CN117390125B - Drainage basin hydrologic forecasting method with intelligent adaptation of flow production mode - Google Patents
Drainage basin hydrologic forecasting method with intelligent adaptation of flow production mode Download PDFInfo
- Publication number
- CN117390125B CN117390125B CN202311234707.4A CN202311234707A CN117390125B CN 117390125 B CN117390125 B CN 117390125B CN 202311234707 A CN202311234707 A CN 202311234707A CN 117390125 B CN117390125 B CN 117390125B
- Authority
- CN
- China
- Prior art keywords
- runoff
- flow
- mode
- alpha
- target
- Prior art date
- Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
- Active
Links
- 238000004519 manufacturing process Methods 0.000 title claims abstract description 55
- 230000006978 adaptation Effects 0.000 title claims abstract description 14
- 238000013277 forecasting method Methods 0.000 title claims abstract description 11
- 238000000034 method Methods 0.000 claims abstract description 64
- 230000008569 process Effects 0.000 claims abstract description 39
- XLYOFNOQVPJJNP-UHFFFAOYSA-N water Substances O XLYOFNOQVPJJNP-UHFFFAOYSA-N 0.000 claims abstract description 37
- 238000004364 calculation method Methods 0.000 claims abstract description 26
- 239000002689 soil Substances 0.000 claims abstract description 25
- 238000011156 evaluation Methods 0.000 claims abstract description 8
- 230000008878 coupling Effects 0.000 claims abstract description 6
- 238000010168 coupling process Methods 0.000 claims abstract description 6
- 238000005859 coupling reaction Methods 0.000 claims abstract description 6
- 238000001764 infiltration Methods 0.000 claims description 15
- 230000008595 infiltration Effects 0.000 claims description 12
- 238000005457 optimization Methods 0.000 claims description 12
- 238000004422 calculation algorithm Methods 0.000 claims description 10
- 238000009825 accumulation Methods 0.000 claims description 5
- 230000008020 evaporation Effects 0.000 claims description 4
- 238000001704 evaporation Methods 0.000 claims description 4
- 230000035699 permeability Effects 0.000 claims description 3
- 238000010586 diagram Methods 0.000 description 3
- 230000000694 effects Effects 0.000 description 3
- 238000004088 simulation Methods 0.000 description 3
- 238000006424 Flood reaction Methods 0.000 description 2
- 230000006872 improvement Effects 0.000 description 2
- 230000011218 segmentation Effects 0.000 description 2
- 230000003068 static effect Effects 0.000 description 2
- 238000003860 storage Methods 0.000 description 2
- 230000000007 visual effect Effects 0.000 description 2
- 238000010835 comparative analysis Methods 0.000 description 1
- 230000007547 defect Effects 0.000 description 1
- 238000013461 design Methods 0.000 description 1
- 238000012850 discrimination method Methods 0.000 description 1
- 230000007774 longterm Effects 0.000 description 1
- 238000007726 management method Methods 0.000 description 1
- 230000007246 mechanism Effects 0.000 description 1
- 238000012986 modification Methods 0.000 description 1
- 230000004048 modification Effects 0.000 description 1
- 238000011160 research Methods 0.000 description 1
Classifications
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F16/00—Information retrieval; Database structures therefor; File system structures therefor
- G06F16/20—Information retrieval; Database structures therefor; File system structures therefor of structured data, e.g. relational data
- G06F16/29—Geographical information databases
-
- 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
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
- G06Q10/00—Administration; Management
- G06Q10/06—Resources, workflows, human or project management; Enterprise or organisation planning; Enterprise or organisation modelling
- G06Q10/063—Operations research, analysis or management
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
- G06Q50/00—Information and communication technology [ICT] specially adapted for implementation of business processes of specific business sectors, e.g. utilities or tourism
- G06Q50/10—Services
- G06Q50/26—Government or public services
Landscapes
- Engineering & Computer Science (AREA)
- Business, Economics & Management (AREA)
- Theoretical Computer Science (AREA)
- Physics & Mathematics (AREA)
- General Physics & Mathematics (AREA)
- Human Resources & Organizations (AREA)
- Economics (AREA)
- Tourism & Hospitality (AREA)
- Strategic Management (AREA)
- Entrepreneurship & Innovation (AREA)
- General Business, Economics & Management (AREA)
- Marketing (AREA)
- General Engineering & Computer Science (AREA)
- Development Economics (AREA)
- Databases & Information Systems (AREA)
- Educational Administration (AREA)
- Remote Sensing (AREA)
- Game Theory and Decision Science (AREA)
- Data Mining & Analysis (AREA)
- Geometry (AREA)
- Operations Research (AREA)
- Quality & Reliability (AREA)
- Evolutionary Computation (AREA)
- Computer Hardware Design (AREA)
- Health & Medical Sciences (AREA)
- General Health & Medical Sciences (AREA)
- Primary Health Care (AREA)
- Feedback Control In General (AREA)
- Management, Administration, Business Operations System, And Electronic Commerce (AREA)
Abstract
The invention belongs to the technical field of hydrologic forecasting, and discloses a watershed hydrologic forecasting method with an intelligent adaptation of a runoff mode, which is characterized in that a novel runoff index is provided by carrying out weighted coupling on a runoff coefficient and a runoff curve number, a correlation model of the runoff index and the water content and rainfall characteristics of watershed soil is established according to hydrologic historical data, a dynamic runoff mode identification model is obtained, a time-varying runoff mode of the watershed is identified, and a corresponding runoff process is simulated; furthermore, a time period unit line is adopted to calculate a converging process, a flood peak target, a peak time target and a deterministic target are used as evaluation indexes, the weight and hydrologic model parameters of each traditional flow production index in the novel flow production index are optimized and calibrated, and a complete prediction system for intelligent adaptation of the flow production mode is constructed. The method can determine the optimal runoff producing mode of the river basin aiming at the water content characteristics and rainfall characteristics of different soils of the river basin, and has the advantages of simplicity in calculation, wide application range and high fitting precision.
