Surface freshwater storage and dynamics in the Amazon basin during the 2005 exceptional drought

Frappart, Frédéric; Papa, Fabrice; Silva, Joécila Santos da; Ramillien, Guillaume; Prigent, Catherine; Seyler, Frédérique; Calmant, Stéphane

doi:10.1088/1748-9326/7/4/044010

Cited by 135 publications

(143 citation statements)

References 29 publications

Supporting

Mentioning

125

Contrasting

Unclassified

Order By: Relevance

“…We used satellite-based products to quantify the monthly distribution and variation of surface freshwater extent from the GIEMS dataset (e.g., [24,26,42]) and freshwater storage from a new product based on the combination of surface water extent from GIEMS and inland water stages from Envisat radar altimetry following the approach proposed earlier by Frappart et al [28]. A total of 83 time series of water level variations were constructed using Envisat radar altimeter observations.…”

Section: Discussionmentioning

confidence: 99%

“…We used both datasets during their period of common availability, from 2003 to 2007. Details of the two-step methodology have been published in [28,62] and are summarized below.…”

Section: Methods For Estimating Surface Water Storagementioning

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%

See 2 more Smart Citations

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

et al. 2014

Self Cite

View full text Add to dashboard Cite

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.

show abstract

Section: Discussionmentioning

confidence: 99%

“…We used both datasets during their period of common availability, from 2003 to 2007. Details of the two-step methodology have been published in [28,62] and are summarized below.…”

Section: Methods For Estimating Surface Water Storagementioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

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

et al. 2014

Self Cite

View full text Add to dashboard Cite

show abstract

“…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%

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

Parrens

Bitar

Frappart

et al. 2017

Water

Self Cite

View full text Add to dashboard Cite

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.

show abstract

“…Recent studies have demonstrated the potential of combining satellite imagery and radar altimetry to estimate the volume of water stored in lakes, rivers, and floodplains, and how these volumes change in response to climate variability and/or anthropogenic effects using SAR images, multispectral images, or multi-satellite observations (Frappart et al, 2005(Frappart et al, , 2006b(Frappart et al, , 2008(Frappart et al, , 2012Yesou et al, 2007;Ding and Li, 2011;Haibo et al, 2011;Duan and Bastiaanssen, 2013). Despite the relevance of these results, these techniques have not been applied yet to study small lakes due to the difficulty in collecting synchronously radar altimeter data, highresolution images and consistent ground data.…”

Section: F Baup Et Al: Combining High-resolution Satellite Images Amentioning

confidence: 99%

Combining high-resolution satellite images and altimetry to estimate the volume of small lakes

Baup

Frappart

Maubant

2014

Hydrol. Earth Syst. Sci.

View full text Add to dashboard Cite

Abstract. This study presents an approach to determining the volume of water in small lakes (< 100 ha) by combining satellite altimetry data and high-resolution (HR) images. In spite of the strong interest in monitoring surface water resources on a small scale using radar altimetry and satellite imagery, no information is available about the limits of the remote-sensing technologies for small lakes mainly used for irrigation purposes. The lake being studied is located in the south-west of France and is only used for agricultural irrigation purposes. The altimetry satellite data are provided by an RA-2 sensor onboard Envisat, and the high-resolution images (< 10 m) are obtained from optical (Formosat-2) and synthetic aperture radar (SAR) antenna (Terrasar-X and Radarsat-2) satellites. The altimetry data (data are obtained every 35 days) and the HR images (77) have been available since 2003 and 2010, respectively. In situ data (for the water levels and volumes) going back to 2003 have been provided by the manager of the lake. Three independent approaches are developed to estimate the lake volume and its temporal variability. The first two approaches (HRBV and ABV) are empirical and use synchronous ground measurements of the water volume and the satellite data. The results demonstrate that altimetry and imagery can be effectively and accurately used to monitor the temporal variations of the lake (R 2 ABV = 0.98, RMSE ABV = 5%, R 2 HRBV = 0.90, and RMSE HRBV = 7.4 %), assuming a time-varying triangular shape for the shore slope of the lake (this form is well adapted since it implies a difference inferior to 2 % between the theoretical volume of the lake and the one estimated from bathymetry). The third method (AHRBVC) combines altimetry (to measure the lake level) and satellite images (of the lake surface) to estimate the volume changes of the lake and produces the best results (R 2 AHRBVC = 0.98) of the three methods, demonstrating the potential of future Sentinel and SWOT missions to monitor small lakes and reservoirs for agricultural and irrigation applications.

show abstract

Surface freshwater storage and dynamics in the Amazon basin during the 2005 exceptional drought

Cited by 135 publications

References 29 publications

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

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

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

Combining high-resolution satellite images and altimetry to estimate the volume of small lakes

Contact Info

Product

Resources

About