Groundwater recharge degree collaborative inversion method and system based on multi-source remote sensing and intelligent algorithm

Abstract

The present application belongs to the technical field of hydrological remote sensing, and relates to a groundwater recharge degree collaborative inversion method and system based on multi-source remote sensing and intelligent algorithm. The method comprises: collecting data and performing pretreatment to obtain a monthly groundwater storage change sequence, a monthly actual evapotranspiration, a surface runoff and a net drainage; reconstructing a vertical direction water balance equation on a monthly scale; calculating a monthly water surplus of each pixel; data downscaling; recharge event identification and estimation of the value of recharge amount; verification of the error of monthly precipitation, monthly actual evapotranspiration and monthly groundwater storage change sequence, and evaluation of the inversion result. The present application avoids a large number of hypothetical parameters of a black box model, does not need an expensive dense ground monitoring network, and has strong realization; realizes spatial explicit estimation of the recharge amount; can generate a monthly or seasonal groundwater recharge amount time sequence, reflects the dynamic change of the recharge process, is superior to a static annual average value provided by a traditional method, and has strong dynamic monitoring capability.

Description

TECHNICAL FIELD

[0001] The application belongs to the technical field of hydrological remote sensing, and in particular, relates to a groundwater recharge degree collaborative inversion method and system based on multi-source remote sensing and intelligent algorithms. BACKGROUND

[0002] Groundwater recharge is the flux of water that seeps from the surface and reaches the groundwater surface, and is the cornerstone of accurately assessing the sustainable exploitation of groundwater and managing water resources. Traditional estimation methods such as water level fluctuation method, chemical tracer method, and physical-based hydrological models (such as modular three-dimensional finite difference groundwater flow model) are all heavily dependent on dense ground monitoring well networks, which are not only costly, time-consuming and labor-intensive, but also have great errors in data sparse or complex geological conditions due to their point-to-surface characteristics, making it difficult to meet the needs of regional water resource management.

[0003] Remote sensing technology provides large-scale and continuous surface observation data, and gravity satellites can directly monitor the changes in groundwater storage in a large area, but the time resolution and spatial resolution limit direct application. Remote sensing evapotranspiration products can quantitatively measure the largest output item in the water cycle at high resolution. Existing research has tried to use these data alone, but there are fundamental flaws: gravity satellites can only give storage change results, and cannot distinguish the reasons for the change (such as recharge, exploitation and natural discharge); using remote sensing evapotranspiration data alone cannot be directly linked to the groundwater system. There is currently no mature technology to effectively couple these two key remote sensing data to directly and physically derive groundwater recharge. SUMMARY

[0004] To solve the above technical problems, the application provides a groundwater recharge degree collaborative inversion method and system based on multi-source remote sensing and intelligent algorithms.

[0005] In a first aspect, the application provides a groundwater recharge degree collaborative inversion method based on multi-source remote sensing and intelligent algorithms, comprising:

[0006] Collecting monthly land water storage change data, remote sensing evapotranspiration data and precipitation data in the target area and preprocessing to obtain a monthly groundwater storage change sequence, monthly actual evapotranspiration, surface runoff and net drainage;

[0007] According to the monthly groundwater storage change sequence and the monthly precipitation, a monthly vertical water balance equation is constructed, the surface runoff and the net drainage are combined as a net outflow item, and the monthly vertical water balance equation is reconstructed;

[0008] According to the monthly precipitation and the monthly actual evapotranspiration, the monthly water surplus of each pixel is calculated;

[0009] The monthly groundwater storage change sequence is down-scaled to the same resolution as the monthly actual evapotranspiration;

[0010] According to the monthly water surplus, a recharge event is identified, and the value of the recharge amount is estimated; it is defined that the monthly water surplus is positive after filling in the consumption of the monthly groundwater system, and the recharge event exists;

[0011] The inverted regional total recharge amount sequence is compared with the reference value of the recharge rate estimated by the monitoring well data or the runoff data of the basin outlet, the errors of the monthly precipitation, the monthly actual evapotranspiration and the monthly groundwater storage change sequence are verified, and the inversion result is evaluated.

[0012] In the second aspect, the application provides a groundwater recharge degree collaborative inversion system based on multi-source remote sensing and intelligent algorithm, which comprises an acquisition and preprocessing unit, a reconstruction unit, a processing unit, a down-scaling unit, an identification unit and a verification unit.

