CN111241473B - Method for improving estimation accuracy of regional groundwater reserves - Google Patents
Method for improving estimation accuracy of regional groundwater reserves Download PDFInfo
- Publication number
- CN111241473B CN111241473B CN201911378489.5A CN201911378489A CN111241473B CN 111241473 B CN111241473 B CN 111241473B CN 201911378489 A CN201911378489 A CN 201911378489A CN 111241473 B CN111241473 B CN 111241473B
- Authority
- CN
- China
- Prior art keywords
- change
- delta
- groundwater
- scale
- gws
- Prior art date
- Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
- Active
Links
- 239000003673 groundwater Substances 0.000 title claims abstract description 109
- 238000000034 method Methods 0.000 title claims abstract description 44
- 230000008859 change Effects 0.000 claims abstract description 87
- XLYOFNOQVPJJNP-UHFFFAOYSA-N water Substances O XLYOFNOQVPJJNP-UHFFFAOYSA-N 0.000 claims abstract description 63
- 238000011156 evaluation Methods 0.000 claims abstract description 15
- 239000002689 soil Substances 0.000 claims abstract description 9
- 238000011160 research Methods 0.000 claims abstract description 5
- 238000004364 calculation method Methods 0.000 claims description 3
- 238000012216 screening Methods 0.000 claims description 3
- 230000005484 gravity Effects 0.000 abstract description 7
- 238000010187 selection method Methods 0.000 abstract description 3
- 238000001556 precipitation Methods 0.000 description 14
- 238000012544 monitoring process Methods 0.000 description 8
- 230000007423 decrease Effects 0.000 description 6
- 230000003247 decreasing effect Effects 0.000 description 4
- 238000010586 diagram Methods 0.000 description 4
- 230000000694 effects Effects 0.000 description 4
- 230000001932 seasonal effect Effects 0.000 description 4
- 238000004458 analytical method Methods 0.000 description 3
- 230000001174 ascending effect Effects 0.000 description 3
- 230000000875 corresponding effect Effects 0.000 description 3
- 238000004519 manufacturing process Methods 0.000 description 3
- 238000012986 modification Methods 0.000 description 3
- 230000004048 modification Effects 0.000 description 3
- 230000000630 rising effect Effects 0.000 description 3
- 230000002596 correlated effect Effects 0.000 description 2
- 230000007613 environmental effect Effects 0.000 description 2
- 230000001747 exhibiting effect Effects 0.000 description 2
- 230000004907 flux Effects 0.000 description 2
- 239000013505 freshwater Substances 0.000 description 2
- 230000007774 longterm Effects 0.000 description 2
- 230000008520 organization Effects 0.000 description 2
- 230000008569 process Effects 0.000 description 2
- 238000012935 Averaging Methods 0.000 description 1
- 238000012952 Resampling Methods 0.000 description 1
- 230000005856 abnormality Effects 0.000 description 1
- 230000003044 adaptive effect Effects 0.000 description 1
- 238000010276 construction Methods 0.000 description 1
- 239000003651 drinking water Substances 0.000 description 1
- 235000020188 drinking water Nutrition 0.000 description 1
- 238000005516 engineering process Methods 0.000 description 1
- 238000002474 experimental method Methods 0.000 description 1
- 230000006872 improvement Effects 0.000 description 1
- 238000011835 investigation Methods 0.000 description 1
- 238000012423 maintenance Methods 0.000 description 1
- 239000011159 matrix material Substances 0.000 description 1
- 238000005259 measurement Methods 0.000 description 1
- 238000002844 melting Methods 0.000 description 1
- 230000008018 melting Effects 0.000 description 1
- 238000012805 post-processing Methods 0.000 description 1
- 230000009467 reduction Effects 0.000 description 1
- 230000002441 reversible effect Effects 0.000 description 1
- 238000005070 sampling Methods 0.000 description 1
- 239000002352 surface water Substances 0.000 description 1
- 230000004083 survival effect Effects 0.000 description 1
- 238000012876 topography Methods 0.000 description 1
- 238000012795 verification Methods 0.000 description 1
Classifications
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F17/00—Digital computing or data processing equipment or methods, specially adapted for specific functions
- G06F17/10—Complex mathematical operations
- G06F17/18—Complex mathematical operations for evaluating statistical data, e.g. average values, frequency distributions, probability functions, regression analysis
Landscapes
- Engineering & Computer Science (AREA)
- Physics & Mathematics (AREA)
- Data Mining & Analysis (AREA)
- General Physics & Mathematics (AREA)
- Mathematical Optimization (AREA)
- Pure & Applied Mathematics (AREA)
- Theoretical Computer Science (AREA)
- Mathematical Physics (AREA)
- Computational Mathematics (AREA)
- Mathematical Analysis (AREA)
- Life Sciences & Earth Sciences (AREA)
- Operations Research (AREA)
- Probability & Statistics with Applications (AREA)
- Bioinformatics & Cheminformatics (AREA)
- Algebra (AREA)
- Evolutionary Biology (AREA)
- Databases & Information Systems (AREA)
- Software Systems (AREA)
- General Engineering & Computer Science (AREA)
- Bioinformatics & Computational Biology (AREA)
- Geophysics And Detection Of Objects (AREA)
Abstract
The invention discloses a handleA method of high area groundwater reserve estimation accuracy, comprising: acquiring a change delta TWS of land water reserves on a month scale 0 The method comprises the steps of carrying out a first treatment on the surface of the Method for extracting global month-scale soil water content change delta SM by using GLDAS hydrologic model 1 Change in snow Water equivalent ΔSWE 1 And vegetation canopy water reserves change ΔPCSW 1 The method comprises the steps of carrying out a first treatment on the surface of the Extracting global month-scale soil water content change delta SM by utilizing WGHM hydrologic model 2 Change in snow Water equivalent ΔSWE 2 Vegetation canopy water reserves change ΔPCSW 2 The method comprises the steps of carrying out a first treatment on the surface of the Solving to obtain the change delta GWS of the groundwater reserve of the month scale 1 And delta GWS 2 The method comprises the steps of carrying out a first treatment on the surface of the Measured month-scale groundwater reserve change ΔGWS from study area 0 For DeltaGWS respectively 1 And delta GWS 2 Evaluating; selecting the groundwater reserve change delta GWS with the optimal month scale according to the evaluation result Excellent (excellent) And outputting the result of the change of the groundwater reserve of the month scale of each pixel of the research area. According to the invention, based on a statistical selection method and combining GRACE satellite gravity data and a global hydrologic model, the estimation accuracy of regional groundwater reserves is improved.
