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https://doi.org/10.5194/hess-2020-46
© Author(s) 2020. This work is distributed under
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/hess-2020-46
© Author(s) 2020. This work is distributed under
the Creative Commons Attribution 4.0 License.

Submitted as: research article 12 Feb 2020

Submitted as: research article | 12 Feb 2020

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This preprint is currently under review for the journal HESS.

Averaging over spatiotemporal heterogeneity substantially biases evapotranspiration rates in a mechanistic large-scale land evaporation model

Elham Rouholahnejad Freund1,2,3, Massimiliano Zappa4, and James W. Kirchner3,4,5 Elham Rouholahnejad Freund et al.
  • 1Laboratory of Hydrology and Water Management, Ghent University, Ghent, Belgium
  • 2Chair of Hydrology, Faculty of Environment and Natural Resources, University of Freiburg, Freiburg, Germany
  • 3Department of Environmental Systems Science, ETH Zurich, CH-8092 Zürich, Switzerland
  • 4Swiss Federal Research Institute WSL, CH-8903 Birmensdorf, Switzerland
  • 5Departmentof Earth and Planetary Science, University of California, Berkeley, CA 94720, USA

Abstract. Evapotranspiration (ET) influences land-climate interactions, regulates the hydrological cycle, and contributes to the Earth's energy balance. Due to its feedbacks to large-scale hydrological processes and its impact on atmospheric dynamics, ET is a key driver of droughts and heatwaves. Existing land surface models differ substantially, both in their estimates of current ET fluxes and in their projections of how ET will evolve in the future. Any bias in estimated ET fluxes will affect the partitioning between sensible and latent heat, and thus alter model predictions of temperature and precipitation. One potential source of bias is the so-called aggregation bias that arises whenever nonlinear processes, such as those that regulate ET fluxes, are modeled using averages of heterogeneous inputs. Here we demonstrate a general mathematical approach to quantifying and correcting for this aggregation bias, using the GLEAM land evaporation model as a relatively simple example. We demonstrate that this aggregation bias can lead to substantial overestimates in ET fluxes in a typical large-scale land surface model when sub-grid heterogeneities in land surface properties are averaged out. Using Switzerland as a test case, we examine the scale-dependence of this aggregation bias and show that it can lead to overestimation of daily ET fluxes by as much as 21 % averaged over the whole country. We show how our approach can be used to identify the dominant drivers of aggregation bias, and to estimate sub-grid closure relationships that can correct for aggregation biases in ET estimates, without explicitly representing sub-grid heterogeneities in large-scale land surface models.

Elham Rouholahnejad Freund et al.

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Elham Rouholahnejad Freund et al.

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Short summary
Evapotranspiration (ET) is the largest flux from the land to the atmosphere and thus contributes to Earth's energy and water balance. Due to its impact on atmospheric dynamics, ET is a key driver of droughts and heatwaves. In this paper, we demonstrate how averaging over land surface heterogeneity contributes to substantial overestimates of ET fluxes. We also demonstrate how one can correct for the effects of small-scale heterogeneity without explicitly representing it in land surface models.
Evapotranspiration (ET) is the largest flux from the land to the atmosphere and thus contributes...
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