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 PDF

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WO2021218457A1
WO2021218457A1 PCT/CN2021/080782 CN2021080782W WO2021218457A1 WO 2021218457 A1 WO2021218457 A1 WO 2021218457A1 CN 2021080782 W CN2021080782 W CN 2021080782W WO 2021218457 A1 WO2021218457 A1 WO 2021218457A1
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runoff
forecast
period
storage
model
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PCT/CN2021/080782
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English (en)
French (fr)
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王浩
杨明祥
戴会超
蒋云钟
赵勇
董宁澎
杨恒
朱永楠
杨朝晖
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中国长江三峡集团有限公司
中国水利水电科学研究院
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Priority to JP2022502259A priority Critical patent/JP7310004B2/ja
Priority to US17/626,037 priority patent/US20230071484A1/en
Priority to GB2200294.3A priority patent/GB2599862B/en
Publication of WO2021218457A1 publication Critical patent/WO2021218457A1/zh

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    • 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
    • G06Q10/04—Forecasting or optimisation specially adapted for administrative or management purposes, e.g. linear programming or "cutting stock problem"
    • 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
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    • 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
    • G06Q10/0635—Risk analysis of enterprise or organisation activities
    • 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
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    • Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
    • Y02—TECHNOLOGIES OR APPLICATIONS FOR MITIGATION OR ADAPTATION AGAINST CLIMATE CHANGE
    • Y02A—TECHNOLOGIES FOR ADAPTATION TO CLIMATE CHANGE
    • Y02A10/00—TECHNOLOGIES FOR ADAPTATION TO CLIMATE CHANGE at coastal zones; at river basins
    • Y02A10/40—Controlling or monitoring, e.g. of flood or hurricane; Forecasting, e.g. risk assessment or mapping

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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

本发明公开了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，包括：收集资料；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；结合收集的资料驱动水文模型，以预测未来径流量；获取上一时段预报误差；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；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。优点是：通过利用上一时刻预报误差可以间接反映水库群蓄放水情况的规律，建立预报误差与水库蓄放水对径流的改变量之间的相关关系，对调蓄影响量预估模型预报结果进行修正，达到不用直接获取上游水库群蓄放水计划，开展水库群影响下径流预报的目的。

