in

A deep learning-based hybrid model of global terrestrial evaporation

  • Good, S. P., Noone, D. & Bowen, G. Hydrologic connectivity constrains partitioning of global terrestrial water fluxes. Science 349, 175–177 (2015).

    ADS 
    CAS 
    PubMed 

    Google Scholar 

  • Masson-Delmotte, V. et al. Climate change 2021: The physical science basis. contribution of working group I to the sixth assessment report of the intergovernmental panel of climate change. Global warming of 1.5 C. An IPCC Special Report (2021).

  • Milly, P. C. D., Dunne, K. A. & Vecchia, A. V. Global pattern of trends in streamflow and water availability in a changing climate. Nature 438, 347–350 (2005).

    ADS 
    CAS 
    PubMed 

    Google Scholar 

  • Konapala, G., Mishra, A. K., Wada, Y. & Mann, M. E. Climate change will affect global water availability through compounding changes in seasonal precipitation and evaporation. Nat. Commun. 11, 3044 (2020).

    ADS 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar 

  • Miralles, D. G., Gentine, P., Seneviratne, S. I. & Teuling, A. J. Land-atmospheric feedbacks during droughts and heatwaves: state of the science and current challenges. Ann. N. Y. Acad. Sci. 1436, 19–35 (2019).

    ADS 
    PubMed 

    Google Scholar 

  • Schwalm, C. R. et al. Global patterns of drought recovery. Nature 548, 202–205 (2017).

    ADS 
    CAS 
    PubMed 

    Google Scholar 

  • Sippel, S. et al. Drought, heat, and the carbon cycle: a review. Curr. Clim. Change Rep. 4, 266–286 (2018).

    Google Scholar 

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

    ADS 
    CAS 
    PubMed 

    Google Scholar 

  • Vicente-Serrano, S. M., Beguería, S. & L`ópez-Moreno, J. I. A multiscalar drought index sensitive to global warming: the standardized precipitation evapotranspiration index. J. Clim. 23, 1696–1718 (2010).

    Google Scholar 

  • Anderson, M. C. et al. The evaporative stress index as an indicator of agricultural drought in brazil: an assessment based on crop yield impacts. Remote Sens. Environ. 174, 82–99 (2016).

    ADS 

    Google Scholar 

  • Fisher, J. B. et al. The future of evapotranspiration: global requirements for ecosystem functioning, carbon and climate feedbacks, agricultural management, and water resources. Water Resour. Res. 53, 2618–2626 (2017).

    ADS 

    Google Scholar 

  • Kalma, J. D., McVicar, T. R. & McCabe, M. F. Estimating land surface evaporation: a review of methods using remotely sensed surface temperature data. Surv. Geophys. 29, 421–469 (2008).

    ADS 

    Google Scholar 

  • Melton, F. S. et al. Openet: Filling a critical data gap in water management for the western united states. JAWRA Journal of the American Water Resources Association (2021). https://onlinelibrary.wiley.com/doi/abs/10.1111/1752-1688.12956. https://onlinelibrary.wiley.com/doi/pdf/10.1111/1752-1688.12956.

  • Lawrence, D. M. et al. The community land model version 5: Description of new features, benchmarking, and impact of forcing uncertainty. J. Adv. Modeling Earth Syst. 11, 4245–4287 (2019).

    ADS 

    Google Scholar 

  • Niu, G.-Y. et al. The community noah land surface model with multiparameterization options (noah-mp): 1. model description and evaluation with local-scale measurements. J. Geophys. Res.: Atmosph. 116 (2011). https://agupubs.onlinelibrary.wiley.com/doi/abs/10.1029/2010JD015139.

  • Miralles, D. G. et al. Global land-surface evaporation estimated from satellite-based observations. Hydrol. Earth Syst. Sci. 15, 453–469 (2011).

    ADS 

    Google Scholar 

  • Fisher, J. B., Tu, K. P. & Baldocchi, D. D. Global estimates of the land-atmosphere water flux based on monthly avhrr and islscp-ii data, validated at 16 fluxnet sites. Remote Sens. Environ. 112, 901–919 (2008).

