CN110175214A - A kind of method and system changed using Gravity Satellite data monitoring extreme climate - Google Patents
A kind of method and system changed using Gravity Satellite data monitoring extreme climate Download PDFInfo
- Publication number
- CN110175214A CN110175214A CN201910104612.8A CN201910104612A CN110175214A CN 110175214 A CN110175214 A CN 110175214A CN 201910104612 A CN201910104612 A CN 201910104612A CN 110175214 A CN110175214 A CN 110175214A
- Authority
- CN
- China
- Prior art keywords
- data
- precipitation
- climatic factor
- gravity satellite
- time
- 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.)
- Pending
Links
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
-
- Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
- Y02—TECHNOLOGIES OR APPLICATIONS FOR MITIGATION OR ADAPTATION AGAINST CLIMATE CHANGE
- Y02A—TECHNOLOGIES FOR ADAPTATION TO CLIMATE CHANGE
- Y02A10/00—TECHNOLOGIES FOR ADAPTATION TO CLIMATE CHANGE at coastal zones; at river basins
- Y02A10/40—Controlling or monitoring, e.g. of flood or hurricane; Forecasting, e.g. risk assessment or mapping
-
- Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
- Y02—TECHNOLOGIES OR APPLICATIONS FOR MITIGATION OR ADAPTATION AGAINST CLIMATE CHANGE
- Y02A—TECHNOLOGIES FOR ADAPTATION TO CLIMATE CHANGE
- Y02A90/00—Technologies having an indirect contribution to adaptation to climate change
- Y02A90/10—Information and communication technologies [ICT] supporting adaptation to climate change, e.g. for weather forecasting or climate simulation
Abstract
The invention discloses a kind of method and systems changed using Gravity Satellite data monitoring extreme climate to pre-process GRACE Gravity Satellite data, obtain level-3 data this method comprises: obtaining GRACE Gravity Satellite data；Call actual measurement precipitation data, Hydrology model and climatic factor, in conjunction with level-3 data, obtain terrestrial water variation, changes and precipitation respectively on time, space with the related coefficient of climatic factor；Further resolve the specific time for obtaining lag climatic factor and maximum correlation coefficient；When anomalous changes of precipitation and climatic factor reach maximum correlation coefficient, the spatial and temporal distributions of terrestrial water anomalous variation are provided as the monitoring result influenced by climatic factor.The monitoring to the variation of regional extreme climate is realized through the invention, facilitate the disasters such as arid, the flood in forecast China region, and then the loss that extreme weather conditions are caused is reduced, influence that there is important scientific value and economic benefit on the terrestrial water of regional.
Description
Technical field
The invention belongs to space flight and Satellite gravity technical field more particularly to a kind of utilization Gravity Satellite data monitoring are extreme
The method and system of climate change.
Background technique
The country most as world population, China, although gross amount of water resources is abundant, there are still the moneys of water per capita
The problems such as source amount occupies less, is unevenly distributed, influencing vulnerable to extreme climate.Therefore, Study of China region land water resource change
Especially influence of the extreme weather events to regional land water storage social benefit with higher and scientific research value.
Land water storage (Terrestrial Water Storage, TWS) refers to that land surface and water below are total
Be the important composition ingredient in water circulation system.From water vertical distribution, land water storage is by soil moisture content
(Soil Moisture Storage, SMS), earth's surface water reserve, dirt band accumulated snow and groundwater storage composition；It is flat from water
It is seen on weighing apparatus equation, the variation of land water storage is by the movable concentrated expression such as precipitation, evaporation, runoff, underground water.In GRACE
Before the transmitting of (Gravity Recovery and Climate Experiment) Gravity Satellite, research land water storage variation
Depend on traditional hydrometric station and Hydrology model.But traditional hydrological observation is vulnerable to observation condition system
About, the factors such as website spatial distribution is uneven, the quality of data is bad influence, and this strongly limits people to Hydrology process
The understanding and research of (loss of such as underground water, the ablation of polar glacier)；The output of Hydrology model is also limited to specific
Hydrographic features, such as GLDAS (Global Land Data Assimilation System) model output only near-earth
The soil moisture content on surface layer changes, and can not reflect the entire water reserve variation from earth's surface to underground.
Summary of the invention
Technology of the invention solves the problems, such as: overcoming the deficiencies of the prior art and provide a kind of utilization Gravity Satellite data monitoring
The method and system of extreme climate variation observes the variation of regional land water storage by the Gravity Satellite data of post-processing
And time lag correlation analysis is carried out with the global climate factor and is compared, and then reaches monitoring regional extreme climate variation
Purpose.
