CN108520165A - Rainfall Runoff Forecasting - Google Patents
Rainfall Runoff Forecasting Download PDFInfo
- Publication number
- CN108520165A CN108520165A CN201810245785.7A CN201810245785A CN108520165A CN 108520165 A CN108520165 A CN 108520165A CN 201810245785 A CN201810245785 A CN 201810245785A CN 108520165 A CN108520165 A CN 108520165A
- Authority
- CN
- China
- Prior art keywords
- forecasting
- runoff
- rainfall
- forecast
- flood
- 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
-
- 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
Landscapes
- Alarm Systems (AREA)
- Management, Administration, Business Operations System, And Electronic Commerce (AREA)
Abstract
Present invention employs the rainfall runoff forecasting technical solutions of innovation, the long-term rainfall runoff forecasting of brachymedial can be achieved, and Real-time Flood Forecasting process can be showed in forecast, be conducive to accurately estimate flood peak, magnanimity, the peb process of flood, it can be to simulate outbound and water level process in forecasting process, realize brachymedial Long Term Runoff Forecasting scheduling of the user including Real-time Flood Forecasting, most Runoff Forecast software forecasting process is too short now for solution, the simple and crude problem of Medium-long Term Prediction function.This system includes regimen database, customer parameter configuration module, rainfall runoff forecasting algorithm module, forecast achievement statistical module, forecast figure module.This system has automatic amendment forecast function, without gaging station, can also realize real-time rainfall flood forecast, substantially reduce system hardware and software cost, solve the problems, such as that most Small Reservoir, power station and some large medium-size stations lack Medium-long Term Prediction.
Description
The application be application No. is：2014103166598, invention and created name is：Rainfall Runoff Forecasting, the applying date be：
The divisional application of application for a patent for invention on 07 06th, 2014.
Technical field
The present invention relates to rainfall runoff forecasting technical field more particularly to it is a kind of can be carried out at the same time short-term, mid-term, it is long-term and
Real-time rainfall runoff forecasting method.
Background technology
Runoff Forecast is flood-control and drought relief and efficiently uses the important evidence of water resource and water project operation, management etc., is forecast
Its power benefit of management and running and social benefit are significantly higher than the management and running pattern for not utilizing forecast information, and runoff now is pre-
The data that reporting system Main Basiss water level monitoring and rainfall monitoring acquire carry out short-term rainfall runoff forecasting and section flood is pre-
Report and the real-time modified Real-time Flood Forecasting of consideration, the Medium-long Term Prediction method in society is still not mature enough at present now, middle length
The only a small number of large reservoirs in the phase Rainfall Runoff Forecasting country are applied, and the condition of popularizing is not had.Water level or flow
The telemetric stations of monitoring will generally set water level well or monitoring station, and cost is saved your breath tens of thousands of to hundreds of thousands, general small reservoir, small hydropower station
Less input for these infrastructure, and the degree of automation is low, and matched Runoff Forecast system is even more almost without Runoff Forecast
System hardware and software development cost is high, and infrastructure input is big, and maintenance cost is expensive, or the medium-term and long-term rainfall runoff forecasting having is from reason
By complicated to the process of realization, it is unfavorable for promoting, these factors are all that the bottleneck that water resource utilizes is improved in Small Reservoir, power station,
Such as by the end of the year 2010, Fujian the whole province is completed rural hydropower station 6606, in contrast possesses the water of Runoff Forecast system
Library, power station are very few, and many large reservoirs, hydroelectric power plant also only have that short-term Runoff Forecast system, to lack medium-term and long-term runoff pre-
Reporting system.These Small Reservoirs, power station are because lack Real-time Flood Forecasting and Medium-and Long-Term Runoff Forecasting blindly with water resource
And the benefit number lost is in terms of necessarily, the Small Reservoir that even more has, power station are because lack Medium-and Long-Term Runoff Forecasting, to flood calamity
Evil is underestimated and is not carried out adequate preparation or have little time to cause casualties due to carrying out Emergency Preparedness and wealth because leading time is too short
Production loss.
Invention content
One of present invention purpose is that neoteric Rainfall Runoff Forecasting is innovated on basic algorithm, only
It can be carried out at the same time real-time rainfall flood forecast and brachymedial Long Term Runoff Forecasting by rainfall is measured, it is pre- to substantially reduce runoff
The hardware and infrastructure cost that reporting system uses；
Another object of the present invention is only to use representativeness, reliability, one to the high actual measurement historical flood data of property
Inquire into Rainfall Runoff Forecasting parameter, and the development cost of the software of system is low, the time is short, solves Rainfall Runoff Forecasting
Popularization bottleneck limitation；
A further object of the present invention is to show Real-time Flood Forecasting process in the long-term rainfall runoff forecasting of brachymedial, is conducive to standard
Really flood peak, magnanimity, the peb process of estimation flood, can simulate outbound and water level process in forecasting process, realize user's packet
The brachymedial Long Term Runoff Forecasting scheduling including Real-time Flood Forecasting is included, solves now that most Runoff Forecast software forecasting process is too
It is short, Medium-long Term Prediction function lack flood peak, magnanimity forecast the problem of.
