Evaporative water loss of 1.42 million global lakes

Zhao, Gang; Li, Yao; Zhou, Liming; Gao, Huilin

doi:10.1038/s41467-022-31125-6

Cited by 109 publications

(84 citation statements)

References 56 publications

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“…Multi annual averages, from 2012 to 2014, were equal to 5.33 mm.d −1 by our estimation, 5.40 mm.d −1 by derived evaporation (Fowe et al, 2015) and 5.38 mm.d −1 by GLEV (Zhao et al, 2022). Evaporation differences by these three methods are lower https://doi.org/10.5194/hess-2022-367 Preprint.…”

Section: Discussionmentioning

confidence: 64%

“…Precipitation is estimated by the Integrated Multi-satEllite Retrievals algorithm of the international satellite mission Global Precipitation Measurement (IMERG-GPM, Huffman et al, 2019). The data are provided by Google Earth Engine, through the "GPM: Global Precipitation Measurement (GPM) v6" collection, with a spatial resolution of 0.1 Global Lake Evaporation Volume (GLEV) dataset (Zhao et al, 2022) and Colorado pan evaporation data over a small reservoir (Boura) from Fowe et al (2015), are used for validation over the April 2012-April 2014 period.…”

Section: Datamentioning

confidence: 99%

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Hydrological regime of Sahelian small water bodies from combined Sentinel-2 MSI and Sentinel-3 SRAL data

FLEURY

Kergoat

Grippa

2022

Preprint

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Abstract. In the Sahelian semi-arid region, water resources, especially small water bodies such as ponds, small lakes and reservoirs in rural areas, are of vital importance. However, because of their high number and the scarce in situ monitoring networks, these resources and their spatio-temporal variability are poorly known at the regional scale. This study investigates the hydrological regime of 37 small water bodies, located in Mali, Niger and Burkina Faso, in Central Sahel. We propose a method based on remote sensing data only, which consists of combining water height data from Sentinel-3 SAR Radar Altimeter (SRAL) with water area data obtained with Sentinel-2 Multispectral Instrument (MSI) to create dense water height time series. Water height variations are then compared to evaporation estimated by the Penman--Monteith method using ERA5 reanalysis by the European Centre for Medium-Range Weather Forecasts (ECMWF) to infer water regimes during the dry season. Three main regimes stand out: a net water loss, mainly resulting from anthropogenic withdrawals, a net water supply occurring after the end of the rainy season through river network or water table exchange, and a balanced behaviour, where water losses during the dry season closely correspond to evaporation rates. Spatial patterns have been identified: in central Burkina Faso, most of the reservoirs show a net dry season water loss, which is explained by frequent irrigation, while reservoirs in northern Burkina Faso, generally show little water loss, indicating that water withdrawal is not significant in this area. Lakes located in the Inner Niger Delta in Mali and connected to the Niger river network generally show an important water supply, particularly at the beginning of the dry season. Lakes in Niger tend to show a weak signal toward water inflow that could be explained by exchange processes with the groundwater. These results show that satellite data are effective in estimating hydrological regimes as well as the anthropogenic impact on water resources, at the large scale, including resources found in small water bodies.

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Section: Discussionmentioning

confidence: 64%

Section: Datamentioning

confidence: 99%

Hydrological regime of Sahelian small water bodies from combined Sentinel-2 MSI and Sentinel-3 SRAL data

FLEURY

Kergoat

Grippa

2022

Preprint

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“…We also see a smaller but salient intra-annual lake area ranges in Asia and in East Africa along the rift valleys and the Nile valley. In this case, the dominant driver remains the seasonal pattern of winter and changes within the single year of our study are harder to link to wider effects as in 13 . We then use the level 3 HydroBASINS vectors and sum the total areas in each basin, in ha, The total is then normalised by basin area, in km 2 .…”

Section: Figure 3 Global Scaling Relationships For Rivers and Lakes A...mentioning

confidence: 86%

Seasonal monitoring of river and lake water surface areas at global scale with deep learning

Carbonneau

Bizzi

2022

Preprint

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Much progress has been made over the last decade to build global inventories of freshwater resources. However, existing freshwater inventories are generally produced for a fixed period in time and/or do not discriminate lakes and rivers. The emergence of deep learning methods and Big Data platforms such as Google Earth Engine offers a potential solution. Here we presenta unique global raster dataset of 6.7 terapixels at a spatial resolution of 10 m produced with a deep learning workflow. This dataset uses Sentinel-2 imagery downloaded from Google Earth Engine to delineatefreshwater as separate semantic classes of rivers and lakes. Ourstudy site covers the non-polar globe, 89% of the terrestrial landmass, with repeat surveys for the months of April, July and October 2021. This gives us the first global-scale direct count of lakes larger than 1 hectare as ~7.3 million. Also the repeat surveys allow us topresent the firstintra-annual ranges for the areas distinctly occupied by rivers and lakes where we find that basins influenced by the Asian summer monsoon (e.g. the Ganges) closely followed by the Amazon display the largest intra-annual range of river area per unit basin area. Finally, we find that whilst maximum of lake water surface occurs in October 2021, the maximum river surfaceis in July 2021 leading us to conclude that the global maximum occurrences of river waterand lake water are not synchronous.

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“…Reservoir evaporation estimates can be extracted from two available global reservoir evaporation products produced by the Zhao et al (2022) and Tian et al (2022). These studies covered a portion of the reservoirs we studied and used the same robust algorithm by Zhao et al (2019) (3) shortwave radiation [MJ m −2 d −1 ]), for the evaporation rate calculation.…”

Section: Data and Methodology For Generating Reservoir Evaporationmentioning

confidence: 99%

“…These studies covered a portion of the reservoirs we studied and used the same robust algorithm by Zhao et al (2019) (3) shortwave radiation [MJ m −2 d −1 ]), for the evaporation rate calculation. In previous studies (Zhao et al, 2022;Tian et al, 2022), the TerraClimate dataset (Abatzoglou et al, 2018) has been shown to be the most appropriate meteorological dataset for reliable estimates of reservoir evaporation rates compared to other global datasets. Thus, we adopted the TerraClimate to generate meteorological data time series by averaging gridded forcing data within reservoir shapefiles.…”

Section: Data and Methodology For Generating Reservoir Evaporationmentioning

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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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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Evaporative water loss of 1.42 million global lakes

Cited by 109 publications

References 56 publications

Hydrological regime of Sahelian small water bodies from combined Sentinel-2 MSI and Sentinel-3 SRAL data

Hydrological regime of Sahelian small water bodies from combined Sentinel-2 MSI and Sentinel-3 SRAL data

Seasonal monitoring of river and lake water surface areas at global scale with deep learning

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

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