CN112785035A - Medium-short term hydrological forecasting method and system integrating multivariate information - Google Patents
Medium-short term hydrological forecasting method and system integrating multivariate information Download PDFInfo
- Publication number
- CN112785035A CN112785035A CN202011596740.8A CN202011596740A CN112785035A CN 112785035 A CN112785035 A CN 112785035A CN 202011596740 A CN202011596740 A CN 202011596740A CN 112785035 A CN112785035 A CN 112785035A
- Authority
- CN
- China
- Prior art keywords
- model
- hydrological
- atmospheric
- numerical
- forecast
- Prior art date
- Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
- Pending
Links
- 238000013277 forecasting method Methods 0.000 title claims abstract description 10
- 238000000034 method Methods 0.000 claims abstract description 62
- 238000010168 coupling process Methods 0.000 claims abstract description 31
- 230000008878 coupling Effects 0.000 claims abstract description 30
- 238000005859 coupling reaction Methods 0.000 claims abstract description 30
- 230000008569 process Effects 0.000 claims abstract description 30
- 238000001556 precipitation Methods 0.000 claims abstract description 15
- 230000002457 bidirectional effect Effects 0.000 claims abstract description 13
- 230000007246 mechanism Effects 0.000 claims abstract description 9
- 238000004422 calculation algorithm Methods 0.000 claims description 9
- 238000005516 engineering process Methods 0.000 claims description 9
- 238000004088 simulation Methods 0.000 claims description 8
- 239000002689 soil Substances 0.000 claims description 7
- 230000008859 change Effects 0.000 claims description 6
- 230000004907 flux Effects 0.000 claims description 3
- 230000015572 biosynthetic process Effects 0.000 claims description 2
- 238000005293 physical law Methods 0.000 claims description 2
- 230000004927 fusion Effects 0.000 claims 3
- 238000012937 correction Methods 0.000 description 9
- 238000004364 calculation method Methods 0.000 description 7
- 238000004800 variational method Methods 0.000 description 4
- 238000004458 analytical method Methods 0.000 description 3
- 230000007547 defect Effects 0.000 description 3
- 238000001704 evaporation Methods 0.000 description 3
- 230000008020 evaporation Effects 0.000 description 3
- 238000011160 research Methods 0.000 description 3
- 238000012360 testing method Methods 0.000 description 3
- 230000008901 benefit Effects 0.000 description 2
- 230000000694 effects Effects 0.000 description 2
- 239000012530 fluid Substances 0.000 description 2
- 239000011159 matrix material Substances 0.000 description 2
- 238000012545 processing Methods 0.000 description 2
- 230000009467 reduction Effects 0.000 description 2
- 230000003068 static effect Effects 0.000 description 2
- WKBPZYKAUNRMKP-UHFFFAOYSA-N 1-[2-(2,4-dichlorophenyl)pentyl]1,2,4-triazole Chemical compound C=1C=C(Cl)C=C(Cl)C=1C(CCC)CN1C=NC=N1 WKBPZYKAUNRMKP-UHFFFAOYSA-N 0.000 description 1
- 238000009825 accumulation Methods 0.000 description 1
- 238000007405 data analysis Methods 0.000 description 1
- 238000013461 design Methods 0.000 description 1
- 238000011161 development Methods 0.000 description 1
- 238000010586 diagram Methods 0.000 description 1
- 238000001914 filtration Methods 0.000 description 1
- 230000006872 improvement Effects 0.000 description 1
- 230000010354 integration Effects 0.000 description 1
- 230000003993 interaction Effects 0.000 description 1
- 230000007774 longterm Effects 0.000 description 1
- 238000007726 management method Methods 0.000 description 1
- 238000005259 measurement Methods 0.000 description 1
- 230000031864 metaphase Effects 0.000 description 1
- 238000012986 modification Methods 0.000 description 1
- 230000004048 modification Effects 0.000 description 1
- 230000002035 prolonged effect Effects 0.000 description 1
- 230000005855 radiation Effects 0.000 description 1
- 238000012216 screening Methods 0.000 description 1
- 238000000926 separation method Methods 0.000 description 1
- 238000007619 statistical method Methods 0.000 description 1
- 238000006467 substitution reaction Methods 0.000 description 1
- 230000009897 systematic effect Effects 0.000 description 1
- 230000002123 temporal effect Effects 0.000 description 1
- 239000011031 topaz Substances 0.000 description 1
- 229910052853 topaz Inorganic materials 0.000 description 1
- 238000012549 training Methods 0.000 description 1
- 238000012795 verification Methods 0.000 description 1
- XLYOFNOQVPJJNP-UHFFFAOYSA-N water Substances O XLYOFNOQVPJJNP-UHFFFAOYSA-N 0.000 description 1
Images
Classifications
-
- 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
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F30/00—Computer-aided design [CAD]
- G06F30/20—Design optimisation, verification or simulation
-
- 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
- G06F30/28—Design optimisation, verification or simulation using fluid dynamics, e.g. using Navier-Stokes equations or computational fluid dynamics [CFD]
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F2111/00—Details relating to CAD techniques
- G06F2111/10—Numerical modelling
Abstract
The invention discloses a medium-short term hydrological forecasting method and system integrating multivariate information, which are used for collecting relevant information of a full drainage basin and extracting characteristic information of the full drainage basin, selecting a distributed hydrological model according to the characteristic information of the full drainage basin, selecting an ARW model as an atmospheric numerical model, and predicting atmospheric motion state and weather phenomenon in a certain period of time according to the characteristics of a dynamic framework of the model; and embedding the established distributed hydrological model into a land model of the atmospheric numerical model, so that the atmospheric numerical model and the distributed hydrological model share the same land process mechanism, and constructing the bidirectional coupling of the numerical weather forecast and the distributed hydrological model. By the method for eliminating the initial error of the meteorological forecast numerical model, the meteorological forecast precision is improved, the watershed small-scale matching application of the gridding quantitative precipitation estimation model is realized, the seamless connection between the meteorological forecast and the hydrological forecast model is ensured, and the hydrological forecast precision is improved.
