Evaporation is a very important flux in the hydrological cycle and links the water and energy balance of a catchment. The Budyko framework is often used to provide a first order estimate of evaporation, since it is a simple model where only rainfall and potential evaporation is required as input. Many researchers have tried to improve the Budyko framework by including more physics and catchment characteristics into the original equation. However, this often resulted in additional parameters, which are unknown or difficult to determine. In this paper we present an improvement of the previously presented Gerrits' model (“Analytical derivation of the Budyko curve based on rainfall characteristics and a simple evaporation model” in Gerrits et al., 2009 WRR), whereby total evaporation is calculated on the basis of simple interception and transpiration thresholds in combination with measurable parameters like rainfall dynamics and storage availability from remotely sensed data sources. While Gerrits' model was investigated for 10 catchments with different climate conditions and also some parameters were assumed to be constant, in this study we applied the model on the global scale and it was fed with remotely sensed input data. The output of the model is compared to two complex land–surface models STEAM and GLEAM, as well as the database of Landflux-EVAL. Our results showed that total evaporation estimated by Gerrits' model is in good agreement with Landflux-EVAL, STEAM and GLEAM. Results also show that Gerrits’ model underestimated interception in comparison to STEAM and overestimated in comparison to GLEAM, while for transpiration the opposite was found. Errors in interception can partly be explained by differences in the interception definition that successively introduce errors in the calculation of transpiration. Comparing to the Budyko framework, the model showed a good performance for total evaporation estimation and the results are closer to Ol'dekop than Schreiber, Pike and Budyko curves.
- Budyko curves
- remote sensing