Development and validation of a global database of lakes, reservoirs and wetlands

Lehner, Bernhard; Döll, Petra

doi:10.1016/j.jhydrol.2004.03.028

Cited by 2,042 publications

(1,737 citation statements)

References 16 publications

(35 reference statements)

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“…The distribution map for different wetland types are determined based on the data from Stillwell-Soller et al (1995); Aselman and Crutzen (1989) and Lehner and Döll (2004). DLEM simulates water transport to rivers based upon catchments, but does not explicitly move water through grid cells and thereby does not influence conditions in neighbouring grid cells.…”

Section: Dlemmentioning

confidence: 99%

“…We separately ran each fraction of the grid cells. Before simulations lakes and rivers were masked out using the GWLD (Global Wetland and Lakes Database) dataset (Lehner and Döll, 2004) and rice with the Leff datset (Leff et al, 2004).…”

Section: Wetchimp Set-upmentioning

confidence: 99%

“…Simulations were run separately for each portion of the grid cell. The lake/wetland portion of each grid cell was determined as the superset of the Sheng et al (2004) peatland map; wetlands, wet tundra, and croplands (so that nearby lakes could have a surrounding catchment) given by the Bartalev et al (2003) land cover classification; and lakes given by the Global Lake and Wetland Database (GLWD; Lehner and Döll, 2004). Bathymetries for the lake/wetlands were estimated by combining lake size distributions from the GLWD; average lake depths from literature for bog pools, Arctic thaw lakes, and other boreal lakes; and topography of surrounding wetlands from the ASTER (Advanced Spaceborne Thermal Emission and Reflection Radiometer) (Hayakawa et al, 2008) and STRM (Shuttle Topography Radar Mission; Digital Elevation Model ) (Farr and Kobrick, 2000) DEMs.…”

Section: Wetchimp Set-upmentioning

confidence: 99%

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Present state of global wetland extent and wetland methane modelling: methodology of a model inter-comparison project (WETCHIMP)

Wania

Melton²,

Hodson³

et al. 2013

Geosci. Model Dev.

189

180

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Abstract. The Wetland and Wetland CH 4 Intercomparison of Models Project (WETCHIMP) was created to evaluate our present ability to simulate large-scale wetland characteristics and corresponding methane (CH 4 ) emissions. A multi-model comparison is essential to evaluate the key uncertainties in the mechanisms and parameters leading to methane emissions. Ten modelling groups joined WETCHIMP to run eight global and two regional models with a common experimental protocol using the same climate and atmospheric carbon dioxide (CO 2 ) forcing datasets. We reported the main conclusions from the intercomparison effort in a companion paper (Melton et al., 2013). Here we provide technical details for the six experiments, which included an equilibrium, a transient, and an optimized run plus three sensitivity experiments (temperature, precipitation, and atmospheric CO 2 concentration). The diversity of approaches used by the models is summarized through a series of conceptual figures, and is used to evaluate the wide range of wetland extent and CH 4 fluxes predicted by the models in the equilibrium run. We discuss relationships among the various approaches and patterns in consistencies of these model predictions. Within this group of models, there are three broad classes of methods used to estimate wetland extent: prescribed based on wetland distribution maps, prognostic relationships between hydrological states based on satellite observations, and explicit hydrological mass balances. A larger variety of approaches was used to estimate the net CH 4 fluxes from wetland systems. Even though modelling of wetland extent and CH 4 emissions has progressed significantly over recent decades, large uncertainties still exist when estimating CH 4 emissions: there is little consensus on model structure or complexity due to knowledge gaps, different aims of the models, and the range of temporal and spatial resolutions of the models.

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Section: Dlemmentioning

confidence: 99%

Section: Wetchimp Set-upmentioning

confidence: 99%

Section: Wetchimp Set-upmentioning

confidence: 99%

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Present state of global wetland extent and wetland methane modelling: methodology of a model inter-comparison project (WETCHIMP)

Wania

Melton²,

Hodson³

et al. 2013

Geosci. Model Dev.