The main object of the present invention is achieved by following technical proposals：
A kind of Rainfall Runoff Forecasting, it is first determined Rainfall-runoff computational methods, the present invention provides a kind of Rainfall-runoff meters
For calculation method as rainfall runoff forecasting technical solution, implementation process is as follows：
The Rainfall-runoff computational methods of the present invention are as follows：The parameter includes that Q is flow (cubic meters per second), and the undergrounds Q are underground
Runoff Forecast flow, the ground Q be surface runoff forecasting runoff, Q forecast j be j moment forecasting runoffs, j be certain forecast the moment (when),
M is concentration of channel time span (hour), and n is production stream concentration time length (hour), when i=1,2 when ..., n when, Pj-m-n
+ i is to forecast that Basin Rainfall (millimeter), α j-m are the runoff coefficient in the moment basins j-m the j-m-n+i moment, and F is drainage area
(square kilometre), Rainfall-runoff calculate function be Q forecast j=∑ ni=1Pj-m-n+i α j-mF × 1000/ (3600 seconds × n+
3600 seconds × i), the preceding generation of rainfall in basin n hours in m hours is exported corresponding to runoff system before m hours to describe the basin in j
The corresponding forecast section flow of number, following peb processes refer to runoff process larger in forecasting period；
Water-break rate obtains, and by typical flood data, divides the water-break rate of different level, including nearly flood peak section, surface layer runoff, underground
Runoff, base flow, nearly flood peak section correspond to the faster epimere of water-break process water-break, and surface layer runoff corresponds to peb process stage casing immediately nearly flood
After crest segment, water-break obviously slows down, and interflow subsurface drainage corresponds to peb process hypomere, this gentle water-break of process longer-term, base flow this
A process in the period of considerably long in maintain main peb process and terminate the process that the latter water-break rate changes very little.Thus side
More typical flood processes of method can get different level runoff or the water-break rate of flood substitutes into more typical flood tune schemes
Tentative calculation simultaneously adjusts, and when forecast flood water-break process is more close with observed flood water-break process, each layering water-break rate value is better；
The undergrounds interflow subsurface drainage Q that function calculates future time period forecast are calculated according to rainfall and Rainfall-runoff；And according to rising
Forecast result is modified with water-break situation, if the undergrounds Q j is less than upper period Q underground j-1, the undergrounds Q j is according to residing water-break
The water-break rate of level is modified；
The ground surface runoff Q that function calculates future time period forecast is calculated according to rainfall and Rainfall-runoff；By the undergrounds Q and Q
The sum of ground calculate future time period forecast section flow Q forecast j, and according to rise with water-break situation to forecast result into
Row is corrected, if Q forecast j be less than upper period Q forecast j-1, Q forecast j according to the water-break rate of residing surface runoff water-break level into
Row is corrected；
Forecast by actual measurement rainfall and subsequently rainfall, calculate each future time period forecast section flow ... Q forecast
J-2, Q forecast j-1, Q forecast j, Q forecast j+1, Q forecast j+2 ..., roll, rainfall update, cover false quasi- when the time
Forecast rainfall, then the slitless connection that actual measurement and false quasi- forecast can be achieved is forecast；
To improve precision, it is also possible to which too Thiessen polygon method or area-time method, by drainage area piecemeal, piecemeal forecasts corresponding rain
The discharge process of the corresponding forecast section of amount, then with the principle of superposition by basin each section block in the forecasting runoff process for forecasting section
It is superimposed, full basin is synthesized in the forecasting runoff process for forecasting section.
A kind of Rainfall Runoff Forecasting, including：
Watermark protocol database stores time needed for rainfall runoff forecasting algorithm module, water level, rainfall, data on flows, and can root
It needs that the quasi- data of the next period vacation in end are arranged according to user, is forecast for real-time rainfall flood, the long-term rainfall runoff forecasting of brachymedial；
Parameter and rainfall runoff data needed for customer parameter configuration module, storage and calculating rainfall runoff forecasting are pre- for rainfall runoff
It reports algorithm module to call, for user setting, adjustment forecast parameter, adjusts forecast precision；
The watermark protocol data and customer parameter configuration module that rainfall runoff forecasting algorithm module is provided according to watermark protocol database carry
The configuration parameter of confession is forecast following runoff process, and is modified to forecast result with water-break situation according to rising；
Rainfall runoff forecasting achievement statistical module, according to the forecasting process of rainfall runoff forecasting algorithm module, to main runoff or
Peb process carries out statistical estimation, and the content of statistical estimation includes that short-term, mid-term, the flood peak of long-term forecasting, magnanimity, peak are current
Between and flood peak magnanimity precision；
Rainfall runoff forecasting figure module, prog chart have short-term rainfall runoff forecasting figure, mid-term rainfall runoff forecasting figure, long pre- drop
Rain Runoff Forecast has period rainfall column diagram and each stage hydrograph, measured discharge graph, forecasting runoff process in prog chart
Line, storage outflow graph.
The Rainfall Runoff Forecasting, with Rainfall-runoff to calculate function be Q forecast j=∑s ni=1Pj-m-n+
F × 1000/ i α j-m (3600 seconds × n+3600 seconds × i), period parameters are not limited to hour in function, can also be several points
Clock or several hours, Ruo Gantian depend on flood data, Streamflow Data or the density for observing data, and are wanted to the precision of forecast
It asks.
The Rainfall Runoff Forecasting calculates function according to rainfall and Rainfall-runoff and calculates future time period forecast
The undergrounds surface runoff Q, if the undergrounds Q j is less than upper period Q underground j-1, the undergrounds Q j is according to the water-break rate of residing water-break level
It is modified；By the sum of the undergrounds Q and the ground Q calculate the forecast section flow Q of future time period forecasts j, and according to rising and move back
Regimen condition is modified forecast result, if Q forecast j, which was less than upper period Q, forecasts j-1, according to moving back for residing water-break level
Water rate is modified, and the real time data by database and false quasi- data realize that Real-time Flood Forecasting or brachymedial long-period runoff are pre-
Report.
