Articles | Volume 16, issue 1
https://doi.org/10.5194/essd-16-201-2024
© Author(s) 2024. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/essd-16-201-2024
© Author(s) 2024. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
GloLakes: water storage dynamics for 27 000 lakes globally from 1984 to present derived from satellite altimetry and optical imaging
Fenner School of Environment & Society, Australian National University, Canberra, ACT, Australia
Albert I. J. M. Van Dijk
Fenner School of Environment & Society, Australian National University, Canberra, ACT, Australia
Luigi J. Renzullo
Fenner School of Environment & Society, Australian National University, Canberra, ACT, Australia
Pablo R. Larraondo
Fenner School of Environment & Society, Australian National University, Canberra, ACT, Australia
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Cited articles
Alsdorf, D. E., Rodriguez, E., and Lettenmaier, D. P.: Measuring surface water from space, Rev. Geophys., 45, RG2002, https://doi.org/10.1029/2006RG000197, 2007.
Avisse, N., Tilmant, A., Müller, M. F., and Zhang, H.: Monitoring small reservoirs' storage with satellite remote sensing in inaccessible areas, Hydrol. Earth Syst. Sci., 21, 6445–6459, https://doi.org/10.5194/hess-21-6445-2017, 2017.
Bastviken, D., Tranvik, L. J., Downing, J. A., Crill, P. M., and Enrich-Prast, A.: Freshwater methane emissions offset the continental carbon sink, Science, 331, 50, 2011.
Beck, H. E., van Dijk, A. I., Larraondo, P. R., McVicar, T. R., Pan, M., Dutra, E., and Miralles, D. G.: MSWX: Global 3-Hourly 0.1∘ Bias-Corrected Meteorological Data Including Near-Real-Time Updates and Forecast Ensembles, B. Am. Meteorol. Soc., 103, E710–E732, 2022.
Birkett, C. M.: Contribution of the TOPEX NASA radar altimeter to the global monitoring of large rivers and wetlands, Water Resour. Res., 34, 1223–1239, 1998.
Birkett, C. M., Reynolds, C., Beckley, B., and Doorn, B.: From Research to Operations: The USDA Global Reservoir and Lake Monitor, chapter 2, in: Coastal Altimetry, edited by: Vignudelli, S., Kostianoy, A. G., Cipollini, P., and Benveniste, J., Springer Publications, ISBN 978-3-642-12795-3, 2010.
Bonnema, M., Sikder, S., Miao, Y., Chen, X., Hossain, F., Ara Pervin, I., Mahbubur Rahman, S., and Lee, H.: Understanding satellite-based monthly-to-seasonal reservoir outflow estimation as a function of hydrologic controls, Water Resour. Res., 52, 4095–4115, 2016.
Busker, T., de Roo, A., Gelati, E., Schwatke, C., Adamovic, M., Bisselink, B., Pekel, J.-F., and Cottam, A.: A global lake and reservoir volume analysis using a surface water dataset and satellite altimetry, Hydrol. Earth Syst. Sci., 23, 669–690, https://doi.org/10.5194/hess-23-669-2019, 2019.
Chen, J., Zhu, X., Vogelmann, J. E., Gao, F., and Jin, S.: A simple and effective method for filling gaps in Landsat ETM+ SLC-off images, Remote Sens. Environ., 115, 1053–1064, 2011.
Cooley, S. W., Ryan, J. C., and Smith, L. C.: Human alteration of global surface water storage variability, Nature, 591, 78–81, 2021.
Crétaux, J.-F., Jelinski, W., Calmant, S., Kouraev, A., Vuglinski, V., Bergé-Nguyen, M., Gennero, M.-C., Nino, F., Del Rio, R. A., and Cazenave, A.: SOLS: A lake database to monitor in the Near Real Time water level and storage variations from remote sensing data, Adv. Space Res., 47, 1497–1507, https://doi.org/10.1016/j.asr.2011.01.004, 2011.