In order to solve the above-mentioned technical problem, changed the invention discloses a kind of using Gravity Satellite data monitoring extreme climate
Method, comprising:
GRACE Gravity Satellite data are obtained, GRACE Gravity Satellite data are pre-processed, level-3 data are obtained；
Actual measurement precipitation data, Hydrology model and climatic factor are called, according to actual measurement precipitation data, Hydrology model
And climatic factor and level-3 data, obtain terrestrial water variation, changes and precipitation respectively on time, space with climatic factor
Related coefficient；
According to terrestrial water variation, changes and precipitation respectively on time, space with the related coefficient of climatic factor, lagged
The specific time of climatic factor and maximum correlation coefficient；
When anomalous changes of precipitation and climatic factor reach maximum correlation coefficient, the when space division of terrestrial water anomalous variation is provided
Cloth is as the monitoring result influenced by climatic factor.
In the above-mentioned method using the variation of Gravity Satellite data monitoring extreme climate, GRACE Gravity Satellite data are obtained,
GRACE Gravity Satellite data are pre-processed, level-3 data are obtained, comprising:
GRACE Gravity Satellite data are obtained, determine spherical harmonic coefficient；
Deduct the average value in spherical harmonic coefficient on the corresponding period；
By spherical harmonic coefficient C20Item is changed to the C observed by satellite laser ranging (SLR)20, while adding the earth's core correction member；
Consider that GIA influences change in long term, tetra- kinds of methods of DDK, Swenson, Duan and P4M6 is selected to inhibit north and south respectively
" band " error influences；
The influence of noise of high-order spherical harmonic coefficient is reduced using Gauss 500km filtering method, and uses Forward modeling
Method carries out leakage errors correction；
Equivalent water pillar height Grid square is converted by the spherical harmonic coefficient after correction, and carries out Area-weighted and averagely switchs to accordingly
Time series；
Trend term, anniversary and half anniversary are asked using least square method, and uses 5 after being deducted in time series
A month sliding average obtains inter-annual scale TWSA；
It takes four kinds of arithmetic mean of instantaneous values for going stripe method and different data sources to obtain result to estimate as final TWSA, obtains
Level-3 data.
In the above-mentioned method using the variation of Gravity Satellite data monitoring extreme climate, actual measurement precipitation data, land are called
Hydrological model and climatic factor are obtained according to actual measurement precipitation data, Hydrology model and climatic factor and level-3 data
To terrestrial water variation, changes and precipitation respectively on time, space with the related coefficient of climatic factor, comprising:
Take two groups of independent time series x1And x2；
Correlation coefficient ρ (τ) is calculated:
Wherein, σ11And σ22Respectively x1And x2Variance, σ12For x1And x2Covariance, τ is time delay factor, | ρ (τ) |
≤ 1, | τ |≤12.
In the above-mentioned method using the variation of Gravity Satellite data monitoring extreme climate, become according to terrestrial water variation, precipitation
Change respectively on time, space with the related coefficient of climatic factor, obtains specific time and the maximal correlation of lag climatic factor
Coefficient, comprising:
According to maximum correlation coefficient and lag time and level-3 data, determine that GRACE Gravity Satellite terrestrial water becomes extremely
The spatial and temporal distributions of change and precipitation data and Hydrology model；
According to the time change and particular geographic location and atmospheric environment of climatic factor, comparative analysis is in lag time inland
The case where ground water anomalous variation and changes and precipitation are affected by climate change；
The time series for the terrestrial water exception that GRACE Gravity Satellite is monitored is as the gas of the hydrological variation of relevant range
Wait the factor.
In the above-mentioned method using the variation of Gravity Satellite data monitoring extreme climate,
Survey precipitation data are as follows: the actual measurement moon precipitation Grid square provided from China Meteorological Administration；
Hydrology model are as follows: in NASA Goddard's space flight center and Environmental forecast
Assimilate data system in the global land face that the heart is established jointly.
In the above-mentioned method using the variation of Gravity Satellite data monitoring extreme climate, terrestrial water and precipitation are as independent
Variable, when analyzing the relationship of terrestrial water and precipitation and climatic factor, will ± 0.5 as whether there is the threshold value of correlation, when
Stagnant range selection was at -12~12 months.
The invention also discloses a kind of systems changed using Gravity Satellite data monitoring extreme climate, comprising:
Module is obtained to pre-process GRACE Gravity Satellite data for obtaining GRACE Gravity Satellite data, obtain
Level-3 data；
It calls and resolves module, for calling actual measurement precipitation data, Hydrology model and climatic factor, according to actual measurement precipitation
Data, Hydrology model and climatic factor and level-3 data, obtain terrestrial water variation, changes and precipitation respectively when
Between, spatially with the related coefficient of climatic factor；
Analysis module, for being changed according to terrestrial water, changes and precipitation it is related to climatic factor on time, space respectively
Coefficient obtains specific time and the maximum correlation coefficient of lag climatic factor；
Prediction module, for it is different to provide terrestrial water when anomalous changes of precipitation and climatic factor reach maximum correlation coefficient
The spatial and temporal distributions often changed are as the monitoring result influenced by climatic factor.
