Groundwater level estimation method and system in arid areas to reduce evapotranspiration calculation errors

Abstract

The present invention relates to a method and system for estimating groundwater levels in arid areas that reduces evapotranspiration calculation errors, and belongs to the technical field of groundwater level estimation in arid areas. The method comprises the following steps: obtaining remote sensing evapotranspiration, a remote sensing evapotranspiration model, and a land surface process model for a study area; generating a predicted evapotranspiration ensemble and a simulated evapotranspiration ensemble using the land surface process model; calculating the temporal and spatial variations of surface evapotranspiration at each integration moment based on the respective errors and co-errors of the predicted evapotranspiration and simulated evapotranspiration at each integration moment on a daily scale; then calculating the temporal and spatial variations of surface evapotranspiration at each integration moment based on the respective errors of the temporal and spatial variations of surface evapotranspiration at each integration moment on a daily scale; expanding the remote sensing evapotranspiration into a daily-scale surface evapotranspiration based on the temporal and spatial variations of surface evapotranspiration at each integration moment on a daily scale; and substituting the daily-scale surface evapotranspiration into a model linking surface evapotranspiration and groundwater level to estimate the spatial distribution of groundwater levels in the arid area.

Description

Technical Field

[0001] The present invention relates to a method for monitoring groundwater levels in arid areas, and in particular to a method and system for estimating groundwater levels in arid areas for reducing evapotranspiration calculation errors. Background Art

[0002] Intense evaporation in arid regions has led to an extreme scarcity of surface water. This lack of surface water has made groundwater the most crucial source of moisture for most arid ecosystems and the sole source of water for sustaining natural plant growth. The depth of groundwater, a direct indicator of water resource conditions in arid regions, influences the type, growth, distribution, and evolution of ecosystems within the region. Therefore, understanding the spatial distribution of groundwater in arid regions plays a crucial role in guiding the quantitative assessment, enrichment, and optimal regulation of water resources.

[0003] Phreatic evapotranspiration (PEV) is the primary channel for groundwater discharge in arid regions. PEP transports water to the vadose zone, where it disappears into the atmosphere through evapotranspiration. For arid regions, PEV evaporation is essentially equivalent to surface evapotranspiration, making surface evapotranspiration an important method for estimating groundwater levels. Currently, land surface process simulation and remote sensing inversion are the two most important methods for estimating surface evapotranspiration. The advantage of estimating evapotranspiration using land surface process models lies in their ability to continuously predict evapotranspiration in both time and space, relying on their inherent dynamic mechanisms. Remote sensing, on the other hand, offers the advantage of high spatial resolution in inverting surface evapotranspiration.

[0004] Although land surface process models (LSPMs) can predict evapotranspiration continuously in space and time thanks to their inherent physical dynamics governing equations, the spatial resolution of their driving data is relatively coarse, resulting in relatively low spatial resolution of surface evapotranspiration simulated by these models. Furthermore, vegetation and soil parameters simulated by these models exhibit spatial and temporal representational errors, inevitably leading to evapotranspiration errors. While remote sensing evapotranspiration inversions offer relatively high spatial resolution, they cannot produce temporally continuous surface evapotranspiration due to the instantaneous nature of remote sensing observations. Furthermore, assumptions in remote sensing evapotranspiration inversions also introduce errors. Most importantly, the equations linking phreatic evaporation to groundwater levels typically use daily-scale surface evapotranspiration, which hinders the estimation of groundwater levels using instantaneous remote sensing evapotranspiration and creates difficulties in linking remote sensing evapotranspiration and groundwater levels.

[0005] In order to reduce the impact of the respective errors of evapotranspiration simulated by the current land surface process model and remote sensing evapotranspiration on the estimation of groundwater levels in arid areas, the accuracy of remote sensing evapotranspiration on the daily scale was improved by using the respective errors of evapotranspiration simulated by the land surface process model and remote sensing evapotranspiration. Based on this, a method for estimating groundwater levels in arid areas that reduces the error in evapotranspiration calculation was invented. Summary of the Invention

[0006] To overcome the deficiencies of the prior art, the present invention aims to provide a method and system for estimating groundwater levels in arid areas that reduces evapotranspiration calculation errors, thereby effectively reducing the impact of errors in different surface evapotranspiration estimation methods on groundwater level monitoring in arid areas.

[0007] To achieve the above object, the present invention provides the following solutions:

[0008] A method for estimating groundwater levels in arid areas to reduce evapotranspiration calculation errors comprises:

[0009] Obtain remote sensing evapotranspiration, remote sensing evapotranspiration models and land surface process models for the study area;

[0010] Generate forecast evapotranspiration ensemble and simulated evapotranspiration ensemble using land surface process models;

[0011] Based on the errors and co-errors of the forecast evapotranspiration ensemble and the simulated evapotranspiration ensemble, the remote sensing evapotranspiration is expanded into the daily evapotranspiration.

[0012] The groundwater level in arid areas was estimated by substituting sky evapotranspiration into the model linking surface evapotranspiration and groundwater level.

[0013] In one embodiment, obtaining remote sensing evapotranspiration, remote sensing evapotranspiration model, and land surface process model for a study area includes:

[0014] Obtain the remote sensing evapotranspiration model of the study area and the remote sensing evapotranspiration during the study period;

[0015] Establish a land surface process model and input dataset for the study area. The input dataset includes: forcing dataset, land use, soil parameter dataset, vegetation parameter dataset, surface albedo, vegetation cover and initial field data within the study period of the study area.

[0016] Preprocessing remote sensing evapotranspiration products and input datasets, including projection conversion, resampling and spatial clipping;

[0017] The state variables of the land surface process model corresponding to the input of the remote sensing evapotranspiration model are determined as the state vector to be disturbed.

[0018] In one embodiment, the steps of generating a forecast evapotranspiration ensemble and a simulated evapotranspiration ensemble using a land surface process model include:

[0019] The perturbation set of the state vector to be disturbed at the initial integration time of remote sensing evapotranspiration is generated as follows:

[0020] {X t1M ,…,X tnM ,…,X tNM}

[0021] Where t represents the initial integration time of the remote sensing evapotranspiration day, N is the number of members in the perturbation set of the state vector to be disturbed, and M is the state vector to be disturbed X = [x1, ..., x m ,…,x M ] dimension, x m is the m-th dimension state variable of X, X tnM is the nth (1≤n≤N) member of the perturbation set of the state vector X to be perturbed at time t;

[0022] Combined with the input data set, each member of the perturbation set of the state vector is used to integrate the land surface process model forward within the remote sensing evapotranspiration day to generate the state vector forecast set and simulated evapotranspiration set at each integration moment within the remote sensing evapotranspiration day, specifically:

[0023] X t+1nM =M(X tnM ,F t )→X t+2nM =M(X t+1nM ,F t+1 )→…→X t+TnM =M(X t+T-1nM ,F t+T-1 )

[0024] SE tn =M(X tnM ,F t )SE t+1n =M(X t+1nM ,F t+1 )…SE t+Tn =M(X t+TnM ,F t+T )

[0025] Where M represents the land surface process model, T represents the number of integration steps of M within the remote sensing evapotranspiration day, and F t , F t+1 ,…,F t+T-1 , F t+T Respectively represent the input data at the integration time t, t+1, ..., t+T-1, t+T, M(X tnM ,F t ) indicates that X tnM 、F t Drive M forward one step, X t+TnM Indicated by X t+T-1nM 、F t+T-1 The prediction result of driving M to integrate one step forward, SE tn =M(X tnM ,F t ),...,SE t+Tn =M(X t+TnM ,F t+T ) indicates that XtnM 、F t ,...,X t+TnM 、F t+T The simulated evapotranspiration SE at time t,…,t+T obtained by driving M respectively tn ,...,SE t+Tn ;