Description

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 技术领域
本发明涉及水文预报技术领域，尤其涉及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。
背景技术
精准的水文预报是开展防汛抗旱、兴利调度的前提，具有较高的经济、社会价值。随着人类科技水平的不断进步，人类对自然的改造能力愈发强大，修建水库对城市供水以及利用水能资源发电就是人类改造自然、利用自然的一种表现。
目前，我国拥有十万余座水库工程，是世界上水库数量最多的国家，大量的水库为我国经济发展带来了便利，但也剧烈的改变了流域水文规律，使天然径流过程变成了人类活动影响下的径流过程，这对水文预报工作带来了难题。这主要是因为，水库工程群具有改变径流时间分配的能力，可以在某一个时间段内对来水进行存蓄(入库量大于出库量)，也可以在某一个时间段内释放存蓄在水库中的水量(出库量大于入库量)，这就打破了降水、产流、汇流、河道演进的天然水文规律，使传统调蓄影响量预估模型的精度严重降低。
目前，针对上游水库群蓄放水造成下游径流预报精度较低的问题，主要是通过实时获取上游水库群的蓄放水计划，在此基础上叠加区间水文预报成果解决的。这种方法适用的前提是能够获取上游水库群的蓄放水计划信息，但实际情况是上游水库在多数情况并不会直接共享或提供这类信息(因涉及商业机密等)，而且预报断面上游的水库群数量往往异常庞大，由此导致在绝大多数情况下并不能获取到预报断面上游的水库群蓄放水计划信息，因此，上游水库群影响造成传统水文预报模型精度较低，而现有解决方法适用条件过为苛刻，不具有实际操作性的问题。
发明内容
本发明的目的在于提供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，从而解决现有技术中存在的前述问题。
为了实现上述目的，本发明采用的技术方案如下：
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，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。
优选的，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。
优选的，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。
优选的，所述已知的水文模型为新安江模型。
优选的，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。
优选的，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。
优选的，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。
优选的，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。
优选的，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。
本发明的有益效果是：1、本发明提供的方法通过利用上一时刻预报误差可以间接 反映水库群蓄放水情况的规律，将预报误差与水库蓄放水对径流的改变量(影响量)之间建立相关关系，进而对调蓄影响量预估模型预报结果进行修正，达到不用直接获取上游水库群蓄放水计划，开展水库群影响下径流预报的目的。2、使用本发明专利的方法开展受上游水库群影响的径流预报，由于在预报过程中考虑了上游水库群对径流的影响量，因此与传统水文预报方法相比，具有更高的精度。3、提前获取上游水库群的调度计划，可以显著提高受水库群影响下的径流预报精度，这是开展受上游水库群影响的径流预报的传统方法和手段，传统方法适用的前提是可以获取上游水库群调度计划，这种资料实际是很难获取的，传统方法的适用条件较为苛刻，与传统方法相比，使用本发明的方法开展受上游水库群影响的径流预报，由于建立了预报误差与水库群调蓄径流的相关关系，因此不再需要收集上游大量水库群的调度计划，所需要的资料均较易获取。
附图说明
图1是本发明实施例中方法的流程示意图。
具体实施方式
为了使本发明的目的、技术方案及优点更加清楚明白，以下结合附图，对本发明进行进一步详细说明。应当理解，此处所描述的具体实施方式仅仅用以解释本发明，并不用于限定本发明。
实施例一
如图1所示，本实施例中提供了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，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。
本实施例中，本发明所提供的方法的适用前提是已经存在一个水文模型可以对预报断面开展预报；该水文模型参数是利用上游未修建水库，或水库影响较小的时期的降水、径流数据率定的；该断面径流预报的主要误差来自于上游水库的调蓄。在以上前提成立的情况下，本发明主要包括四个步骤：收集资料；建立调蓄影响量预估模型，预报未来径流量；获取上一时段预报误差，获取未来调蓄影响量预估值；叠加未来径流量预报值与调蓄影响量预估值。
本实施例中，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。需要收集的资料具体如下表。
Figure PCTCN2021080782-appb-000001
本实施例中，步骤S2具体包括如下内容，
S21、以预见期为1天的日尺度预报为例，根据本发明方法的适用前提，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，因此，预报误差可表达为，
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；(单位：m 3/s)。
S22、由于预报误差主要由上游水库调蓄引起，且误差量即为因调蓄引起的径流变化量，而水库调蓄的依据是水库的初始时刻的状态，包括蓄水量、蓄水位等，因此，设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；因水库当前状态为上一时刻状态加上水库调蓄量(与对径流的影响符号相反，若水库增加蓄水量，则径流量减少)，则当前时刻水库状态计算为
state i＝state i-1-86400×δ i；
由当前时刻水库状态计算公式可见，由于上一时刻水库状态为已知，当前水库状态与当前预报误差成线性相关关系，而当前水库状态又对下一时段的径流过程有影响，进而决定下一时段的预报误差，即当前预报误差与下一时段水库调蓄引起的径流变化相关。
基于以上结论，可利用当前时段已知的预报误差来预报未来一个时段水库调蓄引起的径流改变量，本发明中选择KNN模型作为已知的水文模型，建立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KNN模型的作用机制，需要建立i时段预报误差δ i与i+1时段水库调蓄引起的径流改变量Δq i+1的数据集{δ i，Δq i+1}；也就是步骤S23的内容，
S23、利用已知的水文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。已知的水文模型即为新安江模型。