The invention has the following advantages that
The present invention is with a completely new angle using the monitoring extreme climate variation of Gravity Satellite method to regional land
The influence of water, observed by the Gravity Satellite data of post-processing regional land water storage variation and with the global climate factor
It carries out time lag correlation analysis and is compared, and then reach monitoring regional extreme climate variation purpose, help to predict
The disasters such as arid, the flood of regional, and then the loss that extreme weather conditions are caused is reduced, to the terrestrial water of regional
Influence that there is important scientific value and economic benefit.
Detailed description of the invention
Fig. 1 is a kind of step process using Gravity Satellite data monitoring extreme climate changing method in the embodiment of the present invention
Figure；
Fig. 2 is 2015/16ElPeriod, regional in September, 2015 to the GRACE of in August, 2016 observation is month by month
TWSA spatial distribution schematic diagram；
Fig. 3 is regional terrestrial water and the relation schematic diagram of extreme weather events in time；
Fig. 4 is the relation schematic diagram in Yangtze river basin upper, middle and lower trip area and extreme weather events.
Specific embodiment
To make the object, technical solutions and advantages of the present invention clearer, below in conjunction with attached drawing to disclosed by the invention
Embodiment is described in further detail.
Such as Fig. 1, in embodiments of the present invention, this utilizes the method for Gravity Satellite data monitoring extreme climate variation, comprising:
Step 101, GRACE Gravity Satellite data are obtained, GRACE Gravity Satellite data are pre-processed, are obtained
Level-3 data.
GRACE Gravity Satellite emits in March, 2002, mainly passes through the variation of the distance between two satellites of high-acruracy survey
(micron accuracies) carry out the change in time and space in the quantitative inversion whole world and regional land water storage.Combination of embodiment of the present invention GRACE
Gravity Satellite data, GLDAS hydrological model and actual measurement precipitation data, have studied in detail 2005-2017 regional terrestrial water
The change in time and space of reserves and correlation and time lag with ENSO, and give concise physical interpretation.
In the present embodiment, GRACE Gravity Satellite data can be pre-processed in the following way: obtains GRACE
Gravity Satellite data, determine spherical harmonic coefficient；Deduct the average value in spherical harmonic coefficient on the corresponding period；By spherical harmonic coefficient C20Xiang Geng
It is changed to the C observed by satellite laser ranging (SLR)20, while adding the earth's core correction member；Consider that GIA influences change in long term, point
It Xuan Yong not tetra- kinds of methods inhibition north and south " band " errors influences of DDK, Swenson, Duan and P4M6；It is filtered using Gauss 500km
Method reduces the influence of noise of high-order spherical harmonic coefficient, and carries out leakage errors correction using Forward modeling method；It will change
Spherical harmonic coefficient after just is converted into equivalent water pillar height Grid square, and carries out Area-weighted and averagely switch to corresponding time series；Benefit
Trend term, anniversary and half anniversary are asked with least square method, and uses sliding in 5 months after being deducted in time series
Averagely obtain inter-annual scale TWSA；Take four kinds of arithmetic mean of instantaneous values for going stripe method and different data sources to obtain result as final
TWSA estimation, obtains level-3 data.
Wherein, it should be understood that
GRACE Gravity Satellite data include but are not limited to: CSR RL05level-2 month gravity field spherical harmonic coefficient, CSR-
Mascons data and JPL-Mascons data.
Four sets of earth's surfaces assimilation data of GLDAS announcement can be used in Hydrology model.
Precipitation data may be from the actual measurement moon precipitation Grid square of China Meteorological Administration's offer.
Various types of data in temporal resolution and spatial resolution it is ensured that be consistent.Wherein, GRACE Gravity Satellite
Data carry out respective handling and obtaining level-3 data.Hydrology model, precipitation data and weather factor data only need to read
Access is according to precipitation data does simple neighbor interpolation.
Step 102, actual measurement precipitation data, Hydrology model and climatic factor are called, according to actual measurement precipitation data, land
Hydrological model and climatic factor and level-3 data, obtain terrestrial water variation, changes and precipitation respectively on time, space with
The related coefficient of climatic factor.
Step 103, according to terrestrial water variation, changes and precipitation respectively on time, space with the related coefficient of climatic factor,
Obtain specific time and the maximum correlation coefficient of lag climatic factor.
In the present embodiment, two groups of independent time series x can first be taken1And x2, correlation coefficient ρ (τ) is calculated:
Wherein, σ11And σ22Respectively x1And x2Variance, σ12For x1And x2Covariance, τ is time delay factor, | ρ (τ) |
≤ 1, | τ |≤12.
