Preprints
https://doi.org/10.5194/hess-2021-283
https://doi.org/10.5194/hess-2021-283

  14 Jun 2021

14 Jun 2021

Review status: this preprint is currently under review for the journal HESS.

Modelling hourly evapotranspiration in urban environments with SCOPE using open remote sensing and meteorological data

Alby Duarte Rocha1, Stenka Vulova1, Christiaan van der Tol2, Michael Förster1, and Birgit Kleinschmit1 Alby Duarte Rocha et al.
  • 1Geoinformation in Environmental Planning Lab, Technische Universität Berlin, 10623 Berlin, Germany
  • 2University of Twente, Faculty of Geo-Information Science and Earth Observation (ITC), P.O. Box 217, Enschede AE7500, The Netherlands

Abstract. Evapotranspiration (ET) is a fundamental variable to assess water balance and urban heat island effect. ET is deeply dependent on the land cover as it derives mainly from the processes of soil evaporation and plant transpiration. The majority of well-known process-based models based on the Penman-Monteith equation focus on the atmospheric interfaces (e.g. radiation, temperature and humidity), lacking explicit input parameters to describe the land surface. The model Soil-Canopy-Observation of Photosynthesis and Energy fluxes (SCOPE) accounts for a broad range of surface-atmosphere interactions to predict ET. However, like most modelling approaches, SCOPE assumes a homogeneous vegetated landscape to estimate ET. Urban environments are highly fragmented, exhibiting a blend of pervious and impervious anthropogenic surfaces. Whereas, high-resolution remote sensing (RS) and detailed GIS information to characterise land surfaces is usually available for major cities. Data describing land surface properties were used in this study to develop a method to correct bias in ET predictions caused by the assumption of homogeneous vegetation by process-based models. Two urban sites equipped with eddy flux towers presenting different levels of vegetation fraction and imperviousness located in Berlin, Germany, were used as study cases. The correction factor for urban environments has increased model accuracy significantly, reducing the relative bias in ET predictions from 0.74 to −0.001 and 2.20 to −0.13 for the two sites, respectively, considering the SCOPE model using RS data. Model errors (i.e. RMSE) were also considerably reduced in both sites, from 0.061 to 0.026 and 0.100 to 0.021, while the coefficient of determination (R2) remained similar after the correction, 0.82 and 0.47, respectively. This study presents a novel method to predict hourly urban ET using freely available RS and meteorological data, independently from the flux tower measurements. The presented method can support actions to mitigate climate change in urban areas, where most the world population lives.

Alby Duarte Rocha et al.

Status: open (until 09 Aug 2021)

Comment types: AC – author | RC – referee | CC – community | EC – editor | CEC – chief editor | : Report abuse
  • RC1: 'Comment on hess-2021-283', Anonymous Referee #1, 24 Jun 2021 reply
  • RC2: 'Comment on hess-2021-283', Anonymous Referee #2, 12 Jul 2021 reply

Alby Duarte Rocha et al.

Alby Duarte Rocha et al.

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Short summary
Evapotranspiration (ET) is a combination of soil evaporation and plant transpiration that produces a cooling effect to mitigate Urban Heat Island. Our method uses a physical model with remote sensing and meteorological data to predict hourly ET. Designed for uniform vegetation, it overestimated urban ET. To correct it, we create a factor using vegetation fraction that proved efficient to reduce bias and improve accuracy. This approach was tested on two Berlin sites, and it can be used to map ET.