The rainfall runoff forecasting algorithm module introduces simultaneously from customer parameter configuration module influences duration, production stream early period
Concentration time, concentration of channel time, basis runoff regulation coefficient early period, user can be according to predicting conditions early period by adjusting this
Four parameters improve the forecast precision of follow-up rainfall runoff forecasting.
The anabolic process of rainfall runoff forecasting described in Rainfall Runoff Forecasting is by surface runoff rising limb, interflow subsurface drainage
The Fitting Calculation is composed respectively for rising limb, surface runoff water-break section, interflow subsurface drainage water-break section, rainfall runoff forecasting algorithm module
Using per hour or shorter time as a period of time segment length realize Rainfall Runoff Forecasting be fitted peb process flood peak, magnanimity, flood
Process.
The Rainfall Runoff Forecasting is according to the forecasting process statistical data of rainfall runoff forecasting algorithm module, to master
Runoff or peb process is wanted to carry out statistical estimation, statistical content includes short-term, mid-term, long-term forecasting runoff process and corresponding actual measurement
Between flood peak, magnanimity, the peak of runoff process are current, peak show time error, assessment forecast flood peak, magnanimity precision.
What the rainfall runoff forecasting figure of the Rainfall Runoff Forecasting was counted according to rainfall runoff forecasting algorithm module
Day part reservoir level, rainfall, measured profile flow, forecast section flow, storage outflow, generating flow, discharge are drop
The value in rain Runoff Forecast diagram data source, using each mutually continuous actual measurement period and forecasting period as category Axis labels, according to classification
Axis time length generates short-term rainfall runoff forecasting figure, mid-term rainfall runoff forecasting figure, long-term rainfall runoff forecasting figure.
Thus the present invention has the advantages that：
One of advantageous effect of the present invention is that this system is to realize that be carried out at the same time real-time rainfall flood pre- based on process rainfall
Report and the long-term rainfall runoff forecasting of brachymedial, the hardware and software cost and infrastructure for substantially reducing Rainfall Runoff Forecasting utilization are thrown
Enter, enriches Runoff Forecast means；The two of advantageous effect of the present invention are only to use representativeness, reliability, one to the high reality of property
Rainfall Runoff Forecasting parameter, and the development cost of Rainfall Runoff Forecasting software can be inquired by surveying historical flood data
Low, the time is short, is promoted and applied rapidly for rainfall runoff forecasting, forms social benefit, economic benefit provides convenience；
The three of advantageous effect advantage of the present invention are that the anabolic process of Runoff Forecast is risen by surface runoff rising limb, interflow subsurface drainage
Section, the Fitting Calculation is composed respectively for surface runoff water-break section, interflow subsurface drainage water-break section, is reduced and is calculated error range, and with every
Hour or it is shorter when a length of a period of time segment length be conducive to the flood peak of Rainfall Runoff Forecasting fitting peb process, magnanimity, flood shape
State enhances the real-time of forecast function；
When the four of advantageous effect of the present invention are during rainfall runoff forecasting while influencing the early period of introducing duration, production stream confluence
Between, the concentration of channel time, runoff coefficient adjustment early period, user can carry according to predicting condition early period by adjusting this four parameters
The forecast precision of high follow-up rainfall runoff forecasting, increases the flexibility of forecast；
The five of advantageous effect of the present invention are can be by the drop of future time period in false quasi- leading time during rainfall runoff forecasting
Rainfall, discharge, generating flow, storage outflow realize real-time flood control program simulation, and very easily false quasi- brachymedial is long
The processes such as rainfall, outbound, water level of day part in phase forecast, convenient for formulating reservoir operation and generation schedule.
Description of the drawings
Fig. 1 is Rainfall Runoff Forecasting structure diagram of the present invention；
Fig. 2 is rainfall runoff forecasting algorithm module structure diagram of the present invention；
Fig. 3 is customer parameter configuration module structure diagram of the present invention；
Fig. 4 is watermark protocol database structure block diagram of the present invention；
Fig. 5 is rainfall runoff forecasting achievement statistical module structure diagram of the present invention；
Fig. 6 is rainfall runoff forecasting figure module structure diagram of the present invention.
Specific implementation mode
Purpose, advantage and the characteristic of the present invention is worked as by the detailed description of certain following reservoir embodiment and schema can be brighter
In vain, such embodiment be it is as embodiment for example, the purpose is to for convenience of elaborate the present invention, rather than to
Limit the present invention.The system diagram of Rainfall Runoff Forecasting of the present invention please refers to Fig.1.The system of the present invention includes mainly five big knots
Structure：Rainfall runoff forecasting algorithm module, customer parameter configuration module, watermark protocol database, rainfall runoff forecasting achievement count mould
Block, rainfall runoff forecasting figure module, this system structure is simplified, running environment requirement is low, and minimum this system is in similar WPS tables
Or it is achieved that extension also can be real by access, SQL, VB, C++ etc. general database and programming in excel tables
It is existing, the main implementation method and implementation process of this system are illustrated below.
Rainfall runoff forecasting algorithm module is referring to Fig. 2, implementation process is as follows：
It is that Q forecasts F × 1000/ j=∑ ni=1Pj-m-n+i α j-m (3600 seconds × n+3600 seconds that Rainfall-runoff, which calculates function,
×i)
The parameter includes that Q is flow (cubic meters per second), and the undergrounds Q are interflow subsurface drainage forecasting runoff, and the ground Q is surface runoff
Forecasting runoff, Q forecast j be j moment forecasting runoffs, j be certain forecast the moment (when), m be concentration of channel time span (hour), n
For production stream concentration time length (hour), i=1,2 ..., n, Pj-m-n+i be j-m-n+i moment Basin Rainfall (millimeter), α j-m
For the runoff coefficient in the moment basins j-m, F is drainage area (square kilometre), and following floods also belong to runoff, and peb process herein refers to
Peak runoff process in forecasting process.
