas. The target area can be areas with different farmland types, such as paddy fields and dry land. The target area can include multiple pixels. Each pixel can be 0.1°. Multi-source data can also include at least one of soil parameter data and irrigated area data. Actual scene evapotranspiration data based on remote sensing observations is also called remote sensing evapotranspiration. Natural scene evapotranspiration data based on land surface models is also called model evapotranspiration. Natural scene multi-layer soil moisture data based on land surface models is also called model soil moisture data. Natural scene multi-layer soil moisture data is multi-layer soil moisture data under natural scenes. Multi-layer soil moisture data can include top layer soil moisture data, middle layer soil moisture data, and bottom layer soil moisture data. The natural scenario is a scenario without irrigation, and the actual scenario is a scenario with irrigation. The impact of other human activities on the water cycle is not considered for the time being. The soil parameter data can be the soil parameter data of the Hamonized World Soil Database (HWSD).

[0059] Step 102 : Determine the actual scene multi-layer soil moisture data of the target area based on the actual scene evapotranspiration data, the natural scene evapotranspiration data, the natural scene multi-layer soil moisture data, and the reference crop evapotranspiration data.

[0060] In this embodiment of the present application, the terminal establishes a linear relationship between soil moisture and evapotranspiration ratio at each preset time step, pixel, and soil layer based on natural scene evapotranspiration data, natural scene multi-layer soil moisture data, and reference crop evapotranspiration data. The terminal then determines the actual scene multi-layer soil moisture data for the target area based on these multiple linear relationships, the actual scene evapotranspiration data, the natural scene evapotranspiration data, the natural scene multi-layer soil moisture data, and the reference crop evapotranspiration data.

[0061] In step 103 , the actual scene multi-layer soil moisture content data is input into a water vertical movement model between two adjacent soil layers to obtain the actual scene soil moisture vertical movement of the target area.

[0062] Among them, the vertical water movement model is generated based on the soil water balance equation, natural scene evapotranspiration data, natural scene multi-layer soil moisture content data and soil parameter data of the target area.

[0063] In the embodiments of the present application, the vertical water movement model is used to characterize the vertical water movement between two adjacent soil layers, that is, the soil water movement law. It can characterize the vertical water movement between the top and middle soil layers, and between the middle and bottom soil layers. The vertical water movement of soil in actual scenarios includes the vertical water movement between the top and middle soil layers, and between the middle and bottom soil layers.

[0064] The terminal generates a model for the vertical movement of water between two adjacent soil layers based on the soil water balance equation, natural scene evapotranspiration data, natural scene multi-layer soil moisture content data, and soil parameter data for the target area. The terminal then inputs the actual multi-layer soil moisture content data into the model to determine the actual vertical movement of soil moisture in the target area.

[0065] Specifically, the terminal establishes a target neural network based on the soil water balance equation. It then trains the target neural network based on natural scene evapotranspiration data, multi-layer soil moisture content data from natural scenes, and soil parameter data from the target area to develop a model of vertical water movement between two adjacent soil layers. The target neural network can be a one-dimensional convolutional neural network, which represents the relationship between vertical soil moisture movement and the soil moisture content and soil parameters of adjacent layers.

[0066] Step 104 : Determine the groundwater recharge in the target area based on the soil water balance equation and the vertical movement of soil moisture in actual scenarios.

[0067] In the embodiments of the present application, groundwater recharge may include actual recharge, and may also include at least one of natural recharge and irrigation recharge. Actual recharge refers to the amount of groundwater recharge in actual scenarios. Natural recharge refers to the amount of groundwater recharge in natural scenarios. Irrigation recharge refers to the amount of groundwater recharge caused by irrigation.

[0068] The terminal determines the actual recharge volume for the target area based on the soil water balance equation and the vertical movement of soil moisture in the actual scenario. Then, the terminal determines the groundwater recharge volume for the target area based on the actual recharge volume.

[0069] In one example, the terminal uses the actual scenario recharge as the groundwater recharge of the target area.

[0070] In another example, the terminal calculates the natural recharge for a target area based on the soil water balance equation, natural multi-layer soil moisture data, precipitation, surface runoff, and natural evapotranspiration data. The terminal then combines the actual recharge and the natural recharge to form the groundwater recharge for the target area.

[0071] In another example, the terminal calculates the natural scenario recharge for a target area based on the soil water balance equation, natural scenario multi-layer soil moisture data, precipitation, surface runoff, and natural scenario evapotranspiration data. The terminal then calculates the irrigation recharge based on the actual scenario recharge and the natural scenario recharge. The terminal then generates the groundwater recharge for the target area, which includes the actual scenario recharge and the irrigation recharge.

[0072] In one example, the terminal may use the bottom soil water balance remainder method to calculate the actual scenario recharge and natural scenario recharge of the target area.

