WO2022016931A1 - 一种基于多点并行校正的分布式水文模型参数率定方法 - Google Patents

一种基于多点并行校正的分布式水文模型参数率定方法 Download PDF

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WO2022016931A1
WO2022016931A1 PCT/CN2021/088985 CN2021088985W WO2022016931A1 WO 2022016931 A1 WO2022016931 A1 WO 2022016931A1 CN 2021088985 W CN2021088985 W CN 2021088985W WO 2022016931 A1 WO2022016931 A1 WO 2022016931A1
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watershed
hydrological
parameter calibration
basin
sub
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PCT/CN2021/088985
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French (fr)
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戴会超
王浩
常文娟
雷晓辉
蒋定国
马海波
王煜
严登华
刘冀
赵汗青
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中国长江三峡集团有限公司
三峡大学
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Priority to JP2022517315A priority Critical patent/JP7337361B2/ja
Priority to GB2203415.1A priority patent/GB2601282B/en
Publication of WO2022016931A1 publication Critical patent/WO2022016931A1/zh

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    • 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
    • G01—MEASURING; TESTING
    • G01W—METEOROLOGY
    • G01W1/00—Meteorology
    • G01W1/10—Devices for predicting weather conditions
    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01W—METEOROLOGY
    • G01W1/00—Meteorology
    • G01W1/18—Testing or calibrating meteorological apparatus
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F17/00—Digital computing or data processing equipment or methods, specially adapted for specific functions
    • G06F17/10—Complex mathematical operations
    • G06F17/17—Function evaluation by approximation methods, e.g. inter- or extrapolation, smoothing, least mean square method
    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01V—GEOPHYSICS; GRAVITATIONAL MEASUREMENTS; DETECTING MASSES OR OBJECTS; TAGS
    • G01V20/00—Geomodelling in general
    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01V—GEOPHYSICS; GRAVITATIONAL MEASUREMENTS; DETECTING MASSES OR OBJECTS; TAGS
    • G01V2210/00—Details of seismic processing or analysis
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    • G01V2210/66—Subsurface modeling
    • 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 field of hydrological forecasting, in particular to a method for parameter calibration of a distributed hydrological model based on multi-point parallel correction.
  • the widely used method of parameter calibration of hydrological model is to establish the objective function of parameter calibration with the flow process of the outlet section of the watershed as a variable, and to use various optimization algorithms or parallel algorithms to calibrate the parameters of the hydrological model.
  • the method of directly using the optimization algorithm to establish the objective function with the flow process at the outlet of the watershed as a variable requires a high computer memory, and the calculation and optimization time is long.
  • the general computer usually suffers from insufficient memory , resulting in the interruption of the optimization program, and the optimal parameters of the watershed cannot be obtained, and the obtained model parameters are only corrected according to the measured flow process at a single point of the outlet section of the entire watershed, so the obtained parameters may not reflect the true nature of each sub-basin in the watershed. Confluence characteristics.
  • the purpose of the present invention is to provide a distributed hydrological model parameter calibration method based on multi-point parallel correction, so as to solve the aforementioned problems existing in the prior art.
  • a distributed hydrological model parameter calibration method based on multi-point parallel correction comprising the following steps:
  • step S1 specifically includes the following contents:
  • the hydrological station and/or the reservoir is used as the outlet section of the sub-basin.
  • step S2 is specifically as follows: adding a hydrological unit to the watershed surface file to generate a watershed model, and configuring a corresponding calculation method for each hydrological unit, where the hydrological unit includes a reservoir unit, a channel unit, a sub-basin unit and a confluence point unit.
  • step S3 specifically includes the following contents:
  • the runoff process of each sub-basin is represented by the area rainfall of each sub-basin, and the area rainfall of the sub-basin is determined according to the product of the flow data of each rainfall station in the sub-basin and the Thiessen polygon weight of each rainfall station;
  • step S4 specifically includes the following contents:
  • each of the parameter calibration units includes at least one sub-basin; and ensure that the outlet sections of each parameter calibration unit have observation data.
  • the flow rate of the outlet section of the other parameter calibration unit adopts the observed flow rate as its corresponding outflow data, that is, the inflow parameter calibration unit of incoming data.
  • step S5 specifically includes the following contents:
  • Each parameter calibration unit selects an appropriate hydrological model parameter calibration objective function according to the needs of its respective watershed hydrology forecast, and the objective function is a peak error percentage function or a mean weighted root mean square error function.
  • the peak error percentage function is selected as the objective function; when it is necessary to reflect the overall situation of the flood process and focus on the simulation of flood peak flow, the mean weighted root mean square error function is selected as the objective function; the peak error percentage
  • the function and the mean weighted root mean square error function are respectively as follows,
  • f 1 is the peak error percentage function
  • q s (peak) is the calculated peak value
  • q o (peak) is the measured peak value
  • f 2 is the mean weighted root mean square error function
  • NQ is the calculated number of process line ordinates
  • q o (i) is the measured flow at the end of the i-th period
  • q s (i) is the calculated flow at the end of the i-th period
  • i is the time sequence
  • each parameter calibration unit uses a computer to use an optimization algorithm to obtain the minimum value of each objective function in parallel, so as to realize the parameter calibration of the hydrological model of the target research basin; the minimum value of each said objective function is the
  • the optimal parameters of the parameter calibration unit calibration are summarized and integrated, and the optimal parameters of the hydrological model of the target study watershed can be obtained.
