the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
| 30 Jul 2024
Mapping the world’s inland surface waters: an update to the Global Lakes and Wetlands Database (GLWD v2)
Abstract. In recognition of the importance of inland waters, numerous datasets mapping their extents, types, or changes have been created using sources ranging from historical wetland maps to real-time satellite remote sensing. However, differences in definitions and methods have led to spatial and typological inconsistencies among individual data sources, confounding their complementary use and integration. The Global Lakes and Wetlands Database (GLWD), published in 2004, with its globally seamless gridded depiction of major vegetated and non-vegetated wetland classes, has emerged over the last decades as a foundational reference map that has advanced research and conservation planning addressing freshwater biodiversity, ecosystem services, greenhouse gas emissions, land surface processes, hydrology, and human health. Here, we present a new iteration of this map, termed GLWD version 2, generated by harmonizing the latest ground- and satellite-based data products into one single database. Following the same design principle as its predecessor, GLWD v2 aims to avoid double-counting of overlapping surface water features while differentiating between natural and non-natural lakes, rivers of multiple sizes, and several other wetland types. The classification of GLWD v2 incorporates information on seasonality (i.e., permanent vs. intermittent vs. ephemeral); inundation vs. saturation (i.e., flooding vs. waterlogged soils); vegetation cover (e.g., forested swamps vs. non-forested marshes); salinity (e.g., salt pans); natural vs. non-natural origins (e.g., rice paddies); and a stratification of landscape position and water source (e.g., riverine, lacustrine, palustrine, coastal/marine). GLWD v2 represents 33 wetland classes and—including all intermittent classes—depicts a maximum of 18.2 million km2 of wetlands (13.4 % of the global land area excluding Antarctica). The spatial extent of each class is provided as the fractional coverage within each grid cell at a resolution of 15 arc-seconds (approximately 500 m at the equator), with cell fractions derived from input data at resolutions as small as 10 m. The updated GLWD v2 offers an improved representation of inland surface water extents and their classification for contemporary conditions. Despite being a static map, it includes classes that denote intrinsic temporal dynamics. GLWD v2 is designed to facilitate large-scale hydrological, ecological, biogeochemical, and conservation applications, aiming to support the study and protection of wetland ecosystems around the world.
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RC1: 'Comment on essd-2024-204', Anonymous Referee #1, 19 Aug 2024
Summary:
This study presents the latest version of The Global Lakes and Wetlands Database (GLWD) V2, a foundational reference map depicting major vegetated and non-vegetated wetland classes globally. GLWD V2 was created by harmonizing ground- and satellite-based data products into a single database. Like its predecessor, GLWD V2 avoids double-counting overlapping surface water features and differentiates between natural and non-natural lakes, rivers, and other wetland types. It represents 33 wetland classes, covering approximately 18.2 million km² (13.4% of global land area). GLWD V2 provides an improved representation of inland surface water extents and supports large-scale hydrological, ecological, and conservation applications.Major comments:
1. The GLWD V2 is a static map, which is insufficient for accurate and comprehensive studies of wetland ecosystems that are fundamental to quantifying their role within the water, carbon, and nutrient cycles, despite its high spatial resolution. I suggest the authors clearly state the significant contributions of this dataset to the field (or its advantages over other similar databases) in the abstract and introduction.
2. The authors only provide some comparisons with other databases. Can the authors add comparisons with different satellite-based data for comparison?
3. There are too many short paragraphs in section 3. Please consider merging some of them. The overall paragraph structure is loose and needs to be reorganized to be logical and concise.
4. The font size of Figure 2 is too small. Please consider making it bigger.
5. The explanation of uncertainties and comparisons with other datasets is insufficient. The potential sources of error for the dataset are also not adequately explained.
6. The advantages of this dataset over others are not clearly highlighted, and the advanced nature of the classification methods is not demonstrated. It appears to be merely an update of the previous version.
7. The discussion in section 5.2 about uncertainties, distortions, and overestimations in regions with overlapping data sources is puzzling. The fusion of multiple data sources should reduce uncertainties in the final results, not increase them.
8. The advantages of this dataset compared to other remote sensing-based land cover classifications are not apparent. Remote sensing data can maintain high spatial resolution and be dynamically updated.
Minor comments:
1. Abstract: The author needs to explain the advantages of GLWD V2 rather than just stating it as an update of GLWD V1.
2. Introduction: Add a table to describe all similar databases from different data sources, including the former GLWD V1, their time period, time step, theory or method, resolution, advantages, and disadvantages.
3. Importance of the Database: Clearly explain to the readers and the community why this database is important.
4. Figure 1: Please explain ‘G3WBM’, ‘GIEMS1’, and all other abbreviations in full the first time they appear in the text.
5. Definitions and Data Sources: Please add a table to describe the comparison data used in section 4.
6. Methods: Please consider rewriting this part, especially merging some short paragraphs in sections 3.2-3.4, as they are not very clear now.
7. Figure 3: Please add explanations for the boxes with different shapes and colors. Also, center Figure 3.
8. Uncertainties: How do you deal with uncertainties arising from different data sources and their spatial and temporal resolutions?
9. Section 4.2.3: Please provide more details or static numbers to describe the differences between the GLWD V2 data and other datasets.
10. Classification Methods: Please explain the necessity of using different classification methods here.
11. Uncertainties and Shortcomings: line 788 Please list all the uncertainties and shortcomings in section 7.8.
12. Bias and Uncertainties: lines 789: The authors need to describe the bias and uncertainties with some static numbers.Citation: https://doi.org/10.5194/essd-2024-204-RC1 -
RC2: 'Comment on essd-2024-204', Anonymous Referee #2, 28 Sep 2024
The manuscript presents an updated version of the Global Lakes and Wetlands Database (GLWD), which integrates modern ground and satellite-based data to create a harmonized global map of inland surface waters and wetlands. This update provides enhanced resolution and additional classification layers compared to its predecessor (GLWD v1), offering a more detailed and consistent representation of inland surface waters. The contribution is substantial and timely, addressing critical gaps in the representation of wetlands and their dynamic properties, which are crucial for studies in hydrology, ecology, and environmental management.