本实施例中，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KNN模型中，获取KNN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、根据所述径流模拟序列获取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。
综上所述，步骤S23的主要过程就是，
1、利用降水等数据驱动水文模型；
2、获得径流模拟(预报)序列{F 1，F 2，F 3，…F n}；
3、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]。
本实施例中，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。
步骤S3也就是将数据集构建完成后，依据KNN算法的运行机制，设置KNN中超参数k＝5，在实际预报过程中，再根据获取的降水等资料，驱动水文模型，即可实现下一个时刻水库调蓄引起的径流改变量，也就是实现对未来径流量进行预测。由于本发明针对的是预见期为1天的日尺度径流预报开展工作，因此此处预报的未来径流量即为“明天”或“第二个时段”的径流量，单位为m 3/s。
本实施例中，，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。
本实施例中，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。
本实施例中，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，单位m 3/s；F i+1为调蓄影响量预估模型输出的i+1时段的径流量，单位m 3/s；Δq i+1为i+1时段调蓄影响量量(预估值)，单位m 3/s。
实施例二
本实施例中，选择丹江口水库为研究对象，预报效果检验时段为2016年7月1日至2016年7月31日，预报目的是获取预见期1天的日尺度径流；以详细说明本发明提供的方法的实施过程。
1、收集资料；需要收集的资料如下表所示(因资料过多，只展示部分资料)：
Figure PCTCN2021080782-appb-000002
2、建立调蓄影响量预估模型；
由于选择的预报效果检验时间为2016年7月1日至2016年7月31日，因此选取2009年1月1日至2016年6月30日的降水数据驱动水文模型获取历史预报信息，并结合2009年1月1日至2016年6月30日的径流数据共同建立调蓄影响量预估模型，具体实施例步骤如下所示：
(1)、利用降水等数据驱动水文模型
本实施例中选用的水文模型是已经在丹江口水库实际应用的新安江模型，该模型使用2009年以前的降水、径流数据进行率定，率定的纳什效率系数达到了0.97，而2009年之前丹江口水库以上流域的水库数量相对较少，调蓄能力有限，对丹江口水库的入库影响较小，可以认为是天然径流过程。使用2009年1月1日至2016年6月30日的日尺度降水数据，输入新安江模型，得到相应时段的日尺度径流预报数据F i，结合同时段的径流观测数据Q i，可以计算出预报误差信息δ i，以及下一时段的径流该变量Δq i+1，如下表所述(由于数据过多，只展示部分数据)。
Figure PCTCN2021080782-appb-000003
Figure PCTCN2021080782-appb-000004
Figure PCTCN2021080782-appb-000005
(2)、获得径流模拟(预报)序列{F 1，F 2，F 3，…F n}
上表中列“预报流量(F i)”即为获得的径流模拟(预报)序列。
(3)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]；
上表中列“预报误差(δ j)”和“下时段径流该变量(Δq j+1)”综合起来就是“数据集{δ j，Δq j+1}”。
将以上数据集构建完成后，依据KNN算法的运行机制，设置KNN模型中的超参数k＝5，至此便完成了调蓄影响量预估模型的建立。
3、预报未来径流量
本实施例选择的预报检验时段为2016年7月2日至2016年7月31日，共计包括30天的日径流预报值。由于本实施例采用的新安江模型仅能预报未来一天的径流量，为了更好的说明本发明的有效性，利用2016年6月1日至2016年6月30日的日累计降水，驱动水文模型预热，在预热的基础上，利用2016年7月1日至2016年7月31日的31个降水数据，驱动水文模型，运行31次，得到31个预报结果，列入下表。
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
4、获取上一时段预报误差
由上表中实测径流减去新安江模型预报径流得到预报误差(相对于预报时段，该预报误差是上一时段预报误差)，如下表最后一列所示。
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
由上表可知，7月份上游水库对径流进行了不同程度的拦蓄，因此，导致新安江模型的预报结果普遍偏高。
5、获取未来调蓄影响量预估值
根据实施例一介绍的方法，得到预报时段调蓄影响量的预估(未来调蓄影响量预估值)，如下表最后一列所示：
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
6、叠加未来径流量预报值与调蓄影响量预估值
叠加未来径流量预报值与调蓄影响量预估值之后，得到最终预报结果，如下表最后一列所示：
Figure PCTCN2021080782-appb-000006
利用纳什效率系数对本发明预报径流以及新安江模型预报径流的精度进行定量评价，可知本发明预报径流的纳什效率系数NS＝0.88，高于直接使用新安江模型预报的纳 什效率系数NS＝0.66。并且，本发明在未使用上游水库群调度计划的情况下，将预报精度从0.66提高到0.88，具有比传统方法更少的资料需求。纳什效率系数的计算公式如下所示：
Figure PCTCN2021080782-appb-000007
其中，Q o指观测值，Q m指模拟值，Q t(上标)表示第t时刻的某个值，Q o(上横线)表示观测值的总平均。E为纳什效率系数，取值为负无穷至1，E接近1，表示模式质量好，模型可信度高；E接近0，表示模拟结果接近观测值的平均值水平，即总体结果可信，但过程模拟误差大；E远远小于0，则模型是不可信的。
通过采用本发明公开的上述技术方案，得到了如下有益的效果：
本发明提供了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，该方法，通过利用上一时刻预报误差可以间接反映水库群蓄放水情况的规律，将预报误差与水库蓄放水对径流的改变量(影响量)之间建立相关关系，进而对调蓄影响量预估模型预报结果进行修正，达到不用直接获取上游水库群蓄放水计划，开展水库群影响下径流预报的目的；使用本发明专利的方法开展受上游水库群影响的径流预报，由于在预报过程中考虑了上游水库群对径流的影响量，因此与传统水文预报方法相比，具有更高的精度。提前获取上游水库群的调度计划，可以显著提高受水库群影响下的径流预报精度，这是开展受上游水库群影响的径流预报的传统方法和手段，传统方法适用的前提是可以获取上游水库群调度计划，这种资料实际是很难获取的，传统方法的适用条件较为苛刻，与传统方法相比，使用本发明的方法开展受上游水库群影响的径流预报，由于建立了预报误差与水库群调蓄径流的相关关系，因此不再需要收集上游大量水库群的调度计划，所需要的资料均较易获取。
以上所述仅是本发明的优选实施方式，应当指出，对于本技术领域的普通技术人员来说，在不脱离本发明原理的前提下，还可以做出若干改进和润饰，这些改进和润饰也应视本发明的保护范围。