Further, according to maximum correlation coefficient and lag time and level-3 data, GRACE Gravity Satellite land is determined
The spatial and temporal distributions of ground water anomalous variation and precipitation data and Hydrology model；According to the time change of climatic factor and specific
Geographical location and atmospheric environment, comparative analysis are affected by climate change in lag time inland basin water anomalous variation and changes and precipitation
The case where；The time series for the terrestrial water exception that GRACE Gravity Satellite is monitored is as the gas of the hydrological variation of relevant range
Wait the factor.
Step 104, when anomalous changes of precipitation and climatic factor reach maximum correlation coefficient, terrestrial water anomalous variation is provided
Spatial and temporal distributions as the monitoring result influenced by climatic factor.
In the preferred embodiment of the present invention, actual measurement precipitation data can be with are as follows: mentions from China Meteorological Administration
The actual measurement moon precipitation Grid square of confession.For example, access time section is in January, 2005 to part of in August, 2016, spatial resolution
It is 0.5 ° × 0.5 °.Consistent for spatial resolution when carrying out data processing, we utilize neighbor interpolation method by precipitation data
Interpolation is 1 ° × 1 °.
In the preferred embodiment of the present invention, Hydrology model can be with are as follows: navigates from NASA Goddard
Assimilate data system in the global land face that its flight center and Environmental forecasting centre establish jointly.For example, to reduce number
Influence according to model error to result assimilates data (Noah, Mosaic, CLM and VIC) using four sets of earth's surfaces that GLDAS is announced,
Its spatial resolution is 1 ° × 1 °, and the period of selection is in January, 2005 to part of in August, 2016.Take the arithmetic of four sets of data
Average value come estimate soil moisture content exception.
In the preferred embodiment of the present invention, terrestrial water and precipitation are as independent variable, in analysis terrestrial water and drop
When the relationship of water and climatic factor, will ± 0.5 as whether with correlation threshold value, time lag range selection at -12~12
Month.
On the basis of the above embodiments, it is illustrated below with reference to a specific example.
(1) experimental subjects is chosen.Chinese ten large watersheds are chosen as experimental subjects.Because of extreme weather events in recent years
It takes place frequently and produces between significant impact, especially 2005-2017 that strong ENSO event makes twice to the whole world and regional water circulation
There is biggish year border fluctuation in regional land water storage.
(2) processing and application of Various types of data.The GRACE Gravity Satellite level-3 number of Chinese ten large watersheds is read respectively
According to, Hydrology model and precipitation data, their irregular item is obtained using harmonic analysis method.Meanwhile read weather because
Time Sub-series.Ten large watershed Hydrology model exception of China, Abnormal Precipitation are calculated separately using correlation and time lag formula
Maximum correlation coefficient and lag month with climatic factor.
(3) survey region relevant to climatic factor in experimental subjects is determined.According to the threshold value of strong correlation, including length
The land water storage of all river valleies in river basin and the southeast abnormal (including TWSA and SMSA), Abnormal Precipitation shown with ENSO compared with
Strong correlation.Therefore, select all river valleies in the Yangtze river basin and the southeast as further research object.And the entire Yangtze river basin
It is wide across range, geographical environment difference is big, climate variability, need for be divided into the Yangtze river basin upper, middle and lower and swim and analyze.
As shown in Fig. 2, in the Yangtze river basin, downstream land water storage abnormal (including TWSA and SMSA), Abnormal Precipitation show with ENSO
Stronger correlation out.Finally, experimental subjects is determined as in Yangtze River in China, all river valleies in Lower Reaches and the southeast.
(4) terrestrial water it is abnormal in time with the relationship of climatic factor.Provide in Yangtze River in China, Lower Reaches and the southeast it is all
River valley terrestrial water is abnormal as shown in Figure 3 with the time series of ENSO.It is obvious that monitoring their terrestrial water using GRACE
Reserves are abnormal and ENSO maximum correlation coefficient is respectively 0.55,0.78,0.70, and about lag 2 months compared with ENSO.
Wherein, Fig. 3 (a) is TWSA, SMSA and Abnormal Precipitation time series of the Yangtze river basin 2005-2016 In The Middle Reaches
Schematic diagram；Fig. 3 (b) is TWSA, SMSA and the signal of Abnormal Precipitation time series in the Changjiang river 2005-2016 lower reaches area
Figure；Fig. 3 (c) is TWSA, SMSA and Abnormal Precipitation time series schematic diagram of all river valleies in the southeast 2005-2016；Fig. 3 (d)3.4 index schematic diagrames.
(5) terrestrial water it is abnormal spatially with the relationship of climatic factor.As shown in figure 4, in 2015/16 year strong EI Nino
The regional terrestrial water monitored during event using GRACE Gravity Satellite is abnormal.
