Analysis of Meteorological Element Variation Characteristics in the Heilongjiang (Amur) River Basin

被引:2
作者
Yue, Qi [1 ,2 ,3 ]
Yu, Gao [1 ,2 ,3 ]
Miao, Yu [3 ,4 ,5 ]
Zhou, Yang [1 ,2 ,3 ]
机构
[1] Heilongjiang Univ, Inst Groundwater Cold Reg, Harbin 150080, Peoples R China
[2] Heilongjiang Univ, Sch Hydraul & Elect Power, Harbin 150080, Peoples R China
[3] Int Joint Lab Hydrol & Hydraul Engn Cold Reg Heilo, Harbin 150080, Peoples R China
[4] North Eastern Fed Univ, Fac Geol & Survey, Yakutsk 677000, Russia
[5] Russian Acad Sci, Melnikov Permafrost Inst, Siberian Branch, Yakutsk 677000, Russia
关键词
Heilongjiang (Amur) River; temperature; precipitation; spatiotemporal distribution characteristics; abrupt change analysis; precipitation value prediction; CLIMATE-CHANGE; WATER-RESOURCES;
D O I
10.3390/w16040521
中图分类号
X [环境科学、安全科学];
学科分类号
08 ; 0830 ;
摘要
Located in the Heilongjiang (Amur) River in north-east Asia, spanning four countries, plays a crucial role as an international border river, and its meteorological changes significantly impact the variation in water resources in the basin. This study utilizes daily average temperature and precipitation data from 282 meteorological stations in the Heilongjiang (Amur) River Basin and its surrounding areas for the period 1980-2022. The analysis employs spatial interpolation, change point testing, and model construction prediction methods. The results indicate a significant increasing trend in both overall temperature and precipitation changes within the Heilongjiang (Amur) River Basin. At the spatial scale, the annual warming rate increases gradually from the southeastern coastal region to the northwestern plateau region, while the rate of precipitation increase decreases from the southern area towards its surroundings. Temporally, the warming amplitude during the growing season decreases gradually from east to west, and the trend in precipitation changes during the growing season aligns with the overall annual precipitation trend. During the non-growing season, the warming trend shows a decrease in the plains and an increase in the plateau, while precipitation increase concentrates in the central and southern plains, and precipitation decrease predominantly occurs in the northwestern plateau region. Temperature and precipitation change points occurred in the years 2001 and 2012, respectively. In precipitation prediction, the Long Short-Term Memory (LSTM) model exhibits higher accuracy, with R (Pearson correlation coefficient) and NSE (Nash-Sutcliffe efficiency coefficient) values approaching 1 and lower NRSME values. This study provides a research foundation for the rational development and utilization of water resources in the Heilongjiang (Amur) River Basin and offers valuable insights for research on climate change characteristics in large transboundary river systems.
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页数:28
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