A new method for constructing scale factors for leakage error correction of terrestrial water storage

Abstract

The present invention discloses a new method for constructing scale factors for correcting leakage errors of terrestrial water reserves, which relates to the technical field of terrestrial water reserve inversion and solves the problem that the traditional scale factor method for correcting leakage errors is heavily dependent on the single hydrological model used, and different hydrological models have regional differences and large uncertainties, so the use of the traditional scale factor method will lead to large systematic deviations in the estimation of leakage errors. The key points of the technical solution are: 1. A new method for constructing scale factors for correcting leakage errors of terrestrial water reserves, characterized in that it specifically includes the following steps: a) calculating GRACE terrestrial water reserve anomaly data by GRACE earth gravity field spherical harmonic coefficients; the effect is that the present invention proposes a new method for weighting scale factors of different hydrological models in theory, which is more perfect, so as to reduce the influence of hydrological model uncertainty on the leakage error correction effect, thereby improving the accuracy of correcting leakage errors.

Description

Technical Field

[0001] The invention relates to the technical field of terrestrial water storage inversion, and in particular to a new method for constructing a scale factor for terrestrial water storage leakage error correction. Background Art

[0002] The GRACE gravity satellite has certain limitations in detecting the time-varying changes in the Earth's gravity field caused by surface mass migration. This is because when the surface mass changes are calculated directly using the spherical harmonic coefficients without filtering, they will be affected by the north-south strip error and other errors, which seriously interfere with the identification of effective physical signals. Therefore, it is necessary to use a suitable filtering method to weaken the influence of these errors, but no matter what filtering method is used, it will cause signal attenuation and distortion. This phenomenon of signal attenuation and distortion is called leakage, and the error caused by it is called "leakage error". Leakage error is one of the main factors affecting the accuracy of time-varying changes in the Earth's gravity field at regional and basin scales. Therefore, leakage error will also affect the accuracy of terrestrial water storage anomalies inverted from time-varying Earth gravity field data. Therefore, correcting leakage error is crucial to improving the accuracy of inverting terrestrial water storage anomalies.

[0003] At present, the scale factor method is widely used in the correction of leakage error. The scale factor method requires a spatially distributed TWSA. model As prior information, the scale factor of each grid is then estimated by least squares fitting between the unfiltered and filtered prior information. Therefore, the advantage of the scale factor method is that the leakage signal can be restored to a specific grid unit. However, since the traditional scale factor method for correcting leakage errors is heavily dependent on the single hydrological model used, and different hydrological models have regional differences and large uncertainties, the use of the traditional scale factor method will lead to large systematic deviations in the estimation of leakage errors. Currently, the only weighted method is to assign weights to the scale factors of different hydrological models based on the root mean square error and correlation coefficient to obtain the fused scale factor. However, this method has the following two disadvantages in theory: 1. The data processing is irrational: The scale factor calculation method is to use TWSA before filtering. model Data and filtered TWSA SHCs The consistency between the data is weighted, and the filtering effect on TWSA is not taken into account. model 2. The evaluation index correlation coefficient has the following disadvantages: it cannot evaluate the systematic deviation, cannot measure the relative mean square error, only measures the linear correlation, and the correlation coefficient may give a highly misleading evaluation in extreme cases (such as when there is a systematic deviation but the trend of change remains consistent).

[0004] In summary, it is particularly important to improve the theoretical shortcomings of the scale factor weighting method, propose a new scale factor weighting method that is more theoretically perfect, and improve the accuracy of the scale factor method in correcting leakage errors. Summary of the invention

[0005] The purpose of the present invention is to provide a new method for constructing a scale factor for land water storage leakage error correction to solve the problems raised in the above background technology.