Wherein, Fig. 4 (a) indicates the correlation of Yangtze river basin upstream area TWSA, SMSA, Abnormal Precipitation and ENSO；Fig. 4
(b) correlation of Yangtze river basin In The Middle Reaches TWSA, SMSA, Abnormal Precipitation and ENSO is indicated；Fig. 4 (c) is indicated under the Yangtze river basin
Swim the correlation of area TWSA, SMSA, Abnormal Precipitation and ENSO；Fig. 4 (d) indicates all river valley TWSA, SMSA in the southeast, precipitation
The abnormal correlation with ENSO.The time lag range of abscissa selected between -12 to 12 month, and wherein negative value indicates TWSA
Or before Abnormal Precipitation is stagnant, positive value indicates TWSA or Abnormal Precipitation lag
Experimental analysis
2015/16ElSince in October, 2014, about reach peak value in November, 2015, and in 2016
May in year terminates, and the duration nearly 20 months altogether.2015/16ElPeriod (such as Fig. 3): in the Yangtze river basin, Lower Reaches
Persistently there is normal anomaly from part of in September, 2015 in July, 2016 with the terrestrial water and precipitation of all river valleies in the southeast.2015
The normal anomaly that autumn and winter (in September, 2015 was to 2 months 2016) occurs is since this EI Nino event is from October, 2014
Caused by starting to be continued above influence in 1 year, we term it the contemporaneous affects of EI Nino.(the 3-5 in 2016 of spring in 2016
Month) in the Yangtze river basin, the terrestrial water of all river valleies in Lower Reaches and the southeast and precipitation occur normal anomaly again, wherein dropping
There is peak value in April, 2016 in water, and terrestrial water about lags and occurs within 1 month peak value in May, 2016, this is because this
Secondary EI Nino peak lag influence in 4~6 months.The terrestrial water in these three regions occurs again in July, 2016
Then there is biggish normal anomaly in June, 2016 in biggish normal anomaly, precipitation.South China since 6~July in 2016
Area has continuously met with the heavy rainfall invasion of large area, and heavy rainfall has aggravated southern area since autumn and winter in 2015
This serious flood situation.
In the Yangtze river basin, Lower Reaches and the terrestrial water in the southeast all river valley in July, 2016 it is different normally due to 2016
Caused by the precipitation in 6~July.And it is due to 2015/16El that the reason of precipitation occurs in 6~July in 2016Knot
The summer of beam, it is abnormal that northwest Pacific still remains apparent anticyclonic circulation.This not only strengthen Tropical western North Pacific to
The vapor transfer of regional, also makes Tibetan high reinforcement and extension westwards causes southern china and drops by force
Water has been eventually led in the Yangtze river basin, normal anomaly occur in all river valley terrestrial waters in Lower Reaches and the southeast.
Experiment conclusion
Comparative analysis from the time and spatially using GRACE Gravity Satellite it is found that can be monitored due to extreme climate
The terrestrial water that event is caused is abnormal.It (is taken off using the standardized middle and lower reach of Yangtze River, all river valley terrestrial water time serieses in the southeast
Season and trend term) it can be used to indicate the extreme exception of Hydroclimate.
On the basis of the above embodiments, become the invention also discloses a kind of using Gravity Satellite data monitoring extreme climate
The system of change, comprising: obtain module and GRACE Gravity Satellite data are located in advance for obtaining GRACE Gravity Satellite data
Reason, obtains level-3 data；It calls and resolves module, for calling actual measurement precipitation data, Hydrology model and climatic factor,
According to actual measurement precipitation data, Hydrology model and climatic factor and level-3 data, terrestrial water variation is obtained, precipitation becomes
Change respectively on time, space with the related coefficient of climatic factor；Analysis module, for according to terrestrial water variation, changes and precipitation
Specific time and the maximal correlation system of lag climatic factor are obtained with the related coefficient of climatic factor on time, space respectively
Number；Prediction module, for providing terrestrial water anomalous variation when anomalous changes of precipitation and climatic factor reach maximum correlation coefficient
Spatial and temporal distributions as the monitoring result influenced by climatic factor.
For system embodiments, since it is corresponding with embodiment of the method, so be described relatively simple, correlation
Place referring to embodiment of the method part explanation.
Various embodiments are described in a progressive manner in this explanation, the highlights of each of the examples are with its
The difference of his embodiment, the same or similar parts between the embodiments can be referred to each other.
The above, optimal specific embodiment only of the invention, but scope of protection of the present invention is not limited thereto,
In the technical scope disclosed by the present invention, any changes or substitutions that can be easily thought of by anyone skilled in the art,
It should be covered by the protection scope of the present invention.
The content that description in the present invention is not described in detail belongs to the well-known technique of professional and technical personnel in the field.
