CN108460814B - Multi-station linkage water level flow relation curve fitting method - Google Patents
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Abstract
The invention discloses a multi-station linkage water level flow relation curve fitting method, which comprises the following steps: selecting a test station; it doesDetermining a flood forecasting forecast period l of the test site; establishing a water level flow relation curve of a test station
Wherein t is sampling time, N is the number of upstream actual measurement stations of the test station, i is the serial number of the upstream actual measurement station, and Zt+l、Qt+l、at+lRespectively is a water level predicted value, a flow predicted value and a flow coefficient of the test station in a forecast periodi,t、Qi,t、bi,t、Zi,tThe flow coefficient, the flow value, the water level coefficient and the water level value of the ith actual measurement station at the time t, c is the offset of the flow of the test station relative to the water level in the forecast period, epsilon is a remainder and trends to be infinitesimal, and l is not larger than the time lag of the flood wave transmitted from the upstream to the downstream. The real-time water level flow relation of the test station is expressed as a function of the water level and the flow of the upstream station at the historical moment, so that more accurate results can be obtained in the real-time flood forecasting application in different forecasting periods, and the real-time flood forecasting level and the forecasting precision are provided.
Description
Technical Field
The invention belongs to the field of forecasting of hydrological station water level flow relation, and particularly relates to a multi-station linkage water level flow relation curve fitting method.
Background
The water level flow relation is important in flood component analysis and real-time flood forecast research, factors influencing the water level flow relation are many, the factors are influenced by not only geometrical factors such as river channel erosion and blockage, but also hydraulic factors such as flood fluctuation trend, branch water quantity exchange and the like, and in the research of plain river channel water level flow relation, factors such as backwater jacking, row flood storage area regulation and storage and the like obviously influence the water level flow relation. Due to the fact that various factors have obvious uncertainty, the hydrological station water level flow relation has strong instability and time-varying characteristics, the water level flow relation is quite complex, and accurate estimation is difficult.
The common water level flow relation fitting method mainly comprises a single-value curve method and a rope sleeve curve method.
The single-value curve method generally summarizes the water level flow relation into a single curve, and the equation is simple in form and easy to popularize.
The rope sleeve curve can more accurately reflect the compound water level flow relation influenced by the flood fluctuation, has certain precision in flood forecasting and is more widely applied. However, in a river section where the influence of flood fluctuation is significant, it is difficult to align the loop curve, and it is generally necessary to perform trial and error.
No matter the traditional single-valued method or the rope sling curve method, only the prior line can be determined for the existing historical data, the parameters and equation structures in the two methods are fixed in the real-time flood forecasting application, and the latest measured data cannot be applied to the updating of the water level flow relation. Therefore, in the two methods, only the conversion relation between the water level and the flow of the test station in the fixed forecast period is considered, only the historical data of the current test station is considered, other factors influencing the current test station are not considered, and the accuracy of flood forecast is influenced in the real-time flood forecast application.
Disclosure of Invention
The existing water level flow relation fitting method only considers the conversion relation between the water level and the flow of a test station in a fixed forecast period, only considers the historical data of the current test station, does not consider other factors influencing the current test station, and is low in flood forecast precision in real-time flood forecast application. The invention aims to provide a multi-station linkage water level flow relation curve fitting method aiming at the defects of the prior art, the real-time water level flow relation of a test station is expressed as a function of the water level and the flow of an upstream station at the historical moment, more accurate results can be obtained in real-time flood forecasting applications in different forecast periods, and the method has a positive effect on improving the real-time flood forecasting level and the forecasting precision.
In order to solve the technical problems, the technical scheme adopted by the invention is as follows:
a multi-station linkage water level flow relation curve fitting method comprises the following steps:
A. selecting a test station;
B. determining a flood forecast period l of a test station;
further comprising the steps of:
C. establishing a water level flow relation curve of a test station
By the method, the invention provides a Multi-station linkage Stage-discharge creating method MISC (multiple-station Interactive Stage-discharge creating vessel) based on the principle of a river channel corresponding water level forecasting method.
The river channel corresponding water level forecasting method is based on a natural river channel flood wave propagation principle, and is a method for analyzing corresponding water level and wave speed change rules of stations along the course when the water level/flow at any phase is propagated from top to bottom in the motion process of flood waves. The river channel corresponding water level forecasting method well considers the translation and flattening effects of flood, and the flood forecasting verification is reliable in river basin such as Yangtze river. According to the method, by taking the corresponding water level forecasting method of the river channel and the traditional water level and flow rate relation theory as reference, the hydrological elements of the upstream station with the same phase are considered to be closely related to the changes of the water level and the flow rate of the test station, and the correlation between the hydrological elements of the upstream station and the downstream station is described by establishing a regression equation.
