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Innovative soil moisture (SM) monitoring and modelling methods are key to reduce irrigation water use in the face of expected water scarcity and increase of droughts related to climate change. A promising irrigation monitoring method is Cosmic-Ray Neutron Sensing (CRNS), which is based on the negative correlation between fast neutrons originating from Cosmic-Ray neutron intensities and SM content. The CRNS key advantage lies in its relatively large sensing volume of several hectares, which allows to use a single CRNS instead of a network of point-scale sensors. Additionally, land surface models such as the Community Land Model (CLM5) that simulate the exchange of water, energy, carbon and nitrogen at the land–atmosphere interface can be a valuable tool to study the efficiency of irrigation and effects on crop growth. In this study, novel CRNS and the newly developed CLM5-FruitTree were tested in two small (~1.2 ha) irrigated apple orchards located in the Pinios Hydrologic Observatory (Greece). In 2020, a climate station (Atmos21) and a network of 12 SoilNet nodes, each with two SM sensors at 5, 20 and 50 cm depth, were installed in each field, as well as water meters to measure irrigation timing and amounts. In addition, a CRNS was installed in each field to test the possibility of monitoring irrigation and informing irrigation models. We found that the CRNS was very sensitive to the weekly irrigation events. However, the magnitude of the SM fluctuations caused by the irrigation was underestimated by the CRNS resulting in an RMSE of up to 0.058 cm3 cm-3. This can be attributed to the fact that the CRNS has a large footprint, and the neutron counts were therefore also influenced by the surroundings of the irrigated field. Therefore, to compensate for this influence, an additional SoilNet node was installed outside one of the two irrigated fields in 2022. By combining these data with neutron transport simulations of the study area, a correction of CRNS-derived SM was developed to better capture both timing and magnitude of SM changes (RMSE reduced to 0.031 cm3 cm-3). In parallel, CLM5-FruitTree was able to reproduce the observed SM response to irrigation when the local irrigation schedule was considered (i.e., defining starting date, timing, and target soil moisture for irrigation). Interestingly, the simulated irrigation in 2021 and 2022 used 10 to 60 % less water than the amount applied by the farmer. This suggests a great water saving potential through a reduction in irrigation amounts or through improvements in irrigation efficiency by reducing losses through evaporation or deep percolation. However, existing model weaknesses in the representation of soil properties and water fluxes need to be further addressed for this modelling approach. Nevertheless, the results of this study are a further step towards the use of novel CRNS and modelling tools as a decision support system in irrigation for more efficient use of water resources.

Pinios River Basin in central Greece (PRB) is region of highly productive agriculture where irrigation intensification and climate change have caused a significant depletion of groundwater resources. In the framework of the EU-Horizon 2020 project ATLAS, a precision irrigation scheduling service has been developed that aims at improving irrigation water management at the field scale. Such service is intended to protect crops from water stress by keeping the soil moisture (SM) in the root zone above the maximum allowable deficit (MAD). The presented approach is developed in two highly instrumented apple orchard pilot fields (~1.2 ha extent each) located at the Pinios Hydrologic Observatory ILTER site in PRB. In each pilot field and for two consecutive cultivation periods (2021 and 2022), intensive monitoring of meteorological parameters plus SM in 12 locations and at three depths (5, 20, 50 cm) was performed. To determine the time and volume of the next irrigation event, the forecast of meteorological variables for the next six days provided by the Global Forecast Model (GFS) was included in the service. The irrigation service performance was evaluated via comparison of the model estimated crop evapotranspiration (ETc) values against the SM content distribution monitored by the cluster of the installed SM sensors. The potential service contribution to reduce irrigation water consumption was assessed via comparison of the modelled irrigation water demands against the actual water consumption monitored at the irrigation blocks that divide each field. Statistical metrics demonstrate a good agreement between modeled crop evapotranspiration (ETc) and the monitored SM dynamics as captured by the SM sensors. Comparisons between the calculated irrigation demands and the actual water consumption monitored at the irrigation blocks of the pilot fields show that irrigation water applied in the fields may be reduced from 15% up to 50% or more in some instances, without considerably impacting crop health and yield. On the contrary, significant gains may be achieved on water saving and consequently on energy consumption to abstract irrigation water, thus contributing considerably to the region’s water-energy-food nexus sustainability.

