Radar Altimetry as a Proxy for Determining Terrestrial Water Storage Variability in Tropical Basins

The Gravity Recovery and Climate Experiment (GRACE) mission has provided us with unforeseen information on terrestrial water-storage (TWS) variability, contributing to our understanding of global hydrological processes, including hydrological extreme events and anthropogenic impacts on water storage...

Full description

Bibliographic Details
Main Authors: Davi de C. D. Melo, Augusto Getirana
Format: Article
Language:English
Published: MDPI AG 2019-10-01
Series:Remote Sensing
Subjects:
Online Access:https://www.mdpi.com/2072-4292/11/21/2487
id doaj-8d1bf01dca694db4b23662129e066f19
record_format Article
spelling doaj-8d1bf01dca694db4b23662129e066f192020-11-25T00:10:07ZengMDPI AGRemote Sensing2072-42922019-10-011121248710.3390/rs11212487rs11212487Radar Altimetry as a Proxy for Determining Terrestrial Water Storage Variability in Tropical BasinsDavi de C. D. Melo0Augusto Getirana1Department of Soils and Rural Engineering, Federal University of Paraíba, Areia, PB 58397-000, BrazilHydrological Sciences Laboratory, NASA Goddard Space Flight Center, Greenbelt, MD 20771, USAThe Gravity Recovery and Climate Experiment (GRACE) mission has provided us with unforeseen information on terrestrial water-storage (TWS) variability, contributing to our understanding of global hydrological processes, including hydrological extreme events and anthropogenic impacts on water storage. Attempts to decompose GRACE-based TWS signals into its different water storage layers, i.e., surface water storage (SWS), soil moisture, groundwater and snow, have shown that SWS is a principal component, particularly in the tropics, where major rivers flow over arid regions at high latitudes. Here, we demonstrate that water levels, measured with radar altimeters at a limited number of locations, can be used to reconstruct gridded GRACE-based TWS signals in the Amazon basin, at spatial resolutions ranging from 0.5 to 3°, with mean absolute errors (MAE) as low as 2.5 cm and correlations as high as 0.98. We show that, at 3° spatial resolution, spatially-distributed TWS time series can be precisely reconstructed with as few as 41 water-level time series located within the basin. The proposed approach is competitive when compared to existing TWS estimates derived from physically based and computationally expensive methods. Also, a validation experiment indicates that TWS estimates can be extrapolated to periods beyond that of the model regression with low errors. The approach is robust, based on regression models and interpolation techniques, and offers a new possibility to reproduce spatially and temporally distributed TWS that could be used to fill inter-mission gaps and to extend GRACE-based TWS time series beyond its timespan.https://www.mdpi.com/2072-4292/11/21/2487graceradar altimetryamazonterrestrial water storagesurface water
collection DOAJ
language English
format Article
sources DOAJ
author Davi de C. D. Melo
Augusto Getirana
spellingShingle Davi de C. D. Melo
Augusto Getirana
Radar Altimetry as a Proxy for Determining Terrestrial Water Storage Variability in Tropical Basins
Remote Sensing
grace
radar altimetry
amazon
terrestrial water storage
surface water
author_facet Davi de C. D. Melo
Augusto Getirana
author_sort Davi de C. D. Melo
title Radar Altimetry as a Proxy for Determining Terrestrial Water Storage Variability in Tropical Basins
title_short Radar Altimetry as a Proxy for Determining Terrestrial Water Storage Variability in Tropical Basins
title_full Radar Altimetry as a Proxy for Determining Terrestrial Water Storage Variability in Tropical Basins
title_fullStr Radar Altimetry as a Proxy for Determining Terrestrial Water Storage Variability in Tropical Basins
title_full_unstemmed Radar Altimetry as a Proxy for Determining Terrestrial Water Storage Variability in Tropical Basins
title_sort radar altimetry as a proxy for determining terrestrial water storage variability in tropical basins
publisher MDPI AG
series Remote Sensing
issn 2072-4292
publishDate 2019-10-01
description The Gravity Recovery and Climate Experiment (GRACE) mission has provided us with unforeseen information on terrestrial water-storage (TWS) variability, contributing to our understanding of global hydrological processes, including hydrological extreme events and anthropogenic impacts on water storage. Attempts to decompose GRACE-based TWS signals into its different water storage layers, i.e., surface water storage (SWS), soil moisture, groundwater and snow, have shown that SWS is a principal component, particularly in the tropics, where major rivers flow over arid regions at high latitudes. Here, we demonstrate that water levels, measured with radar altimeters at a limited number of locations, can be used to reconstruct gridded GRACE-based TWS signals in the Amazon basin, at spatial resolutions ranging from 0.5 to 3°, with mean absolute errors (MAE) as low as 2.5 cm and correlations as high as 0.98. We show that, at 3° spatial resolution, spatially-distributed TWS time series can be precisely reconstructed with as few as 41 water-level time series located within the basin. The proposed approach is competitive when compared to existing TWS estimates derived from physically based and computationally expensive methods. Also, a validation experiment indicates that TWS estimates can be extrapolated to periods beyond that of the model regression with low errors. The approach is robust, based on regression models and interpolation techniques, and offers a new possibility to reproduce spatially and temporally distributed TWS that could be used to fill inter-mission gaps and to extend GRACE-based TWS time series beyond its timespan.
topic grace
radar altimetry
amazon
terrestrial water storage
surface water
url https://www.mdpi.com/2072-4292/11/21/2487
work_keys_str_mv AT davidecdmelo radaraltimetryasaproxyfordeterminingterrestrialwaterstoragevariabilityintropicalbasins
AT augustogetirana radaraltimetryasaproxyfordeterminingterrestrialwaterstoragevariabilityintropicalbasins
_version_ 1725409322160095232