CN107316095A - A kind of region meteorological drought grade prediction technique for coupling multi-source data - Google Patents
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Abstract
The invention discloses a kind of region meteorological drought grade prediction technique for coupling multi-source data, by carrying out space NO emissions reduction to gridding satellite remote sensing precipitation data, build high-resolution Regional Precipitation spatial database, arid grade is divided using standardization drought index, the large scale meteorological factor of reflection Atmospheric Circulation Characteristics is introduced as the covariant of drought status transition probability, there is the meteorological drought grade forecast model of time-varying transition probability based on non-stationary Markov chain model construction.Variability of the present invention using multi-source remote sensing information and basic underground properties capture region precipitation spatially, it compensate for conventional station observation rainfall not enough, make full use of the outside stress and precursor signal that can reflect that this drought and waterlogging of the large scale meteorological factor of Atmospheric Circulation Characteristics are developed, drought and waterlogging formation and development mechanism is considered to a certain extent, the Dynamic Evolution Characteristics for meteorological model system of more fitting, can be to build Regional Floods/Droughts early-warning and predicting system to lay the foundation with stronger science and practicality.
Description
Technical field
The invention belongs to hazard forecasting early warning technology field, more particularly to a kind of region meteorological drought for coupling multi-source data
Grade prediction technique.
Background technology
Arid is a kind of natural phenomena of moisture continuation shortage, with occurrence frequency height, duration length, involves scope
Wide the features such as.China is located in Asian Monsoon climatic province, and precipitation quantity space and Tendency analysis are seriously uneven, and monsoon path and intensity
Year border luffing it is very big, the factor such as domestic landform is caused in addition hydro-thermal is uneven so that China's drought takes place frequently, be in the world by
One of Droughts the most serious country.Arid is divided into by meteorology institute of the U.S. on the basis of various Definitions of Drought are summarized
Four types, i.e. meteorological drought, Hydrologic Drought, agricultural arid and social economy's arid.Meteorological drought refers to decrease in precipitation or nothing
Precipitation, the formation of other types arid all has with meteorological drought directly to be contacted.
" impact " consequence brought compared to other extreme weather events such as flood, typhoon, arid generation
There is significant disguised and " creep " property with development.The starting of one drought event and end time are often difficult to define, and
Once its coverage and the extent of injury are shown, reply and remedial measure are often seriously delayed.Therefore, promptly and accurately
Drought forccast, for instructing drought relief work to carry out, strengthens calamity source contingency management, improves disaster response level etc., be respectively provided with
Important meaning.
Draught monitor is the basis of drought forccast, and existing method is mostly based on actual measurement rainfall station data, but due to observation
The problem of site density and skewness, it is difficult to embody the special heterogeneity of rainfall distribution；In some remote Data scarces
Or Cross Some Region Without Data, even more it is difficult to obtain observation data.The development of remote sensing technology in recent years, is provided for a wide range of draught monitor
One brand-new approach.Gao Zhiqiang[1]A kind of drought monitoring method based on earth's surface water and heat remote-sensing inversion is invented and has been
System, for estimating the region surface energy under Different climate, orographic condition and distribution of evapotranspiring, is provided for regional agriculture Monitoring of drought
Technical support.Lee is all right etc.[2]A kind of drought monitoring method based on HJ-1A/1B ccd datas is invented, with reference to by HJ-1A/
MPDI data that 1B CCD remotely-sensed datas are obtained and Crop growing stage determine agricultural arid situation；Feng Jie etc.[3]There is provided one kind
Drought monitoring method based on data mining, the multi-source remote sensing spatial information considered in draught monitor is carried out to remote sensing precipitation
Space NO emissions reduction, draught monitor model is built using Spatial Data Mining Technique.But limited by the present art, arid is distant
The low deficiency of Existential Space resolution ratio is gone back in sense monitoring.
Zonal drought and waterlogging phenomenon is generally caused by the temporary transient sexual abnormality of local atmospheric water revenue and expenditure, but due to its influence because
Element is numerous and interaction is complicated, due to lacking the full appreciation to catastrophe mechanism, and the accurate of the condition of a disaster is still difficult at present and is commented
Estimate and forecast.It the substitute is, the relative frequency and intensity (grade) that drought and flood events can occur carry out quantitative estimate.