Certain Watershed Runoff process such as table 1 (to check forecast precision, if forecast rainfall and actual measurement rainfall are same)：
Table 1；
| Period | 1 | 2 | 3 | 4 | 5 | 6 | 7 | 8 | 9 | 10 | 11 | 12 |
| Rainfall | 2.79 | 2.95 | 9.00 | 12.5 | 7.42 | 5.95 | 7.26 | 5.37 | 2.80 | 2.0 | 3.0 | 2.16 |
| Period | 13 | 14 | 15 | 16 | 17 | 18 | 19 | 20 | 21 | 22 | 23 | 24 |
| Storage | 4570 | 4958 | 4349 | 5622 | 6732 | 5792 | 5679 | 5641 | 5735 | 5681 | 5645 | 5503 |
Interflow subsurface drainage process calculate, interflow subsurface drainage calculate function be the undergrounds Q j=∑ ni=1Pj-m- n+i α underground j-m F ×
1000/ (3600 seconds × n+3600 seconds × i), if concentration of channel time m is 7 hours, production stream concentration time n is 8 hours, forecast
When being 17 when basin Outlet Section flow-time j is carried out at end, drainage area is 7907 square kilometres, if 9 ground face diameter when 12
Stream coefficient is respectively 0.261,0.264,0.267, the undergrounds 0.270, α j-m=0.264, the undergrounds Q 17=∑s ni=1P2+i ×
0.264 × 7907 × 1000/ (3600 × n+3600 × i)=2603 cubic meters per second, can similarly acquire 16 ground when 19
Lower runoff is 2452,2603,2739,2495 cubic meters per seconds；
Interflow subsurface drainage water-break process amendment, if obtaining water-break rate 0.985 on base flow by flood analysis, water-break under base flow
Rate 0.995, the base flow upper limit are that then the undergrounds Q j is constant such as the undergrounds the Q undergrounds j >=Q j-1 for 500 cubic meters per seconds, no such as the undergrounds Q j-
1 ＞ base flows are, on base flow water-break be calculated as the undergrounds the Q undergrounds j=Q j-1 × 0.985, no base flow water-break is the undergrounds Q j=Q
Underground j-1 × 0.995；The undergrounds Q j >=(Q underground j-1=2452)；Because when the undergrounds Q j >=(Q underground j-1=2572), j
The interflow subsurface drainage flow at quarter is that interflow subsurface drainage water-break process correction value is 2603 cubic meters per seconds.
Surface runoff process calculates, and it is the ground Q j=∑ ni=1 Pj-m-n+i α ground j-m F that surface runoff, which calculates function,
× 1000/ (3600 seconds × n+3600 seconds × i), if 9 surface runoff coefficient when 12 is respectively 0.394,0.407,
The ground the 0.420, ground 0.431, α j-m=0.407, Q P3+i × 0.407 × 7907 × 1000/ 17=∑ ni=1 (3600 ×
3600 × i of n+)=4013 cubic meters per second, can similarly acquire 16 surface runoff when 19 be 3702,4013,4309,
3983 cubic meters per seconds.
Forecast that runoff process calculates Q and forecasts the undergrounds the j=Q ground j+Q j, acquiring 16 forecast runoff process when 19 is
6154,6616,7048,6478 cubic meters per second；Forecast that runoff water-break process amendment, surface runoff water-break process amendment are included in
In the amendment for forecasting runoff water-break process, if water-break rate is 0.945 on nearly flood peak section, water-break rate is on the runoff of surface layer
0.965, water-break rate is 0.985 on interflow subsurface drainage, if nearly flood peak section lower limit is forecast flood peak value, 7048 half is 3524
Cubic meters per second, surface layer runoff lower limit are that flood peak value one third is 2349 cubic meters per seconds, and the interflow subsurface drainage upper limit is flood peak value
1/5th be 1410 cubic meters per seconds, is that then Q forecasts that j is constant, otherwise as Q is pre- if Q forecast j >=Q forecasts j-1 × 0.985
Report j>Nearly flood peak section water-break is that water-break is that Q forecasts that j=Q forecasts j-1 × 0.945 on nearly flood peak, otherwise as Q forecasts j>Table
Water-break on layer runoff is that then water-break is that Q forecasts that j=Q forecasts j-1 × 0.965 on the runoff of surface layer, and otherwise interflow subsurface drainage moves back
Water is that Q forecasts that j=Q forecasts j-1 × 0.985；
Because Q forecasts j >=(Q forecasts j-1=6154), the forecasting runoff at j moment is forecast runoff process correction value 6616
Cubic meters per second, it is 6154,6616,7048,6660 cubes that can similarly acquire 16 modified forecast runoff process when 19
Metre per second (m/s),.
