A Flood Forecasting Method for Watersheds Without Runoff Data

Abstract

The invention discloses a flood forecasting method for watersheds without runoff data. For completed watersheds with historical runoff data and better forecasting effects, the model parameters and watershed characteristic information after clustering processing are stored in a database in advance; When forecasting a watershed without runoff data, the characteristic information of the underlying surface in the watershed is first automatically clustered, and then the watersheds similar to each model parameter are found one by one in the large watershed where the watershed is located, and the model parameters of the similar watersheds are Carry out correlation analysis with the characteristic information determined in advance, and finally determine each parameter. Based on the accumulation of a large number of watershed forecasts, the present invention completes the automatic selection of similar watersheds without data forecasting, automatic correlation analysis and final determination of model parameters, which can effectively avoid human subjective judgment errors and improve efficiency. Lay the groundwork for regional forecasts.

Description

technical field [0001] The invention relates to a flood forecasting method for a watershed without runoff data, and belongs to the technical field of flood forecasting. Background technique [0002] Hydrological processes in nature are extremely complex and affected by many factors. Generally, generalized hydrological models are used for prediction. On the basis of , determine the hydrological model parameters of the current forecast basin. In most watersheds, the parameters of the hydrological model are calculated by simulating a large amount of historical runoff data (substituting information such as rainfall and evaporation into the model, and adjusting the parameters to make the calculated runoff process consistent with the actual situation); , the historical runoff data sequence is very short, which cannot meet the forecasting requirements; at the same time, due to human activities and other influences, the characteristics of runoff production and confluence in the bas...

AI Technical Summary

Problems solved by technology

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, and the existing studies are only based on one or Several characteristic variables look for reference watersheds, but all parameters are transferred uniformly or use the same watersheds, which cannot reflect the uniqueness of parameters

[0007] Subjective discrimination and low efficiency: the existing technologies all use manual discrimination and processing to try to solve the problem of flood forecasting in areas without data. The technology is difficult to promote in large quantities, and it contains human subjectivity, which is inefficient

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