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Siyuan Tian

Researcher at Australian National University

Publications -  26
Citations -  765

Siyuan Tian is an academic researcher from Australian National University. The author has contributed to research in topics: Data assimilation & Water storage. The author has an hindex of 11, co-authored 20 publications receiving 415 citations. Previous affiliations of Siyuan Tian include Lam Research & Cooperative Research Centre.

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Global GRACE data assimilation for groundwater and drought monitoring: Advances and challenges

TL;DR: In this paper, the authors assimilated a state-of-the-art terrestrial water storage product derived from Gravity Recovery and Climate Experiment (GRACE) satellite observations into NASA's Catchment land surface model (CLSM) at the global scale, with the goal of generating groundwater storage time series that are useful for drought monitoring and other applications.
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Improved water balance component estimates through joint assimilation of GRACE water storage and SMOS soil moisture retrievals

TL;DR: In this article, the Ensemble Kalman Smoother (EKS) model was used to assimilate satellite water storage and soil moisture data into a water balance model for the Australian continent.
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Airborne multi-temporal L-band polarimetric SAR data for biomass estimation in semi-arid forests

TL;DR: In this article, the impact of high revisit cycle and full polarimetric acquisitions on biomass retrieval was investigated by means of backscatter-based multi-temporal methods using both parametric and non-parametric models.
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A Review of Industrial MIMO Decoupling Control

TL;DR: The scattered coupling interaction analysis and decoupling algorithms are collected and divided into different categories with their characteristics, application domains and informative comments for selection in order to benefit researchers and engineers with different academic backgrounds.
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Evaluation of groundwater storage variations estimated from GRACE data assimilation and state-of-the-art land surface models in Australia and the North China Plain

TL;DR: This study assimilates GRACE data into the PCRaster Global Water Balance model and reveals that PCR-GLOBWB and CABLE provide a more accurate ΔGWS estimate in Australia while PCR-PLWB and WGHM are more accurate in the NCP (subject to the inclusion of anthropogenic factors).