Description
Technical Field
The invention belongs to the technical field of hydrologic forecasting, and particularly relates to a drainage basin hydrologic forecasting method with intelligent adaptation of a production flow mode.
Background
The high-precision hydrologic forecast is an important guarantee for flood control, scientific management and reasonable scheduling of water resources. The runoff calculation is a key process of rainfall runoff modeling, and a common runoff model in a river basin comprises: a full-reservoir runoff model, a super-osmotic runoff model and a mixed runoff model.
Traditional hydrologic forecasting often adopts a single full-accumulation and super-seepage flow production mode to describe the flow production process, and for a plurality of rivers, the underlying surface and rainfall condition are duplicated and changeable, the river basin flow production has space-time variation characteristics, and the single static flow production mode cannot accurately describe the river basin flow production process. For example, under long-term drought conditions, the basin runoff pattern may be a super-osmotic runoff during the first rainfall in the flood season, but the subsequent runoff pattern is changed from super-osmotic to full-reservoir runoff.
The traditional method for judging the flow production mode comprises the following steps: the rainfall runoff correlation diagram method, the runoff segmentation method and the runoff coefficient discrimination method are used for comprehensively analyzing historical flood data of the river basin to obtain a main runoff generating mode of the river basin, and are static; the runoff segmentation method judges the water flow mode of the field flood through the comparative analysis of the proportion of different runoff components to the field flood, and has the advantages of dynamic, complex steps, inadequately visual and certain limitation in application. Therefore, there is a need to design a method for dynamically and intuitively determining the river basin flow pattern.
Disclosure of Invention
Aiming at the defects or improvement demands of the prior art, the invention provides a river basin hydrologic forecasting method with intelligent adaptation of a production flow mode, solves the problems of inaccurate simulation of the production flow process and lack of a dynamic and visual production flow mode distinguishing method, and improves the accuracy of runoff forecasting results of small and medium-sized river basins.
In order to achieve the above object, according to one aspect of the present invention, there is provided a drainage basin hydrologic forecasting method with intelligent adaptation of a production flow mode, including: s1: acquiring hydrological historical data of a target river basin, and further acquiring initial soil water content, rainfall characteristic values and runoff of each flood of the target river basin, wherein the rainfall characteristic values comprise rainfall and average rainfall intensity; s2: obtaining a runoff coefficient and a runoff curve number of the target river basin according to the rainfall and the runoff; s3: carrying out weighted coupling on the runoff coefficient and the runoff curve number to construct a novel runoff index model and a novel runoff index threshold for judging a runoff mode; s4: presetting a weight parameter and a novel runoff index threshold in a novel runoff index model, and introducing the hydrologic historical data into the novel runoff index model to obtain a novel runoff index value; s5: constructing a dynamic identification model of a runoff generating mode taking rainfall, initial soil water content and average rain intensity as influence factors;
S6: calibrating the flow mode dynamic identification model by using the hydrologic historical data and the corresponding novel flow index value, and obtaining model parameters in the flow mode dynamic identification model; s7: based on the hydrologic historical data, carrying out the flow generation mode determination by adopting a rated flow generation mode dynamic identification model, carrying out flow generation calculation and confluence calculation based on the flow generation mode, obtaining an initial flood process, optimizing the weight parameters and model parameters by adopting an intelligent optimization algorithm by taking a flood peak target, a peak time target and a deterministic target as evaluation indexes, and obtaining an optimized flow generation mode dynamic identification model; s8: and forecasting the hydrology of the target river basin by adopting the optimized runoff generating mode dynamic identification model.
Preferably, the runoff coefficient r of the target river basin is:
r＝R/P
wherein R is total runoff, P is total rainfall, and R is more than 0 and less than 1;
the runoff curve number CN of the target river basin is as follows:
In the method, in the process of the invention, S m is the potential infiltration amount; q is the actual flow, and CN is more than 0 and less than 100.
