WO2021218457A1 - 一种利用预报误差开展上游水库群影响下径流预报的方法 - Google Patents
一种利用预报误差开展上游水库群影响下径流预报的方法 Download PDFInfo
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- the invention relates to the technical field of hydrological forecasting, and in particular to a method for using forecast errors to carry out runoff forecasting under the influence of upstream reservoir groups.
- the problem of low accuracy of downstream runoff forecast caused by upstream reservoir group storage and release is mainly solved by obtaining the upstream reservoir group's storage and release plan in real time and superimposing the results of interval hydrological forecasting on this basis.
- the prerequisite for this method is to be able to obtain information on the water storage and release plan of the upstream reservoir group, but the actual situation is that the upstream reservoir does not directly share or provide such information in most cases (due to commercial secrets, etc.), and forecasts upstream of the section
- the number of reservoir groups is often unusually large. As a result, in most cases, it is impossible to obtain information on the storage and release plan of the reservoir group upstream of the forecast section. Therefore, the influence of the upstream reservoir group causes the traditional hydrological forecast model to have low accuracy, and the existing solutions
- the application conditions of the method are too harsh, and there are no practical problems.
- the purpose of the present invention is to provide a method for forecasting runoff under the influence of upstream reservoir groups by using forecast errors, so as to solve the aforementioned problems in the prior art.
- a method for using forecast errors to carry out runoff forecasting under the influence of upstream reservoir groups includes the following steps:
- the data collected in step S1 specifically includes precipitation data and runoff data.
- the precipitation data is the precipitation data from the time when the upstream reservoir begins to significantly affect the downstream runoff process to the current time; the runoff data is upstream The precipitation data from the time the reservoir began to significantly affect the downstream runoff process to the current time.
- step S2 specifically includes the following content:
- ⁇ is the total forecast error
- ⁇ is the forecast error caused by upstream reservoir regulation and storage
- ⁇ is other forecast errors
- state i-1 is the state of the reservoir at the initial moment, that is , the state of the reservoir at the end of the previous period
- ⁇ i is the runoff forecast error at the current moment, that is, the amount of runoff change caused by reservoir regulation
- the known hydrological model is the Xin'anjiang model.
- step S23 specifically includes the following content:
- step S3 specifically includes selecting the date to be forecasted, combining precipitation data and runoff data, and driving the hydrological model to obtain the runoff of the date to be forecasted and realizing the prediction of future runoff.
- step S4 is specifically that the forecast period is i+1, then the previous period is i, and the forecast error in period i is obtained, that is, the runoff forecast value in period i is subtracted from the runoff forecast value in period i, which can be expressed as:
- ⁇ i is the prediction error period i; Q i i runoff data period; F. I i runoff predictive value for the period.
- step S5 specifically includes inputting the forecast error of period i into the regulation and storage influence forecasting model, and obtaining the difference between the forecast error of period i and the forecast error of each period j in the data set ⁇ j, ⁇ q j+1 ⁇ Distance
- step S6 is specifically calculated by the following formula:
- F'i+1 is the runoff forecast value in the future i+1 period
- F i+1 is the runoff in the i+1 period output by the regulation and storage influence forecast model
- ⁇ q i+1 is the runoff in the i+1 period. Estimated value of storage influence.
- the beneficial effects of the present invention are: 1.
- the method provided by the present invention can indirectly reflect the law of the storage and release of the reservoir group by using the forecast error at the previous time, and the prediction error is between the amount of change (influence) of the reservoir storage and release on the runoff. Establish correlations, and then revise the forecast results of the regulation and storage impact forecast model, so as to achieve the goal of carrying out runoff forecast under the influence of the reservoir group without directly obtaining the storage and release plan of the upstream reservoir group.
- Obtaining the scheduling plan of the upstream reservoir group in advance can significantly improve the accuracy of the runoff forecast under the influence of the reservoir group.
- This is the traditional method and means to carry out the runoff forecast affected by the upstream reservoir group.
- the premise of the application of the traditional method is that the upstream Reservoir group scheduling plan, this kind of data is actually difficult to obtain, and the application conditions of the traditional method are relatively harsh.