[0026] Combined with the input data set, each member of the forecast set of the state vector at each integration moment of the remote sensing evapotranspiration day is input into the remote sensing evapotranspiration model to generate the forecast evapotranspiration set at each integration moment of the remote sensing evapotranspiration day, specifically:

[0027] PE tn =R(X tnM ,F t )PE t+1n =R(X t+1nM ,F t+1 )…PE t+Tn =R(X t+TnM ,F t+T )

[0028] In the formula, R represents the remote sensing evapotranspiration model, PE tn =R(X tnM ,F t ), PE t+1n =R(X t+1nM ,F t+1 ),…,PE t+Tn =R(X t+TnM ,F t+T ) means to convert X tnM 、F t , X t+1nM 、F t+1 ,...,X t+TnM 、F t+T Input R to obtain the predicted evapotranspiration PE at t, t+1, ..., t+T respectively tn , PE t+1n ,...,PE t+Tn .

[0029] In one embodiment, the remote sensing evapotranspiration is expanded into the daily evapotranspiration based on the errors and co-errors of the forecast evapotranspiration ensemble and the simulated evapotranspiration ensemble, including:

[0030] The ensemble of forecast evapotranspiration and simulated evapotranspiration at each integration moment is spatially downscaled to the spatial resolution of remote sensing evapotranspiration;

[0031] The error of simulated evapotranspiration at each integration moment is calculated based on the simulated evapotranspiration ensemble, specifically:

[0032]

[0033] Where, represent the simulated evapotranspiration errors at t, …, t+i, …, t+T respectively;

[0034] The error of the forecast evapotranspiration is calculated based on the ensemble of forecast evapotranspiration at each integration moment, specifically:

[0035]

[0036] Where, represent the forecast evapotranspiration errors at t, …, t+i, …, t+T respectively;

[0037] The co-error of the simulated evapotranspiration is calculated based on the simulated evapotranspiration ensemble at each integration moment and the simulated evapotranspiration ensemble covering the remote sensing evapotranspiration time integration moment, specifically:

[0038]

[0039] Where τ is the integration time covering the remote sensing evapotranspiration time, δ SEt ,…,δ SEt+i ,…,δ SEt+T denote the co-errors of the simulated evapotranspiration ensemble at time t,…,t+i,…,t+T and the simulated evapotranspiration ensemble at time τ, respectively;

[0040] The co-error of the forecast evapotranspiration is calculated based on the ensemble of forecast evapotranspiration at each integration moment and the ensemble of forecast evapotranspiration at time τ, specifically:

[0041]

[0042] Where, δ PEt ,…,δ PEt+i ,…,δ PEt+T denote the co-errors of the ensemble of forecast evapotranspiration at time t,…,t+i,…,t+T and the ensemble of forecast evapotranspiration at time τ, respectively;

[0043] The temporal variation of surface evapotranspiration at each integration moment relative to time τ is calculated based on the simulated evapotranspiration ensemble and its error, the forecast evapotranspiration ensemble and its error, the simulated evapotranspiration co-error at each integration moment, and the forecast evapotranspiration co-error at each integration moment. Specifically,

[0044]

[0045] Where i represents the subscript of the integration time, 0≤i≤T;

[0046] The spatial variation of surface evapotranspiration at each integration moment relative to remote sensing evapotranspiration at time τ is calculated based on the simulated evapotranspiration ensemble, the forecast evapotranspiration ensemble, and RE, including:

[0047] Based on the simulated evapotranspiration ensemble, the forecast evapotranspiration ensemble and RE, the regression equations between the mean simulated evapotranspiration, the mean forecast evapotranspiration and RE at each integration time are calculated as follows:

[0048]

[0049] Where, κ SEt ,…,κ SEt+i …,κ SEt+T and b SEt ,…,b SEt+i …, b SEt+T , κ PEt ,…,κ PEt+i …, κ PEt+T and b PEt ,…,b PEt+i …, b PEt+T are the regression coefficients and regression constants between the simulated evapotranspiration mean and RE, and the predicted evapotranspiration mean and RE at t, …, t+i, …, t+T, respectively;

[0050] Calculate the remote sensing simulated evapotranspiration ensemble and remote sensing forecast evapotranspiration ensemble at each integration moment as follows:

[0051] RSE tn =κ SEt SE tn +b SEt ,…,RSE t+in =κ SEt+i SE t+in +b SEt+i ,…,RSE t+Tn =κ SEt+T SE t+Tn +b SEt+T

[0052] RPE tn =κ PEt PE tn +b PEt ,…,RPE t+in =κ PEt+i PE t+in +b PEt+i ,…,RPE t+Tn =κ PEt+T PE t+Tn +b PEt+T

[0053] Where, RSE tn ,…,RSE t+in …, RSE t+Tn and RPE tn ,…,RPE t+in ..., RPE t+Tn are the nth members of the remote sensing simulated evapotranspiration ensemble and the remote sensing predicted evapotranspiration ensemble at time t,…,t+i,…,t+T, respectively;

[0054] Calculate the error of the remote sensing simulated evapotranspiration ensemble and the error of the remote sensing predicted evapotranspiration ensemble at each integration moment as follows:

[0055]

[0056] Where, and are the errors of the remote sensing simulated evapotranspiration ensemble and the errors of the remote sensing predicted evapotranspiration ensemble at t,…,t+i,…,t+T respectively;

[0057] Calculate the co-errors between the remote sensing simulated evapotranspiration ensemble, the remote sensing predicted evapotranspiration ensemble and the remote sensing simulated evapotranspiration ensemble, the remote sensing predicted evapotranspiration ensemble at each integration moment, specifically:

[0058]

[0059] Where, δ RSEt ,…,δ RSEt+i ,…,δ RSEt+T denote the co-errors between the remote sensing simulated evapotranspiration ensemble at time t,…,t+i,…,t+T and the remote sensing simulated evapotranspiration ensemble at time τ; δ RPEt ,…,δ RPEt+i ,…,δ RPEt+T denote the co-errors between the remote sensing evapotranspiration ensemble at time t, …, t+i, …, t+T and the remote sensing evapotranspiration ensemble at time τ, respectively;

[0060] The spatial variation of surface evapotranspiration at each integration moment relative to time τ is calculated based on the remote sensing simulated evapotranspiration ensemble and its error, the remote sensing predicted evapotranspiration ensemble and its error, the remote sensing simulated evapotranspiration co-error and the remote sensing predicted evapotranspiration co-error at each integration moment. Specifically,

[0061]

[0062] According to γ t+i→t+τn , χ t+i→t+τn Calculate the spatiotemporal variation of surface evapotranspiration at each integration moment, specifically:

[0063]

[0064] Where, α t+i is the spatiotemporal variation of surface evapotranspiration at the integration time t+i;

[0065] According to the spatiotemporal variation of surface evapotranspiration at each integration moment, remote sensing evapotranspiration is expanded into daytime evapotranspiration, specifically:

[0066]

[0067] Where DE is the expanded evapotranspiration.

[0068] In one embodiment, the groundwater level in the arid region is estimated by substituting the sky evapotranspiration into the model linking surface evapotranspiration and groundwater level, specifically:

[0069] H=G -1 (DE)

[0070] Where G is the link model between surface evapotranspiration and groundwater level. The dimension of DE should be consistent with the dimension of surface evapotranspiration obtained by inputting the dryland groundwater level H (dimension of meters) into model G.