Preferably, the new product stream index model in step S3 is:
α＝λ1r+λ2(1-CN/100)
Wherein alpha is a novel runoff index, alpha is more than 0 and less than 1, lambda 1、λ2 is a weight parameter, and CN is a runoff curve number.
Preferably, the new flow index thresholds in step S3 are α 1 and α 2, wherein:
α1＝λ1r1+λ2(1-CN2/100)
α2＝λ1r2+λ2(1-CN1/100)
Wherein, alpha 1 is a first critical value, alpha 2 is a second critical value, the production flow mode is super-osmotic production flow when alpha is less than alpha 1, full production flow when alpha is more than alpha 2, and mixed production flow when alpha 1＜α＜α2.
Preferably, the generating flow pattern dynamic identification model in step S5 is:
Wherein, alpha is a novel yield index, w 1,w2,w3,w4,w5,w6,w7 is a model coefficient to be rated, P is total rainfall, P a is initial soil water content, and I_avg is average rainfall intensity.
Preferably, the flow mode is a super-osmotic flow when alpha < alpha 1, and the flow formula is:
Wherein f t is the drainage basin seepage capacity at the moment t; w t is the water content of the river basin soil at the moment t; WM is field water holding capacity; f c is the stable infiltration capacity of the river basin; KF is the permeability coefficient;
When alpha is larger than alpha 2, the full production flow is realized, and the production flow formula is as follows:
Wherein R is total diameter flow; PE is the amount of rainfall to remove evaporation; w 0 is the initial soil water content, WM is the field water holding capacity; w' mm is the maximum soil moisture content; a and B are water storage capacity curve coefficients;
When alpha 1＜α＜α2 is the mixed production flow, the calculation is carried out by adopting a vertical mixed production flow method, and the calculation expression of the surface runoff RS is as follows:
In the method, in the process of the invention, For the average infiltration capacity of the river basin, the value of f t in the super-infiltration product flow formula is the same,
According to the water balance principle, the infiltration amount FA is as follows:
FA＝PE-RS
The FA obtained by the calculation of the formula is taken as input and is brought into a full-accumulation runoff formula, and the calculation expression of the underground runoff RR can be obtained as follows:
The total diameter flow R of the vertical mixed flow production mode is as follows:
R＝RS+RR。
Preferably, in step S7, the flood peak target, the peak time target and the deterministic target are used as evaluation indexes, and the weight parameter and the model parameter are optimized by adopting an intelligent optimization algorithm to obtain an optimized flow generation mode dynamic identification model, which specifically comprises the following steps: on the basis of the runoff calculation, calculating a converging process by using a time interval unit line, simulating an initial flood process Q-t of a target river basin, and optimizing the weight parameters and the model parameters by adopting an intelligent optimization algorithm so that the errors of the flood peak and the peak time and the deterministic in the initial flood process and the flood peak and the peak time and the deterministic in the hydrologic historical data are in a preset range.
Preferably, the expression of the flood peak target Obj 1 is:
The expression of the peak time target Obj 2 is:
the deterministic target Obj 3 has the expression:
Wherein Q obs,i is the actual flow value in the hydrologic history data, Q sim,i is the predicted flow value in the initial flood process, T obs,i is the actual peak value in the hydrologic history data, T sim,i is the predicted peak value in the initial flood process, For the measured mean of the flow in the hydrographic history data, Q 'obs,i is the measured field flood Hong Fengzhi in the hydrographic history data, Q' sim,i is the field flood predictor, and N is the field flood number.
In general, compared with the prior art, the intelligent adaptation river basin hydrologic forecasting method for the flow generating mode has the following advantages:
1. According to the invention, a novel runoff index is constructed by carrying out weighted coupling on the runoff coefficient and the runoff curve number, and a correlation model of the index and the water content and rainfall characteristics of the river basin soil is established according to hydrologic historical data, so that a runoff mode dynamic identification model is obtained, a river basin time-varying runoff mode is identified, a corresponding runoff process is simulated, and the simulation accuracy of the runoff process is improved.
2. According to the application, on the basis of the runoff calculation, a time period unit line is adopted to calculate a converging process, a flood peak target, a peak time target and a deterministic target are used as evaluation indexes, the weight and hydrologic model parameters of each traditional runoff index in the novel runoff indexes are optimized and calibrated, a final river basin flooding process is obtained, and a complete forecasting system for intelligent adaptation of a runoff mode is constructed, so that the accuracy of the runoff forecasting result of the middle and small river basins is improved.