[0073] In this groundwater recharge estimation method, real-world multi-layer soil moisture data are determined based on remotely sensed evapotranspiration data, natural-world evapotranspiration data from land surface models, and natural-world multi-layer soil moisture data, as well as reference crop evapotranspiration data from meteorological observations. This data is then fed into a vertical water movement model generated using the soil water balance equation, natural-world evapotranspiration data, natural-world multi-layer soil moisture data, and soil parameter data. This model then calculates the vertical soil moisture movement under the real-world scenario. Combined with the soil water balance equation, the groundwater recharge for the target area is determined. This method, while taking into account the impact of irrigation and other environmental factors in the real-world scenario, is consistent with actual conditions and can accurately determine groundwater recharge. Furthermore, this method does not require complex land surface and groundwater models, nor does it assume ideal irrigation scenarios. It can reflect actual irrigation conditions and their impact on groundwater recharge. It also does not require the daily-scale data often required for hydrological models. This eliminates the need for acquiring long-term, high-uncertainty, daily-scale, high-spatial-resolution (0.1°) remotely sensed soil moisture and evapotranspiration products, making it suitable for widespread use. Furthermore, this method does not rely on specific empirical parameters or formulas. Instead, it uses neural network training to capture patterns in the vertical movement of water, thereby obtaining high-spatial-resolution, long-term groundwater recharge data. This method not only considers soil moisture but also its association with other environmental factors, such as groundwater level. The model parameters are clearly linked to specific soil physical parameters, enabling the establishment of universal patterns and large-scale characterization. Furthermore, this method is applicable to diverse climatic conditions (arid and humid regions), irrigation conditions (sprinkler, flood, drip irrigation), multiple cropping indices (single-season and double-season), and farmland types (paddy fields and dry land), reflecting the relationship between irrigation recharge and different irrigation methods and natural conditions. Moreover, this method relies less on measured data and has a wide range of applications for calculating irrigation water consumption. It does not require a complex parameter calibration process and is less affected by parameter uncertainty. It can calculate various groundwater recharge amounts in a region separately, reflecting the spatial distribution characteristics at the pixel scale. It can reflect the seasonal distribution characteristics of irrigation water, which is consistent with the monthly irrigation water demand characteristics of crops. It can reflect its relationship with the characteristics of complex aquifers and can serve regional water resources management and land use management.

[0074] In an exemplary embodiment, as shown in FIG2 , a specific process of determining the actual scene multi-layer soil moisture content data of a target area based on the actual scene evapotranspiration data, the natural scene evapotranspiration data, the natural scene multi-layer soil moisture content data, and the reference crop evapotranspiration data includes the following steps:

[0075] Step 201 : performing system error correction on the actual scene evapotranspiration data based on the actual scene evapotranspiration data of non-irrigated pixels and the natural scene evapotranspiration data to obtain corrected actual scene evapotranspiration data.

[0076] In this embodiment of the present application, a terminal calculates the systematic error of the actual scene evapotranspiration data based on a kriging interpolation function and the actual scene evapotranspiration data and natural scene evapotranspiration data for non-irrigated pixels. The terminal then corrects the actual scene evapotranspiration data based on the systematic error to obtain corrected actual scene evapotranspiration data. Non-irrigated pixels are pixels that are not irrigated.

[0077] In one embodiment, step 201 can be expressed as:

[0078]

[0079]

[0080] Among them, Krig is the Kriging interpolation function, 、 are the actual scene evapotranspiration data of non-irrigated pixels and the natural scene evapotranspiration data of non-irrigated pixels, respectively. e is the systematic error obtained by spatial interpolation. To correct the actual scene evapotranspiration data.

[0081] Step 202 : determining the actual scene multi-layer soil moisture data of the target area based on the corrected actual scene evapotranspiration data, the natural scene evapotranspiration data, the natural scene multi-layer soil moisture data, and the reference crop evapotranspiration data.

[0082] In this embodiment of the present application, a terminal establishes a linear relationship between soil moisture and evapotranspiration ratio at each preset time step, pixel, and soil layer based on natural scene evapotranspiration data, natural scene multi-layer soil moisture data, and reference crop evapotranspiration data. The terminal then uses these multiple linear relationships, calibrates the actual scene evapotranspiration data, natural scene evapotranspiration data, natural scene multi-layer soil moisture data, and reference crop evapotranspiration data, and determines the actual scene multi-layer soil moisture data for the target area.

[0083] In the above-mentioned groundwater recharge estimation method, the actual scene evapotranspiration data is corrected for systematic errors based on the actual scene evapotranspiration data and natural scene evapotranspiration data for non-irrigated pixels, resulting in corrected actual scene evapotranspiration data. The actual scene multi-layer soil moisture content data for the target area is then determined based on the corrected actual scene evapotranspiration data, natural scene evapotranspiration data, natural scene multi-layer soil moisture content data, and reference crop evapotranspiration data. This correction aims to ensure consistency between the actual scene evapotranspiration data and the natural scene evapotranspiration data for non-irrigated pixels. The systematic errors of the actual scene evapotranspiration data and the natural scene evapotranspiration data for the non-irrigated area are calculated, and the average of these errors is calculated for each watershed. This average is then extended to the entire spatial range using kriging interpolation to obtain the corrected actual scene evapotranspiration data. Compared to directly using existing global remote sensing evapotranspiration datasets as actual scene evapotranspiration data, this method can minimize the systematic errors caused by using different data sources and improve the accuracy of groundwater recharge estimation.

[0084] In an exemplary embodiment, as shown in FIG3 , a specific process of determining the actual scene multi-layer soil moisture content data of a target area based on the calibrated actual scene evapotranspiration data, the natural scene evapotranspiration data, the natural scene multi-layer soil moisture content data, and the reference crop evapotranspiration data includes the following steps:

[0085] Step 301 : Calculate the natural scene evapotranspiration ratio based on the natural scene evapotranspiration data and the reference crop evapotranspiration data.

[0086] In this embodiment of the present application, the terminal uses the ratio of the natural scene evapotranspiration data to the reference crop evapotranspiration data as the natural scene evapotranspiration ratio. The natural scene evapotranspiration ratio is the evapotranspiration ratio under a natural scene, which is the ratio of the natural scene evapotranspiration to the reference crop evapotranspiration.

[0087] In one embodiment, step 301 can be expressed as:

[0088]

[0089] Among them, EF N is the evapotranspiration ratio of natural scene, ET N For natural scene evapotranspiration data, ET ref Reference crop evapotranspiration data.

[0090] In step 302, for each soil layer, linear fitting parameters corresponding to the soil layer are obtained based on a preset linear relationship between the soil moisture content and the evapotranspiration ratio of the soil layer, the evapotranspiration ratio of the natural scene, and the multi-layer soil moisture content data of the natural scene corresponding to the soil layer.

