CN106202788A - A kind of tide flood combined probability analysis method based on Copula function and application thereof - Google Patents
A kind of tide flood combined probability analysis method based on Copula function and application thereof Download PDFInfo
- Publication number
- CN106202788A CN106202788A CN201610575905.0A CN201610575905A CN106202788A CN 106202788 A CN106202788 A CN 106202788A CN 201610575905 A CN201610575905 A CN 201610575905A CN 106202788 A CN106202788 A CN 106202788A
- Authority
- CN
- China
- Prior art keywords
- rainfall
- flood
- tidal level
- peak value
- distribution
- 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.)
- Granted
Links
Classifications
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F30/00—Computer-aided design [CAD]
- G06F30/20—Design optimisation, verification or simulation
-
- 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
Landscapes
- Engineering & Computer Science (AREA)
- Physics & Mathematics (AREA)
- Theoretical Computer Science (AREA)
- Computer Hardware Design (AREA)
- Evolutionary Computation (AREA)
- Geometry (AREA)
- General Engineering & Computer Science (AREA)
- General Physics & Mathematics (AREA)
- Management, Administration, Business Operations System, And Electronic Commerce (AREA)
- Complex Calculations (AREA)
Abstract
The present invention relates to a kind of tide flood combined probability analysis method based on Copula function and application thereof, the purpose of this tide flood analysis method is to provide the flood-control construction foundation of tidal reach, by setting up " tidal level ", " rainfall peak value " and the joint probability distribution function of " single storm ", can reflect that affecting flood facility crosses the function of stream and Regulation capacity characteristic quantity, provides reliable basis for flood-control construction.
Description
Technical field
The present invention relates to combined probability analysis field, especially relate to a kind of tide flood joint probability based on Copula function
Analysis method and application thereof.
Background technology
Sea outfall section is referred to as tidal reach, and so-called tidal reach refers to be pushed up by tide at the lower exit of section
Torr, flow and water level are by the section of tidal effect.Upstream, basin is affected relatively big by Basin Rainfall, and downstream should be held and be let out upstream flood
Water enters river, is affected by the jacking of tidal level again, causes the flood that basin suffers not only to have relation with internal heavy rain, also with
The tidal level met with mutually is closely related.Therefore, in order to reasonably analyze the flood risk in basin, sudden and violent in understanding basin
The probability risk rule that rain meets with tidal level, in research basin, heavy rain just seems particularly significant with the combination of the experience of tidal level.
Tidal reach river complicated movement, and interference factor is numerous, but main by upstream flood and downstream tidal level the two
The impact of factor.To simplify the analysis, the probabilistic risk analysis of experience is mainly with the Joint Distribution of basin heavy rain and flood level as base
Plinth, and they simply have certain contact, dependency is not very strong, and its Joint Distribution can not be simple by marginal distribution phase
Multiplied arriving, this is accomplished by a kind of more perfect theory and analyzes and researches.Copula is theoretical is building multivariate experience event associating
The flexibility advantage possessed during distribution, the flood risk for research tidal reach provides a great convenience, and expands new research
Means.
The method carrying out tidal level and flood combined probability analysis at present has limitation.First, current analysis method is only
It is analyzed only for the water level of upstream and the tidal level in downstream, does not consider the factors such as rainfall duration, only by two dimension
Copula function not can completely analyzes the relation of flood meeting with tide;Secondly, current analysis method first chooses simulation often
Preferably marginal distribution function, carries out combined probability analysis showing preferable marginal distribution by two series, and in fact
Marginal distribution performance well might not determine the optimality of Joint Distribution.