- the method of the present invention is used to carry out the runoff forecast affected by the upstream reservoir group, due to the establishment of the forecast error and Reservoir groups regulate and store runoff related relations, so it is no longer necessary to collect the scheduling plans of a large number of upstream reservoir groups, and the required data are easier to obtain.
- Fig. 1 is a schematic flowchart of a method in an embodiment of the present invention.
- this embodiment provides a method for using forecast errors to carry out runoff forecasting under the influence of upstream reservoir groups.
- the method includes the following steps:
- the prerequisite for the application of the method provided by the present invention is that there is already a hydrological model that can perform forecasting on the forecast section; the hydrological model parameters are to use the precipitation and runoff data of the period when the upstream reservoir is not built or the reservoir is less affected. Rated; the main error of the runoff forecast of this section comes from the regulation and storage of the upstream reservoir.
- the present invention mainly includes four steps: collecting data; establishing an estimated model for the impact of regulation and storage to forecast future runoff; obtaining the forecast error of the previous period and obtaining the estimated value of the impact of the future regulation and storage; Superimpose the forecast value of future runoff and the estimated value of the influence of regulation and storage.
- the data collected in step S1 specifically includes precipitation data and runoff data.
- the precipitation data is the precipitation data from the time the upstream reservoir starts to significantly affect the downstream runoff process to the current time; the runoff data It is the precipitation data from the time when the upstream reservoir began to significantly affect the downstream runoff process to the current time.
- the specific information to be collected is as follows.
- step S2 specifically includes the following content:
- the main source of forecast error is the natural runoff change caused by upstream reservoir regulation and storage. Therefore, the forecast error can be expressed as:
- ⁇ is the total forecast error
- ⁇ is the forecast error caused by upstream reservoir regulation
- ⁇ is other forecast errors
- ⁇ (unit: m 3 /s).
- state i-1 is the state of the reservoir at the initial moment, that is , the state of the reservoir at the end of the previous period
- ⁇ i is the runoff forecast error at the current moment, that is, the amount of runoff change caused by reservoir regulation
- the current reservoir state is linearly related to the current forecast error, and the current reservoir state has an impact on the runoff process in the next period, and then determines the next time.
- the forecast error of the time period that is, the current forecast error is related to the runoff change caused by the reservoir regulation and storage in the next time period.
- the known forecast error in the current period can be used to predict the change in runoff caused by the reservoir regulation and storage in a period in the future.
- the KNN model is selected as the known hydrological model to establish the prediction error in the current period and the reservoir in the next period.
- step S23 specifically includes the following content:
- step S23 the main process of step S23 is:
- step S3 specifically includes selecting the date to be forecasted, combining precipitation data and runoff data, and driving the hydrological model to obtain the runoff of the date to be forecasted to realize the prediction of future runoff.
- the hydrological model is driven according to the obtained precipitation and other data to realize the next moment
- the amount of change in runoff caused by reservoir regulation and storage is to realize the prediction of future runoff. Since the present invention is aimed at the daily-scale runoff forecast with a forecast period of 1 day, the future runoff forecast here is the runoff of "tomorrow" or "the second time period", and the unit is m 3 /s.
- step S4 is specifically that the forecast period is i+1, then the previous period is i, and the forecast error of period i is obtained, that is, the runoff forecast value of period i is subtracted from the runoff data of period i, which can be expressed as:
- ⁇ i is the prediction error period i; Q i i runoff data period; F. I i runoff predictive value for the period.
- step S5 specifically includes inputting the forecast error of period i into the regulation and storage influence forecasting model, and obtaining the prediction error of period i and the prediction error of each period j in the data set ⁇ j, ⁇ q j+1 ⁇ The distance between
- step S6 is specifically calculated by the following formula:
- F'i+1 is the runoff forecast value in the future i+1 period, in m 3 /s
- F i+1 is the runoff in the i+1 period output by the regulation and storage influence forecast model, in m 3 /s s
- ⁇ q i+1 is the amount of influence (estimated value) of regulation and storage in the period of i+1, in m 3 /s.