[0006] To achieve the above object, the present invention provides the following technical solutions:

[0007] A new method for constructing scaling factors for leakage error correction of terrestrial water storage based on TWSA provided by different single hydrological models model Should be equal to TWSA SHCs Theory, for TWSA model After truncation and filtering, the TWSA obtained is model There is leakage error (signal attenuation). The scale factor of each single hydrological model is obtained by the least squares method, and then the scale factors of different single hydrological models are weighted by the RMS_NSE method to obtain a new scale factor. The new scale factor can restore the signal strength and improve the accuracy of correcting the leakage error. Specifically, it includes the following steps:

[0008] Step a) Calculate the GRACE terrestrial water storage anomaly data TWSA from the GRACE Earth gravity field spherical harmonic coefficients SHCs .TWSA SHCs It will participate in the process of calculating the new scale factor using the RMS_NSE method (step c);

[0009] Step b) reading the terrestrial water storage data of different hydrological models, performing spherical harmonicization and filtering, and calculating a single scale factor based on the terrestrial water storage anomaly data before and after the hydrological model filtering;

[0010] Step c) is based on the GRACE terrestrial water storage anomaly data TWSA in step a). SHCs and the terrestrial water storage anomaly data of different hydrological models after filtering in step b), and calculating the weights of the scale factors of different hydrological models according to the proposed new weighting method: RMS_NSE method;

[0011] Step d) assigns a weight to the single scale factor calculated in step b) according to the weight calculated in step c) to construct a new scale factor.

[0012] As a further technical solution of the present invention: GRACE terrestrial water storage anomaly data is calculated from GRACE spherical harmonic data: ① conventional post-processing operations are performed on the GRACE spherical harmonic data: the first-order terms are replaced by the TN 13GEOC CSR RL06 product, and the C 20Item; ② deduct the average value of GRACE spherical harmonic data from January 2004 to December 2009; ③ use SWENSON filtering to weaken the north-south strip error, and Gaussian filtering to weaken the high-frequency error and remove the GIA effect; ④ then convert the spherical harmonic data into grid data.

[0013] As a further technical solution of the present invention, the scale factor calculation process of different hydrological models is as follows:

[0014] Single scale factor recovery TWSA SHCs The theory is: TWSA based on the hydrological model model Should be equal to TWSA SHCs According to the theory of TWSA model The calculated scale factor should be equal to TWSA SHCs The scale factor of , so the calculated scale factor can restore TWSA SHCs Signal strength;

[0015] ① Obtain TWSA of different hydrological models model data;

[0016] ② TWSA of different hydrological models model Grid data converted into spherical harmonic coefficients SHCs model , as shown in formula (2).

[0017]

[0018] Where a represents the average radius of the Earth, ρ w and ρ earth They represent the density of water and the average density of the earth, l and m represent the order and degree of the earth's gravity field, respectively. l represents the load Love number, θ represents the co-latitude (90° minus the latitude), λ represents the longitude, and Δh represents the land water storage anomaly. ΔC lm , ΔS lm represents the change of spherical harmonic coefficients, represents the normalized Legendre function.

[0019] ③ The SHCs of the hydrological model model The same filtering process as GRACE spherical harmonic data is performed to ensure that the hydrological model TWSA model With TWSA SHCs The leakage error caused by filtering is consistent. The filtering process includes smoothing filtering and decorrelation filtering.

[0020] Decorrelation filtering: Because there is an obvious correlation between the odd and even terms of certain orders of the spherical harmonic coefficients, this correlation will cause obvious north-south stripe errors in the inversion results of the spherical harmonic coefficients. For this reason, a method of using a sliding polynomial to eliminate its correlation is proposed, which is called Swenson decorrelation filtering. Swenson decorrelation filtering usually uses a quadratic polynomial filter window centered at order l and with a width of w. The relationship between the smoothing window width w and the spherical harmonic coefficient order m is:

[0021]

[0022] In the formula, max() represents the larger value of 1 or 5, and e represents a natural constant. Through repeated experiments, the empirical parameters are A=30 and k=10.