[0071] A groundwater level estimation system in arid areas for reducing evapotranspiration calculation errors comprises:

[0072] Preparation module, used to obtain remote sensing evapotranspiration, remote sensing evapotranspiration model and land surface process model of the study area;

[0073] Ensemble module, used to generate forecast evapotranspiration ensemble and simulated evapotranspiration ensemble using land surface process model;

[0074] Extension module, used to expand remote sensing evapotranspiration into daily evapotranspiration based on the errors and co-errors of the forecast evapotranspiration ensemble and the simulated evapotranspiration ensemble;

[0075] The estimation module is used to substitute sky evapotranspiration into the connection model between surface evapotranspiration and groundwater level to estimate the groundwater level in arid areas.

[0076] In one embodiment, the preparation module includes a remote sensing preparation unit, a land surface process model preparation unit, a preprocessing unit, and a disturbance state vector preparation unit:

[0077] Remote sensing preparation unit, used to obtain remote sensing evapotranspiration model of the study area and remote sensing evapotranspiration during the study period;

[0078] The land surface process model preparation unit is used to establish the land surface process model and input data sets of the study area. The input data sets include: the forcing data set, land use, soil parameter data set, vegetation parameter data set, surface albedo, vegetation cover and initial field within the study period of the study area;

[0079] A preprocessing unit, used to preprocess remote sensing evapotranspiration products and input data sets, wherein the preprocessing includes projection conversion, resampling and spatial clipping;

[0080] The disturbance state vector preparation unit is used to determine the state variables of the land surface process model corresponding to the remote sensing evapotranspiration model input as the state vector to be disturbed.

[0081] In one embodiment, the ensemble module includes a perturbation ensemble unit and an ensemble simulation unit:

[0082] The disturbance set unit is used to generate the disturbance set of the state vector to be disturbed at the initial integration time of the remote sensing evapotranspiration day;

[0083] The ensemble simulation unit is used to combine the input data set and use each member of the perturbation set of the state vector to forward integrate the land surface process model within the remote sensing evapotranspiration day to generate the state vector forecast ensemble and simulated evapotranspiration ensemble at each integration moment within the remote sensing evapotranspiration day.

[0084] In one embodiment, the expansion module includes a simulated evapotranspiration and forecasted evapotranspiration spatial downscaling unit, a simulated evapotranspiration and forecasted evapotranspiration error unit, a simulated evapotranspiration and forecasted evapotranspiration co-error unit, a surface evapotranspiration temporal variation unit, a regression equation unit, a remotely sensed simulated evapotranspiration and remotely sensed forecasted evapotranspiration unit, a remotely sensed simulated evapotranspiration and remotely sensed forecasted evapotranspiration error unit, a remotely sensed simulated evapotranspiration and remotely sensed forecasted evapotranspiration co-error unit, a surface evapotranspiration spatial variation unit, a surface evapotranspiration spatiotemporal variation unit, and a remotely sensed evapotranspiration day-scale unit:

[0085] The spatial downscaling unit of simulated evapotranspiration and forecast evapotranspiration is used to spatially downscale the forecast evapotranspiration ensemble and simulated evapotranspiration ensemble at each integration moment to the spatial resolution of remote sensing evapotranspiration;

[0086] The simulated evapotranspiration and forecast evapotranspiration error unit is used to calculate the error of the simulated evapotranspiration based on the simulated evapotranspiration ensemble at each integration moment, and to calculate the error of the forecast evapotranspiration based on the forecast evapotranspiration ensemble at each integration moment;

[0087] The simulated evapotranspiration and forecast evapotranspiration co-error unit is used to calculate the co-error of the simulated evapotranspiration based on the simulated evapotranspiration ensemble at each integration moment and the simulated evapotranspiration ensemble covering the remote sensing evapotranspiration time integration moment, and to calculate the co-error of the forecast evapotranspiration based on the forecast evapotranspiration ensemble at each integration moment and the forecast evapotranspiration ensemble covering the remote sensing evapotranspiration time integration moment;

[0088] The surface evapotranspiration temporal variation unit is used to calculate the temporal variation of surface evapotranspiration at each integration moment relative to the remote sensing evapotranspiration time based on the simulated evapotranspiration ensemble and its error, the forecast evapotranspiration ensemble and its error, the simulated evapotranspiration co-error at each integration moment, and the forecast evapotranspiration co-error at each integration moment;

[0089] The regression equation unit is used to calculate the regression equation between the simulated evapotranspiration mean, the predicted evapotranspiration mean and the remote sensing evapotranspiration at each integration moment based on the simulated evapotranspiration ensemble, the predicted evapotranspiration ensemble and the remote sensing evapotranspiration;

[0090] Remote sensing simulated evapotranspiration and remote sensing predicted evapotranspiration units are used to calculate the remote sensing simulated evapotranspiration ensemble and remote sensing predicted evapotranspiration ensemble at each integration moment;

[0091] Remote sensing simulated evapotranspiration and remote sensing predicted evapotranspiration error units are used to calculate the error of the remote sensing simulated evapotranspiration ensemble and the error of the remote sensing predicted evapotranspiration ensemble at each integration moment;

[0092] The remote sensing simulated evapotranspiration and remote sensing predicted evapotranspiration co-error unit is used to calculate the co-error between the remote sensing simulated evapotranspiration ensemble, the remote sensing predicted evapotranspiration ensemble at each integration moment and the remote sensing simulated evapotranspiration ensemble, the remote sensing predicted evapotranspiration ensemble covering the remote sensing evapotranspiration time integration moment;

[0093] The surface evapotranspiration spatial variation unit is used to calculate the spatial variation of surface evapotranspiration at each integration moment relative to the remote sensing evapotranspiration time based on the remote sensing simulated evapotranspiration ensemble and its error at each integration moment, the remote sensing predicted evapotranspiration ensemble and its error at each integration moment, the remote sensing simulated evapotranspiration co-error at each integration moment, and the remote sensing predicted evapotranspiration co-error at each integration moment;

[0094] The spatiotemporal variation unit of surface evapotranspiration is used to calculate the spatiotemporal variation of surface evapotranspiration based on the temporal variation and spatial variation of surface evapotranspiration at each integration moment;

[0095] The remote sensing evapotranspiration day-scale unit is used to expand the remote sensing evapotranspiration into day-scale evapotranspiration based on the spatiotemporal changes of surface evapotranspiration at each integration moment.

[0096] According to the specific embodiments provided by the present invention, the present invention discloses the following technical effects:

[0097] The present invention provides a method and system for estimating groundwater levels in arid areas with reduced evapotranspiration calculation errors. Compared with the groundwater level estimation method based on evapotranspiration simulation and remote sensing evapotranspiration using a land surface process model, the present invention effectively utilizes the respective calculation errors and co-errors of evapotranspiration simulation and predicted evapotranspiration using a land surface process model, and more accurately calculates the temporal changes of each integral moment of surface evapotranspiration relative to the remote sensing evapotranspiration moment by weighting the error values ​​and co-error values; through a regression model between the simulated evapotranspiration and predicted evapotranspiration after spatial downscaling and the remote sensing evapotranspiration, and utilizing the respective errors and co-errors of the remote sensing simulated evapotranspiration and the remote sensing predicted evapotranspiration calculation, the present invention provides a method and system for estimating groundwater levels in arid areas with reduced evapotranspiration calculation errors. By weighting the remote sensing simulated evapotranspiration and the remote sensing predicted evapotranspiration with the error value and the co-error value, the spatial variation of surface evapotranspiration at each integration moment relative to the remote sensing evapotranspiration moment is more accurately calculated; finally, the temporal variation of surface evapotranspiration at each integration moment relative to the remote sensing evapotranspiration moment is obtained by weighting the temporal variation error and the spatial variation error of surface evapotranspiration at each integration moment; based on the temporal variation of surface evapotranspiration at each integration moment relative to the remote sensing evapotranspiration moment, the remote sensing evapotranspiration is expanded to the daily scale, thereby minimizing the daily scale expansion error of remote sensing evapotranspiration and improving the accuracy of groundwater level estimation in arid areas. BRIEF DESCRIPTION OF THE DRAWINGS

[0098] In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the following briefly introduces the drawings required for use in the embodiments of the present invention. It should be understood that the following drawings are merely some embodiments of the present invention and should not be regarded as limiting the scope. For those skilled in the art, other relevant drawings can be obtained based on these drawings without paying any creative work.