Drawings
FIG. 1 is a step diagram of a watershed hydrologic forecasting method of the application with intelligent adaptation of a stream generating mode;
FIG. 2 is a flow chart of a watershed hydrologic forecasting method of the application for intelligent adaptation of the stream generating mode;
FIG. 3 is a flow pattern dynamic identification model of the present application;
FIG. 4 is a bus bar graph of the present application;
fig. 5A is a diagram of a session flood rainfall path in 2011;
Fig. 5B is a 2018 session flood rainfall diameter flow graph;
FIG. 6A is a graph comparing the prediction results of the 2011 full-reservoir and mixed-reservoir models;
fig. 6B is a graph comparing the prediction results of the 2018 full-reservoir and mixed-reservoir models.
Detailed Description
The present invention will be described in further detail with reference to the drawings and examples, in order to make the objects, technical solutions and advantages of the present invention more apparent. It should be understood that the specific embodiments described herein are for purposes of illustration only and are not intended to limit the scope of the invention. In addition, the technical features of the embodiments of the present invention described below may be combined with each other as long as they do not collide with each other.
The invention provides a drainage basin hydrologic forecasting method with intelligent adaptation of a production flow mode, which is characterized by comprising the following steps S1-S8 as shown in fig. 1 and 2.
S1: acquiring hydrological historical data of a target river basin, and further acquiring initial soil water content, rainfall characteristic values and runoff of each flood of the target river basin, wherein the rainfall characteristic values comprise rainfall and average rainfall intensity.
And collecting relevant data of flood forecast of the target river basin, and acquiring initial soil water content, rainfall characteristic values, runoff, evaporation data and the like of each flood.
S2: and obtaining the runoff coefficient and the runoff curve number of the target river basin according to the rainfall and the runoff quantity.
The runoff coefficient r of the target river basin is as follows:
r＝R/P
wherein R is total runoff, P is total rainfall, and R is more than 0 and less than 1;
the runoff curve number CN of the target river basin is as follows:
In the method, in the process of the invention, S m is the potential infiltration amount; q is the actual flow, and CN is more than 0 and less than 100.
In general, the runoff mode can be judged according to the runoff coefficient or the runoff curve number, for example, a runoff coefficient threshold r 1 and a runoff coefficient threshold r 2 which are changed in the runoff mode of a river basin are preset, when r is smaller than r 1, the runoff mode of the river basin can be judged to be super-osmotic runoff, when r is larger than r 2, the runoff mode can be judged to be full runoff, and when r 1＜r＜r2, the runoff mode of the river basin can be judged to be mixed runoff; when the runoff curve number is adopted to judge the runoff mode, runoff coefficient thresholds CN 1 and CN 2 which are changed in the runoff mode are preset, the runoff mode is full-reserved runoff when CN < CN 1, super-seepage runoff when CN > CN 2 and mixed runoff when CN 1＜CN＜CN2.
S3: and carrying out weighted coupling on the runoff coefficient and the runoff curve number to construct a novel runoff index model and a novel runoff index threshold for judging the runoff mode.
The novel flow index model is as follows:
α＝λ1r+λ2(1-CN/100)
Wherein alpha is a novel runoff index, alpha is more than 0 and less than 1, lambda 1、λ2 is a weight parameter, and CN is a runoff curve number.
The novel flow index thresholds are alpha 1 and alpha 2, wherein:
α1＝λ1r1+λ2(1-CN2/100)
α2＝λ1r2+λ2(1-CN1/100)
Wherein, alpha 1 is a first critical value, alpha 2 is a second critical value, the production flow mode is super-osmotic production flow when alpha is less than alpha 1, full production flow when alpha is more than alpha 2, and mixed production flow when alpha 1＜α＜α2.
S4: the weight parameters and the novel flow index threshold value in the novel flow index model are preset, and the hydrologic historical data are brought into the novel flow index model to obtain the novel flow index value.
The weight parameters lambda 1 and lambda 2 in the novel runoff index model are preset, r 1、r2、CN1 and CN 2 in the novel runoff index threshold value are preset, the novel runoff index threshold value can be obtained, the novel runoff index model can be brought into the novel runoff index model by hydrologic historical data, and the novel runoff index value and the novel runoff index threshold value are compared to obtain a runoff mode.
S5: and constructing a dynamic identification model of the runoff production mode taking rainfall, initial soil water content and average rain intensity as influence factors.
The influence mechanism of rainfall, initial soil water content and average rainfall intensity on the novel runoff index is clarified by adopting a diagonal function, a natural exponential function and a reciprocal function respectively, so that a runoff mode dynamic identification model based on soil water content and rainfall characteristics is established, and the runoff mode dynamic identification model is as follows:
Wherein, alpha is a novel yield index, w 1,w2,w3,w4,w5,w6,w7 is a model coefficient to be rated, P is total rainfall, P a is initial soil water content, and I_avg is average rainfall intensity.
S6: and calibrating the flow mode dynamic identification model by using the hydrologic historical data and the corresponding novel flow index value, and obtaining model parameters in the flow mode dynamic identification model.