[0091] In the embodiment of the present application, the soil may include: a top layer of soil, a middle layer of soil, and a bottom layer of soil. The linear relationship between the moisture content of the top layer of soil and the evapotranspiration ratio may be: ; The linear relationship between the middle soil moisture content and evapotranspiration ratio can be: ; The linear relationship between the bottom soil moisture content and evapotranspiration ratio can be: .in, , and The multi-layer soil moisture data of natural scenes corresponding to the top soil, middle soil and bottom soil, EF N is the evapotranspiration ratio of the natural scene, k1, k2 and k3 are the first linear fitting parameters of the top layer, the middle layer and the bottom layer, respectively; b1, b2 and b3 are the second linear fitting parameters of the top layer, the middle layer and the bottom layer, respectively.

[0092] For each layer of soil and each month, the terminal fits the linear fitting parameters corresponding to the soil layer of that month based on the preset linear relationship between the moisture content of the soil layer and the evapotranspiration ratio, the natural scene evapotranspiration ratio of that month, and the natural scene multi-layer soil moisture content data corresponding to the soil layer of that month.

[0093] In one example, for each layer of soil and each month, the terminal fits the linear fitting parameters corresponding to the layer of soil in each target time period corresponding to the month based on the preset linear relationship between the moisture content of the layer of soil and the evapotranspiration ratio, the natural scene evapotranspiration ratio in each target time period corresponding to the month, and the natural scene multi-layer soil moisture content data corresponding to the layer of soil in each target time period. Then, the terminal uses the linear fitting parameter with the largest determination coefficient as the linear fitting parameter corresponding to the layer of soil in the month. Each month corresponds to a target time period, and the target time period includes its corresponding month. The duration of the target time period corresponding to each month is the same and can be in months. For example, the target time periods corresponding to March can be January-March, February-April, and March-May, respectively. The determination coefficient is R 2 , is a statistic that measures the goodness of fit of the regression model. In this way, the linear relationship between soil moisture content and evapotranspiration ratio in each layer is established pixel by pixel and month by month, and the linear fitting R 2 By comparing the linear relationships and selecting the most robust one, the accuracy of the linear fitting parameters can be improved, thereby improving the accuracy of groundwater recharge estimation.

[0094] Step 303 : Calculate the actual scene evapotranspiration ratio based on the corrected actual scene evapotranspiration data and the reference crop evapotranspiration data.

[0095] In this embodiment of the present application, the terminal uses the ratio of the corrected actual scene evapotranspiration data to the reference crop evapotranspiration data as the actual scene evapotranspiration ratio. The actual scene evapotranspiration ratio is the evapotranspiration ratio in the actual scene, which is the ratio of the actual scene evapotranspiration to the reference crop evapotranspiration.

[0096] In one embodiment, step 303 can be expressed as:

[0097]

[0098] Among them, EF A is the evapotranspiration ratio in the actual scenario, To correct the actual scene evapotranspiration data.

[0099] Step 304 : For each layer of soil, the actual scenario multi-layer soil moisture content data corresponding to the layer of soil is calculated based on the preset linear relationship between the soil moisture content and the evapotranspiration ratio of the layer, the actual scenario evapotranspiration ratio, the natural scenario evapotranspiration ratio, the natural scenario multi-layer soil moisture content data corresponding to the layer of soil, and the linear fitting parameters corresponding to the layer of soil.

[0100] In the embodiment of the present application, step 304 can be expressed as:

[0101]

[0102]

[0103]

[0104] in, 、 and Represents the actual scenario multi-layer soil moisture data corresponding to the top soil layer, middle soil layer and bottom soil layer respectively.

[0105] In the above-mentioned groundwater recharge estimation method, a linear relationship between the soil moisture content in natural scenarios and the natural evapotranspiration ratio is established on a monthly, pixel-by-pixel, and layer-by-layer (top, middle, and bottom) basis. This linear relationship is then applied to the actual evapotranspiration ratio to calculate the soil moisture content in actual scenarios. This method can accurately obtain the soil moisture content in actual scenarios including irrigation, which is in line with actual conditions and can further improve the accuracy of groundwater recharge acquisition.

[0106] In an exemplary embodiment, as shown in FIG4 , the method further includes the following steps:

[0107] Step 401: constructing the water balance residual of the top layer of soil and the water balance residual of the middle layer of soil according to the soil water balance equation.

[0108] In an embodiment of the present application, the soil water balance equation may include: a soil water balance equation in a natural scenario and a soil water balance equation in an actual scenario.

[0109] In one embodiment, for the top soil layer of a pixel (0-70 mm, indicated by subscript 1), in a natural scene, the soil water balance formula is as follows:

[0110]

[0111] in, is the top soil thickness (mm), is the change in top soil moisture content within one time step ( ), superscript N refers to the natural scene (the same below), P is the precipitation (mm / month), is surface runoff (mm / month), is the evapotranspiration under natural scenarios (mm / month), is the vertical movement of water between the top and middle soil layers under natural conditions (mm / month). All variables describing water movement have a downward positive direction. The water balance formulas for the middle soil layer (70-280 mm, denoted by subscript 2) and the bottom soil layer (280-1000 mm, denoted by subscript 3) are as follows:

[0112]

[0113]

[0114] in, is the vertical movement of water between the middle and bottom layers of soil in natural scenarios (mm / month), This is the vertical movement of water between the underlying soil and groundwater in natural scenarios (mm / month). A positive value indicates water flowing from the soil to the groundwater, meaning groundwater recharge is dominant; a negative value indicates water flowing from the groundwater to the underlying soil, meaning capillary rise is dominant. Part of this value is used to replenish groundwater reserves, while the other part forms groundwater runoff.

[0115] In actual scenarios, the water input to the top soil includes not only precipitation but also irrigation water. This method does not consider the impact of irrigation on precipitation, that is, it assumes that the precipitation in the natural scene and the actual scene is the same. In addition, this method assumes that irrigation water will maximize the utilization of water resources and will not generate additional surface runoff. Therefore, the soil water balance formulas for the top, middle, and bottom layers in the actual scene are as follows:

[0116]

[0117]

[0118]

[0119] Among them, the superscript A represents the actual scenario (the same below), and the meaning and unit of each variable are the same as above.