- the Danjiangkou Reservoir is selected as the research object, and the forecast effect verification period is from July 1, 2016 to July 31, 2016.
- the purpose of the forecast is to obtain daily runoff with a forecast period of 1 day; to explain in detail what the present invention provides The implementation process of the method.
- the precipitation data from January 1, 2009 to June 30, 2016 is selected to drive the hydrological model to obtain historical forecast information, and combine The runoff data from January 1, 2009 to June 30, 2016 are used to jointly establish a regulation and storage impact estimation model.
- the specific implementation steps are as follows:
- the hydrological model selected in this example is the Xin'anjiang model that has been actually applied in Danjiangkou Reservoir.
- the model is calibrated using precipitation and runoff data before 2009, and the Nash efficiency coefficient has been calibrated to 0.97, compared with Danjiangkou before 2009
- the number of reservoirs in the basin above the reservoir is relatively small, and the storage capacity is limited, which has little impact on Danjiangkou Reservoir's inflow, which can be considered as a natural runoff process.
- the forecast check period selected in this embodiment is from July 2, 2016 to July 31, 2016, which includes a total of 30 days of daily runoff forecast value. Since the Xin'an River model used in this embodiment can only predict the runoff of the next day, in order to better illustrate the effectiveness of the present invention, the daily accumulated precipitation from June 1, 2016 to June 30, 2016 is used to drive hydrology. Model preheating, based on preheating, using 31 precipitation data from July 1, 2016 to July 31, 2016, to drive the hydrological model, run 31 times, and obtain 31 forecast results, which are listed in the table below.
- the forecast error is obtained by subtracting the Xin'anjiang model forecast runoff from the measured runoff in the above table (relative to the forecast period, the forecast error is the forecast error of the previous period), as shown in the last column of the following table.
- the estimated value of the impact of regulation and storage in the forecast period (estimated value of the impact of regulation and storage in the future) is obtained, as shown in the last column of the following table:
- 2016-07-22 1090 1504.468346 -414.4685456 -370.7669409 2016-07-23 336 469.0391414 -133.0391414 -16.23080435 2016-07-24 911 1128.836117 -217.8361174 -149.1279152 2016-07-25 474 672.294591 -198.294591 -191.6265372 2016-07-26 898 936.365495 -38.36549498 -35.92955197 2016-07-27 586 669.6127829 -83.61278287 -49.33584972 2016-07-28 1430 1846.677136 -416.677136 -494.268071 2016-07-29 1160 1383.445582 -223.4455816 -173.2191907 2016-07-30 1180 1518.267742 -338.2677419 -175.5355712 2016-07-31 1080 1451.723783 -371.7
- the calculation formula of Nash efficiency coefficient is as follows:
- Q o refers to the observed value
- Q m refers to the simulated value
- Q t (superscript) refers to a certain value at time t
- Q o upper horizontal line
- E is the Nash efficiency coefficient, with a value of negative infinity to 1, E close to 1, indicating that the model is of good quality and high model credibility; E close to 0, indicating that the simulation result is close to the average level of the observed value, that is, the overall result is credible, But the process simulation error is large; E is far less than 0, the model is not credible.
- the present invention provides a method for using forecast errors to carry out runoff forecast under the influence of upstream reservoir groups.
- the method can indirectly reflect the law of storage and release of reservoirs by using the forecast error at the previous moment, and the forecast error is compared with the impact of reservoir storage and release on runoff. Establish a correlation between the amount of change (influence), and then revise the forecast results of the adjustment and storage influence forecast model, so as to achieve the purpose of carrying out runoff forecast under the influence of the reservoir group without directly obtaining the upstream reservoir group storage and release plan;
- the method of the invention patent carries out the runoff forecast affected by the upstream reservoir group. Since the influence of the upstream reservoir group on the runoff is considered in the forecasting process, it has higher accuracy than traditional hydrological forecasting methods.
