CN114036127A - Method for improving hydrological model runoff simulation - Google Patents

Method for improving hydrological model runoff simulation Download PDF

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CN114036127A
CN114036127A CN202111270007.1A CN202111270007A CN114036127A CN 114036127 A CN114036127 A CN 114036127A CN 202111270007 A CN202111270007 A CN 202111270007A CN 114036127 A CN114036127 A CN 114036127A
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丁振宙
吕海深
朱永华
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Hohai University HHU
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Abstract

The invention discloses a method for improving hydrological model runoff simulation, which comprises the following steps: adopting an MISDc model as a hydrological model to carry out parameter calibration; means 1, replacing soil moisture data of a hydrological model by a direct insertion method, and using actual soil moisture data for simulating runoff of the hydrological model; means 2, combining actual soil moisture data with soil moisture data simulated by a hydrological model by adopting an ensemble Kalman filter method, and using the obtained state variable value for simulating runoff by the model; and respectively comparing NS coefficients and NRMSE values of the two optimization results, and obtaining the runoff simulation optimization result by adopting a means corresponding to the larger NS coefficient and the smaller NRMSE value. The invention utilizes a data assimilation method to assimilate more accurate actual soil moisture data into the hydrological model, and improves the runoff simulation result of the hydrological model, thereby providing help for hydrological flood forecasting.

Description

Method for improving hydrological model runoff simulation
Technical Field
The invention belongs to the technical field of hydrological model parameter optimization, and particularly relates to a method for improving hydrological model runoff simulation.
Background
In recent years, the number and intensity of flood and mountain torrent events has increased, not only creating environmental problems, but also causing significant personnel and economic losses. Among them, soil moisture is a key state variable in hydrology, because it controls the proportion of rainfall that permeates, runs off or evaporates from the soil, playing an important role in hydrological forecasting. However, due to the spatial heterogeneity of soil texture, soil moisture varies significantly in spatial distribution, and it is far from possible to achieve soil moisture in a precise area or even globally by observation only with ground sites. Therefore, remote sensing observation of soil moisture becomes a necessary means, and soil moisture observation data on a large scale can be obtained more conveniently through remote sensing monitoring. However, the remote sensing monitoring has defects in time and space resolution, and the remote sensing monitoring data has larger error compared with the ground measurement data. Therefore, scientists introduce data assimilation methods into hydrological and terrestrial process research in order to solve the problem of estimation accuracy of key variables such as soil moisture and soil temperature.
Data assimilation is taken as a key technology of data processing and is gradually the leading edge and hot spot problem of ecological hydrology process research and remote sensing inversion research, uncertainty of model structure and model input data can be considered, the uncertainty is combined with remote sensing information, remote sensing observation data are introduced, model state variables are corrected in real time at the satellite transit time, advantages of a land surface process model and the remote sensing data can be fully exerted, and a new way is provided for continuous simulation of soil water content. And the method plays an important role in coordinating and fusing remote sensing technology and ecological modeling, and can effectively fuse multi-source remote sensing data and simulation results of models, thereby improving the prediction precision of soil moisture.
Hydrological models are also an effective method for obtaining accurate estimates of soil moisture conditions and have been used worldwide. However, hydrologic simulations are "imperfect" because they contain uncertainties mainly related to the quality and quantity of hydrologic data used to drive the model and representative errors due to scale incompatibility; simple representation of real physical processes leads to inevitable assumptions and simplifications, inevitably approximating imperfect reality and errors in parameter estimation, which may lead to large errors in model output. Therefore, in recent years, some scientists propose that data assimilation technology can be applied to the hydrological model, and the state variable of soil moisture in the hydrological model is updated in real time, so that the forecasting precision of the hydrological model is improved, and the application of the hydrological model in a research area is improved.
Disclosure of Invention
The technical problem to be solved is as follows: in view of the above technical problems, the present invention provides a method for improving the runoff simulation of a hydrological model, which can improve the accuracy of the runoff simulation result, thereby providing help for the hydrological flood forecasting.
