Method for estimating runoff in non-data area based on ensemble kalman filter

Abstract

The present invention discloses a method for estimating the runoff in the non-data area based on the ensemble Kalman filter, and relates to the technical field of hydrological simulation. The method comprises: calculating an optimized parameter value of the whole basin; calculating the estimated flow and the distribution regularity of the point C in the exit point of the sub-basin A; calculating the ensemble perturbation value of the variable and the parameter; calculating the runoff forecast set of the exit point in the downstream sub basic B, i.e., the hydrological station D; carrying out Gaussian perturbation on the actually measured runoff value of the hydrological station D, and taking the obtained Gaussian perturbation value as the observation data set; integrating in the observation data set to carry out assimilate update; and finally obtaining an optimal estimate value of the runoff QC of the point C. According to the method disclosed by the present invention, the filtering assimilation process is simplified, and the assimilation precision is better improved; and the shortcomings of the conventional method for calculating the runoff in the non-data area are avoided.

Description

technical field [0001] The invention relates to the technical field of hydrological simulation, in particular to a method for deriving runoff in areas without data based on ensemble Kalman filtering. Background technique [0002] In nature, there are many areas in nature that cannot meet the forecasting requirements due to the lack of rainfall or the early construction of hydrological stations and the short sequence of historical runoff data. At the same time, due to the influence of human activities, the characteristics of runoff production and confluence in the basin have undergone major changes, and historical data cannot reflect the current flow. Water properties, historical data not available. Therefore, research on areas without data is urgently needed. [0003] At present, the commonly used method for runoff forecasting in areas without data is the regionalization method, that is, through some means, the model parameters of the watershed with data are used to calcula...

AI Technical Summary

Problems solved by technology

But in fact, each parameter in the hydrological model represents a different physical meaning, because each watershed has its own uniqueness, it is difficult to find a watershed with similar characteristics to all the parameters in the model, although the current research only bases on one One or several related features are used to find reference watersheds, and all parameters are transferred uniformly or use the same watersheds, but this method obviously cannot reflect the uniqueness of parameters

[0006] 2. The selection of similar watersheds lacks comprehensiveness and objectivity: Existing studies usually use one or several comprehensive indexes of terrain and geomorphic characteristic parameters, such as topographic index, as attribute discrimination to find similar watersheds, and then transfer or analyze the parameters

Because the physical meanings of the parameters of different hydrological models are different, it is not comprehensive and objective to judge only based on one or several watershed characteristic parameters, and it is difficult to reflect the real watershed similarity. Categorize complex and undiagnosed problems

[0007] 3. Subjective discrimination and low efficiency: the existing technology adopts manual discrimination and processing to solve the problem of flood forecasting in areas without data. This technology is difficult to promote in large quantities, and it contains human subjectivity and low efficiency. Find other effective A simple method is urgently needed

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