  • the beneficial effects of the present invention are: 1. Divide the target research watershed into a plurality of parameter calibration units according to the distribution of hydrological stations with actual measurement data, and on multiple computers, different parameter calibration units are based on the hydrology of their outlet sections. The actual measured flow process of the station is calibrated and calibrated to improve the calibration efficiency. 2. Under the condition that the computer performance is not very high, the hydrological model parameters that can more truly reflect the runoff and runoff characteristics of the basin can still be obtained through simple operations. 3. Parallel computing Write sensitivity analysis and multi-objective calibration programs based on the parallel language MPI, coupled with the open source program of the hydrological model, and the sensitive parameters obtained according to the global sensitivity analysis method are used for multi-objective calibration of model parameters to obtain the optimal solution. , the use of parallel computing greatly improves the efficiency of parameter calibration and saves a lot of time for parameter optimization.
  • FIG. 1 is a schematic flowchart of a method in an embodiment of the present invention
  • Fig. 2 is the distribution map of rainfall stations in the basin above Linyi in the embodiment of the present invention
  • Fig. 3 is the distribution map of hydrological stations in the basin above Linyi in the embodiment of the present invention.
  • Fig. 4 is the distribution map of reservoirs in the basin above Linyi in the embodiment of the present invention.
  • Fig. 5 is the sub-watershed division diagram of the watershed above Linyi in the embodiment of the present invention.
  • FIG. 6 is a schematic diagram of a Thiessen polygon in the basin above Linyi in the embodiment of the present invention.
  • FIG. 7 is a schematic diagram of the hydrological model of the basin above Linyi in the embodiment of the present invention.
  • FIG. 11 is a comparison diagram of the measured flow and the calculated flow excluding other three parameter calibration units in the basin above Linyi in the embodiment of the present invention.
  • Fig. 12 is an overall calibration effect diagram of the basin above Linyi in the embodiment of the present invention.
  • a method for parameter calibration of a distributed hydrological model based on multi-point parallel correction includes the following steps:
  • the method specifically includes five parts, which are data collection and processing, generation of a watershed model, determination of the rainfall process of each sub-basin in the process of flood rainfall and runoff, and the flow process of each hydrological station section in the watershed, for research
  • the watershed is divided into parallel parameter calibration units, formulating calculation rules, selecting and optimizing the objective function and obtaining the minimum value of the objective function.
  • step S1 corresponds to the first part, and specifically includes the following contents:
  • the hydrological station and/or the reservoir is used as the outlet section of the sub-basin.
  • step S15 since the impermeability is a fixed parameter with physical meaning of the hydrological model; therefore, in step S15, the impermeability of each sub-basin needs to be calculated, so as to obtain the optimal parameters of the hydrological model later.
  • step S2 corresponds to the second part.
  • a watershed model is generated by adding a hydrological unit to the watershed surface file, and a corresponding calculation method is configured for each hydrological unit, and the hydrological unit includes a reservoir unit and a channel unit. , sub-basin units, and confluence point units, etc.
  • each hydrological unit has different calculation methods; for example, a sub-basin unit needs to set a runoff calculation method, a confluence calculation method, and a base flow calculation method; a river channel unit needs to set a channel flood calculation method; Sets the calculation method of reservoir outflow.
  • each different hydrological unit adopts different calculation methods to perform corresponding calculation, so as to prepare for calculation in step S3.
  • step S3 corresponds to the third part, specifically:
  • the runoff process of each sub-basin is represented by the area rainfall of each sub-basin, and the area rainfall of the sub-basin is determined according to the product of the flow data of each rainfall station in the sub-basin and the Thiessen polygon weight of each rainfall station;
  • step S3 specifically includes three steps.
  • the start and end time of the rainfall-runoff simulation is determined according to the runoff process of the outlet section of the target study watershed and the rainfall process of each rainfall station;
  • the area rainfall is determined by multiplying the hourly precipitation data of each rainfall station by the Thiessen polygon weight of each rainfall station; finally, the hourly flow process is adopted for the flow process of each hydrological station.
  • step S4 corresponds to the fourth part, which specifically includes the following content:
  • each of the parameter calibration units includes at least one sub-basin; and ensure that the outlet sections of each parameter calibration unit have observation data.
  • the flow rate of the outlet section of the other parameter calibration unit adopts the observed flow rate as its corresponding outflow data, that is, the inflow parameter calibration unit of incoming data.
  • the division of parameter calibration units is specifically: according to the location of the hydrological station, the target research watershed is divided into several parameter calibration units, and these parameter calibration units respectively include one or more sub-basins, and these parameter calibration units
  • the exit location must be a hydrological station with observational data.
  • the calculation rule of the parameter calibration unit is: for the sub-basin whose outlet is a reservoir unit, the outflow process is processed by the actual outflow flow of the reservoir.
  • the flow rate of the outlet section of the other parameter calibration unit adopts the observed flow rate as its corresponding outflow data, that is, the inflow of the inflow parameter calibration unit. data.
  • step S5 corresponds to the fifth part, which specifically includes the following contents:
  • Each parameter calibration unit selects an appropriate hydrological model parameter calibration objective function according to the needs of its respective watershed hydrology forecast, and the objective function is a peak error percentage function or a mean weighted root mean square error function.