The technical scheme is as follows: a method for improving hydrological model runoff simulation comprises the following steps:
s1, adopting an MISDc model as a hydrological model to carry out parameter calibration;
s2, means 1, replacing soil moisture data of the hydrological model by a direct insertion method, and using the actual soil moisture data for simulating runoff of the hydrological model; means 2, combining actual soil moisture data with soil moisture data simulated by a hydrological model by adopting an ensemble Kalman filter method, and using the obtained state variable value for simulating runoff by the model;
and S3, obtaining the optimized result of the hydrological model runoff simulation by two means in S2, respectively comparing NS coefficients of the two optimized results with the normalized root mean square error NRMSE before and after optimization, and obtaining the optimized result of the runoff parameter simulation by adopting the means corresponding to the larger NS coefficient and the smaller NRMSE.
Preferably, the parameter calibration criterion in step S1 is determined by the following calculation:
Figure BDA0003328401740000021
in the formula, QobsFor measuring the flow rate, QsimFor the flow obtained by model simulation, the superscript "-" represents the average value, and the parameter when the calculated err value reaches the minimum value is the optimal parameter.
Preferably, the step S1 includes:
s1.1, obtaining the water storage capacity of the soil by linking the processes of infiltration, evaporation and drainage through a water quantity balance equation, wherein the process is the SWB model part in the MISDc model;
s1.2, inputting soil water storage capacity data into an MISDc model, and outputting a runoff simulation result;
wherein the water balance equation is as follows:
Figure BDA0003328401740000022
wherein t is time; w (t) is the soil water storage capacity; wmaxThe maximum water storage capacity of the soil; f (t) is the process of infiltration of rainfall into the soil; e (t) is the evapotranspiration rate; g (t) is the process of soil drainage due to gravity.
Preferably, the infiltration process uses the Green-Ampt equation, which is expressed as follows:
Figure BDA0003328401740000023
wherein r (t) represents a rainfall process; ksRepresenting the saturated hydraulic conductivity of the soil; psi denotes the suction at the wetting front; f represents the cumulative infiltration depth at the beginning of the rainfall event; l represents the thickness of the soil layer.
Preferably, the evaporation process is represented as follows:
Figure BDA0003328401740000031
in the formula, ETp represents potential evapotranspiration.
Preferably, the calculation method of the potential evapotranspiration is as follows:
ETp＝-2+b[ξ(0.46Ta(t)+8.13)]
in the formula, Ta(t) represents air temperature in units of; ξ represents the ratio of the day's hours of sunshine to the whole year's hours of sunshine (365 × 12 h); b is a parameter to be calibrated.
Preferably, the calculation method of the drainage process is as follows:
g(t)＝Ks[W(t)/Wmax]3+2/m
in the formula, KsRepresenting the saturated hydraulic conductivity of the soil; and m represents the pore size distribution index of the soil layer.
Preferably, step S1.2 includes:
s1.2.1, calculating the excess of rainfall epsilon according to the following calculation formula:
Figure BDA0003328401740000032
wherein r is rainfall intensity; r is the rainfall depth at the beginning of the rainfall event; lambda is a parameter to be calibrated; s is a parameter for connecting the soil water storage capacity W and the excess rainfall, and the calculation formula is as follows:
S＝a[Wmax-W(t)]
in the formula, a is a parameter to be calibrated;
s1.2.2 runoff calculation, namely calculating the runoff of a watershed by using a direct runoff hydrological process line method, wherein the process is a rainfall-runoff model part in the MISDc model; the calculation formula of the average lag time f of the watershed is as follows:
Γ＝η1.19A0.33
in the formula, eta is a parameter to be calibrated; a is the area of the basin, in km2。
Preferably, the alternative method of the means 1 is: converting the actual soil water content into the actual soil water storage capacity, and replacing the soil water storage capacity obtained by calculation in the model; wherein, the conversion relation of soil water content and soil water storage is as follows:
Figure BDA0003328401740000033
wherein W (t) is the soil water storage capacity; theta (t) is the soil moisture content; thetarThe residual water content of the soil.