Major Points:
- I recommend emphasizing the distinct applications and improvements over other recent global wetland datasets. While the paper touches on this, a more detailed comparative analysis between GLWD v2 and existing databases (e.g., GIEMS, GLOWABO) would strengthen the argument for its uniqueness and applicability in contemporary research.
- While the authors acknowledge persistent issues in defining and classifying wetlands globally, consider proposing potential solutions or standardization efforts to improve consistency in future wetland mapping initiatives. Since the authors mention that there are very significant differences in the definitional criteria for wetlands used in different data products or studies, are the wetland classification criteria used in this dataset comparable to those used in other studies, and are the wetland products obtained comparable to other products?
- Clarify and potentially expand on the validation methods used to assess the accuracy of the new dataset. Although the area estimates of GLWD V2 was compared with other datasets, please consider comparing results against independent observations or field data where possible. I recommend adding a section that describes field-based or independent validation efforts for other wetland types, especially in regions with significant wetland coverage, such as Southeast Asia or the Amazon basin, to compare GLWD v2 classifications against in-situ observations or higher-resolution local datasets. This would provide empirical validation of the classification system and spatial accuracy.
- Are there inconsistencies or conflicts between the 25 major global data products used to generate the GLWD V2 data? What measures have been taken in this work to avoid the impacts on wetland classification when there are inconsistencies between the surface types of the input data (e.g., the HydroLAKES and Global Surface Water dataset, these two estimates are highly inconsistent)? reported by Rajib et al., (2024): A call for consistency and integration in global surface water estimates)? Is it possible to be specific in the section on selection criteria for input data (coherency between datasets)?
- While GLWD v2 is described as a static map representing contemporary conditions, and although they provide more detailed wetland classification information than the previous version of the data, they cannot be used to quantify seasonal fluctuations and inter-annual scales in wetland ecosystems. The importance of the data is diminished by the fact that wetlands can change significantly over relatively short periods of time. The authors may need to go into more depth to explain the critical role of this wetland classification information and potential application scenarios to highlight the importance of this dataset.
- Offer more detailed guidance on appropriate uses and limitations of the dataset for various applications. This could help users better understand how to effectively utilize the data in different contexts.
- Discuss integration with other datasets: Explore how GLWD v2 could be integrated or used in conjunction with other global environmental datasets (e.g., land cover, climate data) to enhance its value for interdisciplinary research.
Minor Points:
- Overall, the manuscript is well-written and clear. However, there are instances where technical jargon may impede accessibility for a broader audience. For example, the use of terms like "mosaicking" and "ancillary data" may need more explanation. Consider simplifying or defining these terms more clearly for non-specialist readers.
- The inclusion of several figures to demonstrate the different stages of data integration and the final wetland classification is excellent. However, Figure 3 could be expanded with more details on the data fusion procedures as the current methods section is complex and is not very clear. A table comparing GLWD v2 with other global wetland maps in terms of resolution, typology, and applications would be a valuable addition.
- Table 1 provides a good overview of the data sources but could be improved by adding information on the temporal coverage of each dataset.
- Please consider adding a section to describe all necessary information on the data files provided in the dataset (e.g., data format, layer names and content). This would make it easy for data users to quickly know what information are provided in each data file.
Citation: https://doi.org/10.5194/essd-2024-204-RC2 -
RC3: 'Comment on essd-2024-204', Anonymous Referee #3, 28 Oct 2024
This article generates GLWD version 2 by integrating the latest ground and satellite data products into one database. GLWD v2 mapped 33 wetland categories worldwide with a minimum resolution of 10m. This database has successfully overcome the differences caused by inconsistent regional or national data sources, filling the gap between field surveys and globally applicable classifications. Beneficial for promoting large-scale hydrological, ecological, biogeochemical, and conservation applications, supporting research and protection of wetland ecosystems around the world. Although GLWD v2 has made significant progress in global wetland classification and representation, it may require careful consideration of the following issues:
(1) GLWD v2 combined different datasets to map a total of 33 wetland categories, including wetlands and water body types. However, how to eliminate the temporal differences in wetland types extracted from different datasets? That is to say, the increase or decrease of wetlands may have biases in different statistical time differences, especially in areas where human activities have a significant impact on wetland area disturbance. How to consider this?
(2) There are still doubts about the clear definition of wetlands, such as whether wetlands in high dynamic change areas can be defined as wetlands? Is the statistical analysis of the high dynamic change area accurate? And this is difficult to accurately model and obtain for wetlands that exist intermittently or in the short term.
(3) What is the significance of distinguishing lakes, saltwater lakes, and reservoirs based on the classification criteria for 33 wetland types? Is the source of the third-party dataset used reliable? How are subcategories specifically classified?
(4) The article uses a hierarchical data processing approach to separately process wetlands, such as high-resolution coastal wetland types, urban and glaciated areas，peatlands, paddy rice class，peatland classes. However, for the extraction of wetland types, only the image data or dataset used to obtain it is introduced, and the specific method needs to be declared or referenced, which further verifies the effectiveness and accuracy of the results. This question also applies to the extraction of lake water bodies.
I think if the author can explain or handle the above issues well, the paper can be published after major revision.
Citation: https://doi.org/10.5194/essd-2024-204-RC3
Data sets
Global Lakes and Wetlands Database version 2.0 Bernhard Lehner et al. https://figshare.com/s/e40017f69f41f80d50df
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