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Ten Years of GLEAM: A Review of Scientific Advances and Applications

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Computational Intelligence for Water and Environmental Sciences

Part of the book series: Studies in Computational Intelligence ((SCI,volume 1043))

Abstract

During the past decades, consistent efforts have been undertaken to model the Earth's hydrological cycle. Multiple mathematical models have been designed to understand, predict, and manage water resources, particularly under the context of climate change. A variable that has traditionally received limited attention by the hydrological community—but that is crucial to understand the links to climate—is terrestrial evaporation. The Global Land Evaporation Amsterdam Model (GLEAM) was developed ten years ago with the goal to derive terrestrial evaporation from satellite imagery. Since then, GLEAM has been used in a variety of applications, including trend analysis, drought and heatwave studies, hydrological model calibration and validation, water budget assessment, and studies of changes in vegetation. To streamline the development of the model and improve its ability and accuracy in capturing the spatiotemporal patterns of evaporation, while tailoring the development to the needs of stakeholders, it is important to review previous studies and highlight the potential strengths and weaknesses of the model. Therefore, in this study, we provide a literature review of the GLEAM model applications and its accuracy. The results of this metanalysis indicate that GLEAM is preferentially used in climate studies, potentially due to its coarse (25 km) spatial resolution being a limiting factor for its use in water management and, particularly, agricultural applications. Validations to date suggest that, while GLEAM provides a relatively accurate evaporation dataset, its performance over short canopies requires further improvement. Two major sources of uncertainty in the GLEAM algorithm have been identified: (1) the modelling of evaporative stress in response to water limitation, (2) the need to consider below canopy evaporation estimates for a more realistic attribution of evaporation to its different sources. These potential drawbacks of the model could be alleviated by combining the current algorithm with a machine learning-based approach for a next generation of the model. Likewise, ongoing activities of running the model at high (100 m–1 km) resolutions open possibilities to utilise the data for water and agricultural management applications.

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References

  • Abdi, A., Ghahreman, N., & Ghamghami, M. (2020). Evaluation of evapotranspiration estimations of GLEAM model in northern part of Karkhe basin. Iranian Journal of Irrigation & Drainage, 14(2), 366–378.

    Google Scholar 

  • Albergel, C., Munier, S., Leroux, D. J., Dewaele, H., Fairbairn, D., Barbu, A. L., Gelati, E., Dorigo, W., Faroux, S., Meurey, C. and Le Moigne, P., & Calvet, J. C. (2017). Sequential assimilation of satellite-derived vegetation and soil moisture products using SURFEX_v8. 0: LDAS-Monde assessment over the Euro-Mediterranean area. Geoscientific Model Development, 10(10), 3889–3912.

    Google Scholar 

  • Bai, P., & Liu, X. (2018). Intercomparison and evaluation of three global high-resolution evapotranspiration products across China. Journal of Hydrology, 566, 743–755.

    Article  Google Scholar 

  • Baik, J., Liaqat, U. W., & Choi, M. (2018). Assessment of satellite-and reanalysis-based evapotranspiration products with two blending approaches over the complex landscapes and climates of Australia. Agricultural and Forest Meteorology, 263, 388–398.

    Article  Google Scholar 

  • Baik, J., Park, J., & Choi, M. (2020). Blending multi-source evapotranspiration datasets via triple collocation approach. Authorea Preprints.

    Google Scholar 

  • Baik, J., Park, J., Lee, S., Kim, U., & Choi, M. (2018b). Assessment of merging technique using Triple Collocation (TC) from satellite and reanalysis dataset over Different Land Covers in East Asia: GLDAS, MOD16, GLEAM, and MERRA. In AGU Fall Meeting Abstracts (Vol. 2018b, pp. H51R-1554).

    Google Scholar 

  • Benedict, I., Heerwaarden, C. C. V., Weerts, A. H., & Hazeleger, W. (2019). The benefits of spatial resolution increase in global simulations of the hydrological cycle evaluated for the Rhine and Mississippi basins. Hydrology and Earth System Sciences, 23(3), 1779–1800.

    Article  Google Scholar 

  • Chao, L., Zhang, K., Wang, J., Feng, J., & Zhang, M. (2021). A Comprehensive evaluation of five evapotranspiration datasets based on ground and GRACE satellite observations: implications for improvement of evapotranspiration retrieval algorithm. Remote Sensing, 13(12), 2414.