  • the peak error percentage function is selected as the objective function; when it is necessary to reflect the overall situation of the flood process and focus on the simulation of flood peak flow, the mean weighted root mean square error function is selected as the objective function; the peak error percentage
  • the function and the mean weighted root mean square error function are respectively as follows,
  • f 1 is the peak error percentage function
  • q s (peak) is the calculated peak value
  • q o (peak) is the measured peak value
  • f 2 is the mean weighted root mean square error function
  • NQ is the calculated number of process line ordinates
  • q o (i) is the measured flow at the end of the i-th period
  • q s (i) is the calculated flow at the end of the i-th period
  • i is the time sequence
  • each parameter calibration unit uses a computer to use an optimization algorithm to obtain the minimum value of each objective function in parallel, so as to realize the parameter calibration of the hydrological model of the target research basin; the minimum value of each said objective function is the
  • the optimal parameters of the parameter calibration unit calibration are summarized and integrated, and the optimal parameters of the hydrological model of the target study watershed can be obtained.
  • the calculation rule determined in step S4 is adopted, and the minimum value of each objective function is obtained in parallel by applying the optimization algorithm through several computers, and the parallel parameter calibration is performed on the hydrological model;
  • the parameter calibration unit calibrates the optimal parameters to obtain the optimal parameter set of the hydrological model of the target study watershed.
  • the method is based on the parallel language MPI to write sensitivity analysis and multi-objective calibration programs, coupled with the open source program of the hydrological model, and the sensitive parameters obtained according to the global sensitivity analysis method are used for the multi-objective calibration of model parameters to obtain Optimal solution; parallel calculation on multiple computers to improve the efficiency of parameter calibration.
  • the parameter calibration of the hydrological model in the basin above the Linyi Hydrological Station of the Yihe River in Shandong is taken as an example to specifically describe the implementation process and the effect achieved by the parameter calibration method of the present invention.
  • the catchment area of Linyi Station is 10315km 2 , and the river channel is 227.8km long.
  • the terrain is high in the northwest and slopes to the southeast plain. Due to the complex terrain in the upper reaches of the Yi River, many tributaries have been formed.
  • the first-class tributaries with the catchment area above Linyi Station are more than 200km 2 , including Dongwen River, Meng River, Yi River, Su River and Liuqing River.
  • the mountainous area in the basin accounts for about 68%, and the plain area accounts for about 32%.
  • the Yihe River Basin has a temperate monsoon continental climate.
  • the average annual rainfall in the basin is 813mm, and the rainfall during the flood season is 600mm, accounting for about 73.9% of the annual rainfall.
  • the distribution map of rainfall stations in the basin above Linyi is shown in Figure 2
  • the distribution map of hydrological stations is shown in Figure 3
  • the distribution map of reservoirs is shown in Figure 4.
  • the example is based on the rainfall data of 21 rainfall stations in the basin above Linyi and the hydrological data of Linyi, Gegou, Jiaoyi and Gaoli with the start and end time from 1:00 am on July 14, 2017 to 15:00 on July 20, 2017
  • the parameters of the hydrological model of the basin above Linyi were calibrated.
  • the steps of the hydrological model parameter calibration method based on multi-point parallel correction are as follows:
  • the watershed above Linyi is used as the target research watershed, and the rainfall data of 21 rainfall stations in the watershed above Linyi from 1:00 am on July 14, 2017 to 15:00 on July 20, 2017, as well as Linyi, Gegou, Jiaoyi, Gaoli4
  • the flow data of each hydrological station, and interpolate these data from unequal time interval data into hourly data collect the DEM map and land use map of the target study watershed, conduct hydrological analysis on the DEM map through GIS software, and obtain the target study
  • the watershed area file of the watershed divides the target study watershed into several sub-watersheds by the method of watershed segmentation to ensure that the main stream and the larger tributaries in the target study watershed have measured data hydrological stations and reservoirs are distributed in the exit positions of each sub-watershed .
  • the sub-basin division map is shown in Figure 5; the soil utilization map is analyzed by GIS software to obtain the impermeability of each sub-basin of the target study watershed; the Thiessen polygon is drawn based on the rainfall stations in the target study watershed to obtain each sub-basin of the target study watershed. The influencing rainfall stations and their respective weights in the sub-watersheds; the Thiessen polygons of the target study watersheds are shown in Figure 6.
  • the starting and ending time of the rainfall runoff simulation is determined from 1:00 am on July 14, 2017 to 15:00 on July 20, 2017.
  • the rainfall process of each sub-basin is represented by the area rainfall of the sub-basin, which is determined by multiplying the hourly precipitation data of each rainfall station obtained in step 1 by the Thiessen polygon weight of each rainfall station.
  • parameter calibration units are divided, namely, the watershed above Gegou, the watershed above Gaoli, the watershed above Jiaoyi, and the watershed above Linyi except for the above three parameter calibration units. part, namely the W1710 sub-watershed.
  • the mean weighted root mean square error function is selected to calibrate the four parameter calibration units; for the four parameter calibration units, the optimization algorithm is applied to four computers to obtain the minimum value of the objective function in parallel.
  • the hydrological model is calibrated in parallel.