Description
Technical Field
The invention relates to the technical field of medium and short term hydrological forecasting, in particular to a medium and short term hydrological forecasting method and system integrating multivariate information.
Background
At present, quantitative precipitation forecast is mainly based on a numerical weather forecast technology, and a GRAPES (Global/Global analysis and Prediction System) model developed by China gradually forms a medium-scale forecast model (GRAPES _ Meso), a Global forecast model (GRAPES _ Global), and the like, after about 10 years of development.
In recent ten years, the land-air coupling model is developed into an advanced hydrologic forecasting tool, a mesoscale numerical weather forecasting model MM5 is used for forecasting weather elements, the hydrologic model is driven by the mesoscale numerical weather forecasting model MM5, and a bidirectional coupling forecasting system of the numerical weather forecasting model and the hydrologic model is constructed, so that the forecasting period is prolonged.
The coupling mode of the multivariate information and the hydrological forecast comprises a one-way coupling mode and a two-way coupling mode. The one-way coupling method is simple, the atmosphere model and the hydrological model respectively and independently operate, the hydrological model cannot forcibly improve the calculation of evaporation by utilizing the atmosphere in real time due to different methods for taking land parameters, and the atmosphere model cannot share the real-time verification of runoff, soil humidity and other results simulated by using the hydrological model and modify the accuracy of land process simulation, so that the boundary layer structure and the precipitation prediction accuracy of the atmosphere model are influenced.
The atmospheric model and the hydrological model jointly utilize one land model, so that the description of the land hydrological process by the atmospheric model can be enhanced, the simulation and prediction capability of the hydrological model can be improved, and higher-quality flux input can be fed back to the atmospheric model; by using different levels of multilevel nesting, the bidirectional coupling of the two models can be realized, and then the two models are respectively subjected to parameter calibration.
At present, most of the land-air coupling researches belong to one-way coupling or partial coupling, namely, output data (mainly rainfall, temperature, evaporation and wind speed) of an atmosphere model is used as input data of a hydrological model, the output data of the hydrological model such as runoff, soil water content, radiation and the like are not fed back to the atmosphere model for calculation, and due to the limited rainfall forecasting capacity and low reliability of the numerical weather forecasting model, a land-air coupling system applied to a flood forecasting system is not common, and the technical field lacks a systematic medium-short term hydrological weather coupling forecasting system which realizes land-air feedback and can be popularized and applied to drainage basin mechanisms.
Disclosure of Invention
The invention aims to overcome the technical defects, provides a medium-short term hydrological forecasting method and system integrating multivariate information, and solves the problems that the existing numerical weather forecasting model is limited in rainfall forecasting capacity, low in reliability and not bidirectionally coupled with a hydrological model.
In order to achieve the technical purpose, the invention provides a medium-short term hydrological forecasting method integrating multivariate information, which comprises the following steps:
collecting related information of the whole watershed, extracting characteristic information of the whole watershed, and selecting a distributed hydrological model according to the characteristic information of the whole watershed;
an ARW model is selected as an atmospheric numerical model, a hydrodynamics and thermodynamics equation set describing a weather evolution process is solved according to the characteristics of a power frame of the ARW model, and the atmospheric motion state and the weather phenomenon in a certain period are predicted;
and embedding the established distributed hydrological model into a land model of the atmospheric numerical model, so that the atmospheric numerical model and the distributed hydrological model share the same land process mechanism, and constructing the bidirectional coupling of the numerical weather forecast and the distributed hydrological model.
