Abstract
Evapotranspiration (ET) is one of the most important components of global hydrologic cycle and has significant impacts on energy exchange and climate change. Numerous models have been developed to estimate ET so far; however, great uncertainties in models still require considerations. The aim of this study is to reduce model errors and uncertainties among multi-models to improve daily ET estimate. The Bayesian model averaging (BMA) method is used to assemble eight ET models to produce ET with Landsat 8 satellite data, including four surface energy balance models (i.e., SEBS, SEBAL, SEBI, and SSEB) and four machine learning algorithms (i.e., polymars, random forest, ridge regression, and support vector machine). Performances of each model and BMA method were validated through in situ measurements of semi-arid region. Results indicated that the BMA method outperformed all eight single models. The four most important models obtained by the BMA method were ranked by random forest, SVM, SEBS, and SEBAL. The BMA method coupled with machine learning can significantly improve the accuracy of daily ET estimate, reducing uncertainties among models, and taking different intrinsic benefits of empirically and physically based models to obtain a more reliable ET estimate.
Change history
23 March 2021
A Correction to this paper has been published: https://doi.org/10.1007/s10661-021-09009-x
References
Abrahao, R., García-Garizábal, I., Merchán, D., & Causapé, J. (2015). Climate change and the water cycle in newly irrigated areas. Environmental Monitoring & Assessment, 187(2), 22.
Allen, R. G., Tasumi, M., & Trezza, R. (2007). Satellite-based energy balance for mapping evapotranspiration with internalized calibration (METRIC)—model. Journal of Irrigation and Drainage Engineering, 133(4), 380–394.
Allen, R. G. (1998). Crop Evapotranspiration-Guidelines for computing crop water requirements. FAO Irrigation & Drainage Paper, 56.
Bastiaanssen, W. G. M., Menenti, M., Feddes, R. A., & Holtslag, A. A. M. (1998). A remote sensing surface energy balance algorithm for land (SEBAL) – 1 Formulation. Journal of Hydrology, 212(1), 198–212.
Breiman, L. (2001). Random forests. Machine Learning, 45, 5–32.
Charles, K., Smarajit, B., & Charles, J. S. (1997). Polychotomous regression. Journal of the American Statistical Association, 92, 117–127.
Chen, Y., Yuan, W., & Xia, J. (2015). Using Bayesian model averaging to estimate terrestrial evapotranspiration in China. Journal of Hydrology, 528, 537–549.
Colaizzi, P. D., Evett, S. R., & Howell, T. A. (2006). Comparison of five models to scale daily evapotranspiration from one-time-of-day measurements. Transactions of the ASABE, 49(5), 1409–1417.
Dirmeyer, P. A., Gao, X., Zhao, M., et al. (2006). GSWP-2: multimodel analysis and implications for our perception of the land surface. Bulletin of the American Meteorological Society, 87(10), 1381–1397.
Eamus, D. (2003). How does ecosystem water balance affect net primary productivity of woody ecosystems? Functional Plant Biology, 30(2), 187–205.
Feldkircher, M., & Stefan, Z. (2009). Benchmark priors revisited: on adaptive shrinkage and the supermodel effect in Bayesian model averaging. Imf Working Papers, 09(202),1–39.
Fernández, C., Ley, E., & Steel, M. F. (2001). Benchmark priors for Bayesian model averaging. Journal of Econometrics, 100, 381–427.
Fisher, J. B., Tu, K. P., & Baldocchi, D. D. (2008). Global estimates of the land–atmosphere water flux based on monthly AVHRR and ISLSCP-II data, validated at 16 FLUXNET sites. Remote Sensing of Environment, 112(3), 901–919.
French, A. N., Hunsaker, D. J., & Thorp, K. R. (2015). Remote sensing of evapotranspiration over cotton using the TSEB and METRIC energy balance models. Remote Sensing of Environment, 158, 281–294.
Friedl, M. A. (1996). Relationships among remotely sensed data, surface energy balance, and area-averaged fluxes over partially vegetated land surfaces. Journal of Applied Meteorology, 35(11), 2091–2103.
George, E., & Foster, D. (2000). Calibration and empirical Bayes variable selection. Biometrika, 87(4), 731–747.
Ghorbani, M. A., Deo, R. C., Yaseen, Z. M., Kashani, M. H., & Mohammadi, B. (2017). Pan evaporation prediction using a hybrid multilayer perceptron-firefly algorithm (MLP-FFA) model: case study in North Iran. Theoretical and Applied Climatology, (2), 1–13.