Customer parameter configuration module is referring to Fig. 3, implementation process is as follows：
Rainfall runoff coefficient correlation table is built, it, can be according to the typical Streamflow Data of actual measurement, flood data, by different brackets rainfall such as table 2
If corresponding coefficient of groundwater runoff, surface runoff coefficient, runoff coefficient value is with representativeness, reliability, one to the high allusion quotation flood of property
Subject to water process, then runoff coefficient is adjusted for reference with middle long Streamflow Data and is fitted each rank flood hydrograph, taken in leading time
Survey luffing 20% is used as permissible error, compares each history actual measurement typical flood data, runoff is adjusted according to each basin characteristic
Coefficient, controls each forecast precision and forecast qualification rate approaches 100% as far as possible；
Table 2；
| Rainfall grade | Coefficient of groundwater runoff | Surface runoff coefficient | Rainfall runoff coefficient |
| … | … | … | … |
| 90 | 0.24 | 0.31 | 0.55 |
| 100 | 0.25 | 0.35 | 0.60 |
| 120 | 0.26 | 0.39 | 0.65 |
| 140 | 0.27 | 0.43 | 0.70 |
| 160 | 0.28 | 0.47 | 0.75 |
| 180 | 0.29 | 0.51 | 0.80 |
| … | … | … | … |
Runoff process coefficient table is built, such as table 3, coefficient of groundwater runoff, the surface runoff coefficient of day part are according to day part m hours
Rainfall looks into the analog value of rainfall runoff coefficient correlation table, the coefficient of groundwater runoff during rainfall runoff forecasting and ground
The value of runoff coefficient is corresponding with runoff process coefficient table；
Table 3；
| Period surface runoff coefficient | Day part rainfall | Rainfall duration | Period coefficient of groundwater runoff |
| … | … | … | … |
| 0.394 | 149.21 | 9 | 0.261 |
| 0.407 | 150.58 | 10 | 0.264 |
| 0.420 | 152.21 | 11 | 0.267 |
| 0.431 | 152.95 | 12 | 0.270 |
| 0.439 | 154.37 | 13 | 0.272 |
| 0.445 | 157.84 | 14 | 0.274 |
| … | … | … | … |
Water level capacity curve is set, it is poor to go out Incoming water quantity according to day part of the water balanced calculation containing forecast, in addition when
Reservoir level at the beginning of section corresponds to storage capacity, calculates subsequent period just storage capacity, can be according to day part storage capacity and water level capacity curve with directly
Line interpolation calculation day part equivalent water level acquires forecast water level process.
User's basic configuration parameter, including surface runoff water-break rate, interflow subsurface drainage water-break rate, production stream concentration time, river
Concentration time, early period basis runoff regulation coefficient, early period influence duration, drainage area, the base flow upper limit, forecast Start Date, pre-
Time started, water-break rate is reported to obtain the water-break rate for dividing different level by typical flood data, including nearly flood peak section, surface layer diameter
Stream, interflow subsurface drainage, base flow, nearly flood peak section correspond to the faster epimere of water-break process water-break, and surface layer runoff corresponds to peb process stage casing
Immediately after flood peak, water-break obviously slows down, and interflow subsurface drainage corresponds to peb process hypomere, this gentle water-break of process longer-term, base
Flow this process in the period of considerably long in maintain the process that a water-break rate changes very little.Thus more typical cases of method
Peb process can get different level runoff or the water-break rate of flood substitutes into more typical flood tune scheme tentative calculations and adjusts, and work as flood
Water forecasts that water-break process is more close with flood actual measurement water-break process, and each layering water-break rate value is better；
Early period, runoff regulation coefficient in basis improved follow-up forecast essence for user according to current rich withered situation path transfer stream coefficient magnitude
Degree, rainfall runoff coefficient magnitude is related to basis runoff regulation coefficient early period, and the production stream concentration time generates master for rainwater to be arranged
Runoff is wanted to come together in river effective time, the concentration of channel time is used to be arranged the time that main runoff reaches basin forecast section,
Early period influences duration influences the rainfall duration of forecast section discharge process, the rainfall of day part for early period to be arranged mainly
It is exactly according to the accumulative preceding hourly precipitation amount of influence early period duration calculation.
Watermark protocol database is referring to Fig. 4, implementation process is as follows：
Including field have：Date, time, reservoir level, rainfall, reservoir inflow, generating flow, discharge, storage outflow；
The field name date is recorded as date type data, and field entitled reservoir level, reservoir inflow, generating flow, is let out Basin Rainfall
Water flow is numeric type data, such as table 4；
Table 4；
1 period 17 of table is corresponded in table 4 when 16 days 0 June in 2010,1 period of table is to facilitate vacation quasi- since the period 1 to understand, is estimated
Measuring rainfall can be placed in after actual measurement rainfall, actual measurement rainfall update directly covering estimation rainfall, when such real-time prediction, you can realization
Actual measurement rainfall forecast and the slitless connection for estimating rainfall forecast, in turn simplify system, can will according to brachymedial long-range weather forecasting
The rainfall accordingly predicted is placed in database corresponding position, is called for rainfall runoff forecasting algorithm module, user can be according to each
It includes the storage outflows processes such as generating flow, flood discharge flow that the forecast section flow and control water level in period, which need to be arranged, is realized
Real-time flood tune library program simulation and the long-term Runoff Forecast of brachymedial dispatch ruleization.
Rainfall runoff forecasting achievement statistical module is referring to Fig. 5, implementation process is as follows：The forecast section of this runoff process and
Measured profile is same section, and corresponding discharge is included in during Runoff Forecast process and actual measurement, can by similar WPS tables or
Max () function in excel tables obtains maximum forecast section flow value and measured profile flow value during required；
Position its time of occurrence according to maximum forecast section flow value and measured profile flow value, and calculate peak it is current between accidentally
Difference can pass through match () in similar WPS tables or excel tables and the realization of max () function；
Associated each day magnanimity is determined according to the time of occurrence of maximum forecast section flow value and measured profile flow value,
It can be realized by sum (), offset (), match () and max () function in similar WPS tables or excel tables, generally
Respectively to influence Main Prediction and measured profile discharge process rainfall time started as starting point, until required main respective process length
The last time terminates；
Associated main drop is determined according to the time of occurrence of most maximum forecast section flow value and measured profile flow value
Rain process, can be real by sum (), offset (), match () and max () function in similar WPS tables or excel tables
It is existing, generally respectively to influence Main Prediction and measured profile discharge process rainfall time started as starting point, until required main corresponding
The process length end time terminates.
