ReaLSAT, a global dataset of reservoir and lake surface area variations

Khandelwal, Ankush; Karpatne, Anuj; Ravirathinam, Praveen; Ghosh, Rahul; Wei, Zhihao; Dugan, Hilary A.; Hanson, Paul C.; Kumar, Vipin

doi:10.1038/s41597-022-01449-5

Cited by 27 publications

(26 citation statements)

References 37 publications

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“…The derived reservoir water area estimations are limited by the spatial coverage and accuracy restrictions of the initial dataset. As such, three available global water area products are developed by using algorithms that reclassify contaminated pixels as water, i.e., the GRSAD (Zhao & Gao, 2018), the RealSAT (Khandelwal et al, 2022), and areas of medium-small reservoirs by Donchyts et al (2022). These products cover only a portion of the reservoirs we studied (e.g., 908 overlapping reservoirs between GRSAD and our product) and use different algorithms and source datasets (e.g., RealSAT and GRSAD use only Landsat).…”

Section: Data and Methodology For Generating Reservoir Water Areamentioning

confidence: 99%

“…Hydroweb (Crétaux et al, 2011), G-REALM (Global reservoirs and lakes monitor, Birkett et al, 2011), DAHITI (Database for hydrological time series of inland waters, Schwatke et al, 2015), AltEx (Markert et al, 2019), HydroSat (Tourian et al, 2022), Water level On VITO, and several studies (e.g., Gao et al, 2012;Tortini et al, 2020;Shen et al, 2022b) offer time series of altimetry-derived water level for inland waters by incorporating multiple laser or radar altimeters such as Jason-1/2/3, CryoSat-2, Sentinel-3A/B, and ICESat-1/2. Imagery-based water area estimates can be extracted from GSW (Global surface water, Pekel et al, 2016), DAHITI, Hydroweb, HydroSat, Bluedot Observatory, GRSAD (Global reservoir surface area dataset, Zhao & Gao, 2018), RealSAT (Khandelwal et al, 2022), and relevant studies (e.g., Busker et al, 2019;Liu et al, 2021;Donchyts et al, 2022). Storage anomalies are available in DAHITI, HydroSat, and several studies (e.g., Gao et al, 2012;Hou et al, 2022) using water level and area from satellite altimeters and images, and/or from imagery-based water area and the area-storage model constructed by digital elevation model (DEM, Vu et al, 2022).…”

Section: Introductionmentioning

confidence: 99%

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Res-CN: hydrometeorological time series and landscape attributes across 3254 Chinese reservoirs

Shen

Nielsen

Revel

et al. 2023

Preprint

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Abstract. Dams and reservoirs are human-made infrastructures that have attracted increasing attentions because of their societal and environmental significance. Towards better management and conservation of reservoirs, a dataset of reservoir-catchment characteristics is needed, considering that the amount water and material flowing into and out of reservoirs depends on their locations on the river network and the properties of upstream catchment. To date, no dataset exists for reservoir-catchment characteristics. The aim of this study is to develop the first database featuring reservoir-catchment characteristics for 3254 reservoirs with storage capacity totaling 682,595 km3 (73.2 % reservoir water storage capacity in China), to support the management and conservation of reservoirs in the context of catchment level. To ensure a more representative and accurate mapping of local variables of large reservoirs, reservoir catchments are delineated into full catchments (their full upstream contributing areas) and intermediate catchments (subtracting the area contributed by upstream reservoirs from full upstream of the current reservoir). Using both full catchments and intermediate catchments, characteristics of reservoir catchments were extracted, with a total of 512 attributes in six categories (i.e., reservoir catchment, topography, climate, soil and geology, land cover and use, and anthropogenic activity). Besides these static attributes, time series of 15 meteorological variables of catchments were extracted to support hydrological simulations for a better understanding of drivers of reservoir environment change. Moreover, we provide a comprehensive and extensive reservoir data set on water level (data available for 20 % of 3,254 reservoirs), water area (99 %), storage anomaly (92 %), and evaporation (98 %) from multisource satellites such as radar and laser altimeters and images from Landsat and Sentinel satellites. These products significantly enhance spatial and temporal coverage in comparison to existing similar products (e.g., 67 % increase in spatial resolution of water level and 225 % increase in storage anomaly) and contribute to our understanding of reservoir properties and functions within the Earth system by incorporated national or global hydrological modeling. In situ data of 138 reservoirs are employed in this study as a valuable reference for evaluation, thus enhancing our confidence in the data quality and enhancing our understanding of accuracy of current satellite datasets. Along with its extensive attributes, the Reservoir dataset in China (Res-CN) can support a broad range of applications such as water resources, hydrologic/hydrodynamic modeling, and energy planning. Res-CN is on Zenodo through https://doi.org/10.5281/zenodo.7390715 (Shen et al., 2022a).