    Article  Google Scholar 

  • Dembélé, M., Hrachowitz, M., Savenije, H. H., Mariéthoz, G., & Schaefli, B. (2020a). Improving the predictive skill of a distributed hydrological model by calibration on spatial patterns with multiple satellite data sets. Water Resources Research, 56(1).

    Google Scholar 

  • Dembélé, M., Ceperley, N., Zwart, S. J., Salvadore, E., Mariethoz, G., & Schaefli, B. (2020). Potential of satellite and reanalysis evaporation datasets for hydrological modelling under various model calibration strategies. Advances in Water Resources, 143, 103667.

    Article  Google Scholar 

  • Dolman, A. J., Miralles, D. G., & de Jeu, R. A. (2014). Fifty years since Monteith’s 1965 seminal paper: The emergence of global ecohydrology. Ecohydrology, 7(3), 897–902.

    Article  Google Scholar 

  • Dorigo, W., Dietrich, S., Aires, F., Brocca, L., Carter, S., Cretaux, J. F., Dunkerley, D., Enomoto, H., Forsberg, R., Güntner, A. Hegglin, M. I., & Aich, V. (2021). Closing the water cycle from observations across scales: Where do we stand? Bulletin of the American Meteorological Society, 102(10), E1897–E1935.

    Google Scholar 

  • Draper, C. S., Reichle, R. H., & Koster, R. D. (2018). Assessment of MERRA-2 land surface energy flux estimates. Journal of Climate, 31(2), 671–691.

    Article  Google Scholar 

  • Duveiller, G., Hooker, J., & Cescatti, A. (2018). The mark of vegetation change on Earth’s surface energy balance. Nature Communications, 9(1), 1–12.

    Article  Google Scholar 

  • Gash, J. H. C. (1979). An analytical model of rainfall interception by forests. The Quarterly Journal of the Royal Meteorological Society, 105, 43–55. https://doi.org/10.1002/qj.49710544304

    Article  Google Scholar 

  • Geirinhas, J. L., Russo, A., Libonati, R., Sousa, P. M., Miralles, D. G., & Trigo, R. M. (2021). Recent increasing frequency of compound summer drought and heatwaves in Southeast Brazil. Environmental Research Letters, 16(3), 034036.

    Article  Google Scholar 

  • Gonsamo, A., Ter-Mikaelian, M. T., Chen, J. M., & Chen, J. (2019). Does earlier and increased spring plant growth lead to reduced summer soil moisture and plant growth on landscapes typical of Tundra-Taiga interface? Remote Sensing, 11(17), 1989.

    Article  Google Scholar 

  • Guillod, B. P., Orlowsky, B., Miralles, D., Teuling, A. J., Blanken, P. D., Buchmann, N., Ciais, P., Ek, M., Findell, K. L., Gentine, P. Lintner, B. R., & Seneviratne, S. (2014). Land-surface controls on afternoon precipitation diagnosed from observational data: uncertainties and confounding factors. Atmospheric Chemistry and Physics, 14(16), 8343–8367.

    Google Scholar 

  • Hobeichi, S., Abramowitz, G., Evans, J., & Ukkola, A. (2018). Derived optimal linear combination evapotranspiration (DOLCE): A global gridded synthesis ET estimate. Hydrology and Earth System Sciences, 22(2), 1317–1336.

    Article  Google Scholar 

  • Jiang, S., Wei, L., Ren, L., Xu, C. Y., Zhong, F., Wang, M., Zhang, L., Yuan, F., & Liu, Y. (2021). Utility of integrated IMERG precipitation and GLEAM potential evapotranspiration products for drought monitoring over mainland China. Atmospheric Research, 247, 105141.

    Google Scholar 

  • Jiménez, C., Martens, B., Miralles, D. M., Fisher, J. B., Beck, H. E., & Fernández-Prieto, D. (2018). Exploring the merging of the global land evaporation WACMOS-ET products based on local tower measurements. Hydrology and Earth System Sciences, 22(8), 4513–4533.

    Article  Google Scholar 

  • Jin, X., & Jin, Y. (2020). Calibration of a distributed hydrological model in a data-scarce basin based on GLEAM datasets. Water, 12(3), 897.