Indicators on the sustainability of productive human sectors help boosting societal awareness and provide remarkable information for political decision-making and resources management. Prominent examples of relevant environmental indicators currently available are those that form the footprint family. In agriculture, the water footprint approach provides indicators that integrate direct and indirect freshwater usage. While a considerable number of studies developed so far used tabulated values for crop parametrization, the less explored application of dense remote sensing time series provides huge benefits.This paper aims to present the spatiotemporal estimation of the green and blue Remote Sensing-based Agriculture Water Footprint (RS-AWAF) at the Pinios River Basin (11,000 km2) in Greece (year 2017), combining two globally accepted and operational methodologies: the Soil Water Balance published by the Food and Agriculture Organization in its irrigation and drainage paper 56 for water accounting purposes, and the standardized methodology for Agricultural Water Footprint estimation of growing a crop or tree published by the Water Footprint Network. Initially, the RS-AWAF applies dense temporal series of the Normalized Difference Vegetation Index produced by Sentinel-2 data at 10m spatial resolution to monitor the crops provided by local authorities through the Land Parcel Information System and derive the biophysical parameters along its development, such as the basal crop coefficient and the fraction of soil surface covered by vegetation. Those are then integrated into a validated and operational Remote Sensing-based Soil Water Balance that day after day and within a pixel spatial scale, estimates among other components of the balance, the adjusted crop evapotranspiration (ETcadj) and the net irrigation requirements (NIR). In a second step, both previous components are combined to estimate the blue crop water use (CWUblue), related to the NIR, and the green crop water use (CWUgreen), related to the fraction of the ETcadj that comes from other freshwater sources different than irrigation, the precipitation. Finally, crop yield values collected from official statistics per crop or crop group are used to estimate the blue water footprint (WFblue) and the green water footprint (WFgreen).Once the green and blue RS-AWAF is estimated, a collection of thematic maps over the Pinios River Basin is ready for use by local stakeholders at their desired working scale. In that sense, monthly and annual thematic maps of ETcadj, NIR, CWUgreen and CWUblue are available, as well as annual thematic maps of WFblue and WFgreen. In parallel, tabulated values are created from these parameters using zonal statistics through GIS at the spatial scale appropriate to the final user (i.e. water user associations).These results are part of the EU Horizon 2020 project REXUS (Managing Resilient Nexus Systems Through Participatory Systems Dynamics Modelling), in which stakeholders from water user associations to river basin water managers are evaluating the information. At this stage, our final goal is to provide spatiotemporal distributed accounting of agricultural freshwater resources over large areas that enhance regional knowledge and increases efficiency in water management and subsequently contributing to energy-saving, since the major agricultural water volume is abstracted from deep groundwater wells.

Given the expected increase of droughts related to climate change, soil moisture (SM) monitoring will likely become essential for farmers as it helps to reduce water consumption while mitigating crop losses. Cosmic-Ray Neutron Sensing (CRNS) is a promising SM monitoring method that is based on the negative correlation between fast neutrons originating from cosmic radiation and SM content. As CRNS integrates SM over a large radius of ~130-210 m with a penetration depth of ~15-85 cm, it has advantages over point-scale and remote-sensing methods. However, it is yet unclear how well CRNS can monitor areas with complex SM heterogeneity, such as small irrigated fields. In this study, two CRNS equipped with a novel gadolinium oxide thermal shielding were installed in two small (~1.2 ha) irrigated apple orchards located in the Pinios Hydrologic Observatory (Greece). Each CRNS was supported by an Atmos41 all-in-one climate station, by water meters measuring irrigation timing and amounts, and by a network of 12 wireless SM measurement nodes (SoilNet) that monitored SM at 5, 20 and 50 cm depth. The results showed that the CRNS was sensitive to the weekly irrigation events, but that it showed a general underestimation of the magnitude of SM fluctuations caused by the irrigation, which resulted in a RMSE of 0.058 cm3 cm-3. To better understand these results, we used the URANOS model to simulate neutron transport for a CRNS placed in the centre of a square irrigated field of varying dimensions (0.5 to 8 ha). The simulation results showed that CRNS can be used to monitor irrigation in fields as small as 0.5 ha in certain SM conditions and that a gadolinium-based thermal shielding provides the best monitoring results due to the much-reduced detection of thermal neutrons. Nonetheless, a considerable number of detected neutrons (above 60%) can originate outside the target field if the irrigated field is small, and in such cases a CRNS may not be able to clearly distinguish irrigation from SM variations in the surroundings. In an attempt to correct for such SM variations not related to irrigation, an additional SoilNet node was installed outside one of the two irrigated apple orchards in September 2021. By combining the results of neutron transport simulations with the information provided by this additional SoilNet node, a correction of CRNS-derived SM was developed that better captures both timing and magnitude of SM changes (RMSE reduced to 0.031 cm3 cm-3). These results show that the combination of real-world studies with neutron transport simulations can help to establish CRNS as a reliable tool in irrigation management.

Robust assessments of variations in freshwater availability are essential for current and future water resource management in the Pinios River Basin (PRB), which is one of the most productive basins of Greece in terms of agriculture. To support sustainable water resources management in the PRB, we set up and calibrated the mGROWA hydrological model at a high spatial (100 m) and temporal (daily) resolution for the period 1971–2000, with particular attention given to deriving crop-specific irrigation requirements. We developed and implemented a comprehensive methodological framework to overcome data scarcity constraints in the PRB, thus enabling the derivation of high-resolution spatially continuous estimates of many input variables required for the mGROWA model. We generated estimates of spatiotemporal variations in the water balance components actual evapotranspiration, irrigation requirements, total runoff, and groundwater recharge for the PRB. In addition, through the calculation of indices, such as the potential irrigation to groundwater recharge ratio (PIQR), we demonstrate a way to identify potential unsustainable water use in irrigated agriculture. The established mGROWA model can be used both as a hydrological reference model providing continuous decision support for water resources management, focusing on irrigation water use, and a basis for climate impact studies for the PRB.

