CN111241473A - Method for improving regional underground water reserve estimation precision - Google Patents

Method for improving regional underground water reserve estimation precision Download PDF

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CN111241473A
CN111241473A CN201911378489.5A CN201911378489A CN111241473A CN 111241473 A CN111241473 A CN 111241473A CN 201911378489 A CN201911378489 A CN 201911378489A CN 111241473 A CN111241473 A CN 111241473A
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郑伟
尹文杰
李钊伟
吴凡
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China Academy of Space Technology CAST
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Abstract

The invention discloses a method for improving regional underground water reserve estimation precision, which comprises the following steps: obtaining monthly-scale land water reserve change delta TWS0(ii) a Method for extracting soil water content change delta SM of monthly scale in global scope by utilizing GLDAS hydrological model1Snow water equivalent change Δ SWE1And vegetation canopy water reserve change delta PCSW1(ii) a Extraction of soil water content change delta SM of monthly scale in global scope by using WGHM hydrological model2Snow water equivalent change Δ SWE2And the water reserve change of the vegetation canopy delta PCSW2(ii) a Calculating to obtain the underground water reserve change delta GWS in the month scale1And Δ GWS2(ii) a Change in groundwater reserves Δ GWS according to measured monthly scale of research area0For Δ GWS, respectively1And Δ GWS2Carrying out evaluation; selecting the underground water reserve change delta GWS with the optimal monthly scale according to the evaluation resultSuperior foodAnd outputting the result of the change of the underground water reserves of the pixel of the research area in the monthly scale. The invention combines GRACE satellite gravity data and global water based on a statistical selection methodThe text model improves the estimation precision of the underground water reserves in the region.

Description

Method for improving regional underground water reserve estimation precision
Technical Field
The invention belongs to the technical field of satellite gravimetry and hydrology intersection, and particularly relates to a method for improving regional underground water reserve estimation accuracy.
Background
Ground Water (GWS) is the largest fresh water resource in the global hydrologic cycle, providing about 50% of the drinking water worldwide. In recent years, extreme climate, population growth, and excessive exploitation of underground water resources have resulted in severe consumption of underground water resources. Therefore, mastering groundwater dynamics is critical to water resource management and human survival.
The traditional underground water change monitoring method mainly depends on an observation well, and although the result can provide high-resolution underground water level estimation, the traditional underground water change monitoring method has a plurality of limitations in practical application. Firstly, the construction and maintenance of an observation well are time-consuming and labor-consuming; secondly, observing the water well to be unevenly distributed; finally, single-point observations are difficult to represent for large area results. The gravity inversion and climate experimentation satellite (GRACE) program was implemented by the United states space and navigation agency (NASA) and the German space flight center (DLR) in combination, and was successfully launched in 3 months of 2002, and the satellite was able to acquire land water reserves at all depths in the area. GRACE satellites are by far the only remote sensing means that can monitor all depth TWS changes under any condition. However, the main disadvantage is that the separate hydrological component cannot be separated from the GRACE data.
In order to separate the groundwater reserves from the land water reserves, the conventional research mainly uses the auxiliary information of individual hydrological models to vertically decompose the GRACE data. The global terrestrial data assimilation system (GLDAS) provides a hydrological flux estimate with 0.25 ° spatial resolution, and has been applied to various hydrological studies. For example, groundwater decline in North China plain, groundwater replenishment rate in loess plateau, radial flow assessment in Qinghai-Tibet plateau, etc.
Currently, many hydrological models and land surface models are developed to describe the respective terrestrial hydrological flux, such as the WaterGAP Global Hydrology Model (WGHM), Community Atmosphere Biosphere LandExchange (CABLE) and World-Wide Water Resources Association (W3 RA). Due to the difference of model structures, parameter settings and driving data, the output results of the hydrological model have partial differences. Typically, these models are developed on a global scale and therefore have both advantages and disadvantages. For example, global hydrological data for the GLDAS model is published, but the amount of groundwater is not simulated; the AWAR model simulates the ground water component, but does not describe the large water consumption that occurs during drought periods. Therefore, the biggest problem with using a single hydrological model is not determining whether the model output fits a particular region.
The Tasmanian island is located in south Australia and has a total area of 68000km2. Although it is less than 1% of the surface area of australia, the region accounts for approximately 12% of the fresh water resources of australia. Groundwater recovery in the state of tasmania is relatively low with a total consumption estimated at 38 GL/yr. However, 90% of the production areas are in the northwest and in the middle of the state, which means that ground water production may cause local problems in these areas. Furthermore, most regions of the west coast of tasmania are protected as world heritage areas, with native vegetation covering 50% of the entire state. Therefore, understanding the dynamic change of GWS is of great significance to the local ecological environment, and it is difficult to reliably estimate groundwater reserves using well observations due to the uneven distribution of groundwater level monitoring wells. Particularly, in 2001-2009, long-term drought, known as thousand-year drought, has occurred in the southeast australia, affecting environmental, agricultural and economic activities. There have been many studies showing that this drought phenomenon affects the water groundwater of tasmania.
