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Communication

Global Terrestrial Water Storage Reconstruction Using Cyclostationary Empirical Orthogonal Functions (1979–2020)

by
Hrishikesh A. Chandanpurkar
1,2,3,*,
Benjamin D. Hamlington
1 and
John T. Reager
1
1
Jet Propulsion Laboratory, California Institute of Technology, Pasadena, CA 91109, USA
2
Global Institute for Water Security, University of Saskatchewan, Saskatoon, SK S7N 3H5, Canada
3
Centre for Sustainability, Environment, and Climate Change, FLAME University, Pune 412115, India
*
Author to whom correspondence should be addressed.
Remote Sens. 2022, 14(22), 5677; https://doi.org/10.3390/rs14225677
Submission received: 16 August 2022 / Revised: 26 September 2022 / Accepted: 2 October 2022 / Published: 10 November 2022
(This article belongs to the Special Issue Remote Sensing of Water Cycle: Recent Developments and New Insights)

Abstract

Terrestrial water storage (TWS) anomalies derived from the Gravity Recovery and Climate Experiment (GRACE) mission have been useful for several earth science applications, ranging from global earth system science studies to regional water management. However, the relatively short record of GRACE has limited its use in understanding the climate-driven interannual-to-decadal variability in TWS. Targeting these timescales, we used the novel method of cyclostationary empirical orthogonal functions (CSEOFs) and the common modes of variability of TWS with precipitation and temperature to reconstruct the TWS record of 1979–2020. Using the same common modes of variability, we also provide a realistic, time-varying uncertainty estimate of the reconstructed TWS. The interannual variability in the resulting TWS record is consistent in space and time, and links the global variations in TWS to the regional ones. In particular, we highlight improvements in the representation of ENSO variability when compared to other available TWS reconstructions.
Keywords: cyclostationary empirical functions; remote sensing; terrestrial water storage; TWS reconstruction; GRACE cyclostationary empirical functions; remote sensing; terrestrial water storage; TWS reconstruction; GRACE

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MDPI and ACS Style

Chandanpurkar, H.A.; Hamlington, B.D.; Reager, J.T. Global Terrestrial Water Storage Reconstruction Using Cyclostationary Empirical Orthogonal Functions (1979–2020). Remote Sens. 2022, 14, 5677. https://doi.org/10.3390/rs14225677

AMA Style

Chandanpurkar HA, Hamlington BD, Reager JT. Global Terrestrial Water Storage Reconstruction Using Cyclostationary Empirical Orthogonal Functions (1979–2020). Remote Sensing. 2022; 14(22):5677. https://doi.org/10.3390/rs14225677

Chicago/Turabian Style

Chandanpurkar, Hrishikesh A., Benjamin D. Hamlington, and John T. Reager. 2022. "Global Terrestrial Water Storage Reconstruction Using Cyclostationary Empirical Orthogonal Functions (1979–2020)" Remote Sensing 14, no. 22: 5677. https://doi.org/10.3390/rs14225677

APA Style

Chandanpurkar, H. A., Hamlington, B. D., & Reager, J. T. (2022). Global Terrestrial Water Storage Reconstruction Using Cyclostationary Empirical Orthogonal Functions (1979–2020). Remote Sensing, 14(22), 5677. https://doi.org/10.3390/rs14225677

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