Global-scale analysis of satellite-derived time series of naturally inundated areas as a basis for floodplain modeling

Adam, Linda; Döll, Petra; Prigent, Catherine; Papa, Fabrice

doi:10.5194/adgeo-27-45-2010

Cited by 12 publications

(9 citation statements)

References 15 publications

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“…It has been used in many GRACE-related studies and has been calibrated against GRACE by Werth and Güntner [2010]. Furthermore, specific model versions exist that consider updated anthropogenic water use , improved floodplain dynamics [Adam et al, 2010], or higher spatial resolution (5 0 Â 5 0 ) [aus der Beek et al, 2011]. Here, we use 0.5 ∘ Â 0.5 ∘ output fields of WGHM provided by Döll et al [2011], with the long-term average TWS removed to obtain anomalies comparable to GRACE results.…”

Section: Wghm Datamentioning

confidence: 99%

Land water contribution to sea level from GRACE and Jason‐1measurements

Jensen¹,

Rietbroek²,

Kusche³

2013

JGR Oceans

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[1] We investigate the effect of water storage changes in the world's major hydrological catchment basins on global and regional sea level change at seasonal and long-term time scales. In a joint inversion using GRACE and Jason-1 data, we estimate the time-dependent sea level contributions of 124 spatial patterns ("fingerprints") including glacier and ice sheet melting, thermal expansion, changes in the terrestrial hydrological cycle, and glacial isostatic adjustment. Particularly, for hydrological storage changes, we derive fingerprints of the 33 world's largest catchment basins, assuming mass distributions derived from the leading EOFs of total water storage in the WaterGap Global Hydrological Model (WGHM). From our inversion, we estimate a contribution of terrestrial hydrological cycle changes to global mean sea level of À 0.20 AE 0.04 mm/yr with an annual amplitude of 6.6 AE 0.5 mm for August 2002 to July 2009. Using only GRACE data in the inversion and comparing to hydrological changes derived from GRACE data directly using a basin averaging method shows a good agreement on a global scale, but considerable differences are found for individual catchment basins (up to 180%). Hydrological storage change estimates in 33 basins from the GRACE/ Jason fingerprint inversion indicate a trend 46% smaller and an annual amplitude 43% bigger compared to WGHM-derived storage changes. Mapping the hydrological trends to regional sea level reveals the strongest sea level rise along the coastlines of South America (max 0.9 mm/yr) and West Africa (max 0.4 mm/yr), whereas around Alaska and Australia, we find the hydrological component of sea level falling (min À2.0 mm/yr and À0.9 mm/yr).Citation: Jensen, L., R. Rietbroek, and J. Kusche (2013), Land water contribution to sea level from GRACE and Jason-1 measurements,

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Section: Wghm Datamentioning

confidence: 99%

Land water contribution to sea level from GRACE and Jason‐1measurements

Jensen¹,

Rietbroek²,

Kusche³

2013

JGR Oceans

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“…Because a key requirement for wetlands to produce CH 4 is by anaerobic soil respiration, where saturated or flooded soil conditions limit oxygen availability and thus create a suitable environment for methanogenesis, the accurate mapping of wetland area is critically important for estimating emissions. We address this issue, and problems related to comprehensively mapping wetland types (Adam et al 2010, by merging dynamic satellite remote sensing data of surface inundation for the 2000-2012 period (Schroeder et al 2015) with a static inventory of wetlands (Lehner and Doll 2004) following the same definition for natural wetlands used in Matthews and Fung (1987) and Melton et al (2013). These wetland definitions include both permanently and seasonally flooded soils, and include soils with either surface inundation or sub-surface saturation or both.…”

Section: Introductionmentioning

confidence: 99%

Global wetland contribution to 2000–2012 atmospheric methane growth rate dynamics

Poulter

Bousquet

Canadell

et al. 2017

Environ. Res. Lett.