And (3) bringing the novel flow production index value obtained in the step (S4) into a flow production mode dynamic identification model, and judging to obtain a flow production mode according to the novel flow production index value, so that the flow production mode dynamic identification model can be calibrated, and model parameters in the flow production mode dynamic identification model can be obtained.
S7: and based on the hydrologic historical data, carrying out the flow generation mode determination by adopting a rated flow generation mode dynamic identification model, carrying out flow generation calculation and confluence calculation based on the flow generation mode, obtaining an initial flood process, and optimizing the weight parameters and model parameters by adopting an intelligent optimization algorithm by taking a flood peak target, a peak time target and a deterministic target as evaluation indexes to obtain an optimized flow generation mode dynamic identification model.
And (3) adopting a rated flow mode dynamic identification model to determine a flow mode, wherein when alpha is smaller than alpha 1, the flow mode is super-osmotic flow, and the flow formula is as follows:
Wherein f t is the drainage basin seepage capacity at the moment t; w t is the water content of the river basin soil at the moment t; WM is field water holding capacity; f c is the stable infiltration capacity of the river basin; KF is the permeability coefficient;
When alpha is larger than alpha 2, the full production flow is realized, and the production flow formula is as follows:
Wherein R is total diameter flow; PE is the amount of rainfall to remove evaporation; w 0 is the initial soil water content, WM is the field water holding capacity; w' mm is the maximum soil moisture content; a and B are water storage capacity curve coefficients;
When alpha 1＜α＜α2 is the mixed production flow, the calculation is carried out by adopting a vertical mixed production flow method, and the calculation expression of the surface runoff RS is as follows:
In the method, in the process of the invention, For the average infiltration capacity of the river basin, the value of f t in the super-infiltration product flow formula is the same,
According to the water balance principle, the infiltration amount FA is as follows:
FA＝PE-RS
The FA obtained by the calculation of the formula is taken as input and is brought into a full-accumulation runoff formula, and the calculation expression of the underground runoff RR can be obtained as follows:
The total diameter flow R of the vertical mixed flow production mode is as follows:
R＝RS+RR
On the basis of the runoff calculation, calculating a converging process by using a time interval unit line, and simulating an initial flood process Q-t of the target river basin. And optimizing the weight parameters and the model parameters by adopting an intelligent optimization algorithm, so that the flood peak and peak time and certainty in the initial flood process and the flood peak and peak time and certainty in the hydrologic historical data are in a preset range. In a further preferred scheme, optimization calibration is performed on the index weight and the parameters of the river basin flow production mode through SCEUA optimization algorithm.
The expression of the flood peak target Obj 1 is:
The expression of the peak time target Obj 2 is:
the deterministic target Obj 3 has the expression:
Wherein Q obs,i is the actual flow value in the hydrologic history data, Q sim,i is the predicted flow value in the initial flood process, T obs,i is the actual peak value in the hydrologic history data, T sim,i is the predicted peak value in the initial flood process, For the measured mean of the flow in the hydrographic history data, Q 'obs,i is the measured field flood Hong Fengzhi in the hydrographic history data, Q' sim,i is the field flood predictor, and N is the field flood number.
S8: and forecasting the hydrology of the target river basin by adopting the optimized runoff generating mode dynamic identification model.
Examples
(1) Taking a certain target river basin as a research object, and giving a novel runoff index threshold of 0.2 and 0.4 by combining related documents;
(2) Based on historical flood data, assuming initial weight, calculating an initial novel runoff yield index, judging an optimal runoff yield mode, and performing runoff yield calculation to obtain an initial flood process of the target river basin;
(3) Respectively taking the flood peak relative error, peak time error and deterministic target as objective functions, and carrying out optimization calibration on the novel runoff index weight and the parameters of the river basin runoff model through SCEUA optimization algorithm; and combining the parameter-optimized runoff generation model with a converging time period unit line, as shown in fig. 4, so as to obtain the final flood process of the target river basin. The novel flow production index weight parameter lambda 1 is 0.3, lambda 2 is 0.7, the dynamic identification model of the flow production mode of the target flow field is shown in fig. 3, and the prior calculation formula of the novel flow production index is as follows:
In order to verify the forecasting precision of the method, two field flood processes of a water basin outlet hydrological station 2011 from 5 to 10 months 2 days, a water basin outlet hydrological station 2011 from 17 to 23 days are taken as an example to conduct flood forecasting, rainfall runoff processes are shown in fig. 5A and 5B, the results of the various runoff mode distinguishing methods are shown in the following table 1, forecasting results are shown in fig. 6A and 6B, and forecasting evaluation indexes are shown in the following table 2.
TABLE 1
TABLE 2
As can be obtained from the data in table 1, according to the novel runoff index proposed by the method, the 20110905 field flood dominant runoff mode is full runoff, and the 20180617 field flood dominant runoff mode is mixed runoff.
