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HomeNatureGlobal dominance of seasonality in shaping lake-surface-extent dynamics

Global dominance of seasonality in shaping lake-surface-extent dynamics

  • Dudgeon, D. et al. Freshwater biodiversity: importance, threats, status and conservation challenges. Biol. Rev. 81, 163–182 (2006).

    Article 
    PubMed 

    Google Scholar
     

  • Holgerson, M. A. & Raymond, P. A. Large contribution to inland water CO2 and CH4 emissions from very small ponds. Nat. Geosci. 9, 222–226 (2016).

    Article 
    ADS 
    CAS 

    Google Scholar
     

  • Qin, B. et al. A drinking water crisis in Lake Taihu, China: linkage to climatic variability and lake management. Environ. Manag. 45, 105–112 (2010).

    Article 
    ADS 

    Google Scholar
     

  • Zhao, G. & Gao, H. Automatic correction of contaminated images for assessment of reservoir surface area dynamics. Geophys. Res. Lett. 45, 6092–6099 (2018).

    Article 
    ADS 
    PubMed 
    PubMed Central 

    Google Scholar
     

  • Nyberg, B., Sayre, R. & Luijendijk, E. Increasing seasonal variation in the extent of rivers and lakes from 1984 to 2022. Hydrol. Earth Syst. Sci. 28, 1653–1663 (2024).

    Article 
    ADS 

    Google Scholar
     

  • Yao, F. et al. Satellites reveal widespread decline in global lake water storage. Science 380, 743–749 (2023).

    Article 
    ADS 
    CAS 
    PubMed 

    Google Scholar
     

  • Pekel, J.-F., Cottam, A., Gorelick, N. & Belward, A. S. High-resolution mapping of global surface water and its long-term changes. Nature 540, 418–422 (2016).

    Article 
    ADS 
    CAS 
    PubMed 

    Google Scholar
     

  • Zhang, G. et al. Regional differences of lake evolution across China during 1960s–2015 and its natural and anthropogenic causes. Remote Sens. Environ. 221, 386–404 (2019).

    Article 
    ADS 

    Google Scholar
     

  • Adrian, R. et al. Lakes as sentinels of climate change. Limnol. Oceanogr. 54, 2283–2297 (2009).

    Article 
    ADS 
    PubMed 
    PubMed Central 

    Google Scholar
     

  • Seekell, D., Cael, B., Norman, S. & Byström, P. Patterns and variation of littoral habitat size among lakes. Geophys. Res. Lett. 48, e2021GL095046 (2021).

    Article 
    ADS 

    Google Scholar
     

  • Vander Zanden, M. J. & Vadeboncoeur, Y. Putting the lake back together 20 years later: what in the benthos have we learned about habitat linkages in lakes? Inland Waters 10, 305–321 (2020).

    Article 

    Google Scholar
     

  • Kuiper, J. J. et al. Food-web stability signals critical transitions in temperate shallow lakes. Nat. Commun. 6, 7727 (2015).

    Article 
    ADS 
    CAS 
    PubMed 

    Google Scholar
     

  • Bastviken, D., Tranvik, L. J., Downing, J. A., Crill, P. M. & Enrich-Prast, A. Freshwater methane emissions offset the continental carbon sink. Science 331, 50–50 (2011).

    Article 
    ADS 
    CAS 
    PubMed 

    Google Scholar
     

  • Johnson, M. S., Matthews, E., Du, J., Genovese, V. & Bastviken, D. Methane emission from global lakes: new spatiotemporal data and observation-driven modeling of methane dynamics indicates lower emissions. J. Geophys. Res. Biogeosci. 127, e2022JG006793 (2022).

    Article 
    ADS 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar
     

  • Lehner, B., Messager, M. L., Korver, M. C. & Linke, S. Global hydro-environmental lake characteristics at high spatial resolution. Sci. Data 9, 351 (2022).

    Article 
    CAS 
    PubMed Central 

    Google Scholar
     

  • Woolway, R. I. et al. Global lake responses to climate change. Nat. Rev. Earth Environ. 1, 388–403 (2020).

    Article 
    ADS 

    Google Scholar
     

  • Verpoorter, C., Kutser, T., Seekell, D. A. & Tranvik, L. J. A global inventory of lakes based on high-resolution satellite imagery. Geophys. Res. Lett. 41, 6396–6402 (2014).