Crétaux, J.-F., Abarca-del-Río, R., Berge-Nguyen, M., Arsen, A., Drolon, V., Clos, G., and Maisongrande, P.: Lake volume monitoring from space, Surv. Geophys., 37, 269–305, 2016.
Da Silva, J. S., Calmant, S., Seyler, F., Rotunno Filho, O. C., Cochonneau, G., and Mansur, W. J.: Water levels in the Amazon basin derived from the ERS 2 and ENVISAT radar altimetry missions, Remote Sens. Environ., 114, 2160–2181, 2010.
De Groeve, T., Brakenridge, G. R., and Paris, S.: Global flood detection system data product specifications, JRC Technical Report, http://www.gdacs.org/flooddetection/Download/Technical_Note_GFDS_Data_Products_v1.pdf (last access: 2 November 2018), 2015.
Donchyts, G., Winsemius, H., Baart, F., Dahm, R., Schellekens, J., Gorelick, N., Iceland, C., and Schmeier, S.: High-resolution surface water dynamics in Earth's small and medium-sized reservoirs, Sci. Rep., 12, 1–13, 2022.
Dorigo, W., Preimesberger, W., Reimer, C., Van der Schalie, R., Pasik, A., De Jeu, R., and Paulik, C.: Soil moisture gridded data from 1978 to present, v201912.0.0, Copernicus Climate Change Service (C3S) Climate Data Store (CDS) [data set], https://cds.climate.copernicus.eu/cdsapp#!/dataset/satellite-soil-moisture?tab=overview, 2019.
Duan, Z. and Bastiaanssen, W. G. M.: Estimating water volume variations in lakes and reservoirs from four operational satellite altimetry databases and satellite imagery data, Remote Sens. Environ., 134, 403–416, https://doi.org/10.1016/j.rse.2013.03.010, 2013.
Frappart, F., Calmant, S., Cauhopé, M., Seyler, F., and Cazenave, A.: Preliminary results of ENVISAT RA-2-derived water levels validation over the Amazon basin, Remote Sens. Environ., 100, 252–264, 2006.
Gao, H., Birkett, C., and Lettenmaier, D. P.: Global monitoring of large reservoir storage from satellite remote sensing, Water Resour. Res., 48, W09504, https://doi.org/10.1029/2012WR012063, 2012.
Hou, J., van Dijk, A. I. J. M., Renzullo, L. J., and Vertessy, R. A.: Using modelled discharge to develop satellite-based river gauging: a case study for the Amazon Basin, Hydrol. Earth Syst. Sci., 22, 6435–6448, https://doi.org/10.5194/hess-22-6435-2018, 2018.
Hou, J., Van Dijk, A. I. J. M., and Beck, H. E.: Global satellite-based river gauging and the influence of river morphology on its application, Remote Sens. Environ., 239, 111629, https://doi.org/10.1016/j.rse.2019.111629, 2020.
Hou, J., van Dijk, A. I. J. M., Beck, H. E., Renzullo, L. J., and Wada, Y.: Remotely sensed reservoir water storage dynamics (1984–2015) and the influence of climate variability and management at a global scale, Hydrol. Earth Syst. Sci., 26, 3785–3803, https://doi.org/10.5194/hess-26-3785-2022, 2022a.
Hou, J., Van Dijk, A. I. J. M., and Renzullo, L. J.: Merging Landsat and airborne LiDAR observations for continuous monitoring of floodplain water extent, depth and volume, J. Hydrol., 609, 127684, https://doi.org/10.1016/j.jhydrol.2022.127684, 2022b.
Hou, J., Van Dijk, A. I. J. M., Renzullo, L. J., and Larraondo, P. R.: GloLakes: Global historical and near real-time lake storage dynamics from 1984–present, NCI Data Catalogue [data set], https://doi.org/10.25914/K8ZF-6G46, 2022c.