[0099] Figure 1 A flow chart of the method in the embodiment provided by the present invention;

[0100] Figure 2 A diagram showing the connection of system modules in an embodiment of the present invention;

[0101] Figure 3 A structural block diagram of a preparation module in an embodiment provided by the present invention;

[0102] Figure 4 A structural block diagram of the collection module in the embodiment provided by the present invention;

[0103] Figure 5 A structural block diagram of the expansion module in the embodiment provided by the present invention; DETAILED DESCRIPTION

[0104] The following will clearly and completely describe the technical solutions in the embodiments of the present invention in conjunction with the accompanying drawings. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. All other embodiments obtained by ordinary technicians in this field based on the embodiments of the present invention without making any creative efforts shall fall within the scope of protection of the present invention.

[0105] The present invention aims to provide a method and system for estimating groundwater levels in arid regions that reduces evapotranspiration calculation errors, thereby improving the accuracy of groundwater level estimation in arid regions. To facilitate understanding of the present invention's objectives, features, and advantages, the present invention is described in detail below with reference to the accompanying drawings and specific embodiments.

[0106] Please refer to Figure 1 , Figure 1 The method flow chart of the embodiment provided by the present invention is as follows Figure 1 The method comprises the following steps:

[0107] S1 obtains remote sensing evapotranspiration, remote sensing evapotranspiration model and land surface process model of the study area;

[0108] S2 uses the land surface process model to generate the forecast evapotranspiration ensemble and the simulated evapotranspiration ensemble;

[0109] S3 expands remote sensing evapotranspiration into daytime evapotranspiration based on the errors and co-errors of the forecast evapotranspiration ensemble and the simulated evapotranspiration ensemble;

[0110] S4 substitutes sky evapotranspiration into the model linking surface evapotranspiration and groundwater level to estimate the groundwater level in arid areas.

[0111] As a further preferred technical solution, in step S1, obtaining remote sensing evapotranspiration, remote sensing evapotranspiration model and land surface process model of the study area includes:

[0112] Obtain the remote sensing evapotranspiration model of the study area and the remote sensing evapotranspiration during the study period;

[0113] Establish a land surface process model and input dataset for the study area. The input dataset includes: forcing dataset, land use, soil parameter dataset, vegetation parameter dataset, surface albedo, vegetation cover and initial field data within the study period of the study area.

[0114] The state variables of the land surface process model corresponding to the input of the remote sensing evapotranspiration model are determined as the state vector to be disturbed;

[0115] Remote sensing evapotranspiration products and input datasets are preprocessed, including projection conversion, resampling and spatial clipping.

[0116] As a further preferred technical solution, in step S2, generating the forecast evapotranspiration ensemble and the simulated evapotranspiration ensemble using the land surface process model includes:

[0117] The perturbation set of the state vector to be disturbed at the initial integration time of remote sensing evapotranspiration is generated as follows:

[0118] {X t1M ,…,X tnM ,…,X tNM}

[0119] Where t represents the initial integration time of the remote sensing evapotranspiration day, N is the number of members in the perturbation set of the state vector to be disturbed, and M is the state vector to be disturbed X = [x1, ..., x m ,…,x M ] dimension, x m is the m-th dimension state variable of X, X tnM is the nth (1≤n≤N) member of the perturbation set of the state vector X to be perturbed at time t;

[0120] Combined with the input data set, each member of the state vector perturbation set is used to integrate the land surface process model forward within the remote sensing evapotranspiration day to generate the state vector forecast set and simulated evapotranspiration set at each integration moment within the remote sensing evapotranspiration day, specifically:

[0121] X t+1nM =M(X tnM ,F t )→X t+2nM =M(Xt+1nM ,F t+1 )→…→X t+TnM =M(X t+T-1nM ,F t+T-1 )

[0122] SE tn =M(X tnM ,F t )SE t+1n =M(X t+1nM ,F t+1 )…SE t+Tn =M(X t+TnM ,F t+T )

[0123] Where M represents the land surface process model, T represents the number of integration steps of M within the remote sensing evapotranspiration day, and F t , F t+1 ,…,F t+T-1 , F t+T They represent the input data sets at the integration time t, t+1, ..., t+T-1, t+T respectively, M(X tnM ,F t ) indicates that X tnM 、F t Drive M forward one step, X t+TnM Indicated by X t+T-1nM 、F t+T-1 Input the prediction result of M one step forward, SE tn =M(X tnM ,F t ),...,SE t+Tn =M(X t+TnM ,F t+T ) indicates that X tnM 、F t ,...,X t+TnM 、F t+T The simulated evapotranspiration SE at time t,…,t+T obtained by driving M respectively tn ,...,SE t+Tn ;

[0124] Combined with the input data set, each member of the forecast set of the state vector at each integration moment during the study period is input into the remote sensing evapotranspiration model to generate the forecast evapotranspiration set at each integration moment during the study period, specifically:

[0125] PE tn =R(X tnM ,F t )PE t+1n =R(X t+1nM ,F t+1 )…PE t+Tn =R(Xt+TnM ,F t+T )

[0126] In the formula, R represents the remote sensing evapotranspiration model, PE tn =R(X tnM ,F t ), PE t+1n =R(X t+1nM ,F t+1 ),…,PE t+Tn =R(X t+TnM ,F t+T ) means to convert X tnM 、F t , X t+1nM 、F t+1 ,...,X t+TnM 、F t+T Input R to obtain the predicted evapotranspiration PE at t, t+1, ..., t+T respectively tn , PE t+1n ,...,PE t+Tn .