    ADS 

    Google Scholar 

  • Mu, Q., Zhao, M. & Running, S. W. Improvements to a modis global terrestrial evapotranspiration algorithm. Remote Sens. Environ. 115, 1781–1800 (2011).

    ADS 

    Google Scholar 

  • Mueller, B. & Seneviratne, S. I. Systematic land climate and evapotranspiration biases in cmip5 simulations. Geophys. Res. Lett. 41, 128–134 (2014).

    ADS 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar 

  • Koppa, A., Alam, S., Miralles, D. G. & Gebremichael, M. Budyko-based long-term water and energy balance closure in global watersheds from earth observations. Water Resour. Res. 57, e2020WR028658 (2021).

    ADS 
    PubMed 
    PubMed Central 

    Google Scholar 

  • Fisher, J. B. et al. The future of evapotranspiration: global requirements for ecosystem functioning, carbon and climate feedbacks, agricultural management, and water resources. Water Resour. Res. 53, 2618–2626 (2017).

    ADS 

    Google Scholar 

  • Penman, H. L. & Keen, B. A. Natural evaporation from open water, bare soil and grass. Proc. R. Soc. Lond. Ser. A. Math. Phys. Sci. 193, 120–145 (1948).

    ADS 
    CAS 

    Google Scholar 

  • Priestley, C. H. B. & Taylor, R. J. On the assessment of surface heat flux and evaporation using large-scale parameters. Monthly Weather Rev. 100, 81–92 (1972).

    Google Scholar 

  • Maes, W. H., Gentine, P., Verhoest, N. E. C. & Miralles, D. G. Potential evaporation at eddy-covariance sites across the globe. Hydrol. Earth Syst. Sci. 23, 925–948 (2019).

    ADS 

    Google Scholar 

  • Zhao, W. L. et al. Physics-constrained machine learning of evapotranspiration. Geophys. Res. Lett. 46, 14496–14507 (2019).

    ADS 

    Google Scholar 

  • Miralles, D. G. et al. The wacmos-et project – part 2: Evaluation of global terrestrial evaporation data sets. Hydrol. Earth Syst. Sci. 20, 823–842 (2016).

    ADS 

    Google Scholar 

  • Green, J. K., Berry, J., Ciais, P., Zhang, Y. & Gentine, P. Amazon rainforest photosynthesis increases in response to atmospheric dryness. Sci. Adv. 6 (2020). https://advances.sciencemag.org/content/6/47/eabb7232. https://advances.sciencemag.org/content/6/47/eabb7232.full.pdf.

  • Verhoef, A. & Egea, G. Modeling plant transpiration under limited soil water: Comparison of different plant and soil hydraulic parameterizations and preliminary implications for their use in land surface models. Agric. For. Meteorol. 191, 22–32 (2014).

    ADS 

    Google Scholar 

  • Powell, T. L. et al. Confronting model predictions of carbon fluxes with measurements of amazon forests subjected to experimental drought. N. Phytologist 200, 350–365 (2013).

    Google Scholar 

  • Wu, X. et al. Parameterization of the water stress reduction function based on soil–plant water relations. Irrig. Sci. 39, 101–122 (2021).

    Google Scholar 

  • Zhang, J., Liu, P., Zhang, F. & Song, Q. Cloudnet: Ground-based cloud classification with deep convolutional neural network. Geophys. Res. Lett. 45, 8665–8672 (2018).

    ADS 

    Google Scholar 

  • Hengl, T. et al. Soilgrids250m: global gridded soil information based on machine learning. PLoS ONE 12, 1–40 (2017).

    Google Scholar 

  • Hansen, M. C. et al. High-resolution global maps of 21st-century forest cover change. Science 342, 850–853 (2013).

    ADS 
    CAS 
    PubMed 

    Google Scholar 

  • Jung, M. et al. The fluxcom ensemble of global land-atmosphere energy fluxes. Sci. Data 6, 74 (2019).

