Prospects for Imaging Terrestrial Water Storage in South America Using Daily GPS Observations

Ferreira, Vagner G.; Ndehedehe, Christopher E.; Castro, Henry Montecino; Yong, Bin; Peng, Yingguo; Abdalla, Ahmed; Mohammed, Abubakar S.

doi:10.3390/rs11060679

Cited by 35 publications

(25 citation statements)

References 67 publications

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“…5a). To this end, poroelastic effects due to the lowering of groundwater aquifers (e.g., Tan et al 2016) do not seem to be apparent in the GPS-VCDs over South America (Ferreira et al 2019). However, few stations demonstrate significant anti-correlation with significant amplitudes, which could indicate phase lags between those displacements observed by GPS and those derived from GRACE and CLMs.…”

Section: Sourcementioning

confidence: 94%

Determining seasonal displacements of Earth’s crust in South America using observations from space-borne geodetic sensors and surface-loading models

Ferreira

Castro

Ndehedehe

et al. 2019

Earth Planets Space

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There are small pieces of evidence, suggesting that South America's hydrological cycle is changing, which impacts its water availability and, consequently, the Earth's surface due to its elastic response to the surface mass loading/ unloading. Therefore, we analyzed 3 to 15 years of vertical crustal displacements (VCDs) due to mass loadings using 292 Global Positioning System (GPS) stations in South America, which are essential for studies related to tectonic phenomena, for example. Thus, we investigated whether the intra-annual variabilities of the displacements could be reduced using modeled VCDs and inverted displacements from Gravity Recovery and Climate Experiment (GRACE) harmonic solutions (Release 06). The modeled VCDs come from the combination of nontidal atmospheric and ocean loadings with the hydrological loadings from the land-surface model (GLDAS) and reanalysis (MERRA). We found that the highest amplitudes of VCDs of the annual signals are concentrated mainly over the Amazon Rainforest and Brazilian Highlands. However, the results also show different behavior throughout other physiographic provinces of South America, which shows low water capabilities as "sensed" by GRACE and described by the GLDAS and MERRA models. Accordingly, when we disregard the stations over the Andes Mountains and Patagonia in the analysis, the highest reduction in the variability of GPS-observed VCDs is achieved while using GRACE (79% of the sites), MERRA (75% of the sites), and GLDAS (74% of the sites). For these stations, the amplitudes (and phases) of the annual signals depicted by the geodetic sensors generally agree, while those from GLDAS and MERRA explain only approximately 50% of the deformation. However, in the southwest region of South America (between the latitude bands of − 40° to − 30°), GPS annual signals, which reached up to 11 mm, are much larger than those from GRACE and the models due to the existence of lakes that are not resolved in global analysis. These inconsistencies between GPS-observed VCDs and those derived from GRACE and the other models require further investigation, specifically for Chile.

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Section: Sourcementioning

confidence: 94%

Determining seasonal displacements of Earth’s crust in South America using observations from space-borne geodetic sensors and surface-loading models

Ferreira

Castro

Ndehedehe

et al. 2019

Earth Planets Space

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“…SA is one the most physiographically diverse wet-dry tropics that is characterised by numerous aquifer types, highlands, lowlands, and highly-productive watersheds (e.g., 21). The continent is home to extremely wet river basins (Amazon river basin) and considerably drier regions where geodetic perturbations and the impacts of other physical processes such as earthquakes and land subsidence have been reported (19,21,45).…”

Section: Study Regionmentioning

confidence: 99%

Identifying the footprints of global climate modes in time-variable gravity hydrological signals

Ndehedehe

Ferreira

2019

Climatic Change

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Modelling continental freshwater dynamics is expected to be challenging in regions with considerable influence of multi-scale global climatic drivers. An assessment of the interplay between these climatic drivers (e.g., El-Niño Southern Oscillation-ENSO) that influence hydro-climatic conditions and hydrological processes is therefore required to optimize predictive frameworks. The main aim of this study is to assess the impacts of eleven key climate modes describing oceanic variability in the nearby oceans on the spatial and temporal distributions of terrestrial water storage (TWS) derived from Gravity Recovery and Climate Experiment (GRACE) (2002 − 2017) over South America (SA). Considering that SA accounts for nearly one fifth of global continental freshwater discharge, this assessment is crucial because of the differences in the intrinsic response of freshwater availability in some regions to several important processes of inter-annual variability. The novel integration of independent component analysis with parameter estimation techniques in this study shows that climate variability drivers (ENSO; Southern Oscillation Index-SOI; Pacific Decadal Oscillation-PDO; Ninos 1+2, 3.0, 3.4 and 4.0; North Tropical Atlantic-NTA and the Caribbean Sea Surface Temperature (SST) anomalies) have considerable association (α = 0.05) with GRACE-derived TWS over SA. The influence of Nino 4.0 (r = −0.72), Nino 3.4 (−0.68), Nino 3.0 (−0.53), ENSO (r = −0.71), PDO (r = −0.69), SOI (r = 0.64), Caribbean SST (r = −0.67), and NTA (r = −0.51) on TWS are relatively stronger in tropical SA (Amazon basin/northern SA) and result in higher amplitudes of TWS (> 100 mm). Given the temporal and spatial relationships of TWS with PDO over SA, there is also evidence to suggest strong multi-decadal variability in TWS.