  • the comparison between the measured flow and the calculated flow of the parameter calibration unit in the watershed above Gegou is shown in Figure 8; the comparison between the measured flow and the calculated flow of the parameter calibration unit in the watershed above Gaoli is shown in Figure 9; the measured flow of the parameter calibration unit in the watershed above Jiaoyi See Figure 10 for the comparison with the calculated flow; see Figure 11 for the comparison between the measured flow and the calculated flow except for the other three parameter calibration units in the watershed above Linyi; see Figure 12 for the overall calibration effect of the target study watershed;
  • the optimal parameters of the element calibration can be obtained to obtain the optimal parameters of the hydrological model of the target study watershed.
  • the invention provides a distributed hydrological model parameter calibration method based on multi-point parallel correction.
  • the invention divides the target research basin into a plurality of parameter calibration units according to the distribution of hydrological stations with actual measurement data.
  • different parameter calibration units are calibrated according to the measured flow process of the hydrological station at their outlet section, so as to improve the calibration efficiency.
  • the hydrological model parameters that can more realistically reflect the runoff and runoff characteristics of the watershed can still be obtained through simple operations.
  • Parallel computing is based on the parallel language MPI to write sensitivity analysis and multi-objective calibration programs, and open source programs for coupling hydrological models.
  • the sensitive parameters obtained by the global sensitivity analysis method are used for multi-objective calibration of model parameters to obtain optimal solutions.
  • Parallel The use of calculation greatly improves the efficiency of parameter calibration and saves a lot of time for parameter optimization.

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Abstract

本发明公开了一种基于多点并行校正的水文模型参数率定方法，根据目标研究流域内干流和大支流上的水文站点位置，将目标研究流域划分为若干个子流域；在进行模型参数的时候，根据子流域的位置关系以及水文站点的数据情况，将目标研究流域划分为几个参数率定单元，通过多台电脑分别对不同的参数率定单元通过参数率定单元流域出口断面的水文站点的观测流量过程进行并行参数校正；将各参数率定单元的水文模型参数进行整合，得到整个流域的水文模型参数。优点是：将目标研究流域根据有实测资料的水文站点的分布情况划分为多个参数率定单元，并在多台计算机上对不同的参数率定单元根据其出口断面的水文站的实测流量过程进行校正率定，提高率定效率。

Description

一种基于多点并行校正的分布式水文模型参数率定方法 技术领域
本发明涉及水文预报领域，尤其涉及一种基于多点并行校正的分布式水文模型参数率定方法。
背景技术
