Evaporation is a crucial 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, as it is a straightforward model with only rainfall and potential evaporation as required input. Many researchers have improved the Budyko framework by including more physics and catchment characteristics in the original equation. However, the parameterization of these improved Budyko models is not so straightforward, is data demanding, and requires local knowledge that is difficult to obtain at the global scale. 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 previously investigated for 10 catchments with different climate conditions and where some parameters were assumed to be constant, in this study we applied the model at the global scale and fed the model with remotely sensed input data. The output of the model has been compared to two complex land-surface models, STEAM and GLEAM, as well as the database of Landflux-EVAL. Our results show that total evaporation estimated by Gerrits' model is in good agreement with Landflux-EVAL, STEAM, and GLEAM. The results also show that Gerrits' model underestimates interception in comparison to STEAM and overestimates it in comparison to GLEAM, whereas the opposite is found for transpiration. Errors in interception can partly be explained by differences in the definition of interception that successively introduce errors in the calculation of transpiration. Relating to the Budyko framework, the model shows a reasonable performance for the estimation of total evaporation. The results also found a unimodal distribution of the transpiration to precipitation fraction (Et/p), indicating that both increasing and decreasing aridity will result in a decline in the fraction of transpired rainfall by plants for growth and metabolism.@en

In arid and semi-arid regions, water scarcity is the crucial issue for crop production. Identifying the spatial and temporal trends in aridity, especially during the crop-growing season, is important for farmers to manage their agricultural practices. This will become especially relevant when considering climate change projections. To reliably determine the actual trends, the influence of short- and long-term memory should be removed from the trend analysis. The objective of this study is to investigate the effect of short- and long-term memory on estimates of trends in two aridity indicators—the inverted De Martonne (ϕ IDM ) and Budyko (ϕ B ) indices. The analysis is done using precipitation and temperature data over Iran for a 50-year period (1966–2015) at three temporal scales: annual, wheat-growing season (October–June), and maize-growing season (May–November). For this purpose, the original and the modified Mann–Kendall tests (i.e., modified by three methods of trend free pre-whitening (TFPT), effective sample size (ESS), and long-term persistence (LTP)) are used to investigate the temporal trends in aridity indices, precipitation, and temperature by taking into account the effect of short- and long-term memory. Precipitation and temperature data were provided by the Islamic Republic of Iran Meteorological Organization (IRIMO). The temporal trend analysis showed that aridity increased from 1966 to 2015 at the annual and wheat-growing season scales, which is due to a decreasing trend in precipitation and an increasing trend in mean temperature at these two timescales. The trend in aridity indices was decreasing in the maize-growing season, since precipitation has an increasing trend for most parts of Iran in that season. The increasing trend in aridity indices is significant in Western Iran, which can be related to the significantly more negative trend in precipitation in the West. This increasing trend in aridity could result in an increasing crop water requirement and a significant reduction in the crop production and water use efficiency. Furthermore, the modified Mann–Kendall tests indicated that unlike temperature series, precipitation, ϕ IDM , and ϕ B series are not affected by short- and long-term memory. Our results can help decision makers and water resource managers to adopt appropriate policy strategies for sustainable development in the field of irrigated agriculture and water resources management.@en

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.@en

In this study, we explore how the differences in soil moisture accounting affect the estimation of actual soil evaporation (E) and transpiration (T). The main objective is, therefore, a comparative assessment of a vapor flux estimation method which has explicit soil moisture accounting, against a vapor flux estimation method that uses satellite observed soil moisture data. Three methods with different representations of water supply dynamics are compared: (1) ETLook, wherein E and T are estimated using Advanced Microwave Scanning Radiometer - Earth Observing System (AMSR-E) derived soil moisture data; (2) a simple evaporation transpiration scheme (SETS) that has a similar vaporization representation as ETLook but with soil moisture accounting based on the MOSAIC Land Surface Model; and (3) SETS-AMS which is similar to SETS except that the AMSR-E derived soil moisture controls the top layer mass balance. The schemes are compared on the Indus River Basin for the year 2007 at 1 km spatial resolution. The results suggest that downward soil water flux influences the estimation of E and T. This effect is especially dominant in areas with high soil moisture content. The comparative assessment reveals how lack of explicit soil moisture accounting may lead to an overestimation of E and underestimation of T, especially in irrigated areas.@en