Summary of the invention
The present invention devises a kind of tide flood combined probability analysis method based on Copula function and application thereof, and it solves
Technical problem is that and lack characterizing the single storm of characteristics of rainfall and the comprehensive of rainfall peak value in terms of tide flood combinative analysis at present
Considering, the method simultaneously choosing marginal distribution has limitation, and the best marginal distribution differs and joined the most accurately
Close distribution.In order to solve the technical problem of above-mentioned existence, present invention employs below scheme:
A kind of tide flood combined probability analysis method based on Copula function, comprises the following steps:
Tidal level station that step 1, Analysis on Selecting section are representative and precipitation station, representational tidal level station should be at section
At estuary and obtain its tidal level series materials;Representational precipitation station is positioned to be analyzed the basin, downstream of section and obtains its rainfall
Data;
Step 2, step 1 gained tidal level series materials is carried out consistent correction;
Step 3, each representational precipitation station selected by step 1, use rainfall intensity method, by continuous print rainfall
Time series is divided into catchment, carries out rainfall data segmentation, precipitation time series is divided into different rainfall plays, enters
And add up " single storm " and " rainfall peak value " two characteristic variables characterizing rainfall pattern;
Step 3 gained " single storm " and " rainfall peak value " are sampled by step 4, employing annual maximum design flood: according to not
Same provincial characteristics, as a example by design lasts 720min (i.e. 12h) or 360min (i.e. 6h) or N min, N is natural number, and sampling is every
" single storm " numerical value sample maximum in 1 year and " rainfall peak value " numerical value sample;
Step 5, for maximum " single storm " the numerical value sample in step 4 and " rainfall peak value " numerical value sample, use Thailand
Gloomy polygon method calculates face, basin " single storm " and " rainfall peak value " two characteristic variables;
Step 6, for single storm, rainfall peak value and tidal level series, calculate the marginal probability distribution of each series respectively,
The multiple distribution linetype being chosen in hydrologic(al) frequency analysis carries out curve to " tidal level ", " rainfall peak value " and " single storm " respectively
Matching;
Step 7, use Probability Point according to related-coefficient test method (PPCC), intend excellent sum-of-squares criterion method (RMSE) and plan excellent absolutely
Three kinds of goodness-of-fit test methods of value Criterion Method (MAE) are determined the two kind limits best with each variable data series fit effect
Edge distribution linetype;
Step 8, employing archimedes type Copula family apply wide Frank Copula and AMH Copula
Function builds the joint probability distribution of characteristics of rainfall variable respectively;
Step 9, selection AIC information criterion method (AIC) and sum of deviation square minimum criteria method (OLS) are to Copula function
The goodness of fit is evaluated, and chooses optimal joint probability distribution.
Further, the order of step 2 and step 3 can be replaced, and does not affect the evaluation result in step 9.
Further, the multiple distribution linetype in the hydrologic(al) frequency analysis in step 6 is respectively as follows: P-III type distribution curve
(P3), Lognormal distribution curve (LN2) and generalized extreme value distribution curve (GEV).
Further, due to the two kinds of linear simulations of each variable data series in step 8, so for each
Plant Copula function to need to calculate 8 times.
A kind of application using above-mentioned modification method reduction tide gauge, it is characterised in that: eliminate change by correction
The environment impact on data, reduces the concordance of tide gauge, it is ensured that use tide gauge to carry out hydrologic(al) frequency analysis
Reliability, the tide gauge using revised moon yardstick is that urban flood defence provides reliable foundation.
The present invention can apply on the flood forecasting of tidal reach, and tidal reach refers to the estuary section in river,
The water level of these sections is not only affected by upland water, is also affected by downstream tidal level simultaneously, and the present invention can provide
To the effective reference information of tidal reach flood forecasting.Specifically, on the premise of determining rainfall peak value, calculate time fall
Rainfall more than some numerical value and tidal level more than the simultaneous probability of some tidal level, or on the premise of single storm determines,
Rainfall peak value is more than the simultaneous probability of a certain tidal level more than a certain numerical value and tidal level, and construction and flood for flood control works are pre-
Report provides foundation.
Should tide flood combined probability analysis method based on Copula function have the advantages that
(1) purpose of tide flood of the present invention analysis method is to provide the flood-control construction foundation of tidal reach, by setting up " tide
Position ", " rainfall peak value " and the joint probability distribution function of " single storm ", can reflect affect flood facility cross flow and regulate and store
The function of ability characteristics amount, provides reliable basis for flood-control construction.
(2) present invention sets up three-dimensional Copula function not only for evaluating preferable marginal distribution, but in marginal distribution
Have chosen preferable two kinds of distribution linetypes, set up multiple joint probability distribution by different combinations, further preferably behave oneself best
Construction method, can avoid the shortcoming that preferred marginal distribution cannot obtain good Joint Distribution.