The prediction of existing Regional Floods/Droughts grade is mainly started with from the randomness of drought and flood events, using time series analysis instrument, with
Reach the purpose for disclosing its Spatio-Temporal Change Characteristics.Yang Zhi is brave etc.[4]Luan River Basin is constructed using dimensional Co pula functions representative
The Joint Distribution of meteorological site seasonal precipitation anomalous percentage sequence, calculating each website drought and waterlogging, alternately and even drought connects flooded two class
The probability of happening of drought and waterlogging combination event.Song Xinshan etc.[5]The representative of Huang-Huai-Hai In The Middle And Lower Reaches 16 is calculated using Markov model
Stand the statistical nature such as transition probability, duration, recurring temporal of different drought and waterlogging states over 540 years.Feng's equality[6]Using three-dimensional
Log-linear model establishes the arid grade forecast model of the precipitation station short-range weather of Luanhe River PJK Reservoir control catchment 21, real
The meteorological drought grade forecast that leading time is 1 month and 2 months is showed.
Many develop drought and waterlogging of the above method is regarded as stationary process, that is, thinks the transition probability of its statistical nature such as drought and waterlogging state
Etc. not changing over, it can be obtained by the meteorology or Hydrologic Series sample statistics of passing actual measurement.However, climate change and the mankind
The interference of activity, meteorological model system has significant Dynamic Evolution Characteristics.In order to successfully manage region under dynamic evolution condition
The calamity of drought and flood events, the Regional Floods/Droughts Forecasting Methodology of the inherent origin cause of formation and outside stress can be considered by needing research and development badly.
The bibliography being related in text is as follows：
[1] drought monitoring method and the system patent No. of the strong of high will based on earth's surface water and heat remote-sensing inversion
ZL201010623662.6.
[2] Lee is all right, Chen Haibo, Yu Changhong, and waiting a kind of, the drought monitoring method based on HJ-1A/1B ccd datas is special
Profit ZL201310379034.1.
[3] Feng Jie, He Qisheng, Yang Zhiyong, the drought monitoring method publication numbers based on data mining that wait a kind of
CN105760814A.
[4] Yang Zhiyong, Yuan's Zhe, Fang Hongyang waits Luan River Basin drought and waterlogging combination event probability characteristicses of the based on Copula functions
Analyze [J] Journal of Hydraulic Engineering, 2013,44 (5):556-569.
[5] Song Xinshan, Yan Denghua, Wang Yuhui, wait to be based on Markov model analysis Huang-Huai-Hai Middle Eastern over 540 years
Drought and waterlogging Characteristics of Evolution [J] Journal of Hydraulic Engineering, 2013,44 (12):1425-1432.
[6] Feng Ping, Hu Rong, Lee build meteorological drought grade forecast research [J] the water of post based on three-dimensional log-linear model
Sharp journal, 2014,45 (5):505-512.
The content of the invention
In view of the deficienciess of the prior art, the invention provides a kind of region meteorological drought grade for coupling multi-source data
Forecasting Methodology, carries out space NO emissions reduction by the satellite remote sensing precipitation data to gridding, builds high-resolution Regional Precipitation
Spatial database, arid grade is divided using standardization drought index, introduce reflection Atmospheric Circulation Characteristics large scale it is meteorological because
Son has the gas of time-varying transition probability based on non-stationary Markov chain model construction as the covariant of drought status transition probability
As arid grade forecast model, to analyze the dynamic evolution with estimation range drought status.
In order to solve the above technical problems, the present invention is adopted the following technical scheme that：
A kind of region meteorological drought grade prediction technique for coupling multi-source data, including step：
Step 1, basic underlying surface information in collecting zone, and precipitation, large scale meteorological factor same step-length series materials；
Step 2, the remote sensing precipitation data space NO emissions reduction model for considering basic underlying surface factor correction is built, is differentiated low
The original remote sensing monitoring precipitation data of rate is processed as the precipitation data of high-resolution, and surveys precipitation number with surface-based observing station point
It is corrected according to output data, obtains high-resolution Regional Precipitation spatial database；
Step 3, each fine-resolution meshes that regional network is formatted in precipitation spatial database are obtained to step 2 successively
Long series precipitation data carries out frequency analysis, calculates it and standardizes drought index, according to the arid grade classification table of drought index,
Obtain the arid rate sequence of each grid；
Step 4, the arid rate sequence obtained according to step 3, large scale gas when having certain stagnant collected with step 1
As covariant of the factor as drought status transition probability, time-varying transition probability is had based on non-stationary Markov chain model construction
Meteorological drought grade forecast model；
Step 5, it is pre- using the non-stationary Markov chain model progress region meteorological drought grade preferably obtained through step 4
Survey, obtain the spatial distribution of region-wide arid grade.Further, in step 2, basis is coupled using multiple linear regression analysis method
Underlying surface information architecture space NO emissions reduction model, yardstick degraded is carried out to original remote sensing monitoring precipitation data.
