Abstract
Evapotranspiration is a key process within the hydrological cycle, so it requires an accurate assessment. This work aims at assessing monthly scale performances of six meteorological data-based methods to predict evapotranspiration by comparing model estimates with observations from six flux tower sites differing for land cover and climate. Three of the proposed methodologies use a potential evapotranspiration approach (Penman, Priestley-Taylor and Blaney-Criddle models) while the additional three an actual evapotranspiration approach (the Advection-Aridity, the Granger and Gray and the Antecedent Precipation Index method). The results show that models efficiency varies from site to site, even though land cover and climate features appear to have some influence. It is difficult to comment on a general accuracy, but an overall moderate better performance of the Advection-Aridity model can be reported within a context where model calibration is not accounted for. If model calibration is further taken into consideration, the Granger and Gray model appears the best performing method but, at the same time, it is also the approach which is mostly affected by the calibration process, and therefore less suited to evapotranspiration prediction tools dealing with a data scarcity context.
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Acknowledgments
This work used eddy covariance data acquired and shared by the FLUXNET community, including these networks: AmeriFlux, AfriFlux, AsiaFlux, CarboAfrica, CarboEuropeIP, CarboItaly, CarboMont, ChinaFlux, Fluxnet-Canada, GreenGrass, ICOS, KoFlux, LBA, NECC, OzFlux-TERN, TCOS-Siberia, and USCCC. The ERA-Interim reanalysis data are provided by ECMWF and processed by LSCE. The FLUXNET eddy covariance data processing and harmonization was carried out by the European Fluxes Database Cluster, AmeriFlux Management Project, and Fluxdata project of FLUXNET, with the support of CDIAC and ICOS Ecosystem Thematic Center, and the OzFlux, ChinaFlux and AsiaFlux offices.
The authors also gratefully acknowledge the support of TERENO, funded by the Helmholtz Association, and the SFB-TR32 “Pattern in Soil–Vegetation–Atmosphere Systems: Monitoring, Modeling and Data Assimilation”, funded by the Deutsche Forschungsgemeinschaft (DFG).
This research has been supported by the U.S. Department of Energy’s Office of Science (Grant No. DE-FG02-06ER64318 and DE-AC02-05CH11231 for the AmeriFlux core site) and the US Department of Energy (Grant DE-SC0012194) and Agriculture and Food Research Initiative of the US Department of Agriculture National Institute of Food and Agriculture (Grants 2013-67003-20652, 2014-67003-22065, and 2014-35100-22066) for our North American Carbon Program studies, “Carbon cycle dynamics within Oregon’s urban-suburban-forested-agricultural landscapes”.
We would like to thank you for providing data, the Office of Biological and Environmental Research of the US Department of Energy under contract No. DE-AC02-05CH11231 as part of the Atmospheric Radiation Measurement Program (ARM) and California Department of Water Resources; USDA/AFRI Funding for AmeriFlux data resources was provided by the U.S. Department of Energy’s Office of Science.
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Mobilia, M., Longobardi, A. (2020). Evaluation of Meteorological Data-Based Models for Potential and Actual Evapotranspiration Losses Using Flux Measurements. In: Gervasi, O., et al. Computational Science and Its Applications – ICCSA 2020. ICCSA 2020. Lecture Notes in Computer Science(), vol 12253. Springer, Cham. https://doi.org/10.1007/978-3-030-58814-4_1
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