In various parameters related to curve fitting, the water level value Z of a test station at the moment of dividing t + lt+lAnd the flow value Q of the test station at the moment t + lt+lNot known, each constant coefficient item (a)t+l、ai,t、bi,tAnd c) the flow and water level value are measured in the actual measurement period to realize calibration (for example, calibration by a regression analysis method), and the water level flow value of each actual measurement station at the upstream is an actual measurement value. When epsilon takes a minimum value, the obtained water level flow relation curve is the result.
Compared with the prior art, the real-time water level flow relation of the test station is expressed as a function of the upstream multi-station water level flow at the historical moment, the upstream station flow and the water level influencing the current test station water flow state are brought into a water level flow relation fitting system besides the current test station hydrological elements, the latest measured data can be applied to updating of the water level flow relation in real-time flood forecasting application, the time-varying water level flow relation is established, simulation and comparative analysis of the water level flow relation shows that the real-time flood forecasting method can obtain more accurate results in real-time flood forecasting at different forecasting periods, and has positive effects on improvement of the technical level and forecasting accuracy of the real-time flood forecasting.
Drawings
FIG. 1 is a diagram of water level flow relationships simulated by the present invention.
Fig. 2 is a diagram showing a relationship between water level and flow rate simulated by a conventional single-value method.
Fig. 3 is a diagram showing the relationship between water level and flow rate simulated by the conventional rope sling curve method.
Fig. 4 is a diagram of the process of the water level prediction value changing with the forecast period in the invention.
Fig. 5 is a diagram of the process of flow prediction value change with forecast period in the present invention.
Detailed Description
In this embodiment, the multi-station linkage water level flow relation curve fitting method of the present invention includes the steps of:
A. selecting a test station:
the small willow tunnel hydrological station is arranged in 4 months in 1981, is located in small willow tunnel town of Mingguang city, Anhui province, is the final control station for the main river to flow into Hongze lake, and has a drainage basin area of 123950km2The downstream 100m is the moored river-leading project, and the downstream 630km is the Hongze lake. Under the influence of additional specific drop, the water level flow relation of the small willow tunnel station is a complex anticlockwise rope sleeve curve, the sizes of the corresponding rope sleeve curves are different due to different flood fluctuation rates at each time, parameters of the flood rope sleeve curves in various fields are greatly different, and the water level flow relation is difficult to determine. In addition, factors such as downstream backwater jacking and dry flood scheduling have a large influence on the relation of the water level and the flow of the small willow tunnel station.
B. Determining a flood forecast period l of the test site:
in the application of real-time flood forecasting, when the method is applied to construct the water level flow relation, the flood forecasting forecast period of a test station needs to be considered. Analyzing historical flood in more than twenty years from 1982 to 2005 in Huaihe river basin using hydrological forecasting supplement scheme, counting and displaying that the time delay of the river flood in the experimental river basin is approximately 0-27 hours, and only considering the influence of upstream water level and flow on the water level and flow relation of the small willow tunnel station within 24 hours or less when constructing the water level and flow relation according to the actual hydrological characteristics of the river above the small willow tunnel and the requirement of flood forecasting. In the present embodiment, the setting of the water level flow relationship is applied to real-time flood forecasting occasions with forecast periods of 3h, 6h, 12h and 24 h.
C. Establishing a water level flow relation curve of a test station
Wherein t is sampling time, and N is actual measurement at upstream of test stationThe number of stations, i is the serial number of the upstream measured station, Zt+lIs a predicted value of the water level of the test station in the forecast period, at+lIs the flow coefficient, Q, of the test station in the forecast periodt+lFor the predicted flow value of the test station in the forecast period, ai,tIs the flow coefficient, Q, of the ith measured station at the time ti,tIs the flow value of the ith measured station at the time t, bi,tIs the water level coefficient, Z, of the ith actual measurement station at the time ti,tThe water level value of the ith actual measurement station at the time t, c is the offset of the flow of the test station relative to the water level in the forecast period, epsilon is a remainder term and trends to be infinitesimal, and l is not larger than the time lag (confluence time lag) when the flood wave is transmitted from the upstream to the downstream. And selecting a proper flood process to simulate the water level flow relation.
In the embodiment, 20050707 flood is selected for a simulation test, the starting time and the stopping time of the flood are 2005/7/714: 00-2005/7/2814: 00, the water level of the small willow roadway is adopted as a dependent variable in the simulation test, the flow of the small willow roadway at the forecasting time or the water level and the flow of an upstream station are taken as independent variables, the method is used for constructing the relation of the water level and the flow of the small willow roadway, and simulation results of different forecasting periods are shown in figure 1. In fig. 1(a), the foreseeable period is 3 h; in FIG. 1(b), the foreseeable period is 6 h; in FIG. 1(c), the foreseeable period is 9 h; in FIG. 1(d), the foreseeable period is 24 h.