Disclosure of Invention
The technical problem of the invention is solved: the method overcomes the defects of the prior art, provides a method for improving the estimation precision of the regional underground water reserves, and improves the estimation precision of the regional underground water reserves by combining GRACE satellite gravity data and a global hydrological model based on a statistical selection method.
In order to solve the technical problem, the invention discloses a method for improving the estimation precision of regional underground water reserves, which comprises the following steps:
obtaining monthly-scale land water reserve change delta TWS0；
Method for extracting soil water content change delta SM of monthly scale in global scope by utilizing GLDAS hydrological model1Snow water equivalent change Δ SWE1And vegetation canopy water reserve change delta PCSW1；
Extracting global range using WGHM hydrological modelSoil water content change delta SM around the moon scale2Snow water equivalent change Δ SWE2And the water reserve change of the vegetation canopy delta PCSW2；
According to Δ TWS0、ΔSM1、ΔSWE1And Δ PCSW1And resolving to obtain the underground water reserve change delta GWS in the month scale1(ii) a According to Δ TWS0、ΔSM2、ΔSWE2And Δ PCSW2Calculating to obtain the underground water reserve change delta GWS in the month scale2；
Change in groundwater reserves Δ GWS according to measured monthly scale of research area0For Δ GWS, respectively1And Δ GWS2Carrying out evaluation;
selecting the underground water reserve change delta GWS with the optimal monthly scale according to the evaluation resultSuperior foodAnd outputting the result of the change of the underground water reserves of the pixel of the research area in the monthly scale.
In the method for improving the estimation precision of the regional underground water reserves, the monthly-scale land water reserve change delta TWS is obtained0The method comprises the following steps:
acquiring m monthly-scale land water reserve change delta TWS (delta TWS) obtained by resolving based on spherical harmonic coefficients from m data sources1、ΔTWS2...ΔTWSm(ii) a Wherein m is more than or equal to 3;
determining Δ TWS1、ΔTWS2...ΔTWSiRegression model Z of each corresponding time series1(t)、Z2(t)、...Zm(t)；
To Z1(t)、Z2(t)、...Zm(t) respectively resolving to obtain values of linear trend terms in the regression models;
from the comparison of the values of the linear trend terms in the respective regression models, from Δ TWS1、ΔTWS2...ΔTWSmThe land water reserve change delta TWS with the optimal month scale is obtained by medium screening0And output.
In the above method for improving the accuracy of regional groundwater reserve estimation, Δ TWS1、ΔTWS2...ΔTWSiOf regression models of respective corresponding time seriesThe general expression is:
Figure BDA0002341651320000031
wherein i ∈ m, βi1Constant term representing the i-th regression model, βi2A linear trend term representing the i-th regression model, βi3Annual sinusoidal signal representing the ith regression model, βi4Annual cosine signal representing the ith regression model, βi5Representing the half-year sinusoidal signal of the ith regression model, βi6Half-year cosine signal, ε, representing the ith regression modeliData error for the ith regression model is indicated.
In the method for improving the estimation accuracy of the regional groundwater reserves, the calculation formula of the change of the groundwater reserves in the monthly scale is as follows:
ΔGWS1＝ΔTWS0-ΔSM1-ΔSWE1-ΔPCSW1
ΔGWS2＝ΔTWS0-ΔSM2-ΔSWE2-ΔPCSW2。
in the method for improving the estimation accuracy of the regional underground water reserves, the underground water reserve change delta GWS according to the measured monthly scale of the research region0For Δ GWS, respectively1And Δ GWS2Performing an evaluation comprising:
determination of Δ GWS0And Δ GWS1Correlation coefficient of (PR)1Root mean square error RMSE1Determining Δ GWS0And Δ GWS2Correlation coefficient of (PR)2Root mean square error RMSE2；
Determination of Δ GWS0、ΔGWS1And Δ GWS2Respective slope Tr0、Tr1And Tr2；
Calculating to obtain delta GWS1Evaluation result Y of1And Δ GWS2Evaluation result Y of2：
Figure BDA0002341651320000041
Figure BDA0002341651320000042
Wherein, F11、F12、F21And F22Respectively represent PR1、RMSE1、PR2And RMSE2The weight coefficient of (b), Consist (·), represents a trend consistency determination function.
In the method for improving the regional groundwater reserve estimation accuracy, determining delta GWS0And Δ GWS1Correlation coefficient of (PR)1Root mean square error RMSE1Determining Δ GWS0And Δ GWS2Correlation coefficient of (PR)2Root mean square error RMSE2The method comprises the following steps:
obtaining Δ GWS0Time series X (t), Δ GWS1Time series Y of1(t) and. DELTA.GWS2Time series Y of2(t)；
The correlation coefficient is calculated as follows:
Figure BDA0002341651320000043
Figure BDA0002341651320000044
the root mean square error is calculated as follows:
Figure BDA0002341651320000045
Figure BDA0002341651320000051
where n represents the length of the time series.