当前为研究水库调度、水资源管理等问题，流域水文模型的构建以及水文模型参数率定是必不可少的步骤之一。
现在广泛使用的水文模型参数率定的方法，是以流域出口断面流量过程为变量建立参数率定的目标函数，采用各种优化算法或者并行算法来对水文模型的参数进行率定。直接采用优化算法进行以流域出口断面流量过程为变量建立目标函数的方法，对计算机内存的需求较高，且计算优化的时间较长，如果流域面积很大的话，一般的计算机通常会由于内存不足，导致优化程序中断，不能得到流域的最优参数，且得到的模型参数仅仅是按照整个流域出口断面单点的实测流量过程进行校正，所以得到的参数可能不能够反映流域中各个子流域的真实产汇流特性。
发明内容
本发明的目的在于提供一种基于多点并行校正的分布式水文模型参数率定方法，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：
一种基于多点并行校正的分布式水文模型参数率定方法，包括如下步骤，
S1、收集目标研究流域内的雨量站和水文站的位置及其对应的观测数据，获取目标研究流域的DEM图和土地利用图；对目标流域的DEM图进行分析，获取目标研究流域的流域面文件；将目标研究流域划分为多个子流域，并分别获取各个子流域内雨量站及各个雨量站的权重；对目标研究流域的土地利用图进行分析，获取目标研究流域内的各个子流域的不透水率；
S2、在所述流域面文件中添加水文单元以生成流域模型，并为各个水文单元选择相应的计算方法；
S3、确定洪水降雨径流过程中各子流域的降雨过程以及各子流域内各水文站断面的流量过程；
S4、将目标研究流域划分为多个参数率定单元，并为参数率定单元制定计算规则；
S5、为每个参数率定单元选择相应的目标函数，并利用优化算法并行求取各个参数率定单元的目标函数的最小值，将各个参数率定单元获取的目标函数最小值汇总整合，即可获取目标流域的水文模型的最优参数。
优选的，步骤S1具体包括如下内容，
S11、收集目标研究流域内有观测资料的雨量站和水文站的位置信息以及雨量站和水文站对应的观测数据，将不等时间间隔的观测数据，通过插值的方法转化为逐小时的 观测数据；并获取目标研究流域的DEM图和土地利用图；
S12、通过GIS软件对目标研究流域的DEM图进行水文分析，获取目标研究流域内的流域面文件；
S13、通过流域分割的方法将目标研究流域划分为若干个子流域；所述流域分割的方法保证目标研究流域的干流和大支流中有实测数据的水文站以及水库，都分布在各个子流域的出口位置；
S14、根据目标研究流域内的雨量站绘制泰森多边形，以获取目标研究流域内的各个子流域的雨量站及其各个雨量站的权重；
S15、通过GIS软件对目标研究流域的土地利用图进行分析，以获取目标研究流域内的各个子流域的不透水率。
优选的，在对目标研究流域进行子流域划分时，将水文站和/或水库作为子流域的出口断面。
优选的，步骤S2具体为，在所述流域面文件中添加水文单元生成流域模型，并为各个水文单元配置相应的计算方法，所述水文单元包括水库单元、河道单元、子流域单元和汇流点单元。
优选的，步骤S3具体包括如下内容，
S31、根据目标研究流域出口断面的径流过程以及各雨量站的降雨过程，确定降雨径流模拟的起止时间；
S32、各个子流域的径流过程以各个子流域的面雨量表示，所述子流域的面雨量根据该子流域中各雨量站的流量数据与各雨量站的泰森多边形权重之间的乘积确定；
S33、各水文站断面的流量过程采用逐小时的流量过程。
优选的，步骤S4具体包括如下内容，
S41、根据具有观测资料的水文站的位置，划分出若干个参数率定单元，各所述参数率定单元包括至少一个子流域；并确保各个参数率定单元的出口断面均为具有观测资料的水文站；
S42、对于出口断面为水库单元的子流域，其出流过程采用水库的实际出库流量代替；
S43、对于有其他参数率定单元流入的参数率定单元，则所述其他参数率定单元的出口断面流量采用观测的流量作为其对应的出流数据，也即为被流入的参数率定单元的入流数据。
优选的，步骤S5具体包括如下内容，
S51、各参数率定单元根据其各自的流域水文预报的需要，选择合适的水文模型参数率定目标函数，所述目标函数为峰值误差百分比函数或均值加权均方根误差函数，当需要对峰值流量进行限制规划和设计时，选择峰值误差百分比函数作为目标函数；当需要反映洪水过程整体情况并偏重于洪峰流量的模拟时，选择均值加权均方根误差函数作为目标函数；所述峰值误差百分比函数和均值加权均方根误差函数分别如下，
Figure PCTCN2021088985-appb-000001
Figure PCTCN2021088985-appb-000002
其中，f 1为峰值误差百分比函数；q s(peak)为计算的峰值；q o(peak)为实测的峰值；f 2为均值加权均方根误差函数；NQ为计算的过程线纵坐标数目；q o(i)为实测第i个时段末的流量；q s(i)为计算第i个时段末的流量；i为时序；
S52、各参数率定单元分别使用一台计算机使用优化算法并行求取各自所用目标函数的最小值，实现对目标研究流域的水文模型的参数率定；各所述目标函数的最小值即为各个参数率定单元率定的最优参数；将各个参数率定单元率定的最优参数汇总整合，即可获取目标研究流域的水文模型最优参数。
本发明的有益效果是：1、将目标研究流域根据有实测资料的水文站点的分布情况划分为多个参数率定单元，在多台计算机上对不同的参数率定单元根据其出口断面的水文站的实测流量过程进行校正率定，提高率定效率。2、在计算机性能不是很高的情况下，通过简单的操作仍然能够得到能够较为真实的反映流域产汇流特性的水文模型参数。3、并行计算基于并行语言MPI编写敏感性分析和多目标率定程序，耦合水文模型的开源程序，根据全局敏感性分析法得到的敏感参数，用于模型参数多目标率定，获得最优解，并行计算的运用很大地提高了参数率定效率，大量的节约了参数优化运行的时间。
附图说明
图1是本发明实施例中方法的流程示意图；
图2是本发明实施例中临沂以上流域雨量站分布图；
