Four Job Satisfaction’s dimensions of secondary education teachers: An Exploratory Factor Analysis based on a Greek sample

BackgroundSurveying and assessing teachers’ job satisfaction constitutes an evergreen crosscutting issue.  It deploys the formation of policies that underpin its’ augmentation aiming for enhanced educational outcomes. Internationally there are not many valid instruments that can be used to assess teachers’ job satisfaction. Especially in Greece, research on the reliability and validity of these instruments when applied on teachers is non-existent.MethodThe instrument used was JSS. JSS describes nine (9) dimensions of Job Satisfaction. 177 secondary education teachers in Western Greece took part in this research. Exploratory Factor Analyses was conducted. ResultsParallel analyses and principal axis factoring revealed a structure of 4 dimensions of the JSS. Confirmatory factor analysis ascertained this structure that features satisfactory reliability and construct validity.ConclusionsWe could say that the JSS, according to the factorial structure, could be considered an appropriate tool for investigating the JS of teachers in Greece as well. The four -dimensions of the JSS revealed by the answers of Greek secondary education teachers are: teachers’ Payment framework, teachers’ Supervision, Nature of work and teachers’ Communication

Multiple Linear Regression Models with Limited Data for the Prediction of Reference Evapotranspiration of the Peloponnese, Greece

The aim of this study was to investigate the utility of multiple linear regression (MLR) for the estimation of reference evapotranspiration (ETo) of the Peloponnese, Greece, for two representative months of winter and summer during 2016–2019. Another objective was to test the number of inputs needed for satisfactorily accurate estimates via MLR. Datasets from sixty-two meteorological stations were exploited. The available independent variables were sunshine hours (N), mean temperature (Tmean), solar radiation (Rs), net radiation (Rn), wind speed (u2), vapour pressure deficit (es − ea), and altitude (Z). Sixteen MLR models were tested and compared to the corresponding ETo estimates computed by FAO-56 Penman–Monteith (FAO PM) in a previous study, via statistical indices of error and agreement. The MLR5 model with five input variables outperformed the other models (RMSE = 0.28 mm d−1, adj. R2 = 98.1%). Half of the tested models (two to six inputs) exhibited very satisfactory predictions. Models of one input (e.g., N, Rn) were also promising. However, the MLR with u2 as the sole input variable presented the worst performance, probably because its relationship with ETo cannot be linearly described. The results indicate that MLR has the potential to produce very good predictive models of ETo for the Peloponnese, based on the literature standards

Reference Evapotranspiration (ETo) Methods Implemented as ArcMap Models with Remote-Sensed and Ground-Based Inputs, Examined along with MODIS ET, for Peloponnese, Greece

The present study develops ArcMap models to implement the following three methods: FAO-56 Penman–Monteith (FAO PM), Hargreaves–Samani (HS) and Hansen, with the former used as a reference. Moreover, three models implementing statistical indices (RMSD, MB, NMB) are also created. The purpose is threefold, as follows: to investigate the variability in the daily mean reference evapotranspiration (ETo) for the Decembers and Augusts during 2016–2019, over Peloponnese, Greece. Furthermore, to investigate the agreement between the methods’ ETo estimates, and examine the former along with MODIS ET (daily) averaged products. The study area is a complex Mediterranean area. Meteorological data from sixty-two stations under the National Observatory of Athens (NOA), and MODIS Terra LST products, have been employed. FAO PM is found sensitive to wind speed and depicts interactions among climate parameters (T, evaporative demand and water availability) in the frame of climate change. The years 2016–2019 are four of the warmest since the preindustrial era. Hargreaves–Samani’s estimations for the Decembers of 2016–2019 were almost identical to MODIS ET, despite their different physical meaning. However, for the Augusts there are considerable discrepancies between the methods’ and MODIS’s estimates, attributed to the higher evaporative demand in the summertime. The GIS models are accurate, reliable, time-saving, and adjustable to any study area

Development of the Statistical Errors Raster Toolbox with Six Automated Models for Raster Analysis in GIS Environments