According to the results shown in fig. 6A and 6B and table 2, the effect of the full-reservoir model of 20110905 field floods is better than that of the mixed model, and the effect of the mixed model of 20180617 field floods is better than that of the full-reservoir model, namely the effect of simulating the flood process by adopting the model selected by the novel flood indexes provided by the method is best, and the errors of various indexes are minimum.
In summary, the novel runoff index is constructed by carrying out weighted coupling on the runoff coefficient and the runoff curve number, and a correlation model of the index and the water content and rainfall characteristics of the river basin soil is built according to the hydrologic historical data, so that a runoff mode dynamic identification model is obtained, the river basin time-varying runoff mode is identified, the corresponding runoff process is simulated, and the accuracy of the runoff process simulation is improved.
It will be readily appreciated by those skilled in the art that the foregoing description is merely a preferred embodiment of the invention and is not intended to limit the invention, but any modifications, equivalents, improvements or alternatives falling within the spirit and principles of the invention are intended to be included within the scope of the invention.
Claims (5)
1. The intelligent flow mode adapting river basin hydrologic forecasting method is characterized by comprising the following steps of:
S1: acquiring hydrological historical data of a target river basin, and further acquiring initial soil water content, rainfall characteristic values and runoff of each flood of the target river basin, wherein the rainfall characteristic values comprise rainfall and average rainfall intensity;
s2: obtaining a runoff coefficient and a runoff curve number of the target river basin according to the rainfall and the runoff; the runoff coefficient r of the target river basin is as follows:
r＝R/P
wherein R is total runoff, P is total rainfall, and 0< R <1;
the runoff curve number CN of the target river basin is as follows:
In the method, in the process of the invention, S m is the potential infiltration amount; q is the actual flow, CN is more than 0 and less than 100;
S3: carrying out weighted coupling on the runoff coefficient and the runoff curve number to construct a novel runoff index model and a novel runoff index threshold for judging a runoff mode; the novel flow index model is as follows:
α＝λ1r+λ2(1-CN/100)
wherein alpha is a novel runoff index, alpha is more than 0 and less than 1, lambda 1、λ2 is a weight parameter, and CN is a runoff curve number;
S4: presetting a weight parameter and a novel runoff index threshold in a novel runoff index model, and introducing the hydrologic historical data into the novel runoff index model to obtain a novel runoff index value; the novel flow index thresholds are alpha 1 and alpha 2, wherein:
α1＝λ1r1+λ2(1-CN2/100)
α2＝λ1r2+λ2(1-CN1/100)
Wherein, alpha 1 is a first critical value, alpha 2 is a second critical value, the flow mode is super-osmotic flow when alpha is smaller than alpha 1, the full flow is stored when alpha is larger than alpha 2, the mixed flow is generated when alpha 1＜α＜α2, the flow mode is judged according to the runoff coefficient or the runoff curve number, r 1 and r 2 are the runoff coefficient threshold value of the change of the runoff coefficient of the runoff domain flow mode, the runoff domain flow mode is judged to be super-osmotic flow when r is smaller than r 1, the full flow is judged to be stored when r is larger than r 2, and the mixed flow is judged to be mixed flow when r 1＜r＜r2; when the runoff curve number is adopted to judge the runoff mode, CN 1 and CN 2 are runoff coefficient thresholds of the runoff mode change, the runoff mode is full-accumulation runoff when CN < CN 1, the super-seepage runoff is generated when CN > CN 2, and the mixed runoff is generated when CN 1＜CN＜CN2;
s5: constructing a dynamic identification model of a runoff generating mode taking rainfall, initial soil water content and average rain intensity as influence factors;
S6: calibrating the flow mode dynamic identification model by using the hydrologic historical data and the corresponding novel flow index value, and obtaining model parameters in the flow mode dynamic identification model;
s7: based on the hydrologic historical data, carrying out the flow generation mode determination by adopting a rated flow generation mode dynamic identification model, carrying out flow generation calculation and confluence calculation based on the flow generation mode, obtaining an initial flood process, optimizing the weight parameters and model parameters by adopting an intelligent optimization algorithm by taking a flood peak target, a peak time target and a deterministic target as evaluation indexes, and obtaining an optimized flow generation mode dynamic identification model;
S8: and forecasting the hydrology of the target river basin by adopting the optimized runoff generating mode dynamic identification model.
2. The method for drainage basin hydrologic forecasting of intelligent adaptation of a production flow mode according to claim 1, wherein the dynamic identification model of the production flow mode in step S5 is as follows:
In the formula, alpha * is a priori novel runoff index, w 1,w2,w3,w4,w5,w6,w7 is a model coefficient to be rated, P is total rainfall, P a is initial soil water content, and I_avg is average rainfall intensity.