- Obtaining the scheduling plan of the upstream reservoir group in advance can significantly improve the accuracy of runoff forecasting under the influence of the reservoir group.
- This is the traditional method and means to carry out runoff forecasting affected by the upstream reservoir group.
- the premise of the application of the traditional method is that the upstream reservoir group can be obtained. For dispatching plans, this kind of data is actually difficult to obtain.
- the application conditions of the traditional method are more stringent.
- the method of the present invention is used to carry out runoff forecasting affected by the upstream reservoir group, due to the establishment of forecast errors and reservoir groups. Regulating and storing the related relationship of runoff, it is no longer necessary to collect the dispatching plans of a large number of upstream reservoir groups, and the required data are easier to obtain.
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Abstract
Description
日期 | 实测径流 | 新安江模型预报径流 |
2016-07-01 | 773 | 981.4777906 |
2016-07-02 | 810 | 968.5364213 |
2016-07-03 | 711 | 832.0640394 |
2016-07-04 | 423 | 807.8978364 |
2016-07-05 | 409 | 615.3510802 |
2016-07-06 | 138 | 469.969122 |
2016-07-07 | 619 | 1117.658142 |
2016-07-08 | 757 | 1043.039863 |
2016-07-09 | 466 | 519.5576857 |
2016-07-10 | 327 | 513.8985641 |
2016-07-11 | 564 | 834.2230372 |
2016-07-12 | 592 | 1042.741363 |
2016-07-13 | 581 | 637.7200687 |
2016-07-14 | 1480 | 1498.464158 |
2016-07-15 | 1640 | 1943.726127 |
2016-07-16 | 1780 | 2242.721982 |
2016-07-17 | 1320 | 1722.908443 |
2016-07-18 | 1040 | 1382.139659 |
2016-07-19 | 1410 | 1706.848477 |
2016-07-20 | 2340 | 2721.277143 |
2016-07-21 | 1540 | 1702.29011 |
2016-07-22 | 1090 | 1504.468346 |
2016-07-23 | 336 | 469.0391414 |
2016-07-24 | 911 | 1128.836117 |
2016-07-25 | 474 | 672.294591 |
2016-07-26 | 898 | 936.365495 |
2016-07-27 | 586 | 669.6127829 |
2016-07-28 | 1430 | 1846.677136 |
2016-07-29 | 1160 | 1383.445582 |
2016-07-30 | 1180 | 1518.267742 |
2016-07-31 | 1080 | 1451.723783 |
日期 | 实测径流 | 新安江模型预报径流 | 预报误差 |
2016-07-01 | 773 | 981.4777906 | -208.4777906 |
2016-07-02 | 810 | 968.5364213 | -158.5364213 |
2016-07-03 | 711 | 832.0640394 | -121.0640394 |
2016-07-04 | 423 | 807.8978364 | -384.8978364 |
2016-07-05 | 409 | 615.3510802 | -206.3510802 |
2016-07-06 | 138 | 469.969122 | -331.969122 |
2016-07-07 | 619 | 1117.658142 | -498.6581421 |