Further, in step 2, using geography information differences method, precipitation is surveyed to output using ground observation website
High resolution precipitation data further corrected.
Further, in step 3, standardization drought index uses Standardized Precipitation index.
Further, in step 4, the parameter of each non-stationary Markov chain model is estimated using generalized crossover entropy method.
Further, in step 4, historical drought grade is calculated by the non-stationary Markov chain model back substitution of calibration, adopted
With the preferred final meteorological drought grade forecast model of akaike information criterion.
Compared with prior art, the present invention has advantages below and effect：
1st, multi-source remote sensing information and basic underground properties are made full use of, the variability of capture region precipitation spatially,
Conventional station observation rainfall point bit density and skewness can be made up, it is impossible to reflect the deficiency of precipitation distribution characteristics on the whole.
2nd, the non-stationary Markov chain model with time varying characteristic for considering covariant is established, overcomes conventional method only to examine
Consider the deficiency of meteorological model sequence autocorrelation performance, the Dynamic Evolution Characteristics for meteorological model system of more fitting.
3rd, make full use of can reflect outside stress that the large scale meteorological factor of Atmospheric Circulation Characteristics this drought and waterlogging are developed and
Precursor signal, considers drought and waterlogging formation and development mechanism to a certain extent, with stronger science and practicality, can be to build
Regional Floods/Droughts early-warning and predicting system lays the foundation.
Brief description of the drawings
Fig. 1 is the particular flow sheet of the inventive method；
Fig. 2 is basin mesh generation schematic diagram；
Fig. 3 is precipitation probability fitting of distribution schematic diagram；
Fig. 4 is arid grade forecast result schematic diagram.
Embodiment
Below in conjunction with the accompanying drawings, technical solution of the present invention is described further.
Fig. 1 is the particular flow sheet of the inventive method, is comprised the following steps that：
Step 1, basic underlying surface information in collecting zone, and precipitation, large scale meteorological factor same step-length series materials.
This step is routine techniques in the art.
Basic underlying surface information refers mainly to lattice point, high-resolution digital elevation model (Digital Elevation
Model, DEM) data, and lattice point, high-resolution windy and sandy soil data (such as vegetation index (NDVI))
Deng；Precipitation data includes the point actual measurement of ground areal rainfall observation station, and lattice point, low resolution Satellite Remote Sensing data；Greatly
Yardstick meteorological factor includes the achievement data for characterizing each large-scale circulation feature in global range.
Step 2, the remote sensing precipitation data space NO emissions reduction model for considering basic underlying surface factor correction is built, is differentiated low
The original remote sensing monitoring precipitation data of rate is processed as the precipitation data of high-resolution；And survey precipitation number with surface-based observing station point
It is corrected according to output data, obtains high-resolution Regional Precipitation spatial database.
In step 2, dropped using multiple linear regression analysis method coupling regime underlying surface information architecture remote sensing precipitation data space
Scale Model, yardstick degraded is carried out to original remote sensing monitoring precipitation data；Using geography information difference (GDA) method, ground is utilized
Acquisition high-resolution Regional Precipitation spatial database of the observation website actual measurement precipitation in face to output is further corrected.Enter one
Step includes following sub-step：
(1) according to the spatial resolution of region underlying surface information, based on GIS-Geographic Information System (GIS) platform by region in sky
Between it is upper carry out discrete, be divided into uniform high-resolution longitude and latitude grid.Basin mesh generation schematic diagram is as shown in Figure 2.
(2) using closest interpolation method to region underlying surface information (digital elevation model (DEM), normalization difference vegetation
Index (NDVI)) resampling is carried out, it is consistent the resolution ratio of itself and original remote sensing monitoring precipitation data.
Closest interpolation method is routine techniques in the art.
(3) height above sea level (h), slope aspect (α) and the gradient (β) factor are extracted from digital elevation model (DEM).
(4) region underlying surface information and original remote sensing precipitation data are built using multiple linear regression methodExperience
Relation：
In formula：B=(b0,b1,b2,b3,b4,b5,b6) be multiple linear regression model parameter matrix, using least square
Method is estimated；Z=(1, X, Y, h, α, β, NDVI) is independent variable matrix；X and Y are respectively mesh point center latitude and latitude.Root
The parameter obtained according to estimatesCalculate remote sensing Simulation of Precipitation value under original low-resolution
ZLR=(1, XLR,YLR,hLR,αLR,βLR,NDVILR) it is independent variable matrix under low resolution.