Description
Technical Field
The invention belongs to the technical field of satellite biomechanics and hydrology intersection, and particularly relates to a method for improving estimation accuracy of regional groundwater reserves.
Background
Groundwater (GWS) is the largest source of fresh water in the global hydrologic cycle, providing about 50% of drinking water worldwide. In recent years, extreme climates, population growth, and over-exploitation of groundwater resources have resulted in serious consumption of groundwater resources. Thus, grasping the dynamic changes of groundwater is critical to water resource management and human survival.
The conventional groundwater change monitoring method mainly depends on an observation well, and although the result can provide high-resolution groundwater level estimation, the method has a plurality of limitations in practical application. Firstly, the construction and maintenance of the observation well are time-consuming and labor-consuming; secondly, observing uneven distribution of water wells; finally, single point observations are difficult to represent for large area results. Gravity inversion was implemented in combination with the climate experiment satellite (GRACE) program by the U.S. space agency (NASA) and the German space center of flight (DLR), which was successfully launched in month 3 of 2002, and was capable of obtaining land water reserves at all depths of the area. To date, GRACE satellites are the only remote sensing means that can monitor TWS changes at all depths under any conditions. However, the main disadvantage is that it is not possible to separate individual hydrologic components from the GRACE data.
In order to separate groundwater reserves from land water reserves, previous studies have mainly utilized the auxiliary information of individual hydrologic models to vertically decompose GRACE data. Global terrestrial data assimilation systems (GLDAS) provide hydrologic flux estimates with 0.25 ° spatial resolution, and have been applied to various hydrologic studies. For example, the method can be used for estimating the groundwater descent in North China plain, the groundwater supply rate of the loess plateau, the runoff of Qinghai-Tibet plateau and the like.
Currently, many hydrologic models and land surface models are developed to describe land hydrologic fluxes, such as WaterGAP Global Hydrology Model (WGHM), community Atmosphere Biosphere Land Exchange (CABLE) and World-Wide Water Resources Assessment (W3 RA). The output results of the hydrologic model are partially different due to the differences of the model structure, parameter setting and driving data. Typically, these models are developed on a global scale, and thus each has advantages and disadvantages. For example, global hydrologic data of the GLDAS model are published, but without simulated groundwater amounts; the AWAR model simulates the amount of groundwater but fails to describe the phenomenon of massive water resource consumption that occurs during periods of drought. Thus, the biggest problem with using a single hydrological model is the uncertainty of whether the model output is appropriate for a particular region.
The tasmania island is located in the southern australia and has a total area of approximately 68000km 2 . Although it is less than 1% of the australian surface area, this area accounts for approximately 12% of the australian fresh water resource. Groundwater production in Tasmanian was relatively low, with a total consumption estimated at 38GL/yr. However, 90% of the production area is in the northwest and north-mid regions of the state, meaning that groundwater production may cause local problems in these areas. In addition, most areas of the west coast of tasmania are protected as areas of world birth, with native vegetation covering 50% of the entire state. Therefore, understanding the dynamic change of GWS is significant to the local ecological environment, and due to the ground water level monitoringWell logging is unevenly distributed, and reliable estimation of groundwater reserves is difficult to achieve by means of well observations. Especially in the 2001-2009, long-term drought, the so-called "thousand year drought", occurs in the southeast region of australia, affecting environmental, agricultural and economic activities. There have been many studies showing that this drought phenomenon affects tasmania groundwater.
Disclosure of Invention
The technical solution of the invention is as follows: the method for improving the estimation precision of the regional groundwater reserves is provided, and based on the combination of the GRACE satellite gravity data and the global hydrologic model, the estimation precision of the regional groundwater reserves is improved.
In order to solve the technical problems, the invention discloses a method for improving the estimation precision of regional groundwater reserves, which comprises the following steps:
acquiring a change delta TWS of land water reserves on a month scale 0 ；
Method for extracting global month-scale soil water content change delta SM by using GLDAS hydrologic model 1 Change in snow Water equivalent ΔSWE 1 And vegetation canopy water reserves change ΔPCSW 1 ；
Extracting global month-scale soil water content change delta SM by utilizing WGHM hydrologic model 2 Change in snow Water equivalent ΔSWE 2 Vegetation canopy water reserves change ΔPCSW 2 ；
According to DeltaTWS 0 、ΔSM 1 、ΔSWE 1 And ΔPCSW 1 Solving to obtain the change delta GWS of the groundwater reserve of the month scale 1 The method comprises the steps of carrying out a first treatment on the surface of the According to DeltaTWS 0 、ΔSM 2 、ΔSWE 2 And ΔPCSW 2 Solving to obtain the change delta GWS of the groundwater reserve of the month scale 2 ；
Measured month-scale groundwater reserve change ΔGWS from study area 0 For DeltaGWS respectively 1 And delta GWS 2 Evaluating;
selecting the groundwater reserve change delta GWS with the optimal month scale according to the evaluation result Excellent (excellent) And outputting the result of the change of the groundwater reserve of the month scale of each pixel of the research area.
In the above method for improving accuracy of estimating groundwater reserves in a region, a terrestrial water reserve change ΔTWS of a lunar scale is obtained 0 Comprising:
obtaining m lunar-scale land water reserve changes delta TWS based on spherical harmonic coefficient calculation from m data sources 1 、ΔTWS 2 ...ΔTWS m The method comprises the steps of carrying out a first treatment on the surface of the Wherein m is more than or equal to 3;
determining ΔTWS 1 、ΔTWS 2 ...ΔTWS i Regression model Z of the respective time series 1 (t)、Z 2 (t)、...Z m (t)；
For Z 1 (t)、Z 2 (t)、...Z m (t) respectively carrying out solution to obtain the value of the linear trend term in each regression model;
from ΔTWS based on the comparison of the values of the linear trend terms in the regression models 1 、ΔTWS 2 ...ΔTWS m Obtaining the optimal terrestrial water reserve change delta TWS of the month scale through medium screening 0 And output.