Claims (7)
1. a kind of method changed using Gravity Satellite data monitoring extreme climate characterized by comprising
GRACE Gravity Satellite data are obtained, GRACE Gravity Satellite data are pre-processed, level-3 data are obtained；
Actual measurement precipitation data, Hydrology model and climatic factor are called, it is gentle according to actual measurement precipitation data, Hydrology model
Wait the factor and level-3 data, obtain terrestrial water variation, changes and precipitation respectively on time, space with the phase of climatic factor
Relationship number；
According to terrestrial water variation, changes and precipitation respectively on time, space with the related coefficient of climatic factor, lag weather is obtained
The specific time of the factor and maximum correlation coefficient；
When anomalous changes of precipitation and climatic factor reach maximum correlation coefficient, the spatial and temporal distributions for providing terrestrial water anomalous variation are made
For the monitoring result influenced by climatic factor.
2. the method according to claim 1 changed using Gravity Satellite data monitoring extreme climate, which is characterized in that obtain
GRACE Gravity Satellite data are taken, GRACE Gravity Satellite data are pre-processed, level-3 data are obtained, comprising:
GRACE Gravity Satellite data are obtained, determine spherical harmonic coefficient；
Deduct the average value in spherical harmonic coefficient on the corresponding period；
By spherical harmonic coefficient C20Item is changed to the C observed by satellite laser ranging (SLR)20, while adding the earth's core correction member；
Consider that GIA influences change in long term, tetra- kinds of methods of DDK, Swenson, Duan and P4M6 is selected to inhibit north and south " item respectively
Band " error influences；
The influence of noise of high-order spherical harmonic coefficient is reduced using Gauss 500km filtering method, and uses Forward modeling method
Carry out leakage errors correction；
Equivalent water pillar height Grid square is converted by the spherical harmonic coefficient after correction, and carries out Area-weighted and averagely switchs to the corresponding time
Sequence；
Trend term, anniversary and half anniversary are asked using least square method, and is used 5 months after being deducted in time series
Sliding average obtain inter-annual scale TWSA；
It takes four kinds of arithmetic mean of instantaneous values for going stripe method and different data sources to obtain result to estimate as final TWSA, obtains
Level-3 data.
3. the method according to claim 1 changed using Gravity Satellite data monitoring extreme climate, which is characterized in that adjust
With actual measurement precipitation data, Hydrology model and climatic factor, according to actual measurement precipitation data, Hydrology model and weather because
Son and level-3 data, obtain terrestrial water variation, changes and precipitation respectively on time, space with the phase relation of climatic factor
Number, comprising:
Take two groups of independent time series x1And x2；
Correlation coefficient ρ (τ) is calculated:
Wherein, σ11And σ22Respectively x1And x2Variance, σ12For x1And x2Covariance, τ is time delay factor, | ρ (τ) |≤1, | τ |
≤12。
4. the method according to claim 3 changed using Gravity Satellite data monitoring extreme climate, which is characterized in that root
According to terrestrial water variation, changes and precipitation respectively on time, space with the related coefficient of climatic factor, lag climatic factor is obtained
Specific time and maximum correlation coefficient, comprising:
According to maximum correlation coefficient and lag time and level-3 data, determine GRACE Gravity Satellite terrestrial water anomalous variation with
And the spatial and temporal distributions of precipitation data and Hydrology model；
According to the time change and particular geographic location and atmospheric environment of climatic factor, comparative analysis is in lag time inland basin water
The case where anomalous variation and changes and precipitation are affected by climate change；
The time series for the terrestrial water exception that GRACE Gravity Satellite is monitored as the weather of the hydrological variation of relevant range because
Son.
5. the method according to claim 1 changed using Gravity Satellite data monitoring extreme climate, which is characterized in that
Survey precipitation data are as follows: the actual measurement moon precipitation Grid square provided from China Meteorological Administration；
Hydrology model are as follows: total from NASA Goddard's space flight center and Environmental forecasting centre
Assimilate data system with the global land face established.
6. the method according to claim 1 changed using Gravity Satellite data monitoring extreme climate, which is characterized in that land
Ground water and precipitation are as independent variable, when analyzing the relationship of terrestrial water and precipitation and climatic factor, whether incite somebody to action ± 0.5 conduct
Threshold value with correlation, time lag range were selected at -12~12 months.
