Absolute water storages in the Congo River floodplains from integration of InSAR and satellite radar altimetry

Ting Yuan, Hyongki Lee, Hahn Chul Jung, Abureli Aierken, Edward Beighley, Douglas E. Alsdorf, Raphael M. Tshimanga, Donghwan Kim

Research output: Contribution to journalArticlepeer-review

40 Citations (Scopus)

Abstract

Floodplains delay the transport of water, dissolved matter and sediments by storing water during flood peak seasons. Estimation of water storage over the floodplains is essential to understand the water balances in the fluvial systems and the role of floodplains in nutrient and sediment transport. However, spatio-temporal variations of water storages over floodplains are not well known due to their remoteness, vastness, and high temporal variability. In this study, we propose a new method to estimate absolute water storages over the floodplains by establishing relations between water depths (d) and water volumes (V) using 2-D water depth maps from the integration of Interferometric Synthetic Aperture Radar (InSAR) and altimetry measurements. We applied this method over the Congo River floodplains and modeled the d − V relation using a power function (note that d − V indicates relation between d and V, not d minus V), which revealed the cross-section geometry of the floodplains as a convex curve. Then, we combined this d − V relation and Envisat altimetry measurements to construct time series of floodplain's absolute water storages from 2002 to 2011. Its mean annual amplitude over the floodplains (~ 7,777 km2) is 3.86 ± 0.59 km3 with peaks in December, which lags behind total water storage (TWS) changes from the Gravity Recovery and Climate Experiment (GRACE) and precipitation changes from Tropical Rainfall Measuring Mission (TRMM) by about one month. The results also exhibit inter-annual variability, with maximum water volume to be 5.9 ± 0.72 km3 in the wet year of 2002 and minimum volume to be 2.01 ± 0.63 km3 in the dry year of 2005. The inter-annual variation of water storages can be explained by the changes of precipitation from TRMM.

Original languageEnglish
Pages (from-to)57-72
Number of pages16
JournalRemote Sensing of Environment
Volume201
DOIs
Publication statusPublished - 2017 Nov

Bibliographical note

Publisher Copyright:
© 2017 Elsevier Inc.

All Science Journal Classification (ASJC) codes

  • Soil Science
  • Geology
  • Computers in Earth Sciences

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