Prediction method for overall change trend of drainage basin runoff

Abstract

The invention discloses a prediction method for overall change trend of drainage basin runoff. The method comprises the following steps: S1, constructing a watershed runoff overall trend change factor with multiple space-time granularity characteristics and life cycles; s2, calculating an area empowerment represented by a meteorological station in the drainage basin by adopting a Thiessen polygon method, and constructing a rainfall object; s3, adopting a correlation coefficient method to screen climate objects with strong correlation with the drainage basin runoff process; s4, constructing a vegetation object covering the whole drainage basin based on a drainage basin normalized vegetation index calculation method of the SPOT image; s5, adopting a feature screening method to realize feature screening of key objects influencing the overall change trend of the drainage basin runoff; and S6, realizing basin runoff overall change trend prediction based on the intelligent prediction model. And the macroscopic research and judgment capability of the long-term runoff change trend in the watershed in a complex environment can be improved.

Description

technical field [0001] The invention relates to the application field of hydrological big data, in particular to a method for predicting the overall change trend of watershed runoff. Background technique [0002] Affected by many factors such as climate, underlying surface, and human activities, the medium- and long-term runoff change process in the basin has certain temporal and spatial uncertainties. The factors affecting runoff mainly include precipitation, temperature, topography, geology, soil, vegetation, and human activities. Wait. The current medium- and long-term runoff prediction in the watershed is mainly based on the runoff changes of some typical stations (single granularity level) in the watershed to predict the medium- and long-term runoff change situation, and does not start from the watershed level (coarse-grained) and combine multiple hydrological time and space in the research area The multi-granularity comprehensive analysis of the object leads to insuff...

AI Technical Summary

Problems solved by technology

The current medium- and long-term runoff prediction in the watershed is mainly based on the runoff changes of some typical stations (single granularity level) in the watershed to predict the medium- and long-term runoff change situation, and does not start from the watershed level (coarse-grained) and combine multiple hydrological time and space in the research area The multi-granularity comprehensive analysis of objects leads to insufficient macro research and judgment ability for medium and long-term runoff forecasting in complex environments

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