On the global scale, agriculture constitutes the most significant non-point pollution source for surface water bodies, especially in terms of nitrogen and phosphorus. Located in northeastern Greece, Vistonis basin constitutes the area that discharges into Vistonis estuarine system, which is very significant both from the environmental and economic perspective. Divided into three sub-basins basin includes 220 km2 of agricultural land cultivated mainly with cotton, corn and wheat. The present study aims to quantify the effects of agricultural activities in nutrient loads that discharge in Vistonis. Therefore, detailed spatial distribution of crops and soil data, combined to cultivation practices were used in order to apply SWAT model and quantify discharge, nitrogen and phosphorus loads for the period 2003-2014. The results presented significant variation in nutrient loads, while their contribution on a massive fish kill event that took place on summer 2014 was found to be significant. Nutrient loads variation were further investigated using projected, bias-corrected climate data from 3 Regional Climate Models (RCMs) for the period 2021-2040 and 2061-2080. The results indicate decreasing trend in nutrient loads due to decrease in river discharge because of precipitation decrease.

The aim of this study is the assessment of desertification risk for a typical Mediterranean island, in the frame of climate change and the application of good agricultural practices. Based on the MEDALUS Environmentally Sensitive Area Index (ESAI) approach, the sensitivity in desertification is estimated by employing 15 quantitative parameters divided in to four main quality indices: climate, vegetation, soil, and management quality. The methodology applied for a baseline scenario (current conditions), two future climate change scenarios (RCP 4.5 and RCP 8.5) and a soil quality improvement scenario. According to the results, more than 13% of the island's area is characterized as critically sensitive to desertification in the current conditions. This percentage will increase in the future under both the RCP 4.5 and the RCP 8.5 climate scenarios, where the critical areas will rise above to 15%. By applying the soil quality improvement scenario simultaneously with the climate change scenarios, a slight mitigation of desertification risk in the future could be achieved. The methodology developed in this study may be used to assess desertification process under various climate, soil, and land use management scenarios in regions of the Mediterranean Sea. HIGHLIGHTS This study investigates, using GIS, the effects of all the above approaches on the sensitivity of desertification risk for the Mediterranean island of Crete (Greece) for the period 2031–2060. This study also highlights the results of the comparison of the future desertification scenarios under the application of GAPs and the maintenance of the traditional-conventional agricultural practices.

Groundwater resources are inevitably considered as the primary source of high-quality water for the Mediterranean region. In critical cases, groundwater is essential to complement inadequate, uncertain, expensive, or even lacking surface-water sources. Especially in coastal areas of the Mediterranean, which show increasing development and population growth, karst aquifers represent vital freshwater sources. Karst aquifers are rather complex water systems, and therefore, though to manage, predict, and protect. Groundwater modeling has proved to be a very effective tool for groundwater management. Physically-based modeling is usually applicable to porous aquifers; numerical modeling application to karst aquifers is very challenging because of their complexity, which combines discontinuity, conduit, and porous medium domains. The need to forecast groundwater quantity and quality in karst aquifer systems is high, with groundwater salinity being very critical. Artificial Intelligence (AI) algorithms have been proved to be an effective alternative in simulating groundwater quality and quantity variables. This study aims to develop and test the performance of 6 AI algorithms to forecast groundwater electrical conductivity (EC) in the highly complex, coastal karst aquifer system of Salento (Puglia, Southern Italy). The AI algorithms applied were: 1) Multilayer Perceptron (MLP), 2) Long short-term memory (LSTM), 3) Bidirectional LSTM (BiLSTM), 4) Convolutional Neural Network (CNN), 5) Recurrent Neural Networks (RNN), and 6) Support Vector Machine (SVR). Except for SVR, which is considered a machine learning (ML) algorithm, all the other approaches are deep learning (DL) neural network architectures. Models’ development was based on 3-year groundwater EC daily data from 7 sensors. Other variables used for EC modeling were groundwater level and temperature, precipitation, and air temperature. The above variables were combined in 11 input variable experiments. In addition, various realizations of training times windows were developed under five scenarios. The total number of trained EC models was 2184. The results show AI models can efficiently provide a 30-day groundwater EC forecast for a wide range of EC values varying from slightly saline (0.7-2 mS/cm) to very saline (25-45 mS/cm). BiLSTM proved to be the most effective algorithm, while the least but still effective algorithm was SVR, thus showing the superior performance of DL algorithms compared to legacy ML approaches. Experimental results showed that increasing the number of input variables did not improve the performance of models. In contrast, including 2-time windows for training (one short-term and one long-term) increased it.

The coastal aquifer of the Rhodope region (NE Greece) is a complex groundwater system impacted by various processes that increase groundwater salinization (seawater intrusion, trapped saline lenses, geothermal fluid impact, irrigation return). In the context of the MEDSAL Project (www.medsal.net), a thorough study of its hydrogeochemical characteristics was performed to assess the spatiotemporal variations of groundwater salinization and identify the dynamics of the phenomenon.To this aim, we used a combination of tools, including multivariate statistics analysis (MVSA) and hydrogeochemical modelling, to decipher the mechanism(s) of groundwater salinization and their evolution in time and space.Results from Hierarchical Cluster Analysis (HCA) classified water samples into four (4) diverse groups and seven (7) subgroups that denote different hydrogeochemical and salinization phases. The different processes that control hydrogeochemistry were further assessed using R-mode factor analysis. The outcomes outlined three (3) factors that supplemented the HCA. The dominant factor is related to the cascading processes of salinization, and the secondary factors are related to anthropogenic contamination (N surplus due to agricultural activities) and the impact from the substrate (water-rock interaction).Hydrogeochemical modelling further supported assessments and provided an overview of the spatiotemporal variability of factors and processes affecting groundwater chemistry. A set of saturation indices of key minerals related to the dominant processes identified by the MVSA were calculated and interpolated to capture the spatiotemporal dynamics. Results facilitated the development of a more representative conceptual model about salinization and the key hydrogeochemical processes affecting water quality in the area.