[0023] Smoothing filter: There are errors in the high-order terms of the spherical harmonic coefficients, and the errors will increase rapidly with the increase of the order. Therefore, in order to reduce the impact of the high-order errors on the results, the spherical harmonic coefficients need to be smoothed. This study uses Gaussian filtering to smooth the spherical harmonic coefficients. The principle of Gaussian smoothing is to give smaller weights to the high-order spherical harmonic coefficients with large errors to reduce the impact of the errors of the high-order spherical harmonic coefficients on the results. The iterative formula of the weight is as shown in formula (4).

[0024]

[0025] in,

[0026]

[0027] Where r is the Gaussian smoothing radius, a is the average radius of the earth, e is a natural constant, l (l = 0, 1, ..., 59) is the order, and W is l represents the weight of the lth order.

[0028] ④Then the obtained SHCs after filtering model Convert to TWSA model ;

[0029]

[0030] Where a represents the average radius of the Earth, ρ earth represents the average density of the Earth, ρ w represents the density of water, Δη represents the filtered land water storage anomaly, l and m represent the order and degree of the earth's gravity field, respectively, and k l The load Love number, P lm represents the normalized Legendre function, θ represents the colatitude (90° minus latitude), λ represents the longitude, and ΔC lm and ΔS lm Represents the spherical harmonic coefficients after filtering.

[0031] ⑤ Obtain TWSA before and after filtering model The data are used to calculate the scale factor of each single hydrological model based on the least squares principle, as shown in formula (7).

[0032]

[0033] Where Δh is the unfiltered TWSA model , Δη is the TWSA after filtering model , k is the scale factor, and N is the length of the time series. The scale factor method uses the scale factor calculated based on the hydrological model and the TWSA SHCs Multiply to recover TWSA SHCs But when TWSA model With TWSA SHCs When there are differences in some areas, it will affect the scale factor method to restore TWSA SHCs Therefore, in order to reduce the TWSA model With TWSA SHCs The new scale factor method combines the advantages of scale factors of various hydrological models. model With TWSA SHCs The weights are determined by the degree of compliance between them, and a scale factor that is more suitable for the study area is calculated.

[0034] As a further technical solution of the present invention: according to the newly proposed weighting method: RMS_NSE method, weights are assigned (weighted) to the scale factors of different hydrological models. The following is a theoretically more perfect new weighting method proposed by the present invention.

[0035] RMS_NSE method: Related literature usually uses the root mean square error (RMS) to reduce the value To evaluate TWSA SHCs TWSA after filtering with different hydrological models model Whether it complies. The calculation formula is:

[0036]

[0037] In the formula, RMS(GRACE i ) and RMS(GRACE i -model i ) are TWSA SHCs Subtract the filtered TWSA model The root mean square error before and after, represents the reduction in the root mean square error of the i-th month, The closer to 1, the TWSA SHCs With filtered TWSA model The higher the degree of compliance.

[0038] Nash-Sutcliffe Eficiency Coefficient (NSE) can be used for TWSA SHCs With filtered TWSA model As a standardized statistical method, the coefficient NSE i The range is between -∞ and 1. Ideally, a value of 1 corresponds to TWSA SHCs With filtered TWSA model A perfect match.

[0039]

[0040] GRACE i and model i are the TWSA of the i-th month SHCs With the filtered TWSA model . For TWSA SHCs Monthly average of NSE i represents the Nash efficiency coefficient of the i-th month. The weight formula (10) of each hydrological model calculated by RMS_NSE method is:

[0041]

[0042] Where i represents the i-th month, W i The RMS_NSE method is used to calculate the weights of each hydrological model. is the root mean square error reduction, NSE i is the Nash efficiency coefficient.

[0043] As a further technical solution of the present invention: construct a new scale factor, after obtaining the weight calculated by the RMS_NSE method, use formula (1) to calculate the new scale factor K NEW .