    Article  Google Scholar 

  • Khan, M. S., Liaqat, U. W., Baik, J., & Choi, M. (2018). Stand-alone uncertainty characterization of GLEAM, GLDAS and MOD16 evapotranspiration products using an extended triple collocation approach. Agricultural and Forest Meteorology, 252, 256–268.

    Article  Google Scholar 

  • Khan, M. S., Baik, J., & Choi, M. (2020). Inter-comparison of evapotranspiration datasets over heterogeneous landscapes across Australia. Advances in Space Research, 66(3), 533–545.

    Article  Google Scholar 

  • Konapala, G., Mishra, A. K., Wada, Y., & Mann, M. E. (2020). Climate change will affect global water availability through compounding changes in seasonal precipitation and evaporation. Nature Communications, 11(1), 1–10.

    Article  Google Scholar 

  • Koppa, A., Rains, D., Hulsman, P., & Miralles, D. (2021a). A deep learning-based hybrid model of global terrestrial evaporation.

    Google Scholar 

  • Koppa, A., Alam, S., Miralles, D. G., & Gebremichael, M. (2021b). Budyko‐based long‐term water and energy balance closure in global watersheds from earth observations. Water Resources Research, 57(5), e2020WR028658.

    Google Scholar 

  • Koppa, A., & Gebremichael, M. (2020). Improving the applicability of hydrologic models for food–energy–water nexus studies using remote sensing data. Remote Sensing, 12(4), 599.

    Article  Google Scholar 

  • Koppa, A., Gebremichael, M., & Yeh, W. W. (2019). Multivariate calibration of large scale hydrologic models: The necessity and value of a Pareto optimal approach. Advances in Water Resources, 130, 129–146.

    Article  Google Scholar 

  • Koppa, A., & Gebremichael, M. (2017). A framework for validation of remotely sensed precipitation and evapotranspiration based on the Budyko hypothesis. Water Resources Research, 53(10), 8487–8499.

    Article  Google Scholar 

  • Lee, Y., Im, B., Kim, K., & Rhee, K. (2020). Adequacy evaluation of the GLDAS and GLEAM evapotranspiration by eddy covariance method. Journal of Korea Water Resources Association, 53(10), 889–902.

    Google Scholar 

  • Liang, C., Chen, T., Dolman, H., Shi, T., Wei, X., Xu, J., & Hagan, D. F. T. (2020). Drying and wetting trends and vegetation covariations in the drylands of China. Water, 12(4), 933.

    Article  Google Scholar 

  • Liu, X., He, B., Guo, L., Huang, L., & Chen, D. (2020). Similarities and differences in the mechanisms causing the European summer heatwaves in 2003, 2010, and 2018. Earth's Future, 8(4), e2019EF001386.

    Google Scholar 

  • Liu, W., Wang, L., Zhou, J., Li, Y., Sun, F., Fu, G., Li, X., & Sang, Y. F. (2016). A worldwide evaluation of basin-scale evapotranspiration estimates against the water balance method. Journal of Hydrology, 538, 82–95.

    Google Scholar 

  • López, P., Sutanudjaja, E. H., Schellekens, J., Sterk, G., & Bierkens, M. F. (2017). Calibration of a large-scale hydrological model using satellite-based soil moisture and evapotranspiration products. Hydrology and Earth System Sciences, 21(6), 3125–3144.

    Article  Google Scholar 

  • Lu, J., Wang, G., Gong, T., Hagan, D. F. T., Wang, Y., Jiang, T., & Su, B. (2019). Changes of actual evapotranspiration and its components in the Yangtze River valley during 1980–2014 from satellite assimilation product. Theoretical and Applied Climatology, 138(3), 1493–1510.

    Article  Google Scholar 

  • Lv, M., Xu, Z., & Lv, M. (2021). Evaluating hydrological processes of the atmosphere-vegetation interaction model and MERRA-2 at global scale. Atmosphere, 12(1), 16.

    Article  Google Scholar 

  • López-Ballesteros, A., Senent-Aparicio, J., Srinivasan, R., & Pérez-Sánchez, J. (2019). Assessing the impact of best management practices in a highly anthropogenic and ungauged watershed using the SWAT model: A case study in the El Beal Watershed (Southeast Spain). Agronomy, 9(10), 576.