Flood peak, magnanimity, each day rainfall, the drainage area of the forecast of each day and actual measurement according to weather report can determine each day forecast and
The runoff coefficient of actual measurement, magnanimity precision, flood peak precision；
Table 5；
| Long-term forecasting outcome table | |||
| Forecast flood peak | 7049 | Survey flood peak | 6732 |
| Forecast ten days magnanimity | 143784 | Survey ten days magnanimity | 147750 |
| The of that month magnanimity of forecast | 266254 | The of that month magnanimity of actual measurement | 291671 |
| Forecast flood peak precision | 104.7% | Survey ten days rainfall | 221.8 |
| Ten days magnanimity precision | 97.3% | The of that month rainfall of actual measurement | 429.4 |
| Of that month water precision | 91.3% | Peak shows time error | -1 |
| Forecast ten days runoff coefficients | 0.82 | Survey ten days runoff coefficients | 0.84 |
| The of that month runoff coefficient of forecast | 0.78 | The of that month runoff coefficient of actual measurement | 0.86 |
| There is the date in forecast flood peak | 2010-6-16 | Forecast flood peak time of occurrence | When 01 |
| There is the date in actual measurement flood peak | 2010-6-16 | Survey flood peak time of occurrence | When 00 |
As shown in table 5, it is exactly to be joined as example with acquired results after the calculation of a certain large reservoir history observed flood Process Forecasting
It examines.
In short term, mid-term, long-term dispatch program simulation, by the rainfall of false quasi- regimen database future time period, generating flow,
Discharge, storage outflow, rainfall runoff forecasting algorithm module can be automatically according to the accordingly pre- count off of these data statistics day parts
According to by adjusting user configuration parameter, the rainfall of watermark protocol database, outbound process and observation day part equivalent water level, rainfall diameter
Stream forecast achievement, forecasting process are compared with actual measurement, by compare user be assured that each period control water level, outbound it is how many and
Corresponding reservoir operation uses process.
Rainfall runoff forecasting figure module is referring to Fig. 6, implementation process is as follows：
Rainfall runoff forecasting figure is classified, and forecasting process is that short-term rainfall runoff forecasting figure, forecasting process are arrived 4 at three days and below
7 days are mid-term rainfall runoff forecasting figure, and forecasting process was long-term rainfall runoff forecasting figure at 10 days or more；
According to day part reservoir level, rainfall, section flow, storage outflow, the hair obtained from rainfall runoff forecasting algorithm module
The magnitude of current, discharge, the value that forecasting runoff is rainfall runoff forecasting diagram data source, with each mutually continuous actual measurement period and pre-
Section of giving the correct time is category Axis labels, and short-term rainfall runoff forecasting figure, mid-term rainfall runoff forecasting are generated according to classification axis time length
Figure, long-term rainfall runoff forecasting figure, have in prog chart period rainfall column diagram and each stage hydrograph, measured discharge graph,
Forecasting runoff graph, storage outflow graph.
Claims (1)
1. a kind of Rainfall Runoff Forecasting, including：
The watermark protocol database, store rainfall runoff forecasting algorithm module needed for time, water level, rainfall, data on flows, and
It needs to be arranged end come the quasi- data of period vacation according to user, is forecast to real-time rainfall flood, the long-term rainfall runoff forecasting of brachymedial；
Parameter and rainfall runoff data are for rainfall diameter needed for the customer parameter configuration module, storage and calculating rainfall runoff forecasting
Stream forecast algorithm module calls, and for user setting, adjustment forecast parameter, adjusts forecast precision；
Rainfall runoff forecasting algorithm module, the watermark protocol data and customer parameter configuration module provided according to watermark protocol database carry
The configuration parameter of confession is forecast following runoff process, and is modified to forecast result with water-break situation according to rising；
Rainfall runoff forecasting achievement statistical module, according to the forecasting process of rainfall runoff forecasting algorithm module, to runoff or flood
Process carries out statistical estimation, and the content of statistical estimation includes, between short-term, mid-term, the flood peak of long-term forecasting, magnanimity, peak are current and
Flood peak magnanimity precision；
Rainfall runoff forecasting figure module, prog chart have short-term rainfall runoff forecasting figure, mid-term rainfall runoff forecasting figure, long-term drop
Rain Runoff Forecast figure has period rainfall column diagram and each stage hydrograph, measured discharge graph, forecasting runoff mistake in prog chart
Journey line, storage outflow graph；
The rainfall runoff forecasting algorithm module introduces simultaneously from customer parameter configuration module influences duration, production stream confluence early period
Time, concentration of channel time, basis runoff regulation coefficient early period, user can be according to predicting conditions early period by adjusting this four