The invention also provides a medium and short term hydrological forecasting system integrating multivariate information, which comprises the following functional modules:
the hydrological model building module is used for collecting relevant information of the full watershed, extracting characteristic information of the full watershed and selecting a distributed hydrological model according to the characteristic information of the full watershed;
the atmospheric numerical model establishing module is used for selecting an ARW model as an atmospheric numerical model, solving a hydromechanics and thermodynamics equation set describing a weather evolution process according to the characteristics of a power frame of the ARW model, and predicting an atmospheric motion state and a weather phenomenon in a certain period of time;
and the model coupling module is used for embedding the established distributed hydrological model into a land model of the atmospheric numerical model, so that the atmospheric numerical model and the distributed hydrological model share the same land process mechanism, and the bidirectional coupling of the numerical weather forecast and the distributed hydrological model is established.
Compared with the prior art, the method has the advantages that the weather forecasting precision is improved by eliminating the initial error of the weather forecasting numerical model, the watershed small-scale matching application of the gridding quantitative precipitation estimation model is realized, the seamless connection between the weather forecasting model and the hydrological forecasting model is ensured, and the hydrological forecasting precision is improved.
Drawings
FIG. 1 is a flow chart of a method for forecasting middle and short term hydrology by fusing multivariate information according to an embodiment of the invention;
FIG. 2 is a flow chart of a method for forecasting short and medium term hydrology by fusing multivariate information according to an embodiment of the invention;
fig. 3 is a block diagram of a medium-short term hydrological forecasting system with multivariate information fused according to an embodiment of the present invention.
Detailed Description
In order to make the objects, technical solutions and advantages of the present invention more apparent, the present invention is described in further detail below with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are merely illustrative of the invention and are not intended to limit the invention.
As shown in fig. 1 and fig. 2, an embodiment of the present invention provides a medium-short term hydrologic forecasting method fusing multivariate information, which includes the following steps:
and S1, collecting related information of the full watershed, extracting characteristic information of the full watershed, and selecting a distributed hydrological model according to the characteristic information of the full watershed.
The collection and management of various information such as the geographic environment, natural resources, ecological environment and the like of the full-flow domain are realized by comprehensively utilizing modern high and new technologies such as remote sensing RS, a geographic information system GIS, a global positioning system GPS, a network technology, multimedia virtual reality and the like. On the basis of a DEM digital elevation model, extracting River basin characteristic information by utilizing GIS software such as River Tools, TOPAZ, ARC/INFO and the like to obtain River basin characteristic information such as River system characteristics (River length, flow direction and catchment area) and terrain characteristics (ridge point, valley point, mountain vertex and the like) of a River basin.
A WRF platform is used for constructing a refined hydrological forecast numerical model, a distributed hydrological model is established, and the distributed hydrological model is selected and can be enriched continuously according to the accumulation of experience and methods aiming at different underlying surface types and precipitation types. The distributed hydrological model can fully reflect the influence of rainfall and underlying surface element space change in the basin on flood formation. And can comprehensively utilize the spatial distribution information of rainfall; the spatial distribution of the model parameters can reflect the spatial change of the natural conditions of the underlying surface; the output of the model has spatial non-uniformities such as evapotranspiration, soil moisture, runoff depth, and the like.
In the early-stage data analysis of forecasting, long-sequence precipitation processes are collected, different forecasting models are tried to be adopted to test historical precipitation data, suitable distributed hydrological models are selected, and different parameters are adopted for forecasting according to different precipitation types.
In addition, in order to improve the prediction accuracy, the current correction may be performed on the parameter or predicted value of the hydrological model using the difference between the predicted value and the measured value at the latest time as a guide. The real-time correction technology used in hydrologic prediction is to use "innovation", which is the difference between the predicted value and the measured value at the latest moment, as the guide to perform the current correction on the parameters or predicted values of the hydrologic model. The adopted real-time correction methods comprise a Kalman filtering method, a recursion least square method, an error self-regression method, a self-adaptive algorithm and the like.
S2, selecting an ARW model as an atmospheric numerical model, solving a hydrodynamics and thermodynamics equation set describing a weather evolution process according to the characteristics of a power frame of the ARW model, and predicting the atmospheric motion state and the weather phenomenon in a certain period.
An ARW model separated from a new generation of high-resolution mesoscale model WRF is selected as an atmospheric numerical model, and the characteristics of a dynamic framework of the model comprise: a completely compressible Euler non-static equation, a vertical coordinate which is a terrain following static air pressure coordinate, a horizontal Arakawa C grid, and a separation format of a 3-order Longge Kuta in time; for the main physical processes: the Kain-Fritsch cumulus cloud parameterization scheme, the short wave radiance (cloud-ground interaction) scheme of MMS, the long wave radiance scheme (RRTM), and the planetary boundary layer scheme in the metaphase prediction model of NCEP are described. And solving a fluid mechanics and thermodynamics equation system describing the weather evolution process according to the characteristics of the power framework of the weather prediction device, and predicting the atmospheric motion state and the weather phenomenon in a certain period.