[0013] The acquisition and preprocessing unit is used for collecting monthly land water storage change data, remote sensing evapotranspiration data and precipitation data in a target region and preprocessing the data to obtain a monthly groundwater storage change sequence, a monthly actual evapotranspiration, a surface runoff and a net drainage.

[0014] The reconstruction unit is used for constructing a monthly scale vertical water balance equation according to the monthly groundwater storage change sequence and the monthly precipitation, combining the surface runoff and the net drainage as a net outflow item, and reconstructing the monthly scale vertical water balance equation.

[0015] The processing unit is used for calculating a monthly water surplus of each pixel according to the monthly precipitation and the monthly actual evapotranspiration.

[0016] The down-scaling unit is used for down-scaling the monthly groundwater storage change sequence to the same resolution as the monthly actual evapotranspiration.

[0017] The identification unit is used for identifying a recharge event according to the monthly water surplus, estimating the value of the recharge amount, and defining that the recharge event exists when the monthly water surplus is positive after filling in the consumption of the monthly groundwater system.

[0018] The verification unit is used for comparing the inverted regional total recharge amount sequence with the reference value of the recharge rate estimated by the monitoring well data or the runoff data of the basin outlet, verifying the errors of the monthly precipitation, the monthly actual evapotranspiration and the monthly groundwater storage change sequence, and evaluating the inversion result.

[0019] On the basis of the above technical solution, the application can be further improved as follows.

[0020] Further, the preprocessing of the monthly land water storage change data comprises: stripping non-groundwater components by using land surface model data, removing strip noise by Gaussian filtering, and calculating the monthly groundwater storage change amount.

[0021] Further, the obtained monthly actual evapotranspiration is synchronous with the monthly land water storage change data, and the spatial and temporal resolution of the monthly actual evapotranspiration is greater than that of the monthly land water storage change data.

[0022] Further, the monthly scale vertical water balance equation is constructed, comprising:

[0023] Let be a monthly groundwater storage change sequence, be a monthly precipitation, be a monthly actual evapotranspiration, be a surface runoff, be a net drainage, then:

[0024] .

[0025] Further, the monthly scale vertical water balance equation is reconstructed, comprising:

[0026] Let be a monthly groundwater storage change sequence, be a monthly precipitation, be a monthly actual evapotranspiration, be a surface runoff, be a net drainage, be a net outflow, then the reconstructed monthly scale vertical water balance equation is expressed as:

[0027] .

[0028] Further, the monthly water surplus of each pixel is calculated, comprising:

[0029] Let the monthly water surplus of each pixel be , be a monthly precipitation, be a monthly actual evapotranspiration, then the monthly water surplus of each pixel is expressed as:

[0030] .

[0031] Further, the monthly groundwater storage change amount is downscaled to the same resolution as the monthly actual evapotranspiration; the high-resolution monthly water surplus of each pixel and the terrain index are used as covariates to spatially distribute the monthly groundwater storage change sequence by using the synergic Kriging interpolation method, so as to obtain the high-resolution monthly groundwater change amount of each pixel.

[0032] Further, according to the monthly water surplus, the replenishment event is identified, and the value of the replenishment amount is estimated, including:

[0033] Let the monthly water surplus of each pixel be , is the surface runoff, the high-resolution monthly groundwater change amount of each pixel is , and the estimated value of the replenishment amount is An effective replenishment event occurs only when , and the estimated value of the replenishment amount is:

[0034] .

[0035] Further, the Monte Carlo method is used to calculate the error of the monthly precipitation, the monthly actual evapotranspiration and the monthly groundwater storage change sequence through random sampling.

[0036] The beneficial effects of the present application are:

[0037] (1) Based on the strict water mass balance principle, a large number of hypothetical parameters of the black box model are avoided, and the physical mechanism is clear;

[0038] (2) The required data are all publicly available remote sensing products and reanalysis data, without the need for expensive and intensive ground monitoring network, especially suitable for areas with scarce ground data, and the realization is strong;

[0039] (3) The present application innovatively uses the monthly precipitation, the monthly actual evapotranspiration and the monthly groundwater storage change sequence as the core operator, and innovatively couples the monthly land water storage change data with the high-resolution evapotranspiration data at the pixel level, realizing the spatial explicit estimation of the replenishment amount;

[0040] (4) The monthly or seasonal groundwater replenishment amount time sequence can be generated, reflecting the dynamic changes of the replenishment process, which is better than the static annual average value provided by the traditional method, and the dynamic monitoring capability is strong. BRIEF DESCRIPTION OF DRAWINGS