In the above method of improving the accuracy of regional groundwater reserve estimation,
when Tr0Trend of (D) and Tr1If the trends are consistent, Consist (Tr)1,Tr0) 1 is ═ 1; otherwise, Consist (Tr)1,Tr0)＝0；
When Tr0Trend of (D) and Tr2If the trends are consistent, Consist (Tr)2,Tr0) 1 is ═ 1; otherwise, Consist (Tr)2,Tr0)＝0。
In the above method of improving the accuracy of regional groundwater reserve estimation,
Figure BDA0002341651320000052
the invention has the following advantages:
the invention discloses a method for improving regional groundwater reserve estimation precision, which combines GRACE satellite gravity data and a global hydrological model based on a statistical selection method, improves the regional groundwater reserve estimation precision, and provides an effective scheme for selecting a proper hydrological model for hydrological application.
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FIG. 1 is a flow chart illustrating steps of a method for improving accuracy of regional groundwater reserve estimation in an embodiment of the invention;
FIG. 2 is a graph illustrating the average results of the region of land water reserves 2003-2015 calculated by different GRACE products in accordance with an embodiment of the present invention;
FIG. 3 is a schematic diagram of the change of groundwater reserves in Tasmanian 2003-2015 obtained by combining GRACE-GLDAS, GRACE-WGHM and WGHM in the embodiment of the present invention; wherein, 3 a: monthly scale, 3 b: seasonal scale, 3 c: correlation coefficient between GRACE-GLDAS and GRACE-WGHM and RMSE;
FIG. 4 is a schematic illustration of the location and number of the distribution of the Tasmanian groundwater level monitoring wells in an embodiment of the invention;
FIG. 5 is a graphical representation of the comparison of GLDAS-GLDAS, GRACE-WGHM and WGHM results with measured data for a four-mesh grid in accordance with an embodiment of the present invention;
FIG. 6 is a 2003-2015 variation of groundwater reserves spatial distribution map in an embodiment of the present invention; wherein, 6 a: GLDAS-GLDAS, 6 b: GRACE-WGHM; d and D represent descending trend areas, and R and R represent ascending trend areas;
FIG. 7 is a graphical depiction of R2 and d4 areas of GRACE-GLDAS and GRACE-WGHM groundwater reserve change estimation results compared to measured data, and temperature and rainfall, in accordance with an embodiment of the present invention;
FIG. 8 is a graph illustrating the combined results of GRACE-GLDAS and GRACE-WGHM in accordance with an embodiment of the present invention; 8 a: optimal selection result, 8 b: simple average results;
FIG. 9 is a schematic diagram of the underground water reserve change and corresponding rainfall change in 2003-2015 according to an embodiment of the present invention; wherein, 9 a: monthly scale, 9 b: annual scale, 9 c: average seasonal scale; the gray bar represents real rainfall data, and the black bar represents rainfall data adjusted in the 7-month lag period;
FIG. 10 is a schematic diagram of the change of underground water reserves in year 2003-2015 according to an embodiment of the present invention; 10 a: average results over the area, 10b drought index and rainfall integration results.
Detailed Description
In order to make the objects, technical solutions and advantages of the present invention more apparent, the embodiments of the present invention will be described in detail with reference to the accompanying drawings.
In the present invention, a method for improving the estimation accuracy of regional groundwater reserves disclosed in the present embodiment is mainly described in the following aspects.
1. Overview of the region of investigation
The Tasmania is located 240km south of Australia, has latitude and longitude ranges of 40-44 degrees S and 144-148 degrees E, and has an area of 6.45 km2. The area is mainly high mountain and hilly, the center is the highest part in the area, the peak altitude exceeds 1500m, and the middle east area and coastal area are relatively flat.
The Tasmanian climate is temperate marine climate, and has cool and mild effects, and the average annual temperature is up to 15.7 ℃ and the minimum temperature is 4.5 ℃. Due to the influence of topography, the difference of rainfall in east and west is large, the rainfall in most regions in west exceeds 2000mm each year, and the rainfall in high mountain regions reaches 4000 mm; average annual precipitation in eastern regions is less than 750mm, and individual regions are less than 400 mm; eastThe northern high land has high rainfall relative to surrounding areas, partly because of snowfall, and annual rainfall is about 900 mm; the precipitation in southeast areas is uniformly distributed all the year round, and is mostly less than 800 mm. The area has about 150000km of water channels, 8800 wetlands and 94000 water bodies. The area of the river basin on the island is 685-11700 km2。
2. Data source
2.1) Grace land water reserve abnormal data
Land water reserve anomaly data used in embodiments of the present invention are Level3 mesh products and Mascon products, both of which are provided by the space research Center (CSR) of the university of Texas, USA. Level3 mesh products were obtained from Swenson and Wahr (2006) and Landerer and Swenson (2012) with spatial resolution of 1 ° × 1 °. Surface quality variation signals at smaller spatial scales tend to decay due to sampling and post-processing of GRACE observations.