In the above method for improving accuracy of estimating groundwater reserves in a region, ΔTWS 1 、ΔTWS 2 ...ΔTWS i The general expression of the regression model of the respective corresponding time series is:
wherein i is m, beta i1 Constant term, beta, representing the ith regression model i2 Linear trend term, beta, representing the ith regression model i3 Annual sinusoidal signal, beta, representing the ith regression model i4 Annual cosine signal, beta, representing the ith regression model i5 Half-year sinusoidal signal, beta, representing the ith regression model i6 Half year cosine signal, epsilon, representing the ith regression model i Representing the data error of the ith regression model.
In the above method for improving the accuracy of estimating the groundwater reserves in the area, the calculation formula of the groundwater reserve change in the month scale is as follows:
ΔGWS 1 ＝ΔTWS 0 -ΔSM 1 -ΔSWE 1 -ΔPCSW 1
ΔGWS 2 ＝ΔTWS 0 -ΔSM 2 -ΔSWE 2 -ΔPCSW 2 。
in the method for improving the accuracy of estimating the groundwater reserves in the area, the groundwater reserve change Δgws is measured according to the measured month scale of the study area 0 For DeltaGWS respectively 1 And delta GWS 2 Performing the evaluation, comprising:
determining ΔGWS 0 And delta GWS 1 Related coefficient PR of (2) 1 Root mean square error RMSE 1 Determining Δgws 0 And delta GWS 2 Related coefficient PR of (2) 2 Root mean square error RMSE 2 ；
Determining ΔGWS 0 、ΔGWS 1 And delta GWS 2 Respective slopes Tr 0 、Tr 1 And Tr 2 ；
Resolving to obtain delta GWS 1 Is the evaluation result Y of (2) 1 And delta GWS 2 Is the evaluation result Y of (2) 2 ：
Wherein F is 11 、F 12 、F 21 And F 22 Respectively represent PR 1 、RMSE 1 、PR 2 And RMSE 2 Consist (·) represents a trend consistency judging function.
In the above method for improving the accuracy of estimating the groundwater reserves in a region, Δgws is determined 0 And delta GWS 1 Related coefficient PR of (2) 1 Root mean square error RMSE 1 Determining Δgws 0 And delta GWS 2 Related coefficient PR of (2) 2 Root mean square error RMSE 2 Comprising:
acquisition of ΔGWS 0 Time series X (t), ΔGWS of (2) 1 Time series Y of (2) 1 (t) and ΔGWS 2 Time series Y of (2) 2 (t)；
The correlation coefficient is calculated as follows:
the root mean square error is calculated as follows:
where n represents the length of the time series.
In the above method of improving accuracy of regional groundwater reserve estimation,
when Tr is 0 Trend and Tr of (2) 1 Is consistent with the trend of Consist (Tr) 1 ,Tr 0 ) =1; otherwise, consist (Tr 1 ,Tr 0 )＝0；
When Tr is 0 Trend and Tr of (2) 2 Is consistent with the trend of Consist (Tr) 2 ,Tr 0 ) =1; otherwise, consist (Tr 2 ,Tr 0 )＝0。
In the above method of improving accuracy of regional groundwater reserve estimation,
the invention has the following advantages:
the invention discloses a method for improving estimation accuracy of regional groundwater reserves, which is based on a statistical selection method combined with GRACE satellite gravity data and a global hydrologic model, improves the estimation accuracy of regional groundwater reserves, and provides an effective scheme for selecting a proper hydrologic model for hydrologic application.
Drawings
FIG. 1 is a flow chart of steps of a method for improving accuracy of regional groundwater reserve estimation in accordance with an embodiment of the invention;
FIG. 2 is a graph showing the average results of land water reserves change areas of the Tasmanian 2003-2015 years calculated by using different GRACE products according to the embodiment of the present invention;
FIG. 3 is a schematic representation of groundwater reserve change in accordance with an embodiment of the invention in the range 2003-2015 in Tasmanian obtained by combining GRACE-GLDAS, GRACE-WGHM and WGHM; wherein, 3a: month scale, 3b: season scale, 3c: correlation coefficient between GRACE-GLDAS and GRACE-WGHM and RMSE;
FIG. 4 is a schematic diagram of distribution positions and numbers of a Tasmanian groundwater level monitoring well according to an embodiment of the invention;
FIG. 5 is a graph showing the comparison of GLDAS-GLDAS, GRACE-WGHM and WGHM results with measured data for one of the four grids according to an embodiment of the present invention;
FIG. 6 is a spatial distribution diagram of groundwater reserve change in 2003-2015 according to an embodiment of the invention; wherein, 6a: GLDAS-GLDAS,6b: grace-WGHM; d and D represent regions exhibiting a downward trend, and R represent regions exhibiting an upward trend;
FIG. 7 is a graph showing the comparison of the groundwater reserve change estimates of the R2 and d4 regions GRACE-GLDAS and GRACE-WGHM with measured data and with temperature and rainfall in accordance with an embodiment of the present invention;
FIG. 8 is a graph showing the combined results of GRACE-GLDAS and GRACE-WGHM in an embodiment of the invention; 8a: optimal selection results, 8b: simple average results;
FIG. 9 is a schematic diagram of a 2003-2015 groundwater reserve change and corresponding rainfall change in an embodiment of the invention; wherein 9a: month scale, 9b: annual scale, 9c: average seasonal scale; gray bars represent real rainfall data, and black bars represent rainfall data adjusted by 7 months of lag period;
FIG. 10 is a schematic diagram of groundwater reserve change in 2003-2015 according to an embodiment of the invention; 10a: average results over the whole area, 10b drought index and rainfall integral results.
Detailed Description
For the purpose of making the objects, technical solutions and advantages of the present invention more apparent, the embodiments of the present invention disclosed herein will be described in further detail with reference to the accompanying drawings.
In the present invention, a method of improving accuracy of estimating a regional groundwater reserve disclosed in this embodiment will be mainly described in the following aspects.
1. Study area overview
Tasmanian is located in Australia in the region 240km in the south of the nature, and has a longitude and latitude range of 40 DEG S-44 DEG S and 144 DEG E-148 DEG E, and an area of 6.45 km 2 . Most of the areas are mountains and hills, the center is the highest part of the area, the peak elevation exceeds 1500m, and the middle eastern area and the coastal area are flat.