Reservoir sedimentation constitutes a major issue worldwide and a long-lasting priority for dam managers, especially when hydropower, and hence profit, is involved. Commonly, the problem of excessive sedimentation is attributed to failed prediction of the sediment supply from the upland basin prior to the construction of dams, namely to the underestimation of sediment inflow to the reservoir.The sediment input in the Rio di Pusteria reservoir (South Tyrol, Italy) between two consecutive sediment flushing operations in June 2014 and May 2019, was determined by obtaining the volumetric difference between very high resolution (0.25 m) reservoir bathymetries conducted after the flushing of 2014 and before the flushing of 2019. The sediment yield in the reservoir during the latter period was found to be 453,783 t.To calculate the sediment yield in the reservoir, we have applied a gridded seven-factor Universal Soil Loss Equation (USLE) combined with a Sediment Delivery Ratio (SDR) module in a high resolution (2.5 m) GIS environment, which enabled an accurate representation of the rapidly shifting Alpine topography. An additional factor for coarse fragments was added to the conventional six-factor USLE to account for the non-erodible part of the basin. This is of great importance as the USLE-based models are criticized to produce extreme erosion rates in uplands and mountain areas. The topographic factor, LS, was refined by the use of a fine scale DEM and the slope length factor, L, was adjusted to the Alpine terrain by means of a regulating threshold. The proposed SDR module does not rely on one but on several physiographic, topographic and hydrologic characteristics of the basin. Finally, the rainfall erosivity factor, R, was determined in two different ways, one representing the rainfall climatology of the study area and one the specific rainfall conditions of the study period, hence the application of the model in two distinct configurations.The application of the combined USLE-SDR model resulted in five-year reservoir sedimentation rates of 439,279.2 t and 589,520.5 t, with deviations from the measured sediment yield of 3.3% and -25.5%. Excluding very high altitudes with glaciers and perennial snow, we consider the proposed modeling approach ideal for upper lands and mountainous areas such as the Alps.

Climate change increases the occurrence and severity of droughts due to increasing temperatures, altered circulation patterns, and reduced snow occurrence. While Europe has suffered from drought events in the last decade unlike ever seen since the beginning of weather recordings, harmonized long-term datasets across the continent are needed to monitor change and support predictions. Here we present soil moisture data from 66 cosmic-ray neutron sensors (CRNSs) in Europe (COSMOS-Europe for short) covering recent drought events. The CRNS sites are distributed across Europe and cover all major land use types and climate zones in Europe. The raw neutron count data from the CRNS stations were provided by 24 research institutions and processed using state-of-the-art methods. The harmonized processing included correction of the raw neutron counts and a harmonized methodology for the conversion into soil moisture based on available in situ information. In addition, the uncertainty estimate is provided with the dataset, information that is particularly useful for remote sensing and modeling applications. This paper presents the current spatiotemporal coverage of CRNS stations in Europe and describes the protocols for data processing from raw measurements to consistent soil moisture products. The data of the presented COSMOS-Europe network open up a manifold of potential applications for environmental research, such as remote sensing data validation, trend analysis, or model assimilation The dataset could be of particular importance for the analysis of extreme climatic events at the continental scale. Due its timely relevance in the scope of climate change in the recent years, we demonstrate this potential application with a brief analysis on the spatiotemporal soil moisture variability. The dataset, entitled "Dataset of COSMOS-Europe: A European network of Cosmic-Ray Neutron Soil Moisture Sensors", is shared via Forschungszentrum Julich: https://doi.org/10.34731/x9s3-kr48 (Bogena and Ney, 2021)., Earth System Science Data, 14 (3), ISSN:1866-3516, ISSN:1866-3508

Water availability is a key challenge in agriculture, especially given the expected increase of droughts related to climate change. Soil moisture (SM) sensors can be used to collect information on water availability in a reliable and accurate way. However, due to their very small measuring volume, the installation of multiple sensors is required. In addition, in-situ sensors may need to be removed during field management and connecting cables are often damaged by rodents and other wilderness animals. Hence, the demand for SM sensors that do not have such limitations will increase in the upcoming years. A promising non-invasive technique to monitor SM is cosmic-ray neutron sensing (CRNS), which is based on the negative correlation between fast neutrons originating from cosmic radiation and SM content. With its large measuring footprint of ~130-210m, CRNS can efficiently cover the field-scale. However, heterogeneous agricultural management (e.g., irrigation) can lead to abrupt SM differences, which pose a challenge for the analysis of CRNS data. Here, we investigate the effects of small-scale soil moisture patterns on the CRNS signal by using both modelling approaches and field studies. The neutron transport model URANOS was used to simulate the neutron signal of a CRNS station located in irrigated plots of different sizes (from 1 to 8 ha) with different soil moisture (from 5 and 50 Vol.%) inside and outside such a plot. A total of 400 different scenarios were simulated and the response functions of multiple detector types were further considered. In addition, two CRNS with Gadolinium shielding were installed in two irrigated apple orchards of ~1.2 ha located in the Pinios Hydrologic Observatory (Greece) in the context of the H2020 ATLAS project. Reference soil moisture was determined using 25 SoilNet stations, each with 6 SM sensors installed in pairs at 5, 20 and 50 cm depth and water potential sensors at 20 cm depth. The orchards were also equipped with two Atmos41 climate stations and eight water meters for irrigation monitoring. The CRNS were calibrated using either soil samples or the SM measured by the SoilNet network. In the URANOS simulations, the percentage of neutrons detected by the CRNS that are representative of an irrigated plot varied between 45 and 90% and was strongly influenced by both the dimension and SM of the irrigated plot. As expected, the CRNS footprint decreased considerably with increasing SM but did not appear to be influenced by the plot dimension. SM variation within the irrigated plot strongly affected the neutron energy at detection, which was not the case for SM variations outside the plot. The instrumented fields corroborated the URANOS findings and the performance of the local CRNS was dependent on a) the timing and intensity of irrigation and precipitation, b) the CRNS calibration strategy, and c) the management of the surrounding fields. These results provide novel and meaningful information on the impact of horizontal SM patterns on CRNS measurements, which will help to make CRNS more useful in irrigated agriculture.