    Article  Google Scholar 

  • Lorenz, C., Kunstmann, H., Devaraju, B., Tourian, M. J., Sneeuw, N., & Riegger, J. (2014). Large-scale runoff from landmasses: A global assessment of the closure of the hydrological and atmospheric water balances. Journal of Hydrometeorology, 15(6), 2111–2139.

    Article  Google Scholar 

  • Majozi, N. P., Mannaerts, C. M., Ramoelo, A., Mathieu, R., Mudau, A. E., & Verhoef, W. (2017). An intercomparison of satellite-based daily evapotranspiration estimates under different eco-climatic regions in South Africa. Remote Sensing, 9(4), 307.

    Article  Google Scholar 

  • Mao, J., Fu, W., Shi, X., Ricciuto, D. M., Fisher, J. B., Dickinson, R. E., Wei, Y., Shem, W., Piao, S., Wang, K., Schwalm, C. R., & Zhu, Z. (2015). Disentangling climatic and anthropogenic controls on global terrestrial evapotranspiration trends. Environmental Research Letters, 10(9), 094008

    Google Scholar 

  • Martens, B., De Jeu, R. A., Verhoest, N. E., Schuurmans, H., Kleijer, J., & Miralles, D. G. (2018). Towards estimating land evaporation at field scales using GLEAM. Remote Sensing, 10(11), 1720.

    Article  Google Scholar 

  • Martens, B., Miralles, D. G., Lievens, H., Van Der Schalie, R., De Jeu, R. A., Fernández-Prieto, D., Beck, H.E., Dorigo, W. A., & Verhoest, N. E. (2017). GLEAM v3: Satellite-based land evaporation and root-zone soil moisture. Geoscientific Model Development, 10(5), 1903–1925.

    Google Scholar 

  • McCabe, M. F., Ershadi, A., Jimenez, C., Miralles, D. G., Michel, D., & Wood, E. F. (2016). The GEWEX LandFlux project: Evaluation of model evaporation using tower-based and globally gridded forcing data. Geoscientific Model Development, 9(1), 283–305.

    Article  Google Scholar 

  • Melo, D. C. D., Anache, J. A. A., Borges, V. P., Miralles, D. G., Martens, B., Fisher, J. B., Nobrega, R. L., Moreno, A., Cabral, O. M., Rodrigues, T. R., Wendland, E. (2021). Are remote sensing evapotranspiration models reliable across South American ecoregions?. Water Resources Research, e2020WR028752.

    Google Scholar 

  • Michel, D., Jiménez, C., Miralles, D. G., Jung, M., Hirschi, M., Ershadi, A., Martens, B., McCabe, M. F., Fisher, J.B., Mu, Q., Seneviratne, S. I. & Fernández-Prieto, D. (2016a). The WACMOS-ET project–Part 1: Tower-scale evaluation of four remote-sensing-based evapotranspiration algorithms. Hydrology and Earth System Sciences, 20(2), 803–822.

    Google Scholar 

  • Miralles, D. G., Jiménez, C., Jung, M., Michel, D., Ershadi, A., McCabe, M. F., Hirschi, M., Martens, B., Dolman, A. J., Fisher, J. B., Mu, Q. & Fernández-Prieto, D. (2016b). The WACMOS-ET project–Part 2: Evaluation of global terrestrial evaporation data sets. Hydrology and Earth System Sciences, 20(2), 823–842.

    Google Scholar 

  • Miralles, D. G., Gash, J. H., Holmes, T. R., de Jeu, R. A., & Dolman, A. J. (2010). Global canopy interception from satellite observations. Journal of Geophysical Research: Atmospheres, 115(D16).

    Google Scholar 

  • Miralles, D. G., Gentine, P., Seneviratne, S. I., & Teuling, A. J. (2019). Land–atmospheric feedbacks during droughts and heatwaves: State of the science and current challenges. Annals of the New York Academy of Sciences, 1436(1), 19.

    Article  Google Scholar 

  • Miralles, D. G., Holmes, T. R. H., De Jeu, R. A. M., Gash, J. H., Meesters, A. G. C. A., & Dolman, A. J. (2011). Global land-surface evaporation estimated from satellite-based observations. Hydrology and Earth System Sciences, 15(2), 453–469.