Detailed description of the invention
Below in conjunction with embodiment, the present invention will be further described:
Step 1, representativeness tidal level station, Analysis on Selecting section and precipitation station, tidal level station should be at section estuary and tidal level money
Material is complete;Representative water precipitation station in appropriate basin, the rainfall series length of different precipitation stations is chosen according to basin, downstream size
Should be essentially identical;
Step 2, tide gauge is carried out consistent correction;
Step 3, for each selected precipitation station, use rainfall intensity method, continuous print precipitation time series split
Become catchment, such as the standard according to " rainfall intensity is less than 0.1mm/h, and the persistent period is more than 10h ", carry out rainfall data and divide
Cut, precipitation time series is divided into different rainfall plays, and then 2 features of statistics " single storm " and " rainfall peak value " become
Amount;
Step 4, employing annual maximum design flood are sampled. according to zones of different feature, last 720min (i.e. 12h) with design
Or as a example by 360min (i.e. 6h) etc., sample sample;
Step 5, utilization Thiessen polygon method calculate face, basin " single storm " and " rainfall peak value " 2 characteristic variables;
Step 6, for single storm, rainfall peak value and tidal level series, calculate the marginal probability distribution of each series respectively,
Choose 3 kinds of distribution linetype (P-III type distribution curve (P3), Lognormal distribution curves conventional in hydrologic(al) frequency analysis
(LN2) and generalized extreme value distribution curve (GEV)) respectively " tidal level ", " rainfall peak value " and " single storm " is carried out curve fitting;
Step 7, use Probability Point according to related-coefficient test method (PPCC), intend excellent sum-of-squares criterion method (RMSE) and plan excellent absolutely
3 kinds of goodness-of-fit test methods of value Criterion Method (MAE) are determined the 2 kind edges best with each variable data series fit effect
Distribution linetype;
Assume that optimizing check is as follows:
Probability Point according to related-coefficient test method (PPCC), intend excellent sum-of-squares criterion method (R MSE) and intend excellent absolute value criterion
Method (MAE) all can have a numerical value to the inspection of each matching, and comprehensive analysis is selected for each project fitting degree
Two kinds of good approximating methods.
Such as the tidal level project in the example above, PPCC test value is the bigger the better, RMSE and MAE is the smaller the better.So selecting
LN2 and GEV is as approximating method.Can be seen that distribution linetype exists minimum and maximum test value quantity the most time, then by
Select;Otherwise, then abandon.
In above table " rainfall peak value " and " single storm " due to the method for inspection with the method for inspection of " tidal level " complete phase
With, special its concrete numerical value of omission.In a word, all of space is required for inspection, is all by PPCC, RMSE, MAE tri-kinds inspection
Mode is checked, and principle is the same, and PPCC test value is the bigger the better, RMSE and MAE is the smaller the better.
Step 8, employing archimedes type Copula family apply wide Frank Copula and AMH Copula
Function builds the Joint Distribution of characteristics of rainfall variable respectively, due to the 2 kinds of linear simulations of each series, so for
Each Copula function needs to calculate 8 times;
Below to each project, we represent selected two kinds of approximating methods with A, B
The above-mentioned combination being just by the calculating of Copula Joint Distribution, chooses the associating of optimum by AIC and OLS inspection
Distributed combination mode.
Minimum (OLS) Criterion Method of step 9, selection AIC information criterion method (AIC) and sum of deviation square is to Copula function
The goodness of fit is evaluated.
Above in conjunction with embodiment, the present invention is carried out exemplary description, it is clear that the realization of the present invention is not by above-mentioned side
The restriction of formula, if the various improvement that the method design that have employed the present invention is carried out with technical scheme, or the most improved by this
Bright design and technical scheme directly apply to other occasion, the most within the scope of the present invention.
