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额尔齐斯河库威站日尺度的降雨融雪径流模拟

赵文龙 吕海深 朱永华 刘涵 吴卓珺

干旱区研究2024,Vol.41Issue(10):1685-1698,14.
干旱区研究2024,Vol.41Issue(10):1685-1698,14.DOI:10.13866/j.azr.2024.10.07

额尔齐斯河库威站日尺度的降雨融雪径流模拟

Simulation of rainfall and snowmelt runoff on the daily scale of the Kuwei Sta-tion in the Irtysh River

赵文龙 1吕海深 1朱永华 1刘涵 1吴卓珺1

作者信息

  • 1. 河海大学水灾害防御全国重点实验室,江苏 南京 210098||河海大学水文水资源学院,江苏 南京 210098
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摘要

Abstract

Due to geographical conditions,there are limited hydrometeorological stations and a lack of basic data in the Irtysh River Basin,and the snowmelt flood exerts a considerable effect on the flood season and water re-sources management in the basin.In this study,precipitation and temperature reanalysis products and AVHRR snow cover data were applied,the K-means clustering method was used to divide the characteristics of different runoff periods,the corresponding SRM+LSTM model in different periods was constructed,and the runoff data observed in the field in 2023 were used.Results showed that the reanalysis product CMFD can be well applied to the Irtysh River Basin according to precipitation and temperature.The relationship between snow cover and run-off was divided into different runoff periods,as follows:December 11th to April 10th of the following year was the snow retreat period,April 11th to August 10th was the snowmelt precipitation runoff period,and August 11th was the precipitation runoff period.The simulation effect of the SRM model was poor,and the Nash efficiency co-efficient of most runoff was<0.The SRM+LSTM model could better simulate the runoff in different periods of the basin,the deterministic coefficient could reach>0.5,and the Nash efficiency coefficient NSE could also reach>0.5,which confirms that the SRM+LSTM model can be better applied to the area with high accuracy.

关键词

K-means聚类法/SRM模型/LSTM模型/径流模拟/额尔齐斯河

Key words

K-means clustering method/SRM model/LSTM model/runoff simulation/Irtysh River

引用本文复制引用

赵文龙,吕海深,朱永华,刘涵,吴卓珺..额尔齐斯河库威站日尺度的降雨融雪径流模拟[J].干旱区研究,2024,41(10):1685-1698,14.

基金项目

国家重点研发计划项目(2019YFC1510504) (2019YFC1510504)

干旱区研究

OA北大核心CSTPCD

1001-4675

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