Preferably, the specific objective function realized by the means 2 is as follows:
Figure BDA0003328401740000041
in the formula, superscripts a and f represent the analysis and prediction processes, respectively; xi,k+1Represents the value of the ith member at time k + 1; z represents an observation vector; h represents an observation operator; v. ofi,kIs a mean of 0 and a variance of RkWhite gaussian noise of (1); kkAnd expressing a Kalman gain matrix, wherein the calculation formula is as follows:
Figure BDA0003328401740000042
in the formula, the superscript T represents the transpose of the matrix,
Figure BDA0003328401740000043
the prior information error variance prediction matrix at the moment of k +1 is expressed by the following calculation formula,
Figure BDA0003328401740000044
in the formula, N represents the number of set members.
Has the advantages that: the invention assimilates the soil water content into the hydrological model by two means, improves the forecasting precision of the hydrological model and can provide help for flood forecasting and alarming.
The method 1 is a direct insertion method (DI), and the main idea is that actually measured soil moisture data is directly inserted into soil moisture data obtained by replacing a model for simulation, and the soil moisture data is an intermediate state variable of model simulation runoff and determines the final runoff simulation of the model, so that the method combines actual soil moisture data with the model, and the prediction precision of the model is improved.
The means 2 is an ensemble Kalman filter method (EnKF), and the important idea is that the actually measured soil moisture data and the soil moisture data obtained by model simulation are combined to calculate and obtain an optimal state estimation value between the actually measured soil moisture data and the soil moisture data.
Drawings
FIG. 1 shows the results of model simulation of the MISDc model in the Wanwuqiao basin;
FIG. 2 is a flow chart of the process of implementing an ensemble Kalman filter among MISDcs;
FIG. 3 is the result of assimilation of soil moisture data at 0-5cm using the CLDAS using the DI method into a model;
FIG. 4 is the result of assimilation of soil moisture data at 0-5cm using CLDAS into a model using the EnKF method.
Detailed Description
In order to make the objects, technical solutions and advantages of the present invention more apparent, the present invention is further described in detail below with reference to the accompanying drawings. It should be understood that the detailed description and specific examples, while indicating the invention, are intended for purposes of illustration only and are not intended to limit the scope of the invention.
Example 1
A method for improving hydrological model runoff simulation comprises the following steps:
s1, adopting an MISDc model as a hydrological model to carry out parameter calibration; the criteria for parameter calibration were confirmed by the following calculation:
Figure BDA0003328401740000051
in the formula, QobsFor measuring the flow rate, QsimFor the flow obtained from the model simulation, the superscript "-" represents the mean. When the calculated err value reaches the minimum value, the parameter is the optimal parameter;
s2, means 1, replacing soil moisture data of the hydrological model by a direct insertion method, and using the actual soil moisture data for simulating runoff of the hydrological model; means 2, combining actual soil moisture data with soil moisture data simulated by a hydrological model by adopting an ensemble Kalman filter method, and using the obtained state variable value for simulating runoff by the model;
and S3, obtaining the optimized result of the hydrological model runoff simulation by two means in S2, respectively comparing NS coefficients of the two optimized results with the normalized root mean square error NRMSE before and after optimization, and obtaining the optimized result of the runoff simulation by adopting the means corresponding to the larger NS coefficient and the smaller NRMSE.
Wherein the step S1 includes:
s1.1, obtaining the water storage capacity of the soil by linking the processes of infiltration, evaporation and drainage through a water quantity balance equation, wherein the process is the SWB model part in the MISDc model;
the water balance equation is as follows:
Figure BDA0003328401740000052
wherein t is time; w (t) is the soil water storage capacity; wmaxThe maximum water storage capacity of the soil; f (t) is the process of infiltration of rainfall into the soil; e (t) is the evapotranspiration rate; g (t) is the process of soil drainage due to gravity.