Claims (5)
1. a tide flood combined probability analysis method based on Copula function, comprises the following steps:
Tidal level station that step 1, Analysis on Selecting section are representative and precipitation station, representational tidal level station should be at section and enters sea
At Kou and obtain its tidal level series materials;Representational precipitation station is positioned to be analyzed the basin, downstream of section and obtains its rainfall money
Material, chooses the precipitation station of respective amount according to the downstream drainage area size analyzing section;
Step 2, step 1 gained tidal level series materials is carried out consistent correction;
Step 3, each representational precipitation station selected by step 1, use rainfall intensity method, by continuous print rain time
Sequences segmentation becomes catchment, carries out rainfall data segmentation, and precipitation time series is divided into different rainfall plays, Jin Ertong
Meter " single storm " and " rainfall peak value " two characterizes the characteristic variable of rainfall pattern;
Step 3 gained " single storm " and " rainfall peak value " are sampled by step 4, employing annual maximum design flood: according to not same district
Characteristic of field, as a example by design lasts 720min (i.e. 12h) or 360min (i.e. 6h) or Nmin, N is natural number, samples in each year
Maximum " single storm " numerical value sample and " rainfall peak value " numerical value sample;
Step 5, for maximum " single storm " the numerical value sample in step 4 and " rainfall peak value " numerical value sample, use Tyson many
Limit shape method calculates face, basin " single storm " and " rainfall peak value " two characteristic variables;
Step 6, for single storm, rainfall peak value and tidal level series, calculate the marginal probability distribution of each series respectively, choose
Multiple distribution linetype in hydrologic(al) frequency analysis carries out curve plan to " tidal level ", " rainfall peak value " and " single storm " respectively
Close;
Step 7, use Probability Point according to related-coefficient test method (PPCC), intend excellent sum-of-squares criterion method (RMSE) and plan excellent absolute value
Three kinds of goodness-of-fit test methods of Criterion Method (MAE) determine that the two kind edges best with each variable data series fit effect divide
Cloth line style;
Step 8, employing archimedes type Copula family apply wide Frank Copula and AMH Copula function
Build the joint probability distribution of characteristics of rainfall variable respectively;
Step 9, selection AIC information criterion method (AIC) and sum of deviation square minimum criteria method (OLS) matching to Copula function
Goodness is evaluated, and chooses optimal joint probability distribution.
Tide flood combined probability analysis method based on Copula function the most according to claim 1, it is characterised in that: step 2
Can replace with the order of step 3, not affect the evaluation result in step 9.
Tide flood combined probability analysis method based on Copula function the most according to claim 1 or claim 2, it is characterised in that: step
Multiple distribution linetype in hydrologic(al) frequency analysis in rapid 6 is respectively as follows: P-III type distribution curve (P3), logarithm normal distribution song
Line (LN2) and generalized extreme value distribution curve (GEV).
4. according to tide flood combined probability analysis method based on Copula function described in claim 1,2 or 3, it is characterised in that:
Due to the two kinds of linear simulations of each variable data series in step 8, so needing for each Copula function
Calculate 8 times.
5. in any of the one of claim 1-4 based on Copula function tide flood combined probability analysis method should
With, it is characterised in that: on the premise of determining rainfall peak value, calculate single storm and be more than more than some numerical value and tidal level
The simultaneous probability of some tidal level, or on the premise of single storm determines, rainfall peak value is big more than a certain numerical value and tidal level
In the simultaneous probability of a certain tidal level, construction and flood forecasting for flood control works provide foundation.
