Evapotranspiration estimates from an energy-water-balance model calibrated on satellite land surface temperature over the Heihe basin

Nicola Paciolla, Chiara Corbari, Guangcheng Hu, Chaolei Zheng, Massimo Menenti, Li Jia, Marco Mancini

Research output: Contribution to journalArticleScientificpeer-review

Abstract

A distributed hydrological energy-water-balance model (FEST-EWB) is calibrated over the Heihe Basin, a mainly desertic basin in China, employing remotely-sensed Land Surface Temperature (LST) (MODIS, 1-km resolution) as calibration variable. This approach overcomes the problem of model parameters characterization, which are usually difficult to define especially over large basins, allowing a pixel-by-pixel calibration, preserving the spatial heterogeneity. Hence, the spatial distribution of the modelled LST, but also of soil moisture (SM) and evapotranspiration (ET) is improved. The accuracy of the calibration process is documented through common statistical indexes. The modelled ET is compared locally against two eddy covariance stations in the agricultural area, while distributively against the ET estimates of the ETMonitor model and some global re-analysis products (ERA-Interim, GLDAS2, GLEAM and MERRA-2). Calibration and validation performed in this study prove that a considerable model accuracy is attainable even in extremely arid environments. An average LST bias of 2.6 °C is obtained over the basin. A good adaptation of FEST-EWB is also obtained against eddy-covariance stations ET with a little bias around −1 mm/d. On the other hand, the reanalysis products display a much worse performance, with higher absolute biases (around −3.5 mm/d), although with high variability among the models.

Original languageEnglish
Article number104466
Pages (from-to)1-13
Number of pages13
JournalJournal of Arid Environments
Volume188
DOIs
Publication statusPublished - 2021

Keywords

  • Distributed calibration
  • Eddy covariance stations
  • Evapotranspiration
  • Land surface temperature
  • Meteorological reanalysis
  • Remote sensing

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