The Statistical Errors Raster Toolbox includes models of the most popular error metrics in the interdisciplinary literature, namely, root mean square error (RMSE), normalized root mean square error (NRMSE), mean bias error (MBE), normalized mean bias error (NMBE), mean absolute error (MAE) and normalized mean absolute error (NMAE), for computing the areal errors of any raster file in .tiff format as compared with a reference raster file. The models are applicable to any size of raster files, no matter if no-data pixels are included. The only prerequisites are that the two raster files share the same units, cell size, and projection system. The novelty lies in the fact that, to date, there is no such application in ArcGIS Pro 3/ArcMap 10.8. Therefore, users who work with raster files require external software, plus the relevant expertise. An application on the reference evapotranspiration (ETo) of Peloponnese peninsula (Greece) is presented. MODIS ET products and ETo raster files for empirical methods are employed. The results of the models (for 20,440 valid values) are compared to the results of external software (for 1000 random points). Considering that the different sample sizes can lead to different accuracies and the inhomogeneity of the area, it is obvious that the results are almost identical

Artificial Neural Networks for the Prediction of the Reference Evapotranspiration of the Peloponnese Peninsula, Greece

The aim of the study was to investigate the utility of artificial neural networks (ANNs) for the estimation of reference evapotranspiration (ETo) on the Peloponnese Peninsula in Greece for two representative months of wintertime and summertime during 2016–2019 and to test if using fewer inputs could lead to satisfactory predictions. Datasets from sixty-two meteorological stations were employed. The available inputs were mean temperature (Tmean), sunshine (N), solar radiation (Rs), net radiation (Rn), vapour pressure deficit (es-ea), wind speed (u2) and altitude (Z). Nineteen Multi-layer Perceptron (MLP) and Radial Basis Function (RBF) models were tested and compared against the corresponding FAO-56 Penman Monteith (FAO PM) estimates of a previous study, via statistical indices. The MLP1 7-2 model with all the variables as inputs outperformed the rest of the models (RMSE = 0.290 mm d−1, R2 = 98%). The results indicate that even ANNs with simple architecture can be very good predictive models of ETo for the Peloponnese, based on the literature standards. The MLP1 model determined Tmean, followed by u2, as the two most influential factors for ETo. Moreover, when one input was used (Tmean, Rn), RBFs slightly outperformed MLPs (RMSE < 0.385 mm d−1, R2 ≥ 96%), which means that even a sole-input ANN resulted in satisfactory predictions of ETo

Artificial Neural Networks for the Prediction of the Reference Evapotranspiration of the Peloponnese Peninsula, Greece

The aim of the study was to investigate the utility of artificial neural networks (ANNs) for the estimation of reference evapotranspiration (ETo) on the Peloponnese Peninsula in Greece for two representative months of wintertime and summertime during 2016–2019 and to test if using fewer inputs could lead to satisfactory predictions. Datasets from sixty-two meteorological stations were employed. The available inputs were mean temperature (Tmean), sunshine (N), solar radiation (Rs), net radiation (Rn), vapour pressure deficit (es-ea), wind speed (u2) and altitude (Z). Nineteen Multi-layer Perceptron (MLP) and Radial Basis Function (RBF) models were tested and compared against the corresponding FAO-56 Penman Monteith (FAO PM) estimates of a previous study, via statistical indices. The MLP1 7-2 model with all the variables as inputs outperformed the rest of the models (RMSE = 0.290 mm d−1, R2 = 98%). The results indicate that even ANNs with simple architecture can be very good predictive models of ETo for the Peloponnese, based on the literature standards. The MLP1 model determined Tmean, followed by u2, as the two most influential factors for ETo. Moreover, when one input was used (Tmean, Rn), RBFs slightly outperformed MLPs (RMSE −1, R2 ≥ 96%), which means that even a sole-input ANN resulted in satisfactory predictions of ETo

Annual Actual Evapotranspiration Estimation via GIS Models of Three Empirical Methods Employing Remotely Sensed Data for the Peloponnese, Greece, and Comparison with Annual MODIS ET and Pan Evaporation Measurements