[0044] Compared with the prior art, the present invention has the following beneficial effects:

[0045] The theoretical loopholes of the existing scale factor weighting method have been improved, including data processing and evaluation indicators, and a more theoretically perfect weighting method has been proposed. In terms of data processing: This patent takes into account the effect of filtering on TWSA modelThe influence of the previous new scale factor calculation method is improved, that is, the RMS_NSE method is used to evaluate the TWSA after filtering. model and filtered TWSA SHCs consistency between the TWSA and the model and filtered TWSA SHCs In terms of evaluation indicators: the previous method assigns weights to the scale factors of different hydrological models according to the root mean square error and the correlation coefficient to obtain the fused scale factor. However, considering that weighting based on the correlation coefficient has the following disadvantages: it is impossible to evaluate the systematic deviation between the two series, nor can it measure the relative mean square error between the two series. It only measures the linear correlation between the two, which may give a highly misleading evaluation in extreme cases (such as when there is a systematic deviation but the trend changes remain consistent). In contrast, we need to provide a more comprehensive and rigorous model evaluation standard, especially when focusing on the fit of the two series, including deviations, change trends and other aspects, which is more advantageous than the correlation coefficient. Compared with the correlation coefficient, NSE is more sensitive to the differences between the two series, especially in the presence of large errors or systematic deviations. It provides a more accurate assessment of the overall fit between the series. Therefore, this patent combines the RMS and NSE indices to improve the accuracy of the weight of each hydrological model. A new method for weighting the scale factors of different hydrological models that is more theoretically perfect is proposed to reduce the impact of hydrological model uncertainty on the leakage error correction effect, thereby improving the accuracy of leakage error correction. BRIEF DESCRIPTION OF THE DRAWINGS

[0046] Figure 1 For the technology roadmap.

[0047] Figure 2 Spatial distribution of CSR MASCON and CSR SHCs trends.

[0048] Figure 3 is the TWSA recovered for each scale factor SHCs Trend spatial distribution. DETAILED DESCRIPTION

[0049] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0050] The present invention mainly includes:

[0051] ① A theoretically more perfect method for weighting the scale factors of various hydrological models is invented: the RMS_NSE method. The new scale factors obtained based on this weighting method can improve the accuracy of leakage error correction.

[0052] ② Improvements have been made in data processing: the only existing new scale factor weighting method is to use the pre-filter TWSA model and filtered TWSA SHCs However, this is not reasonable in theory, and the consistency between the two should be weighted after the same processing (i.e., after filtering). Therefore, the present invention is based on this and uses the filtered TWSA model and filtered TWSA SHCs The consistency between them is weighted.

[0053] The purpose of this project is to construct a new method for the scale factor correction of terrestrial water storage leakage errors, so as to improve the accuracy of the scale factor method in correcting leakage errors. It solves the problem that the traditional scale factor method is too dependent on a single hydrological model in the process of correcting leakage errors, and the insufficiency of the only existing weighting method. In order to solve the theoretical problems of the existing scale factor weighting method, a new scale factor weighting method that is more theoretically perfect is proposed to improve the correction effect of the new scale factor on leakage errors.

[0054] See also Figure 1 The present invention discloses a new method for constructing a scale factor for correcting leakage errors of terrestrial water reserves.

[0055] First, the GRACE terrestrial water storage anomaly data are calculated from the GRACE spherical harmonic data;

[0056] Then, taking the Yellow River Basin as an example, the single scaling factors of six hydrological models (WGHM hydrological model and GLDAS series hydrological models: NOAH, VIC, MOS, CLM and CLSM) in the study area are calculated;

[0057] Secondly, the weights of each single scale factor in the study area were calculated using the RMS_NSE method;

[0058] Finally, the calculated weights and the single scale factors of each hydrological model are used to weight the new scale factor of the present invention, and the filtered TWSA of the study area is corrected using the new scale factor. SHCs In order to analyze the effect of the new scale factor on TWSA SHCs The recovery accuracy of GRACE MASCON and SHCs direct inversion is calculated. Figure 2 ), TWSA restored by each scale factor method SHCs ( Figure 3) in the Yellow River Basin (Table 1). Using MASCON data as a reference for terrestrial water storage anomalies, the effects of different scale factors in correcting leakage errors are further compared and analyzed.