159

236

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Increasing atmospheric methane (CH 4 ) concentrations have contributed to approximately 20% of anthropogenic climate change. Despite the importance of CH 4 as a greenhouse gas, its atmospheric growth rate and dynamics over the past two decades, which include a stabilization period (1999)(2000)(2001)(2002)(2003)(2004)(2005)(2006), followed by renewed growth starting in 2007, remain poorly understood. We provide an updated estimate of CH 4 emissions from wetlands, the largest natural global CH 4 source, for 2000-2012 using an ensemble of biogeochemical models constrained with remote sensing surface inundation and inventory-based wetland area data. Between 2000-2012, boreal wetland CH 4 emissions increased by 1.2 Tg yr −1 (−0.2-3.5 Tg yr −1 ), tropical emissions decreased by 0.9 Tg yr −1 (−3.2−1.1 Tg yr −1 ), yet globally, emissions remained unchanged at 184 ± 22 Tg yr −1 . Changing air temperature was responsible for increasing high-latitude emissions whereas declines in low-latitude wetland area decreased tropical emissions; both dynamics are consistent with features of predicted centennial-scale climate change impacts on wetland CH 4 emissions. Despite uncertainties in wetland area mapping, our study shows that global wetland CH 4 emissions have not contributed significantly to the period of renewed atmospheric CH 4 growth, and is consistent with findings from studies that indicate some combination of increasing fossil fuel and agriculture-related CH 4 emissions, and a decrease in the atmospheric oxidative sink.

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“…Previous analysis [2,21,38] suggested that GIEMS overestimates the actual surface water extents in regions of very saturated soils. To overcome this issue, we use external information on flood coverage from the Dartmouth Flood Observatory (DFO) database that provides surface water extent for the period 2002-2015 [59].…”

Section: Giems Surface Water Extent Thresholdingmentioning

confidence: 99%

“…Over the Indian Sub-Continent (and especially GB), the spatial distribution of GIEMS was evaluated against static surface water dataset (Global Lake and Wetland Dataset, GLWD-3, [37]) and other related hydrological variables (precipitation, altimeter-derived river heights, river discharge) in [5,21], as well as using other regional surveys representing various components of wetland and open-water distributions [38]. Figure 2 shows GIEMS characteristics over the GB basin.…”

Section: Global Inundation Extent From Multi-satellites (Giems)mentioning

confidence: 99%

Fifteen Years (1993–2007) of Surface Freshwater Storage Variability in the Ganges-Brahmaputra River Basin Using Multi-Satellite Observations

Salameh

Frappart

Papa

et al. 2017

Water

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Surface water storage is a key component of the terrestrial hydrological and biogeochemical cycles that also plays a major role in water resources management. In this study, surface water storage (SWS) variations are estimated at monthly time-scale over 15 years (1993)(1994)(1995)(1996)(1997)(1998)(1999)(2000)(2001)(2002)(2003)(2004)(2005)(2006)(2007)) using a hypsographic approach based on the combination of topographic information from Advance Spaceborne Thermal Emission and Reflection Radiometer (ASTER) and Hydrological Modeling and Analysis Platform (HyMAP)-based Global Digital Elevation Models (GDEM) and the Global Inundation Extent Multi-Satellite (GIEMS) product in the Ganges-Brahmaputra basin. The monthly variations of the surface water storage are in good accordance with precipitation from Global Precipitation Climatology Project (GPCP), river discharges at the outlet of the Ganges and the Brahmaputra, and terrestrial water storage (TWS) from the Gravity Recovery And Climate Experiment (GRACE), with correlations higher than 0.85. Surface water storage presents a strong seasonal signal (~496 km 3 estimated by GIEMS/ASTER and~378 km 3 by GIEMS/HyMAPs), representing~51% and 41% respectively of the total water storage signal and it exhibits a large inter-annual variability with strong negative anomalies during the drought-like conditions of 1994 or strong positive anomalies such as in 1998. This new dataset of SWS is a new, highly valuable source of information for hydrological and climate modeling studies of the Ganges-Brahmaputra river basin.

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Global-scale analysis of satellite-derived time series of naturally inundated areas as a basis for floodplain modeling

Abstract: ADGE

Cited by 12 publications

References 15 publications

Land water contribution to sea level from GRACE and Jason‐1measurements

Land water contribution to sea level from GRACE and Jason‐1measurements

Global wetland contribution to 2000–2012 atmospheric methane growth rate dynamics

Fifteen Years (1993–2007) of Surface Freshwater Storage Variability in the Ganges-Brahmaputra River Basin Using Multi-Satellite Observations

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