    Article  Google Scholar 

  • Miralles, D. G., Teuling, A. J., Van Heerwaarden, C. C., & De Arellano, J. V. G. (2014). Mega-heatwave temperatures due to combined soil desiccation and atmospheric heat accumulation. Nature Geoscience, 7(5), 345–349.

    Article  Google Scholar 

  • Miralles, D. G., Van Den Berg, M. J., Teuling, A. J., & De Jeu, R. A. M. (2012). Soil moisture‐temperature coupling: A multiscale observational analysis. Geophysical Research Letters, 39(21).

    Google Scholar 

  • Moletto-Lobos, I., Mattar, C., & Barichivich, J. (2020). Performance of satellite-based evapotranspiration models in temperate pastures of Southern Chile. Water, 12(12), 3587.

    Article  Google Scholar 

  • Moreira, A. A., Ruhoff, A. L., Roberti, D. R., de Arruda Souza, V., da Rocha, H. R., & de Paiva, R. C. D. (2019). Assessment of terrestrial water balance using remote sensing data in South America. Journal of Hydrology, 575, 131–147.

    Article  Google Scholar 

  • Mueller, B., Hirschi, M., Jimenez, C., Ciais, P., Dirmeyer, P. A., Dolman, A. J., Fisher, J.B., Jung, M., Ludwig, F., Maignan, F. and Miralles, D. G., & Seneviratne, S. I. (2013). Benchmark products for land evapotranspiration: LandFlux-EVAL multi-data set synthesis. Hydrology and Earth System Sciences, 17(10), 3707–3720.

    Google Scholar 

  • Mueller, B., Seneviratne, S. I., Jimenez, C., Corti, T., Hirschi, M., Balsamo, G., Ciais, P., Dirmeyer, P., Fisher, J.B., Guo, Z., Jung, M., Zhang, Y. (2011). Evaluation of global observations‐based evapotranspiration datasets and IPCC AR4 simulations. Geophysical Research Letters, 38(6).

    Google Scholar 

  • Niyogi, D., Jamshidi, S., Smith, D., & Kellner, O. (2020). Evapotranspiration climatology of Indiana using in situ and remotely sensed products. Journal of Applied Meteorology and Climatology, 59(12), 2093–2111.

    Article  Google Scholar 

  • Nooni, I. K., Wang, G., Hagan, D. F. T., Lu, J., Ullah, W., & Li, S. (2019). Evapotranspiration and its components in the Nile River Basin based on long-term satellite assimilation product. Water, 11(7), 1400.

    Article  Google Scholar 

  • Paca, V. H. D. M., Espinoza-Dávalos, G. E., Hessels, T. M., Moreira, D. M., Comair, G. F., & Bastiaanssen, W. G. (2019). The spatial variability of actual evapotranspiration across the Amazon River Basin based on remote sensing products validated with flux towers. Ecological Processes, 8(1), 1–20.

    Article  Google Scholar 

  • Papagiannopoulou, C., Miralles, D. G., Dorigo, W. A., Verhoest, N. E. C., Depoorter, M., & Waegeman, W. (2017). Vegetation anomalies caused by antecedent precipitation in most of the world. Environmental Research Letters, 12(7), 074016.

    Article  Google Scholar 

  • Peng, J., Dadson, S., Leng, G., Duan, Z., Jagdhuber, T., Guo, W., & Ludwig, R. (2019). The impact of the Madden-Julian Oscillation on hydrological extremes. Journal of Hydrology, 571, 142–149.

    Article  Google Scholar 

  • Peng, J., Dadson, S., Hirpa, F., Dyer, E., Lees, T., Miralles, D. G., Vicente-Serrano, S. M., & Funk, C. (2020). A pan-African high-resolution drought index dataset. Earth System Science Data, 12(1), 753–769

    Google Scholar 

  • Porada, P., Van Stan, J. T., & Kleidon, A. (2018). Significant contribution of non-vascular vegetation to global rainfall interception. Nature Geoscience, 11(8), 563–567.

    Article  Google Scholar 

  • Pourmansouri, F., & Rahimzadegan, M. (2020). Evaluation of vegetation and evapotranspiration changes in Iran using satellite data and ground measurements. Journal of Applied Remote Sensing, 14(3), 034530.