[0059] By comparison, it is found that a) and the trend signal before the leakage error is corrected ( Figure 2 b) Comparison, based on NOAH ( Figure 3 a) MOS( Figure 3 c) CLM Figure 3 e) and CLSM( Figure 3 f) The trend spatial distribution of the single scale factor restored by the hydrological model shows the situation of incorrect restoration in the purple line area; b) The trend spatial distribution of the single scale factor restored by the MOS hydrological model shows the situation of excessive restoration in the blue line area, which is caused by excessive correction of leakage error in this area; c) The trend spatial distribution of the single scale factor restored by the MOS hydrological model shows the situation of excessive restoration in the blue line area. Figure 3 c) TWSA restored by a single scale factor calculated by the hydrological model SHCs The trend space distribution also has false recovery in the source of the Yellow River. After recovery, the trend did not decrease but increased; d) Based on WGHM ( Figure 3 b) TWSA restored by a single scale factor calculated by the hydrological model SHCs There are two cases of false recovery in the trend space distribution in the purple line marked area. After recovery, the trend items in the two areas increase. Therefore, when using a single scale factor to restore TWSA SHCs When the trend space is distributed, false recovery and over-recovery may occur. Figure 2 and Figure 3 g comparison shows that the spatial distribution of terrestrial water storage trends restored based on the new scale factor has effectively solved the problems of erroneous recovery and over-recovery that may occur with a single scale factor. Figure 2 b is closer Figure 2 a. By comparison, the downward trend is more obvious in the middle and lower reaches of the Yellow River Basin, and TWSA is effectively weakened at the source of the Yellow River. SHCs A signal that the uptrend is too strong.

[0060] As shown in Table 1, positive values ​​indicate incorrect recovery of the trend item, and compared with the SHCs before recovery, positive values ​​indicate a larger difference with MASCON. Therefore, it can be seen that the time series trend item restored by the scale factor calculated by RMS_NSE is the best, better than all single scale factors. The trend item of the SHCs before recovery is closer to that of MASCON, with an improvement of 0.054 cm / a.

[0061] Table 1 Trends in time series and their improvements compared to SHCs solutions

[0062]

[0063] Therefore, the TWSA is recovered relative to a single scale factor SHCs ,The new scale factor can effectively avoid the situations of false recovery and over-recovery in space, and improve the accuracy of estimating trend items in time series.

[0064] The following is an explanation of some of the industry's terminology:

[0065] 1. Gravity Recovery and Climate Experiment (GRACE): The GRACE gravity satellite provides a direct means of observation for continuous monitoring of the time-varying Earth gravity field, which can be used to invert the mass migration and redistribution of Earth's terrestrial water storage, polar ice sheets, mountain glaciers, and seawater mass.

[0066] 2. Spherical harmonic coefficients: They are the expression of the Earth's gravity field. Spherical harmonic coefficients can be used to calculate the change of the geoid, which is regarded as a gravity equipotential surface. Therefore, spherical harmonic coefficients can be used to express the change of the time-varying Earth's gravity field. The GRACE satellite expresses the time-varying gravity field model sequence with discrete gravitational potential spherical harmonic coefficients.

[0067] 3. Terrestrial water reserves: refers to the sum of all water changes on the surface and underground, including global groundwater, glaciers, biological water, soil water, etc.

[0068] 4. North-South striping error: Due to the measurement error of the GRACE gravity satellite's payload instrument, mixing error and satellite orbit, the GRACE spherical harmonic coefficient inversion results have obvious north-south striping in space, that is, the north-south striping error, and the existence of this error will hinder the interpretation of the signal.