Parameter improves the forecast precision of follow-up rainfall runoff forecasting.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201810245785.7A CN108520165A (en) | 2014-07-06 | 2014-07-06 | Rainfall Runoff Forecasting |
Applications Claiming Priority (2)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201410316659.8A CN105320827B (en) | 2014-07-06 | 2014-07-06 | Rainfall Runoff Forecasting |
| CN201810245785.7A CN108520165A (en) | 2014-07-06 | 2014-07-06 | Rainfall Runoff Forecasting |
Related Parent Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN201410316659.8A Division CN105320827B (en) | 2014-07-06 | 2014-07-06 | Rainfall Runoff Forecasting |
Publications (1)
| Publication Number | Publication Date |
|---|---|
| CN108520165A true CN108520165A (en) | 2018-09-11 |
Family
ID=55248207
Family Applications (4)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN201410316659.8A Active CN105320827B (en) | 2014-07-06 | 2014-07-06 | Rainfall Runoff Forecasting |
| CN201810246621.6A Active CN108491682B (en) | 2014-07-06 | 2014-07-06 | Rainfall runoff forecasting system |
| CN201810632402.1A Active CN108983325B (en) | 2014-07-06 | 2014-07-06 | Rainfall runoff forecasting method |
| CN201810245785.7A Pending CN108520165A (en) | 2014-07-06 | 2014-07-06 | Rainfall Runoff Forecasting |
Family Applications Before (3)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN201410316659.8A Active CN105320827B (en) | 2014-07-06 | 2014-07-06 | Rainfall Runoff Forecasting |
| CN201810246621.6A Active CN108491682B (en) | 2014-07-06 | 2014-07-06 | Rainfall runoff forecasting system |
| CN201810632402.1A Active CN108983325B (en) | 2014-07-06 | 2014-07-06 | Rainfall runoff forecasting method |
Country Status (1)
| Country | Link |
|---|---|
| CN (4) | CN105320827B (en) |
Cited By (1)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN112766531A (en) * | 2019-11-06 | 2021-05-07 | 中国科学院国家空间科学中心 | Runoff prediction system and method based on satellite microwave observation data |
Families Citing this family (15)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN106777724B (en) * | 2016-12-23 | 2020-03-31 | 吉林大学 | Basic flow segmentation method for semi-humid and semi-arid regions and humid regions |
| CN106971237B (en) * | 2017-02-27 | 2018-04-24 | 中国水利水电科学研究院 | A kind of Medium-and Long-Term Runoff Forecasting method for optimization algorithm of being looked for food based on bacterium |
| CN108804634B (en) * | 2018-06-01 | 2021-10-29 | 深圳乐信软件技术有限公司 | Data rolling method and device, front-end equipment, background server and medium |
| CN109298468A (en) * | 2018-09-10 | 2019-02-01 | 柳创新 | A kind of rainfall runoff and river-level traffic forecast DSS |
| CN109507759B (en) * | 2018-09-13 | 2020-10-20 | 广州积雨云科技有限公司 | Method for forecasting rainfall |
| CN109710883A (en) * | 2018-12-25 | 2019-05-03 | 河海大学 | A kind of runoff yield excess area rainfall runoff counts description method |
| CN111008362B (en) * | 2019-11-13 | 2023-06-02 | 广东省水文局韶关水文分局 | Dynamic rainfall judging method for flood forecast starting and stopping calculation |
| CN111047213B (en) * | 2019-12-25 | 2023-06-20 | 长江水利委员会水文局 | Medium-long term water resource quantity critical early warning index method based on multi-element joint discrimination |
| CN111598354B (en) * | 2020-05-26 | 2023-04-21 | 河南郑大水利科技有限公司 | Method for predicting current day runoff of small reservoir |
| CN111948739B (en) * | 2020-07-21 | 2022-08-02 | 重庆交通大学 | Flood forecasting system |
| CN112950033B (en) * | 2021-03-04 | 2024-03-29 | 吴统明 | Reservoir dispatching decision method and system based on reservoir dispatching rule synthesis |
| CN112967476A (en) * | 2021-04-09 | 2021-06-15 | 黑龙江文图测绘地理信息有限责任公司 | Flood forecasting system based on hyperbolic flow distribution model |
| CN113589404B (en) * | 2021-07-30 | 2023-02-03 | 郑州大学 | Method for predicting runoff volume of storm at scene |
| CN114020975B (en) * | 2021-10-27 | 2024-06-14 | 华能西藏雅鲁藏布江水电开发投资有限公司 | Method for automatically screening flood orders |
| CN116258278A (en) * | 2023-05-10 | 2023-06-13 | 青岛研博数据信息技术有限公司 | Method, system and equipment for deducing constructed water level reservoir capacity |
Citations (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN101692273A (en) * | 2009-09-28 | 2010-04-07 | 北京工业大学 | Modeling method of on-line hydraulic model of municipal drainage pipe network |
| CN102289570A (en) * | 2011-07-23 | 2011-12-21 | 浙江大学 | Flood forecast method based on rainfall-runoff-flood routing calculation |
| CN102867106A (en) * | 2012-08-14 | 2013-01-09 | 贵州乌江水电开发有限责任公司 | Method and system for predicting short-term running water |
Family Cites Families (13)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| JP4682178B2 (en) * | 2007-05-31 | 2011-05-11 | 財団法人河川情報センター | Distributed runoff forecasting system using nationwide synthetic radar rainfall |
| CN101794495B (en) * | 2010-02-05 | 2011-08-24 | 国网电力科学研究院 | Optimization method of real-time correction models in flood forecast system |
| CN102296562A (en) * | 2010-06-25 | 2011-12-28 | 华东电网有限公司 | Step reservoir joint flood scheduling optimization method coupling flood protection with power generation |
| CN101864750B (en) * | 2010-06-29 | 2011-11-16 | 西安理工大学 | Multi-model meta-synthesis flood forecasting system and forecasting method thereof |
| KR101219506B1 (en) * | 2010-09-13 | 2013-02-18 | 대한민국 | Monitoring system and Method for rainfall and water level in dam |
| CN102096118A (en) * | 2010-11-26 | 2011-06-15 | 河海大学 | Hillside point rainfall runoff measurement method |
| CN102034003B (en) * | 2010-12-16 | 2012-11-28 | 南京大学 | Watershed hydrological model design method based on storage capacity curve and TOPMODEL |