Hai, T., Lamine, D., Ansoumana, B., Koffi, D., Malick, N. P., & Mundher, Y. Z. (2018). Reference evapotranspiration prediction using hybridized fuzzy model with firefly algorithm: regional case study in burkina faso. Agricultural Water Management, 208, 140–151.
Henriques, J. F., Caseiro, R., Martins, P., et al. (2015). High-speed tracking with kernelized correlation filters. IEEE Transactions on Pattern Analysis and Machine Intelligence, 37(3), 583–596.
Hoeting, J. A., Madigan, D., & Volinsky, R. C. T. (1999). Bayesian model averaging: a tutorial. Statistical Science, 14(4), 382–401.
Jing, W., Yaseen, Z. M., Shahid, S., Saggi, M. K., Tao, H., Kisi, O., & Chau, K. W. (2019). Implementation of evolutionary computing models for reference evapotranspiration modeling: Short review, assessment and possible future research directions. Engineering Applications of Computational Fluid Mechanics, 13(1), 811–823.
Jung, M., Reichstein, M., & Bondeau, A. (2009). Towards global empirical upscaling of FLUXNET eddy covariance observations: Validation of a model tree ensemble approach using a biosphere model. Biogeosciences, 6(10), 2001–2013.
Jung, M., Reichstein, M., & Ciais, P. (2010). Recent decline in the global land evapotranspiration trend due to limited moisture supply. Nature, 467(7318), 951–954.
Khosravi, K., Daggupati, P., Alami, M. T., Awadh, S. M., & Yaseen, Z. M. (2019). Meteorological data mining and hybrid data-intelligence models for reference evaporation simulation: A case study in Iraq. Computers and Electronics in Agriculture, 167, 105041.
Legates, D. R., & McCabe, G. J. (1999). Evaluating the use of ‘goodness-of-fit’ measures in hydrologic and hydroclimatic model validation. Water Resources Research, 35, 233–241.
Leuning, R., Zhang, Y. Q., Rajaud, A., et al. (2008). A simple surface conductance model to estimate regional evaporation using MODIS leaf area index and the Penman-Monteith equation. Water Resources Research, 44(10).
Liang, F., Paulo, R., Molina, G., Clyde, M. A., & Berger, J. O. (2008). Mixtures of g priors for Bayesian variable selection. Journal of the American Statistical Association, 103, 410–423.
Liang, Z., Wang, D., Guo, Y., et al. (2013). Application of Bayesian model averaging approach to multimodel ensemble hydrologic forecasting. Journal of Hydrologic Engineering, 18(11), 1426–1436.
Lu, X., & Zhuang, Q. (2010). Evaluating evapotranspiration and water-use efficiency of terrestrial ecosystems in the conterminous United States using MODIS and AmeriFlux data. Remote Sensing of Environment, 114, 1924–1939.
Malik, A., Kumar, A., Kim, S., Kashani, M., Karimi, V., Sharafati, A., & Chau, K. W. (2020). Modeling monthly pan evaporation process over the Indian central Himalayas: application of multiple learning artificial intelligence model. Engineering Applications of Computational Fluid Mechanics, 14(1), 323–338.
Mu, Q., Heinsch, F. A., Zhao, M., et al. (2007). Development of a global evapotranspiration algorithm based on MODIS and global meteorology data. Remote Sensing of Environment, 111(4), 519–536.
Mu, Q., Zhao, M., & Running, S. W. (2011). Improvements to a MODIS global terrestrial evapotranspiration algorithm. Remote Sensing of Environment, 115(8), 1781–1800.
Pipunic, R. C., Walker, J. P., & Western, A. (2008). Assimilation of remotely sensed data for improved latent and sensible heat flux prediction: A comparative synthetic study. Remote Sensing of Environment, 112(4), 1295–1305.
Priestley, C., & Taylor, R. (1972). On the assessment of surface heat flux and evaporationusing large-scale parameters. Monthly Weather Review, 100, 81–92.
Raftery, A. E., Gneiting, T., Balabdaoui, F., et al. (2005). Using Bayesian Model Averaging to Calibrate Forecast Ensembles. Monthly Weather Review, 133(5), 1155–1174.
Roerink, G. J., Su, Z., & Menenti, M. (2000). S-SEBI: A simple remote sensing algorithm to estimate the surface energy balance. Physics & Chemistry of the Earth Part B Hydrology Oceans & Atmosphere, 25(2), 147–157.