Least square method is routine techniques in the art.
(5) calculate under original low-resolutionWithResidual error：
(6) row interpolation is entered to low resolution grid residual error using anti-distance weighting interpolation method and obtains high-resolution residual error value
Anti- distance weighting interpolation method is routine techniques in the art.
(7) step (4) being estimated to, the parameter of obtained multiple linear regression model is applied to high resolution grid point, is obtained
Initial high resolution remote sensing Simulation of Precipitation valueAnd by high-resolution residual error valueAmendment is obtained under high-resolution
Remote sensing Simulation of Precipitation value
ZHR=(1, XHR,YHR,hHR,αHR,βHR,NDVIHR) it is independent variable matrix under high-resolution.
(8) calculate each and obtained ground observation website actual measurement precipitation is collected by step 1With including this website
The remote sensing Simulation of Precipitation value of high resolution grid pointBetween residual error：
(9) using anti-distance weighting interpolation method to website precipitation residual delta PpointEnter row interpolation and obtain high resolution precipitation
Modifying factor subvalue
(10) with high resolution precipitation modifying factorPlus high-definition remote sensing Simulation of Precipitation valueObtain final height
Resolution remote sense precipitation correction valueForm high-resolution Regional Precipitation spatial database.
Step 3, each high-resolution in the high-resolution Regional Precipitation spatial database obtained successively to step 2
The long serial precipitation data of grid carries out frequency analysis, calculates it and standardizes drought index, is drawn according to the arid grade of drought index
Divide table, obtain the arid rate sequence of each grid.
In step 3, standardization drought index uses Standardized Precipitation index (Standardized Precipitation
Index, SPI).Its calculation procedure is as follows：
(1) each revised high resolution grid point remote sensing precipitation obtained to step 2, using Gamma points
Each of cloth linear fitting in month different time scales accumulative rainfall amount.The probability density function of Gamma distributions is as follows：
In formula：a1And a2It is the shape and scale parameter of Gamma distributions respectively, is estimated using maximum-likelihood method；Γ () is
Gamma functions；P is period accumulative rainfall amount, such as ten days, the moon, season, year.Precipitation probability fitting of distribution figure is as shown in Figure 3.
Preferably, it is contemplated that current remote sensing Precipitation Products are overall still more credible in the moon and scale above, but in smaller chi
Larger uncertainty is still suffered from degree, therefore this specific implementation is temporarily using the moon as time scale.It is further with Remote Sensing Products precision
Improve, the inventive method can be applied to smaller time scale.
Maximum-likelihood method is the ordinary skill in the art.
(2) cumulative probability of a certain period precipitation is calculated：
(3) cumulative probability is converted into the quantile of standardized normal distribution, i.e. SPI according to equal probability principle：
SPI=Φ-1(G(p)) (9)
In formula：Φ-1() is the inverse function of standardized normal distribution probability-distribution function.
In step 3, the arid grade classification of drought index deviates normal level (50% quantile) using its cumulative probability
Degree.
Arid grade classification based on SPI is as shown in table 1.
The SPI indexes of table 1 arid grade classification table
As seen from table, SPI can be not only used for draught monitor, can be used for monitored area flood situation.
Step 4, the arid rate sequence obtained according to step 3, using with it is certain stagnant when large scale meteorological factor as
The covariant of drought status transition probability, the meteorological drought based on non-stationary Markov chain model construction with time-varying transition probability
Grade forecast model.
Using whole survey region as total system, high resolution grid point is drought and waterlogging knot in computing unit, system in region
Structure accounts for total ratio (i.e. disaster area ratio for calculating number of unit with a certain moment t mesh point numbers for being in different drought grade
Example A (t)=(A1t,A2t,...,A7t)) represent, and Temporal Evolution, often described with Markov chain model.
The arid level status of any computing unit is converted by i in etching system during stationary Markov chain model hypothesis future t+1
For j probability πij(i, j=1,2 ..., 7) only relevant with state i known to current time t in this specific implementation, and with before
State is unrelated, and does not change over, i.e.,：
In formula：I, j are state value, and S is status field, and T is time-domain.Then the evolutionary process of disaster area ratio can be stated
For：
A (t+1)=A (t) π (11)
In formula：π is state-transition matrix.