2016-07-08 | 757 | 1043.039863 | -286.0398629 |
2016-07-09 | 466 | 519.5576857 | -53.55768565 |
2016-07-10 | 327 | 513.8985641 | -186.8985641 |
2016-07-11 | 564 | 834.2230372 | -270.2230372 |
2016-07-12 | 592 | 1042.741363 | -450.7413634 |
2016-07-13 | 581 | 637.7200687 | -56.72006866 |
2016-07-14 | 1480 | 1498.464158 | -18.46415784 |
2016-07-15 | 1640 | 1943.726127 | -303.7261268 |
2016-07-16 | 1780 | 2242.721982 | -462.7219825 |
2016-07-17 | 1320 | 1722.908443 | -402.9084426 |
2016-07-18 | 1040 | 1382.139659 | -342.1396595 |
2016-07-19 | 1410 | 1706.848477 | -296.8484772 |
2016-07-20 | 2340 | 2721.277143 | -381.2771434 |
2016-07-21 | 1540 | 1702.29011 | -162.2901097 |
2016-07-22 | 1090 | 1504.468346 | -414.4683456 |
2016-07-23 | 336 | 469.0391414 | -133.0391414 |
2016-07-24 | 911 | 1128.836117 | -217.8361174 |
2016-07-25 | 474 | 672.294591 | -198.294591 |
2016-07-26 | 898 | 936.365495 | -38.36549498 |
2016-07-27 | 586 | 669.6127829 | -83.61278287 |
2016-07-28 | 1430 | 1846.677136 | -416.677136 |
2016-07-29 | 1160 | 1383.445582 | -223.4455816 |
2016-07-30 | 1180 | 1518.267742 | -338.2677419 |
2016-07-31 | 1080 | 1451.723783 | -371.7237834 |
日期 | 实测径流 | 新安江模型预报径流 | 预报误差 | 未来调蓄影响量预估值 |
2016-07-01 | 773 | 981.4777906 | -208.4777906 | |
2016-07-02 | 810 | 968.5364213 | -158.5364213 | -137.572579 |
2016-07-03 | 711 | 832.0640394 | -121.0640394 | -104.8745406 |
2016-07-04 | 423 | 807.8978364 | -384.8978364 | -355.8711378 |
2016-07-05 | 409 | 615.3510802 | -206.3510802 | -149.9599759 |
2016-07-06 | 138 | 469.969122 | -331.969122 | -156.6991408 |
2016-07-07 | 619 | 1117.658142 | -498.6581421 | -232.0724935 |
2016-07-08 | 757 | 1043.039863 | -286.0398629 | -222.6857634 |
2016-07-09 | 466 | 519.5576857 | -53.55768565 | -50.95472822 |
2016-07-10 | 327 | 513.8985641 | -186.8985641 | -34.91305245 |
2016-07-11 | 564 | 834.2230372 | -270.2230372 | -160.6209183 |
2016-07-12 | 592 | 1042.741363 | -450.7413634 | -62.05214966 |
2016-07-13 | 581 | 637.7200687 | -56.720068.66 | -31.77375448 |
2016-07-14 | 1480 | 1498.464158 | -18.46415784 | -18.6329034 |
2016-07-15 | 1640 | 1943.726127 | -303.7261268 | -124.0817908 |
2016-07-16 | 1780 | 2242.721982 | -462.7219825 | -17.71318935 |
2016-07-17 | 1320 | 1722.908443 | -402.9084426 | -10.29585157 |
2016-07-18 | 1040 | 1382.139659 | -342.1396595 | -26.41614625 |
2016-07-19 | 1410 | 1706.848477 | -296.8484772 | -100.3125198 |
2016-07-20 | 2340 | 2721.277143 | -381.2771434 | -455.4021723 |