[0041] Figure 1 The principle diagram of the groundwater replenishment degree collaborative inversion method based on multi-source remote sensing and intelligent algorithm provided by the embodiment 1 of the present application;

[0042] Figure 2 The structural block diagram of the groundwater replenishment degree collaborative inversion system based on multi-source remote sensing and intelligent algorithm provided by the embodiment 2 of the present application. DETAILED DESCRIPTION

[0043] In order to make the purposes, technical solutions and advantages of the embodiments of the present application clearer, the technical solutions in the embodiments of the present application will be described clearly and completely below with reference to the drawings in the embodiments of the present application. Obviously, the described embodiments are some but not all of the embodiments of the present application. The components of the embodiments of the present application described and shown in the drawings herein can be arranged and designed in various different configurations.

[0044] Embodiment 1

[0045] As an embodiment, as shown in the accompanying drawings, Figure 1 To solve the above technical problems, the embodiments provide a groundwater recharge degree collaborative inversion method based on multi-source remote sensing and intelligent algorithm, which comprises the following steps:

[0046] Collecting monthly terrestrial water storage change data, remote sensing evapotranspiration data and precipitation data in a target area and performing preprocessing to obtain a monthly groundwater storage change sequence, a monthly actual evapotranspiration, a surface runoff and a net drainage;

[0047] According to the monthly groundwater storage change sequence and the monthly precipitation, a monthly vertical water balance equation is constructed, and the surface runoff and the net drainage are combined as a net outflow item, and the monthly vertical water balance equation is reconstructed;

[0048] According to the monthly precipitation and the monthly actual evapotranspiration, the monthly water surplus of each pixel is calculated;

[0049] The monthly groundwater storage change sequence is downscaled to the same resolution as the monthly actual evapotranspiration;

[0050] According to the monthly water surplus, a recharge event is identified, and the value of the recharge amount is estimated; the monthly water surplus is positive after the consumption of the monthly groundwater system is filled, and it is defined that the recharge event exists;

[0051] The inverted regional total recharge amount sequence is compared with the reference value of the recharge rate estimated by the monitoring well data or the runoff data of the basin outlet, the errors of the monthly precipitation, the monthly actual evapotranspiration and the monthly groundwater storage change sequence are verified, and the inversion result is evaluated.

[0052] The present application provides a remote sensing inversion method for groundwater recharge amount without relying on dense groundwater well network, with clear physical mechanism and can be directly implemented on regional scale. The method realizes quantitative and dynamic monitoring of groundwater recharge amount by innovatively coupling the groundwater storage change sequence inverted from the monthly terrestrial water storage change data and the evapotranspiration data estimated by remote sensing.

[0053] The present application has the following advantages:

[0054] (1) Based on the strict water quality balance principle, a large number of assumed parameters of the black box model are avoided, and the physical mechanism is clear;

[0055] (2) The required data are all publicly available remote sensing products and reanalysis data, without the need for expensive and intensive ground monitoring network, especially suitable for areas with scarce ground data, and the realization is strong;

[0056] (3) The application innovatively uses monthly precipitation, monthly actual evapotranspiration and monthly groundwater storage change sequence as core operators, and innovatively couples monthly land water storage change data with high-resolution evapotranspiration data at the pixel level, and realizes spatial explicit estimation of recharge;

[0057] (4) The monthly or seasonal groundwater recharge time series can be generated, reflecting the dynamic changes of the recharge process, which is better than the static annual average value provided by the traditional method, and has strong dynamic monitoring capability.

[0058] Optionally, the preprocessing of the monthly land water storage change data includes: stripping non-groundwater components by using land surface model data, removing strip noise by Gaussian filtering, and calculating the monthly groundwater storage change.

[0059] In actual application process, the monthly land water storage change data is obtained, and the non-groundwater components such as soil water and snow water are stripped by using GLDAS (Global Land Data Assimilation System, global land data assimilation system) and other land surface model data, to obtain the monthly groundwater storage change sequence.

[0060] Optionally, the obtained monthly actual evapotranspiration is synchronous with the monthly land water storage change data, and the temporal and spatial resolution of the monthly actual evapotranspiration is greater than that of the monthly land water storage change data.

[0061] The monthly actual evapotranspiration uses remote sensing evapotranspiration products such as Penman-Monteith-Liu Evapotranspiration Model Version 2, and satellite-ground fusion precipitation products are used to obtain precipitation data.