[0069] 5. Filtering: Because the existence of north-south strip errors and high-frequency errors will hinder the interpretation of signals, it is necessary to reduce the impact of these errors by selectively passing or suppressing signals of specific frequencies during the processing process. Generally, it can be divided into two categories: smoothing filtering and decorrelation filtering.

[0070] 6. Smoothing filtering is a method of reducing noise by reducing the weight of the high-order spherical harmonic coefficients with large errors. The most commonly used filtering methods are Gaussian filtering, as well as Han filtering and fan filtering improved from Gaussian filtering.

[0071] 7. Decorrelation filtering. The basic principle of this type of filtering is to perform function fitting on the odd and even spherical harmonic coefficients respectively, deduct the fitting terms from the original spherical harmonic coefficients, and obtain the decorrelated spherical harmonic coefficients to achieve the purpose of removing banding errors.

[0072] 8. Leakage error: The signal attenuation and distortion caused by the filtering method is called leakage, and the error caused by leakage is called "leakage error", including leakage from within the area to outside the area "leakage out" and leakage from outside the area to within the area "leakage in".

[0073] 9. Hydrological model: The hydrological model integrates human understanding of the hydrological cycle process, expresses the temporal and spatial movement laws of the hydrological process through mathematical formulas, and can provide simulated terrestrial water storage anomaly data.

[0074] 10. Terrestrial water storage anomaly: The terrestrial water storage data during the study period were deducted from the average terrestrial water storage from 2004 to 2009. The terrestrial water storage anomaly can be calculated using the GRACE spherical harmonic coefficients.

[0075] 11. Least squares: In the absence of a definite solution, a method of finding an approximate solution is used to make the model achieve the "best" fit at each observation point. The "best" considered by the least squares fitting is the minimum sum of the errors (true value - theoretical value).

[0076] 12. Scale factor method: A method for correcting leakage errors. The scale factor method uses the spatially distributed terrestrial water storage anomaly of the hydrological model as prior information, and then estimates the scale factor of each grid by least squares fitting between the unfiltered and filtered prior information. The traditional scale factor method uses a hydrological model to calculate the scale factor to correct leakage errors. The new scale factor method corrects leakage errors by weighting the scale factors calculated by different hydrological models to obtain a new scale factor.

[0077] 13. Weighting / weighting: Weights are different in the adjustment calculation according to the different precision of the measured values. The higher the precision, the greater the weight. "Weighting / weighting" means "multiply by weight", that is, "multiply by coefficient".

[0078] It will be apparent to those skilled in the art that the invention is not limited to the details of the exemplary embodiments described above and that the invention can be implemented in other specific forms without departing from the spirit or essential features of the invention. Therefore, the embodiments should be considered exemplary and non-limiting in all respects, and the scope of the invention is defined by the appended claims rather than the foregoing description, and it is intended that all variations falling within the meaning and scope of the equivalent elements of the claims be included in the invention. Any reference numeral in a claim should not be considered as limiting the claim to which it relates.

[0079] In addition, it should be understood that although the present specification is described according to implementation modes, not every implementation mode contains only one independent technical solution. This description of the specification is only for the sake of clarity. Those skilled in the art should regard the specification as a whole. The technical solutions in each embodiment may also be appropriately combined to form other implementation modes that can be understood by those skilled in the art.