    Article 
    ADS 

    Google Scholar
     

  • Pi, X. et al. Mapping global lake dynamics reveals the emerging roles of small lakes. Nat. Commun. 13, 5777 (2022).

    Article 
    ADS 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar
     

  • Bastviken, D., Cole, J., Pace, M. & Tranvik, L. Methane emissions from lakes: dependence of lake characteristics, two regional assessments, and a global estimate. Glob. Biogeochem. Cycles 18, GB4009 (2004).

  • Khazaei, B., Read, L. K., Casali, M., Sampson, K. M. & Yates, D. N. GLOBathy, the global lakes bathymetry dataset. Sci. Data 9, 36 (2022).

    Article 
    PubMed 
    PubMed Central 

    Google Scholar
     

  • Messager, M. L. et al. Global prevalence of non-perennial rivers and streams. Nature 594, 391–397 (2021).

    Article 
    ADS 
    CAS 
    PubMed 

    Google Scholar
     

  • Cooley, S. W., Ryan, J. C. & Smith, L. C. Human alteration of global surface water storage variability. Nature 591, 78–81 (2021).

    Article 
    ADS 
    CAS 
    PubMed 

    Google Scholar
     

  • Carpenter, S. R. et al. Early warnings of regime shifts: a whole-ecosystem experiment. Science 332, 1079–1082 (2011).

    Article 
    ADS 
    CAS 
    PubMed 

    Google Scholar
     

  • Peterson, T. J., Saft, M., Peel, M. C. & John, A. Watersheds may not recover from drought. Science 372, 745–749 (2021).

    Article 
    ADS 
    CAS 
    PubMed 

    Google Scholar
     

  • Klein, I. et al. Global WaterPack—the development of global surface water over the past 20 years at daily temporal resolution. Sci. Data 11, 472 (2024).

    Article 
    PubMed 
    PubMed Central 

    Google Scholar
     

  • Zhao, G., Li, Y., Zhou, L. & Gao, H. Evaporative water loss of 1.42 million global lakes. Nat. Commun. 13, 3686 (2022).

    Article 
    ADS 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar
     

  • Hou, J., Van Dijk, A. I. J. M., Renzullo, L. J. & Larraondo, P. R. GloLakes: water storage dynamics for 27,000 lakes globally from 1984 to present derived from satellite altimetry and optical imaging. Earth Syst. Sci. Data 16, 201–218 (2024).

    Article 
    ADS 

    Google Scholar
     

  • Yang, X. et al. Monthly estimation of the surface water extent in France at a 10-m resolution using Sentinel-2 data. Remote Sens. Environ. 244, 111803 (2020).

    Article 

    Google Scholar
     

  • Chen, J. et al. Remote sensing big data for water environment monitoring: current status, challenges, and future prospects. Earths Future 10, e2021EF002289 (2022).

    Article 
    ADS 

    Google Scholar
     

  • Zhu, X. et al. A flexible spatiotemporal method for fusing satellite images with different resolutions. Remote Sens. Environ. 172, 165–177 (2016).

    Article 
    ADS 

    Google Scholar
     

  • Pickens, A. H. et al. Mapping and sampling to characterize global inland water dynamics from 1999 to 2018 with full Landsat time-series. Remote Sens. Environ. 243, 111792 (2020).

    Article 

    Google Scholar
     

  • Xiao, Z. et al. Unveiling the hidden dynamics of intermittent surface water: a remote sensing framework. Remote Sens. Environ. 311, 114285 (2024).

    Article 

    Google Scholar
     

  • Cleveland, R. B., Cleveland, W. S., McRae, J. E. & Terpenning, I. STL: a seasonal-trend decomposition procedure based on loess (with discussion). J. Off. Stat. 6, 3–73 (1990).


    Google Scholar
     

  • Wik, M., Varner, R. K., Anthony, K. W., MacIntyre, S. & Bastviken, D. Climate-sensitive northern lakes and ponds are critical components of methane release. Nat. Geosci. 9, 99–105 (2016).

    Article 
    ADS 
    CAS 

    Google Scholar
     

  • van Dijk, A. I. J. M. et al. The Millennium Drought in southeast Australia (2001–2009): natural and human causes and implications for water resources, ecosystems, economy, and society. Water Resour. Res. 49, 1040–1057 (2013).