Actual evapotranspiration (ETa) has been insufficiently investigated in Greece. This study aimed to estimate annual ETa by empirical methods (Turc, modified Turc, and Coutagne) for the Peloponnese, Greece, a Mediterranean testbed, between 2016–2019, four of the warmest years since the preindustrial era, and compare them to MODIS ET. Furthermore, measurements of annual pan evaporation (Epan) were performed for two Class A pan stations in the Peloponnese with different reliefs and conditions. The empirical methods and statistical formulae (RMSD, MB, and NMB) were developed as models in ArcMap. The outcomes of the Turc method resembled MODIS ET ranges for all years, followed by those of Coutagne. The estimates by the modified Turc method were almost identical to MODIS ET. Therefore, the modified Turc method can be used as an alternative to MODIS ET (and vice versa) for the Peloponnese for 2016–2019. Moreover, the Epan at Patras University station (semiurban, low elevation) exhibited an upward trend resembling the trends of the empirical methods over the study years, whereas the Epan at Ladonas station (higher elevation, lakeside) required investigation on a monthly time scale. Additionally, the gradual decrease of pan-water icing at Ladonas in December (from 20 d in 2016 to 0 d in 2019) could imply an undergoing decrease in snowpack storage retention across the mountains of the Peloponnese

Evapotranspiration Trends and Interactions in Light of the Anthropogenic Footprint and the Climate Crisis: A Review

Evapotranspiration (ET) is a parameter of major importance participating in both hydrological cycle and surface energy balance. Trends of ET are discussed along with the dependence of evaporation to key environmental variables. The evaporation paradox can be approached via natural phenomena aggravated by anthropogenic impact. ET appears as one of the most affected parameters by human activities. Complex hydrological processes are governed by local environmental conditions thus generalizations are difficult. However, in some settings, common hydrological interactions could be detected. Mediterranean climate regions (MCRs) appear vulnerability to the foreseen increase in ET, aggravated by precipitation shifting and air temperature warming, whereas in tropical forests its role is rather beneficial. ET determines groundwater level and quality. Groundwater level appeared to be a robust predictor of annual ET for peatlands in Southeast Asia. In semi-arid to arid areas, increases in ET have implications on water availability and soil salinization. ET-changes after a wildfire can be substantial for groundwater recharge if a canopy-loss threshold is surpassed. Those consequences are site-specific. Post-fire ET rebound seems climate and fire-severity-dependent. Overall, this qualitative structured review sets the foundations for interdisciplinary researchers and water managers to deploy ET as a means to address challenging environmental issues such as water availability

Interpretation of the Factors Defining Groundwater Quality of the Site Subjected to the Wildfire of 2007 in Ilia Prefecture, South-Western Greece

The present study examines the factors that define groundwater quality of a site subjected to the wildfire of 2007 in Ilia Prefecture, Peloponnese. This wildfire was the most severe in Greece in the last decade. An extensive sampling was carried out three months after the fire. Ninety-nine samples were analyzed in the Hydrogeology Laboratory of University of Patras for major and trace elements. The groundwater samples were classified into three hydrochemical types: Ca-HCO3, Ca-HCO3-SO4 and Ca-Na-HCO3. The hydrochemical results, processed using R-type factor analysis, resulted in a three-factor model that did not indicate any wildfire impact. The values of pH and electrical conductivity ranged between the expected levels for the area. The most abundant cations (Ca, Mg, Na, K) and trace elements (Mn, Zn, Cu, Cd, Pb, V) in the ash, occurred in the majority of the groundwater samples at concentrations below the potable limits set by the European Council. The concentrations of NO3−, NO2−, NH4+, phosphates, and sulphates, where present, were attributed to agricultural land uses of the area. No hydrochemical disturbances were found that could indicate a fire-retardant effect. It is likely that the prominent thickness of the unsaturated zone, of the granular aquifers that prevail in the area, limited the infiltration of the elements and trace elements usually found in abundance in fire ash