The objective of the study is to provide global grids (0.5°) of revised annual coefficients for the Priestley–Taylor (P-T) and Hargreaves–Samani (H-S) evapotranspiration methods after calibration based on the ASCE (American Society of Civil Engineers)-standardized Penman–Monteith method (the ASCE method includes two reference crops: short-clipped grass and tall alfalfa). The analysis also includes the development of a global grid of revised annual coefficients for solar radiation (R_s) estimations using the respective R_s formula of H-S. The analysis was based on global gridded climatic data of the period 1950–2000. The method for deriving annual coefficients of the P-T and H-S methods was based on partial weighted averages (PWAs) of their mean monthly values. This method estimates the annual values considering the amplitude of the parameter under investigation (ET_o and R_s) giving more weight to the monthly coefficients of the months with higher ET_o values (or R_s values for the case of the H-S radiation formula). The method also eliminates the effect of unreasonably high or low monthly coefficients that may occur during periods where ET_o and R_s fall below a specific threshold. The new coefficients were validated based on data from 140 stations located in various climatic zones of the USA and Australia with expanded observations up to 2016. The validation procedure for ET_o estimations of the short reference crop showed that the P-T and H-S methods with the new revised coefficients outperformed the standard methods reducing the estimated root mean square error (RMSE) in ET_o values by 40 and 25 %, respectively. The estimations of R_s using the H-S formula with revised coefficients reduced the RMSE by 28 % in comparison to the standard H-S formula. Finally, a raster database was built consisting of (a) global maps for the mean monthly ET_o values estimated by ASCE-standardized method for both reference crops, (b) global maps for the revised annual coefficients of the P-T and H-S evapotranspiration methods for both reference crops and a global map for the revised annual coefficient of the H-S radiation formula and (c) global maps that indicate the optimum locations for using the standard P-T and H-S methods and their possible annual errors based on reference values. The database can support estimations of ET_o and solar radiation for locations where climatic data are limited and it can support studies which require such estimations on larger scales (e.g. country, continent, world). The datasets produced in this study are archived in the PANGAEA database (https://doi.org/10.1594/PANGAEA.868808) and in the ESRN database (http://www.esrn-database.org or http://esrn-database.weebly.com).

Lakes, either artificial or natural, are greatly important as a component in their catchments’ hydrology, but also as ecosystem service providers. However, due to climate change, they have begun to face numerous problems with their water quality and quantity. Furthermore, general circulation models (GCMs) show future climate change with a reduction in rainfall and increase in temperature. The aim of the current study is to present an application where GCMs and state-of-the-art hydrological modelling system MIKE SHE/MIKE 11 are combined for assessing the response of a Greek lake in terms of its water balance and water level under climate change. Four general circulation models (GCMs; GFDL-CM3, MIROC-ESM-CHEM, MIROC-ESM, IPSL-CM5A-LR) for the extreme climate change scenario of RCP8.5 were used in the basin of Lake Zazari in Greece as a case study. Results showed that, by keeping the irrigated demands (the main water user) unchanged in the future, the lake exhibited a lower water level for all GCMs, fluctuating from −0.70 to −1.8 m for the mean (min) water level and from −0.30 to −1.20 m for the mean (max) water level. Instead of the above and by preserving the amount of withdraw water n from the lake at a certain percentage of inflows, the irrigated area should be reduced from 54.1% to 64.05% depending on the circulation model.

The WEAP (Water Evaluation and Planning) model was used to estimate the effects of drought in addition to rural and tourist activity on the operation of two multi-purpose reservoirs in the Chalkidiki region of Greece. In scenarios based on historical data from 1975–2005, the fully effective months exceeded 85% of the total simulation period, while short-term droughts (less than two years’ duration) made the reservoirs ineffective to cover summer water demand. The results show that short-term droughts, in combination with high competition for water supply during summer between tourist and rural activities, may have environmental and social implications in semi-arid environments.

A classic method for assessing the reference crop evapotranspiration (ET_o) is the pan evaporation (E_pan) method that uses E_pan measurements and pan coefficient (k_p) models, which can be functions of relative humidity (RH), wind speed (u₂), and temperature (T). The aim of this study is to present a methodology for evaluating the robustness of regression coefficients associated to climate parameters (RH, u₂, and T) in pan method models during recalibration at local conditions. Two years of daily data from April to October (warm season) of meteorological parameters, E_pan measurements from class A pan evaporimeter and ET_o estimated by ASCE-standardized method for the climatic conditions of Thessaloniki (Greece, semi-arid environment), were used. The regression coefficients of six general nonlinear (NLR) regression E_pan models were analyzed through recalibration using a technique called “random cross-validation nonlinear regression RCV-NLR” that produced 1000 random splits of the initial dataset into calibration and validation sets using a constant proportion (70% and 30%, respectively). The variance of the regression coefficients was analyzed based on the 95% interval of the highest posterior density distribution. NLR models that included coefficients with a 95% HPD interval that fluctuates in both positive and negative values were considered nonrobust. The machine-learning technique of random forests (RF) was also used to build a RF model that includes E_pan, u₂, RH, and T parameters. This model was used as a benchmark for evaluating the predictive accuracy of NLR models but, also, for assessing the relative importance of the predictor climate variables if they were all included in one NLR model. The findings of this study indicated that locally calibrated NLR functions that use only the E_pan parameter presented better results, while the inclusion of additional climate parameters was redundant and led to underfitting.

The study presents a combination of techniques for integrated analysis of reference crop evapotranspiration (ETo) in GIS environment. The analysis is performed for Greece and includes the use of (a) ASCE-standardized Penman-Monteith method for the estimation of 50-year mean monthlyETo, (b) cross-correlation and principal components analysis for the analysis of the spatiotemporal variability ofETo, (c)K-means clustering for terrain segmentation to regions with similar temporal variability ofETo, and (d) general linear models for the description ofETobased on clusters attributes. Cross-correlation revealed a negative correlation ofETowith both elevation and latitude and a week positive correlation with longitude. The correlation betweenEToand elevation was maximized during the warm season, while the correlation with latitude was maximized during winter. The first two principal components accounted for the 97.9% of total variance of mean monthlyETo.K-means segmented Greece to 11 regions/clusters. The categorical factor of cluster number together with the parameters of elevation, latitude, and longitude described satisfactorily theETothrough general linear models verifying the robustness of the cluster analysis. This research effort can contribute to hydroclimatic studies and to environmental decision support in relation to water resources management in agriculture.

Simple formulas for estimating annual actual evapotranspiration (AET) based on annual climate data are widely used in large scale applications. Such formulas do not have distinct compartments related to topography, soil and irrigation, and for this reason may be limited in basins with high slopes, where runoff is the dominant water balance component, and in basins where irrigated agriculture is dominant. Thus, a simplistic method for assessing AET in both natural ecosystems and agricultural systems considering the aforementioned elements is proposed in this study. The method solves AET through water balance based on a set of formulas that estimate runoff and percolation. These formulas are calibrated by the results of the deterministic hydrological model GLEAMS (Groundwater Loading Effects of Agricultural Management Systems) for a reference surface. The proposed methodology is applied to the country of Greece and compared with the widely used climate-based methods of Oldekop, Coutagne and Turk. The results show that the proposed methodology agrees very well with the method of Turk for the lowland regions but presents significant differences in places where runoff is expected to be very high (sloppy areas and areas of high rainfall, especially during December–February), suggesting that the proposed method performs better due to its runoff compartment. The method can also be applied in a single application considering irrigation only for the irrigated lands to more accurately estimate AET in basins with a high percentage of irrigated agriculture.