Quantitative Classification of Desertification Severity for Degraded Aquifer Based on Remotely Sensed Drought Assessment

Sidiropoulos, Pantelis; Dalezios, Nicolas R.; Λουκάς, Αθανάσιος; Mylopoulos, Nikitas; Spiliotopoulos, Marios; Faraslis, Ioannis; Alpanakis, Nikos; Sakellariou, Stavros

doi:10.3390/hydrology8010047

Cited by 24 publications

(14 citation statements)

References 33 publications

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“…Therefore, several methods for estimating ETo have been developed, ranging from simple empirical or physically based models [13,14] to complex algorithms and techniques, such as fuzzy logic and machine learning (ML) [15][16][17][18][19][20][21]. These methods employ data from meteorological stations, or retrieved data via remote sensors [22][23][24][25][26][27][28][29][30][31][32]. The FAO-56 Penman-Monteith (FAO PM) equation (Equation ( 1)) is the most established method used to compute ETo worldwide.…”

Section: Introductionmentioning

confidence: 99%

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

Dimitriadou

Nikolakopoulos

2022

Hydrology

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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.

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Section: Introductionmentioning

confidence: 99%

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

Dimitriadou

Nikolakopoulos

2022

Hydrology

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“…The necessity of acquiring ETo values led to the development of several methods of estimation, ranging from simple empirical or physically based models [9,10] to complex neuro-fuzzy and machine learning algorithms . The methods incorporate data from meteorological stations or, due to the scarcity of the former, remotely sensed data [48][49][50][51][52][53][54][55][56][57][58]. The FAO-56 Penman Monteith (FAO PM) equation requires numerous meteorological variables for effective application [59,60].…”

Section: Introductionmentioning

confidence: 99%

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

Dimitriadou

Nikolakopoulos

2022

Water

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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.

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“…Higher leaching by runoff means higher export of organic matter from soil to surface waters, provided that average precipitation is high enough to preserve soil moisture, which is a prerequisite for the long-term occurrence of organic matter in soil. Actually, heavy rain on organic-poor soil cannot leach much organic matter [62].…”

Section: Water Browning In Boreal Environmentsmentioning

confidence: 99%

Photochemical reactions and the self-depuration of sunlit freshwaters

Vione

2022

Environ. Res.: Ecology

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Reactions induced by sunlight (direct photolysis and indirect photochemistry) are important ecosystem services that aid freshwater bodies in removing contaminants, although they may also exacerbate pollution in some cases. Without photoinduced reactions, pollution problems would be considerably worse overall. The photochemical reaction rates depend on seasonality, depth, water chemistry (which also significantly affects the reaction pathways), and pollutant photoreactivity. They are also deeply impacted by less studied factors, including hydrology, water dynamics, and precipitation regimes, which are key to understanding the main impacts of climate change on surface-water photochemistry. Climate change is often expected to both exacerbate freshwater pollution, and enhance photochemical decontamination. Therefore, photochemical knowledge will be essential to understand the future evolution of freshwater environments.

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Quantitative Classification of Desertification Severity for Degraded Aquifer Based on Remotely Sensed Drought Assessment

Cited by 24 publications

References 33 publications

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

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

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

Photochemical reactions and the self-depuration of sunlit freshwaters

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