The scale factor method is used to recover signal leakage (called CSR-scaled), and is provided by ftp:// podaac-ftp. jpl. nasa. gov/allData/tellus/L3/land _ mass.
Mascon is another fundamental equation for gravitational field solution with a spatial resolution of 0.5 ° x 0.5 °. CSR Mascon (CSR-M) is constrained by a time-varying regularization matrix and is derived only from the GRACE information, with no other models or data applied for constraint. Thus, the CSR-M solution has no significant banding error and can capture the signal of GRACE within the measured noise level.
2.2) hydrological model surface water reserve data
The Global Land Data Assimilation System (GLDAS) is jointly developed by the United states space administration (NASA), the United states environmental prediction center (NCEP) and the United states National Oceanic Atmospheric Administration (NOAA), meanwhile, a global hydrological mode is established, real-time satellite remote sensing observation data and earth surface observation data are published in a public mode, and 28 items of land meteorological data can be generated by driving four land process models, namely CLM, MOS, VIC and NOAH, through the data. The invention uses NOAH2.1 spatial resolution of 0.25 ° × 0.25 °, and resampling of 0.5 ° × 0.5 °.
The Water GAP Global hydrological Model (WGHM) was developed by The Natural geography Institute (IPG), university of Farfford, Germany, and provides Water resource information at 0.5 degrees by 0.5 degrees worldwide excluding Antarctic and Greenland islands. The WGHM model takes into account not only the groundwater component, but also the impact of human activity on water consumption.
2.3) rainfall data
TRMM 3B43 is a standard monthly rainfall product incorporating precipitation data sets including TMI (TRMM microwave imager), PR (precipitation radar), VIRS (visible and infrared scanners), SSM/I (special sensor microwave imager) and rain gauge data. TRMM 3B43 was obtained from average TRMM 3B42V6 precipitation products and was widely used in climatological applications. It provides an estimate of the total monthly rainfall recorded from 1988 to the present, with a spatial resolution of 0.25 °.
2.4) ground Observation data
Actual measurement data of the underground water monitoring well is from a Global Ground Monitoring Network (GGMN), the GGMN is initiated by a United nations textbook organization and is implemented by an IGRAC (International group resources Association center) organization, the quality and the acquireability of underground water monitoring information are improved, a website (https:// ggmn.un-igrac.org /) provides Global underground water space-time monitoring data, and the data are collected for 1-2 times every day. The invention averages monthly water level information, and does not convert the water level into reserve volume because the water supply level is unknown.
The Australian weather service (BoM, http:// www.bom.gov.au/close/data /) provides precipitation and temperature data based on ground observations. Despite the limited range and inherent error of climate stations, they are still the most direct and accurate measurement tools. Thus, in the following discussion, measurements based on the ground are considered "true precipitation" and "true temperature".
3. Method of producing a composite material
In this embodiment, as shown in fig. 1, the method for improving the estimation accuracy of regional groundwater reserves includes:
step 101, acquiring monthly land water reserve change delta TWS0。
In this embodiment, first, m bases can be obtained from m data sourcesLunar-scale land water reserve change delta TWS obtained by spherical harmonic coefficient calculation1、ΔTWS2...ΔTWSm(ii) a Wherein Δ TWS is determined1、ΔTWS2...ΔTWSiRegression model Z of each corresponding time series1(t)、Z2(t)、...Zm(t); then, for Z1(t)、Z2(t)、...Zm(t) respectively resolving to obtain values of linear trend terms in the regression models; finally, from the results of the comparison of the values of the linear trend terms in the respective regression models, Δ TWS1、ΔTWS2...ΔTWSmThe land water reserve change delta TWS with the optimal month scale is obtained by medium screening0And output.
Preferably,. DELTA.TWS1、ΔTWS2...ΔTWSiThe general expression of the regression model for each corresponding time series may be as follows:
Figure BDA0002341651320000091
wherein m is more than or equal to 3, i belongs to m, βi1Constant term representing the i-th regression model, βi2A linear trend term representing the i-th regression model, βi3Annual sinusoidal signal representing the ith regression model, βi4Annual cosine signal representing the ith regression model, βi5Representing the half-year sinusoidal signal of the ith regression model, βi6Half-year cosine signal, ε, representing the ith regression modeliData error for the ith regression model is indicated.