Jasinski, M. F., Stoll, J. D., Hancock III, D. W., Robbins, J., Nattala, J., Pavelsky, T. M., Morison, J., Jones, B. M., Ondrusek, M. E., Parrish, C., Carabajal, C., and the ICESat-2 Science Team: ATLAS/ICESat-2 L3A Along Track Inland Surface Water Data, Version 6. Boulder, Colorado USA. NASA National Snow and Ice Data Center Distributed Active Archive Center [data set], https://doi.org/10.5067/ATLAS/ATL13.006, 2023.
Ji, L., Gong, P., Wang, J., Shi, J., and Zhu, Z.: Construction of the 500-m Resolution Daily Global Surface Water Change Database (2001–2016), Water Resour. Res., 54, 10270–10292, https://doi.org/10.1029/2018WR023060, 2018.
Khazaei, B., Read, L. K., Casali, M., Sampson, K. M., and Yates, D. N.: GLOBathy, the global lakes bathymetry dataset, Sci. Data, 9, 1–10, 2022.
Klein, I., Gessner, U., Dietz, A. J., and Kuenzer, C.: Global WaterPack–A 250 m resolution dataset revealing the daily dynamics of global inland water bodies, Remote Sens. Environ., 198, 345–362, 2017.
Kraemer, B. M., Seimon, A., Adrian, R., and McIntyre, P. B.: Worldwide lake level trends and responses to background climate variation, Hydrol. Earth Syst. Sci., 24, 2593–2608, https://doi.org/10.5194/hess-24-2593-2020, 2020.
Lehner, B. and Döll, P.: Development and validation of a global database of lakes, reservoirs and wetlands, J. Hydrol., 296, 1–22, 2004.
Lehner, B. and Grill, G.: Global river hydrography and network routing: baseline data and new approaches to study the world's large river systems, Hydrol. Process., 27, 2171–2186, 2013.
Lehner, B., Verdin, K., and Jarvis, A.: New global hydrography derived from spaceborne elevation data, Eos, Transactions American Geophysical Union, 89, 93–94, 2008.
Lehner, B., Liermann, C. R., Revenga, C., Vörösmarty, C., Fekete, B., Crouzet, P., Döll, P., Endejan, M., Frenken, K., and Magome, J.: High-resolution mapping of the world's reservoirs and dams for sustainable river-flow management, Front. Ecol. Environ., 9, 494–502, https://doi.org/10.1890/100125, 2011.
Li, X., Ling, F., Foody, G. M., Boyd, D. S., Jiang, L., Zhang, Y., Zhou, P., Wang, Y., Chen, R., and Du, Y.: Monitoring high spatiotemporal water dynamics by fusing MODIS, Landsat, water occurrence data and DEM, Remote Sens. Environ., 265, 112680, https://doi.org/10.1016/j.rse.2021.112680, 2021.
Markus, T., Neumann, T., Martino, A., Abdalati, W., Brunt, K., Csatho, B., Farrell, S., Fricker, H., Gardner, A., Harding, D., Jasinski, M., Kwok, R., Magruder, L., Lubin, D., Luthcke, S., Morison, J., Nelson, R., Neuenschwander, A., Palm, S., Popescu, S., Shum, C., Schutz, B. E., Smith, B., Yang, Y., and Zwally, J.: The Ice, Cloud, and land Elevation Satellite-2 (ICESat-2): Science requirements, concept, and implementation, Remote Sens. Environ., 190, 260–273, https://doi.org/10.1016/j.rse.2016.12.029, 2017.
Messager, M. L., Lehner, B., Grill, G., Nedeva, I., and Schmitt, O.: Estimating the volume and age of water stored in global lakes using a geo-statistical approach, Nat. Commun., 7, 13603, https://doi.org/10.1038/ncomms13603, 2016.
Mulligan, M., van Soesbergen, A., and Sáenz, L.: GOODD, a global dataset of more than 38,000 georeferenced dams, Sci. Data, 7, 1–8, 2020.
