Dense flux observations reveal the incapability of evapotranspiration products to capture the heterogeneity of evapotranspiration

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  • Wu, Jie
  • Yu Feng
  • Chunmiao Zheng
  • Zhenzhong Zeng

Accurate terrestrial evapotranspiration (ET) estimation over complex surfaces with spatial heterogeneity is crucial for local, regional, and global applications. Currently, a variety of global ET products with different spatiotemporal resolutions have been developed and evaluated. However, little is known about their performance in capturing the heterogeneity of ET over complex surfaces. Focusing on the Heihe River Basin (HRB), a typical arid and semi-arid region that is hydrologically vulnerable, this study compared ET from eleven global ET products (six remotely- sensed ET, two land surface model ET, one hydrological model-based ET, one reanalysis ET, and one synthesis ET) and one regional ET dataset against dense eddy covariance observations in terms of the magnitude, seasonal cycle, and spatial pattern. In general, the remotely-sensed ET and synthesis ET outperformed other categories, with the operational Simplified Surface Energy Balance (SSEBop), Penman-Monteith–Leuning (PML), Moderate Resolution Imaging Spectroradiometer (MOD16) and Global LAnd Surface Satellite (GLASS) performing relatively better (root mean square error ranging from 1.22 to 1.57 mm d-1). Across all the land cover types, ET products reproduced a relatively feasible ET over the desert steppe, meadow, and barren, but substantially underestimated ET over ecosystems with high ET values but a low land area fraction over HRB (cropland, wetland, and forest). This highlights the importance of accurately representing the heterogeneity and local climates in the complex land surfaces for ET quantification in regional water resource management.

OriginalsprogEngelsk
Artikelnummer129743
TidsskriftJournal of Hydrology
Vol/bind622
Antal sider12
ISSN0022-1694
DOI
StatusUdgivet - 2023

Bibliografisk note

Funding Information:
This study was supported by the National Natural Science Foundation of China ( 42071022 and 41861124003 ) and the startup fund provided by the Southern University of Science and Technology(29/Y01296122).

Publisher Copyright:
© 2023 Elsevier B.V.

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