[0062] For a closed watershed or hydrogeological unit, ignore the horizontal lateral exchange (or estimate it as an input item), and the monthly scale vertical water balance equation can be simplified.

[0063] Optionally, the monthly scale vertical water balance equation is constructed, including:

[0064] Let be the monthly groundwater storage change sequence, be the monthly precipitation, be the monthly actual evapotranspiration, Surface runoff, Net water discharge,

[0065] .

[0066] In many regions without large surface reservoirs and where the amount of artificial exploitation is negligible or can be estimated, surface runoff and net water discharge can be combined as a net outflow term. We reformulate the equation as:

[0067] Optionally, the monthly vertical water balance equation is reformulated, including:

[0068] Let be the monthly groundwater storage change series, be the monthly precipitation, be the monthly actual evapotranspiration, be the surface runoff, be the net water discharge, be the net outflow term, the reformulated monthly vertical water balance equation is expressed as:

[0069] .

[0070] The calculated value of essentially represents the part of the precipitation that has not been consumed by evapotranspiration or stored in the groundwater system. This part of water must have left the system through a route other than runoff or infiltration, so in the month when recharge occurs, this calculated value should theoretically be zero or negative.

[0071] Specifically, the true recharge occurs after and the water is sufficient to fill the soil in the vadose zone. Therefore, the present application proposes an innovative algorithm for recharge event identification.

[0072] Optionally, the monthly water surplus of each pixel is calculated, including:

[0073] Let the monthly water surplus of each pixel be , be the monthly precipitation, be the monthly actual evapotranspiration, the monthly water surplus of each pixel is expressed as:

[0074] .

[0075] Specifically, the monthly groundwater storage change series provided by the monthly land water storage change data is a regional average value, which needs to be spatially downscaled to the same resolution as the monthly actual evapotranspiration. The monthly water surplus The topographic index was used as a covariate to spatially allocate the monthly groundwater storage change series.

[0076] Optionally, the monthly groundwater storage change is downscaled to the same resolution as the actual monthly evapotranspiration; the co-kriging interpolation method is used to spatially allocate the monthly groundwater storage change sequence of each high-resolution cell as covariates using the monthly water surplus and topographic index of each cell, so as to obtain the high-resolution monthly groundwater change of each cell.

[0077] Optionally, replenishment events can be identified based on monthly water surplus, and the replenishment amount can be estimated, including:

[0078] Let the monthly water surplus of each pixel be... , For surface runoff, the high-resolution monthly groundwater variation per pixel is: The estimated supply amount is If and only if A valid supply event will only occur when the estimated supply amount is:

[0079] .

[0080] The physical meaning of the formula for calculating this estimate is: when A negative value (net system consumption) only occurs when there is a monthly water surplus. After satisfying the surface runoff R, its remaining amount ( It is still greater than the observed consumption. Only then can it be proven that a valid resupply event has occurred. The amount of resupply at this point... Its physical meaning is "the amount of water that actually infiltrates after offsetting the system's net consumption in the current period." When the value is positive (net increase in the system), the water surplus for the current period is... It has been completely converted into an increase in surface runoff R and groundwater storage. At this point, the conditions This is not true; the formula calculation result is incorrect. This means that the model did not experience any independent or significant vertical recharge events that exceeded the reserve increase signal. This design avoids simply repeating the calculation of reserve increases as recharge flux, ensuring the physical rigor of the estimation results.

[0081] Optionally, the Monte Carlo method can be used to calculate the error of the monthly precipitation, monthly actual evapotranspiration and monthly groundwater storage change series through random sampling.

[0082] Example 2

[0083] Based on the same principle as the multi-source remote sensing and intelligent algorithm based groundwater recharge degree collaborative inversion method shown in Embodiment 1 of the present application, as shown in the accompanying Figure 2 The present application also provides a multi-source remote sensing and intelligent algorithm based groundwater recharge degree collaborative inversion system in the embodiments, which comprises an acquisition and preprocessing unit, a reconstruction unit, a processing unit, a downscaling unit, an identification unit and a verification unit;

[0084] The acquisition and preprocessing unit is used for collecting and preprocessing the monthly terrestrial water storage change data, remote sensing evapotranspiration data and precipitation data in the target area to obtain a monthly groundwater storage change sequence, a monthly actual evapotranspiration, a surface runoff and a net drainage;