The Tasmanian climate type is temperate marine climate, cool and mild, and the average annual air temperature is up to 15.7 ℃ and the minimum is 4.5 ℃. Due to the influence of terrain, the precipitation difference in the east and west is large, the precipitation amount in most of the west areas exceeds 2000mm each year, and the precipitation amount in the mountain areas reaches 4000mm; the annual average precipitation in eastern regions is less than 750mm, and the annual average precipitation in individual regions is less than 400mm; the northeast highland has high rainfall relative to surrounding areas, due in part to snowfall and annual rainfall of about 900mm; the annual precipitation in southeast areas is evenly distributed and is mostly lower than 800mm. The area has approximately 150000km of waterways, 8800 wetlands and 94000 bodies of water. River basin area 685-11700 km on island 2 。
2. Data source
2.1 GRACE land water reserve anomaly data
The land water reserves anomaly data used in the examples of the present invention are the Level3 grid product and the Mascon product, both offered by the space research Center (CSR) of the university of texas, usa. The Level3 grid product was processed by the algorithm of Swenson and Wahr (2006) and Landerer and Swenson (2012) with a spatial resolution of 1 ° x 1 °. Surface quality variation signals at smaller spatial scales tend to decay due to sampling and post-processing of the GRACE observations.
The scale factor method is used to recover signal leakage (called CSR-scaled), and the scale factor is provided by ftp:// podaac-ftp. Jpl.
Mascon is another basic equation for gravitational field resolution, with a spatial resolution of 0.5 DEG x 0.5 deg. CSR Mascon (CSR-M) is constrained by a time-varying regularization matrix and is derived only from GRACE information, without applying other models or data to constrain. Thus, the CSR-M solution has no significant banding errors and can capture the signal of GRACE within the measured noise level.
2.2 Hydrologic model surface water reserve data
The Global Land Data Assimilation System (GLDAS) is jointly developed by the U.S. astronavigation agency (NASA), the national environmental prediction center (NCEP) and the U.S. National Ocean Atmospheric Agency (NOAA), and simultaneously establishes a global hydrologic mode to publicly release real-time satellite remote sensing observation data and earth surface observation data, and 28 land meteorological data can be generated by driving CLM, MOS, VIC and NOAH four land process models through the data. The invention uses NOAH2.1 spatial resolution of 0.25 ° x 0.25 ° and resampling of 0.5 ° x 0.5 °.
The Water GAP Global Hydrology Model (WGHM) global hydrologic model developed by the university of farafol, germany, institute of geography (IPG), provides global 0.5 ° x 0.5 ° water resource information excluding south poles and the isles of holland. The WGHM model considers not only the amount of groundwater but also the impact of human activity on water consumption.
2.3 Rainfall data)
TRMM 3B43 is a standard lunar precipitation product incorporating precipitation datasets 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 is obtained by averaging TRMM 3B42V6 precipitation products and is widely used in climatology applications. It provides an estimate of the total rainfall from 1988 to the month it has recorded, with a spatial resolution of 0.25 °.
2.4 Ground observation data)
The measured data of the underground water monitoring well come from a global underground water monitoring network (Global Ground Monitoring Network, GGMN), the GGMN is initiated by a United nations textbook organization, implemented by IGRAC (International Groundwater Resources Assessment Centre) organization, and aims to improve the quality and the acquisition of underground water monitoring information, a website (https:// GGMN. Un-igrac. Org /) provides the global space-time underground water monitoring data, and the data is collected 1-2 times per day. The invention obtains monthly water level information by taking the average monthly water level, and the water level is not converted into reserves because the water supply degree is unknown.
Australian weather bureau (BoM, http:// www.bom.gov.au/date/data /) provides precipitation and temperature data based on ground observations. Although climate stations are limited in scope and have inherent errors, they are still the most direct and accurate measuring tools. Thus, in the following discussion, ground-based measurements are considered to be "real precipitation" and "real temperature".
3. Method of
In this embodiment, as shown in fig. 1, the method for improving the estimation accuracy of the groundwater reserve in the area includes:
step 101, acquiring land water reserve change delta TWS of month scale 0 。
In this embodiment, first, m lunar-scale land water reserve changes Δtws based on spherical harmonic coefficient resolution may be obtained from m data sources 1 、ΔTWS 2 ...ΔTWS m The method comprises the steps of carrying out a first treatment on the surface of the Wherein ΔTWS is determined 1 、ΔTWS 2 ...ΔTWS i Regression model Z of the respective time series 1 (t)、Z 2 (t)、...Z m (t); then, to Z 1 (t)、Z 2 (t)、...Z m (t) respectively carrying out solution to obtain the value of the linear trend term in each regression model; finally, based on the comparison result of the values of the linear trend terms in each regression model, the regression model is calculated from ΔTWS 1 、ΔTWS 2 ...ΔTWS m Obtaining the optimal terrestrial water reserve change delta TWS of the month scale through medium screening 0 And output.
Preferably, ΔTWS 1 、ΔTWS 2 ...ΔTWS i The general expression of the regression model of the respective corresponding time series may be as follows:
wherein m is larger than or equal to 3,i E m, beta i1 Constant term, beta, representing the ith regression model i2 Linear trend term, beta, representing the ith regression model i3 Annual sinusoidal signal, beta, representing the ith regression model i4 Annual cosine signal, beta, representing the ith regression model i5 Half-year sinusoidal signal, beta, representing the ith regression model i6 Half year cosine signal, epsilon, representing the ith regression model i Representing the data error of the ith regression model.
Step 102, extracting the global monthly soil water content change delta SM by using GLDAS hydrologic model 1 Change in snow Water equivalent ΔSWE 1 And vegetation canopy water reserves change ΔPCSW 1 。
Step 103, extracting the change delta SM of the soil water content of the lunar scale in the global range by utilizing a WGHM hydrological model 2 Change in snow Water equivalent ΔSWE 2 Vegetation canopy water reserves change ΔPCSW 2 。
Step 104, according to ΔTWS 0 、ΔSM 1 、ΔSWE 1 And ΔPCSW 1 Solving to obtain the change delta GWS of the groundwater reserve of the month scale 1 The method comprises the steps of carrying out a first treatment on the surface of the According to DeltaTWS 0 、ΔSM 2 、ΔSWE 2 And ΔPCSW 2 Solving to obtain the change delta GWS of the groundwater reserve of the month scale 2 。
In this embodiment, the solution formula for the change in groundwater reserve on a monthly scale is as follows:
ΔGWS 1 ＝ΔTWS 0 -ΔSM 1 -ΔSWE 1 -ΔPCSW 1
ΔGWS 2 ＝ΔTWS 0 -ΔSM 2 -ΔSWE 2 -ΔPCSW 2
step 105Measured month-scale groundwater reserve change ΔGWS from study area 0 For DeltaGWS respectively 1 And delta GWS 2 An evaluation is performed.