[0127] As a further preferred technical solution, in step S3, the step of expanding the remote sensing evapotranspiration into daily evapotranspiration based on the errors and co-errors of the forecast evapotranspiration ensemble and the simulated evapotranspiration ensemble includes:

[0128] The ensemble of forecast evapotranspiration and simulated evapotranspiration at each integration moment is spatially downscaled to the spatial resolution of remote sensing evapotranspiration;

[0129] The error of the simulated evapotranspiration is calculated based on the simulated evapotranspiration ensemble at each integration moment, specifically:

[0130]

[0131] Where, represent the simulated evapotranspiration errors at t, …, t+i, …, t+T respectively;

[0132] The error of the forecast evapotranspiration is calculated based on the ensemble of forecast evapotranspiration at each integration moment, specifically:

[0133]

[0134] Where, represent the forecast evapotranspiration errors at t, …, t+i, …, t+T respectively;

[0135] The co-error of the simulated evapotranspiration is calculated based on the simulated evapotranspiration ensemble at each integration moment and the simulated evapotranspiration ensemble covering the remote sensing evapotranspiration time integration moment, specifically:

[0136]

[0137] Where τ is the integration time covering the remote sensing evapotranspiration time, δ SEt ,…,δ SEt+i ,…,δ SEt+T denote the co-errors of the simulated evapotranspiration ensemble at time t,…,t+i,…,t+T and the simulated evapotranspiration ensemble at time τ, respectively;

[0138] The co-error of the forecast evapotranspiration is calculated based on the ensemble of forecast evapotranspiration at each integration moment and the ensemble of forecast evapotranspiration at time τ, specifically:

[0139]

[0140] Where, δ PEt ,…,δ PEt+i ,…,δ PEt+T denote the co-errors of the ensemble of forecast evapotranspiration at time t,…,t+i,…,t+T and the ensemble of forecast evapotranspiration at time τ, respectively;

[0141] The temporal variation of surface evapotranspiration at each integration moment relative to time τ is calculated based on the simulated evapotranspiration ensemble and its error, the forecast evapotranspiration ensemble and its error, the simulated evapotranspiration co-error at each integration moment, and the forecast evapotranspiration co-error at each integration moment. Specifically,

[0142]

[0143] Where i represents the subscript of the integration time, 0≤i≤T;

[0144] The spatial variation of surface evapotranspiration at each integration moment relative to remote sensing evapotranspiration at time τ is calculated based on the simulated evapotranspiration ensemble, the forecast evapotranspiration ensemble, and RE, including:

[0145] Based on the simulated evapotranspiration ensemble, the forecast evapotranspiration ensemble and RE, the regression equations between the mean simulated evapotranspiration, the mean forecast evapotranspiration and RE at each integration time are calculated as follows:

[0146]

[0147] Where, κ SEt ,…,κ SEt+i …, κ SEt+T and b SEt ,…,b SEt+i …, b SEt+T , κ PEt ,…,κ PEt+i …, κ PEt+T and b PEt ,…,b PEt+i …, b PEt+T are the regression coefficients and regression constants between the simulated evapotranspiration mean and RE, and the predicted evapotranspiration mean and RE at t, …, t+i, …, t+T, respectively;

[0148] Calculate the remote sensing simulated evapotranspiration ensemble and remote sensing forecast evapotranspiration ensemble at each integration moment as follows:

[0149] RSE tn =κ SEt SE tn +b SEt ,…,RSE t+in =κ SEt+i SE t+in +b SEt+i ,…,RSE t+Tn =κ SEt+T SE t+Tn +b SEt+T

[0150] RPE tn =κ PEt PE tn +b PEt ,…,RPE t+in =κ PEt+i PE t+in +b PEt+i ,…,RPE t+Tn =κ PEt+T PE t+Tn +b PEt+T

[0151] Where, RSE tn ,…,RSE t+in …, RSE t+Tn and RPE tn ,…,RPE t+in ..., RPE t+Tn are the nth members of the remote sensing simulated evapotranspiration ensemble and the remote sensing predicted evapotranspiration ensemble at time t,…,t+i,…,t+T, respectively;

[0152] Calculate the error of the remote sensing simulated evapotranspiration ensemble and the error of the remote sensing predicted evapotranspiration ensemble at each integration moment as follows:

[0153]

[0154] Where, and are the errors of the remote sensing simulated evapotranspiration ensemble and the errors of the remote sensing predicted evapotranspiration ensemble at t,…,t+i,…,t+T respectively;

[0155] Calculate the co-errors between the remote sensing simulated evapotranspiration ensemble, the remote sensing predicted evapotranspiration ensemble and the remote sensing simulated evapotranspiration ensemble, the remote sensing predicted evapotranspiration ensemble at each integration moment, specifically:

[0156]

[0157] Where, δ RSEt,…,δ RSEt+i ,…,δ RSEt+T denote the co-errors between the remote sensing simulated evapotranspiration ensemble at time t,…,t+i,…,t+T and the remote sensing simulated evapotranspiration ensemble at time τ; δ RPEt ,…,δ RPEt+i ,…,δ RPEt+T denote the co-errors between the remote sensing evapotranspiration ensemble at time t, …, t+i, …, t+T and the remote sensing evapotranspiration ensemble at time τ, respectively;

[0158] The spatial variation of surface evapotranspiration at each integration moment relative to time τ is calculated based on the remote sensing simulated evapotranspiration ensemble and its error, the remote sensing predicted evapotranspiration ensemble and its error, the remote sensing simulated evapotranspiration co-error and the remote sensing predicted evapotranspiration co-error at each integration moment. Specifically,

[0159]

[0160] According to γ t+i→t+τn , χ t+i→t+τn Calculate the spatiotemporal variation of surface evapotranspiration at each integration moment, specifically:

[0161]

[0162] Where, α t+i is the spatiotemporal variation of surface evapotranspiration at the integration time t+i;

[0163] According to the spatiotemporal variation of surface evapotranspiration at each integration moment, remote sensing evapotranspiration is expanded into daytime evapotranspiration, specifically:

[0164]

[0165] Where DE is the expanded evapotranspiration.

[0166] As a further preferred technical solution, in step S4, substituting the sky evapotranspiration into the relationship model between surface evapotranspiration and groundwater level to estimate the groundwater level in the arid area is specifically as follows:

[0167] H=G -1 (DE)

[0168] Where G is the link model between surface evapotranspiration and groundwater level. The dimension of DE should be consistent with the dimension of surface evapotranspiration obtained by inputting the dryland groundwater level H (dimension of meters) into model G.

[0169] Figure 2 This is a system module connection diagram in the embodiment provided by the present invention, such as Figure 2 As shown, this embodiment also provides a groundwater level estimation system in arid areas for reducing evapotranspiration calculation errors, including:

[0170] Preparation module 1 is used to obtain remote sensing evapotranspiration, remote sensing evapotranspiration model and land surface process model of the study area;

[0171] Ensemble module 2, used to generate forecast evapotranspiration ensemble and simulated evapotranspiration ensemble using land surface process model;

[0172] Extension module 3 is used to expand remote sensing evapotranspiration into daily evapotranspiration based on the errors and co-errors of the forecast evapotranspiration ensemble and the simulated evapotranspiration ensemble;

[0173] The estimation module 4 is used to substitute the sky evapotranspiration into the connection model between surface evapotranspiration and groundwater level to estimate the groundwater level in the arid area.

[0174] Figure 3 The structural block diagram of the preparation module in the embodiment provided by the present invention is as follows Figure 3 The preparation module shown includes a remote sensing preparation unit 101, a land surface process model preparation unit 102, a pre-processing unit 103 and a disturbance state vector preparation unit 104:

[0175] Remote sensing preparation unit 101, used to obtain remote sensing evapotranspiration model of the study area and remote sensing evapotranspiration within the study period;

[0176] The land surface process model preparation unit 102 is used to establish a land surface process model and input data sets for the study area. The input data sets include: a forcing data set, land use, a soil parameter data set, a vegetation parameter data set, surface albedo, vegetation cover, and an initial field within the study period of the study area.

[0177] A preprocessing unit 103 is used to preprocess the remote sensing evapotranspiration product and the input data set, wherein the preprocessing includes projection conversion, resampling and spatial clipping;

[0178] The disturbance state vector preparation unit 104 is used to determine the state variables of the land surface process model corresponding to the remote sensing evapotranspiration model input as the state vector to be disturbed.