    PubMed 
    PubMed Central 

    Google Scholar 

  • McGovern, A. et al. Using artificial intelligence to improve real-time decision-making for high-impact weather. Bull. Am. Meteorological Soc. 98, 2073–2090 (2017).

    Google Scholar 

  • Kratzert, F. et al. Toward improved predictions in ungauged basins: exploiting the power of machine learning. Water Resour. Res. 55, 11344–11354 (2019).

    ADS 

    Google Scholar 

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

    ADS 
    CAS 
    PubMed 

    Google Scholar 

  • Rasp, S., Pritchard, M. S. & Gentine, P. Deep learning to represent subgrid processes in climate models. Proc. Natl Acad. Sci. USA 115, 9684–9689 (2018).

    CAS 
    PubMed 
    PubMed Central 

    Google Scholar 

  • de Bézenac, E., Pajot, A. & Gallinari, P. Deep learning for physical processes: incorporating prior scientific knowledge. J. Stat. Mech.: Theory Exp. 2019, 124009 (2019).

    MathSciNet 
    MATH 

    Google Scholar 

  • Kraft, B., Jung, M., Körner, M. & Reichstein, M. Hybrid modeling: Fusion of a deep learning approach and a physics-based model for global hydrological modeling. Int. Arch. Photogramm., Remote Sens. Spat. Inf. Sci. XLIII-B2-2020, 1537–1544 (2020).

    Google Scholar 

  • Chen, H., Huang, J. J., Dash, S. S., Wei, Y. & Li, H. A hybrid deep learning framework with physical process description for simulation of evapotranspiration. J. Hydrol. 606, 127422 (2022).

    Google Scholar 

  • Martens, B. et al. Gleam v3: satellite-based land evaporation and root-zone soil moisture. Geoscientific Model Dev. 10, 1903–1925 (2017).

    ADS 

    Google Scholar 

  • Gash, J. H. C. An analytical model of rainfall interception by forests. Q. J. R. Meteorological Soc. 105, 43–55 (1979).

    ADS 

    Google Scholar 

  • Grossiord, C. et al. Plant responses to rising vapor pressure deficit. N. Phytologist 226, 1550–1566 (2020).

    Google Scholar 

  • Urban, J., Ingwers, M., McGuire, M. A. & Teskey, R. O. Stomatal conductance increases with rising temperature. Plant Signal. Behav. 12, e1356534 (2017). PMID: 28786730.

    PubMed 
    PubMed Central 

    Google Scholar 

  • Matthews, J. S. A., Vialet-Chabrand, S. & Lawson, T. Role of blue and red light in stomatal dynamic behaviour. J. Exp. Bot. 71, 2253–2269 (2019).

    PubMed Central 

    Google Scholar 

  • Xu, Z., Jiang, Y., Jia, B. & Zhou, G. Elevated-co2 response of stomata and its dependence on environmental factors. Front. Plant Sci. 7, 657 (2016).

    PubMed 
    PubMed Central 

    Google Scholar 

  • Jung, M. et al. Recent decline in the global land evapotranspiration trend due to limited moisture supply. Nature 467, 951–954 (2010).

    ADS 
    CAS 
    PubMed 

    Google Scholar 

  • Peng, Y., Bloomfield, K. J., Cernusak, L. A., Domingues, T. F. & Colin Prentice, I. Global climate and nutrient controls of photosynthetic capacity. Commun. Biol. 4, 462 (2021).

    CAS 
    PubMed 
    PubMed Central 

    Google Scholar 

  • Knoben, W. J. M., Freer, J. E. & Woods, R. A. Technical note: Inherent benchmark or not? comparing nash–sutcliffe and kling–gupta efficiency scores. Hydrol. Earth Syst. Sci. 23, 4323–4331 (2019).

    ADS 

    Google Scholar 

  • Pagán, B. R., Maes, W. H., Gentine, P., Martens, B. & Miralles, D. G. Exploring the potential of satellite solar-induced fluorescence to constrain global transpiration estimates. Remote Sens. 11 (2019). https://www.mdpi.com/2072-4292/11/4/413.