| CN102314554B (en) * | 2011-08-08 | 2013-12-25 | 大唐软件技术股份有限公司 | Land-atmosphere coupling-based method and system for flood forecast of minor watersheds |
| CN102902870B (en) * | 2012-06-13 | 2016-04-06 | 中国科学院水利部成都山地灾害与环境研究所 | A kind of Debris Flow Forecast method based on basin water-soil coupling |
| CN102776870B (en) * | 2012-07-12 | 2015-04-01 | 贵州乌江水电开发有限责任公司 | Non-forecast flood regulation method for cascade hydropower station based on water level flow |
| CN103106248A (en) * | 2013-01-06 | 2013-05-15 | 长江水利委员会水文局 | Stage-discharge data assimilation flood informing method, device and flood informing system |
| CN103729550B (en) * | 2013-12-18 | 2016-08-17 | 河海大学 | Multiple-model integration Flood Forecasting Method based on propagation time cluster analysis |
| CN103882827B (en) * | 2014-04-14 | 2015-09-02 | 武汉大学 | Based on flood control by reservoir regulation relative risk Forecasting Methodology and the reservoir regulation for flood control schemes evaluation method of runoff DATA PROCESSING IN ENSEMBLE PREDICTION SYSTEM |
-
2014
- 2014-07-06 CN CN201410316659.8A patent/CN105320827B/en active Active
- 2014-07-06 CN CN201810246621.6A patent/CN108491682B/en active Active
- 2014-07-06 CN CN201810632402.1A patent/CN108983325B/en active Active
- 2014-07-06 CN CN201810245785.7A patent/CN108520165A/en active Pending
Patent Citations (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN101692273A (en) * | 2009-09-28 | 2010-04-07 | 北京工业大学 | Modeling method of on-line hydraulic model of municipal drainage pipe network |
| CN102289570A (en) * | 2011-07-23 | 2011-12-21 | 浙江大学 | Flood forecast method based on rainfall-runoff-flood routing calculation |
| CN102867106A (en) * | 2012-08-14 | 2013-01-09 | 贵州乌江水电开发有限责任公司 | Method and system for predicting short-term running water |
Non-Patent Citations (3)
| Title |
|---|
| 刘佳: "中长期径流预报技术及应用系统研究", 《中国优秀硕士学位论文全文数据库 基础科学辑》 * |
| 杨哲等: "基于水文倒推法的中小洪水过程模拟", 《河海大学学报(自然科学版)》 * |
| 郭君: "清江流域降雨径流预报模型的研究", 《中国优秀硕士学位论文全文数据库 基础科学辑》 * |
Cited By (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN112766531A (en) * | 2019-11-06 | 2021-05-07 | 中国科学院国家空间科学中心 | Runoff prediction system and method based on satellite microwave observation data |
| CN112766531B (en) * | 2019-11-06 | 2023-10-31 | 中国科学院国家空间科学中心 | Runoff prediction system and method based on satellite microwave observation data |
Also Published As
| Publication number | Publication date |
|---|---|
| CN108983325B (en) | 2021-12-14 |
| CN108491682B (en) | 2020-08-18 |
| CN105320827B (en) | 2018-07-13 |
| CN108491682A (en) | 2018-09-04 |
| CN108983325A (en) | 2018-12-11 |
| CN105320827A (en) | 2016-02-10 |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| CN105320827B (en) | Rainfall Runoff Forecasting | |
| CN110991687B (en) | Water resource scheduling optimization method based on empirical model | |
| CA3031517C (en) | Method and apparatus for model-based control of a water distribution system | |
| Xu et al. | Comparison of three global optimization algorithms for calibration of the Xinanjiang model parameters | |
| CN109815611B (en) | Basin boundary generating method based on digital basin | |
| CN113762618B (en) | Lake water level forecasting method based on multi-factor similarity analysis | |
| CN107609716B (en) | A kind of power station load setting prediction technique | |
| KR20090093520A (en) | Decision support method for integrated river basin water resources management | |
| CN102360468A (en) | Flood forecasting system | |
| US20240256746A1 (en) | Scheduling method and system for operation of reservoirs to recharge freshwater for repelling saltwater intrusion under changing conditions | |
| KR100741691B1 (en) | Water Management System and method for Optimal Operation and Control in Wide-area Waterworks using ubiquitous technology | |
| CN111260159B (en) | Meteorological hydrologic coupling flood forecasting method | |
| Etkin et al. | Stochastic programming for improved multiuse reservoir operation in Burkina Faso, West Africa | |
| CN114091277B (en) | Xinanjiang model parameter calibration method considering influence of initial state variables | |
| CN102912760A (en) | Lowest navigable water level generating method of downstream river of reservoir | |
| JP7201969B2 (en) | Learning device and sewage inflow prediction device | |
| Ponte et al. | Intelligent decision support system for real-time water demand management | |
| CN106991274A (en) | Basin storehouse group and regional flood combined scheduling method, system | |
| CN117172359A (en) | Reservoir storm resistance probability forecasting method, system, medium and electronic equipment | |
| CN113723708B (en) | Urban daily water consumption prediction method based on machine learning | |
| CN106355264A (en) | Combined prediction method of reference crop evapotranspiration | |
| KR20240000915A (en) | Water supply and demand analysis method based on historical data by using downscaling watershed and recording medium storing program for executing the method | |
| CN105260948A (en) | City water supply system daily plan scheduling decision-making method | |
| JP5561643B2 (en) | Demand forecasting device and water operation monitoring system | |
| JP2020166529A (en) | Power consumption prediction system, method and program |
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 | ||
| WD01 | Invention patent application deemed withdrawn after publication | ||
| WD01 | Invention patent application deemed withdrawn after publication |
Application publication date: 20180911 |