Normandin, C., Frappart, F., Lubac, B., Bélanger, S., Marieu, V., Blarel, F., Robinet, A., and Guiastrennec-Faugas, L.: Quantification of surface water volume changes in the Mackenzie Delta using satellite multi-mission data, Hydrol. Earth Syst. Sci., 22, 1543–1561, https://doi.org/10.5194/hess-22-1543-2018, 2018.
Ogilvie, A., Belaud, G., Massuel, S., Mulligan, M., Le Goulven, P., and Calvez, R.: Surface water monitoring in small water bodies: potential and limits of multi-sensor Landsat time series, Hydrol. Earth Syst. Sci., 22, 4349–4380, https://doi.org/10.5194/hess-22-4349-2018, 2018.
Oki, T. and Kanae, S.: Global Hydrological Cycles and World Water Resources, Science, 313, 1068–1072, https://doi.org/10.1126/science.1128845, 2006.
Papa, F., Prigent, C., Aires, F., Jimenez, C., Rossow, W., and Matthews, E.: Interannual variability of surface water extent at the global scale, 1993–2004, J. Geophys. Res.-Atmos., 115, D12111, https://doi.org/10.1029/2009JD012674, 2010.
Papa, F., Crétaux, J.-F., Grippa, M., Robert, E., Trigg, M., Tshimanga, R. M., Kitambo, B., Paris, A., Carr, A., and Fleischmann, A. S.: Water Resources in Africa under Global Change: Monitoring Surface Waters from Space, Surv. Geophys., 1–51, 2022.
Pekel, J.-F., Cottam, A., Gorelick, N., and Belward, A. S.: High-resolution mapping of global surface water and its long-term changes, Nature, 540, 418–422, https://doi.org/10.1038/nature20584, 2016.
Prigent, C., Papa, F., Aires, F., Rossow, W., and Matthews, E.: Global inundation dynamics inferred from multiple satellite observations, 1993–2000, J. Geophys. Res.-Atmos., 112, 2007.
Raymond, P. A., Hartmann, J., Lauerwald, R., Sobek, S., McDonald, C., Hoover, M., Butman, D., Striegl, R., Mayorga, E., and Humborg, C.: Global carbon dioxide emissions from inland waters, Nature, 503, 355–359, 2013.
Robinson, N., Regetz, J., and Guralnick, R. P.: EarthEnv-DEM90: a nearly-global, void-free, multi-scale smoothed, 90m digital elevation model from fused ASTER and SRTM data, ISPRS Journal of Photogrammetry and Remote Sensing, 87, 57–67, 2014.
Schwatke, C., Dettmering, D., Bosch, W., and Seitz, F.: DAHITI – an innovative approach for estimating water level time series over inland waters using multi-mission satellite altimetry, Hydrol. Earth Syst. Sci., 19, 4345–4364, https://doi.org/10.5194/hess-19-4345-2015, 2015.
Sheng, Y., Song, C., Wang, J., Lyons, E. A., Knox, B. R., Cox, J. S., and Gao, F.: Representative lake water extent mapping at continental scales using multi-temporal Landsat-8 imagery, Remote Sens. Environ., 185, 129–141, 2016.
Shugar, D. H., Burr, A., Haritashya, U. K., Kargel, J. S., Watson, C. S., Kennedy, M. C., Bevington, A. R., Betts, R. A., Harrison, S., and Strattman, K.: Rapid worldwide growth of glacial lakes since 1990, Nat. Clim. Change, 10, 939–945, 2020.
Storn, R. and Price, K.: Differential evolution – a simple and efficient heuristic for global optimization over continuous spaces, J. Global Optimization, 11, 341–359, https://doi.org/10.1023/A:1008202821328, 1997.
Tao, S., Fang, J., Zhao, X., Zhao, S., Shen, H., Hu, H., Tang, Z., Wang, Z., and Guo, Q.: Rapid loss of lakes on the Mongolian Plateau, P. Natl. Acad. Sci. USA, 112, 2281–2286, 2015.