[0179] Figure 4 This is a structural block diagram of the collection module in the embodiment provided by the present invention, specifically as follows Figure 4 The illustrated ensemble module includes a perturbation ensemble unit 201 and an ensemble simulation unit 202:

[0180] The disturbance set unit 201 is used to generate a disturbance set of the state vector to be disturbed at the initial integration moment of the remote sensing evapotranspiration day;

[0181] The ensemble simulation unit 202 is used to combine the input data set and use each member of the perturbation set of the state vector to forward integrate the land surface process model within the remote sensing evapotranspiration day to generate the state vector forecast set and the simulated evapotranspiration set at each integration moment within the remote sensing evapotranspiration day.

[0182] Figure 5 This is a structural block diagram of the expansion module in the embodiment provided by the present invention, specifically as follows Figure 5 The extended modules shown include a simulated evapotranspiration and predicted evapotranspiration spatial downscaling unit 301, a simulated evapotranspiration and predicted evapotranspiration error unit 302, a simulated evapotranspiration and predicted evapotranspiration co-error unit 303, a surface evapotranspiration temporal variation unit 304, a regression equation unit 305, a remotely sensed simulated evapotranspiration and remotely sensed predicted evapotranspiration unit 306, a remotely sensed simulated evapotranspiration and remotely sensed predicted evapotranspiration error unit 307, a remotely sensed simulated evapotranspiration and remotely sensed predicted evapotranspiration co-error unit 308, a surface evapotranspiration spatial variation unit 309, a surface evapotranspiration spatiotemporal variation unit 310, and a remotely sensed evapotranspiration day-scale unit 311.

[0183] The simulated evapotranspiration and forecast evapotranspiration spatial downscaling unit 301 is used to spatially downscale the forecast evapotranspiration ensemble and the simulated evapotranspiration ensemble at each integration moment to the spatial resolution of remote sensing evapotranspiration;

[0184] The simulated evapotranspiration and forecast evapotranspiration error unit 302 is used to calculate the error of the simulated evapotranspiration based on the set of simulated evapotranspiration at each integration moment, and calculate the error of the forecast evapotranspiration based on the set of forecast evapotranspiration at each integration moment;

[0185] The simulated evapotranspiration and forecast evapotranspiration co-error unit 303 is used to calculate the co-error of the simulated evapotranspiration based on the simulated evapotranspiration ensemble at each integration moment and the simulated evapotranspiration ensemble covering the remote sensing evapotranspiration time integration moment, and calculate the co-error of the forecast evapotranspiration based on the forecast evapotranspiration ensemble at each integration moment and the forecast evapotranspiration ensemble covering the remote sensing evapotranspiration time integration moment;

[0186] The surface evapotranspiration temporal variation unit 304 is configured to calculate the temporal variation of the surface evapotranspiration at each integration moment relative to the remote sensing evapotranspiration time based on the simulated evapotranspiration ensemble and its error, the predicted evapotranspiration ensemble and its error, the simulated evapotranspiration co-error at each integration moment, and the predicted evapotranspiration co-error at each integration moment;

[0187] The regression equation unit 305 is used to calculate the regression equation between the simulated evapotranspiration mean, the predicted evapotranspiration mean and the remote sensing evapotranspiration at each integration moment based on the simulated evapotranspiration ensemble, the predicted evapotranspiration ensemble and the remote sensing evapotranspiration;

[0188] The remote sensing simulated evapotranspiration and remote sensing predicted evapotranspiration unit 306 is used to calculate the remote sensing simulated evapotranspiration ensemble and the remote sensing predicted evapotranspiration ensemble at each integration moment;

[0189] The remote sensing simulated evapotranspiration and remote sensing predicted evapotranspiration error unit 307 is used to calculate the error of the remote sensing simulated evapotranspiration ensemble and the error of the remote sensing predicted evapotranspiration ensemble at each integration moment;

[0190] The remote sensing simulated evapotranspiration and remote sensing predicted evapotranspiration co-error unit 308 is used to calculate the co-error between the remote sensing simulated evapotranspiration ensemble and the remote sensing predicted evapotranspiration ensemble at each integration moment and the remote sensing simulated evapotranspiration ensemble and the remote sensing predicted evapotranspiration ensemble covering the remote sensing evapotranspiration time integration moment;

[0191] The surface evapotranspiration spatial variation unit 309 is used to calculate the spatial variation of surface evapotranspiration at each integration moment relative to the remote sensing evapotranspiration time based on the remote sensing simulated evapotranspiration ensemble and its error at each integration moment, the remote sensing predicted evapotranspiration ensemble and its error at each integration moment, the remote sensing simulated evapotranspiration co-error at each integration moment, and the remote sensing predicted evapotranspiration co-error at each integration moment;

[0192] The surface evapotranspiration temporal and spatial variation unit 310 is used to calculate the temporal and spatial variation of surface evapotranspiration based on the temporal variation of surface evapotranspiration and the spatial variation of surface evapotranspiration at each integration moment;

[0193] The remote sensing evapotranspiration day scale unit 311 is used to expand the remote sensing evapotranspiration into day evapotranspiration according to the spatiotemporal variation of surface evapotranspiration at each integration moment.

[0194] The beneficial effects of the present invention are as follows:

[0195] The present invention takes into account the calculation errors of evapotranspiration simulated by land surface process models and remote sensing evapotranspiration, and can improve the accuracy of the time expansion of remote sensing evapotranspiration on the daily scale through their respective calculation errors. Then, by means of the physical connection between surface evapotranspiration and groundwater level in arid areas, the spatial distribution of groundwater level in arid areas can be effectively obtained, providing data support for the assessment, rational allocation and efficient utilization of groundwater resources in arid areas.

[0196] This specification describes various embodiments in a step-by-step manner. Each embodiment focuses on the differences from other embodiments. The similar or identical parts between the embodiments can be referenced from each other.

[0197] Specific examples are used in this specification to illustrate the basic principles, main features and implementation methods of the present invention. The introduction of the above embodiments is only used to assist in understanding the main ideas and key technologies of the present invention. At the same time, for ordinary technicians in this field, based on the ideas of the present invention, certain links in the method proposed by the present invention can be modified, transformed, improved, and equivalently replaced in actual implementation steps and application scope. These changes do not deviate from the spirit and scope of the technical solutions of the embodiments of the present invention and are all within the scope of protection of the present invention. In summary, the content of this specification should not be regarded as a limitation of the present invention. Therefore, the scope of protection of the present invention shall be based on the scope of protection of the claims.