[0120] Similar to the usual land surface and groundwater models, the soil water balance formula does not consider lateral soil flow. S In areas where the vertical distribution changes abruptly, the characteristic depth is 700 mm. This assumption introduces little error for the top and middle soil layers, and its impact on the accuracy of irrigation water estimation is negligible. Furthermore, because long-term, daily-scale, and high-spatial-resolution (0.1°) remotely sensed soil moisture and evapotranspiration products are difficult to obtain over large areas or have significant uncertainty, this method uses monthly data.

[0121] Step 402: construct a loss function of the target neural network based on the water balance residual of the top soil layer and the water balance residual of the middle soil layer.

[0122] In the embodiment of the present application, step 402 can be expressed as:

[0123]

[0124] Among them, L is the loss function of the target neural network, is the water balance residual of the top soil layer, is the water balance residual of the middle soil layer.

[0125] Step 403 : Based on the natural scene evapotranspiration data, the natural scene multi-layer soil moisture data, and the soil parameter data, a target neural network is trained to obtain a vertical water movement model between two adjacent soil layers.

[0126] In this embodiment of the present application, the terminal generates a training dataset using natural scene evapotranspiration data, multi-layer soil moisture content data, and soil parameter data. The terminal then trains a target neural network based on the training dataset to develop a model for the vertical movement of water between two adjacent soil layers. The soil parameter data may include saturated hydraulic conductivity and saturated water content.

[0127] In one embodiment, the terminal uses the Pytorch package of Python 3.10 to train the target neural network.

[0128] In one embodiment, according to the van Genuchten water characteristic curve, the soil water potential (kPa) and effective saturation Satisfies the following relationship:

[0129]

[0130] Among them, α(kPa -1 ) and m are soil parameters, and S e From soil moisture content θ, saturated moisture content θ S , residual water content θ r The calculation formula is as follows:

[0131]

[0132] According to the laws of physics, the vertical movement of water is determined by the soil water potential. Determine, assuming a natural scenario, the vertical movement of water between two adjacent soil layers , Still determined by the effective saturation S e , soil parameter φ, but its law can be expressed as:

[0133]

[0134]

[0135] Among them, f is the soil water movement law simulated by using convolutional neural network. Convolutional neural network can effectively capture the intrinsic coupling between the time series of soil moisture content and water movement, and improve the prediction ability of the model. is the effective saturation of each layer of soil in the natural scene, is a set of soil parameters for each layer, including soil saturated hydraulic conductivity ,thickness and moisture characteristic curve parameters and :

[0136]

[0137] This method assumes that the law of vertical movement of soil moisture in actual scenarios is similar to that in natural scenarios, that is, the vertical movement of moisture between two adjacent soil layers is still described by f:

[0138]

[0139]

[0140] in, is the effective saturation of each layer of soil in the actual scenario.

[0141] In the above-mentioned groundwater recharge estimation method, based on multi-source image data, a one-dimensional convolutional neural network is used to establish the relationship between the vertical movement of water between two soil layers and the soil moisture content and soil parameters of the adjacent layers to describe the law of vertical movement of soil water. The loss function is constructed through the water balance of the top and middle layers of soil in natural scenes, and the neural network is trained. The obtained vertical water movement model can be applied to actual scenes, accurately predict the vertical water movement in actual scenes, conform to actual conditions, and can further improve the accuracy of groundwater recharge acquisition.

[0142] In an exemplary embodiment, as shown in FIG5 , the specific process of determining the groundwater recharge in the target area based on the soil water balance equation and the vertical movement of soil moisture in actual scenarios includes the following steps:

[0143] Step 501 : Calculate the actual scene soil moisture change of the bottom soil according to the actual scene multi-layer soil moisture content data of the bottom soil and a preset time step.

[0144] In the embodiment of the present application, the time step may be 1 month.

[0145] Step 502 , calculate the actual scenario recharge amount of the target area based on the soil water balance equation, the vertical movement of soil water in the actual scenario, the actual scenario soil water content change of the underlying soil, and the soil thickness of the underlying soil.

[0146] In this embodiment of the present application, the terminal calculates the product of the actual scene soil moisture change of the underlying soil and the soil thickness of the underlying soil. The terminal then uses the difference between the actual scene vertical movement of water between the middle and bottom layers of soil and this product as the actual scene recharge amount for the target area.

[0147] In one embodiment, step 502 may be expressed as:

[0148]

[0149] in, is the actual scenario supply volume of the target area, is the vertical movement of water between the middle and bottom layers of soil in actual scenarios, is the soil thickness of the underlying soil, is the actual scenario soil moisture change of the underlying soil.

[0150] Step 503: Determine the groundwater recharge amount in the target area based on the actual recharge amount in the scenario.

[0151] In an embodiment of the present application, the terminal determines the groundwater recharge amount in the target area based on the actual scenario recharge amount.

[0152] In one example, the terminal generates a groundwater recharge amount for the target area including the actual scenario recharge amount. For example, the terminal uses the actual scenario recharge amount as the groundwater recharge amount for the target area.

[0153] In another example, the terminal determines the irrigation recharge amount for the target area based on the actual scenario recharge amount, and then generates the groundwater recharge amount for the target area including the irrigation recharge amount.

[0154] In the above-mentioned groundwater recharge estimation method, the bottom soil water balance residual method is used in actual scenarios to calculate the actual scenario recharge, which is in line with the actual situation and can further improve the accuracy of groundwater recharge acquisition.

[0155] In an exemplary embodiment, as shown in FIG6 , the specific process of determining the groundwater recharge amount in the target area according to the actual scenario recharge amount includes the following steps:

[0156] Step 601 , calculate the vertical movement of soil moisture in a natural scene based on the soil water balance equation, multi-layer soil moisture data of a natural scene, precipitation, surface runoff, and evapotranspiration data of a natural scene.