Priority Applications (1)
Application Number | Priority Date | Filing Date | Title |
---|---|---|---|
CN201610575905.0A CN106202788B (en) | 2016-07-20 | 2016-07-20 | A kind of damp big vast combined probability analysis method and its application based on Copula functions |
Applications Claiming Priority (1)
Application Number | Priority Date | Filing Date | Title |
---|---|---|---|
CN201610575905.0A CN106202788B (en) | 2016-07-20 | 2016-07-20 | A kind of damp big vast combined probability analysis method and its application based on Copula functions |
Publications (2)
Publication Number | Publication Date |
---|---|
CN106202788A true CN106202788A (en) | 2016-12-07 |
CN106202788B CN106202788B (en) | 2017-11-10 |
Family
ID=57490974
Family Applications (1)
Application Number | Title | Priority Date | Filing Date |
---|---|---|---|
CN201610575905.0A Active CN106202788B (en) | 2016-07-20 | 2016-07-20 | A kind of damp big vast combined probability analysis method and its application based on Copula functions |
Country Status (1)
Country | Link |
---|---|
CN (1) | CN106202788B (en) |
Cited By (20)
Publication number | Priority date | Publication date | Assignee | Title |
---|---|---|---|---|
CN107133473A (en) * | 2017-05-09 | 2017-09-05 | 河海大学 | Hydrologic design values method of estimation in two variable hydrologic(al) frequency analysis under a kind of changing environment |
CN107423496A (en) * | 2017-07-10 | 2017-12-01 | 浙江大学 | A kind of random generation method of new catchment |
CN107423546A (en) * | 2017-04-18 | 2017-12-01 | 武汉大学 | Multivariable hydrological uncertainty processing method based on Copula functions |
CN108320091A (en) * | 2018-01-26 | 2018-07-24 | 中国海洋大学 | A kind of joint probability method calculating the extreme water level in river mouth harbour |
CN108596998A (en) * | 2018-04-24 | 2018-09-28 | 江西省水利科学研究院 | A kind of rainfall runoff correlation drawing drawing method based on Copula functions |
CN109033578A (en) * | 2018-07-11 | 2018-12-18 | 河海大学 | A kind of inversion method of river mouth along journey fresh water fraction |
CN109214588A (en) * | 2018-09-28 | 2019-01-15 | 郑州大学 | Mountain flood probability rainfall pattern calculation method based on copula function |
CN109657409A (en) * | 2019-01-15 | 2019-04-19 | 西南交通大学 | A kind of sea-crossing bridge structural optimization method extremely responded based on stormy waves Joint Distribution |
CN109753633A (en) * | 2018-12-05 | 2019-05-14 | 中国能源建设集团江苏省电力设计院有限公司 | A kind of structures under wind Failure risk evaluation method based on non-stationary GEV distributed model |
CN109858180A (en) * | 2019-02-26 | 2019-06-07 | 中国农业大学 | A kind of area crops water consumption spatial framework optimum design method |
CN109902266A (en) * | 2019-03-07 | 2019-06-18 | 江西省水利科学研究院 | A kind of streamflow routing method based on Copula function |
CN109902267A (en) * | 2019-03-07 | 2019-06-18 | 江西省水利科学研究院 | A kind of river safety discharge calculation method influenced by downstream lake jacking |
CN109960891A (en) * | 2019-04-04 | 2019-07-02 | 长江水利委员会水文局 | A kind of nonuniformity methods for calculating designed flood |
CN110673230A (en) * | 2019-07-12 | 2020-01-10 | 中山大学 | Entropy decision method based dynamic critical rainfall calculation method |
CN111831966A (en) * | 2020-05-21 | 2020-10-27 | 中山大学 | Combined river water level forecasting method based on high-dimensional probability distribution function |
CN112085298A (en) * | 2020-09-23 | 2020-12-15 | 中国电建集团成都勘测设计研究院有限公司 | Non-continuous sequence flood frequency analysis method considering historical flood |
CN112396297A (en) * | 2020-11-03 | 2021-02-23 | 华中科技大学 | Method and system for analyzing encounter time and magnitude occurrence rule in flood process |
CN113378389A (en) * | 2021-06-11 | 2021-09-10 | 中国长江三峡集团有限公司 | Uncertainty evaluation method and device for flood encounter combined risk analysis |
CN114722744A (en) * | 2022-06-08 | 2022-07-08 | 水利部交通运输部国家能源局南京水利科学研究院 | Evaluation method and system for rainstorm and tide level coordination of plain basin design |
CN114756817A (en) * | 2022-02-22 | 2022-07-15 | 南方科技大学 | Copula function-based combined probability analysis method for composite flood disasters |
Citations (1)
Publication number | Priority date | Publication date | Assignee | Title |