[0085] The reconstruction unit is used for constructing a monthly vertical water balance equation according to the monthly groundwater storage change sequence and the monthly precipitation, combining the surface runoff and the net drainage as a net outflow item, and reconstructing the monthly vertical water balance equation;

[0086] The processing unit is used for calculating the monthly water surplus of each pixel according to the monthly precipitation and the monthly actual evapotranspiration;

[0087] The downscaling unit is used for downscaling the monthly groundwater storage change sequence to the same resolution as the monthly actual evapotranspiration;

[0088] The identification unit is used for identifying a recharge event according to the monthly water surplus and estimating the value of the recharge amount; defining that the monthly water surplus is positive after filling in the consumption of the monthly groundwater system as the existence of a recharge event;

[0089] The verification unit is used for comparing the total recharge amount sequence obtained by inversion with the reference value of the recharge rate estimated by the monitoring well data or the runoff data of the basin outlet, verifying the error of the monthly precipitation, the monthly actual evapotranspiration and the monthly groundwater storage change sequence, and evaluating the inversion result.

[0090] Optionally, the preprocessing of the monthly terrestrial water storage change data comprises: stripping non-groundwater components by using land surface model data, removing strip noise by Gaussian filtering, and calculating the monthly groundwater storage change amount.

[0091] Optionally, the obtained monthly actual evapotranspiration is synchronous with the monthly terrestrial water storage change data, and the spatiotemporal resolution of the monthly actual evapotranspiration is greater than that of the monthly terrestrial water storage change data.

[0092] Optionally, the construction of the monthly vertical water balance equation comprises:

[0093] Let be the monthly groundwater storage change sequence, be the monthly precipitation, is the monthly actual evapotranspiration, is the surface runoff, is the net drainage, then:

[0094] .

[0095] Optionally, the monthly vertical water balance equation is reconstructed, including:

[0096] Let be the monthly groundwater storage change sequence, be the monthly precipitation, be the monthly actual evapotranspiration, be the surface runoff, be the net drainage, be the net outflow, then the reconstructed monthly vertical water balance equation is expressed as:

[0097] .

[0098] Optionally, the monthly water surplus of each pixel is calculated, including:

[0099] Let the monthly water surplus of each pixel be , be the monthly precipitation, be the monthly actual evapotranspiration, then the monthly water surplus of each pixel is expressed as:

[0100] .

[0101] Optionally, the monthly groundwater storage change is downscaled to the same resolution as the monthly actual evapotranspiration; the high-resolution monthly water surplus of each pixel and the terrain index are used as covariates to spatially distribute the monthly groundwater storage change sequence using the cokriging interpolation method, to obtain the high-resolution monthly groundwater change of each pixel.

[0102] Optionally, the recharge event is identified according to the monthly water surplus, and the estimated value of the recharge amount is estimated, including:

[0103] Let the monthly water surplus of each pixel be , be the surface runoff, the high-resolution monthly groundwater change of each pixel be , and the estimated value of the recharge amount be , then an effective recharge event occurs only when , at which time the estimated value of the recharge amount is:

[0104] .

[0105] Optionally, the Monte Carlo method is used to calculate the error of the monthly precipitation, the monthly actual evapotranspiration and the monthly change sequence of the groundwater storage by random sampling.

[0106] The above only is the preferred embodiment of the present application, and is not used to limit the present application, for the person skilled in the art, the present application can have various changes and changes. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present application shall be included in the protection scope of the present application.

Claims

1. A method for collaborative inversion of groundwater recharge based on multi-source remote sensing and intelligent algorithms, characterized in that, include: Monthly land water storage change data, remote sensing evapotranspiration data and precipitation data are collected and preprocessed in the target area to obtain monthly groundwater storage change sequence, monthly actual evapotranspiration, surface runoff and net drainage. Based on the monthly groundwater storage change sequence and monthly precipitation, a monthly vertical water balance equation is constructed. Surface runoff and net drainage are combined as a net outflow term to reconstruct the monthly vertical water balance equation. Calculate the monthly water surplus for each pixel based on monthly precipitation and actual monthly evapotranspiration. The monthly groundwater storage change series was downscaled to the same resolution as the actual monthly evapotranspiration. Replenishment events are identified based on monthly water surplus, and the replenishment amount is estimated. A monthly water surplus is defined as a regular replenishment event after the monthly groundwater system's consumption has been replenished. The total regional recharge sequence obtained by inversion is compared with the reference value of recharge rate estimated by monitoring well data or the runoff data of the watershed outlet to verify the error of the monthly precipitation, monthly actual evapotranspiration and monthly groundwater storage change sequence, and to evaluate the inversion results.

