Development and validation of a global database of lakes, reservoirs and wetlands
Introduction
It is estimated that today more than 8 million lakes larger than 1 ha (Meybeck, 1995), about 40,000 dams higher than 15 m (ICOLD, 1998) and some 800,000 smaller ones (McCully, 1996), and more than 10 million km2 of wetlands (Finlayson and Davidson, 1999) exist worldwide. Due to their basic ability to retain, store, clean, and evenly provide water, as well as their distinct characteristics as still-water bodies, lakes, reservoirs and wetlands constitute essential components of the hydrological and biogeochemical water cycles, and influence many aspects of ecology, economy, and human welfare. Knowledge about the distributions of lakes, reservoirs and wetlands is therefore of great interest in many scientific disciplines. Besides their regional significance, global distributions are of particular interest for assessments of present and future water resources, for climate change modeling (global land surface parameterization, methane emissions), and for large-scale studies of the environment, biodiversity, health (spreading of water-borne diseases), and agricultural suitability (Dugan, 1993, Meybeck, 1995, Hagemann and Dümenil, 1997, Vörösmarty et al., 1997, Groombridge and Jenkins, 1998, Mitsch and Gosselink, 2000, Revenga et al., 2000, Sanderson, 2001, Wetzel, 2001).
Despite this importance, few comprehensive data sets exist which comprise information on location, extent and other basic characteristics of open water bodies and wetland areas on a global scale. Birkett and Mason (1995) found that quantity and quality of replies in their extensive search for global lake databases were poor, and very few authors have considered lake censuses on a global scale (Meybeck, 1995). A review of the Ramsar Wetlands Convention concluded that available data are too incomplete to provide a reliable estimate of the global extent of wetlands (Finlayson and Davidson, 1999).
As an alternative data source, recent developments in the field of remote sensing promise global land cover images in increasing quality and resolution, including the possibility to monitor spatio-temporal changes in lake and wetland extents. Based solely on the remotely received signal, however, the correct classification of an open water surface or a mixed vegetation area, say into ‘lake’, ‘reservoir’ or ‘temporarily flooded wetland’, is difficult. Misinterpretation of the signal may lead to errors, and the provided raster-cell representation hinders a clear identification of separate lake pools, individual wetland complexes or single components of braided river and lake systems.
Despite their individual limitations, the existing lake and wetland registers, maps and databases are unique and highly valuable sources of information, focusing on different geographic regions or aspects. The majority of currently available data sets can be grouped into two categories (compare Table 1).
(A) Databases, registers and inventories that focus on descriptive attributes (Table 1, Nos. 1–7). These data sets can provide extensive characterizations for individual lakes or wetlands. However, they generally tend to select only the largest or most important representatives, and they often lack detailed geo-referencing information. In a review, Birkett and Mason (1995) list 13 global and regional lake data sets, which partly include coordinate data, the largest of them comprising 1755 lakes. Since this review, some new databases were compiled, including up to 40,000 individual records (Ryanzhin et al., 2001), but all of them provide geo-referencing information only in terms of longitude/latitude point coordinates, rather than shoreline polygons.
(B) Analog or digital maps that show lakes, reservoirs and wetlands in their spatial extent. The digital maps include (i) polygon data sets of global hydrography, i.e. vectorized maps of river, lake and wetland outlines as derived from various source maps (Table 1, Nos. 8–11), and (ii) rasterized global land use or land cover characterizations as derived from remote sensing or other sources (Table 1, Nos. 12–17). Both type (i) and (ii) data provide information on extent and distribution of lakes and wetlands, but have limitations when individual attributes, e.g. name or ecological condition, are of interest. An important difference between type (i) and (ii) data sets is that remote sensing maps span only the most recent time period, while the polygon data are largely based on analog maps which were drawn from local observations and knowledge over a longer period of time. The polygon maps can thus be assumed to incorporate, at least to some extent, historic conditions and may tend towards representing lakes or wetlands as known in their maximum recorded extents.
This paper presents a new comprehensive database, which combines information of both categories A and B in a consistent manner. The compilation and linkage of attribute and geometric data of the different sources was realized within a Geographic Information System (GIS). The result is a Global Lakes and Wetlands Database (GLWD), organized in three levels: Level 1 comprises the shoreline polygons of the 3067 largest lakes (surface area ≥50 km2) and 654 largest reservoirs (storage capacity ≥0.5 km3) worldwide, and offers extensive attribute data. Level 2 contains the shoreline polygons of approx. 250,000 smaller lakes, reservoirs and rivers (surface area ≥0.1 km2), excluding all water bodies of level 1. Finally, level 3 represents lakes, reservoirs, rivers, and different wetland types in the form of a global raster map at 30-second resolution, including all water bodies of levels 1 and 2.
The three levels of GLWD were originally developed to be applied in a global hydrological model in order to improve calculations of open water evaporation and lateral flow regimes (Alcamo et al., 2003, Döll et al., 2003). These objectives may have influenced some of the displayed characteristics of GLWD, e.g. some unmapped reservoirs were introduced as circular polygons in order to represent their evaporation surface. However, the generated database is believed to provide important information for the broader scientific community and to support a variety of applications.
Section snippets
Definitions of lakes and wetlands
Lakes. There are various definitions of lakes, based on criteria like volume, surface area, depth, or presence of certain habitat types. For small lakes, lakes in floodplains or lakes adjacent to the sea, the distinction between slow-flowing rivers and lakes may be ambiguous (Leonard and Crouzet, 1999), there may be a continuum between lakes and wetlands (Meybeck, 1995), and a strict separation from the sea may be questionable. In the generation of GLWD we were restricted by the given
GLWD Level 1
Level 1 of GLWD represents a digital global polygon map of the world's largest lakes and reservoirs. It contains 3721 water bodies, i.e. 3067 lakes with a surface area ≥50 km2 and 654 reservoirs with a storage capacity ≥0.5 km3. In total, the large lakes and reservoirs of GLWD-1 cover 1.9 million km2 (including Caspian Sea) or 1.4% of the global land surface area (excluding Antarctica and glaciated Greenland).
In order to validate the completeness of GLWD-1, we compared it to various other data
Conclusions
Drawing upon a variety of existing maps, data and information, a new global lakes and wetlands database has been created. The combination of best available sources for lakes and wetlands on a global scale (1:1 to 1:3 million resolution), and the application of GIS functionality enabled the generation of a database which focuses in three coordinated levels on (1) large lakes and reservoirs, (2) smaller water bodies, and (3) wetlands.
Level 1 comprises the shoreline polygons of the 3067 largest
Acknowledgements
We wish to thank all data providers whose sources and information we utilized to generate and validate GLWD, as it is their enduring efforts and extensive work that we draw upon. We received and analyzed valuable data from many organizations and individuals, including ESRI, UNEP-WCMC (Simon Blyth), Charon Birkett and Ian Mason, Charles Vörösmarty et al., Michel Meybeck, Sergei Ryanzhin, and Stefan Hagemann. This work was financially supported by the German Federal Ministry of Education and
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Present address: Institute of Physical Geography, University of Frankfurt, P.O. Box 11 19 32, D-60054 Frankfurt am Main, Germany.