In fact, being limited to limited drought and waterlogging state transfer observation sample, often result in state transfer experience matrix and occur
Irrational null value phenomenon.Meanwhile, Regional Precipitation, by many factors control, is the coefficient typical table of sea~land~gas system
Existing, its state metastatic rule not only with being presently in that state is relevant, should also become with the change of external environment condition (such as air stress)
Change, therefore, the present invention describes drought and waterlogging state migration procedure using the time-varying Markov chain model of non-stationary, it is assumed that drought and waterlogging state
Transition probability change over time, introduce external interpretation variable build its quantitative relationship with transition probability：
πij(t)=fij(zij(t),ηij) (12)
In formula：zij(t) it is explanatory variable matrix, ηijFor its regression parameter, fij() is connection covariant and transition probability
Function.The empirical relation between explanatory variable and transition probability is built frequently with equation of linear regression：
Φ-1(πij(t))=ηijzij(t) (13)
In formula：Φ-1() is the inverse function of standardized normal distribution probability-distribution function.It is for general using quantile conversion
Dependent variable is converted to continuous real number space by probability interval [0,1].
In step 4, by correlation analysis from alternative large scale meteorological factor the preliminary big chi selected as covariant
Spend meteorological factor species and it is stagnant when；The parameter of each non-stationary Markov chain model is estimated using generalized crossover entropy method；Pass through
The non-stationary Markov chain model back substitution of calibration calculates historical drought grade, using akaike information criterion (Akaike
Information Criterion, AIC criterion) preferred final meteorological drought grade forecast model.Further comprise following son
Step：
(1) the achievement data length series for collecting each large-scale circulation feature in obtained sign global range with step 1 is provided
Expect alternately large scale meteorological factor collection, setting area arid grade, which develops to fluctuate large scale meteorological factor, produces what is responded
Lag when maximum stagnantmaxWith it is minimum stagnant when Lagmin, to all alternative large scale meteorological factors, progressively from it is minimum stagnant when increase to most
When big stagnant, the serial correlation analysis and inspection with alternative large scale meteorological factor asynchronous sequence of precipitation is carried out respectively using coefficient correlation
Test, some groups of large scale meteorological factor covariants being had a significant impact to drought and waterlogging evolution process are preferably gone out according to coefficient correlation size
Amount.
(2) parameter of each non-stationary Markov chain model is estimated using generalized crossover entropy method.
(3) historical drought grade is calculated by the non-stationary Markov chain model back substitution of calibration, using AIC criterion preferably most
Whole meteorological drought grade forecast model.
In step 4, alternative large scale meteorological factor collection can be NAO index (NAO), Arctic oscillation index
(AO), Pacific Ocean Decadal Oscillation (PDO), Southern oscillation index (SOI), multivariable ENSO indexes (MEI), North Atlantic Ocean age
One or more in border vibration (AMO) etc..
In step 4, this specific implementation is using the moon as time step, Lag when minimum stagnantminTake 1 month, Lag when maximum stagnantmaxTake
12 months.
In step 4, the parameter of non-stationary Markov model is estimated using generalized crossover entropy method, can be attributed in abundant profit
With all information without increasing redundancy on the premise of, solving makes the Posterior estimator of Model transfer probability and prior estimate is entrained believes
The problem of breath amount difference is minimum.
(1) object function (cross entropy)：
In formula：It is priori transition probability matrix, information is provided to solve posteriority transition probability.
(2) constraints：
Priori transition probability matrixThe experience transfer matrix that can be obtained by stationary Markov chain model is replaced.
Step 5, the pre- of region meteorological drought grade is carried out using the non-stationary Markov chain model preferably obtained through step 4
Survey, obtain the spatial distribution of region-wide arid grade.
In step 5, the arid grade forecast model preferably obtained is sequentially applied to each high resolution grid point, obtained
The probability shifted to the arid grade of the subsequent time mesh point to different brackets, using the maximum level status of transition probability as
The arid grade of the subsequent time mesh point, proclamation form of prediction estimates each grade disaster area.Arid grade forecast knot of a certain moment
Fruit schematic diagram is as shown in Figure 4.
The foregoing is only a specific embodiment of the invention, but protection scope of the present invention is not limited thereto, any
Belong to those skilled in the art in the technical scope that the present invention is illustrated, the change or replacement that can be readily occurred in all should
It is included within the scope of the present invention.Therefore, protection scope of the present invention should be defined by scope of the claims.