2. The groundwater recharge retrieval method based on multi-source remote sensing and intelligent algorithms according to claim 1, characterized in that, Preprocessing of monthly land water storage change data includes: using land surface model data to remove non-groundwater components, removing strip noise through Gaussian filtering, and calculating the monthly groundwater storage change.

3. The groundwater recharge retrieval method based on multi-source remote sensing and intelligent algorithms according to claim 1, characterized in that, The obtained monthly actual evapotranspiration data is concurrent with the monthly land water storage change data, and the spatiotemporal resolution of the monthly actual evapotranspiration data is greater than that of the monthly land water storage change data.

4. The groundwater recharge retrieval method based on multi-source remote sensing and intelligent algorithms according to claim 1, characterized in that, Constructing the vertical water balance equation at the monthly scale includes: set up This is a monthly series of groundwater storage changes. This refers to monthly precipitation. This represents the actual monthly evaporation. As surface runoff, For net drainage volume, then: 。 5. The groundwater recharge retrieval method based on multi-source remote sensing and intelligent algorithms according to claim 1, characterized in that, The vertical water balance equations at the lunar scale are reconstructed, including: assuming... This is a monthly series of groundwater storage changes. This refers to monthly precipitation. This represents the actual monthly evaporation. As surface runoff, This refers to net drainage volume. If the term is net outflow, then the reconstructed monthly-scale vertical water balance equation is expressed as: 。 6. The groundwater recharge retrieval method based on multi-source remote sensing and intelligent algorithms according to claim 1, characterized in that, Calculate the monthly water surplus for each cell, including: Let the monthly water surplus of each pixel be... , This refers to monthly precipitation. Given the actual monthly evapotranspiration, the monthly water surplus for each pixel is expressed as: 。 7. The groundwater recharge retrieval method based on multi-source remote sensing and intelligent algorithms according to claim 1, characterized in that, The monthly groundwater storage change was downscaled to the same resolution as the actual monthly evapotranspiration. The co-kriging interpolation method was used to spatially allocate the monthly groundwater storage change sequence of each high-resolution cell as covariates, and the high-resolution monthly groundwater change of each cell was obtained.

8. The groundwater recharge retrieval method based on multi-source remote sensing and intelligent algorithms according to claim 1, characterized in that, Based on the monthly water surplus, replenishment events are identified, and the replenishment amount is estimated, including: Let the monthly water surplus of each pixel be... , For surface runoff, the high-resolution monthly groundwater variation per pixel is: The estimated supply amount is If and only if A valid supply event will only occur when the estimated supply amount is: 。 9. The groundwater recharge retrieval method based on multi-source remote sensing and intelligent algorithms according to claim 1, characterized in that, The Monte Carlo method was used to calculate the error of the monthly precipitation, monthly actual evapotranspiration and monthly groundwater storage change series by random sampling.

10. A groundwater recharge retrieval system based on multi-source remote sensing and intelligent algorithms, characterized in that, It includes an acquisition and preprocessing unit, a reconstruction unit, a processing unit, a downscaling unit, an identification unit, and a verification unit; The acquisition and preprocessing unit is used to collect monthly land water storage change data, remote sensing evapotranspiration data and precipitation data in the target area and preprocess them to obtain monthly groundwater storage change sequence, monthly actual evapotranspiration, surface runoff and net drainage. The reconstruction unit is used to construct a monthly vertical water balance equation based on the monthly groundwater storage change sequence and monthly precipitation, and to reconstruct the monthly vertical water balance equation by merging surface runoff and net drainage as a net outflow term. The processing unit is used to calculate the monthly water surplus of each pixel based on the monthly precipitation and the actual monthly evapotranspiration. Downscaling unit, used to downscale the monthly groundwater storage change series to the same resolution as the actual monthly evapotranspiration; The identification unit is used to identify replenishment events based on the monthly water surplus and estimate the replenishment amount; it is defined that a monthly water surplus after replenishing the consumption of the groundwater system in the current month is a regular replenishment event. The verification unit is used to compare the total regional recharge sequence obtained by inversion with the reference value of the recharge rate estimated by monitoring well data or the runoff data of the watershed outlet, to verify the error of the monthly precipitation, monthly actual evapotranspiration and monthly groundwater storage change sequences, and to evaluate the inversion results.

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