[0157] In this embodiment of the present application, vertical soil moisture movement in a natural scene includes vertical moisture movement between the top and middle layers of soil in a natural scene, and vertical moisture movement between the middle and bottom layers of soil in a natural scene. The terminal calculates the vertical moisture movement between the middle and bottom layers of soil in a natural scene based on the soil water balance equation, multi-layer soil moisture data for the natural scene, precipitation, surface runoff, and evapotranspiration data for the natural scene.

[0158] Step 602 : Calculate the change in soil moisture content in the natural scene of the bottom soil based on the multi-layer soil moisture content data in the natural scene of the bottom soil and a preset time step.

[0159] Step 603 , calculate the natural scene recharge of the target area based on the soil water balance equation, the vertical movement of soil water in the natural scene, the change in soil water content in the natural scene of the underlying soil, and the soil thickness of the underlying soil.

[0160] In this embodiment of the present application, the terminal calculates the product of the change in soil moisture content in the natural scene of the underlying soil and the soil thickness of the underlying soil. The terminal then uses the difference between the vertical movement of water between the middle and bottom layers of the natural scene and this product as the natural scene recharge amount for the target area.

[0161] In one embodiment, step 603 may be expressed as:

[0162]

[0163] in, is the natural scene supply of the target area, is the vertical movement of water between the middle and bottom layers of soil in natural scenes, is the change in soil moisture content of the underlying soil in the natural scenario.

[0164] Step 604: The difference between the actual scene supply amount and the natural scene supply amount is used as the irrigation supply amount of the target area.

[0165] In the embodiment of the present application, step 604 can be expressed as:

[0166]

[0167] in, is the irrigation recharge for the target area.

[0168] Step 605: Determine the groundwater recharge amount in the target area based on the irrigation recharge amount.

[0169] In an embodiment of the present application, the terminal determines the groundwater recharge amount in the target area based on the irrigation recharge amount.

[0170] In one example, the terminal uses irrigation recharge as groundwater recharge for the target area.

[0171] In another example, the terminal generates a groundwater recharge amount for the target area that includes the actual scenario recharge amount and the irrigation recharge amount. For example, the terminal combines the actual scenario recharge amount, the natural scenario recharge amount, and the irrigation recharge amount to form the groundwater recharge amount for the target area.

[0172] The aforementioned groundwater recharge estimation method also uses the residual term method of the underlying soil water balance to calculate recharge in natural scenarios. The difference between the actual and natural recharge scenarios is used as the irrigation recharge, enabling accurate estimation of various groundwater recharge rates. Furthermore, this method provides a monthly groundwater recharge estimation method based on multi-source data, convolutional neural networks, and soil water balance. This method includes groundwater recharge in natural scenarios (excluding irrigation), groundwater recharge in actual scenarios (including irrigation), and groundwater recharge caused by irrigation. This method can be used for groundwater management, groundwater recharge calculation, and irrigation water consumption accounting.

[0173] In one embodiment, the method further includes the following steps: calculating the natural scene soil moisture content change of the top soil based on the natural scene multi-layer soil moisture content data of the top soil and a preset time step; and calculating the monthly irrigation intensity of the target area based on the natural scene soil moisture content change of the top soil, the soil thickness of the top soil, precipitation, corrected actual scene evapotranspiration data, and the vertical movement of water between the top and middle soil layers in the actual scene. The irrigation intensity is the unit irrigation water consumption.

[0174] In one embodiment, the terminal calculates the irrigation intensity of the target area in each month, which can be expressed as:

[0175]

[0176] Among them, I a is the irrigation intensity, z1 is the soil thickness of the top soil, is the change in soil moisture content in the natural scenario of the top soil, It is the vertical movement of water between the top and middle layers of soil in actual scenarios.

[0177] In one embodiment, the method further includes the following steps: calculating the irrigation water consumption of the target area based on the monthly irrigation intensity and the area of ​​the irrigation pixel of the target area. For example, the terminal calculates the annual irrigation water consumption of the target area, which can be expressed as:

[0178]

[0179] in, is the annual irrigation water consumption of the target area ( ), is the area of ​​the irrigated pixel. In this way, this method can simultaneously quantify the amount of irrigation water and the impact of irrigation on groundwater, that is, the irrigation recharge.

[0180] In one embodiment, the method further comprises the following steps: calculating the ratio of the groundwater recharge to the inflow of the target area based on the groundwater recharge and the inflow of the target area.

[0181] In an embodiment of the present application, the inflow water volume includes: the natural scene inflow water volume, the actual scene inflow water volume and the irrigation water volume. The terminal uses the ratio of the natural scene supply volume to the precipitation to calculate the proportion of the natural scene supply volume of the target area to the natural scene inflow water volume. And / or, the terminal uses the sum of the precipitation and the irrigation water volume as the actual scene inflow water volume, and uses the ratio of the actual scene supply volume to the actual scene inflow water volume as the proportion of the actual scene supply volume to the actual scene inflow water volume of the target area. And / or, the terminal uses the ratio of the irrigation supply volume to the irrigation water volume as the proportion of the irrigation supply volume to the irrigation water volume of the target area. This can be used to analyze the nature of groundwater recharge.

[0182] In one embodiment, the terminal calculates the ratio of groundwater recharge to inflow in the target area, which can be expressed as:

[0183]

[0184]

[0185]

[0186] Among them, r N 、r A and r I are the ratio of natural scenario recharge to natural scenario inflow, the ratio of actual scenario recharge to actual scenario inflow, and the ratio of irrigation recharge to irrigation water consumption, respectively. I is the irrigation water consumption.