The infiltration process employs the Green-Ampt equation, which is expressed as follows:
Figure BDA0003328401740000053
wherein r (t) represents a rainfall process; ksRepresenting the saturated hydraulic conductivity of the soil; psi denotes the suction at the wetting front; f represents the cumulative infiltration depth at the beginning of the rainfall event; l represents the thickness of the soil layer.
The evaporation process is represented as follows:
Figure BDA0003328401740000061
in the formula, ETpIndicating potential evapotranspiration.
The potential evapotranspiration is calculated as follows:
ETp＝-2+b[ξ(0.46Ta(t)+8.13)]
in the formula, Ta(t) represents air temperature in units of; ξ represents the ratio of the day's hours of sunshine to the whole year's hours of sunshine (365 × 12 h); b is a parameter to be calibrated.
The calculation method of the drainage process is as follows:
g(t)＝Ks[W(t)/Wmax]3+2/m
in the formula, KsRepresenting the saturated hydraulic conductivity of the soil; and m represents the pore size distribution index of the soil layer.
S1.2, inputting soil water storage capacity data into an MISDc model, and outputting a runoff simulation result;
wherein step S1.2 comprises:
s1.2.1, calculating the excess of rainfall epsilon according to the following calculation formula:
Figure BDA0003328401740000062
wherein r is rainfall intensity; r is the rainfall depth at the beginning of the rainfall event; lambda is a parameter to be calibrated; s is a parameter for connecting the soil water storage capacity W and the excess rainfall, and the calculation formula is as follows:
S＝a[Wmax-W(t)]
in the formula, a is a parameter to be calibrated;
s1.2.2 runoff calculation, namely calculating the runoff of a watershed by using a direct runoff hydrological process line method, wherein the process is a rainfall-runoff model part in the MISDc model; the rainfall on the drainage basin needs a certain time from the landing point to the drainage basin outlet section, the time for the rainfall at each position on the drainage basin to converge on the outlet section is different, but hydrologists prove that the drainage basin time lag is equivalent to the average drainage basin convergence time, so the calculation formula of the average delay time f of the drainage basin is as follows:
Γ＝η1.19A0.33
in the formula, eta is a parameter to be calibrated; a is a streamArea of the domain in km2。
The two measures involved in step S2 are as follows:
an alternative to the means 1 is: converting the actual soil water content into the actual soil water storage capacity, and replacing the soil water storage capacity obtained by calculation in the model; wherein, the conversion relation of soil water content and soil water storage is as follows:
Figure BDA0003328401740000071
wherein W (t) is the soil water storage capacity; theta (t) is the soil moisture content; thetarThe residual water content of the soil.
The specific objective function realized by the means 2 is as follows:
Figure BDA0003328401740000072
in the formula, superscripts a and f represent the analysis and prediction processes, respectively; xi,k+1Represents the value of the ith member at time k + 1; z represents an observation vector; h represents an observation operator; v. ofi,kIs a mean of 0 and a variance of RkWhite gaussian noise of (1); kkAnd expressing a Kalman gain matrix, wherein the calculation formula is as follows:
Figure BDA0003328401740000073
in the formula, the superscript T represents the transpose of the matrix,
Figure BDA0003328401740000074
the prior information error variance prediction matrix at the moment of k +1 is expressed by the following calculation formula,
Figure BDA0003328401740000075
in the formula, N represents the number of set members.
Specifically, in the present embodiment:
(1) the research area is selected from the Queen bridge drainage basin on the Huaihe river drainage basin, and the area of the drainage basin is about 200km2。
(2) In step S1, the hydrological model is a MISDc model, parameters are calibrated using 2015-2016 rainfall-air temperature data of CLDAS products as input data of the model, and the result is shown in fig. 1, where Q is the graphobsIndicating the measured flow rate, QsimRepresenting the flow of the model simulation.
(3) In step S2, the DI method was used to assimilate the soil moisture data at 0-5cm of CLDAS into the hydrological model, and the results are shown in FIG. 3, in which Q is shownaThe runoff volume obtained by the data assimilation method is shown.