189

180

View full text Add to dashboard Cite

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“…These were mostly derived from the US Defense Mapping Agency Operational Navigation Charts (ESRI 1992, Lehner andDoll 2004). The latest update to any of the published data is 1992 according to the User's Guide (Lehner and Doll 2004).…”

Section: Vectormentioning

confidence: 99%

A new global raster water mask at 250 m resolution

Carroll

Townshend

Dimiceli

et al. 2009

International Journal of Digital Earth

331

238

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Accurate depiction of the land and water is critical for the production of land surface parameters from remote sensing data products. Certain parameters, including the land surface temperature, active fires and surface reflectance, can be processed differently when the underlying surface is water as compared with land. Substantial errors in the underlying water mask can then pervade into these products and any products created from them.Historically many global databases have been created to depict global surface water. These databases still fall short of the current needs of the terrestrial remote sensing community working at 250 m spatial resolution. The most recent attempt to address the problem uses the Shuttle Radar Topography Mission (SRTM) data set to create the SRTM Water Body Data set (SWBD 2005). The SWBD represents a good first step but still requires additional work to expand the spatial coverage to include the whole globe and to address some erroneous discontinuities in major river networks.To address this issue a new water mask product has been created using the SWBD in combination with MODIS 250 m data to create a complete global map of surface water at 250 m spatial resolution. This effort is automated and intended to produce a dataset for use in processing of raster data (MODIS and future instruments) and for masking out water in final terrestrial raster data products.This new global dataset is produced from remotely sensed data and provided to the public in digital format, free of charge. The data set can be found on the Global Land Cover Facility (GLCF) website at http://landcover.org. This dataset is expected to be a base set of information to describe the surface of Earth as either land or water which is a fundamental distinction upon which other descriptions can be made.

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“…In recent years, several global inundation analyses have been released that have moved from static, coarse scale (>1 km) products (e.g., Bontemps et al, 2011;Lehner and Döll, 2004;Verpoorter et al, 2014) to more dynamic offerings, such as the Global Inundation Extent from Multi-Satellites (Fluet-Chouinard et al, 2015) and the MODIS water mask (Carroll et al 2009). Studies of wetland dynamics at global scales have typically focused on relatively short time periods with coarse spatial resolution (e.g., Papa et al, 2010;Prigent et al, 2007;25-km grid cells), and therefore have been unable to capture smaller wetland features, or account for longer term cyclical hydrologic variability.…”

Section: Introductionmentioning

confidence: 99%

Three decades of Landsat-derived spring surface water dynamics in an agricultural wetland mosaic; Implications for migratory shorebirds

Schaffer‐Smith

Swenson

Barbaree

et al. 2017

Remote Sensing of Environment

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Satellite measurements of surface water offer promise for understanding wetland habitat availability at broad spatial and temporal scales; reliable habitat is crucial for the persistence of migratory shorebirds that depend on wetland networks. We analyzed water extent dynamics within wetland habitats at a globally important shorebird stopover site for a 1983-2015 Landsat time series, and evaluated the effect of climate on water extent. A range of methods can detect open water from imagery, including supervised classification approaches and thresholds for spectral bands and indices. Thresholds provide a time advantage; however, there is no universally superior index, nor single best threshold for all instances. We used random forest to model the presence or absence of water from >6200 reference pixels, and derived an optimal water probability threshold for our study area using receiver operating characteristic curves. An optimized mid-infrared (1.5-1.7 μm) threshold identified open water in the Sacramento Valley of California at 30-m resolution with an average of 90% producer's accuracy, comparable to approaches that require more intensive user input. SLC-off Landsat 7 imagery was integrated by applying a customized interpolation that mapped water in missing data gaps with 99% user's accuracy. On average we detected open water on ~26000 ha (~3% of the study area) in early April at the peak of shorebird migration, while water extent increased five-fold after the migration rush. Over the last three decades, late March water extent declined by ~1300 ha per year, primarily due to changes in the extent and timing of agricultural flood-irrigation. Water within shorebird habitats was significantly associated with an index of water availability at the peak of migration. Our approach can be used to optimize thresholds for time series analysis and near-real-time mapping in other regions, and requires only marginally more time than generating a confusion matrix.

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Development and validation of a global database of lakes, reservoirs and wetlands

Cited by 2,042 publications

References 16 publications

Present state of global wetland extent and wetland methane modelling: methodology of a model inter-comparison project (WETCHIMP)

Present state of global wetland extent and wetland methane modelling: methodology of a model inter-comparison project (WETCHIMP)

A new global raster water mask at 250 m resolution

Three decades of Landsat-derived spring surface water dynamics in an agricultural wetland mosaic; Implications for migratory shorebirds

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