Human-caused climate change increases the occurrence and severity of droughts due to increasing temperatures, altered circulation patterns and reduced snow occurrence. For example, Europe has suffered from drought events in the last decade like never since the beginning of weather recording. Here we present soil moisture data from 65 Cosmic-ray neutron sensors (CRNS) in Europe (COSMOS-Europe for short) covering recent drought events. The CRNS sites are distributed across Europe and cover all major land use types and climate zones in Europe. The raw neutron count data from the CRNS stations were provided by 23 research institutions and processed using state-of-the-art methods. The harmonised processing included correction of the raw neutron counts, and a harmonised methodology for the conversion into soil moisture based on available in-situ information. In addition, information on the data uncertainty is provided with the dataset, information that is particularly useful for remote sensing and modelling applications. This paper presents the current spatiotemporal coverage of CRNS stations in Europe and describes the protocols for data processing from raw measurements to consistent soil moisture products as well as first results on how the recent drought events have been captured by the CRNS network. This harmonised European soil moisture dataset will help both hydrologists and climate scientists to study individual drought events, to understand their causes, to evaluate and improve their modelling, and to estimate the extremity of current events. The dataset, entitled “Dataset of COSMOS-Europe: A European network of Cosmic-Ray Neutron Soil Moisture Sensors”, is shared via Forschungszentrum Jülich: https://doi.org/10.34731/x9s3-kr48 (Bogena and Ney, 2021).

Despite the fact that the olive tree is one of the best-adapted species in Mediterranean hydroclimate conditions, climate extremes impose negative effects on olive fruit set and development and subsequently on crop yield. Considering that the frequency of climate extremes is increasing in the last years due to climate change, Good Agricultural Practices (GAPs) have to be applied in order to mitigate their impact on olive trees. In this context, 18 experimental olive groves (irrigated and rainfed) were established, located on the island of Crete (south Greece). A set of 13 GAPs were applied in different combinations, mainly targeting to reduce water losses and erosion, alleviate heat stress and increase water use efficiency. Each experimental orchard was divided into two parts, the control (business-as-usual) and experimental (GAPs implementation). Four indicators were used for the assessment of GAPs performance, namely, Water Productivity (WP), Economic Water Productivity (EWP), Runoff (RF), and Yield (Y). WP and EWP were found to be up to 2.02 and 2.20 times higher, respectively, in the demonstration part of the orchards compared to the control, while Y was found to be up to 119% higher. RF was higher up to 190% in the control compared to the demonstration part of the experimental orchards. The above results clearly demonstrate that the implementation of the proposed GAPs can significantly support the adaptation of olive crops to extreme conditions.

Coastal delta plains are areas with high agricultural potential for the Mediterranean region because of their high soil fertility, but they also constitute fragile systems in terms of water resources management because of the interaction of underlying aquifers with the sea. Such a case is the Pinios River delta plain located in central Greece, which also constitutes a significant ecosystem. Soil and Water Assessment Tool (SWAT) and SEAWAT models were combined in order to simulate the impact of current water resources management practices in main groundwater budget components and groundwater salinization of the shallow aquifer developed in the area. Moreover, potential climate change impact was investigated using climate data from Regional Climate Model for two projected periods (2021&ndash, 2050 and 2071&ndash, 2100) and two sea level rise scenarios (increase by 0.5 and 1 m). Modeling results are providing significant insight: although the contribution of the river to groundwater inflows is significant, direct groundwater recharge from precipitation was found to be higher, while capillary rise constitutes a major part of groundwater outflows from the aquifer. Moreover, during the simulation period, groundwater flow from the aquifer to the sea were found to be higher than the inflows of seawater to the aquifer. Regarding climate change impact assessment, the results indicate that the variability in groundwater recharge posed by the high variability of precipitation during the projected periods is increasing the aquifer&rsquo, s deterioration potential of both its quantity and quality status, the latter expressed by the increased groundwater Cl&minus, concentration. This evidence becomes more significant because of the limited groundwater storage capacity of the aquifer. Concerning sea level rise, it was found to be less significant in terms of groundwater salinization impact compared to the decrease in groundwater recharge and increase in crop water needs.