7. a kind of system changed using Gravity Satellite data monitoring extreme climate characterized by comprising
Module is obtained to pre-process GRACE Gravity Satellite data for obtaining GRACE Gravity Satellite data, obtain
Level-3 data；
It calls and resolves module, for calling actual measurement precipitation data, Hydrology model and climatic factor, according to actual measurement precipitation money
Material, Hydrology model and climatic factor and level-3 data, obtain terrestrial water variation, changes and precipitation respectively the time,
Spatially with the related coefficient of climatic factor；
Analysis module, for being changed according to terrestrial water, changes and precipitation respectively on time, space with the phase relation of climatic factor
Number obtains specific time and the maximum correlation coefficient of lag climatic factor；
Prediction module, for providing terrestrial water and becoming extremely when anomalous changes of precipitation and climatic factor reach maximum correlation coefficient
The spatial and temporal distributions of change are as the monitoring result influenced by climatic factor.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201910104612.8A CN110175214A (en) | 2019-02-01 | 2019-02-01 | A kind of method and system changed using Gravity Satellite data monitoring extreme climate |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201910104612.8A CN110175214A (en) | 2019-02-01 | 2019-02-01 | A kind of method and system changed using Gravity Satellite data monitoring extreme climate |
Publications (1)
| Publication Number | Publication Date |
|---|---|
| CN110175214A true CN110175214A (en) | 2019-08-27 |
Family
ID=67689018
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN201910104612.8A Pending CN110175214A (en) | 2019-02-01 | 2019-02-01 | A kind of method and system changed using Gravity Satellite data monitoring extreme climate |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN110175214A (en) |
Cited By (8)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN110852474A (en) * | 2019-09-24 | 2020-02-28 | 广州地理研究所 | Land water reserve prediction method, device and equipment based on decision tree algorithm |
| CN111241473A (en) * | 2019-12-27 | 2020-06-05 | 中国空间技术研究院 | Method for improving regional underground water reserve estimation precision |
| CN111291944A (en) * | 2020-03-16 | 2020-06-16 | 中国人民解放军61540部队 | Marine climate prediction method and system based on NPSDV driving factor identification |
| CN111797491A (en) * | 2020-04-30 | 2020-10-20 | 中国空间技术研究院 | Analysis method and system for vertical displacement seasonality and temporal-spatial variation of original crust of North China plain |
| 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 |
| CN113268869A (en) * | 2021-05-19 | 2021-08-17 | 南方科技大学 | Method and system for monitoring change of earth surface quality |
| CN113407524A (en) * | 2021-06-30 | 2021-09-17 | 国家气候中心 | Climate system mode multi-circle layer coupling data assimilation system |
| CN115630686A (en) * | 2022-10-11 | 2023-01-20 | 首都师范大学 | Method for recovering land water reserve abnormity from satellite gravity data by machine learning |
Citations (5)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN103076639A (en) * | 2012-12-28 | 2013-05-01 | 中国科学院测量与地球物理研究所 | Method for inverting earth gravity field of residual inter-star velocity |
| CN103678885A (en) * | 2013-11-22 | 2014-03-26 | 河海大学 | Method for establishing and analyzing drought index based on gravity satellite |
| CN106529164A (en) * | 2016-11-03 | 2017-03-22 | 清华大学 | Method and system for acquiring ground water storage variation value by combining GRACE satellite |
| CN108594335A (en) * | 2018-04-25 | 2018-09-28 | 中国科学院国家空间科学中心 | A kind of Global Precipitation detection method |
| CN109035105A (en) * | 2018-06-15 | 2018-12-18 | 河海大学 | A kind of quantitative estimation method of month scale evapotranspiration amount |
-
2019
- 2019-02-01 CN CN201910104612.8A patent/CN110175214A/en active Pending
Patent Citations (5)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN103076639A (en) * | 2012-12-28 | 2013-05-01 | 中国科学院测量与地球物理研究所 | Method for inverting earth gravity field of residual inter-star velocity |
| CN103678885A (en) * | 2013-11-22 | 2014-03-26 | 河海大学 | Method for establishing and analyzing drought index based on gravity satellite |
| CN106529164A (en) * | 2016-11-03 | 2017-03-22 | 清华大学 | Method and system for acquiring ground water storage variation value by combining GRACE satellite |
| CN108594335A (en) * | 2018-04-25 | 2018-09-28 | 中国科学院国家空间科学中心 | A kind of Global Precipitation detection method |
| CN109035105A (en) * | 2018-06-15 | 2018-12-18 | 河海大学 | A kind of quantitative estimation method of month scale evapotranspiration amount |
Non-Patent Citations (2)
| Title |
|---|
| LONG D，ET AL: "GRACE satellite monitoring of large depletion in water storage in response to the 2011 drought in Texas", 《GEOPHYSICAL RESEARCH LETTERS》 * |
| 金钟炜: "利用时变重力场研究长江流域水储量变化与极端气候事件", 《中国优秀硕士学位论文全文数据库 基础科学辑》 * |
Cited By (13)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN110852474A (en) * | 2019-09-24 | 2020-02-28 | 广州地理研究所 | Land water reserve prediction method, device and equipment based on decision tree algorithm |