Step 102, extracting soil water content change delta SM of monthly scale in global scope by utilizing GLDAS hydrological model1Snow water equivalent change Δ SWE1And vegetation canopy water reserve change delta PCSW1。
Step 103, extracting the soil water content change delta SM of the monthly scale in the global scope by utilizing the WGHM hydrological model2Snow water equivalent change Δ SWE2And the water reserve change of the vegetation canopy delta PCSW2。
Step 104, according to Δ TWS0、ΔSM1、ΔSWE1And Δ PCSW1And resolving to obtain the underground water reserve change delta GWS in the month scale1(ii) a According to Δ TWS0、ΔSM2、ΔSWE2And Δ PCSW2Calculating to obtain the underground water reserve change delta GWS in the month scale2。
In this embodiment, the formula for solving the variation of the groundwater reserves on the monthly scale is as follows:
ΔGWS1＝ΔTWS0-ΔSM1-ΔSWE1-ΔPCSW1
ΔGWS2＝ΔTWS0-ΔSM2-ΔSWE2-ΔPCSW2
105, underground water reserve change delta GWS according to the measured monthly scale of the research area0For Δ GWS, respectively1And Δ GWS2Evaluation was performed.
In this embodiment, the specific flow of the evaluation is as follows:
1) determination of Δ GWS0And Δ GWS1Correlation coefficient of (PR)1Root mean square error RMSE1Determining Δ GWS0And Δ GWS2Correlation coefficient of (PR)2Root mean square error RMSE2。
In this embodiment, Δ GWS may be obtained0Time series X (t), Δ GWS1Time series Y of1(t) and. DELTA.GWS2Time series Y of2(t), then:
the correlation coefficient is calculated as follows:
Figure BDA0002341651320000101
Figure BDA0002341651320000102
the root mean square error is calculated as follows:
Figure BDA0002341651320000103
Figure BDA0002341651320000104
where n represents the length of the time series.
2) Determination of Δ GWS0、ΔGWS1And Δ GWS2Respective slope Tr0、Tr1And Tr2。
3) Calculating to obtain delta GWS1Evaluation result Y of1And Δ GWS2Evaluation result Y of2：
Figure BDA0002341651320000105
Figure BDA0002341651320000106
Wherein, F11、F12、F21And F22Respectively represent PR1、RMSE1、PR2And RMSE2The weight coefficient of (2).
Preferably, Consist (-) represents a trend consistency determination function. Wherein when Tr0Trend of (D) and Tr1If the trends are consistent, Consist (Tr)1,Tr0) 1 is ═ 1; otherwise, Consist (Tr)1,Tr0) 0. When Tr0Trend of (D) and Tr2If the trends are consistent, Consist (Tr)2,Tr0) 1 is ═ 1; otherwise, Consist (Tr)2,Tr0)＝0。
106, selecting the underground water reserve change delta GWS with the optimal monthly scale according to the evaluation resultSuperior foodAnd outputting the result of the change of the underground water reserves of the pixel of the research area in the monthly scale.
In the present embodiment, the larger the evaluation value obtained in step 105, the smaller the difference between the output result of the selected hydrological model and the actually measured data. Namely:
Figure BDA0002341651320000107
4. results and analysis
4.1) land Water reserves Change
The average results of the area of land water reserve change in Tasmania 2003-2015 calculated using different GRACE products are shown in FIG. 2. The CSR-scaled results were larger than the CSR-SH and CSR-M results, and the difference in amplitude from these two results was 42.6mm and 21.34mm, respectively. This indicates that in the tasmanian region, the scale factor corrected signal is overestimated, probably due to some error in the TWS in the CLM4.0 model. All TWS sequences of change showed an increasing trend in the years 2003-2015 with slopes ranging from 0.33mm/yr (CSR-SH) to 1.49mm/yr (CSR-scaled). The uncertainty of the CSR-scaled and CSR-Mascon results is shown shaded and has values of 46.26mm and 21.34 mm.
The land water reserves of CSR-SH are abnormal, so that the uncertainty is large, and the coarse spatial resolution is one of the main defects of the data; in addition, the CSR-M can clearly define land and sea areas, can effectively reduce the influence of leakage errors, and can inhibit noise in the processing process, and almost has no empirical post-processing requirement. Therefore, the CSR-M results are selected in the following discussion to describe the land water reserve abnormality feature.
4.2) analysis of Long-term changes in groundwater reserves
FIG. 3 shows the total change of underground water reserves in Tasmania area 2003-2015 obtained from GRACE-GLDAS, GRACE-WGHM and WGHM. The general trend of the graph-GLDAS and graph-WGHM results are substantially the same with a correlation coefficient of 0.82, both results showing significant periodicity (fig. 3 a). The annual amplitude of the GRACE-GLDAS and GRACE-WGHM is 40.75mm and 65.41mm, respectively, and the annual phase is 76.37 ° and 69.91 °, respectively. However, in terms of seasonal characteristics, the WGHM is opposite to the other two results, which are valley bottoms when the WGHM results reach a peak. Furthermore, the amplitude of the WGHM results cannot be determined in the region of about 30% of tasmania, which is quite inaccurate for groundwater estimation.