The atmospheric numerical model is a set of modes which are designed by utilizing atmospheric motion equations on the basis of long-term observation of meteorological elements and are used for meeting the weather forecast requirements, and the numerical weather forecast emphasizes that a computer is utilized to carry out discretization solution on a partial differential equation set which cannot be directly solved, so that the weather condition which possibly appears in the future is calculated. The atmospheric numerical mode lays a foundation for the accuracy of numerical weather forecast, and is a precondition for the numerical weather forecast.
The key factors that restrict further improvement of the accuracy of the numerical prediction result are the accuracy of the numerical prediction model itself and how good the initial conditions for integration of the model are. Therefore, on the basis of considering data space-time distribution and errors between an observation field and a background field, new observation data are continuously fused in the dynamic operation process of the atmospheric numerical model by utilizing a four-dimensional data assimilation technology to update the system state and parameters in real time, various observation data which are irregularly and dispersedly distributed in space-time are fused in the model based on the physical law by utilizing the physical constraint and the time continuity constraint, and the initial field of the atmospheric model integral is closer to the real atmospheric environment, so that the simulation or forecast precision of the physical process is improved.
The three-dimensional variational method (3D-VAR) is a generalization of an optimal interpolation method, can be used for processing the condition that an observation matrix is nonlinear, has larger calculation amount than the optimal interpolation, and can carry out global analysis in a three-dimensional space. The biggest difficulty of the three-dimensional variational method is that an error covariance matrix conforming to the actual, positive background field must be defined for the model variables, and some extra terms can be added to the target functional to implement some external weak constraints, but lack continuity in time. The four-dimensional variational method (4D-VAR) is an integral adjoint model to the three-dimensional variational method.
Through data assimilation, mainly reach 4 purposes: estimating the system time-space state, correcting the model parameters, improving the simulation and prediction precision of the system and quantitatively analyzing the simulation and prediction uncertainty of the system. The data assimilation is a data processing technology which is originally derived from numerical weather forecast and provides an initial field for the numerical weather forecast, and the data assimilation is a method for fusing new observation data in the dynamic operation process of a numerical model on the basis of considering data space-time distribution and errors of an observation field and a background field. In a dynamic frame of a process model, direct or indirect observation information of different sources and different resolutions which are discretely distributed in space-time is continuously fused by a data assimilation algorithm to automatically adjust a model track so as to improve the estimation precision of the state of the dynamic model and improve the prediction capability of the model.
And S3, embedding the established distributed hydrological model into a land model of the atmospheric numerical model, enabling the atmospheric numerical model and the distributed hydrological model to share the same land process mechanism, and establishing bidirectional coupling of the numerical weather forecast and the distributed hydrological model.
The atmospheric numerical model provides meteorological input conditions (such as precipitation, air temperature, evaporation and the like) for the land hydrological model, and the land hydrological model can simulate and output a runoff result under the data drive of the atmospheric numerical model.
The bidirectional coupling mode of the numerical weather forecast and the distributed hydrological model comprises the following steps:
the atmospheric numerical model provides meteorological element forecast of the current operation time period to the hydrological model, the soil humidity, runoff and the like calculated by the operation of the hydrological model are fed back to the atmospheric model, the atmospheric numerical model continuously improves the initial boundary conditions according to the feedback information, and further meteorological output of the next step can be provided for the hydrological model.
Due to the large difference between atmospheric and hydrological spatiotemporal scales: atmospheric processes vary more uniformly in space and develop more aggressively in time, whereas hydrologic processes do the opposite. The high nonlinear characteristic of the atmospheric process causes the model resolution to have an operation bottleneck of a certain scale, and the horizontal resolution of a general space of numerical weather forecast is dozens of kilometers in a large-scale basin at present; the hydrological model can reflect the spatial distribution nonuniformity of input and underlying surfaces in the basin as much as possible, and small sub-basins or small grids are generally used as the calculation unit scale. Because the output of the atmospheric model is in a point shape, in the bidirectional coupling of the numerical weather forecast and the distributed hydrological model, the output of the atmospheric model needs to be converted into the unit input of the hydrological model by adopting a gridding quantitative precipitation estimation model for scale reduction matching.