图3是本发明实施例中临沂以上流域水文站分布图；
图4是本发明实施例中临沂以上流域水库分布图；
图5是本发明实施例中临沂以上流域子流域划分图；
图6是本发明实施例中临沂以上流域泰森多边形示意图；
图7是本发明实施例中临沂以上流域水文模型示意图；
图8是本发明实施例中葛沟以上流域参数率定单元的实测流量与计算流量对比图；
图9是本发明实施例中高里以上流域参数率定单元的实测流量与计算流量对比图；
图10是本发明实施例中角沂以上流域参数率定单元的实测流量与计算流量对比图；
图11是本发明实施例中临沂以上流域中除去其他三个参数率定单元的实测流量与计算流量对比图；
图12是本发明实施例中临沂以上流域总体率定效果图。
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，提供了一种基于多点并行校正的分布式水文模型参数率定方法，包括如下步骤，
S1、收集目标研究流域内的雨量站和水文站的位置及其对应的观测数据，获取目标研究流域的DEM图和土地利用图；对目标流域的DEM图进行分析，获取目标研究流域的流域面文件；将目标研究流域划分为多个子流域，并分别获取各个子流域内雨量站及各个雨量站的权重；对目标研究流域的土地利用图进行分析，获取目标研究流域内的各个子流域的不透水率；
S2、在所述流域面文件中添加水文单元以生成流域模型，并为各个水文单元选择相应的计算方法；
S3、确定洪水降雨径流过程中各子流域的降雨过程以及各子流域内各水文站断面的流量过程；
S4、将目标研究流域划分为多个参数率定单元，并为参数率定单元制定计算规则；
S5、为每个参数率定单元选择相应的目标函数，并利用优化算法并行求取各个参数率定单元的目标函数的最小值，将各个参数率定单元获取的目标函数最小值汇总整合，即可获取目标流域的水文模型的最优参数。
本实施例中，所述方法具体包括五部分，分别为资料收集与处理、生成流域模型、确定场次洪水降雨径流过程中各子流域的降雨过程以及流域内各水文站断面的流量过程、为研究流域划分并行参数率定单元并制定计算规则、选择优化目标函数并求取目标函数的最小值。
一、资料收集与处理
本实施例中，步骤S1对应第一部，具体包括如下内容，
S11、收集目标研究流域内有观测资料的雨量站和水文站的位置信息以及雨量站和水文站对应的观测数据，将不等时间间隔的观测数据，通过插值的方法转化为逐小时的观测数据；并获取目标研究流域的DEM图和土地利用图；
S12、通过GIS软件对目标研究流域的DEM图进行水文分析，获取目标研究流域内的流域面文件；
S13、通过流域分割的方法将目标研究流域划分为若干个子流域；所述流域分割的方法保证目标研究流域的干流和大支流中有实测数据的水文站以及水库，都分布在各个子流域的出口位置；
S14、根据目标研究流域内的雨量站绘制泰森多边形，以获取目标研究流域内的各个子流域的雨量站及其各个雨量站的权重；
S15、通过GIS软件对目标研究流域的土地利用图进行分析，以获取目标研究流域内的各个子流域的不透水率。
本实施例中，在对目标研究流域进行子流域划分时，将水文站和/或水库作为子流域的出口断面。
本实施例中，由于不透水率是水文模型的一个有物理意义的固定的参数；因此，步骤S15中需要计算各个子流域的不透水率，以便于后面求取水文模型的最优参数。
二、生成流域模型
本实施例中，步骤S2对应第二部分，具体为，在所述流域面文件中添加水文单元生成流域模型，并为各个水文单元配置相应的计算方法，所述水文单元包括水库单元、河道单元、子流域单元和汇流点单元等。
本实施例中，各水文单元有不同的计算方法；比如子流域单元需要设置产流计算的方法、汇流计算的方法、基流计算的方法；河道单元需要设置河道洪水演算的方法；水库单元需要设置水库出流的计算方法。这样每个不同的水文单元采用不同的计算方法进行相应的计算，以为步骤S3做好计算准备。
三、确定场次洪水降雨径流过程中各子流域的降雨过程以及流域内各水文站断面的流量过程
本实施例中，步骤S3对应第三部分，具体为，
S31、根据目标研究流域出口断面的径流过程以及各雨量站的降雨过程，确定降雨径流模拟的起止时间；
S32、各个子流域的径流过程以各个子流域的面雨量表示，所述子流域的面雨量根据该子流域中各雨量站的流量数据与各雨量站的泰森多边形权重之间的乘积确定；
S33、各水文站断面的流量过程采用逐小时的流量过程。
也就是说，步骤S3具体包括三个步骤，首先根据目标研究流域出口断面的径流过程以及各雨量站的降雨过程确定降雨径流模拟的起止时间；其次，使用子流域的面雨量表示各子流域的降雨过程，面雨量是根据之前得到的各雨量站的逐小时的降水数据乘以各个雨量站的泰森多边形权重确定；最后，各水文站的流量过程采用逐小时的流量过程。
四、为研究流域划分并行参数率定单元并制定计算规则
本实施例中，步骤S4对应第四部分，具体包括如下内容，
S41、根据具有观测资料的水文站的位置，划分出若干个参数率定单元，各所述参数率定单元包括至少一个子流域；并确保各个参数率定单元的出口断面均为具有观测资料的水文站；
S42、对于出口断面为水库单元的子流域，其出流过程采用水库的实际出库流量代替；
S43、对于有其他参数率定单元流入的参数率定单元，则所述其他参数率定单元的出口断面流量采用观测的流量作为其对应的出流数据，也即为被流入的参数率定单元的入流数据。
本实施例中，参数率定单元划分具体为：根据水文站的位置，将目标研究流域划分为几个参数率定单元，这些参数率定单元分别包含一个或多个子流域，这些参数率定单元的出口位置一定是有观测数据的水文站。
本实施例中，参数率定单元的计算规则为：对于出口断为水库单元的子流域，其出流过程采用水库的实际出库流量进行处理。对于有其他参数率定单元流入的参数率定单元，则所述其他参数率定单元的出口断面流量采用观测的流量作为其对应的出流数据，也即为被流入的参数率定单元的入流数据。
五、选择优化目标函数并求取目标函数的最小值