In order to verify the technical effect of the method, a comparison test is carried out, the water level flow relation of a test station is simulated by a single-value method and a rope sleeve curve method in the prior art respectively, and the simulation result is compared with the simulation result of the method respectively.
The existing single-value method is used for constructing the water level flow relation of the small willow tunnel station for No. 20050707 flood, and the simulation result is shown in FIG. 2. The existing rope sling curve method is utilized to construct the water level flow relation of the small willow tunnel station for No. 20050707 flood, and the simulation result is shown in figure 3.
The water level is lower than 14m, medium water is 14-16 m and high water is above 16 m. By comparing the simulation results of the three fitting methods, it can be found that:
the simulation results of the invention are approximately distributed near the 45-degree line, which shows that in the flood forecasting application with forecast periods of 3h, 6h, 12h and 24h, the simulated water level value of the invention is closer to the measured value, and shows higher simulation precision and stability, but with the increase of the forecast period, the simulated water level value of the invention deviates from the 45-degree line more and more, namely, the simulation precision of the invention is gradually reduced.
The traditional single-valued method has better simulation effect in a low water state, but the water level values of the middle water and the high water have obvious difference with the measured values.
Compared with a single-valued method, the rope sling curve method has the advantages that the overall simulation effect is greatly improved, and particularly the simulation effect is better at the middle water and high water parts.
In order to accurately compare the effects of the three methods in flood forecasting within 3-24 h of a simulation test, the simulation accuracy of the three methods in low-water, medium-water and high-water positions is counted by taking a certainty coefficient as an index. The certainty Coefficient is also called Nash Efficiency Coefficient (NSE), which is a statistical index proposed by Nash and Sutcliffe in 1970 and is widely used to describe the fitting accuracy between a calculated value and a target value. The NSE has a value range of (-infinity, 1), the larger the value is, the closer the calculated value is to the target value, and the better the simulation effect is, and the NSE index statistical values of the simulation effects of the three methods at low-water, middle-water and high-water parts are shown in the table 1 (the forecast period is 3-24 h).
TABLE 1 NSE statistical table of simulation results of MISC, univalent and rope sleeve curve method on low, medium and high water parts (forecast period is 3-24 h)
The statistical result shows that the NSE index value of the simulated high-water part is larger, the NSE value of the reclaimed water part is smaller, and the NSE value of the low-water part is relatively minimum; the amplitude of NSE value of the simulated high-water part changing along with the forecast period is minimum, the amplitude of the simulated low-water part changing along with the forecast period is second, and the amplitude of the simulated high-water part changing along with the forecast period is maximum. Research results show that the simulation precision and stability of the invention are highest at high water level, and the invention is suitable for real-time flood forecast of high water level.
The reason why the simulation effect of the present invention is better than the existing two methods is analyzed as follows:
the partial correlation coefficient is an index for measuring the linear correlation degree between two variables in the variables, and compared with a simple correlation coefficient, the partial correlation coefficient can avoid the influence of the covariate connection possibly existing between the variables and can judge whether the direct linear correlation relation exists between the two variables more reasonably. The partial correlation coefficient is in a range of [ -1,1], and the closer the absolute value is to 1, the higher the linear correlation degree between the two variables is.
In order to analyze the reason that the simulation effect of the MISC method is better than that of the other two methods, the partial correlation coefficients of all variables in the MISC method are counted, and the partial correlation coefficients of independent variables and dependent variables (small willow tunnel water level) in each scheme are shown in a table 2. In the table, zobs (qobs) represents an actual measured value of the water level (flow rate) at the latest time of the site in the real-time flood forecast (or an actual measured water level (flow rate) at the forecast execution time), and Q represents a calculated value of the flow rate at the site at the forecast cutoff time.
Note: the "-" symbol indicates that the process did not have this variable
TABLE 2MISC, INDIVIDUAL VARIABLE AND DEPENDENT VARIABLE PARALLEL-RELATED COEFFICIENT TABLE IN SINGLE VALUED AND ROPE SLEEVE CURVE METHOD
The statistical results of the common variable (small willow lane Q) of the three methods show that the partial correlation coefficient of the variable in the three methods is approximately above 0.995, and the partial correlation coefficients are all larger, which indicates that the small willow lane Q and Z are significant. The correlation coefficients of the small willow lane Q at the water rising and falling sections of the rope sleeve curve method are obviously different, which shows that the linear correlation relationship between the small willow lane Q and the water falling sections is relatively weak, and more other factors possibly exist in the water falling sections and influence the change of the water level of the small willow lane Q. The simulation result is reasonable in consideration of the downstream berthing river-leading engineering of the small willow tunnel station.