    Article 
    ADS 

    Google Scholar
     

  • Li, X. et al. High-temporal-resolution water level and storage change data sets for lakes on the Tibetan Plateau during 2000–2017 using multiple altimetric missions and Landsat-derived lake shoreline positions. Earth Syst. Sci. Data 11, 1603–1627 (2019).

    Article 
    ADS 

    Google Scholar
     

  • Finger-Higgens, R. Diminishing Arctic lakes. Nat. Clim. Change 12, 782–783 (2022).

    Article 
    ADS 

    Google Scholar
     

  • Linke, S. et al. Global hydro-environmental sub-basin and river reach characteristics at high spatial resolution. Sci. Data 6, 283 (2019).

    Article 
    PubMed 
    PubMed Central 

    Google Scholar
     

  • Center for International Earth Science Information Network (CIESIN), Columbia University. Gridded Population of the World, Version 4 (GPWv4): Population Count (NASA Socioeconomic Data and Applications Center, 2016).

  • Spears, B. M. et al. Ecological resilience in lakes and the conjunction fallacy. Nat. Ecol. Evol. 1, 1616–1624 (2017).

    Article 
    PubMed 

    Google Scholar
     

  • Okpara, U. T., Stringer, L. C. & Dougill, A. J. Lake drying and livelihood dynamics in Lake Chad: unravelling the mechanisms, contexts and responses. Ambio 45, 781–795 (2016).

    Article 
    ADS 
    PubMed 
    PubMed Central 

    Google Scholar
     

  • Messager, M. L., Lehner, B., Grill, G., Nedeva, I. & Schmitt, O. Estimating the volume and age of water stored in global lakes using a geo-statistical approach. Nat. Commun. 7, 13603 (2016).

    Article 
    ADS 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar
     

  • Webb, E. E. & Liljedahl, A. K. Diminishing lake area across the northern permafrost zone. Nat. Geosci. 16, 202–209 (2023).

    Article 
    ADS 
    CAS 

    Google Scholar
     

  • Qi, W., Feng, L., Yang, H. & Liu, J. Warming winter, drying spring and shifting hydrological regimes in Northeast China under climate change. J. Hydrol. 606, 127390 (2022).

    Article 

    Google Scholar
     

  • Brown, E., Ferrians, O. J., Heginbottom, J. A. &. Melnikov, E. Circum-Arctic map of permafrost and ground-ice conditions, version 2. National Snow and Ice Data Center https://doi.org/10.7265/skbg-kf16 (2002).

  • Zomer, R. J., Trabucco, A., Bossio, D. A. & Verchot, L. V. Climate change mitigation: a spatial analysis of global land suitability for clean development mechanism afforestation and reforestation. Agric. Ecosyst. Environ. 126, 67–80 (2008).

    Article 

    Google Scholar
     

  • Mann, H. B. Nonparametric tests against trend. Econometrica 13, 245–259 (1945).

    Article 
    MathSciNet 
    MATH 

    Google Scholar
     

  • Woolway, R. I. et al. Multivariate extremes in lakes. Nat. Commun. 15, 4559 (2024).

    Article 
    ADS 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar
     

  • Samuelsson, P., Kourzeneva, E. & Mironov, D. The impact of lakes on the European climate as simulated by a regional climate model. Boreal Environ. Res. 15, 113–129 (2010).


    Google Scholar
     

  • Bertani, I., Primicerio, R. & Rossetti, G. Extreme climatic event triggers a lake regime shift that propagates across multiple trophic levels. Ecosystems 19, 16–31 (2016).

    Article 

    Google Scholar
     

  • Webb, E. E. et al. Permafrost thaw drives surface water decline across lake-rich regions of the Arctic. Nat. Clim. Change 12, 841–846 (2022).

    Article 
    ADS 
    CAS 

    Google Scholar
     

  • Ghamisi, P. et al. Multisource and multitemporal data fusion in remote sensing: a comprehensive review of the state of the art. IEEE Geosci. Remote Sens. Mag. 7, 6–39 (2019).

    Article 

    Google Scholar
     

  • Jumper, J. et al. Highly accurate protein structure prediction with AlphaFold. Nature 596, 583–589 (2021).