Claims

1. A method for estimating groundwater levels in arid areas to reduce evapotranspiration calculation errors, characterized in that: include: Obtain remote sensing evapotranspiration, remote sensing evapotranspiration models and land surface process models for the study area; The land surface process model is used to generate forecast evapotranspiration ensembles and simulated evapotranspiration ensembles, including: The perturbation set of the state vector to be disturbed at the initial integration time of remote sensing evapotranspiration is generated as follows: {X t1M ,…,X tnM ,…,X tNM } Where t represents the initial integration time of the remote sensing evapotranspiration day, N is the number of members in the perturbation set of the state vector to be disturbed, and M is the state vector to be disturbed X = [x1, ..., x m ,…,x M ] dimension, x m is the m-th dimension state variable of X, X tnM is the nth member of the perturbation set of the state vector X to be perturbed at time t, 1≤n≤N; Combined with the input data set, each member of the state vector perturbation set is used to integrate the land surface process model forward within the remote sensing evapotranspiration day to generate the state vector forecast set and simulated evapotranspiration set at each integration moment within the remote sensing evapotranspiration day, specifically: X t+1nM =M(X tnM ,F t )→X t+2nM =M(X t+1nM ,F t+1 )→…→X t+TnM =M(X t+T-1nM ,F t+T-1 ) SE tn =M(X tnM ,F t )SE t+1n =M(X t+1nM ,F t+1 )…SE t+Tn =M(X t+TnM ,F t+T ) Where M represents the land surface process model, T represents the number of integration steps of M within the remote sensing evapotranspiration day, and F t , F t+1 ,…,F t+T-1 , F t+T Respectively represent the input data at the integration time t, t+1, ..., t+T-1, t+T, M(X tnM ,F t ) indicates that X tnM 、F t Drive M forward one step, X t+TnM Indicated by X t+T-1nM 、F t+T-1 The prediction result of driving M to integrate one step forward, SE tn =M(X tnM ,F t ),...,SE t+Tn =M(X t+TnM ,F t+T ) indicates that X tnM 、F t ,...,X t+TnM 、F t+T The simulated evapotranspiration SE at time t,…,t+T obtained by driving M respectively tn ,...,SE t+Tn ; Combined with the input data set, each member of the forecast set of the state vector at each integration moment of the remote sensing evapotranspiration day is input into the remote sensing evapotranspiration model to generate the forecast evapotranspiration set at each integration moment of the remote sensing evapotranspiration day, specifically: ON tn =R(X tnM ,F t )ON t+1n =R(X t+1nM ,F t+1 )…ON t+Tn =R(X t+TnM ,F t+T ) In the formula, R represents the remote sensing evapotranspiration model, PE tn =R(X tnM ,F t ), PE t+1n =R(X t+1nM ,F t+1 ),…,PE t+Tn =R(X t+TnM ,F t+T ) means to convert X tnM 、F t , X t+1nM 、F t+1 ,...,X t+TnM 、F t+T Input R to obtain the predicted evapotranspiration PE at time t, t+1, ..., t+T respectively tn , PE t+1n ,...,PE t+Tn ; Based on the errors and co-errors of the forecast evapotranspiration ensemble and the simulated evapotranspiration ensemble, the remote sensing evapotranspiration is expanded into the daily evapotranspiration. The groundwater level in arid areas was estimated by substituting sky evapotranspiration into the model linking surface evapotranspiration and groundwater level.

2. The method according to claim 1, characterized in that The acquisition of remote sensing evapotranspiration, remote sensing evapotranspiration model and land surface process model of the study area includes: Obtain the remote sensing evapotranspiration model of the study area and the remote sensing evapotranspiration during the study period; Establish a land surface process model and input dataset for the study area. The input dataset includes: forcing dataset, land use, soil parameter dataset, vegetation parameter dataset, surface albedo, vegetation cover and initial field data within the study period of the study area. Preprocessing remote sensing evapotranspiration products and input datasets, including projection conversion, resampling and spatial clipping; The state variables of the land surface process model corresponding to the input of the remote sensing evapotranspiration model are determined as the state vector to be disturbed.

3. The method according to claim 1, characterized in that The expansion of remote sensing evapotranspiration into daily evapotranspiration based on the errors and co-errors of the forecast evapotranspiration ensemble and the simulated evapotranspiration ensemble includes: The forecast evapotranspiration ensemble and simulated evapotranspiration ensemble at each integration moment are spatially downscaled to the spatial resolution of remote sensing evapotranspiration; The error of the simulated evapotranspiration is calculated based on the simulated evapotranspiration ensemble at each integration moment, specifically: Where, represent the simulated evapotranspiration errors at t, …, t+i, …, t+T respectively; The error of the forecast evapotranspiration is calculated based on the ensemble of forecast evapotranspiration at each integration moment, specifically: Where, represent the forecast evapotranspiration errors at t, …, t+i, …, t+T respectively; The co-error of the simulated evapotranspiration is calculated based on the simulated evapotranspiration ensemble at each integration moment and the simulated evapotranspiration ensemble covering the remote sensing evapotranspiration time integration moment, specifically: Where τ is the integration time covering the remote sensing evapotranspiration time, δ SEt ,…,δ SEt+i ,…,δ SEt+T denote the co-errors of the simulated evapotranspiration ensemble at time t,…,t+i,…,t+T and the simulated evapotranspiration ensemble at time τ, respectively; The co-error of the forecast evapotranspiration is calculated based on the ensemble of forecast evapotranspiration at each integration moment and the ensemble of forecast evapotranspiration at time τ, specifically: Where, δ PEt ,…,δ PEt+i ,…,δ PEt+T denote the co-errors of the ensemble of forecast evapotranspiration at time t,…,t+i,…,t+T and the ensemble of forecast evapotranspiration at time τ, respectively; The temporal variation of surface evapotranspiration at each integration moment relative to time τ is calculated based on the simulated evapotranspiration ensemble and its error, the forecast evapotranspiration ensemble and its error, the simulated evapotranspiration co-error at each integration moment, and the forecast evapotranspiration co-error at each integration moment. Specifically, Where i represents the subscript of the integration time, 0≤i≤T; The spatial variation of surface evapotranspiration at each integration moment relative to remote sensing evapotranspiration at time τ is calculated based on the simulated evapotranspiration ensemble, the forecast evapotranspiration ensemble, and RE, including: Based on the simulated evapotranspiration ensemble, the forecast evapotranspiration ensemble and RE, the regression equations between the mean simulated evapotranspiration, the mean forecast evapotranspiration and RE at each integration time are calculated as follows: Where, κ SEt ,…,κ SEt+i …,κ SEt+T and b SEt ,…,b SEt+i …, b SEt+T , κ PEt ,…,κ PEt+i …,κ PEt+T and b PEt ,…,b PEt+i …, b PEt+T are the regression coefficients and regression constants between the simulated evapotranspiration mean and RE, and the predicted evapotranspiration mean and RE at t, …, t+i, …, t+T, respectively; Calculate the remote sensing simulated evapotranspiration ensemble and remote sensing forecast evapotranspiration ensemble at each integration moment as follows: RFE / RL tn =κ SEt SE tn +b SEt ,…,RFE t+in =κ SEt+i SE t+in +b SEt+i ,…,RFE t+Tn =κ SEt+T SE t+Tn +b SEt+T RPE tn =k PEt PE tn +b PEt ,…,RPE t+in =k PEt+i PE t+in +b PEt+i ,…,RPE t+Tn =k PEt+T PE t+Tn +b PEt+T Where, RSE tn ,…,RSE t+in …, RSE t+Tn and RPE tn ,…,RPE t+in ..., RPE t+Tn are the nth members of the remote sensing simulated evapotranspiration ensemble and the remote sensing predicted evapotranspiration ensemble at time t,…,t+i,…,t+T, respectively; Calculate the error of the remote sensing simulated evapotranspiration ensemble and the error of the remote sensing predicted evapotranspiration ensemble at each integration moment as follows: Where, and are the errors of the remote sensing simulated evapotranspiration ensemble and the errors of the remote sensing predicted evapotranspiration ensemble at t,…,t+i,…,t+T respectively; Calculate the co-errors between the remote sensing simulated evapotranspiration ensemble, the remote sensing predicted evapotranspiration ensemble and the remote sensing simulated evapotranspiration ensemble, the remote sensing predicted evapotranspiration ensemble at each integration moment, specifically: Where, δ RSEt ,…,δ RSEt+i ,…,δ RSEt+T denote the co-errors between the remote sensing simulated evapotranspiration ensemble at time t,…,t+i,…,t+T and the remote sensing simulated evapotranspiration ensemble at time τ; δ RPEt ,…,δ RPEt+i ,…,δ RPEt+T denote the co-errors between the remote sensing evapotranspiration ensemble at time t,…,t+i,…,t+T and the remote sensing evapotranspiration ensemble at time τ, respectively; The spatial variation of surface evapotranspiration at each integration moment relative to time τ is calculated based on the remote sensing simulated evapotranspiration ensemble and its error, the remote sensing predicted evapotranspiration ensemble and its error, the remote sensing simulated evapotranspiration co-error and the remote sensing predicted evapotranspiration co-error at each integration moment. Specifically, According to γ t+i→t+τn , χ t+i→t+τn Calculate the spatiotemporal variation of surface evapotranspiration at each integration moment, specifically: Where, α t+i is the spatiotemporal variation of surface evapotranspiration at the integration time t+i; According to the spatiotemporal variation of surface evapotranspiration at each integration moment, remote sensing evapotranspiration is expanded into daytime evapotranspiration, specifically: Where DE is the expanded evapotranspiration.