Statistical results of the MISC method in flood forecasting applications in different forecast periods show that the partial correlation coefficient between the water level value at the forecasting execution time and the water level value at the forecasting cut-off time of the small willow tunnel station reaches 0.999999, and is far higher than the flow value of the small willow tunnel at the forecasting cut-off time and the partial correlation coefficient values of other variables in the actual measurement period. Research results show that besides the small willow roadway Q at the forecasting time, hydrological variables in the actual measurement period also have strong influence on the water level of the small willow roadway in the forecasting time period, and the influence of the hydrological variables in the actual measurement period on the water level of the small willow roadway in the forecasting time period cannot be ignored. Therefore, the traditional water level flow relation fitting method (such as a single-valued and rope sleeve curve method) only considers the influence of the small willow tunnel flow on the water level, and the method for establishing a binary equation to describe the water level flow relation is low in efficiency and inaccurate.
According to the variation of partial correlation coefficients of variables in the MISC method in different forecast periods, considering that the water level of an upstream station is larger than the flow correlation coefficient, the application performance of the MISC method is further analyzed according to the graph shown in FIG. 4 and FIG. 5.
The partial correlation coefficient of wu jiafu Zobs in fig. 4 decreases with the increase of the prediction period, while the wu jiafu Qobs in fig. 5 increases first and then decreases. This phenomenon further corroborates the aforementioned fact that the water level and flow rate variations are not synchronized and do not have a significant linear correlation.
The following focus was on analysis of wu jiafu Qobs. The partial correlation coefficient of the Wu Jia Du Qobs is increased and then decreased, and reaches a maximum value point 0.994419 when the forecast period is 12 h. Considering that the sampling number of the simulation test is limited, only four conditions of 3h, 6h, 12h and 24h in the forecast period are selected, and the variation of the partial correlation coefficient between 3h to 6h and 6h to 12h cannot be considered in detail, the actual minimum value point of the partial correlation coefficient of the variable temporary-critical-flow Zobs should be in the range of 6h to 24h on the abscissa. According to the actual measurement records of all stations, the Wu house ferry water level and the maximum flow rate are shown to be at the 218 th moment, and the small willow tunnel flood peak water level is shown to be at the 234 th moment. In the flood of the field, the peak lag time of Wu Jia crossing to the small willow lane is about 16h, and the Wu Jia crossing water before 16h is not transmitted to the small willow lane station, so the partial correlation coefficient is relatively small; and the Wu Jia river water after 16h continues to be transmitted downwards through the small willow tunnel, and the partial correlation coefficient of the Wu Jia river Qobs is relatively high but is obviously reduced compared with the partial correlation coefficient value of the corresponding moment in the forecast period of 16 h. The simulation result is consistent with the actual situation considering flood propagation factors, the physical mechanism of the MISC method which is accurately attached to the river flow motion is explained, and the method is a reliable water level flow relation fitting method.
In conclusion, the multi-station linkage water level flow relation fitting Method (MISC) can introduce more factors related to the water level and the flow of the measuring station into a water level flow relation fitting system, and can accurately reflect the real-time change of the water level flow relation. By utilizing the MISC method, the flow can be calculated in the flood evolution model, and then the water level analog value is obtained through the conversion of the water level flow relation updated in real time, so that the error expansion can be effectively prevented through the verification, and the method can be used as a stable and reliable lower boundary condition of the hydrodynamic model. The method is verified to be reliable in multi-field real-time flood forecasting application from 2003 to 2014, and plays a positive role in improving the technical level of flood forecasting.
While the present invention has been described with reference to the embodiments shown in the drawings, the present invention is not limited to the embodiments, which are illustrative and not restrictive, and it will be apparent to those skilled in the art that various changes and modifications can be made therein without departing from the spirit and scope of the invention as defined in the appended claims.
Claims (1)
1. A multi-station linkage water level flow relation curve fitting method comprises the following steps:
A. selecting a test station;
B. determining a flood forecast period l of a test station;
it is characterized by also comprising the following steps:
C. establishing a water level flow relation curve of a test station
Wherein t is sampling time, N is the number of upstream actual measurement stations of the test station, i is the serial number of the upstream actual measurement station, and Zt+lIs a predicted value of the water level of the test station in the forecast period, at+lIs the flow coefficient, Q, of the test station in the forecast periodt+lFor the predicted flow value of the test station in the forecast period, ai,tIs the flow coefficient, Q, of the ith measured station at the time ti,tIs the flow value of the ith measured station at the time t, bi,tIs the water level coefficient, Z, of the ith actual measurement station at the time ti,tThe water level value of the ith actual measurement station at the time t, c is the offset of the flow of the test station relative to the water level in the forecast period, epsilon is a remainder term and trends to be infinitesimal, and l is not larger than the time lag of the flood wave passing from the upstream to the downstream.
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