[0187] In one embodiment, the target region, A, faces a shortage of available freshwater resources, high agricultural water consumption, and a need to further improve the effective utilization coefficient of irrigation water. Many areas within A rely heavily on groundwater for irrigation, leading to groundwater overexploitation. Therefore, timely and accurate acquisition of groundwater recharge and assessment of the impact of irrigation on groundwater recharge are crucial for the coordinated management of surface and groundwater resources and guiding sustainable agricultural development. Using SSEBop remote sensing evapotranspiration products, ERA5Land model evapotranspiration data, ERA5Land model soil moisture data, and ICC irrigated area data, a model was trained to estimate natural scenario recharge, actual scenario recharge, and irrigation recharge for target region A from 2003 to 2023. Groundwater recharge calculated using this method can be further used to calculate irrigation water use. The linear fit relationship between the model-calculated irrigation water use and irrigation water withdrawal / use reported in the Water Resources Bulletin for target region A and its five subregions, bf, is shown in Figure 7. A trend comparison of the irrigation water withdrawals from the statistical bulletins and the irrigation water consumption calculated by the model for multiple sub-areas 01-16 included in the target area A from 2003 to 2022 is shown in FIG8A ; a trend comparison of the irrigation water withdrawals from the statistical bulletins and the irrigation water consumption calculated by the model for multiple sub-areas 17-31 included in the target area A from 2003 to 2022 is shown in FIG8B .

[0188] It should be understood that, although the various steps in the flowcharts involved in the various embodiments described above are displayed in sequence according to the instructions of the arrows, these steps are not necessarily executed in sequence in the order indicated by the arrows. Unless otherwise specified herein, there is no strict order restriction on the execution of these steps, and these steps can be executed in other orders. Moreover, at least a portion of the steps in the flowcharts involved in the various embodiments described above can include multiple steps or multiple stages, and these steps or stages are not necessarily executed and completed at the same time, but can be executed at different times, and the execution order of these steps or stages is not necessarily to be carried out in sequence, but can be executed in turn or alternately with other steps or at least a portion of steps or stages in other steps.

[0189] Based on the same inventive concept, embodiments of the present application also provide a groundwater recharge estimation device for implementing the aforementioned groundwater recharge estimation method. The solution provided by this device is similar to the solution described in the aforementioned method. Therefore, the specific limitations of one or more of the following embodiments of the groundwater recharge estimation device can be found in the above-described limitations of the groundwater recharge estimation method and will not be further elaborated here.

[0190] In an exemplary embodiment, as shown in FIG9 , a groundwater recharge estimation device 900 is provided, comprising: an acquisition module 910 , a first determination module 920 , a second determination module 930 , and a third determination module 940 , wherein:

[0191] Acquisition module 910 is used to acquire multi-source data of the target area; the multi-source data includes actual scene evapotranspiration data based on remote sensing observations, natural scene evapotranspiration data based on land surface models, multi-layer soil moisture data of natural scenes based on land surface models, and reference crop evapotranspiration data based on meteorological observations;

[0192] A first determining module 920 is configured to determine the actual scene multi-layer soil moisture data of the target area based on the actual scene evapotranspiration data, the natural scene evapotranspiration data, the natural scene multi-layer soil moisture data, and the reference crop evapotranspiration data;

[0193] A second determination module 930 is configured to input the actual scenario multi-layer soil moisture content data into a water vertical movement model between two adjacent soil layers to obtain the actual scenario soil moisture vertical movement of the target area; the water vertical movement model is generated based on the soil water balance equation, the natural scenario evapotranspiration data, the natural scenario multi-layer soil moisture content data, and the soil parameter data of the target area;

[0194] The third determination module 940 is configured to determine the groundwater recharge in the target area according to the soil water balance equation and the vertical movement of soil moisture in the actual scenario.

[0195] Optionally, the first determining module 920 is specifically configured to:

[0196] performing systematic error correction on the actual scene evapotranspiration data according to the actual scene evapotranspiration data and the natural scene evapotranspiration data of the non-irrigated pixels to obtain corrected actual scene evapotranspiration data;

[0197] The actual scene multi-layer soil moisture content data of the target area is determined based on the corrected actual scene evapotranspiration data, the natural scene evapotranspiration data, the natural scene multi-layer soil moisture content data and the reference crop evapotranspiration data.

[0198] Optionally, the first determining module 920 is specifically configured to:

[0199] Calculating a natural scene evapotranspiration ratio based on the natural scene evapotranspiration data and the reference crop evapotranspiration data;

[0200] For each layer of soil, a linear fitting parameter corresponding to the layer of soil is obtained by fitting according to a preset linear relationship between the moisture content of the layer of soil and the evapotranspiration ratio, the evapotranspiration ratio of the natural scene, and the moisture content data of multiple layers of soil in the natural scene corresponding to the layer of soil;

[0201] Calculating an actual scene evapotranspiration ratio based on the corrected actual scene evapotranspiration data and the reference crop evapotranspiration data;

[0202] For each layer of soil, the actual scene multi-layer soil moisture content data corresponding to the layer of soil is calculated based on the preset linear relationship between the moisture content of the layer of soil and the evapotranspiration ratio, the actual scene evapotranspiration ratio, the natural scene evapotranspiration ratio, the natural scene multi-layer soil moisture content data corresponding to the layer of soil, and the linear fitting parameters corresponding to the layer of soil.

[0203] Optionally, the apparatus 900 further includes:

[0204] A first construction module is used to construct a water balance residual of the top soil layer and a water balance residual of the middle soil layer according to the soil water balance equation;

[0205] A second construction module is used to construct a loss function of a target neural network according to the water balance residual of the top soil layer and the water balance residual of the middle soil layer;

[0206] A training module is used to train the target neural network based on the natural scene evapotranspiration data, the natural scene multi-layer soil moisture data and the soil parameter data to obtain a vertical water movement model between two adjacent soil layers.

[0207] Optionally, the third determining module 940 is specifically configured to:

[0208] Calculating a change in soil moisture content in the actual scenario of the bottom soil according to the actual scenario multi-layer soil moisture content data of the bottom soil and a preset time step;

[0209] Calculating the actual scenario recharge of the target area based on the soil water balance equation, the vertical movement of soil moisture in the actual scenario, the actual scenario soil moisture content change of the underlying soil, and the soil thickness of the underlying soil;

[0210] The groundwater recharge amount of the target area is determined based on the actual scenario recharge amount.