4. The method according to claim 1, wherein Substituting evapotranspiration into the model of the connection between surface evapotranspiration and groundwater level to estimate the groundwater level in arid areas is as follows: H=G -1 (DE) Where G is the link model between surface evapotranspiration and groundwater level. The dimension of DE should be consistent with the dimension of surface evapotranspiration obtained by inputting the dryland groundwater level H (dimension of meters) into model G.

5. A system for implementing the method for estimating groundwater level in arid areas by reducing evapotranspiration calculation error according to any one of claims 1 to 4, characterized in that: include: Preparation module, used to obtain remote sensing evapotranspiration, remote sensing evapotranspiration model and land surface process model of the study area; Ensemble module, used to generate forecast evapotranspiration ensemble and simulated evapotranspiration ensemble using land surface process model; Extension module, used to expand remote sensing evapotranspiration into daily evapotranspiration based on the errors and co-errors of the forecast evapotranspiration ensemble and the simulated evapotranspiration ensemble; The estimation module is used to substitute sky evapotranspiration into the connection model between surface evapotranspiration and groundwater level to estimate the groundwater level in arid areas.

6. The system according to claim 5, characterized in that The preparation module includes a remote sensing preparation unit, a land surface process model preparation unit, a preprocessing unit and a disturbance state vector preparation unit; The remote sensing preparation unit is used to obtain the remote sensing evapotranspiration model of the study area and the remote sensing evapotranspiration within the study period; The land surface process model preparation unit is used to establish a land surface process model and input data sets for the study area. The input data sets include: a forcing data set, land use, a soil parameter data set, a vegetation parameter data set, surface albedo, vegetation coverage, and an initial field within the study period of the study area; The preprocessing unit is used to preprocess the remote sensing evapotranspiration product and the input data set, and the preprocessing includes projection conversion, resampling and spatial clipping; The disturbance state vector preparation unit is used to determine the state variables of the land surface process model corresponding to the remote sensing evapotranspiration model input as the state vector to be disturbed.

7. The system according to claim 5, characterized in that The ensemble module includes a disturbance ensemble unit and an ensemble simulation unit; The disturbance set unit is used to generate a disturbance set of the state vector to be disturbed at the initial integration moment of the remote sensing evapotranspiration day; The ensemble simulation unit is used to combine the input data set, use each member of the perturbation set of the state vector, forward integrate the land surface process model within the remote sensing evaporation day, and generate the state vector forecast set and simulated evaporation set at each integration moment within the remote sensing evaporation day.

8. The system according to claim 5, wherein: The expansion module includes a simulated evapotranspiration and forecasted evapotranspiration spatial downscaling unit, a simulated evapotranspiration and forecasted evapotranspiration error unit, a simulated evapotranspiration and forecasted evapotranspiration co-error unit, a surface evapotranspiration temporal variation unit, a regression equation unit, a remotely sensed simulated evapotranspiration and remotely sensed forecasted evapotranspiration unit, a remotely sensed simulated evapotranspiration and remotely sensed forecasted evapotranspiration error unit, a remotely sensed simulated evapotranspiration and remotely sensed forecasted evapotranspiration co-error unit, a surface evapotranspiration spatial variation unit, a surface evapotranspiration spatiotemporal variation unit, and a remotely sensed evapotranspiration day-scale unit; The simulated evapotranspiration and forecast evapotranspiration spatial downscaling unit is used to spatially downscale the forecast evapotranspiration ensemble and the simulated evapotranspiration ensemble at each integration moment to the spatial resolution of remote sensing evapotranspiration; The simulated evapotranspiration and forecast evapotranspiration error unit is used to calculate the error of the simulated evapotranspiration based on the simulated evapotranspiration ensemble at each integration moment, and calculate the error of the forecast evapotranspiration based on the forecast evapotranspiration ensemble at each integration moment; The simulated evapotranspiration and forecast evapotranspiration co-error unit is used to calculate the co-error of the simulated evapotranspiration based on the simulated evapotranspiration ensemble at each integration moment and the simulated evapotranspiration ensemble covering the remote sensing evapotranspiration time integration moment, and calculate the co-error of the forecast evapotranspiration based on the forecast evapotranspiration ensemble at each integration moment and the forecast evapotranspiration ensemble covering the remote sensing evapotranspiration time integration moment; The surface evapotranspiration temporal variation unit is used to calculate the temporal variation of surface evapotranspiration at each integration moment relative to the remote sensing evapotranspiration time based on the simulated evapotranspiration ensemble and its error, the predicted evapotranspiration ensemble and its error, the simulated evapotranspiration co-error at each integration moment, and the predicted evapotranspiration co-error at each integration moment; The regression equation unit is used to calculate the regression equation between the simulated evapotranspiration mean, the predicted evapotranspiration mean and the remote sensing evapotranspiration at each integration moment based on the simulated evapotranspiration ensemble, the predicted evapotranspiration ensemble and the remote sensing evapotranspiration; The remote sensing simulated evapotranspiration and remote sensing predicted evapotranspiration units are used to calculate the remote sensing simulated evapotranspiration ensemble and the remote sensing predicted evapotranspiration ensemble at each integration moment; The remote sensing simulated evapotranspiration and remote sensing predicted evapotranspiration error units are used to calculate the error of the remote sensing simulated evapotranspiration ensemble and the error of the remote sensing predicted evapotranspiration ensemble at each integration moment; The remote sensing simulated evapotranspiration and remote sensing predicted evapotranspiration co-error unit is used to calculate the co-error between the remote sensing simulated evapotranspiration ensemble and the remote sensing predicted evapotranspiration ensemble at each integration moment and the remote sensing simulated evapotranspiration ensemble and the remote sensing predicted evapotranspiration ensemble covering the remote sensing evapotranspiration time integration moment; The surface evapotranspiration spatial variation unit is used to calculate the spatial variation of surface evapotranspiration at each integration moment relative to the remote sensing evapotranspiration time based on the remote sensing simulated evapotranspiration ensemble and its error at each integration moment, the remote sensing predicted evapotranspiration ensemble and its error at each integration moment, the remote sensing simulated evapotranspiration co-error at each integration moment, and the remote sensing predicted evapotranspiration co-error at each integration moment; The surface evapotranspiration temporal and spatial variation unit is used to calculate the temporal and spatial variation of surface evapotranspiration based on the temporal variation of surface evapotranspiration and the spatial variation of surface evapotranspiration at each integration moment; The remote sensing evapotranspiration day-scale unit is used to expand the remote sensing evapotranspiration into day-scale evapotranspiration according to the spatiotemporal variation of surface evapotranspiration at each integration moment.

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