    Article  Google Scholar 

  • Priestley, C. H. B., & Taylor, R. J. (1972). On the assessment of surface heat flux and evaporation using large-scale parameters. Monthly Weather Review, 100(2), 81–92.

    Article  Google Scholar 

  • Qiu, L., Wu, Y., Shi, Z., Chen, Y., & Zhao, F. (2021). Quantifying the responses of evapotranspiration and its components to vegetation restoration and climate change on the Loess Plateau of China. Remote Sensing, 13(12), 2358.

    Article  Google Scholar 

  • Rains, D., Lievens, H., De Lannoy, G. J., McCabe, M. F., de Jeu, R. A., & Miralles, D. G. (2021). Sentinel-1 backscatter assimilation using support vector regression or the water cloud model at European soil moisture sites. IEEE Geoscience and Remote Sensing Letters.

    Google Scholar 

  • Rasmy, M., Sayama, T., & Koike, T. (2019). Development of water and energy Budget-based Rainfall-Runoff-Inundation model (WEB-RRI) and its verification in the Kalu and Mundeni River Basins, Sri Lanka. Journal of Hydrology, 579, 124163.

    Article  Google Scholar 

  • Rehana, S., & Monish, N. T. (2021). Impact of potential and actual evapotranspiration on drought phenomena over water and energy-limited regions. Theoretical and Applied Climatology, 144(1), 215–238.

    Article  Google Scholar 

  • Rehana, S., & Naidu, G. S. (2021). Development of hydro-meteorological drought index under climate change—Semi-arid river basin of Peninsular India. Journal of Hydrology, 594, 125973.

    Article  Google Scholar 

  • Reichle, R. H., Draper, C. S., Liu, Q., Girotto, M., Mahanama, S. P., Koster, R. D., & De Lannoy, G. J. (2017). Assessment of MERRA-2 land surface hydrology estimates. Journal of Climate, 30(8), 2937–2960.

    Article  Google Scholar 

  • Romanovsky, V. E., Smith, S. L., Isaksen, K., Shiklomanov, N. I., Streletskiy, D. A., Kholodov, A. L., Christiansen, H. H., Drozdov, D. S., Malkova, G. V., & Marchenko, S. S. (2019). Terrestrial permafrost [in “State of the Climate in 2018”]. Bulletin of the American Meteorological Society, 100(9).

    Google Scholar 

  • Rouholahnejad Freund, E., Zappa, M., & Kirchner, J. W. (2020). Averaging over spatiotemporal heterogeneity substantially biases evapotranspiration rates in a mechanistic large-scale land evaporation model. Hydrology and Earth System Sciences, 24(10), 5015–5025.

    Article  Google Scholar 

  • Satgé, F., Hussain, Y., Xavier, A., Zolá, R. P., Salles, L., Timouk, F., Seyler, F., Garnier, J., Frappart, F. & Bonnet, M. P. (2019). Unraveling the impacts of droughts and agricultural intensification on the Altiplano water resources. Agricultural and Forest Meteorology, 279, 107710.

    Google Scholar 

  • Schwingshackl, C., Hirschi, M., & Seneviratne, S. I. (2017). Quantifying spatiotemporal variations of soil moisture control on surface energy balance and near-surface air temperature. Journal of Climate, 30(18), 7105–7124.

    Article  Google Scholar 

  • Schumacher, D. L., Keune, J., Van Heerwaarden, C. C., de Arellano, J. V. G., Teuling, A. J., & Miralles, D. G. (2019). Amplification of mega-heatwaves through heat torrents fuelled by upwind drought. Nature Geoscience, 12(9), 712–717.

    Article  Google Scholar 

  • Shi, Q., & Liang, S. (2014). Surface-sensible and latent heat fluxes over the Tibetan Plateau from ground measurements, reanalysis, and satellite data. Atmospheric Chemistry and Physics, 14(11), 5659–5677.

    Article  Google Scholar 

  • Sirisena, T. A., Maskey, S., & Ranasinghe, R. (2020). Hydrological model calibration with streamflow and remote sensing based evapotranspiration data in a data poor basin. Remote Sensing, 12(22), 3768.