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“…Through a comparison of vertical displacements derived from hydrologic models, GRACE, and ground‐based GPS, the capability of ground‐based GPS observations to infer TWS changes has been demonstrated in regions across the globe such as in the Himalayas (Chanard et al., 2014; Fu & Freymueller, 2012; Zhang et al., 2018), the western United States (Knappe et al., 2019; Tan et al., 2016; Wahr et al., 2013; Yin et al., 2020), the North China Plain (Wang et al., 2017), and Australia (Han & Razeghi, 2017). A number of studies have attempted to use ground‐based GPS observations of vertical displacement to estimate TWS changes at both regional (Argus et al., 2014, 2017) and continental (Ferreira et al., 2019) scales, and GPS‐based TWS anomalies have been successfully applied to study the impact of drought (Borsa et al., 2014) and hurricanes (Milliner et al., 2018) on the terrestrial water cycle. The potential of using GPS‐based observations to infer TWS components such as snow (Enzminger et al., 2018; Ouellette et al., 2013) and groundwater (Gautam et al., 2017; Ojha et al., 2019) has also been examined.…”

Section: Introductionmentioning

confidence: 99%

“…

Accurate estimation of terrestrial water storage (TWS) changes in space and time is critically important in understanding the global hydrological cycle and changes in regional climate (Ferreira et al, 2019;Rodell & Famiglietti, 2002;Syed et al, 2008). However, it is difficult to study TWS using in situ measurements due to the limited spatial coverage of networks, especially across continental and global scales (Chew & Small, 2014).

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confidence: 99%

Assimilation of Ground‐Based GPS Observations of Vertical Displacement into a Land Surface Model to Improve Terrestrial Water Storage Estimates

Yin

Forman

Wang

2021

Water Resources Research

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Ground‐based Global Positioning System (GPS) observations of vertical surface displacement can be used to study terrestrial water storage (TWS) change after properly accounting for nonhydrological loading effects. This study systematically merged ground‐based GPS observations of vertical displacement into a land surface model in order to better estimate TWS. Assimilation was conducted across two snow‐dominated watersheds in the western United States using a one‐dimensional ensemble Kalman filter (EnkF). Modeled estimates were compared against TWS retrievals derived from the Gravity Recovery and Climate Experiment (GRACE) mission and in situ measurements of snow water equivalent (SWE), soil moisture, and runoff. The GPS data assimilation (GPS DA) technique improves prediction skill of TWS anomalies relative to the Open Loop (OL; model run without assimilation) when compared against GRACE TWS retrievals, especially during an extended drought period post‐2011 (e.g., correlation coefficient ROL = 0.46 and RGPSDA = 0.82 in the Great Basin). Furthermore, GPS DA improves SWE estimation with improved R values found over 76% and 69% of all pixels collocated with in situ stations in the Great Basin and Upper Colorado watersheds, respectively. GPS DA estimates of surface soil moisture and runoff, however, exhibit degraded performance relative to the OL due to the limited sensitivity of GPS observations to surface soil moisture variations and the lack of a dynamic surface routing scheme as well as other missing model physics (e.g., river and dam regulation, irrigation‐related water withdrawals, surface water impoundments) that are not included in the Catchment Land Surface Model.

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Prospects for Imaging Terrestrial Water Storage in South America Using Daily GPS Observations

Cited by 35 publications

References 67 publications

Determining seasonal displacements of Earth’s crust in South America using observations from space-borne geodetic sensors and surface-loading models

Determining seasonal displacements of Earth’s crust in South America using observations from space-borne geodetic sensors and surface-loading models

Identifying the footprints of global climate modes in time-variable gravity hydrological signals

Assimilation of Ground‐Based GPS Observations of Vertical Displacement into a Land Surface Model to Improve Terrestrial Water Storage Estimates

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