Small river flood forecasting method

Abstract

The invention discloses a small river flood forecasting method. By using the open source GIS codes for the secondary development, the GIS requirement of flood forecasting can be met. Meanwhile, through the classification of the model calculation engines, the flexible configuration of the models, the automatic extraction of a drainage basin and the matching association of a node topological relation are achieved, the automatic and flexible construction of a scheme is achieved, and the workload of scheme operators can be saved to a large extent while the forecasting precision of medium and smallrivers is improved.

Description

technical field [0001] The invention relates to the technical field of water conservancy and hydropower engineering, in particular to a small river flood forecasting method. Background technique [0002] Most of the central and western regions of my country are mountainous and hilly areas, where mountain torrent disasters occur frequently, and the affected areas are relatively scattered, with small watershed area, short confluence time, and lack of data. The construction scheme has the characteristics of low efficiency and inconvenient parameter adjustment. [0003] The process of the traditional small and medium-sized river forecasting method is mainly as follows: select the Xin'an River three water source model or other hydrological models (including lumped and distributed hydrological models), etc., use existing watershed layers or use commercial GIS tools such as ARCGIS to import DEM data extraction Relevant elements such as the watershed layer; then enter the informatio...

AI Technical Summary

Problems solved by technology

However, when faced with the forecasting of small and medium-sized rivers, there are the following problems that cannot meet the requirements: 1) The area of ​​the small and medium-sized river forecast basin is small and there is no representative rainfall station; 2) There are many sections in the forecast of small and medium-sized rivers. Large workload; 3) data is scarce, confluence parameters cannot be calibrated, and prediction accuracy is low; 4) when there are many schemes, the task of adjusting and modifying schemes is heavy

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