In this embodiment, the specific flow of evaluation is as follows:
1) Determining ΔGWS 0 And delta GWS 1 Related coefficient PR of (2) 1 Root mean square error RMSE 1 Determining Δgws 0 And delta GWS 2 Related coefficient PR of (2) 2 Root mean square error RMSE 2 。
In the present embodiment, Δgws can be obtained 0 Time series X (t), ΔGWS of (2) 1 Time series Y of (2) 1 (t) and ΔGWS 2 Time series Y of (2) 2 (t) then there is:
the correlation coefficient is calculated as follows:
the root mean square error is calculated as follows:
where n represents the length of the time series.
2) Determining ΔGWS 0 、ΔGWS 1 And delta GWS 2 Respective slopes Tr 0 、Tr 1 And Tr 2 。
3) Resolving to obtain delta GWS 1 Is the evaluation result Y of (2) 1 And delta GWS 2 Is the evaluation result Y of (2) 2 ：
Wherein F is 11 、F 12 、F 21 And F 22 Respectively represent PR 1 、RMSE 1 、PR 2 And RMSE 2 Weight coefficient of (c) in the above-mentioned formula (c).
Preferably, consist (.) represents a trend consistency judging function. Wherein when Tr 0 Trend and Tr of (2) 1 Is consistent with the trend of Consist (Tr) 1 ,Tr 0 ) =1; otherwise, consist (Tr 1 ,Tr 0 ) =0. When Tr is 0 Trend and Tr of (2) 2 Is consistent with the trend of Consist (Tr) 2 ,Tr 0 ) =1; otherwise, consist (Tr 2 ,Tr 0 )＝0。
Step 106, selecting the groundwater reserve change delta GWS with the optimal month scale according to the evaluation result Excellent (excellent) And outputting the result of the change of the groundwater reserve of the month scale of each pixel of the research area.
In this embodiment, the larger the evaluation value obtained in step 105, the smaller the difference between the output result of the selected hydrologic model and the measured data is. Namely:
4. results and analysis
4.1 Land water reserve change
The average results of the land water reserves change area from 2003 to 2015 in tasmania 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 differences in amplitude from these two results were 42.6mm and 21.34mm, respectively. This suggests that in tasmania, the signal corrected with scale factors is overestimated, probably due to some error in TWS in CLM4.0 model. In 2003-2015, all TWS change sequences were in an ascending trend with slopes ranging from 0.33mm/yr (CSR-SH) to 1.49mm/yr (CSR-scaled). The shading in the figure shows uncertainty in the CSR-scaled and CSR-Mascon results, which values are 46.26mm and 21.34mm.
CSR-SH has large uncertainty in land water reserve abnormality, and coarse spatial resolution is one of main disadvantages of data; in addition, CSR-M can clearly define land and ocean areas, can effectively reduce the influence of leakage errors, and can suppress noise in the treatment process, and has little empirical post-treatment requirement. Thus, the CSR-M results selected in the following discussion describe land water reserves anomaly characteristics.
4.2 Long-term change analysis of groundwater reserves
FIG. 3 shows the overall change in groundwater reserves from GRACE-GLDAS, GRACE-WGHM and WGHM in the area of Tasmanian from 2003 to 2015. The overall trend of the GRACE-GLDAS and GRACE-WGHM results was essentially consistent, with a correlation coefficient of 0.82, and both results exhibited significant periodicity (FIG. 3 a). The annual amplitudes of GRACE-GLDAS and GRACE-WGHM are 40.75mm and 65.41mm, respectively, and the annual phases are 76.37 DEG and 69.91 deg, respectively. However, in terms of seasonal characteristics, the WGHM is contrary to the other two results, which are valley-bottom when the result of the WGHM peaks. 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 change of the underground water reserves shows stronger periodicity, surplus occurs in 1-5 months, and loss occurs in 6-11 months. In the colder season, the difference between GRACE-GLDAS and GRACE-WGHM was large, with the maximum difference at 9 months, reaching 42.48mm (FIG. 3 b). Overall, the GWS changes of the GRACE-GLDAS are consistent with the GRACE-WGHM in the grid except for G23 and G24, with correlation coefficients between 0.59 and 0.85. The RMSE between GRACE-GLDAS and GRACE-WGHM was about 55mm, with maximum and minimum values occurring in G21 and G40, 61.61mm and 27.61mm respectively (fig. 3 c).
4.3 Ground water reserve change verification based on GRACE satellite gravity and hydrologic model
The state of tasmania is spatially divided into 48 grids at 0.5 ° x 0.5 ° latitude and longitude, as shown in fig. 4, with the dots representing the monitoring wells, not fully covering the entire investigation region. Grace-GLDAS, GRACE-WGHM and WGHM versus groundwater reserve variation estimates as shown in FIG. 5, only 4 grids were selected according to the present invention. In each grid, the seasonality and periodicity of the GRACE-GLDAS and GRACE-WGHM are relatively close to those of the measured data. Furthermore, the amplitude of the GRACE-WGHM results is significantly greater than that of GRACE-GLDAS at the G8 grid, while the GRACE-GLDAS has a large amplitude of variation in G32. The vibration amplitude of the measured data changes greatly, for example, from-5 to 5m in G14 (FIG. 5 b) to-0.3 to 0.3m in G34 (FIG. 5 d). The WGHM results exhibited the reverse seasonal character with little trend in G35. This illustrates that the WGHM model requires a great improvement in groundwater reserve estimation in tasmania.
