Surface freshwater storage and variability in the Amazon basin from multi‐satellite observations, 1993–2007

Papa, Fabrice; Frappart, Frédéric; Güntner, Andreas; Prigent, Catherine; Aires, Filipe; Getirana, Augusto; Maurer, Raffael

doi:10.1002/2013jd020500

Cited by 56 publications

(56 citation statements)

References 101 publications

(159 reference statements)

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“…Covering more than 300,000 km 2 , the Amazon extensive floodplains play a crucial role for global climate and biodiversity, but they are still poorly monitored at a large scale, limiting our understanding of their role in flood hazard, carbon production, sediment transport, nutriment exchange and air-land interactions. Surface water stored in floodplains represents about half of the terrestrial water storage and 15-20% of the water that flowed out of the Amazon floodplains [49][50][51][52][53]. Because it extends over two hemispheres, the Amazon region is characterized by several rainfall regimes.…”

Section: Study Areasmentioning

confidence: 99%

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Mapping Dynamic Water Fraction under the Tropical Rain Forests of the Amazonian Basin from SMOS Brightness Temperatures

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Frappart

et al. 2017

Water

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Inland surface waters in tropical environments play a major role in the water and carbon cycle. Remote sensing techniques based on passive, active microwave or optical wavelengths are commonly used to provide quantitative estimates of surface water extent from regional to global scales. However, some of these estimates are unable to detect water under dense vegetation and/or in the presence of cloud coverage. To overcome these limitations, the brightness temperature data at L-band frequency from the Soil Moisture and Ocean Salinity (SMOS) mission are used here to estimate flood extent in a contextual radiative transfer model over the Amazon Basin. At this frequency, the signal is highly sensitive to the standing water above the ground, and the signal provides information from deeper vegetation density than higher-frequencies. Three-day and (25 km × 25 km) resolution maps of water fraction extent are produced from 2010 to 2015. The dynamic water surface extent estimates are compared to altimeter data (Jason-2), land cover classification maps (IGBP, GlobeCover and ESA CCI) and the dynamic water surface product (GIEMS). The relationships between the water surfaces, precipitation and in situ discharge data are examined. The results show a high correlation between water fraction estimated by SMOS and water levels from Jason-2 (R > 0.98). Good spatial agreements for the land cover classifications and the water cycle are obtained.

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Section: Study Areasmentioning

confidence: 99%

“…This product is fully described in [42]. These data were subsequently employed in estimating surface water storage variations in large river basins [50,65,66]. To be compared with our data, the GIEMS product was averaged during the full period and considered as a static climatological product.…”

Section: Dynamics and Climatoloy Of Water Fraction Datamentioning

confidence: 99%

Mapping Dynamic Water Fraction under the Tropical Rain Forests of the Amazonian Basin from SMOS Brightness Temperatures

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Frappart

et al. 2017

Water

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“…Following Frappart et al [27,62], water levels for a given month were linearly interpolated over GIEMS inundation. Each monthly map of surface water levels had a spatial resolution of 0.25°, and the elevation of each pixel was provided with reference to a map of minimum water levels estimated for the entire observation period (2003-2007) using a hypsometric approach (see Frappart et al [28,29]). The hypsometric approach permitted us to account for the difference of elevation in each cell area of the multi-satellite inundation dataset corresponding to, for instance, the difference in elevation between the river and the floodplain.…”

Section: Monthly Maps Of Surface Water Levelsmentioning

confidence: 99%

“…In South America, previous studies using remotely-sensed observations have mainly provided a multi-year monitoring of changes in extent (e.g., [20][21][22][23][24][25][26]) and storage (e.g., [27][28][29]) at the basin-scale. In this study, we analyzed changes in inundation extent and water storage in the floodplains of the Orinoco Basin using multi-satellite observations.…”

Section: Introductionmentioning

confidence: 99%

Surface Freshwater Storage Variations in the Orinoco Floodplains Using Multi-Satellite Observations

et al. 2014

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Variations in surface water extent and storage are poorly characterized from regional to global scales. In this study, a multi-satellite approach is proposed to estimate the water stored in the floodplains of the Orinoco Basin at a monthly time-scale using remotely-sensed observations of surface water from the Global Inundation Extent Multi-Satellite (GIEMS) and stages from Envisat radar altimetry. Surface water storage variations over [2003][2004][2005][2006][2007] exhibit large interannual variability and a strong seasonal signal, peaking during summer, OPEN ACCESSRemote Sens. 2015, 7 90 and associated with the flood pulse. The volume of surface water storage in the Orinoco Basin was highly correlated with the river discharge at Ciudad Bolivar (R = 0.95), the closest station to the mouth where discharge was estimated, although discharge lagged one month behind storage. The correlation remained high (R = 0.73) after removing seasonal effects. Mean annual variations in surface water volume represented ~170 km 3 , contributing to ~45% of the Gravity Recovery and Climate Experiment (GRACE)-derived total water storage variations and representing ~13% of the total volume of water that flowed out of the Orinoco Basin to the Atlantic Ocean.

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“…Multi-criteria calibration should comprise more than two observables to further constrain the space of plausible model realizations. Besides the types of satellite-based data on states and fluxes mentioned above, information on water storage in surface water bodies has a high potential as a large-scale calibration constraint, based on currently available multi-sensor combination data (for example, Papa et al 2013) and future satellite missions such as SWOT. With the development of multi-scale modelling and parameterization concepts (Samaniego et al 2010), even observation data with a small spatial measurement support but a global coverage such as evapotranspiration from Fluxnet eddy-covariance sites or near-surface soil moisture based on GNSS reflectometry (Larson et al 2008), for instance, may inform parameter adjustment in global models within a multi-criteria calibration approach.…”

Section: Multi-criteria Calibration and Data Assimilationmentioning

confidence: 99%

Modelling Freshwater Resources at the Global Scale: Challenges and Prospects

et al. 2015

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Quantification of spatially and temporally resolved water flows and water storage variations for all land areas of the globe is required to assess water resources, water scarcity and flood hazards, and to understand the Earth system. This quantification is done with the help of global hydrological models (GHMs). What are the challenges and prospects in the development and application of GHMs? Seven important challenges are presented. (1) Data scarcity makes quantification of human water use difficult even though significant progress has been achieved in the last decade. (2) series of water availability and use are needed to provide good indicators of water scarcity.(6) Integration of gradient-based groundwater modelling into GHMs is necessary for a better simulation of groundwater-surface water interactions and capillary rise. (7) Detection and attribution of human interference with freshwater systems by using GHMs are constrained by data of insufficient quality but also GHM uncertainty itself. Regarding prospects for progress, we propose to decrease the uncertainty of GHM output by making better use of in situ and remotely sensed observations of output variables such as river discharge or total water storage variations by multi-criteria validation, calibration or data assimilation. Finally, we present an initiative that works towards the vision of hyperresolution global hydrological modelling where GHM outputs would be provided at a 1-km resolution with reasonable accuracy.

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Surface freshwater storage and variability in the Amazon basin from multi‐satellite observations, 1993–2007

Cited by 56 publications

References 101 publications

Mapping Dynamic Water Fraction under the Tropical Rain Forests of the Amazonian Basin from SMOS Brightness Temperatures

Mapping Dynamic Water Fraction under the Tropical Rain Forests of the Amazonian Basin from SMOS Brightness Temperatures

Surface Freshwater Storage Variations in the Orinoco Floodplains Using Multi-Satellite Observations

Modelling Freshwater Resources at the Global Scale: Challenges and Prospects

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