Spatial patterns in long-term average evapotranspiration (ET) represent a unique source of information for evaluating the spatial pattern performance of distributed hydrological models on a river basin to continental scale. This kind of model evaluation is getting increased attention, acknowledging the shortcomings of traditional aggregated or timeseries-based evaluations. A variety of satellite remote sensing (RS)-based ET estimates exist, covering a range of methods and resolutions. There is, therefore, a need to evaluate these estimates, not only in terms of temporal performance and similarity, but also in terms of long-term spatial patterns. The current study evaluates four RS-ET estimates at moderate resolution with respect to spatial patterns in comparison to two alternative continental-scale gridded ET estimates (water-balance ET and Budyko). To increase comparability, an empirical correction factor between clear sky and all-weather ET, based on eddy covariance data, is derived, which could be suitable for simple corrections of clear sky estimates. Three RS-ET estimates (MODIS16, TSEB and PT-JPL) and the Budyko method generally display similar spatial patterns both across the European domain (mean SPAEF = 0.41, range 0.25–0.61) and within river basins (mean SPAEF range 0.19–0.38), although the pattern similarity within river basins varies significantly across basins. In contrast, the WB-ET and PML_V2 produced very different spatial patterns. The similarity between different methods ranging over different combinations of water, energy, vegetation and land surface temperature constraints suggests that robust spatial patterns of ET can be achieved by combining several methods.
Bibliographical noteFunding Information:
This research was funded by the Villum Foundation (http://villumfonden.dk/) through their Young Investigator Programme (grant VKR023443). In addition, funding was obtained through the GEOERA TACTIC project funded from the European Union?s Horizon 2020 research and innovation programme under grant agreement No 731166. Acknowledgments: We acknowledge the E-OBS dataset from the EU-FP6 project UERRA (http://www.uerra.eu; https://www.ecad.eu/download/ensembles/download.php) (accessed 1 June 2020) and the Copernicus Climate Change Service, and the data providers in the ECA&D project (https://www.ecad.eu ) (accessed 1 June 2020). PET data are accessed through https://wci.earth2observe.eu/. in1 June 2020. Likewise, we acknowledge the E-Run dataset provided through the PANGAEA repository, Gudmundsson, Lukas; Seneviratne, Sonia I (2016): E-RUN version 1.1: Observational gridded runoff estimates for Europe, link to data in NetCDF format (69 MB). PANGAEA, https://doi.org/10.1594/PANGAEA.861371 (accessed 1 June 2020). We acknowledge ECMWF for providing access to the ERA-Interim data and NASA for access to MODIS products. Both MODIS16 and PML_V2 data are accessed through Google Earth Engine. This work benefited from eddy covariance data acquired and shared by the FLUXNET community. We would like to thank Hector Nieto for sharing the pyTSEB package through https://github.com/hectornieto/pyTSEB (accessed 1 January 2018).
© 2021 by the authors. Licensee MDPI, Basel, Switzerland.
- Hydrological modeling
- Remote sensing
- Spatial patterns