The underground water reserve change shows strong periodicity, surplus occurs in 1-5 months, and loss occurs in 6-11 months. In the colder seasons, the difference between GRACE-GLDAS and GRACE-WGHM is larger, with the difference being greatest at 9 months and reaching 42.48mm (FIG. 3 b). Generally speaking, the GWS change of the GRACE-GLDAS is consistent with the grid of the GRACE-WGHM except G23 and G24, and the correlation coefficient is 0.59-0.85. The RMSE between GRACE-GLDAS and GRACE-WGHM is about 55mm, with maximum and minimum values appearing in G21 and G40, 61.61mm and 27.61mm, respectively (FIG. 3 c).
4.3) groundwater reservoir change validation based on GRACE satellite gravity and hydrological models
The state of tasmania was spatially divided into 48 grids by latitude and longitude 0.5 x 0.5 as shown in fig. 4, with the dots representing the monitoring wells and not completely covering the entire area of study. Comparing the results of estimating groundwater reservoir changes with GRACE-GLDAS, GRACE-WGHM and WGHM with the measured data as shown in fig. 5, only 4 grids were selected in the present invention. The seasonality and periodicity of both GRACE-GLDAS and GRACE-WGHM in each grid are relatively close to the measured data. Furthermore, the amplitude of the Grace-WGHM results was significantly greater than the Grace-GLDAS at the G8 grid, whereas the Grace-GLDAS had a larger variation in the G32 grid. The measured data has a large variation in vibration amplitude, for example, from-5 to 5m in G14 (FIG. 5b) and from-0.3 to 0.3m in G34 (FIG. 5 d). The WGHM results exhibited an opposite seasonal character with almost no trend of change in G35. This suggests that the WGHM model requires major improvements in groundwater reserves estimation in the Tasmanian area.
In eastern areas of tasmania, the GRACE-GLDAS results were highly correlated with the measured data, and the correlation coefficients varied from 0.64(G41) to 0.85 (G33). In the northern region, the GRACE-WGHM results were highly correlated with the measured data, and the correlation coefficients varied from 0.69(G21) to 0.88(G26), and the RMSE results agreed with the correlation coefficient conclusions except for G27 and G41.
For the time series trend terms, the GRACE-hydrological model results and the measured data all showed an upward trend, except for G21 and G27. Furthermore, the slope of GRACE-GLDAS is typically 1.8 times that of GRACE-WGHM. In both G21 and G27, the GRACE-WGHM and measured data trend downward, while the GRACE-GLDAS trend the opposite. The reason may be that the GLDAS model cannot produce accurate estimated hydrological variables in high altitude areas due to the shortcomings of meteorological forcing data and model parameters.
4.4) spatial distribution of the trend of the groundwater reserve
The spatial distribution of the rate of change of groundwater reserves in Tasmania between 2003-2015 was calculated using GRACE-GLDAS and GRACE-WGHM and the results are shown in FIG. 6. D1-D3 and D1-D4 are the main areas of decreasing trend shown by the GRACE-GLDAS and GRACE-WGHM results, respectively. R1-R2 and R1 are the main areas of ascending trend shown by the GRACE-GLDAS and GRACE-WGHM results, respectively. As can be seen from FIG. 6, the spatial distribution of the change rate of groundwater reserves in the research area obtained by the GRACE-GLDAS and GRACE-WGHM inversion is relatively consistent, and the results of both models show that: in 2003-2015, underground water reserves in west coastal, southwest and south regions of Tasmanian show a descending trend, the region with the highest descending rate is mainly in the west coastal region, and the region with the higher ascending rate is mainly in the middle and north regions. The GRACE-GLDAS results show that the underground water reserves have a larger ascending area range and a larger ascending rate than the GRACE-WGHM, and have a smaller descending area range and a smaller descending rate than the GRACE-WGHM, i.e., the GRACE-GLDAS change rate is higher in most areas. The greatest difference occurred in the central plateau (R2 and d4) and the two results showed opposite trends with rates of 2.93mm/yr and-2.36 mm/yr, respectively.
The reason why the two results show opposite trends in R2 and d4 can be explained by fig. 7. FIG. 7a shows that the correlation between GRACE-WGHM and measured data is higher than the correlation between GRACE-GLDAS and measured data with correlation coefficients of 0.70 and 0.41, respectively. The reasons for causing the opposite trend mainly include: (1) the area is located in the middle plateau, and the groundwater supply source mainly comes from accumulated snow and glacier melt water, so that the GWS change is greatly influenced by temperature. FIG. 7b shows that the GRACE-WGHM results are more sensitive to temperature changes, whereas the GRACE-GLDAS results cannot capture the dynamic changes in temperature, especially in 5-7 months. Correlation coefficients of Grace-WGHM and Grace-GLDAS with temperature data were 0.89 and 0.55, respectively; (2) the SM variation obtained by WGHM is larger in amplitude than GLDAS as shown in fig. 7 c. The main reasons are the difference in rainfall driving data between GLDAS and WGHM and the difference in soil layer and soil depth as defined by the model.