Snow monitoring instruments like snow pillows are influenced by disturbances such as energy transport into the snowpack, influences from wind fields or varying snow properties within the snowpack (e.g. ice layers). The intensity of epithermal neutrons that are produced in the soil by cosmic radiation and measured above the ground surface is sensitive to soil moisture in the upper decimetres of the ground within a radius of hectometres. Recently, it has been shown that aboveground cosmic ray neutron sensors (CRNS) are also a promising technique to monitor snow pack development thanks to the larger support that they provide and to the lower need for maintenance compared to conventional sensor systems. The basic principle is that snow water moderates neutron intensity in the footprint of the CRNS probe. The epithermal neutrons originating from the soil become increasingly attenuated with increasing depth of the snow cover, so that the neutron intensity measured by the CRN probe above the snow cover is directly related to the snow water equivalent.In this paper, we use long-term CRNS measurements in the Pinios Hydrologic Observatory, Greece, to test different methods for the conversion from neutron count rates to snow pack characteristics, namely: i) linear regression, ii) the standard N0-calibration function, iii) a physically-based calibration approach and iv) the thermal to epithermal neutron ratio. The latter was also tested for its reliability in determining the start and end of snowpack development, respectively. The CRNS-derived snow pack dynamics are compared with snow depth measurements by a sonic sensor located near the CRNS probe. In the presentation, we will discuss the accuracy of the four conversion methods and provide recommendations for the application of CRNS-based snow pack measurements.

A numerical analysis of the groundwater flow and contaminant transport equations, based on the variable density flow approach, is used for the construction of non-dimensional diagrams to predict seawater intrusion to confined coastal aquifers. The classical Henry’s seawater intrusion problem is analysed by using a finite element model. The model’s equations are written in non-dimensional form and the numerical solutions depend solely on three non-dimensional parameters:α=q΄/Κ0, β=(bΚ0)/(nDm), α΄=bS0/n (eq. 1 a,b,c)where q’ is the freshwater recharge rate (m/d), K0 the freshwater hydraulic conductivity (m/d), b the aquifer thickness, n the porosity (-), Dm the molecular diffusion coefficient (m2/d) and S0 the freshwater specific storage (1/m). Please note that hydraulic conductivity appears in two of the non-dimensional parameters, α and β.The non-dimensional formulation has led to the construction of non-dimensional diagrams of salt distribution for a homogeneous and isotropic confined aquifer with horizontal base and constant thickness that is uniformly recharged with freshwater. These diagrams illustrate the influence of the key hydrological and hydraulic parameters, and furthermore, can be used to predict the evolution of seawater intrusion in real case studies.The numerical simulations were carried out up to the equilibrium state for different values of the non-dimensional parameters of equation 1. By decreasing the value of parameter α=q΄/Κ0, seawater intrusion is advancing inland and the width of dispersion zone is increasing. By increasing the parameter β=(bΚ0)/(nDm), the seawater-freshwater transition zone is narrowing and shifted to the seaside at the upper part of the aquifer, while the intrusion of saltwater is advancing inland at the lower part of the aquifer. The distribution of the salts in the aquifer was found essentially identical for different values of the parameter α΄=bS0/n; hence this parameter exhibits very low sensitivity, which makes it of low importance, especially for real case studies.Overall, the non-dimensional diagrams – constructed by following the variable density flow approach and under specific assumptions – can be used for a quick and direct prediction of seawater intrusion in real aquifers. These diagrams would be useful for an initial prediction at the case studies of the PRIMA MEDSAL project (www.medsal.net), namely the coastal aquifers in Rhodope (Greece), Samos island (Greece), Bouficha (Tunisia), Bouteldja (Algeria), Tarsus (Turkey) and under specific assumptions to the karstic aquifer in Salento (Italy).

Olive trees constitute one of the most dynamic cultivations for Mediterranean countries, while their economic importance is high. As water constitutes a fundamental factor affecting olive tree production, soil water content is a most critical parameter that must be monitored to improve olive trees’ cultivation management. Effects of precipitation, irrigation, and soil management on water content in four soil depths (10, 20, 30, 40 cm), four periods of the year (February–March, April–May, June–July, August–September) and three successive years were determined in 12 Mediterranean olive groves (Trifilia, southern Greece) as well as their respective fruit and olive oil yields. Significantly higher soil water content was recorded in the first (+16.8%) and third (+27.4%) year compared to the second year. Higher (+6.8%) water content was observed in irrigated olive groves compared to rainfed fields. Higher (+5.6%) water content was observed in sustainable olive groves compared to intensively managed fields. Significantly, higher soil moisture was recorded at 40 and 30 cm depth compared to 10 cm depth while intermediate values were observed at 20 cm. Marked increase in fruit yield was achieved through sustainable management (+39%) compared to intensive olive groves. The potential to improve irrigation practices in the area was also indicated by results of the present study.