Claims (9)

  1. 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。
  2. 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。
  3. 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。
  4. 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。
  5. 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。
  6. 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。
  7. 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。
  8. 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。
  9. 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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Cited By (3)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN114840989A (zh) * 2022-04-22 2022-08-02 河海大学 一种栅格尺度考虑水利工程拦蓄的河道汇流演算方法
CN117131977A (zh) * 2023-08-09 2023-11-28 长江勘测规划设计研究有限责任公司 一种基于误判风险最小准则的径流预报样本集划分方法
CN117332908A (zh) * 2023-12-01 2024-01-02 长江水利委员会水文局 一种耦合集合预报的梯级水库多目标优化调度方法及系统

Families Citing this family (9)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN111401666B (zh) * 2020-04-28 2021-07-27 中国水利水电科学研究院 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
CN112711896B (zh) * 2021-01-05 2021-09-03 浙江大学 一种考虑多源预报误差不确定性的复杂水库群优化调度方法
CN113468464B (zh) * 2021-06-30 2023-04-18 国能黄骅港务有限责任公司 调蓄湖库水位预测方法、装置、计算机设备和存储介质
CN114819322B (zh) * 2022-04-18 2024-05-14 武汉大学 湖泊入湖流量的预报方法
CN115600749B (zh) * 2022-10-24 2023-07-18 北京市生态环境保护科学研究院 地下水位预测方法、装置及电子设备
CN115795258B (zh) * 2022-10-27 2023-11-28 华能伊敏煤电有限责任公司 一种定量分析改变取水方式对河流下游污染物浓度影响的方法
CN116681202B (zh) * 2023-05-08 2024-02-06 广东省水利水电科学研究院 一种基于蓄水模数的水资源分析方法、系统、装置及介质
CN117172965B (zh) * 2023-11-03 2024-02-09 长江三峡集团实业发展(北京)有限公司 一种考虑气候变化的梯级水库群水能资源评估方法及装置
CN117454778B (zh) * 2023-12-26 2024-05-07 海纳云物联科技有限公司 蓄水量数据处理方法、设备及存储介质

