New Flood Early Warning and Forecasting Method Based on Similarity Theory

Abstract

The challenge of achieving reliable flood forecasting results in semiarid regions remains stark. We developed a flood early warning and forecasting method based on similarity theory and a hydrological model to extend the lead time and achieve dynamic rolling forecasting. A multimeasure-based rainfall event similarity analysis (MRESA) method was proposed for rainfall forecasting based on the similarity evaluation between two rainstorms from multiple perspectives (including the quantity similarity, pattern similarity, earth mover's distance, and rainstorm spatial distribution similarity). Moreover, an ideal sample experiment was conducted to verify the method's rationality and feasibility. The MRSA method for rainfall prediction and the vertically mixed runoff model were applied to the Beiniuchuan River located in the middle Yellow River basin. Results showed that the flood forecast would be continuously updated and the prediction accuracy gradually increases with the increase of rainstorm and flood information. Therefore, in this study the proposed flood forecasting method based on the similarity analysis is effective and applicable.