2016-07-21 | 1540 | 1702.29011 | -162.2901097 | -100.5748648 |
2016-07-22 | 1090 | 1504.468346 | -414.4685456 | -370.7669409 |
2016-07-23 | 336 | 469.0391414 | -133.0391414 | -16.23080435 |
2016-07-24 | 911 | 1128.836117 | -217.8361174 | -149.1279152 |
2016-07-25 | 474 | 672.294591 | -198.294591 | -191.6265372 |
2016-07-26 | 898 | 936.365495 | -38.36549498 | -35.92955197 |
2016-07-27 | 586 | 669.6127829 | -83.61278287 | -49.33584972 |
2016-07-28 | 1430 | 1846.677136 | -416.677136 | -494.268071 |
2016-07-29 | 1160 | 1383.445582 | -223.4455816 | -173.2191907 |
2016-07-30 | 1180 | 1518.267742 | -338.2677419 | -175.5355712 |
2016-07-31 | 1080 | 1451.723783 | -371.7237834 | -212.5363952 |
Claims (9)
- 一种利用预报误差开展上游水库群影响下径流预报的方法,其特征在于:所述方法包括如下步骤,S1、收集资料;S2、根据收集的资料,利用已知的水文模型和KNN模型建立调蓄影响量预估模型;S3、结合收集的资料驱动水文模型,以预测未来径流量;S4、获取上一时段预报误差;S5、根据上一时段预报误差,结合调蓄影响量预估模型,获取未来调蓄影响量预估值;S6、将未来径流量与未来调蓄影响量预估值叠加,以获取未来时段的径流预报值。
- 根据权利要求1所述的利用预报误差开展上游水库影响下径流预报的方法,其特征在于:步骤S1中收集的资料具体包括降水数据和径流数据,所述降水数据是上游水库开始显著影响下游径流过程的时刻到当前时刻这一时间段内的降水数据;所述径流数据为上游水库开始显著影响下游径流过程的时刻到当前时刻这一时间段内的降水数据。
- 根据权利要求2所述的利用预报误差开展上游水库影响下径流预报的方法,其特征在于:步骤S2具体包括如下内容,S21、以预报误差的主要来源为上游水库调蓄引起的天然径流变化为前提,获取预报误差计算公式,ω=δ+ε其中,ω为总的预报误差;δ为因上游水库调蓄引起的预报误差;ε为其他预报误差;ω≈δ;S22、水库调蓄引起径流变化机制可概化为δ i=T(state i-1)其中,state i-1为水库初始时刻的状态,即上一时段末的水库状态,δ i为当前时刻径流预报误差,即因水库调蓄引起的径流变化量;则当前时刻水库状态计算为state i=state i-1-86400×δ i;S23、利用已知的水文模型和KNN模型建立调蓄影响量预估模型,所述调蓄影响量预估模型即为当前时段预报误差与下一个时段水库调蓄引起的径流改变量的关系。
- 根据权利要求3所述的利用预报误差开展上游水库影响下径流预报的方法,其特征在于:所述已知的水文模型为新安江模型。
- 根据权利要求3所述的利用预报误差开展上游水库影响下径流预报的方法,其特征在于:步骤S23具体包括如下内容,S231、将降水数据和径流数据输入到水文模型中,获取水文模型输出的径流预报序列{F 1,F 2,F 3,…F n},所述径流预报序列中包括各个时段的径流量;S232、根据水文模型输出的径流预报序列,结合同时段的径流数据,即可获取j时段预报误差δ j与j+1时段水库调蓄引起的径流改变量Δq j+1组成的数据集{δ j,Δq j+1};其中,j∈(0,n];S233、结合步骤S232中的数据集,并设置KNN模型中的超参数k=5,即可获取调蓄影响量预估模型,即当前时段预报误差与下一个时段水库调蓄引起的径流改变量的关系。
- 根据权利要求5所述的利用预报误差开展上游水库影响下径流预报的方法,其特征在于:步骤S3具体为,选取待预报日期,结合降水数据和径流数据,驱动水文模型,即可获取 待预报日期的径流量,实现未来径流量的预测。
- 根据权利要求6所述的利用预报误差开展上游水库影响下径流预报的方法,其特征在于:步骤S4具体为,预报时段为i+1,则上一时段为i,获取i时段的预报误差,即用i时段径流数据减去i时段径流预报值,可表达为,δ i=Q i-F i其中,δ i为i时段的预报误差;Q i为i时段的径流数据;F i为i时段的径流预报值。
- 根据权利要求7所述的利用预报误差开展上游水库影响下径流预报的方法,其特征在于:步骤S5具体为,将i时段的预报误差输入调蓄影响量预估模型,获取i时段的预报误差与数据集{δ j,Δq j+1}中各个j时段预报误差之间的距离|δ i-δ j|,提取五个距离最小的j时段预报误差对应的径流改变量Δq j+1,并计算该五个径流改变量Δq j+1的平均值,即可获取i+1时段调蓄影响量预估值Δq i+1。
- 根据权利要求8所述的利用预报误差开展上游水库影响下径流预报的方法,其特征在于:步骤S6具体通过如下公式计算,F’ i+1=F i+1+Δq i+1其中,F’ i+1为未来i+1时段的径流预报值;F i+1为调蓄影响量预估模型输出的i+1时段的径流量;Δq i+1为i+1时段调蓄影响量预估值。
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