4.5) spatial distribution of optimal estimation of groundwater reservoir variation
According to the statistical selection method, the overall index display: YGRACE-WGHM and GRACE-GLDAS are larger in the gray area (FIG. 4). This indicates that the Grace-WGHM and GRACE-GLDAS results are more consistent with the measured data in the gray area. For the area lacking measured data, the correlation coefficient and RMSE values between GRACE-GLDAS and GRACE-WGHM were 0.74 and 44.17, respectively, better than the entire tasmania mean level (0.68mm and 45.15 mm). Therefore, the final spatial distribution result of the change rate of the groundwater reserves in the research area is obtained by taking the GRACE-WGHM result as the final estimation of the middle and north areas, the GRACE-GLDAS result of the east coast and the east south and taking the average value of the GRACE-GLDAS result of the other areas as shown in fig. 8 a. Furthermore, fig. 8b shows a simple average of the two results over the entire area.
Compared to the simple average results in fig. 8b, the improved results can be combined with different model results and retain specific region features, as shown in fig. 8 a. DF1-DF4 is the main area of groundwater reserves with descending trend, and the descending rate is about-2.21 mm/yr, -3.37mm/yr, -3.19mm/yr and-2.36 mm/yr respectively. RF1 is the main region with a rising trend, with a rising rate of about 5.43 mm/yr. Dynamic changes in GWS are mainly affected by rainfall, human activity, geological and topographic conditions, and other factors. In the very marked down-trend areas (DF1-DF2) mainly rough mountains and extensive forests are involved. Thus, human activities are minimal and annual changes in rainfall may play an important role in the drastic decline of groundwater in these areas.
Fig. 9 compares the DF2 groundwater reserves change results with the actual precipitation results. It can be seen that the groundwater reserves change results lag behind the rainfall data, as rainfall takes time to recharge groundwater. According to the method, the lag correlation coefficients of rainfall and underground water reserves time series of 4 regions are counted, the lag period is set to be 0-8 months, and the lag period of the four regions is 7 months (the lag correlation coefficient is 0.49-0.63). In 2003-2015, the groundwater reserves and rainfall both showed a significant decline with rates of-3.36 mm/yr and-39.04 mm/yr, respectively (fig. 9b), indicating that groundwater reserves decline was mainly caused by a reduction in precipitation. For the seasonal period, rainfall occurred mainly from 9 months to 5 months of the following year, accounting for 60% of the rainfall throughout the year, consistent with groundwater reserves changing (fig. 9 c).
4.6) analysis of time variation trend of underground water reserves and climate influence
In the measured well data area, the results of groundwater reserve changes estimated by the GRACE satellite gravity and hydrological model have higher consistency with the measured data, which indicates that the GRACE satellite gravity and hydrological model can be used to estimate groundwater reserve changes throughout the tasmanian area (fig. 10 a). The underground water reserve change result shows that the underground water reserve changes in a descending trend from 1 month to 9 months in 2003 to a descending rate of-2.57 mm/yr, and simultaneously shows that Tasmania is influenced by thousand-year drought. The method utilizes a self-adaptive Palmer drought index (scPDSI) as a meteorological drought index. scPDSI data was from ClimatcResearch (http:// www.cru.uea.ac.uk/cru/data), with PDSI less than-2 indicating severe drought. As can be seen from FIG. 10b, in 2006-2009, scPDSI was lower than-2, indicating that severe drought climate occurred in this area. This drought climate affects the groundwater reserves, causing severe decline in groundwater reserves at this stage at a rate of-9.47 mm/yr. During the drought phase, rainfall is below average and exhibits a downward trend, which may be the main cause of drought. In 2011-2015, the groundwater reserves are restored at a rate of 3.94mm/yr, mainly due to increased rainfall.
Although the present invention has been described with reference to the preferred embodiments, it is not intended to limit the present invention, and those skilled in the art can make variations and modifications of the present invention without departing from the spirit and scope of the present invention by using the methods and technical contents disclosed above.
Those skilled in the art will appreciate that the invention may be practiced without these specific details.