The scale reduction matching of the gridding quantitative precipitation estimation model can be divided into direct and indirect types, and the direct method directly carries out scale matching by using remote sensing data with high space-time resolution; establishing an expression related to the radar estimation grid rainfall by using actually measured data of a rainfall station in a drainage basin; and substituting the estimated rainfall of each grid of the radar into the expression to obtain the plane rainfall of the whole basin after calibration, and finally, corresponding to the hydrological model to be used as the initial boundary condition of the rainfall input. The indirect method uses statistical and kinetic methods for scale matching: and (3) deducing a downscaling relation by using a statistical principle to perform calculation analysis based on a multiple nesting scheme and on the condition of a large-scale mean value according to an aerodynamic equation set.
The adopted grids are often thick due to the limitation of the global model by calculation conditions and the like. The numerical weather forecast model of the coarse grid cannot reflect the detailed structure of rainfall under the complex convection meteorological condition, and particularly, the exact time and place of occurrence of the rainfall are difficult to simulate. The need to match temporal and spatial resolutions is an important content and key technology. The global numerical weather forecast model is the only method for realizing the spatial distribution type rainfall forecast with a long forecast period. Therefore, the rainfall forecast of the coarse grid has certain defects in the flood forecast of the basin scale, and in the coupling of the numerical weather forecast and the distributed hydrological model, three methods of dynamic downscaling, statistical downscaling and dynamic-statistical downscaling are required to be used for downscaling so as to provide more accurate rainfall spatial-temporal distribution characteristics, so that reliable runoff simulation can be provided.
The power downscaling method drives Regional Climate Models (RCMs) based on initial and boundary conditions of gcms (global Climate models) based on the atmospheric dynamics equations to generate higher resolution Climate information.
The statistical downscaling method can make up the defects of the dynamic downscaling method to a certain extent, and the principle is that downscaling is carried out by establishing a statistical relationship between a large-scale climate factor output by a regional climate mode and a local climate variable. The method comprises the following specific steps:
1. selecting a forecast area and screening forecast factors, wherein the selected forecast factors are required to be sensitive enough to large-scale climate change; easy to obtain, continuous and accurate to simulate, has good correlation with hydrological meteorological variables, and keeps significant correlation with downscaling parameters
2. Selecting a statistical downscaling model, selecting a model with the most suitable research area conditions, and accurately predicting future climate scenes
3. And (4) calibrating the model parameters, selecting a statistical downscaling model, inputting the actual measurement data (such as precipitation, air temperature and the like) of the long-time sequence into the model, and establishing a statistical relationship with the selected forecasting factor. Generally, the long sequence measured data is divided into two sections, the front section is used for model calibration, and a statistical relationship of a forecasting factor is established; the back section is used for model test to test whether the established statistical relationship is reasonable.
The dynamic-statistical downscaling method is based on an atmospheric dynamics process, utilizes the output of a global climate mode as a boundary condition, and simultaneously incorporates the statistical result of observation data into model training, so that the downscaling method uses a statistical method to quantitatively describe the relationship between a large-scale climate factor and a local climate variable under the support of a physical mechanism.
Meanwhile, on the basis of a land model and a hydrological model, different data assimilation algorithms are adopted to assimilate earth surface observation data, satellite data and radar data, and estimation of earth surface and root zone soil moisture, temperature and earth surface energy flux is optimized. The data assimilation algorithm needs to simulate a dynamic model of a real process in the nature, the state quantity of the data assimilation algorithm needs to directly or indirectly observe data, newly observed data are continuously blended into a process model through the data assimilation algorithm for calculation, model parameters are corrected, the model simulation precision is improved, and the model prediction value and the uncertainty of the model are quantitatively analyzed; different data assimilation algorithms are used for better matching with research targets, and the algorithms for simulating the effects are found and continuously optimized to adapt to fluctuation generated by hydrologic information change.
In addition, accurate quantitative precipitation forecast is critical to land-air coupling short-term forecast, so that the numerical weather forecast model must be capable of accurately reporting the spatial-temporal distribution and rainfall intensity of precipitation, and in the subsequent flood forecast, relatively small errors generated in all aspects form relatively large errors. In order to avoid the butterfly effect and cause the large deviation of the land-air coupling model, the error real-time correction is needed, and the error real-time correction content comprises: WRF model forecast error correction, data assimilation error correction, and land-air coupling forecast model error correction.
As shown in fig. 3, the invention also discloses a medium and short term hydrologic forecast system integrating multivariate information, which comprises the following functional modules:
the hydrological model building module 10 is used for collecting relevant information of the whole watershed, extracting characteristic information of the whole watershed, selecting a distributed hydrological model according to the characteristic information of the whole watershed,
the atmospheric numerical model establishing module 20 is used for selecting an ARW model as an atmospheric numerical model, solving a fluid mechanics and thermodynamics equation set describing a weather evolution process according to the characteristics of a power frame of the ARW model, and predicting an atmospheric motion state and a weather phenomenon in a certain period of time;
and the model coupling module 30 is used for embedding the established distributed hydrological model into a land model of the atmospheric numerical model, so that the atmospheric numerical model and the distributed hydrological model share the same land process mechanism, and bidirectional coupling of the numerical weather forecast and the distributed hydrological model is established.