The Integrated Coastal Zone Management (ICZM) Observatory of Kavala Municipality has been developed during the Mare Nostrum Project following the well-known DPSIR concept (Drivers, Pressures, States, Impacts and Responses). The Observatory aims to aid the day-to-day operations of the Municipality, collecting, analyzing and processing historic, present and forecasted environmental, socio-economic and legal-institutional ICZM datasets collected from a variety of sources. Data are presented in a series of well-structured layers imported in a Geographic Information System (GIS) web-environment, to aid decision-makers at Municipal, Regional and National level to formulate policies on ICZM matters; to provide reliable datasets to scientists and engineers; to engage active citizens and the broader public into coastal zone issues; to promote the distribution of information among coastal managers, stakeholders and the general public. Static datasets as topographic, bathymetric, geologic and other base maps, urban and rural planning maps, local municipal acts, public networks and locations of public interest were digitized and embedded in the system. Historic environmental data together with dynamic data produced daily by a series of coupled operational models (meteorological, hydrologic, hydrodynamic, wave and eutrophication) and climate change datasets were also included. The legal-institutional framework of the coastline and the interactive tools to enhance public participation represent novel elements of the herein presented ICZM Observatory. The Kavala Gulf mussel farmers, the local Port Authority and the decision-makers on siting hydroplane landing-zones are the main user groups of the ICZM Observatory.

Sedimentation in reservoirs constitutes a major issue worldwide, especially when hydropower, and hence profit, is involved. In this study, the sedimentation rates, due to soil erosion and sediment yields, are analyzed in the reservoir of the Rio di Pusteria hydropower dam, in South Tyrol (Italian Alps). The focus is on a five-year period, between two consecutive sediment flushing operations occurred in June 2014 and May 2019. Based on the difference between the pre- and post-flushing bathymetries, the five-year aggregated sediment yield was determined. The soil erosion from the basin surface and the sediment budget in the reservoir are calculated by means of a gridded Universal Soil Loss Equation (USLE), combined with a Sediment Delivery Ratio (SDR) module. The topographic factor is improved by the use of a fine-scale DEM and the slope length factor is adjusted to the Alpine terrain by means of a fitting threshold. The rainfall erosivity factor is determined in two different ways, one representing the precipitation regime of the study area and one the specific rainfall conditions of the study period. The two different applications result to calculated reservoir sedimentation rates of 439,279.2 tons and 589,520.5 tons with deviations from the measured value of 3.1% and −23.2%, respectively, while the sediment delivery ratio to the reservoir was found to be 12.5%. The calculations are also compared with results of the application of the conventional six-factor USLE. The comparison between calculations and measurements shows that the methods used can be efficiently applied in an Alpine basin for the estimation of reservoir sedimentation rates.

In this paper, we tested the operational capacity of an interoperable model coupling system for the irrigation scheduling (IMCIS) at an experimental cotton (Gossypium hirsutum L.) field in Northern Greece. IMCIS comprises a meteorological model (TAPM), downscaled at field level, and a water-driven cultivation tool (AquaCrop), to optimize irrigation and enhance crop growth and yield. Both models were evaluated through on-site observations of meteorological variables, soil moisture levels and canopy cover progress. Based on irrigation management (deficit, precise and farmer’s practice) and method (drip and sprinkler), the field was divided into six sub-plots. Prognostic meteorological model results exhibited satisfactory agreement in most parameters affecting ETo, simulating adequately the soil water balance. Precipitation events were fairly predicted, although rainfall depths needed further adjustment. Soil water content levels computed by the crop growth model followed the trend of soil humidity measurements, while the canopy cover patterns and the seed cotton yield were well predicted, especially at the drip irrigated plots. Overall, the system exhibited robustness and good predicting ability for crop water needs, based on local evapotranspiration forecasts and crop phenological stages. The comparison of yield and irrigation levels at all sub-plots revealed that drip irrigation under IMCIS guidance could achieve the same yield levels as traditional farmer’s practice, utilizing approximately 32% less water, thus raising water productivity up to 0.96 kg/m3.

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&ndash, 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.

The aim of the present study is to assess the future spatial and temporal distribution of precipitation and temperature, and relate the corresponding change to water resources’ quantitative status in Pinios River Basin (PRB), Thessaly, Greece. For this purpose, data from four Regional Climate Models (RCMs) for the periods 2021–2100 driven by several General Circulation Models (GCMs) were collected and bias-correction was performed based on linear scaling method. The bias-correction was made based on monthly precipitation and temperature data collected for the period 1981–2000 from 57 meteorological stations in total. The results indicate a general trend according to which precipitation is decreasing whilst temperature is increasing to an extent that varies depending on each particular RCM–GCM output. On the average, annual precipitation change for the period 2021–2100 was about − 80 mm, ranging between − 149 and + 35 mm, while the corresponding change for temperature was 2.81 °C, ranging between 1.48 and 3.72 °C. The investigation of potential impacts to the water resources demonstrates that water availability is expected to be significantly decreased in the already water-stressed PRB. The water stresses identified are related to the potential decreasing trend in groundwater recharge and the increasing trend in irrigation demand, which constitutes the major water consumer in PRB.

In order to assess the potential impacts of climate change in the hydrologic regime of River Pinios Basin, an area-differentiated model for total run-off (Qt) estimation based on the GROWA model was applied with bias-corrected precipitation and temperature data from four regional climate models (RCMs) for the projected periods 2020–2050 (period A) and 2050–2080 (period B). Bias correction was performed using the linear scaling approach. As a reference basis, monthly precipitation data from 57 meteorological stations and average temperature data from 17 stations were analyzed for the period 1980–2000. Relative assessments were achieved by comparing reference to projected periods values for Qt, after incorporating bias-corrected projected climate data from the four RCMs driven by several general circulation models (GCMs) as input data to the hydrological model. Results showed that all RCM–GCM combinations lead to a considerable decrease in total run-off with variable rates between the examined proje...