本实施例中，步骤S5对应第五部分，具体包括如下内容，
S51、各参数率定单元根据其各自的流域水文预报的需要，选择合适的水文模型参数率定目标函数，所述目标函数为峰值误差百分比函数或均值加权均方根误差函数，当需要对峰值流量进行限制规划和设计时，选择峰值误差百分比函数作为目标函数；当需要反映洪水过程整体情况并偏重于洪峰流量的模拟时，选择均值加权均方根误差函数作为目标函数；所述峰值误差百分比函数和均值加权均方根误差函数分别如下，
Figure PCTCN2021088985-appb-000003
Figure PCTCN2021088985-appb-000004
其中，f 1为峰值误差百分比函数；q s(peak)为计算的峰值；q o(peak)为实测的峰值；f 2为均值加权均方根误差函数；NQ为计算的过程线纵坐标数目；q o(i)为实测第i个时段末的流量；q s(i)为计算第i个时段末的流量；i为时序；
S52、各参数率定单元分别使用一台计算机使用优化算法并行求取各自所用目标函数的最小值，实现对目标研究流域的水文模型的参数率定；各所述目标函数的最小值即为各个参数率定单元率定的最优参数；将各个参数率定单元率定的最优参数汇总整合，即可获取目标研究流域的水文模型最优参数。
本实施例中，对个参数率定单元，采用步骤S4中确定的计算规则，通过几台电脑应用优化算法并行求取各个目标函数的最小值，对水文模型进行并行参数率定；之后汇总各参数率定单元率定的最优参数，得到目标研究流域的水文模型最优参数集。
本实施例中，本方法基于并行语言MPI编写敏感性分析和多目标率定程序，耦合水文模型的开源程序，根据全局敏感性分析法得到的敏感参数，用于模型参数多目标率定，获得最优解；在多台计算机上并行计算，提高参数率定效率。
实施例二
本实施例中，以山东沂河临沂水文站以上流域的水文模型参数率定为例，具体说明本发明的参数率定方法的实施过程和达到的效果。
临沂站集水面积10315km 2，河道长227.8km。地势西北高，向东南平原倾斜。由于沂河上游地势复杂，形成了众多支流。临沂站以上集水面积大于200km 2的一级支流有东汶河、蒙河、祊河、涑河、柳青河。流域内山区面积约占68％，平原区约占32％。沂河流域属温带季风性大陆气候，流域多年平均年降水量813mm，汛期降雨量600mm，约占年降水量的73.9％。临沂以上流域有21个雨量站，位于干流和较大支流上的水文站6个，5座大型水库。临沂以上流域雨量站分布图如附图2所示，水文站分布图如图3所示，水库分布图如附图4所示。实施例以起止时间为2017年7月14日凌晨1点至2017年7月20日15点的临沂以上流域21个雨量站的降雨量资料以及临沂、葛沟、角沂、高里4个水文站的流量资料为基础，对临沂以上流域的水文模型进行参数率定。则基于多点并行校正的水文模型参数率定方法的步骤如下：
一、资料收集与处理
临沂以上流域作为目标研究流域，收集2017年7月14日凌晨1点至2017年7月20日15点临沂以上流域21个雨量站的降雨量资料以及临沂、葛沟、角沂、高里4个水文站的流量资料，并将这些数据由不等时间间隔数据插值成逐小时的数据；收集该目标研究流域的DEM 图以及土地利用图，通过GIS软件对DEM图进行水文分析，得到目标研究流域的流域面文件，通过流域分割的方法将目标研究流域划分为若干个子流域，确保目标研究流域中的干流和较大支流的有实测数据的水文站以及水库都分布在各个子流域的出口位置。子流域划分图见附图5；通过GIS软件对土里利用图进行分析，得到目标研究流域各个子流域的不透水率；基于目标研究流域内的雨量站绘制泰森多边形，获取目标研究流域各个子流域的影响雨量站以及各自的权重；目标研究流域的泰森多边形见附图6。
二、生成流域模型
在流域面文件中添加水文单元生成流域模型，并为各自的水文单元选择相应的计算方法，所述水文单元包括水库单元、河道单元、子流域单元、汇流点单元等等。构建的临沂以上流域模型见附图7。
三、确定场次洪水降雨径流过程中各子流域的降雨过程以及流域内各水文站断面的流量过程
1、根据目标研究流域出口断面的径流过程以及各雨量站的降雨过程确定降雨径流模拟的起止时间为2017年7月14日凌晨1点到2017年7月20日15点。
2、各子流域的降雨过程以子流域的面雨量表示，根据步骤一中得到的各雨量站的逐小时的降水数据乘以各个雨量站的泰森多边形权重确定。
3、各水文站的流量过程采用逐小时的流量过程。
四、为研究流域划分并行参数率定单元并制定计算规则
1、参数率定单元划分：
根据有观测数据的水文站点的位置，划分四个参数率定单元，分别为葛沟以上流域，高里以上流域，角沂以上流域，以及临沂以上流域中除去以上三个参数率定单元的流域部分，即W1710子流域。
2、参数率定单元计算规则：
A、对于出口断面为水库单元(分别为田庄水库、跋山水库、岸堤水库、唐村水库、许家崖水库)的五个子流域，其出流过程采用水库的实际出库流量进行处理。
B、率定W1710子流域的参数时，葛沟、高里、角沂的来水按照这三个水文站的观测出流进行处理。
五、选择优化目标函数并求取目标函数的最小值
本实施例中，选择均值加权均方根误差函数对四个参数率定单元进行率定；对四个参数率定单元，通过4台电脑应用优化算法分别并行求取目标函数的最小值，对水文模型进行并行率定。葛沟以上流域参数率定单元的实测流量与计算流量对比见图8；高里以上流域参数率定单元的实测流量与计算流量对比图见图9；角沂以上流域参数率定单元的实测流量与计算流量对比图见图10；临沂以上流域除去其他三个参数率定单元的实测流量与计算流量对比图见图11；目标研究流域总体率定效果见附图12；汇总整合各参数率定单元率定的最优参数，得到目标研究流域的水文模型最优参数。
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：
