A
Alexander Y. Sun
Researcher at University of Texas at Austin
Publications - 129
Citations - 4945
Alexander Y. Sun is an academic researcher from University of Texas at Austin. The author has contributed to research in topics: Environmental science & Deep learning. The author has an hindex of 35, co-authored 114 publications receiving 3421 citations. Previous affiliations of Alexander Y. Sun include Centrum Wiskunde & Informatica & University of California, Berkeley.
Papers
More filters
Journal ArticleDOI
Global models underestimate large decadal declining and rising water storage trends relative to GRACE satellite data
Bridget R. Scanlon,Zizhan Zhang,Himanshu Save,Alexander Y. Sun,Hannes Müller Schmied,Ludovicus P. H. van Beek,David N. Wiese,Yoshihide Wada,Yoshihide Wada,Di Long,Robert C. Reedy,Laurent Longuevergne,Petra Döll,Marc F. P. Bierkens +13 more
TL;DR: The inability of models to capture large decadal water storage trends based on GRACE indicates that model projections of climate and human-induced water storage changes may be underestimated.
Journal ArticleDOI
GRACE satellite monitoring of large depletion in water storage in response to the 2011 drought in Texas
Di Long,Bridget R. Scanlon,Laurent Longuevergne,Alexander Y. Sun,D. Nelun Fernando,Himanshu Save +5 more
TL;DR: In this paper, the authors assess the value of Gravity Recovery and Climate Experiment (GRACE) satellite-derived total water storage (TWS) change as an alternative remote sensing-based drought indicator, independent of traditional drought indicators based on in situ monitoring.
Journal ArticleDOI
Drought and flood monitoring for a large karst plateau in Southwest China using extended GRACE data
Di Long,Di Long,Yanjun Shen,Alexander Y. Sun,Yang Hong,Yang Hong,Laurent Longuevergne,Yuting Yang,Bin Li,Lu Chen +9 more
TL;DR: In this article, the authors show that both the frequency and severity of droughts and floods over the plateau are intensified during therecent decade from three-decade total water storage anomalies (TWSA) generated by Gravity Recovery andClimate Experiment (GRACE) satellite data and artificial neural network (ANN) models.
Journal ArticleDOI
How can Big Data and machine learning benefit environment and water management: a survey of methods, applications, and future directions
TL;DR: This survey examines the potential and benefits of data-driven research in EWM, gives a synopsis of key concepts and approaches in BigData andML, provides a systematic review of current applications, and discusses major issues and challenges to recommend future research directions.
Journal ArticleDOI
Monthly streamflow forecasting using Gaussian Process Regression
TL;DR: Gaussian Process Regression (GPR), an effective kernel-based machine learning algorithm, is applied to probabilistic streamflow forecasting and indicates relatively strong persistence of streamflow predictability in the extended period, although the low-predictability basins tend to show more variations.