In the above embodiments, the descriptions of the respective embodiments have respective emphasis, and reference may be made to the related descriptions of other embodiments for parts that are not described or illustrated in a certain embodiment.
Those of ordinary skill in the art would appreciate that the modules, elements, and/or method steps of the various embodiments described in connection with the embodiments disclosed herein may be implemented as electronic hardware, or combinations of computer software and electronic hardware. Whether such functionality is implemented as hardware or software depends upon the particular application and design constraints imposed on the implementation. Skilled artisans may implement the described functionality in varying ways for each particular application, but such implementation decisions should not be interpreted as causing a departure from the scope of the present invention.
The above-mentioned embodiments are only used for illustrating the technical solutions of the present invention, and not for limiting the same; although the present invention has been described in detail with reference to the foregoing embodiments, it will be understood by those of ordinary skill in the art that: the technical solutions described in the foregoing embodiments may still be modified, or some technical features may be equivalently replaced; and such modifications or substitutions do not depart from the spirit and scope of the corresponding technical solutions of the embodiments of the present invention.
Claims (10)
1. A medium and short term hydrological forecasting method integrating multivariate information is characterized by comprising the following steps:
collecting related information of the whole watershed, extracting characteristic information of the whole watershed, and selecting a distributed hydrological model according to the characteristic information of the whole watershed;
an ARW model is selected as an atmospheric numerical model, a hydrodynamics and thermodynamics equation set describing a weather evolution process is solved according to the characteristics of a power frame of the ARW model, and the atmospheric motion state and the weather phenomenon in a certain period are predicted;
and embedding the established distributed hydrological model into a land model of the atmospheric numerical model, so that the atmospheric numerical model and the distributed hydrological model share the same land process mechanism, and constructing the bidirectional coupling of the numerical weather forecast and the distributed hydrological model.
2. The method for forecasting middle and short term hydrologic forecast with multi-information fusion as claimed in claim 1, wherein said distributed hydrologic model is required to satisfy the following conditions:
the model can fully reflect the influence of rainfall and underlying surface element space change in the basin on flood formation;
the model can comprehensively utilize the spatial distribution information of rainfall;
the spatial distribution of the model parameters can reflect the spatial change of the natural conditions of the underlying surface;
the output of the model has a spatial non-uniformity.
3. The method for forecasting middle and short term hydrologic forecast with multiple information fusion as claimed in claim 1, wherein the bidirectional coupling mode of numerical weather forecast and distributed hydrologic model comprises:
the atmospheric numerical model provides meteorological element forecast of the current operation time period to the hydrological model, the soil humidity, runoff and the like calculated by the operation of the hydrological model are fed back to the atmospheric model, the atmospheric numerical model continuously improves the initial boundary conditions according to the feedback information, and further meteorological output of the next step can be provided for the hydrological model.
4. The method for forecasting middle and short term hydrologic forecast with multiple information fusion of claim 1, wherein in the bidirectional coupling of the numerical weather forecast and the distributed hydrologic model, the unit input for converting the atmospheric model output into the hydrologic model is subjected to downscaling matching by adopting a gridding quantitative precipitation estimation model.
5. The multivariate information-fused short and medium term hydrological forecasting method as claimed in claim 1, wherein in the coupling of the numerical weather forecast and the distributed hydrological model, three methods of dynamic downscaling, statistical downscaling and dynamic-statistical downscaling are used for downscaling to provide more accurate rainfall spatiotemporal distribution characteristics so as to be able to provide reliable runoff simulation.
6. The method for forecasting middle and short term hydrology integrating multivariate information according to claim 1, wherein new observation data are continuously integrated in the dynamic operation process of the atmospheric numerical model by using a four-dimensional data assimilation technology to update the system state and parameters in real time.
7. The method for forecasting middle and short term hydrology integrating multivariate information according to claim 1, wherein different data assimilation algorithms are adopted to assimilate earth surface observation data, satellite data and radar data on the basis of a land model and a hydrology model, and estimation of earth surface and root zone soil moisture, temperature and earth surface energy flux is optimized.
8. The method according to claim 1, wherein the parameters or predicted values of the hydrological model are corrected at present using the difference between the predicted value and the measured value at the latest time as a guide.