Tortini, R., Noujdina, N., Yeo, S., Ricko, M., Birkett, C. M., Khandelwal, A., Kumar, V., Marlier, M. E., and Lettenmaier, D. P.: Satellite-based remote sensing data set of global surface water storage change from 1992 to 2018, Earth Syst. Sci. Data, 12, 1141–1151, https://doi.org/10.5194/essd-12-1141-2020, 2020.
Verpoorter, C., Kutser, T., Seekell, D. A., and Tranvik, L. J.: A global inventory of lakes based on high-resolution satellite imagery, Geophys. Res. Lett., 41, 6396–6402, 2014.
Vorosmarty, C. J., Green, P., Salisbury, J., and Lammers, R. B.: Global water resources: vulnerability from climate change and population growth, Science, 289, 284–288, 2000.
Vörösmarty, C. J., McIntyre, P. B., Gessner, M. O., Dudgeon, D., Prusevich, A., Green, P., Glidden, S., Bunn, S. E., Sullivan, C. A., and Liermann, C. R.: Global threats to human water security and river biodiversity, Nature, 467, 555, https://doi.org/10.1038/nature09440, 2010.
Vu, D. T., Dang, T. D., Galelli, S., and Hossain, F.: Satellite observations reveal 13 years of reservoir filling strategies, operating rules, and hydrological alterations in the Upper Mekong River basin, Hydrol. Earth Syst. Sci., 26, 2345–2364, https://doi.org/10.5194/hess-26-2345-2022, 2022.
Wang, J., Song, C., Reager, J. T., Yao, F., Famiglietti, J. S., Sheng, Y., MacDonald, G. M., Brun, F., Schmied, H. M., and Marston, R. A.: Recent global decline in endorheic basin water storages, Nat. Geosci., 11, 926–932, 2018.
Wang, J., Walter, B. A., Yao, F., Song, C., Ding, M., Maroof, A. S., Zhu, J., Fan, C., McAlister, J. M., Sikder, S., Sheng, Y., Allen, G. H., Crétaux, J.-F., and Wada, Y.: GeoDAR: georeferenced global dams and reservoirs dataset for bridging attributes and geolocations, Earth Syst. Sci. Data, 14, 1869–1899, https://doi.org/10.5194/essd-14-1869-2022, 2022.
Yang, J., Huang, X., and Tang, Q.: Satellite-derived river width and its spatiotemporal patterns in China during 1990–2015, Remote Sens. Environ., 247, 111918, https://doi.org/10.1016/j.rse.2020.111918, 2020.
Yao, F., Wang, J., Wang, C., and Crétaux, J.-F.: Constructing long-term high-frequency time series of global lake and reservoir areas using Landsat imagery, Remote Sens. Environ., 232, 111210, https://doi.org/10.1016/j.rse.2019.111210, 2019.
Yigzaw, W., Li, H. Y., Demissie, Y., Hejazi, M. I., Leung, L. R., Voisin, N., and Payn, R.: A new global storage-area-depth data set for Modeling reservoirs in land surface and earth system models, Water Resour. Res., 54, 10372–10386, https://doi.org/10.1029/2017WR022040, 2018.
Zhang, S., Gao, H., and Naz, B. S.: Monitoring reservoir storage in South Asia from multisatellite remote sensing, Water Resour. Res., 50, 8927–8943, https://doi.org/10.1002/2014WR015829, 2014.
Zhao, G. and Gao, H.: Automatic correction of contaminated images for assessment of reservoir surface area dynamics, Geophys. Res. Lett., 45, 6092–6099, 2018.
Short summary
The GloLakes dataset provides historical and near-real-time time series of relative (i.e. storage change) and absolute (i.e. total stored volume) storage for more than 27 000 lakes worldwide using multiple sources of satellite data, including laser and radar altimetry and optical remote sensing. These data can help us understand the influence of climate variability and anthropogenic activities on water availability and system ecology over the last 4 decades.
The GloLakes dataset provides historical and near-real-time time series of relative (i.e....
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