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Section: Data and Methodology For Generating Reservoir Water Areamentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Res-CN: hydrometeorological time series and landscape attributes across 3254 Chinese reservoirs

Shen

Nielsen

Revel

et al. 2023

Preprint

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“…As the random forest classifier could provide the posterior probability for each pixel, we determined the labels of the confused pixels by comparing the posterior probabilities. In addition, as the tidal flats were demonstrated to overestimate some coastal ponds as tidal flats, the global lake and reservoir dataset, developed by Khandelwal et al (2022), was applied to optimize the tidal flat. ASTER GDEM Topographical features: elevation, slope and aspect.…”

Section: The Stratified Classification Strategy For Wetland Mappingmentioning

confidence: 99%

GWL_FCS30: a global 30 m wetland map with a fine classification system using multi-sourced and time-series remote sensing imagery in 2020

Zhang

Liu

Zhao

et al. 2023

Earth Syst. Sci. Data

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Abstract. Wetlands, often called the “kidneys of the earth”, play an important role in maintaining ecological balance, conserving water resources, replenishing groundwater and controlling soil erosion. Wetland mapping is very challenging because of its complicated temporal dynamics and large spatial and spectral heterogeneity. An accurate global 30 m wetland dataset that can simultaneously cover inland and coastal zones is lacking. This study proposes a novel method for wetland mapping by combining an automatic sample extraction method, existing multi-sourced products, satellite time-series images and a stratified classification strategy. This approach allowed for the generation of the first global 30 m wetland map with a fine classification system (GWL_FCS30), including five inland wetland sub-categories (permanent water, swamp, marsh, flooded flat and saline) and three coastal tidal wetland sub-categories (mangrove, salt marsh and tidal flats), which was developed using Google Earth Engine platform. We first combined existing multi-sourced global wetland products, expert knowledge, training sample refinement rules and visual interpretation to generate large and geographically distributed wetland training samples. Second, we integrated the Landsat reflectance time-series products and Sentinel-1 synthetic aperture radar (SAR) imagery to generate various water-level and phenological information to capture the complicated temporal dynamics and spectral heterogeneity of wetlands. Third, we applied a stratified classification strategy and the local adaptive random forest classification models to produce the wetland dataset with a fine classification system at each 5∘×5∘geographical tile in 2020. Lastly, GWL_FCS30, mosaicked by 961 5∘×5∘ regional wetland maps, was validated using 25 708 validation samples, which achieved an overall accuracy of 86.44 % and a kappa coefficient of 0.822. The cross-comparisons with other global wetland products demonstrated that the GWL_FCS30 dataset performed better in capturing the spatial patterns of wetlands and had significant advantages over the diversity of wetland sub-categories. The statistical analysis showed that the global wetland area reached 6.38 million km2, including 6.03 million km2 of inland wetlands and 0.35 million km2 of coastal tidal wetlands, approximately 72.96 % of which were distributed poleward of 40∘ N. Therefore, we can conclude that the proposed method is suitable for large-area wetland mapping and that the GWL_FCS30 dataset is an accurate wetland mapping product that has the potential to provide vital support for wetland management. The GWL_FCS30 dataset in 2020 is freely available at https://doi.org/10.5281/zenodo.7340516 (Liu et al., 2022).

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“…Characterizing how and where lakes are changing on broad scales is, therefore, critical to understanding the drivers of change. However, studies that have investigated patterns of waterbody surface area change, rather than quantifying variability or linear change, are typically performed on short-term time series (∼2 years), long-term but low-resolution time series (i.e., only five measurements in 22 years), or on few (<30) waterbodies. − Recently, the Reservoir and Lake Surface Area Time series (ReaLSAT) data set, a long-term, spatially extensive water body data set, has become available which makes possible, using new analytic tools, a comprehensive analysis of long-term patterns of change across broad spatial scales.…”

Section: Introductionmentioning

confidence: 99%

Using Knowledge-Guided Machine Learning To Assess Patterns of Areal Change in Waterbodies across the Contiguous United States

Wander

Farruggia

Fuente

et al. 2024

Environ. Sci. Technol.

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Lake and reservoir surface areas are an important proxy for freshwater availability. Advancements in machine learning (ML) techniques and increased accessibility of remote sensing data products have enabled the analysis of waterbody surface area dynamics on broad spatial scales. However, interpreting the ML results remains a challenge. While ML provides important tools for identifying patterns, the resultant models do not include mechanisms. Thus, the “black-box” nature of ML techniques often lacks ecological meaning. Using ML, we characterized temporal patterns in lake and reservoir surface area change from 1984 to 2016 for 103,930 waterbodies in the contiguous United States. We then employed knowledge-guided machine learning (KGML) to classify all waterbodies into seven ecologically interpretable groups representing distinct patterns of surface area change over time. Many waterbodies were classified as having “no change” (43%), whereas the remaining 57% of waterbodies fell into other groups representing both linear and nonlinear patterns. This analysis demonstrates the potential of KGML not only for identifying ecologically relevant patterns of change across time but also for unraveling complex processes that underpin those changes.

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ReaLSAT, a global dataset of reservoir and lake surface area variations

Cited by 27 publications

References 37 publications

Res-CN: hydrometeorological time series and landscape attributes across 3254 Chinese reservoirs

Res-CN: hydrometeorological time series and landscape attributes across 3254 Chinese reservoirs

GWL_FCS30: a global 30 m wetland map with a fine classification system using multi-sourced and time-series remote sensing imagery in 2020

Using Knowledge-Guided Machine Learning To Assess Patterns of Areal Change in Waterbodies across the Contiguous United States

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