(4) In step S2, soil moisture data at the position of CLDAS0-5cm was also assimilated into the hydrological model by the EnKF method, the process is shown in FIG. 2, and the results are shown in FIG. 4.
Through the above process, two measures of results were obtained (fig. 3 and 4), and it can be seen that the NS coefficient of the model simulation results increased from 0.326 to 0.374 by about 14.72% using the DI method, and that the NRMSE value was 0.964 and less than 1, indicating that the results of assimilation are improved. Using the EnKF method, the NS coefficient of the model simulation results increased from 0.326 to 0.385, which is about 18.10%, and the NRMSE value was 0.962, which is less than 1, indicating that the results of assimilation are improved. And comparing the two results, the result improvement using EnKF method is higher.
The present example uses soil moisture data at 0-5cm for assimilation studies with only one watershed, but the method is not limited thereto. The influence of using the soil moisture data of different layers on the model runoff simulation result can be compared, and an optimal means and an optimal depth using the soil moisture data can be found.
By adopting the technical scheme disclosed by the invention, the following beneficial effects are obtained:
the invention assimilates the measured soil moisture data into the hydrological model by two means respectively, thereby improving the runoff simulation precision of the hydrological model. The method has the advantages that only rainfall and air temperature data are needed as input data of the model, and the assimilated data is soil moisture data. The three data acquisition ways are many, can be conveniently applied to various drainage basin regions, and avoid a large amount of data processing work. Moreover, for the present invention, the two approaches taken to assimilate soil moisture data are improved with respect to the accuracy of flood forecasting.
The foregoing is only a preferred embodiment of the present invention, and it should be noted that, for those skilled in the art, various modifications and improvements can be made without departing from the principle of the present invention, and such modifications and improvements should also be considered within the scope of the present invention.

Claims (10)

1. A method for improving hydrological model runoff simulation is characterized by comprising the following steps:
s1, adopting an MISDc model as a hydrological model to carry out parameter calibration;
s2, means 1, replacing soil moisture data of the hydrological model by a direct insertion method, and using the actual soil moisture data for simulating runoff of the hydrological model; means 2, combining actual soil moisture data with soil moisture data simulated by a hydrological model by adopting an ensemble Kalman filter method, and using the obtained state variable value for simulating runoff by the model;
and S3, obtaining the optimized result of the hydrological model runoff simulation by two means in S2, respectively comparing NS coefficients of the two optimized results with the normalized root mean square error NRMSE before and after optimization, and obtaining the optimized result of the runoff parameter simulation by adopting the means corresponding to the larger NS coefficient and the smaller NRMSE.
2. A method for improving hydrological model runoff simulation according to claim 1, wherein the criterion for parameter calibration in step S1 is determined by calculating:
Figure FDA0003328401730000011
in the formula, QobsFor measuring the flow rate, QsimFor the flow obtained by model simulation, the superscript "-" represents the average value, and the parameter when the calculated err value reaches the minimum value is the optimal parameter.
3. The method for improving hydrological model runoff simulation of claim 2, wherein said step S1 comprises:
s1.1, obtaining the water storage capacity of the soil by linking the processes of infiltration, evaporation and drainage through a water quantity balance equation, wherein the process is the SWB model part in the MISDc model;
s1.2, inputting soil water storage capacity data into an MISDc model, and outputting a runoff simulation result;
wherein the water balance equation is as follows:
Figure FDA0003328401730000012
wherein t is time; w (t) is the soil water storage capacity; wmaxThe maximum water storage capacity of the soil; f (t) is the process of infiltration of rainfall into the soil; e (t) is the evapotranspiration rate; g (t) is the process of soil drainage due to gravity.
4. The method for improving hydrological model runoff simulation of claim 3, wherein the infiltration process uses the Green-Ampt equation expressed as follows:
Figure FDA0003328401730000021
wherein r (t) represents a rainfall process; ksRepresenting the saturated hydraulic conductivity of the soil; psi denotes the suction at the wetting front; f represents the cumulative infiltration depth at the beginning of the rainfall event; l represents the thickness of the soil layer.