3. The method for intelligently adapting the flow mode to the watershed hydrologic forecasting of the invention according to claim 1, wherein when alpha < alpha 1, the flow mode is super-seepage flow, and the flow formula is:
Wherein f t is the drainage basin seepage capacity at the moment t; w t is the water content of the river basin soil at the moment t; WM is field water holding capacity; f c is the stable infiltration capacity of the river basin; KF is the permeability coefficient;
When alpha is larger than alpha 2, the full production flow is realized, and the production flow formula is as follows:
wherein R is total diameter flow; PE is the amount of rainfall to remove evaporation; w 0 is the initial soil water content, WM is the field water holding capacity; w m'm is the maximum soil moisture content;
When alpha 1＜α＜α2 is the mixed production flow, the calculation is carried out by adopting a vertical mixed production flow method, and the calculation expression of the surface runoff RS is as follows:
In the method, in the process of the invention, For the average infiltration capacity of the river basin, the value of f t in the super-infiltration product flow formula is the same,
According to the water balance principle, the infiltration amount FA is as follows:
FA＝PE-RS
The FA obtained by the calculation of the formula is taken as input and is brought into a full-accumulation runoff formula, and the calculation expression of the underground runoff RR can be obtained as follows:
The total diameter flow R of the vertical mixed flow production mode is as follows:
R＝RS+RR。
4. The method for predicting the watershed hydrologic system with intelligent adaptation of the production flow mode according to claim 1 or 3, wherein in the step S7, a flood peak target, a peak time target and a deterministic target are used as evaluation indexes, and an intelligent optimization algorithm is adopted to optimize the weight parameters and the model parameters so as to obtain an optimized dynamic identification model of the production flow mode, and the method is specifically as follows:
On the basis of the runoff calculation, calculating a converging process by using a time interval unit line, simulating an initial flood process Q-t of a target river basin, and optimizing the weight parameters and the model parameters by adopting an intelligent optimization algorithm so that the errors of the flood peak and the peak time and the deterministic in the initial flood process and the flood peak and the peak time and the deterministic in the hydrologic historical data are in a preset range.
5. The method for intelligently adapting a river basin hydrologic forecasting of a runoff generating mode according to claim 4, wherein the expression of the flood peak target Obj 1 is as follows:
The expression of the peak time target Obj 2 is:
the deterministic target Obj 3 has the expression:
wherein Q obs,i is the actual flow value in the hydrologic history data, Q sim,i is the predicted flow value in the initial flood process, T obs,i is the actual peak value in the hydrologic history data, T sim,i is the predicted peak value in the initial flood process, For the measured mean of the flow in the hydrographic history data, Q' obs,i is the measured field flood Hong Fengzhi in the hydrographic history data, Q s'im,i is the field flood predictor, and N is the field flood number.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202311234707.4A CN117390125B (en) | 2023-09-21 | 2023-09-21 | Drainage basin hydrologic forecasting method with intelligent adaptation of flow production mode |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202311234707.4A CN117390125B (en) | 2023-09-21 | 2023-09-21 | Drainage basin hydrologic forecasting method with intelligent adaptation of flow production mode |
Publications (2)
| Publication Number | Publication Date |
|---|---|
| CN117390125A CN117390125A (en) | 2024-01-12 |
| CN117390125B true CN117390125B (en) | 2024-06-04 |
Family
ID=89436387
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN202311234707.4A Active CN117390125B (en) | 2023-09-21 | 2023-09-21 | Drainage basin hydrologic forecasting method with intelligent adaptation of flow production mode |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN117390125B (en) |
Citations (6)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN106951612A (en) * | 2017-03-06 | 2017-07-14 | 河海大学 | Dynamic water storage capacity Runoff calculation method in freeze-thawing process of soil |
| CN110188922A (en) * | 2019-05-05 | 2019-08-30 | 中国长江电力股份有限公司 | A kind of long-term Runoff Forecast method in the RBF neural based on runoff mechanism |
| CN112950062A (en) * | 2021-03-24 | 2021-06-11 | 郑州大学 | Mountain torrent forecast real-time correction method based on dynamic system response curve and LSTM |
| CN113435630A (en) * | 2021-06-04 | 2021-09-24 | 华中科技大学 | Basin hydrological forecasting method and system with self-adaptive runoff yield mode |
| CN115293391A (en) * | 2022-02-12 | 2022-11-04 | 浙江省水利河口研究院(浙江省海洋规划设计研究院) | Runoff prediction method and system based on mixed runoff production mode |
| CN115935717A (en) * | 2023-02-16 | 2023-04-07 | 天津市气象台 | Basin runoff prediction method and device based on statistical model |