Claims (6)
1. a kind of region meteorological drought grade prediction technique for coupling multi-source data, it is characterised in that including step：
Step 1, basic underlying surface information in collecting zone, and precipitation, large scale meteorological factor same step-length series materials；
Step 2, the remote sensing precipitation data space NO emissions reduction model for considering basic underlying surface factor correction is built, by low resolution
Original remote sensing monitoring precipitation data is processed as the precipitation data of high-resolution, and surveys precipitation data pair with surface-based observing station point
Output data is corrected, and obtains high-resolution Regional Precipitation spatial database；
Step 3, the long system for each fine-resolution meshes that regional network is formatted in precipitation spatial database is obtained to step 2 successively
Row precipitation data carries out frequency analysis, calculates it and standardizes drought index, according to the arid grade classification table of drought index, obtains
The arid rate sequence of each grid；
Step 4, the arid rate sequence obtained according to step 3, large scale when having certain stagnant collected with step 1 it is meteorological because
Son has the gas of time-varying transition probability based on non-stationary Markov chain model construction as the covariant of drought status transition probability
As arid grade forecast model；
Step 5, region meteorological drought grade forecast is carried out using the non-stationary Markov chain model preferably obtained through step 4, obtained
To the spatial distribution of region-wide arid grade.
2. a kind of region meteorological drought grade prediction technique for coupling multi-source data as claimed in claim 1, it is characterised in that：
In step 2, basic underlying surface information architecture space NO emissions reduction model is coupled using multiple linear regression analysis method, to original distant
Sense monitoring precipitation data carries out yardstick degraded.
3. a kind of region meteorological drought grade prediction technique for coupling multi-source data as claimed in claim 1 or 2, its feature exists
In：
In step 2, using geography information differences method, high resolution precipitation of the precipitation to output is surveyed using ground observation website
Data are further corrected.
4. a kind of region meteorological drought grade prediction technique for coupling multi-source data as claimed in claim 1, it is characterised in that：
In step 3, standardization drought index uses Standardized Precipitation index.
5. a kind of region meteorological drought grade prediction technique for coupling multi-source data as claimed in claim 1, it is characterised in that：
In step 4, the parameter of each non-stationary Markov chain model is estimated using generalized crossover entropy method.
6. a kind of region meteorological drought grade prediction technique of coupling multi-source data as described in claim 1 or 5, its feature exists
In：
In step 4, historical drought grade is calculated by the non-stationary Markov chain model back substitution of calibration, it is accurate using red pond information content
Then preferred final meteorological drought grade forecast model.
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Cited By (16)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN107944387A (en) * | 2017-11-22 | 2018-04-20 | 重庆邮电大学 | A kind of analysis method of the urban heat island special heterogeneity based on semivariation theory |
| CN107944219A (en) * | 2017-12-13 | 2018-04-20 | 广东电网有限责任公司电力科学研究院 | A kind of method and apparatus for characterizing different periods drought and waterlogging and causing calamity feature |
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Citations (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US20060080858A1 (en) * | 2000-06-16 | 2006-04-20 | Maguire Stephen B | Low pressure high capacity dryer for resins and other granular and powdery materials |
| CN102779391A (en) * | 2012-07-24 | 2012-11-14 | 中国农业科学院农田灌溉研究所 | Drought early-warning method and drought early-warning system |
| CN103577720A (en) * | 2013-11-29 | 2014-02-12 | 民政部国家减灾中心 | Method for estimating regional drought risk |
| CN105550501A (en) * | 2015-12-09 | 2016-05-04 | 中国水利水电科学研究院 | Method for recognizing drought evolution driving mechanism of basin/region |
-
2016
- 2016-09-23 CN CN201610850079.6A patent/CN107316095B/en active Active
Patent Citations (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US20060080858A1 (en) * | 2000-06-16 | 2006-04-20 | Maguire Stephen B | Low pressure high capacity dryer for resins and other granular and powdery materials |
| CN102779391A (en) * | 2012-07-24 | 2012-11-14 | 中国农业科学院农田灌溉研究所 | Drought early-warning method and drought early-warning system |
| CN103577720A (en) * | 2013-11-29 | 2014-02-12 | 民政部国家减灾中心 | Method for estimating regional drought risk |
| CN105550501A (en) * | 2015-12-09 | 2016-05-04 | 中国水利水电科学研究院 | Method for recognizing drought evolution driving mechanism of basin/region |
Non-Patent Citations (2)
| Title |
|---|
| 孙鹏 等: ""基于马尔科夫模型的新疆水文气象干旱研究"", 《地理研究》 * |
| 苏宏新 等: ""基于SPEI的北京低频干旱与气候指数关系"", 《生态学报》 * |
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