In the eastern region of tasmania, the GRACE-GLDAS results are highly correlated with measured data, with correlation coefficients varying from 0.64 (G41) to 0.85 (G33). In the northern region, the GRACE-WGHM results were highly correlated with the measured data, the correlation coefficients varied from 0.69 (G21) to 0.88 (G26), and the RMSE results were consistent with the correlation coefficient conclusions except for G27 and G41.
For the time series trend term, the GRACE-hydrologic model results and the measured data are in an ascending trend except for G21 and G27. Furthermore, the slope of GRACE-GLDAS is typically 1.8 times the slope of GRACE-WGHM. In both G21 and G27, both the GRACE-WGHM and the measured data were in a decreasing trend, while the GRACE-GLDAS had the opposite trend. The reason may be due to the drawbacks of meteorological forcing data and model parameters, the GLDAS model cannot produce accurate estimates of hydrographic variables in high altitude areas.
4.4 Spatial distribution of groundwater reserve trend
The spatial distribution of groundwater reserve rate of change was calculated between 2003 and 2015 years with 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 rising trend shown by GRACE-GLDAS and GRACE-WGHM results, respectively. As can be seen from fig. 6, the spatial distribution of groundwater reserve change rate in the study area obtained by inversion of GRACE-GLDAS and GRACE-WGHM is more consistent, and the results of both models show that: in 2003-2015, the groundwater reserves in the western coast, southwest and south areas of tasmania tended to decrease, the fastest decreasing area was mainly in the western coast, and the faster increasing area was mainly in the middle and north areas. The GRACE-GLDAS results show that the area range of the groundwater reserves is larger than the area range of the GRACE-WGHM, the ascending rate is large, the area range of the groundwater reserves is smaller than the area range of the GRACE-WGHM, the descending rate is small, namely, the change rate of the GRACE-GLDAS is higher in most areas. The greatest difference occurs in the mid-plateau region (R2 and d 4), and the two results show opposite trends, 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 from fig. 7. FIG. 7a shows that the correlation of GRACE-WGHM with measured data is higher than that of GRACE-GLDAS, and the correlation coefficients are 0.70 and 0.41, respectively. Causes of the opposite trend mainly include: (1) The area is located on the middle plateau, and the groundwater supply source mainly comes from snow and glacier melting water, so that the change of GWS is greatly affected by temperature. Figure 7b shows that the GRACE-WGHM results are more sensitive to temperature changes, whereas the GRACE-GLDAS results cannot capture dynamic changes in temperature, especially in the 5-7 months. The correlation coefficients of GRACE-WGHM and GRACE-GLDAS with temperature data are respectively 0.89 and 0.55; (2) SM variation from WGHM is greater in amplitude than GLDAS, as shown in fig. 7 c. The main reasons are the difference in rainfall driving data for GLDAS and WGHM and the difference in model defined soil layer and depth.
4.5 Spatial distribution of optimal estimates of groundwater reserve change
According to the statistical selection method, the overall index is displayed: YGRACE-WGHM and GRACE-GLDAS are larger in the gray area (FIG. 4). This suggests that the GRACE-WGHM and GRACE-GLDAS results are more consistent with the measured data in the gray area. For the areas lacking measured data, the correlation coefficient and RMSE values between GRACE-GLDAS and GRACE-WGHM were 0.74 and 44.17, respectively, superior to the whole tasmania average levels (0.68 mm and 45.15 mm). Thus, taking the GRACE-WGHM result as the final estimate in the middle and north regions, and taking GRACE-GLDAS as the final estimate in the east coast and south, the other regions averaged to obtain the final groundwater reserve change rate spatial distribution result in the study area as shown in FIG. 8 a. Furthermore, fig. 8b shows a simple average of the two results over the whole area.
The improved results may combine different model results compared to the simple average results in fig. 8b, and preserve the specific region characteristics, as shown in fig. 8 a. DF1-DF4 are the main areas with descending trend of groundwater reserves, and the descending rates are about-2.21 mm/yr, -3.37mm/yr, -3.19mm/yr and-2.36 mm/yr respectively. RF1 is the main region in the rising trend, and the rising rate is about 5.43mm/yr. Dynamic changes in GWS are mainly affected by factors such as rainfall, human activity, geological topography conditions, etc. In very significant downhill areas (DF 1-DF 2), the main include bumpy mountains and broad forests. Thus, human activity is minimal and the annual changes in rainfall may play an important role in the dramatic decline of groundwater in these areas.
Fig. 9 compares DF2 groundwater reserve change results with actual precipitation results. It can be seen that the groundwater reserve change results lag behind the rainfall data, as the replenishment of groundwater by rainfall takes time. The invention counts the lag correlation coefficients of the time sequences of rainfall and groundwater reserves in 4 areas, the lag period is respectively set to be 0-8 months, and the lag period of the four areas can be seen to be 7 months (the lag correlation coefficient is 0.49-0.63). In 2003-2015, both groundwater reserves and rainfall showed a significant trend of decrease, with rates of decrease of-3.36 mm/yr and-39.04 mm/yr, respectively (FIG. 9 b), indicating that groundwater reserves decrease mainly caused by precipitation decrease. For seasonal periods, rainfall occurs mainly at 9 months to 5 months of the next year, accounting for 60% of the total annual rainfall, which is consistent with groundwater reserve changes (fig. 9 c).
4.6 Time change trend of groundwater reserves and climate influence analysis
In the measured well data area, the ground water reserve change results estimated by the GRACE satellite gravity and hydrologic model have higher consistency with the measured data, which indicates that the ground water reserve change in the whole Tasmanian area can be estimated by using the GRACE satellite gravity and hydrologic model (fig. 10 a). The groundwater reserves change result shows that the groundwater reserves change in the 1 st year from the 2010 st year and the 9 th year show that the groundwater reserves change in the groundwater reserves change result, the descent speed is-2.57 mm/yr, and the groundwater reserves change result shows that the Tasmanian is influenced by 'thousand years drought'. The invention utilizes an adaptive Palmer drought index (scPDSI) as a weather drought index. scPDSI data was from Climatic Research (http:// www.cru.uea.ac.uk/cru/data), PDSI less than-2 indicated severe drought. As can be seen from fig. 10b, in 2006-2009, scPDSI was below-2, indicating severe drought climate in this region. The drought climate affects the groundwater reserves, so that the groundwater reserves are severely reduced at the stage, and the reduction rate is-9.47 mm/yr. In the drought phase, rainfall is below average and shows a decreasing trend, which may be the main cause of drought. In 2011-2015, groundwater reserves are recovered at a rate of 3.94mm/yr, mainly due to increased rainfall.