---|---|---|---|---|
CN104992245A (en) * | 2015-07-09 | 2015-10-21 | 南京信息工程大学 | Generalized-entropy-theory-based dynamic intelligent comprehensive analysis method for water environment risk management |
-
2016
- 2016-07-20 CN CN201610575905.0A patent/CN106202788B/en active Active
Patent Citations (1)
Publication number | Priority date | Publication date | Assignee | Title |
---|---|---|---|---|
CN104992245A (en) * | 2015-07-09 | 2015-10-21 | 南京信息工程大学 | Generalized-entropy-theory-based dynamic intelligent comprehensive analysis method for water environment risk management |
Non-Patent Citations (1)
Title |
---|
高玉琴等: "《秦淮河流域不同频率降雨联合概率分析》", 《水电能源科学》 * |
Cited By (30)
Publication number | Priority date | Publication date | Assignee | Title |
---|---|---|---|---|
CN107423546A (en) * | 2017-04-18 | 2017-12-01 | 武汉大学 | Multivariable hydrological uncertainty processing method based on Copula functions |
CN107423546B (en) * | 2017-04-18 | 2020-07-24 | 武汉大学 | Multivariable hydrological uncertainty processing method based on Copula function |
CN107133473B (en) * | 2017-05-09 | 2019-09-10 | 河海大学 | Hydrologic design values estimation method in two variable hydrologic(al) frequency analysis under a kind of changing environment |
CN107133473A (en) * | 2017-05-09 | 2017-09-05 | 河海大学 | Hydrologic design values method of estimation in two variable hydrologic(al) frequency analysis under a kind of changing environment |
CN107423496A (en) * | 2017-07-10 | 2017-12-01 | 浙江大学 | A kind of random generation method of new catchment |
CN107423496B (en) * | 2017-07-10 | 2020-06-23 | 浙江大学 | Novel random rainfall event generation method |
CN108320091A (en) * | 2018-01-26 | 2018-07-24 | 中国海洋大学 | A kind of joint probability method calculating the extreme water level in river mouth harbour |
CN108320091B (en) * | 2018-01-26 | 2021-12-07 | 中国海洋大学 | Joint probability method for calculating extreme water level of estuary and harbor |
CN108596998A (en) * | 2018-04-24 | 2018-09-28 | 江西省水利科学研究院 | A kind of rainfall runoff correlation drawing drawing method based on Copula functions |
CN109033578A (en) * | 2018-07-11 | 2018-12-18 | 河海大学 | A kind of inversion method of river mouth along journey fresh water fraction |
CN109033578B (en) * | 2018-07-11 | 2022-06-07 | 河海大学 | Inversion method of estuary on-way fresh water guarantee rate |
CN109214588A (en) * | 2018-09-28 | 2019-01-15 | 郑州大学 | Mountain flood probability rainfall pattern calculation method based on copula function |
CN109753633A (en) * | 2018-12-05 | 2019-05-14 | 中国能源建设集团江苏省电力设计院有限公司 | A kind of structures under wind Failure risk evaluation method based on non-stationary GEV distributed model |
CN109657409A (en) * | 2019-01-15 | 2019-04-19 | 西南交通大学 | A kind of sea-crossing bridge structural optimization method extremely responded based on stormy waves Joint Distribution |
CN109858180A (en) * | 2019-02-26 | 2019-06-07 | 中国农业大学 | A kind of area crops water consumption spatial framework optimum design method |
CN109858180B (en) * | 2019-02-26 | 2020-11-10 | 中国农业大学 | Regional crop water consumption space pattern optimization design method |
CN109902266A (en) * | 2019-03-07 | 2019-06-18 | 江西省水利科学研究院 | A kind of streamflow routing method based on Copula function |
CN109902267A (en) * | 2019-03-07 | 2019-06-18 | 江西省水利科学研究院 | A kind of river safety discharge calculation method influenced by downstream lake jacking |
CN109902266B (en) * | 2019-03-07 | 2022-10-28 | 江西省水利科学研究院 | Riverway flow calculation method based on Copula function |
CN109902267B (en) * | 2019-03-07 | 2022-09-23 | 江西省水利科学研究院 | River channel safety discharge amount calculation method influenced by downstream lake jacking |
CN109960891A (en) * | 2019-04-04 | 2019-07-02 | 长江水利委员会水文局 | A kind of nonuniformity methods for calculating designed flood |
CN110673230A (en) * | 2019-07-12 | 2020-01-10 | 中山大学 | Entropy decision method based dynamic critical rainfall calculation method |
CN111831966A (en) * | 2020-05-21 | 2020-10-27 | 中山大学 | Combined river water level forecasting method based on high-dimensional probability distribution function |
CN111831966B (en) * | 2020-05-21 | 2023-08-15 | 中山大学 | Combined river channel water level forecasting method based on high-dimensional probability distribution function |