    Article  Google Scholar 

  • Sneeuw, N., Lorenz, C., Devaraju, B., Tourian, M. J., Riegger, J., Kunstmann, H., & Bárdossy, A. (2014). Estimating runoff using hydro-geodetic approaches. Surveys in Geophysics, 35(6), 1333–1359.

    Article  Google Scholar 

  • Talsma, C. J., Good, S. P., Jimenez, C., Martens, B., Fisher, J. B., Miralles, D. G., McCabe, M. F., & Purdy, A. J. (2018a). Partitioning of evapotranspiration in remote sensing-based models. Agricultural and Forest Meteorology, 260, 131–143.

    Google Scholar 

  • Talsma, C. J., Good, S. P., Miralles, D. G., Fisher, J. B., Martens, B., Jimenez, C., & Purdy, A. J. (2018b). Sensitivity of evapotranspiration components in remote sensing-based models. Remote Sensing, 10(10), 1601.

    Article  Google Scholar 

  • Tian, Y. (2019). A priori parameter estimates for distribution of soil moisture storage capacity in Hymod model using information extracted from GLEAM root-zone soil moisture data. In Geophysical Research Abstracts (Vol. 21).

    Google Scholar 

  • Valente, F., David, J. S., & Gash, J. H. C. (1997). Modelling interception loss for two sparse eucalypt and pine forests in central Portugal using reformulated Rutter and Gash analytical models. Journal of Hydrology, 190(1–2), 141–162.

    Article  Google Scholar 

  • Vicente-Serrano, S. M., Miralles, D. G., Domínguez-Castro, F., Azorin-Molina, C., El Kenawy, A., McVicar, T. R., Tomás-Burguera, M., Beguería, S., Maneta, M., & Peña-Gallardo, M. (2018). Global assessment of the Standardized Evapotranspiration Deficit Index (SEDI) for drought analysis and monitoring. Journal of Climate, 31(14), 5371–5393.

    Google Scholar 

  • Wang, G., Pan, J., Shen, C., Li, S., Lu, J., Lou, D., & Hagan, D. F. (2018). Evaluation of evapotranspiration estimates in the Yellow River Basin against the water balance method. Water, 10(12), 1884.

    Article  Google Scholar 

  • Wang, Z., Zhan, C., Ning, L., & Guo, H. (2021). Evaluation of global terrestrial evapotranspiration in CMIP6 models. Theoretical and Applied Climatology, 143(1), 521–531.

    Article  Google Scholar 

  • Wagner, S., Fersch, B., Yuan, F., Yu, Z., & Kunstmann, H. (2016). Fully coupled atmospheric-hydrological modeling at regional and long-term scales: Development, application, and analysis of WRF-HMS. Water Resources Research, 52(4), 3187–3211.

    Article  Google Scholar 

  • Wati, T., & Sopaheluwakan, A. (2018). Comparison pan evaporation data with global land-surface evaporation GLEAM in Java and Bali Island Indonesia. The Indonesian Journal of Geography, 50(1), 87–96.

    Article  Google Scholar 

  • Wong, J. S., Zhang, X., Gharari, S., Shrestha, R. R., Wheater, H. S., & Famiglietti, J. S. (2021). Assessing water balance closure using multiple data assimilation—and remote sensing-based datasets for Canada. Journal of Hydrometeorology, 22(6), 1569–1589.

    Google Scholar 

  • Xu, T., Guo, Z., Xia, Y., Ferreira, V. G., Liu, S., Wang, K., Yao, Y., Zhang, X., & Zhao, C. (2019). Evaluation of twelve evapotranspiration products from machine learning, remote sensing and land surface models over conterminous United States. Journal of Hydrology, 578, 124105

    Google Scholar 

  • Yang, L., Feng, Q., Adamowski, J. F., Alizadeh, M. R., Yin, Z., Wen, X., & Zhu, M. (2021). The role of climate change and vegetation greening on the variation of terrestrial evapotranspiration in northwest China’s Qilian Mountains. Science of the Total Environment, 759, 143532.

    Article  Google Scholar 

  • Yang, J., Wang, W., Hua, T., & Peng, M. (2021). Spatiotemporal variation of actual evapotranspiration and its response to changes of major meteorological factors over China using multi-source data. Journal of Water and Climate Change, 12(2), 325–338.

