Spatiotemporal variability of global river extent and the natural driving factors revealed by decades of Landsat observations, GRACE gravimetry observations, and land surface model simulations

Shang Gao, Zhi Li, Mengye Chen, Peirong Lin, Zhen Hong, Daniel Allen, Thomas Neeson, Yang Hong

Research output: Contribution to journalArticlepeer-review

10 Scopus citations

Abstract

Rivers are among the most dynamic components in Earth's terrestrial water cycle and provide critical ecosystem services. Yet, the spatiotemporal variability of river surface extents remains largely unquantified at the global scale. Satellite remote sensing provides a promising alternative to in-situ observations, which can enable a more comprehensive survey and systematic analysis of global rivers at fine spatial resolutions. The study examines the spatiotemporal variability of river surface extent globally and its natural driving factors, by combining the use of Landsat-based Global Surface Water (GSW) and Global River Widths from Landsat (GRWL) databases. In addition to examining the long-term mean river surface extent in various climate zones, we perform statistical analyses to correlate monthly times series of fractional river extent with the terrestrial water storage (TWS) components obtained from the Gravity Recovery and Climate Experiment (GRACE) satellite observation and the Global Land Data Assimilation System (GLDAS) model simulations. Results show that the spatiotemporal variability of water presence in rivers can be explained well via differentiating climate zones. The analysis also shows that 52.7% of the global maximum river extent is covered by water less than half of time. Changes of fractional river extent are found to be highly correlated with groundwater storage in low- and mid-latitudes, whereas snow melting dominates the river dynamics in high latitudes. By examining the extremes of fractional river extent, we found that the abrupt changes of fractional river extent are well linked to precipitation anomalies in the equatorial, arid, and warm temperate areas. This study offers an innovative perspective to study spatiotemporal dynamics of rivers by combining optical remote sensing (Landsat), gravimetry observations (GRACE), and land surface simulations; and it highlights the significant role of low-flow-generating processes (snow melting, infiltration, and recharge-discharge) in controlling river dynamics in certain regions, which warrants future investigation.

Original languageEnglish (US)
Article number112725
JournalRemote Sensing of Environment
Volume267
DOIs
StatePublished - Dec 15 2021

All Science Journal Classification (ASJC) codes

  • Soil Science
  • Geology
  • Computers in Earth Sciences

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