Although the present invention has been described in terms of the preferred embodiments, it is not intended to be limited to the embodiments, and any person skilled in the art can make any possible variations and modifications to the technical solution of the present invention by using the methods and technical matters disclosed above without departing from the spirit and scope of the present invention, so any simple modifications, equivalent variations and modifications to the embodiments described above according to the technical matters of the present invention are within the scope of the technical matters of the present invention.
What is not described in detail in the present specification belongs to the known technology of those skilled in the art.
Claims (6)
1. A method for improving accuracy of regional groundwater reserve estimation, comprising:
acquiring a change delta TWS of land water reserves on a month scale 0 ；
Method for extracting global month-scale soil water content change delta SM by using GLDAS hydrologic model 1 Change in snow Water equivalent ΔSWE 1 And vegetation canopy water reserves change ΔPCSW 1 ；
Extracting global month-scale soil water content change delta SM by utilizing WGHM hydrologic model 2 Change in snow Water equivalent ΔSWE 2 Vegetation canopy water reserves changeConversion of ΔPCSW 2 ；
According to DeltaTWS 0 、ΔSM 1 、ΔSWE 1 And ΔPCSW 1 Solving to obtain the change delta GWS of the groundwater reserve of the month scale 1 The method comprises the steps of carrying out a first treatment on the surface of the According to DeltaTWS 0 、ΔSM 2 、ΔSWE 2 And ΔPCSW 2 Solving to obtain the change delta GWS of the groundwater reserve of the month scale 2 ；
Measured month-scale groundwater reserve change ΔGWS from study area 0 For DeltaGWS respectively 1 And delta GWS 2 Evaluating;
selecting the groundwater reserve change delta GWS with the optimal month scale according to the evaluation result Excellent (excellent) Outputting a result of the change of the groundwater reserve of the month scale of each pixel of the research area;
wherein:
measured month-scale groundwater reserve change ΔGWS from study area 0 For DeltaGWS respectively 1 And delta GWS 2 Performing the evaluation, comprising:
determining ΔGWS 0 And delta GWS 1 Related coefficient PR of (2) 1 Root mean square error RMSE 1 Determining Δgws 0 And delta GWS 2 Related coefficient PR of (2) 2 Root mean square error RMSE 2 ；
Determining ΔGWS 0 、ΔGWS 1 And delta GWS 2 Respective slopes Tr 0 、Tr 1 And Tr 2 ；
Resolving to obtain delta GWS 1 Is the evaluation result Y of (2) 1 And delta GWS 2 Is the evaluation result Y of (2) 2 ：
Wherein F is 11 、F 12 、F 21 And F 22 Respectively represent PR 1 、RMSE 1 、PR 2 And RMSE 2 Consist (·) represents a trend consistency judgment function;
determining ΔGWS 0 And delta GWS 1 Related coefficient PR of (2) 1 Root mean square error RMSE 1 Determining Δgws 0 And delta GWS 2 Related coefficient PR of (2) 2 Root mean square error RMSE 2 Comprising:
acquisition of ΔGWS 0 Time series X (t), ΔGWS of (2) 1 Time series Y of (2) 1 (t) and ΔGWS 2 Time series Y of (2) 2 (t)；
The correlation coefficient is calculated as follows:
the root mean square error is calculated as follows:
where n represents the length of the time series.
2. The method for improving accuracy of regional groundwater reserve estimation according to claim 1, wherein a terrestrial water reserve variation Δtws of a lunar scale is obtained 0 Comprising:
obtaining m lunar-scale land water reserve changes delta TWS based on spherical harmonic coefficient calculation from m data sources 1 、ΔTWS 2 ...ΔTWS m The method comprises the steps of carrying out a first treatment on the surface of the Wherein,,m≥3；
determining ΔTWS 1 、ΔTWS 2 ...ΔTWS i Regression model Z of the respective time series 1 (t)、Z 2 (t)、...Z m (t)；
For Z 1 (t)、Z 2 (t)、...Z m (t) respectively carrying out solution to obtain the value of the linear trend term in each regression model;
from ΔTWS based on the comparison of the values of the linear trend terms in the regression models 1 、ΔTWS 2 ...ΔTWS m Obtaining the optimal terrestrial water reserve change delta TWS of the month scale through medium screening 0 And output.
3. The method for improving accuracy of regional groundwater reserve estimation according to claim 2, wherein Δtws 1 、ΔTWS 2 ...ΔTWS i The general expression of the regression model of the respective corresponding time series is:
wherein i is m, beta i1 Constant term, beta, representing the ith regression model i2 Linear trend term, beta, representing the ith regression model i3 Annual sinusoidal signal, beta, representing the ith regression model i4 Annual cosine signal, beta, representing the ith regression model i5 Half-year sinusoidal signal, beta, representing the ith regression model i6 Half year cosine signal, epsilon, representing the ith regression model i Representing the data error of the ith regression model.