本发明提供了一种基于多点并行校正的分布式水文模型参数率定方法，本发明将目标研究流域根据有实测资料的水文站点的分布情况划分为多个参数率定单元，在多台计算机上对不同的参数率定单元根据其出口断面的水文站的实测流量过程进行校正率定，， 提高率定效率。在计算机性能不是很高的情况下，通过简单的操作仍然能够得到能够较为真实的反映流域产汇流特性的水文模型参数。并行计算基于并行语言MPI编写敏感性分析和多目标率定程序，耦合水文模型的开源程序，根据全局敏感性分析法得到的敏感参数，用于模型参数多目标率定，获得最优解，并行计算的运用很大地提高了参数率定效率，大量的节约了参数优化运行的时间。
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 (7)

  1. 一种基于多点并行校正的分布式水文模型参数率定方法，其特征在于：包括如下步骤，
    S1、收集目标研究流域内的雨量站和水文站的位置及其对应的观测数据，获取目标研究流域的DEM图和土地利用图；对目标流域的DEM图进行分析，获取目标研究流域的流域面文件；将目标研究流域划分为多个子流域，并分别获取各个子流域内雨量站及各个雨量站的权重；对目标研究流域的土地利用图进行分析，获取目标研究流域内的各个子流域的不透水率；
    S2、在所述流域面文件中添加水文单元以生成流域模型，并为各个水文单元选择相应的计算方法；
    S3、确定洪水降雨径流过程中各子流域的降雨过程以及各子流域内各水文站断面的流量过程；
    S4、将目标研究流域划分为多个参数率定单元，并为参数率定单元制定计算规则；
    S5、为每个参数率定单元选择相应的目标函数，并利用优化算法并行求取各个参数率定单元的目标函数的最小值，将各个参数率定单元获取的目标函数最小值汇总整合，即可获取目标流域的水文模型的最优参数。
  2. 根据权利要求1所述的基于多点并行校正的分布式水文模型参数率定方法，其特征在于：步骤S1具体包括如下内容，
    S11、收集目标研究流域内有观测资料的雨量站和水文站的位置信息以及雨量站和水文站对应的观测数据，将不等时间间隔的观测数据，通过插值的方法转化为逐小时的观测数据；并获取目标研究流域的DEM图和土地利用图；
    S12、通过GIS软件对目标研究流域的DEM图进行水文分析，获取目标研究流域内的流域面文件；
    S13、通过流域分割的方法将目标研究流域划分为若干个子流域；所述流域分割的方法保证目标研究流域的干流和大支流中有实测数据的水文站以及水库，都分布在各个子流域的出口位置；
    S14、根据目标研究流域内的雨量站绘制泰森多边形，以获取目标研究流域内的各个子流域的雨量站及其各个雨量站的权重；
    S15、通过GIS软件对目标研究流域的土地利用图进行分析，以获取目标研究流域内的各个子流域的不透水率。
  3. 根据权利要求2所述的基于多点并行校正的分布式水文模型参数率定方法，其特征在于：在对目标研究流域进行子流域划分时，将水文站和/或水库作为子流域的出口断面。
  4. 根据权利要求3所述的基于多点并行校正的分布式水文模型参数率定方法，其特征在于：步骤S2具体为，在所述流域面文件中添加水文单元生成流域模型，并为各个水文单元配置相应的计算方法，所述水文单元包括水库单元、河道单元、子流域单元和汇流点单元。
  5. 根据权利要求4所述的基于多点并行校正的分布式水文模型参数率定方法，其特征在于：步骤S3具体包括如下内容，
    S31、根据目标研究流域出口断面的径流过程以及各雨量站的降雨过程，确定降雨径流模拟的起止时间；
    S32、各个子流域的径流过程以各个子流域的面雨量表示，所述子流域的面雨量根据该 子流域中各雨量站的流量数据与各雨量站的泰森多边形权重之间的乘积确定；
    S33、各水文站断面的流量过程采用逐小时的流量过程。
  6. 根据权利要求5所述的基于多点并行校正的分布式水文模型参数率定方法，其特征在于：步骤S4具体包括如下内容，
    S41、根据具有观测资料的水文站的位置，划分出若干个参数率定单元，各所述参数率定单元包括至少一个子流域；并确保各个参数率定单元的出口断面均为具有观测资料的水文站；
    S42、对于出口断面为水库单元的子流域，其出流过程采用水库的实际出库流量代替；
    S43、对于有其他参数率定单元流入的参数率定单元，则所述其他参数率定单元的出口断面流量采用观测的流量作为其对应的出流数据，也即为被流入的参数率定单元的入流数据。
  7. 根据权利要求6所述的基于多点并行校正的分布式水文模型参数率定方法，其特征在于：步骤S5具体包括如下内容，
    S51、各参数率定单元根据其各自的流域水文预报的需要，选择合适的水文模型参数率定目标函数，所述目标函数为峰值误差百分比函数或均值加权均方根误差函数，当需要对峰值流量进行限制规划和设计时，选择峰值误差百分比函数作为目标函数；当需要反映洪水过程整体情况并偏重于洪峰流量的模拟时，选择均值加权均方根误差函数作为目标函数；所述峰值误差百分比函数和均值加权均方根误差函数分别如下，
    Figure PCTCN2021088985-appb-100001
    Figure PCTCN2021088985-appb-100002
    其中，f 1为峰值误差百分比函数；q s(peak)为计算的峰值；q o(peak)为实测的峰值；f 2为均值加权均方根误差函数；NQ为计算的过程线纵坐标数目；q o(i)为实测第i个时段末的流量；q s(i)为计算第i个时段末的流量；i为时序；
    S52、各参数率定单元分别使用一台计算机使用优化算法并行求取各自所用目标函数的最小值，实现对目标研究流域的水文模型的参数率定；各所述目标函数的最小值即为各个参数率定单元率定的最优参数；将各个参数率定单元率定的最优参数汇总整合，即可获取目标研究流域的水文模型最优参数。
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