    Article 
    ADS 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar
     

  • Zhang, Y. et al. Skilful nowcasting of extreme precipitation with NowcastNet. Nature 619, 526–532 (2023).

    Article 
    ADS 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar
     

  • LeCun, Y., Bengio, Y. & Hinton, G. Deep learning. Nature 521, 436–444 (2015).

    Article 
    ADS 
    CAS 
    PubMed 

    Google Scholar
     

  • Li, Z. et al. Deep learning based cloud detection for medium and high resolution remote sensing images of different sensors. ISPRS J. Photogramm. Remote Sens. 150, 197–212 (2019).

    Article 
    ADS 

    Google Scholar
     

  • Reichstein, M. et al. Deep learning and process understanding for data-driven Earth system science. Nature 566, 195–204 (2019).

    Article 
    ADS 
    CAS 
    PubMed 

    Google Scholar
     

  • Weiss, M., Jacob, F. & Duveiller, G. Remote sensing for agricultural applications: a meta-review. Remote Sens. Environ. 236, 111402 (2020).

    Article 

    Google Scholar
     

  • Azad, R. et al. Medical image segmentation review: the success of U-Net. IEEE Trans. Pattern Anal. Mach. Intell. 46, 10076–10095 (2024).

  • Ronneberger, O., Fischer, P. & Brox, T. U-Net: convolutional networks for biomedical image segmentation. Preprint at https://arxiv.org/abs/1505.04597 (2015).

  • Brandt, M. et al. An unexpectedly large count of trees in the West African Sahara and Sahel. Nature 587, 78–82 (2020).

    Article 
    ADS 
    PubMed 

    Google Scholar
     

  • Suh, J. W., Zhu, Z. & Zhao, Y. Monitoring construction changes using dense satellite time series and deep learning. Remote Sens. Environ. 309, 114207 (2024).

    Article 

    Google Scholar
     

  • Gorelick, N. et al. Google Earth Engine: planetary-scale geospatial analysis for everyone. Remote Sens. Environ. 202, 18–27 (2017).

    Article 
    ADS 

    Google Scholar
     

  • Zhang, Q., Yuan, Q., Zeng, C., Li, X. & Wei, Y. Missing data reconstruction in remote sensing image with a unified spatial–temporal–spectral deep convolutional neural network. IEEE Trans. Geosci. Remote Sens. 56, 4274–4288 (2018).

    Article 
    ADS 

    Google Scholar
     

  • Chen, Y., Shi, K., Ge, Y. & Zhou, Y. Spatiotemporal remote sensing image fusion using multiscale two-stream convolutional neural networks. IEEE Trans. Geosci. Remote Sens. 60, 1–12 (2022).

    CAS 

    Google Scholar
     

  • Goudsmit, G.-H., Burchard, H., Peeters, F. & Wüest, A. Application of kϵ turbulence models to enclosed basins: the role of internal seiches. J. Geophys. Res. Oceans 107, 23-1–23-13 (2002).

  • Gaudard, A., Råman Vinnå, L., Bärenbold, F., Schmid, M. & Bouffard, D. Toward an open access to high-frequency lake modeling and statistics data for scientists and practitioners—the case of Swiss lakes using Simstrat v2.1. Geosci. Model Dev. 12, 3955–3974 (2019).

  • Zomer, R. J., Xu, J. & Trabucco, A. Version 3 of the Global Aridity Index and Potential Evapotranspiration Database. Sci. Data 9, 409 (2022).

    Article 
    PubMed 
    PubMed Central 

    Google Scholar
     

  • Olofsson, P. et al. Good practices for estimating area and assessing accuracy of land change. Remote Sens. Environ. 148, 42–57 (2014).

    Article 
    ADS 

    Google Scholar
     

  • Pi, X. et al. Mapping global lake dynamics reveals the emerging roles of small lakes: code and data. Zenodo https://doi.org/10.5281/zenodo.7016547 (2022).

  • Li, L., Long, D., Wang, Y. & Woolway, R. I. Global dominance of seasonality in shaping lake surface extent dynamics. Zenodo https://doi.org/10.5281/zenodo.14568609 (2025).

  • Li, L., Long, D., Wang, Y., & Woolway, R. I. Global dominance of seasonality in shaping lake surface extent dynamics. Sci. Data Bank https://doi.org/10.57760/sciencedb.19653 (2025).

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