CN112085298A (en) * | 2020-09-23 | 2020-12-15 | 中国电建集团成都勘测设计研究院有限公司 | Non-continuous sequence flood frequency analysis method considering historical flood |
CN112085298B (en) * | 2020-09-23 | 2023-06-16 | 中国电建集团成都勘测设计研究院有限公司 | Non-sequential flood frequency analysis method considering historical flood |
CN112396297A (en) * | 2020-11-03 | 2021-02-23 | 华中科技大学 | Method and system for analyzing encounter time and magnitude occurrence rule in flood process |
CN113378389A (en) * | 2021-06-11 | 2021-09-10 | 中国长江三峡集团有限公司 | Uncertainty evaluation method and device for flood encounter combined risk analysis |
CN114756817A (en) * | 2022-02-22 | 2022-07-15 | 南方科技大学 | Copula function-based combined probability analysis method for composite flood disasters |
CN114722744A (en) * | 2022-06-08 | 2022-07-08 | 水利部交通运输部国家能源局南京水利科学研究院 | Evaluation method and system for rainstorm and tide level coordination of plain basin design |
Also Published As
Publication number | Publication date |
---|---|
CN106202788B (en) | 2017-11-10 |
Similar Documents
Publication | Publication Date | Title |
---|---|---|
CN106202788A (en) | A kind of tide flood combined probability analysis method based on Copula function and application thereof | |
Shabalova et al. | Assessing future discharge of the river Rhine using regional climate model integrations and a hydrological model | |
Sadeghi et al. | Spatiotemporal mapping of rainfall erosivity index for different return periods in Iran | |
Hicks et al. | Variation of suspended sediment yields around New Zealand: the relative importance of rainfall and geology | |
Chang et al. | Sensitivity of urban water consumption to weather and climate variability at multiple temporal scales: The case of Portland, Oregon | |
Loi et al. | Automated procedure of real-time flood forecasting in Vu Gia–Thu Bon river basin, Vietnam by integrating SWAT and HEC-RAS models | |
CN105335603B (en) | A kind of method for measuring diversion irrigation area degree of drought | |
CN102819677B (en) | Rainfall site similarity evaluation method on basis of single rainfall type | |
Hussain | Application of the regional flood frequency analysis to the upper and lower basins of the Indus River, Pakistan | |
Aydoğan et al. | Regional flood frequency analysis for Ç oruh B asin of T urkey with L‐moments approach | |
CN106875048A (en) | Emergent Flood Forecasting Method based on success experience pattern | |
Johansson et al. | Estimation of areal precipitation for runoff modelling using wind data: a case study in Sweden | |
Mimikou et al. | Assessment of climate change impacts in Greece: a general overview | |
Parchure et al. | Probability distribution analysis of extreme rainfall events in a flood-prone region of Mumbai, India | |
Gericke et al. | Evaluation of the standard design flood method in selected basins in South Africa | |
Sene et al. | An assessment of the difficulties in quantifying the surface water resources of Lebanon | |
Stojkovic et al. | Mixed general extreme value distribution for estimation of future precipitation quantiles using a weighted ensemble-case study of the Lim River Basin (Serbia) | |
Ahmad et al. | An analysis of annual maximum streamflows in Terengganu, Malaysia using TL-moments approach | |
Görgens et al. | Modernised South African design flood practice in the context of dam safety | |
Bahrawi | Rainfall distribution and its characteristics in Makkah Al-Mukarramah region, Saudi Arabia. | |
Ouyang et al. | Discussion on the analysis method of urban design rainstorm under the “new normal” of" city seeing the sea” | |
Ahmadpari et al. | Evaluation of the most appropriate statistical distribution for monthly rainfall prediction in the Zarrineh river watershed | |
Aldrees | Water management in Saudi Arabia: a case study of Makkah Al Mukarramah region | |
Babaali et al. | Estimation of design flood using fractal theory and HEC-HMS model (case study: Khorramabad River Basin) | |
Apsite et al. | Changes in river runoff in Latvia at the end of the 21st century |
Legal Events
Date | Code | Title | Description |
---|---|---|---|
C06 | Publication | ||
PB01 | Publication | ||
C10 | Entry into substantive examination | ||
SE01 | Entry into force of request for substantive examination | ||
GR01 | Patent grant | ||
GR01 | Patent grant |