Family Cites Families (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN105512767B (en) * | 2015-12-15 | 2019-06-11 | 武汉大学 | A kind of Flood Forecasting Method of more leading times |
| AR109623A1 (en) * | 2018-02-16 | 2019-01-09 | Pescarmona Enrique Menotti | PROCESS AND SYSTEM OF ANALYSIS AND HYDROLOGICAL MANAGEMENT FOR BASINS |
-
2023
- 2023-09-21 CN CN202311234707.4A patent/CN117390125B/en active Active
Patent Citations (6)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN106951612A (en) * | 2017-03-06 | 2017-07-14 | 河海大学 | Dynamic water storage capacity Runoff calculation method in freeze-thawing process of soil |
| CN110188922A (en) * | 2019-05-05 | 2019-08-30 | 中国长江电力股份有限公司 | A kind of long-term Runoff Forecast method in the RBF neural based on runoff mechanism |
| CN112950062A (en) * | 2021-03-24 | 2021-06-11 | 郑州大学 | Mountain torrent forecast real-time correction method based on dynamic system response curve and LSTM |
| CN113435630A (en) * | 2021-06-04 | 2021-09-24 | 华中科技大学 | Basin hydrological forecasting method and system with self-adaptive runoff yield mode |
| CN115293391A (en) * | 2022-02-12 | 2022-11-04 | 浙江省水利河口研究院(浙江省海洋规划设计研究院) | Runoff prediction method and system based on mixed runoff production mode |
| CN115935717A (en) * | 2023-02-16 | 2023-04-07 | 天津市气象台 | Basin runoff prediction method and device based on statistical model |
Non-Patent Citations (5)
| Title |
|---|
| Derivation of Probabilistic Thresholds of Spatially Distributed Rainfall for Flood Forecasting;Saeed Golian 等;《Water Resources Management》;20100313;第24卷;3547–3559 * |
| Dynamics of hydrological-model parameters: mechanisms, problems and solutions;Tian Lan 等;《Hydrology and Earth System Sciences》;20200323;第24卷(第3期);1347–1366 * |
| 不同水土保持工程下山洪预报模型研究;王燕;《中国优秀硕士学位论文全文数据库 农业科技辑》;20180215(第02期);D043-213 * |
| 嫩江-库漠屯站以上流域的洪水预报研究;郑海洋;《中国优秀硕士学位论文全文数据库 基础科学辑》;20220115(第01期);A012-75 * |
| 洞庭湖流域洪水遭遇规律研究;仇红亚 等;《水力发电学报》;20200610;第39卷(第11期);59-70 * |
Also Published As
| Publication number | Publication date |
|---|---|
| CN117390125A (en) | 2024-01-12 |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| CN106599571B (en) | Watershed hydrological model stage calibration method considering both flow and evaporation | |
| CN106202935B (en) | A kind of bearing calibration of Watershed Runoff forecast and its system | |
| CN108021773B (en) | DSS database-based distributed hydrological model multi-field secondary flood parameter calibration method | |
| CN115759459B (en) | Intelligent forecasting method for drainage basin flood flow set probability of rainwater amorous condition coupling | |
| CN114741987B (en) | Flood probability prediction model considering absolute error fitting residual distribution of flood prediction model | |
| CN114970377B (en) | Method and system for field flood forecasting based on Xinanjiang and deep learning coupling model | |
| CN113435630B (en) | Basin hydrological forecasting method and system with self-adaptive runoff yield mode | |
| CN112528563B (en) | Urban waterlogging early warning method based on SVM algorithm | |
| CN111815043B (en) | Flood flow prediction method and system based on storm characteristics | |
| CN112883558A (en) | Hydrological model parameter time-varying form construction method | |
| CN111914487B (en) | Data-free regional hydrological parameter calibration method based on antagonistic neural network | |
| CN111914488B (en) | Data area hydrologic parameter calibration method based on antagonistic neural network | |
| Gao et al. | A framework for automatic calibration of SWMM considering input uncertainty | |
| CN110186533A (en) | A kind of short-term tide prediction method in high-precision river mouth | |
| CN114819322B (en) | Forecasting method for flow of lake entering lake | |
| CN113887847B (en) | Mixed production area secondary flood forecasting method based on WRF-Hydro model | |
| Zhao et al. | Evaluating influences of the Manwan Dam and climate variability on the hydrology of the Lancang-Mekong River, Yunnan Province, southwest China | |
| CN117390125B (en) | Drainage basin hydrologic forecasting method with intelligent adaptation of flow production mode | |
| Ma et al. | Bayesian statistic forecasting model for middle-term and long-term runoff of a hydropower station | |
| Yan et al. | Study on the dynamic characteristics of shallow groundwater level under the influence of climate change and human activities in Cangzhou, China | |
| Wang et al. | A statistical hydrological model for Yangtze river watershed based on stepwise cluster analysis | |
| CN106777724B (en) | Basic flow segmentation method for semi-humid and semi-arid regions and humid regions | |
| WO2024221631A1 (en) | Base flow simulation method based on multiple linear regression model | |
| CN112699611A (en) | Drainage pipe network model parameter optimization method based on artificial ant colony algorithm | |
| CN114707753B (en) | Regional LSTM flood forecasting method |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| PB01 | Publication | ||
| PB01 | Publication | ||
| SE01 | Entry into force of request for substantive examination | ||
| SE01 | Entry into force of request for substantive examination | ||
| GR01 | Patent grant | ||
| GR01 | Patent grant |