    Article  Google Scholar 

  • Yang, X., Yong, B., Ren, L., Zhang, Y., & Long, D. (2017). Multi-scale validation of GLEAM evapotranspiration products over China via ChinaFLUX ET measurements. International Journal of Remote Sensing, 38(20), 5688–5709.

    Article  Google Scholar 

  • Yang, X., Yong, B., Yin, Y., & Zhang, Y. (2018). Spatio-temporal changes in evapotranspiration over China using GLEAM_V3. 0a products (1980–2014). Hydrology Research, 49(5), 1330–1348.

    Google Scholar 

  • Yin, G., Wang, G., Zhang, X., Wang, X., Hu, Q., Shrestha, S., & Hao, F. (2022). Multi-scale assessment of water security under climate change in North China in the past two decades. Science of the Total Environment, 805, 150103.

    Article  Google Scholar 

  • Zanin, P. R., & Satyamurty, P. (2021). Interseasonal and interbasins hydrological coupling in South America. Journal of Hydrometeorology, 22(6), 1609–1625.

    Google Scholar 

  • Zhang, B., AghaKouchak, A., Yang, Y., Wei, J., & Wang, G. (2019). A water-energy balance approach for multi-category drought assessment across globally diverse hydrological basins. Agricultural and Forest Meteorology, 264, 247–265.

    Article  Google Scholar 

  • Zhang, Y., Kong, D., Gan, R., Chiew, F. H., McVicar, T. R., Zhang, Q., & Yang, Y. (2019). Coupled estimation of 500 m and 8-day resolution global evapotranspiration and gross primary production in 2002–2017. Remote Sensing of Environment, 222, 165–182.

    Article  Google Scholar 

  • Zhang, Y., Peña-Arancibia, J. L., McVicar, T. R., Chiew, F. H., Vaze, J., Liu, C., Lu, X., Zheng, H., Wang, Y., Liu, Y. Y., Miralles, D. G., Pan, M. (2016). Multi-decadal trends in global terrestrial evapotranspiration and its components. Scientific Reports, 6(1), 1–12.

    Google Scholar 

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Acknowledgements

The development of GLEAM has been enabled by the Belgian Science Policy Office (BESLPO) ET-Sense (contract SR/02/377) and ALBERI (SR/02/373) projects, and the European Space Agency (ESA) DTE–Hydrology (contract 4000129870/20/I-NB) project. DGM acknowledges support from the European Research Council (ERC), DRY–2–DRY project (grant no. 715254). AK acknowledges support from the European Union Horizon 2020 Pro-640gramme (DOWN2EARTH, 869550).

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Authors and Affiliations

  1. Department of Water Engineering, School of Agriculture, Shiraz University, Shiraz, Iran

    Mojtaba Naghdyzadegan Jahromi & Shahrokh Zand-Parsa

  2. Hydro-Climate Extremes Lab (HCEL), Ghent University, Ghent, Belgium

    Mojtaba Naghdyzadegan Jahromi, Diego Miralles, Akash Koppa & Dominik Rains

  3. National Research Council, Research Institute for Geo-Hydrological Protection, Perugia, Italy

    Hamidreza Mosaffa

  4. Department of Agronomy, Purdue University, West Lafayette, IN, 47907, USA

    Sajad Jamshidi

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  1. Mojtaba Naghdyzadegan Jahromi
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  3. Akash Koppa
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  5. Shahrokh Zand-Parsa
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Correspondence to Mojtaba Naghdyzadegan Jahromi .

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Editors and Affiliations

  1. College of Agriculture & Natural Resources, University of Tehran, Karaj, Alborz, Iran

    Omid Bozorg-Haddad

  2. QUEX Joint-researcher, Centre for Water in the Minerals Industry, Sustainable Minerals Institute, The University of Queensland, Brisbane, QLD, Australia

    Babak Zolghadr-Asli

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Jahromi, M.N. et al. (2022). Ten Years of GLEAM: A Review of Scientific Advances and Applications. In: Bozorg-Haddad, O., Zolghadr-Asli, B. (eds) Computational Intelligence for Water and Environmental Sciences. Studies in Computational Intelligence, vol 1043. Springer, Singapore. https://doi.org/10.1007/978-981-19-2519-1_25

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