Claims

1. A new method for constructing scale factors for terrestrial water storage leakage error correction, characterized in that: The specific steps include: a) Calculate GRACE terrestrial water storage anomaly data from GRACE Earth gravity field spherical harmonic coefficients (SHCs); b) Read the terrestrial water storage data of different hydrological models, perform spherical harmonicization and filtering, and calculate a single scale factor based on the terrestrial water storage anomaly data before and after the hydrological model filtering; c) Based on the GRACE terrestrial water storage anomaly data in a) and b) and the filtered terrestrial water storage anomaly data of different hydrological models, the weights of the single scale factors of different hydrological models are calculated according to the proposed new weighting method: RMS_NSE method; d) assigning weights to the single scale factor calculated in b) according to the weights calculated in c) to construct a new scale factor; The RMS_NSE method: Use the root mean square error to reduce the value Evaluating TWSA SHCs TWSA after filtering with different hydrological models model Whether it complies with The calculation formula is: （8） In the formula, and TWSA SHCs Subtract the filtered TWSA model The root mean square error before and after, Indicates The RMS error reduction for the month is The closer to 1, the TWSA SHCs With filtered TWSA model The higher the degree of compliance; Nash efficiency coefficient NSE for TWSA SHCs With filtered TWSA model The evaluation of the degree of compliance between The range of variation is to 1, a value of 1 corresponds to TWSA SHCs With filtered TWSA model A perfect match for: （9） and Respectively Months of TWSA SHCs With the filtered TWSA model , For TWSA SHCs The monthly average Indicates The Nash efficiency coefficient of the month, the weight formula (10) of each hydrological model calculated by RMS_NSE method is: （10） In the formula, Indicates Month, The RMS_NSE method is used to calculate the weights of each hydrological model. is the root mean square error reduction value, is the Nash efficiency coefficient.

2. A new method for constructing scale factors for terrestrial water storage leakage error correction according to claim 1, characterized in that: Step a includes the following sub-steps: a1. Post-processing of GRACE spherical harmonic data: using TN 13 GEOC CSR RL06 product to replace the first-order term, SLR RL06 product to replace C 20 item; a2. Deduct the average value of GRACE spherical harmonic data from January 2004 to December 2009; a3. Use SWENSON filtering to weaken the north-south strip error, and Gaussian filtering to reduce high-frequency errors; a4. Remove the GIA effect; a5. Convert GRACE spherical harmonic data into terrestrial water storage grid data, and obtain terrestrial water storage anomaly grid data calculated by spherical harmonic data (SHCs Terrestrial Water Storage Anomaly, TWSA SHCs ).

3. A new method for constructing scale factors for terrestrial water storage leakage error correction according to claim 1, characterized in that: Step b includes the following sub-steps: b1. Read the terrestrial water storage data simulated by different hydrological models, deduct the average value of the terrestrial water storage data from January 2004 to December 2009 to obtain the hydrological model terrestrial water storage anomaly grid data (Model Terrestrial Water Storage Anomaly, TWSA model ); b2. TWSA of different hydrological models model Converted to spherical harmonic coefficients of hydrological model (Model SphericalHarmonic Coefficients, SHCs model ); b3. SHCs for different hydrological models model Perform smoothing filtering and decorrelation filtering processing; b4. SHCs of different hydrological models after filtering model Convert grid data to obtain filtered TWSA model ; b5. Based on the least squares principle, TWSA before and after filtering is used model Data, calculate the scaling factors of a single hydrological model.

4. A new method for constructing scale factors for terrestrial water storage leakage error correction according to claim 1, characterized in that: Step c includes the following sub-steps: c1. In data processing, using TWSA SHCs TWSA after data and different hydrological models filtering model Subtract to get the difference; c2. Based on the difference calculated in c1, the weights of the scale factors of different hydrological models are calculated according to the proposed new weighting method: RMS_NSE method.

5. A new method for constructing scale factors for terrestrial water storage leakage error correction according to claim 1, characterized in that: Step d: Calculate the new scale factor based on the scale factors of different hydrological models obtained in step b5 and the weights calculated in step c2, and obtain it according to formula (1): In the above formula (1), represents the weight, Indicates The scale factor of the hydrological model, represents the number of hydrological models, ( =1,…, ) indicates the A hydrological model, Represents the new scale factor constructed.

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