4. The method for improving accuracy of regional groundwater reserve estimation according to claim 1, wherein a solution formula for a change in groundwater reserve on a monthly scale is as follows:
ΔGWS 1 ＝ΔTWS 0 -ΔSM 1 -ΔSWE 1 -ΔPCSW 1
ΔGWS 2 ＝ΔTWS 0 -ΔSM 2 -ΔSWE 2 -ΔPCSW 2 。
5. the method for improving accuracy of regional groundwater reserve estimation according to claim 1,
when Tr is 0 Trend and Tr of (2) 1 Is consistent with the trend of Consist (Tr) 1 ,Tr 0 ) =1; otherwise, consist (Tr 1 ,Tr 0 )＝0；
When Tr is 0 Trend and Tr of (2) 2 Is consistent with the trend of Consist (Tr) 2 ,Tr 0 ) =1; otherwise, consist (Tr 2 ,Tr 0 )＝0。
6. The method for improving accuracy of regional groundwater reserve estimation according to claim 1,
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201911378489.5A CN111241473B (en) | 2019-12-27 | 2019-12-27 | Method for improving estimation accuracy of regional groundwater reserves |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201911378489.5A CN111241473B (en) | 2019-12-27 | 2019-12-27 | Method for improving estimation accuracy of regional groundwater reserves |
Publications (2)
| Publication Number | Publication Date |
|---|---|
| CN111241473A CN111241473A (en) | 2020-06-05 |
| CN111241473B true CN111241473B (en) | 2023-09-29 |
Family
ID=70865595
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN201911378489.5A Active CN111241473B (en) | 2019-12-27 | 2019-12-27 | Method for improving estimation accuracy of regional groundwater reserves |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN111241473B (en) |
Families Citing this family (7)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN112785031B (en) * | 2020-11-30 | 2024-05-17 | 中国空间技术研究院 | Method for improving drought monitoring space-time accuracy based on total water reserve loss index principle |
| CN112949158B (en) * | 2020-12-23 | 2024-06-11 | 中国空间技术研究院 | Method for improving spatial resolution and precision of underground water level variation |
| CN112989557A (en) * | 2021-01-14 | 2021-06-18 | 中国空间技术研究院 | Method for improving water reserve change prediction reliability based on neural network selectable model |
| CN113268869B (en) * | 2021-05-19 | 2022-02-01 | 南方科技大学 | Method and system for monitoring change of earth surface quality |
| CN113899301B (en) * | 2021-09-15 | 2022-07-15 | 武汉大学 | Regional land water reserve change inversion method and system combining GNSS three-dimensional deformation |
| CN113868855B (en) * | 2021-09-24 | 2023-03-10 | 首都师范大学 | Groundwater reserve change satellite gravity forward modeling method fusing water level data |
| CN113887064A (en) * | 2021-10-18 | 2022-01-04 | 生态环境部卫星环境应用中心 | Large-scale underground water reserve remote sensing dynamic monitoring and driving factor quantitative splitting method |
Citations (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN109035105A (en) * | 2018-06-15 | 2018-12-18 | 河海大学 | A kind of quantitative estimation method of month scale evapotranspiration amount |
| CN110175214A (en) * | 2019-02-01 | 2019-08-27 | 中国空间技术研究院 | A kind of method and system changed using Gravity Satellite data monitoring extreme climate |
| CN110398753A (en) * | 2019-06-28 | 2019-11-01 | 武汉大学 | GNSS survey station coordinate time sequence periodicity detection method and system |
Family Cites Families (1)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US20140343855A1 (en) * | 2013-05-15 | 2014-11-20 | The Regents Of The University Of California | Drought Monitoring and Prediction Tools |
-
2019
- 2019-12-27 CN CN201911378489.5A patent/CN111241473B/en active Active
Patent Citations (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN109035105A (en) * | 2018-06-15 | 2018-12-18 | 河海大学 | A kind of quantitative estimation method of month scale evapotranspiration amount |
| CN110175214A (en) * | 2019-02-01 | 2019-08-27 | 中国空间技术研究院 | A kind of method and system changed using Gravity Satellite data monitoring extreme climate |
| CN110398753A (en) * | 2019-06-28 | 2019-11-01 | 武汉大学 | GNSS survey station coordinate time sequence periodicity detection method and system |
Non-Patent Citations (1)
| Title |
|---|
| 刘任莉 等.利用卫星观测数据评估GLDAS与WGHM水文模型的适用性.《武汉大学学报》.2019,第44卷(第11期),第1596-1604页. * |
Also Published As
| Publication number | Publication date |
|---|---|
| CN111241473A (en) | 2020-06-05 |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| CN111241473B (en) | Method for improving estimation accuracy of regional groundwater reserves | |
| Boilley et al. | Comparison between meteorological re-analyses from ERA-Interim and MERRA and measurements of daily solar irradiation at surface | |
| Chen et al. | Seasonal sea level change from TOPEX/Poseidon observation and thermal contribution | |
| Döll et al. | Impact of water withdrawals from groundwater and surface water on continental water storage variations | |
| Liu et al. | ENSO drought onset prediction in northeast Brazil using NDVI | |
| Schanze et al. | The global oceanic freshwater cycle: A state-of-the-art quantification | |
| Perry et al. | The development of a new set of long‐term climate averages for the UK | |
| Bock et al. | West African Monsoon observed with ground‐based GPS receivers during African Monsoon Multidisciplinary Analysis (AMMA) | |
| Morin et al. | Radar‐based quantitative precipitation estimation over Mediterranean and dry climate regimes | |
| Dong et al. | On the relationship between temperature and MODIS snow cover retrieval errors in the Western US | |
| CN112785031B (en) | Method for improving drought monitoring space-time accuracy based on total water reserve loss index principle | |
| Cronin et al. | Surface cloud forcing in the east Pacific stratus deck/cold tongue/ITCZ complex | |
| Yeh et al. | Determining the precipitable water vapor thresholds under different rainfall strengths in Taiwan | |
| Stammer et al. | How well do we know ocean salinity and its changes? | |
| Griesser et al. | Reconstruction of global monthly upper-level temperature and geopotential height fields back to 1880 | |
| Serra | Precipitation measurements from the Tropical Moored Array: A review and look ahead | |
| Bair et al. | Comparison of modeled snow properties in Afghanistan, Pakistan, and Tajikistan | |
| CN112507558A (en) | Reference crop evapotranspiration amount prediction method based on main control factor correction | |
| Lee et al. | Improvement of AMSR2 soil moisture products over South Korea | |
| Sharma et al. | A neural network approach to improve the vertical resolution of atmospheric temperature profiles from geostationary satellites | |
| Dai et al. | Estimation of rainfall erosivity on the Chinese Loess Plateau: A new combination of the ERA5 dataset and machine learning | |
| Umakanth et al. | Increasing pre-monsoon rain days over four stations of Kerala, India | |
| JP4586171B2 (en) | Temperature estimation method and temperature estimation system | |
| Meng et al. | Quality Assessment of FY-4A/AGRI Official Sea Surface Temperature Product | |
| Behifar et al. | Combination of meteorological indices and satellite data for drought monitoring in two different environments in Iran |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| PB01 | Publication | ||
| PB01 | Publication | ||
| SE01 | Entry into force of request for substantive examination | ||
| SE01 | Entry into force of request for substantive examination | ||
| GR01 | Patent grant | ||
| GR01 | Patent grant |