Sakine, C., & Eyüp, S. K. (2018). Potential use of remote sensing techniques in evapotranspiration estimations at watershed level. Environmental Monitoring & Assessment, 190, 601.
Salih, S. Q., Allawi, M. F., Yousif, A. A., Armanuos, A. M., & Yaseen, Z. M. (2019). Viability of the advanced adaptive neuro-fuzzy inference system model on reservoir evaporation process simulation: case study of nasser lake in egypt. Engineering Applications of Computational Fluid Mechanics, 13(1), 878–891.
Sanikhani, H., Kisi, O., Maroufpoor, E., & Yaseen, Z. M. (2018). Temperature-based modeling of reference evapotranspiration using several artificial intelligence models: Application of different modeling scenarios. Theoretical & Applied Climatology, 135, 449–462.
Senay, G. B., Bohms, S., Singh, R. K., Gowda, P. H., Velpuri, N. M., Alemu, H., & Verdin, J. P. (2013). Operational evapotranspiration mapping using remote sensing and weather datasets: A new parameterization for the SSEB approach. JAWRA Journal of the American Water Resources Association, 49(3), 577–591.
Shrestha, N. K., & Shukla, S. (2013). Support vector machine based modeling of evapotranspiration using hydro-climatic variables in a sub-tropical environment[J]. Bioresource Technology, 128(2), 351–358.
Su, Z. (2002). The surface energy balance system (SEBS) for stimation of turbulent fluxes. Hydrology and Earth System Sciences, 6, 85–99.
Sun, H., Yang, Y., Wu, R., Gui, D., et al. (2019). Improving estimation of cropland evapotranspiration by the Bayesian model averaging method with surface energy balance models. Atmosphere, 10(4). https://doi.org/10.3390/atmos10040188
Vapnik, V. N. (1998). Statistical learning theory (pp. 401–441). New York: Wiley.
Wang, K., & Dickinson, R. E. (2012). A review of global terrestrial evapotranspiration: Observation, modeling, climatology, and climatic variability. Reviews of Geophysics, 50(2).
Watras, C. J., Morrow, M., Morrison, K., Scannell, S., Yaziciaglu, S., Read, J. S., et al. (2014). Evaluation of wireless sensor networks (wsns) for remote wetland monitoring: design and initial results. Environmental Monitoring and Assessment, 186(2), 919–934.
Xia, J., Liang, S., Chen, J., Yuan, W., Liu, S., Li, L, Xia, J., Liang, S., & Chen, J. (2014). Satellite-based analysis of evapotranspiration and water balance in the grassland ecosystems of Dryland East Asia. Plos One, 9(5), e97295.
Yang, Y., Shang, S., & Jiang, L. (2012). Remote sensing temporal and spatial patterns of evapotranspiration and the responses to water management in a large irrigation district of North China. Agricultural and Forest Meteorology, 164, 112–122.
Yao, Y., et al. (2014). Bayesian multimodel estimation of global terrestrial latent heat flux from eddy covariance, meteorological, and satellite observations. Journal of Geophysical Research: Atmospheres, 119, 4521–4545.
Yao, Y., Liang, S., Li, X., Chen, J., Liu, S., et al. (2017). Improving global terrestrial evapotranspiration estimation using support vector machine by integrating three process-based algorithms. Agricultural and Forest Meteorology, 242, 55–74.
Yuan, W., Liu, S., Liang, S., Tan, Z., Liu, H., & Young, C. (2012). Estimations of evapotranspiration and water balance with uncertainty over the yukon river basin. Water Resources Management, 26(8), 2147–2157.
Zhu, G., Li, X., Zhang, K., et al. (2016). MULTI-model ensemble prediction of terrestrial evapotranspiration across north China using Bayesian model averaging. Hydrological Processes, 30(16), 2861–2879.
Funding
This work was partially supported by the National Natural Science Foundation of China under Grant 51879110, 52079055, and 52011530128, and by the Hubei Provincial Water Resources Key Scientific Research Project under Grant HBSLKY201907, and Special project of basic resources investigation of Ministry of Science and Technology under Grant 2019FY100205.
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Yang, Y., Sun, H., Xue, J. et al. Estimating evapotranspiration by coupling Bayesian model averaging methods with machine learning algorithms. Environ Monit Assess 193, 156 (2021). https://doi.org/10.1007/s10661-021-08934-1
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DOI: https://doi.org/10.1007/s10661-021-08934-1