Kunjun Tian
5 Papers
4 Citations
Kunjun Tian is an academic researcher. The author has contributed to research in topics: Environmental science & Structural basin. The author has co-authored 2 publications.
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Papers
Drought Events over the Amazon River Basin (1993–2019) as Detected by the Climate-Driven Total Water Storage Change
TL;DR: In this paper, the authors extended the gridded GRACE TWSC to 1993 by combining principal component analysis (PCA), least square (LS) fitting, and multiple linear regression (MLR) methods using climate variables as input drivers.
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Using Swarm to Detect Total Water Storage Changes in 26 Global Basins (Taking the Amazon Basin, Volga Basin and Zambezi Basin as Examples)
TL;DR: In this paper, the authors explored the optimal data processing strategy for Swarm and then obtained the Swarm-TWSC of each watershed based on the optimal results, which is related to the area size, runoff volume, total annual mass change, and instantaneous mass change of watershed itself.
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An Improved Spatiotemporal Weighted Mean Temperature Model over Europe Based on the Nonlinear Least Squares Estimation Method
TL;DR: In this paper , a weighted average temperature model in Europe (ETm) was proposed by using the nonlinear least squares estimation method to estimate the ground temperature, water vapor pressure, latitude, and their annual variation.
Analysis of Spatiotemporal Evolution and Influencing Factors of Vegetation Net Primary Productivity in the Yellow River Basin from 2000 to 2022
Kunjun Tian,Xing Liu,Bingbing Zhang,Zhengtao Wang,Gong Xu,Kai Chang,Pengfei Xu,Baomin Han +7 more
TL;DR: The vegetation NPP in the YRB showed an increasing trend from 2000 to 2022, with the most significant changes occurring in the middle reaches of the YRB. The dominant factors influencing vegetation NPP are soil type, precipitation, and temperature. TWS has a significant negative impact on vegetation NPP, with a strong correlation of 39%.
4
Gravity field recovery of inter-satellite links between Beidou Navigation Satellite System (BDS) and LEO based on Geodesy and Time Reference in Space (GETRIS)
Yang Xiao,Zhengtao Wang,Nengfang Chao,Kunjun Tian,Tangting Wu +4 more
TL;DR: This study recovers the Earth's gravity field using inter-satellite links between Beidou Navigation Satellite System (BDS) and Low Earth Orbit (LEO) satellites, achieving 16% error reduction by combining GEO, IGSO, and MEO tracking modes.