5. A method of improving hydrological model runoff simulation according to claim 3 wherein the evaporation process is represented as follows:
Figure FDA0003328401730000022
in the formula, ETpIndicating potential evapotranspiration.
6. The method for improving hydrological model runoff simulation of claim 5, wherein the potential evapotranspiration is calculated as follows:
ETp＝-2+b[ξ(0.46Ta(t)+8.13)]
in the formula, Ta(t) represents air temperature in units of; ξ represents the ratio of the day's hours of sunshine to the whole year's hours of sunshine (365 × 12 h); b is a parameter to be calibrated.
7. A method for improving hydrological model runoff simulation according to claim 3, wherein the drainage process is calculated as follows:
g(t)＝Ks[W(t)/Wmax]3+2/m
in the formula, KsRepresenting the saturated hydraulic conductivity of the soil; and m represents the pore size distribution index of the soil layer.
8. A method of improving hydrological model runoff simulation according to claim 3, wherein said step S1.2 comprises:
s1.2.1, calculating the excess of rainfall epsilon according to the following calculation formula:
Figure FDA0003328401730000023
wherein r is rainfall intensity; r is the rainfall depth at the beginning of the rainfall event; lambda is a parameter to be calibrated; s is a parameter for connecting the soil water storage capacity W and the excess rainfall, and the calculation formula is as follows:
S＝a[Wmax-W(t)]
in the formula, a is a parameter to be calibrated;
s1.2.2 runoff calculation, namely calculating the runoff of a watershed by using a direct runoff hydrological process line method, wherein the process is a rainfall-runoff model part in the MISDc model; the calculation formula of the average lag time f of the watershed is as follows:
Γ＝η1.19A0.33
in the formula, eta is a parameter to be calibrated; a is the area of the basin, in km2。
9. The method for improving hydrological model runoff simulation of claim 1, wherein the means 1 is replaced by: converting the actual soil water content into the actual soil water storage capacity, and replacing the soil water storage capacity obtained by calculation in the model; wherein, the conversion relation of soil water content and soil water storage is as follows:
Figure FDA0003328401730000031
wherein W (t) is the soil water storage capacity; theta (t) is the soil moisture content; thetarThe residual water content of the soil.
10. The method for improving hydrological model runoff simulation of claim 1, wherein the specific objective function realized by the means 2 is as follows:
Figure FDA0003328401730000032
in the formula, superscripts a and f represent the analysis and prediction processes, respectively; xi,k+1Represents the value of the ith member at time k + 1; z represents an observation vector; h represents an observation operator; v. ofi,kIs a mean of 0 and a variance of RkWhite gaussian noise of (1); kkAnd expressing a Kalman gain matrix, wherein the calculation formula is as follows:
Figure FDA0003328401730000033
in the formula, the superscript T represents the transpose of the matrix,
Figure FDA0003328401730000034
the prior information error variance prediction matrix at the moment of k +1 is expressed by the following calculation formula,
Figure FDA0003328401730000035
in the formula, N represents the number of set members.
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Cited By (2)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN117057174A (en) * 2023-10-13 2023-11-14 长江三峡集团实业发展(北京)有限公司 Runoff prediction method for data-missing area
CN117473791A (en) * 2023-12-22 2024-01-30 水发科技信息(山东)有限公司 Public data storage management system based on artificial intelligence

Cited By (4)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN117057174A (en) * 2023-10-13 2023-11-14 长江三峡集团实业发展(北京)有限公司 Runoff prediction method for data-missing area
CN117057174B (en) * 2023-10-13 2024-01-26 长江三峡集团实业发展(北京)有限公司 Runoff prediction method for data-missing area
CN117473791A (en) * 2023-12-22 2024-01-30 水发科技信息(山东)有限公司 Public data storage management system based on artificial intelligence
CN117473791B (en) * 2023-12-22 2024-03-29 水发科技信息(山东)有限公司 Public data storage management system based on artificial intelligence

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