The Surface Water and Ocean Topography Mission: Observing Terrestrial Surface Water and Oceanic Submesoscale Eddies

Durand, M. T.; Fu, Lee‐Lueng; Lettenmaier, Dennis P.; Alsdorf, Douglas; Rodríguez, Ernesto; Esteban-Fernández, Daniel

doi:10.1109/jproc.2010.2043031

Cited by 305 publications

(183 citation statements)

References 49 publications

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“…As the fundamental variable for determining water table depth at global scale (Fan et al, 2013), soil moisture plays a key role in simulating the spatiotemporal variability of wetland dynamics. Since it is impossible to produce accurate large-scale estimates of soil moisture from in situ measurement networks (Bindlish et al, 2008;Dorigo et al, 2011), simulation combined with long-term surface and root zone remotely sensed estimates (de Rosnay et al, 2013; Kerr (Entekhabi et al, 2010) and Surface Water and Ocean Topography (SWOT) (Durand et al, 2010) are expected to provide soil moisture and will improve the capacity of global soil moisture simulations.…”

Section: Improved Modeling Of Soil Moisturementioning

confidence: 99%

Modeling spatiotemporal dynamics of global wetlands: comprehensive evaluation of a new sub-grid TOPMODEL parameterization and uncertainties

et al. 2016

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Abstract.Simulations of the spatiotemporal dynamics of wetlands are key to understanding the role of wetland biogeochemistry under past and future climate. Hydrologic inundation models, such as the TOPography-based hydrological model (TOPMODEL), are based on a fundamental parameter known as the compound topographic index (CTI) and offer a computationally cost-efficient approach to simulate wetland dynamics at global scales. However, there remains a large discrepancy in the implementations of TOPMODEL in landsurface models (LSMs) and thus their performance against observations. This study describes new improvements to TOPMODEL implementation and estimates of global wetland dynamics using the LPJ-wsl (Lund-Potsdam-Jena Wald Schnee und Landschaft version) Dynamic Global Vegetation Model (DGVM) and quantifies uncertainties by comparing three digital elevation model (DEM) products (HYDRO1k, GMTED, and HydroSHEDS) at different spatial resolution and accuracy on simulated inundation dynamics. In addition, we found that calibrating TOPMODEL with a benchmark wetland data set can help to successfully delineate the seasonal and interannual variation of wetlands, as well as improve the spatial distribution of wetlands to be consistent with inventories. The HydroSHEDS DEM, using a riverbasin scheme for aggregating the CTI, shows the best accuracy for capturing the spatiotemporal dynamics of wetlands among the three DEM products. The estimate of global wetland potential/maximum is ∼ 10.3 Mkm 2 (10 6 km 2 ), with a mean annual maximum of ∼ 5.17 Mkm 2 for 1980-2010. When integrated with wetland methane emission submodule, the uncertainty of global annual CH 4 emissions from topography inputs is estimated to be 29.0 Tg yr −1 . This study demonstrates the feasibility of TOPMODEL to capture spatial heterogeneity of inundation at a large scale and highlights the significance of correcting maximum wetland extent to improve modeling of interannual variations in wetland area. It additionally highlights the importance of an adequate investigation of topographic indices for simulating global wetlands and shows the opportunity to converge wetland estimates across LSMs by identifying the uncertainty associated with existing wetland products.

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Section: Improved Modeling Of Soil Moisturementioning

confidence: 99%

Modeling spatiotemporal dynamics of global wetlands: comprehensive evaluation of a new sub-grid TOPMODEL parameterization and uncertainties

et al. 2016

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“…that will cover most of the freshwater bodies in the world and will provide fine temporal and spatial resolution information (i.e., elevation and area) for water body dynamics (Durand et al, 2010;Fu et al, 2009). Additional approaches that may reduce uncertainty from currently available remote sensing data is to use a probabilistic framework to propagate the error from remote sensing data into modelling results (Callaghan et al, 2008;de Bruin et al, 2008;Hengl et al, 2010;Leon et al, 2014).…”

Section: Uncertainty and Limitationsmentioning

confidence: 99%

Where does all the water go? Partitioning water transmission losses in a data-sparse, multi-channel and low-gradient dryland river system using modelling and remote sensing