| CN111241473B (en) * | 2019-12-27 | 2023-09-29 | 中国空间技术研究院 | Method for improving estimation accuracy of regional groundwater reserves |
| CN111241473A (en) * | 2019-12-27 | 2020-06-05 | 中国空间技术研究院 | Method for improving regional underground water reserve estimation precision |
| CN111291944A (en) * | 2020-03-16 | 2020-06-16 | 中国人民解放军61540部队 | Marine climate prediction method and system based on NPSDV driving factor identification |
| CN111291944B (en) * | 2020-03-16 | 2022-09-23 | 中国人民解放军61540部队 | Marine climate prediction method and system based on NPSDV driving factor identification |
| CN111797491A (en) * | 2020-04-30 | 2020-10-20 | 中国空间技术研究院 | Analysis method and system for vertical displacement seasonality and temporal-spatial variation of original crust of North China plain |
| CN111797491B (en) * | 2020-04-30 | 2024-04-12 | 中国空间技术研究院 | Method and system for analyzing seasonal and space-time variation of vertical displacement of North China plain crust |
| 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 |
| CN113268869A (en) * | 2021-05-19 | 2021-08-17 | 南方科技大学 | Method and system for monitoring change of earth surface quality |
| CN113268869B (en) * | 2021-05-19 | 2022-02-01 | 南方科技大学 | Method and system for monitoring change of earth surface quality |
| CN113407524A (en) * | 2021-06-30 | 2021-09-17 | 国家气候中心 | Climate system mode multi-circle layer coupling data assimilation system |
| CN115630686B (en) * | 2022-10-11 | 2023-06-23 | 首都师范大学 | Method for recovering land water reserve anomalies from satellite gravity data using machine learning |
| CN115630686A (en) * | 2022-10-11 | 2023-01-20 | 首都师范大学 | Method for recovering land water reserve abnormity from satellite gravity data by machine learning |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| CN110175214A (en) | A kind of method and system changed using Gravity Satellite data monitoring extreme climate | |
| Long et al. | Deriving scaling factors using a global hydrological model to restore GRACE total water storage changes for China's Yangtze River Basin | |
| Tian et al. | Precipitation and temperature changes in the major Chinese river basins during 1957–2013 and links to sea surface temperature | |
| Ran et al. | Delineation of reservoirs using remote sensing and their storage estimate: an example of the Yellow River basin, China | |
| Seyoum | Characterizing water storage trends and regional climate influence using GRACE observation and satellite altimetry data in the Upper Blue Nile River Basin | |
| Jung et al. | Analysis of the relationship between flooding area and water height in the Logone floodplain | |
| Fujinami et al. | Characteristic intraseasonal oscillation of rainfall and its effect on interannual variability over Bangladesh during boreal summer | |
| Oki et al. | Global water resources assessment under climatic change in 2050 using TRIP | |
| Zhang et al. | Analysis of the expanding process of the Spartina alterniflora salt marsh in Shanyutan wetland, Minjiang River estuary by remote sensing | |
| Jiang et al. | Insights into hydrological drought characteristics using GNSS-inferred large-scale terrestrial water storage deficits | |
| Wei et al. | Bias correction of GPM IMERG Early Run daily precipitation product using near real-time CPC global measurements | |
| Zeng et al. | Spatial downscaling of IMERG considering vegetation index based on adaptive lag phase | |
| Szabó et al. | Geospatial analysis of drought tendencies in the Carpathians as reflected in a 50-year time series | |
| Zhang et al. | Flow regime changes in the Lancang River, revealed by integrated modeling with multiple Earth observation datasets | |
| Fan et al. | Monitoring of soil erosion and assessment for contribution of sediments to rivers in a typical watershed of the Upper Yangtze River Basin | |
| Maciel et al. | Flood forecast in Manaus, Amazonas, brazil | |
| Zhang et al. | On the linkage between the extreme drought and pluvial patterns in China and the large-scale atmospheric circulation | |
| Nakayama | Integrated assessment system using process‐based eco‐hydrology model for adaptation strategy and effective water resources management | |
| Xu et al. | Projected seasonal changes in future rainfall erosivity over the Lancang-Mekong River basin under the CMIP6 scenarios | |
| Gowri et al. | A hydro-climatological outlook on the long-term availability of water resources in Cauvery river basin | |
| Zhao et al. | Spatiotemporal variations in ecological quality of Otindag Sandy Land based on a new modified remote sensing ecological index | |
| Lv et al. | Effects of Climate Change and Human Activity on Runoff in a Typical Loess Gullied-Hilly Region Watershed. | |
| Xu | Study on water storage change and its consideration in water balance in the mountain regions over arid Northwest China | |
| De Linage et al. | Forecasting terrestrial water storage changes in the Amazon Basin using Atlantic and Pacific sea surface temperatures. | |
| Sokneth et al. | Evaluating aquifer stress and resilience with GRACE information at different spatial scales in Cambodia |
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 | ||
| RJ01 | Rejection of invention patent application after publication |
Application publication date: 20190827 |
|
| RJ01 | Rejection of invention patent application after publication |