[0211] The third determining module 940 is specifically configured to:

[0212] Calculate the vertical movement of soil moisture in the natural scene based on the soil water balance equation, the multi-layer soil moisture content data of the natural scene, precipitation, surface runoff, and the evapotranspiration data of the natural scene;

[0213] Calculating a change in the natural scene soil moisture content of the bottom soil according to the natural scene multi-layer soil moisture content data of the bottom soil and a preset time step;

[0214] Calculating the natural scene recharge of the target area based on the soil water balance equation, the vertical movement of soil water in the natural scene, the change in soil water content in the natural scene of the underlying soil, and the soil thickness of the underlying soil;

[0215] The difference between the actual scene supply amount and the natural scene supply amount is used as the irrigation supply amount of the target area;

[0216] The groundwater recharge amount of the target area is determined based on the irrigation recharge amount.

[0217] Each module in the aforementioned groundwater recharge estimation device can be implemented in whole or in part through software, hardware, or a combination thereof. Each module can be embedded in or independent of a processor in a computer device in the form of hardware, or can be stored in a memory in the computer device in the form of software, so that the processor can call and execute the corresponding operations of each module.

[0218] In an exemplary embodiment, a computer device is provided. The computer device may be a terminal, and its internal structure may be shown in Figure 10. The computer device includes a processor, memory, an input / output interface, a communication interface, a display unit, and an input device. The processor, memory, and input / output interface are connected via a system bus, and the communication interface, display unit, and input device are connected to the system bus via the input / output interface. The processor of the computer device provides computing and control capabilities. The memory of the computer device includes a non-volatile storage medium and internal memory. The non-volatile storage medium stores an operating system and a computer program. The internal memory provides an environment for the operating system and computer program stored in the non-volatile storage medium. The input / output interface of the computer device is used to exchange information between the processor and external devices. The communication interface of the computer device is used to communicate with external terminals via wired or wireless communication, which may be achieved via Wi-Fi, a mobile cellular network, near-field communication (NFC), or other technologies. When executed by the processor, the computer program implements a method for estimating groundwater recharge. The display unit of the computer device is used to produce a visual display and may be a display screen, a projection device, or a virtual reality imaging device. The display screen can be a liquid crystal display screen or an electronic ink display screen, and the input device of the computer device can be a touch layer covering the display screen, or a button, trackball or touchpad set on the computer device casing, or an external keyboard, touchpad or mouse.

[0219] Those skilled in the art will understand that the structure shown in FIG10 is merely a block diagram of a portion of the structure related to the solution of the present application, and does not constitute a limitation on the computer device to which the solution of the present application is applied. The specific computer device may include more or fewer components than shown in the figure, or combine certain components, or have a different arrangement of components.

[0220] In an exemplary embodiment, a computer device is provided, including a memory and a processor. The memory stores a computer program, and the processor implements the steps in the above method embodiments when executing the computer program.

[0221] In one embodiment, a computer-readable storage medium is provided, on which a computer program is stored. When the computer program is executed by a processor, the steps in the above-mentioned method embodiments are implemented.

[0222] In one embodiment, a computer program product is provided, including a computer program, which implements the steps in the above method embodiments when executed by a processor.

[0223] It should be noted that the user information (including but not limited to user device information, user personal information, etc.) and data (including but not limited to data used for analysis, stored data, displayed data, etc.) involved in this application are all information and data authorized by the user or fully authorized by all parties, and the collection, use and processing of relevant data must comply with relevant regulations.

[0224] Those skilled in the art will understand that all or part of the processes in the above-mentioned embodiments can be implemented by instructing the relevant hardware through a computer program. The computer program can be stored in a non-volatile computer-readable storage medium. When the computer program is executed, it can include the processes of the embodiments of the above-mentioned methods. In particular, any reference to memory, database, or other media used in the embodiments provided in this application can include at least one of non-volatile memory and volatile memory. Non-volatile memory can include read-only memory (ROM), magnetic tape, floppy disk, flash memory, optical memory, high-density embedded non-volatile memory, resistive random access memory (ReRAM), magnetic random access memory (MRAM), ferroelectric random access memory (FRAM), phase change memory (PCM), graphene memory, etc. Volatile memory can include random access memory (RAM) or external cache memory, etc. By way of illustration and not limitation, RAM can take various forms, such as static random access memory (SRAM) or dynamic random access memory (DRAM). The databases involved in the various embodiments provided herein may include at least one of a relational database and a non-relational database. Non-relational databases may include, but are not limited to, blockchain-based distributed databases. The processors involved in the various embodiments provided herein may be, but are not limited to, general-purpose processors, central processing units (CPUs), graphics processing units (GPUs), digital signal processors (DSPs), programmable logic devices (PLDs), quantum computing-based data processing logic devices, artificial intelligence (AI) processors, and the like.

[0225] The technical features of the above embodiments can be combined arbitrarily. In order to make the description concise, not all possible combinations of the technical features in the above embodiments are described. However, as long as there is no contradiction in the combination of these technical features, they should be considered to be within the scope of this application.

[0226] The above-described embodiments merely represent several implementation methods of the present application. While the descriptions are relatively specific and detailed, they should not be construed as limiting the scope of the present application. It should be noted that a person of ordinary skill in the art may make various modifications and improvements without departing from the spirit of the present application, and these modifications and improvements fall within the scope of protection of the present application. Therefore, the scope of protection of the present application shall be determined by the appended claims.