9. The method for forecasting middle and short term hydrology fused with multivariate information according to claim 1, wherein new observation data are continuously fused in the dynamic operation process of the atmospheric numerical model by using a four-dimensional data assimilation technology on the basis of considering data space-time distribution and errors between an observation field and a background field to update the system state and parameters in real time, and various observation data irregularly distributed in a scattered way in space-time are fused into a model based on a physical law by using physical constraints and time continuity constraints.
10. A medium and short term hydrological forecasting method integrating multivariate information is characterized by comprising the following steps:
the hydrological model building module is used for collecting relevant information of the full watershed, extracting characteristic information of the full watershed and selecting a distributed hydrological model according to the characteristic information of the full watershed;
the atmospheric numerical model establishing module is used for selecting an ARW model as an atmospheric numerical model, solving a hydromechanics and thermodynamics equation set describing a weather evolution process according to the characteristics of a power frame of the ARW model, and predicting an atmospheric motion state and a weather phenomenon in a certain period of time;
and the model coupling module is used for embedding the established distributed hydrological model into a land model of the atmospheric numerical model, so that the atmospheric numerical model and the distributed hydrological model share the same land process mechanism, and the bidirectional coupling of the numerical weather forecast and the distributed hydrological model is established.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202011596740.8A CN112785035A (en) | 2020-12-29 | 2020-12-29 | Medium-short term hydrological forecasting method and system integrating multivariate information |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202011596740.8A CN112785035A (en) | 2020-12-29 | 2020-12-29 | Medium-short term hydrological forecasting method and system integrating multivariate information |
Publications (1)
| Publication Number | Publication Date |
|---|---|
| CN112785035A true CN112785035A (en) | 2021-05-11 |
Family
ID=75751345
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN202011596740.8A Pending CN112785035A (en) | 2020-12-29 | 2020-12-29 | Medium-short term hydrological forecasting method and system integrating multivariate information |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN112785035A (en) |
Cited By (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN113222283A (en) * | 2021-05-31 | 2021-08-06 | 中国水利水电科学研究院 | Mountain torrent forecasting and early warning method and system based on digital twin |
| CN114779370A (en) * | 2022-04-19 | 2022-07-22 | 中国民用航空华东地区空中交通管理局 | Rainfall forecasting method and system combining satellite cloud picture and numerical evaluation |
| US20230161072A1 (en) * | 2021-11-23 | 2023-05-25 | At&T Intellectual Property I, L.P. | Predictive Hydrological Impact Diagnostic System |
Citations (6)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN108491664A (en) * | 2018-03-30 | 2018-09-04 | 南京上古网络科技有限公司 | A kind of Distributed Hydrological forecasting model paradigmatic system |
| US20190230875A1 (en) * | 2015-08-05 | 2019-08-01 | Clearag, Inc. | Customized land surface modeling in a soil-crop system using satellite data to detect irrigation and precipitation events for decision support in precision agriculture |
| CN111027175A (en) * | 2019-11-06 | 2020-04-17 | 中国地质大学（武汉） | Method for evaluating social and economic influences of flood based on coupling model integrated simulation |
| CN111079282A (en) * | 2019-12-12 | 2020-04-28 | 北京师范大学 | Hydrological forecasting method and equipment |
| CN111460686A (en) * | 2020-04-23 | 2020-07-28 | 中国水利水电科学研究院 | Atmospheric, land and hydrological three-way coupling method |
| CN112036093A (en) * | 2020-08-13 | 2020-12-04 | 河海大学 | Land hydrologic coupling model |
-
2020
- 2020-12-29 CN CN202011596740.8A patent/CN112785035A/en active Pending
Patent Citations (6)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US20190230875A1 (en) * | 2015-08-05 | 2019-08-01 | Clearag, Inc. | Customized land surface modeling in a soil-crop system using satellite data to detect irrigation and precipitation events for decision support in precision agriculture |
| CN108491664A (en) * | 2018-03-30 | 2018-09-04 | 南京上古网络科技有限公司 | A kind of Distributed Hydrological forecasting model paradigmatic system |
| CN111027175A (en) * | 2019-11-06 | 2020-04-17 | 中国地质大学（武汉） | Method for evaluating social and economic influences of flood based on coupling model integrated simulation |
| CN111079282A (en) * | 2019-12-12 | 2020-04-28 | 北京师范大学 | Hydrological forecasting method and equipment |