Claims (8)

1. A method for improving regional groundwater reserve estimation accuracy, comprising:
obtaining monthly-scale land water reserve change delta TWS0；
Method for extracting soil water content change delta SM of monthly scale in global scope by utilizing GLDAS hydrological model1Snow water equivalent change Δ SWE1And vegetation canopy water reserve change delta PCSW1；
Extraction of soil water content change delta SM of monthly scale in global scope by using WGHM hydrological model2Snow water equivalent change Δ SWE2And the water reserve change of the vegetation canopy delta PCSW2；
According to Δ TWS0、ΔSM1、ΔSWE1And Δ PCSW1And resolving to obtain the underground water reserve change delta GWS in the month scale1(ii) a According to Δ TWS0、ΔSM2、ΔSWE2And Δ PCSW2Calculating to obtain the underground water reserve change delta GWS in the month scale2；
Change in groundwater reserves Δ GWS according to measured monthly scale of research area0For Δ GWS, respectively1And Δ GWS2Carrying out evaluation;
selecting the underground water reserve change delta GWS with the optimal monthly scale according to the evaluation resultSuperior foodAnd outputting the result of the change of the underground water reserves of the pixel of the research area in the monthly scale.
2. The method for improving estimation accuracy of regional groundwater reserves according to claim 1, wherein a monthly scale change in land water reserves Δ TWS is obtained0The method comprises the following steps:
acquiring m monthly-scale land water reserve change delta TWS (delta TWS) obtained by resolving based on spherical harmonic coefficients from m data sources1、ΔTWS2...ΔTWSm(ii) a Wherein m is more than or equal to 3;
determining Δ TWS1、ΔTWS2...ΔTWSiRegression model Z of each corresponding time series1(t)、Z2(t)、...Zm(t)；
To Z1(t)、Z2(t)、...Zm(t) respectively resolving to obtain linear trends in the regression modelsThe value of the term;
from the comparison of the values of the linear trend terms in the respective regression models, from Δ TWS1、ΔTWS2...ΔTWSmThe land water reserve change delta TWS with the optimal month scale is obtained by medium screening0And output.
3. The method of improving regional groundwater reserve estimation accuracy of claim 2, wherein Δ TWS1、ΔTWS2...ΔTWSiThe general expression of the regression model for each corresponding time series is:
Figure FDA0002341651310000021
wherein i ∈ m, βi1Constant term representing the i-th regression model, βi2A linear trend term representing the i-th regression model, βi3Annual sinusoidal signal representing the ith regression model, βi4Annual cosine signal representing the ith regression model, βi5Representing the half-year sinusoidal signal of the ith regression model, βi6Half-year cosine signal, ε, representing the ith regression modeliData error for the ith regression model is indicated.
4. The method for improving estimation accuracy of regional groundwater reserves according to claim 1, wherein the formula for solving the variation of groundwater reserves in the monthly scale is as follows:
ΔGWS1＝ΔTWS0-ΔSM1-ΔSWE1-ΔPCSW1
ΔGWS2＝ΔTWS0-ΔSM2-ΔSWE2-ΔPCSW2。
5. the method of improving accuracy of regional groundwater reserve estimation according to claim 1, wherein the change in groundwater reserve Δ GWS is based on a measured monthly scale of the area of interest0For Δ GWS, respectively1And Δ GWS2Performing an evaluation comprising：
Determination of Δ GWS0And Δ GWS1Correlation coefficient of (PR)1Root mean square error RMSE1Determining Δ GWS0And Δ GWS2Correlation coefficient of (PR)2Root mean square error RMSE2；
Determination of Δ GWS0、ΔGWS1And Δ GWS2Respective slope Tr0、Tr1And Tr2；
Calculating to obtain delta GWS1Evaluation result Y of1And Δ GWS2Evaluation result Y of2：
Figure FDA0002341651310000022
Figure FDA0002341651310000023
Wherein, F11、F12、F21And F22Respectively represent PR1、RMSE1、PR2And RMSE2The weight coefficient of (b), Consist (·), represents a trend consistency determination function.
6. Method for improving regional groundwater reservoir estimation accuracy as claimed in claim 5, wherein determining Δ GWS0And Δ GWS1Correlation coefficient of (PR)1Root mean square error RMSE1Determining Δ GWS0And Δ GWS2Correlation coefficient of (PR)2Root mean square error RMSE2The method comprises the following steps:
obtaining Δ GWS0Time series X (t), Δ GWS1Time series Y of1(t) and. DELTA.GWS2Time series Y of2(t)；
The correlation coefficient is calculated as follows:
Figure FDA0002341651310000031
Figure FDA0002341651310000032
the root mean square error is calculated as follows:
Figure FDA0002341651310000033
Figure FDA0002341651310000034
where n represents the length of the time series.
7. The method of improving regional groundwater reservoir estimation accuracy of claim 5,
when Tr0Trend of (D) and Tr1If the trends are consistent, Consist (Tr)1,Tr0) 1 is ═ 1; otherwise, Consist (Tr)1,Tr0)＝0；
When Tr0Trend of (D) and Tr2If the trends are consistent, Consist (Tr)2,Tr0) 1 is ═ 1; otherwise, Consist (Tr)2,Tr0)＝0。
8. The method of improving regional groundwater reservoir estimation accuracy of claim 5,
Figure FDA0002341651310000035
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