Jarihani

Larsen

Callow

et al. 2015

Journal of Hydrology

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Where does all the water go? Partitioning water transmission losses in a data-sparse, multi-channel and low-gradient dryland river system using modelling and remote sensing, Journal of Hydrology (2015), doi: http://dx.doi.org/10.1016/j.jhydrol. 2015.08.030 This is a PDF file of an unedited manuscript that has been accepted for publication. As a service to our customers we are providing this early version of the manuscript. The manuscript will undergo copyediting, typesetting, and review of the resulting proof before it is published in its final form. Please note that during the production process errors may be discovered which could affect the content, and all legal disclaimers that apply to the journal pertain. 1Where does all the water go? Partitioning water transmission losses in a data-sparse, multi-channel and low-gradient dryland river system using modelling and remote sensing and April 2012 along a 180 km reach of the Diamantina River in the Lake Eyre Basin, Australia.Transmission losses were found to be high, on average 46% of total inflow within 180 km reach segment or 7 GL/km (range: 4 to 10 GL/km). However, in 180 km reach, transmission losses vary non-linearly with flood discharge, with smaller flows resulting in higher losses (up to 68%), which diminish in higher flows (down to 24%) and in general there is a minor increase in losses with distance downstream. Partitioning these total losses into the major components shows that actual evaporation was the most significant component (21.6% of total inflow), followed by infiltration (13.2%) and terminal water storage (11.2%). Lateral inflow can be up to 200% of upstream inflow (mean = 86%) and is therefore a critical parameter in the water balance and 3 transmission loss calculations. This study shows that it is possible to constrain the water balance using hydrodynamic models in dryland river systems using remote sensing and simple field measurements to address the otherwise scarce availability of data. The results of this study also enable a better understanding of the water resources available for ecosystems in these unique multi-channel and large floodplain rivers. The combined modelling / remote sensing approach of this study can be applied elsewhere in the world to better understand the water balances and water transmission losses, important drivers of ecohydrological processes in dryland environments.

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“…Even wellmonitored countries have sparsely distributed networks, thus limiting current understanding of water losses along river courses, habitat changes, and flood risk (3,4). Satellites, in contrast, provide spatially dense coverage globally, attracting calls for a global river discharge mapping capacity from space (5)(6)(7)(8)(9)(10). However, previous efforts to estimate river discharge from remotely sensed observations have all required inclusion of some form of ancillary ground-based information, such as gauge measurements, bathymetric surveys, and/or calibrated hydrology models that are simply unavailable for most of the planet (11)(12)(13)(14)(15)(16)(17)(18).…”

mentioning

confidence: 99%

Toward global mapping of river discharge using satellite images and at-many-stations hydraulic geometry

Gleason

Smith

2014

Proc. Natl. Acad. Sci. U.S.A.

285

288

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Rivers provide critical water supply for many human societies and ecosystems, yet global knowledge of their flow rates is poor. We show that useful estimates of absolute river discharge (in cubic meters per second) may be derived solely from satellite images, with no ground-based or a priori information whatsoever. The approach works owing to discovery of a characteristic scaling law uniquely fundamental to natural rivers, here termed a river's at-many-stations hydraulic geometry. A first demonstration using Landsat Thematic Mapper images over three rivers in the United States, Canada, and China yields absolute discharges agreeing to within 20-30% of traditional in situ gauging station measurements and good tracking of flow changes over time. Within such accuracies, the door appears open for quantifying river resources globally with repeat imaging, both retroactively and henceforth into the future, with strong implications for water resource management, food security, ecosystem studies, flood forecasting, and geopolitics. S ome 80% of the world's population and 65% of its river ecosystems are threatened by insecure water supply, yet global knowledge of the river discharges upon which these depend is surprisingly poor (1, 2). For much of the world, river gauge measurements are rare, nonexistent, or proprietary. Even wellmonitored countries have sparsely distributed networks, thus limiting current understanding of water losses along river courses, habitat changes, and flood risk (3, 4). Satellites, in contrast, provide spatially dense coverage globally, attracting calls for a global river discharge mapping capacity from space (5-10). However, previous efforts to estimate river discharge from remotely sensed observations have all required inclusion of some form of ancillary ground-based information, such as gauge measurements, bathymetric surveys, and/or calibrated hydrology models that are simply unavailable for most of the planet (11-18). To remove this dependence on ground-based information, we show that useful estimates of absolute river discharge (i.e., in units of cubic meters per second) may be derived solely from multiple satellite images of a river, with no ground-based or a priori information whatsoever, through use of a characteristic scaling law, here termed a river's atmany-stations hydraulic geometry (AMHG). As will be shown in this paper, AMHG effectively halves the number of parameters required by traditional hydraulic geometry, thus paving the way for remote estimation of a single remaining parameter-and thus river discharge-through repeated satellite image observations along a river course. The presence of AMHG is verified in 12 of 12 rivers examined, using 88 in situ gauging stations, three fieldcalibrated hydrodynamic models incorporating 772 field-surveyed bathymetric cross-sections, and 42 Landsat Thematic Mapper (TM) satellite images (SI Text, section S1, Materials and Methods and Tables S1 and S2). Following a description of width AMHG, an innovative satellite discharge estimation approach i...

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The Surface Water and Ocean Topography Mission: Observing Terrestrial Surface Water and Oceanic Submesoscale Eddies

Cited by 305 publications

References 49 publications

Modeling spatiotemporal dynamics of global wetlands: comprehensive evaluation of a new sub-grid TOPMODEL parameterization and uncertainties

Modeling spatiotemporal dynamics of global wetlands: comprehensive evaluation of a new sub-grid TOPMODEL parameterization and uncertainties

Where does all the water go? Partitioning water transmission losses in a data-sparse, multi-channel and low-gradient dryland river system using modelling and remote sensing

Toward global mapping of river discharge using satellite images and at-many-stations hydraulic geometry

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