Claims

1. A method for estimating groundwater recharge, characterized in that: The method comprises: Acquire multi-source data of the target area; the multi-source data includes actual scene evapotranspiration data based on remote sensing observation, natural scene evapotranspiration data based on land surface model, natural scene multi-layer soil moisture data based on land surface model and reference crop evapotranspiration data based on meteorological observation; Determining the actual scene multi-layer soil moisture content data of the target area based on the actual scene evapotranspiration data, the natural scene evapotranspiration data, the natural scene multi-layer soil moisture content data and the reference crop evapotranspiration data; Inputting the actual scene multi-layer soil moisture content data into the water vertical movement model between two adjacent soil layers to obtain the soil moisture vertical movement in the actual scene of the target area; the water vertical movement model is generated according to the soil water balance equation, the natural scene evapotranspiration data, the natural scene multi-layer soil moisture content data and the soil parameter data of the target area; The groundwater recharge in the target area is determined based on the soil water balance equation and the vertical movement of soil moisture in the actual scenario.

2. The method according to claim 1, characterized in that The determining the actual scene multi-layer soil moisture content data of the target area based on the actual scene evapotranspiration data, the natural scene evapotranspiration data, the natural scene multi-layer soil moisture content data and the reference crop evapotranspiration data includes: According to the actual scene evapotranspiration data of the non-irrigated pixel and the natural scene evapotranspiration data, performing system error correction on the actual scene evapotranspiration data to obtain corrected actual scene evapotranspiration data; Based on the corrected actual scene evapotranspiration data, the natural scene evapotranspiration data, the natural scene multi-layer soil moisture content data and the reference crop evapotranspiration data, the actual scene multi-layer soil moisture content data of the target area is determined.

3. The method according to claim 2, characterized in that The determining the actual scene multi-layer soil moisture content data of the target area based on the corrected actual scene evapotranspiration data, the natural scene evapotranspiration data, the natural scene multi-layer soil moisture content data and the reference crop evapotranspiration data comprises: Calculating the natural scene evapotranspiration ratio based on the natural scene evapotranspiration data and the reference crop evapotranspiration data; For each layer of soil, a linear fitting parameter corresponding to the layer of soil is obtained by fitting according to a preset linear relationship between the moisture content of the layer of soil and the evapotranspiration ratio, the natural scene evapotranspiration ratio and the moisture content data of multiple layers of soil in the natural scene corresponding to the layer of soil; Calculating the actual scene evapotranspiration ratio based on the corrected actual scene evapotranspiration data and the reference crop evapotranspiration data; For each layer of soil, the actual scene multi-layer soil moisture content data corresponding to the layer of soil is calculated based on the preset linear relationship between the moisture content of the layer of soil and the evapotranspiration ratio, the actual scene evapotranspiration ratio, the natural scene evapotranspiration ratio, the natural scene multi-layer soil moisture content data corresponding to the layer of soil and the linear fitting parameters corresponding to the layer of soil.

4. The method according to claim 1, characterized in that The method further comprises: According to the soil water balance equation, constructing the water balance residual of the top layer soil and the water balance residual of the middle layer soil; Constructing a loss function of a target neural network according to the water balance residual of the top layer of soil and the water balance residual of the middle layer of soil; Based on the natural scene evapotranspiration data, the natural scene multi-layer soil moisture content data and the soil parameter data, the target neural network is trained to obtain a vertical water movement model between two adjacent soil layers.

5. The method according to claim 1, characterized in that: Determining the groundwater recharge in the target area according to the soil water balance equation and the vertical movement of soil moisture in the actual scenario includes: Calculating the actual scene soil moisture content change of the bottom soil according to the actual scene multi-layer soil moisture content data of the bottom soil and a preset time step; Calculate the actual scenario recharge of the target area according to the soil water balance equation, the vertical movement of soil moisture in the actual scenario, the actual scenario soil moisture content change of the bottom soil and the soil thickness of the bottom soil; The groundwater recharge amount of the target area is determined according to the actual scenario recharge amount.

6. The method according to claim 5, characterized in that Determining the groundwater recharge amount in the target area according to the actual scenario recharge amount includes: Calculate the vertical movement of soil moisture in the natural scene based on the soil water balance equation, the multi-layer soil moisture content data of the natural scene, precipitation, surface runoff, and the evapotranspiration data of the natural scene; Calculating the change in soil moisture content in the natural scene of the bottom soil according to the natural scene multi-layer soil moisture content data of the bottom soil and a preset time step; Calculate the natural scene recharge of the target area according to the soil water balance equation, the vertical movement of soil moisture in the natural scene, the change in soil moisture content in the natural scene of the bottom soil, and the soil thickness of the bottom soil; The difference between the actual scene supply amount and the natural scene supply amount is used as the irrigation supply amount of the target area; The groundwater recharge amount of the target area is determined based on the irrigation recharge amount.

7. A groundwater recharge estimation device, characterized in that: The device comprises: An acquisition module is used to acquire multi-source data of a target area; the multi-source data includes actual scene evapotranspiration data based on remote sensing observations, natural scene evapotranspiration data based on land surface models, multi-layer soil moisture data of natural scenes based on land surface models, and reference crop evapotranspiration data based on meteorological observations; A first determination module is used to determine the actual scene multi-layer soil moisture content data of the target area based on the actual scene evapotranspiration data, the natural scene evapotranspiration data, the natural scene multi-layer soil moisture content data and the reference crop evapotranspiration data; The second determination module is used to input the actual scene multi-layer soil moisture content data into the water vertical movement model between two adjacent soil layers to obtain the soil moisture vertical movement in the actual scene of the target area; the water vertical movement model is generated according to the soil water balance equation, the natural scene evapotranspiration data, the natural scene multi-layer soil moisture content data and the soil parameter data of the target area; The third determination module is used to determine the groundwater recharge in the target area according to the soil water balance equation and the vertical movement of soil moisture in the actual scenario.

8. A computer device comprising a memory and a processor, wherein the memory stores a computer program, wherein: When the processor executes the computer program, the steps of the method according to any one of claims 1 to 6 are implemented.

9. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the steps of the method according to any one of claims 1 to 6 are implemented.

10. A computer program product, comprising a computer program, characterized in that When the computer program is executed by a processor, the steps of the method according to any one of claims 1 to 6 are implemented.

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