| CN111460686A (en) * | 2020-04-23 | 2020-07-28 | 中国水利水电科学研究院 | Atmospheric, land and hydrological three-way coupling method |
| CN112036093A (en) * | 2020-08-13 | 2020-12-04 | 河海大学 | Land hydrologic coupling model |
Non-Patent Citations (8)
| Title |
|---|
| 丁一汇等: "《地球气候的演变》", 31 January 2016, 科学普及出版社, pages: 137 * |
| 中国环境监测总站: "《南水北调西线工程水源区水循环模拟与水资源定量评价》", 31 December 2008, 中国环境出版集团, pages: 73 - 74 * |
| 中国环境监测总站: "《水环境质量预报预警方法技术指南》", 中国环境出版集团, pages: 73 - 74 * |
| 张俊;郭生练;陈桂亚;陈飞;: "大气水文耦合模式在洪水预报中的应用研究", 水电能源科学, no. 09, pages 291 * |
| 杨明祥: "基于陆气耦合的降水径流预报研究", 中国博士学位论文全文数据库工程科技Ⅱ辑 * |
| 郝春沣;贾仰文;王浩;: "气象水文模型耦合研究及其在渭河流域的应用", 水利学报, no. 09 * |
| 雷晓辉: "变化环境下气象水文预报研究进展", 水利学报 * |
| 高冰等: "基于数值天气模式和分布式水文模型的三峡入库洪水预报研究", 水力发电学报, pages 21 * |
Cited By (5)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN113222283A (en) * | 2021-05-31 | 2021-08-06 | 中国水利水电科学研究院 | Mountain torrent forecasting and early warning method and system based on digital twin |
| CN113222283B (en) * | 2021-05-31 | 2023-12-26 | 中国水利水电科学研究院 | Mountain torrent forecasting and early warning method and system based on digital twinning |
| US20230161072A1 (en) * | 2021-11-23 | 2023-05-25 | At&T Intellectual Property I, L.P. | Predictive Hydrological Impact Diagnostic System |
| CN114779370A (en) * | 2022-04-19 | 2022-07-22 | 中国民用航空华东地区空中交通管理局 | Rainfall forecasting method and system combining satellite cloud picture and numerical evaluation |
| CN114779370B (en) * | 2022-04-19 | 2023-10-13 | 中国民用航空华东地区空中交通管理局 | Precipitation prediction method and system combining satellite cloud image and numerical evaluation |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| Mahmoud et al. | Assessment of global precipitation measurement satellite products over Saudi Arabia | |
| Oubeidillah et al. | A large-scale, high-resolution hydrological model parameter data set for climate change impact assessment for the conterminous US | |
| Quintana-Segui et al. | Analysis of near-surface atmospheric variables: Validation of the SAFRAN analysis over France | |
| CN112785035A (en) | Medium-short term hydrological forecasting method and system integrating multivariate information | |
| Voisin et al. | Application of a medium-range global hydrologic probabilistic forecast scheme to the Ohio River basin | |
| CN105243435B (en) | A kind of soil moisture content prediction technique based on deep learning cellular Automation Model | |
| Giebel et al. | Shortterm forecasting using advanced physical modelling-the results of the anemos project | |
| Abera et al. | Estimating the water budget components and their variability in a pre-alpine basin with JGrass-NewAGE | |
| Singh | Long-term streamflow forecasting based on ensemble streamflow prediction technique: a case study in New Zealand | |
| US20220043180A1 (en) | Method and system of real-time simulation and forecasting in a fully-integrated hydrologic environment | |
| Harader et al. | Correcting the radar rainfall forcing of a hydrological model with data assimilation: application to flood forecasting in the Lez catchment in Southern France | |
| Tapiador et al. | Precipitation estimates for hydroelectricity | |
| CN112100922A (en) | Wind resource prediction method based on WRF and CNN convolutional neural network | |
| Jaberzadeh et al. | SWAT and IHACRES models for the simulation of rainfall-runoff of Dez watershed | |
| Feng et al. | Evaluating applicability of multi-source precipitation datasets for runoff simulation of small watersheds: A case study in the United States | |
| CN112766531B (en) | Runoff prediction system and method based on satellite microwave observation data | |
| Nykanen | Linkages between orographic forcing and the scaling properties of convective rainfall in mountainous regions | |
| Zanella et al. | Internet of things for hydrology: Potential and challenges | |
| Förster et al. | A snow and ice melt seasonal prediction modelling system for Alpine reservoirs | |
| Unkrich et al. | Real-time flash flood forecasting using weather radar and a distributed rainfall-runoff model | |
| Zanella et al. | Sensor Networks, Data Processing, and Inference: The Hydrology Challenge | |
| Guo et al. | Projections of daily mean surface temperature over the Beijing-Tianjin-Hebei region through a stepwise cluster downscaling method | |
| De Blasi | Scale dependence of hydrological effects from different climatic conditions on glacierized catchments | |
| Choi et al. | Storm identification and tracking algorithm for modeling of rainfall fields using 1-h NEXRAD rainfall data in Texas | |
| Fava et al. | Integration of information technology systems for flood forecasting with hybrid data sources |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| PB01 | Publication | ||
| PB01 | Publication | ||
| SE01 | Entry into force of request for substantive examination | ||
| SE01 | Entry into force of request for substantive examination |