  • Jonard, F. et al. Value of sun-induced chlorophyll fluorescence for quantifying hydrological states and fluxes: current status and challenges. Agric. For. Meteorol. 291, 108088 (2020).

    ADS 

    Google Scholar 

  • Bauer, P. et al. The digital revolution of earth-system science. Nat. Comput. Sci. 1, 104–113 (2021).

    Google Scholar 

  • Bonan, G. B. Forests and climate change: forcings, feedbacks, and the climate benefits of forests. Science 320, 1444–1449 (2008).

    ADS 
    CAS 
    PubMed 

    Google Scholar 

  • Pastorello, G. et al. The fluxnet2015 dataset and the oneflux processing pipeline for eddy covariance data. Sci. Data 7, 225 (2020).

    PubMed 
    PubMed Central 

    Google Scholar 

  • Wei, Z. et al. Revisiting the contribution of transpiration to global terrestrial evapotranspiration. Geophys. Res. Lett. 44, 2792–2801 (2017).

    ADS 

    Google Scholar 

  • Stoy, P. C. et al. Reviews and syntheses: turning the challenges of partitioning ecosystem evaporation and transpiration into opportunities. Biogeosciences 16, 3747–3775 (2019).

    ADS 
    CAS 

    Google Scholar 

  • Poyatos, R. et al. Global transpiration data from sap flow measurements: the sapfluxnet database. Earth Syst. Sci. Data 13, 2607–2649 (2021).

    ADS 

    Google Scholar 

  • Falster, D. S. et al. Baad: a biomass and allometry database for woody plants. Ecology 96, 1445–1445 (2015).

    Google Scholar 

  • Granier, A. & Loustau, D. Measuring and modelling the transpiration of a maritime pine canopy from sap-flow data. Agric. For. Meteorol. 71, 61–81 (1994).

    ADS 

    Google Scholar 

  • Aumann, H. et al. Airs/amsu/hsb on the aqua mission: design, science objectives, data products, and processing systems. IEEE Trans. Geosci. Remote Sens. 41, 253–264 (2003).

    ADS 

    Google Scholar 

  • Wielicki, B. A. et al. Clouds and the earth’s radiant energy system (ceres): an earth observing system experiment. Bull. Am. Meteorological Soc. 77, 853–868 (1996).

    ADS 

    Google Scholar 

  • Moesinger, L. et al. The global long-term microwave vegetation optical depth climate archive (vodca). Earth Syst. Sci. Data 12, 177–196 (2020).

    ADS 

    Google Scholar 

  • Abadi, M. et al. TensorFlow: Large-scale machine learning on heterogeneous systems (2015). https://www.tensorflow.org/. Software available from tensorflow.org.

  • Gupta, H. V., Kling, H., Yilmaz, K. K. & Martinez, G. F. Decomposition of the mean squared error and NSE performance criteria: implications for improving hydrological modelling. J. Hydrol. 377, 80–91 (2009).

    ADS 

    Google Scholar 

  • Yu, L., Wen, J., Chang, C. Y., Frankenberg, C. & Sun, Y. High-resolution global contiguous sif of oco-2. Geophys. Res. Lett. 46, 1449–1458 (2019).

    ADS 

    Google Scholar 

  • Koppa, A., Rains, D., Hulsman, P., Poyatos, R. & Miralles, D. G. A Deep learning-based hybrid model of global terrestrial evaporation (2022). https://doi.org/10.5281/zenodo.5886608.

  • Koppa, A., Rains, D., Hulsman, P., Poyatos, R. & Miralles, D. G. A Deep learning-based hybrid model of global terrestrial evaporation (2022). https://doi.org/10.5281/zenodo.6343005.


  • Source: Ecology - nature.com

    Q&A: Climate Grand Challenges finalists on using data and science to forecast climate-related risk

    Leveraging science and technology against the world’s top problems