Citations (4)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN103258109A (zh) * 2012-02-15 2013-08-21 连和政 水位预报实时校正的方法
CN110110339A (zh) * 2018-01-29 2019-08-09 中国电力科学研究院有限公司 一种日前水文预报误差校正方法及系统
WO2020047240A1 (en) * 2018-08-31 2020-03-05 The Climate Corporation Subfield moisture model improvement using overland flow modeling with shallow water computations
CN111401666A (zh) * 2020-04-28 2020-07-10 中国水利水电科学研究院 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

Family Cites Families (13)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
JP4146053B2 (ja) 1999-12-13 2008-09-03 富士電機システムズ株式会社 ダムまたは河川における流量予測方法
GB0808985D0 (en) * 2008-05-19 2008-06-25 Univ Exeter The Analysis of pressure management economics in water distribution systems and conceptual risk-based decision support methodology for improved near real time-
US10115158B2 (en) * 2010-10-25 2018-10-30 Trimble Inc. Generating a crop recommendation
CN103116701A (zh) * 2013-01-30 2013-05-22 南京理工大学连云港研究院 一种用于水利信息系统的水利信息调度方法
EP3282433A4 (en) 2015-04-06 2019-04-17 Kabushiki Kaisha Toshiba DISASTER PREVENTION SYSTEM
US10529036B2 (en) * 2016-01-22 2020-01-07 The Climate Corporation Forecasting national crop yield during the growing season using weather indices
CN106529176B (zh) * 2016-11-11 2018-04-06 中国水利水电科学研究院 一种双核双驱洪水预报方法
CN106951980B (zh) * 2017-02-21 2021-07-09 河海大学 一种基于rcp情景的水库群适应性调度方法
CN108108838B (zh) * 2017-12-18 2021-08-27 华电福新能源股份有限公司福建分公司 一种高水量利用率的季调节水库优化调度方法
AR109623A1 (es) 2018-02-16 2019-01-09 Pescarmona Enrique Menotti Proceso y sistema de análisis y gestión hidrológica para cuencas
CN109816167A (zh) * 2019-01-18 2019-05-28 昆仑(重庆)河湖生态研究院(有限合伙) 径流预报方法及径流预报装置
CN109886461A (zh) * 2019-01-18 2019-06-14 昆仑(重庆)河湖生态研究院(有限合伙) 一种径流预报方法及装置
CN111311026A (zh) * 2020-03-19 2020-06-19 中国地质大学（武汉） 一种顾及数据特征、模型和校正的径流非线性预测方法

Patent Citations (4)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN103258109A (zh) * 2012-02-15 2013-08-21 连和政 水位预报实时校正的方法
CN110110339A (zh) * 2018-01-29 2019-08-09 中国电力科学研究院有限公司 一种日前水文预报误差校正方法及系统
WO2020047240A1 (en) * 2018-08-31 2020-03-05 The Climate Corporation Subfield moisture model improvement using overland flow modeling with shallow water computations
CN111401666A (zh) * 2020-04-28 2020-07-10 中国水利水电科学研究院 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

Cited By (6)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN114840989A (zh) * 2022-04-22 2022-08-02 河海大学 一种栅格尺度考虑水利工程拦蓄的河道汇流演算方法
CN114840989B (zh) * 2022-04-22 2022-11-11 河海大学 一种栅格尺度考虑水利工程拦蓄的河道汇流演算方法
CN117131977A (zh) * 2023-08-09 2023-11-28 长江勘测规划设计研究有限责任公司 一种基于误判风险最小准则的径流预报样本集划分方法
CN117131977B (zh) * 2023-08-09 2024-01-23 长江勘测规划设计研究有限责任公司 一种基于误判风险最小准则的径流预报样本集划分方法
CN117332908A (zh) * 2023-12-01 2024-01-02 长江水利委员会水文局 一种耦合集合预报的梯级水库多目标优化调度方法及系统
CN117332908B (zh) * 2023-12-01 2024-02-13 长江水利委员会水文局 一种耦合集合预报的梯级水库多目标优化调度方法及系统

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