This study addresses the effects of land use change from agricultural to photovoltaic parks (PVPs) on the hydrology of an area. Although many environmental effects have been identified and analyzed, only minor attention has been given to the hydrologic effects of the installation and operation of PVPs. The effects of current PVP installation and operation practices on major hydrologic budget constituents (surface runoff, evapotranspiration and percolation) were identified, conceptualized, quantified and simulated using SWAT model. Vosvozis river basin located in north Greece was selected as a test site. Additionally, long-term effects were simulated using dynamically downscaled climate projections by a Regional Climate Model (RCM) driven by 5 different General Circulation Models (GCMs) for the period 2011–2100. Results indicate that surface runoff and percolation potential are significantly increased at the local scale and have to be considered during PVP siting, especially when sensitive and protected ecosystems are involved.

A decision-aiding methodology for agricultural groundwater management is presented; it is based on the combination of a watershed model, a groundwater flow model, and an optimization model. This methodology was applied to an agricultural watershed in northeastern Greece. The watershed model used was the Soil and Water Assessment Tool (SWAT), which provided recharge rates for the aquifers. These recharge rates were imported in the well-known MODFLOW groundwater flow model. Both models were calibrated and verified using field data. Then, the nonlinear optimization problem was solved by a piecewise linearization process, in which the Simplex algorithm was applied sequentially. Apart from several pumping and climate change sensitivity scenarios, a land use change scenario and a climate change scenario, combining the three models, were tested, showing the ability of this methodology to be used in the decision-making process. Copyright © 2012 John Wiley & Sons, Ltd.

The newest version of Soil and Water Assessment Tool (SWAT2005), coupled with a GIS interface (AVSWATX), was applied to Kosynthos River watershed located in Northeastern Greece. The 440 km2 drainage basin was discretized into 32 sub-basins using an automated delineation routine. The multiple hydrologic response unit (HRU) approach was used and the basin was discretized into 135 HRUs. The model was calibrated and verified using continuous meteorological data from three stations, and runoff and nutrient concentrations measured at four monitoring sites located within the main tributaries of the watershed, for the time period from November 2003 to November 2006. Calibration and verification results showed good agreement between simulated and measured data. Model performance was evaluated using several statistical parameters, such as the Nash–Sutcliffe coefficient and the normalized objective function. The validated model was also used to test the effect of several land use change and crop management scenarios in runoff and nutrient loadings. The study showed that SWAT model, if properly validated, can be used effectively in testing management scenarios in Mediterranean watersheds. The SWAT model application, supported by GIS technology, proved to be a very flexible and reliable tool for water decision-making, especially under the need for harmonization with the Water Framework Directive.

The objective of this study was to identify seasonal and spatial trends and soil salinization patterns in a part of Rhodope District irrigated land, northeastern Greece, located east of Vistonis Lagoon. The study area is irrigated from a coastal aquifer, where salt water intrusion occurs because of extensive groundwater withdrawals. Fourteen monitoring sites were established in harvest fields in the study area, where soil samples were collected. Electrical conductivity (ECe), pH, and ion concentrations were determined in the saturated paste extract of the soil samples in the laboratory using standard methods. A clear tendency was observed for ECe to increase from April to September, i.e., within the irrigation period, indicating the effect of saline groundwater to soil. In the last years, the change from moderately sensitive (e.g., corn) to moderately tolerant crops (e.g., cotton) in the south part of the study area indicates the impacts of soil salinity. The study proposes management methods to alleviate this problem.

The geological, hydrogeological and hydrochemical regimes of the coastal Rhodope aquifer system, northeastern Greece, are described. The aquifer system includes two aquifers within coarse grained alluvial sediments. Both vertical and lateral saline water intrusion occurs, usually caused by over pumping. Water has been pumped from the aquifer system at an ever-increasing rate for many years. Water samples for chemical analyses were obtained from 36 productive wells and from 5 research wells at several depths to cover the entire study area. The EC and chloride concentration distribution clearly illustrate the large extent of saline water intrusion in the aquifer system of the study area. Although the ionic content of groundwater of the study area is highly variable, the dominant anions are HCO3− and Cl− and the dominant cations are Na+ and Ca2+. Water in the saline parts of the confined aquifer is generally of the Ca2+-Cl− type. Evidence of cation exchange and reverse cation reaction between fresh and saltwater in the Rhodope aquifer system are reflected in the Piper diagram and the expanded Durov hydrochemical diagram, respectively, both for productive and research wells. The results of this study show that the development of a strategy for managing the aquifer system is vitally necessary.

Neural network techniques combined with Geographical Information Systems (GIS), are used in the spatial prediction of nitrate pollution in groundwaters. Initially, the most important parameters controlling groundwater pollution by nitrates are determined. These include hydraulic conductivity of the aquifer, depth to the aquifer, land uses, soil permeability, and fine to coarse grain ratio in the unsaturated zone. All these parameters were quantified in a GIS environment, and were standardized in a common scale. Subsequently, a neural network classification was applied, using a multi-layer perceptron classifier with the back propagation (BP) algorithm, in order to categorize the examined area into categories of groundwater nitrate pollution potential. The methodology was applied to South Rhodope aquifer (Thrace, Greece). The calculation was based on information from 214 training sites, which correspond to monitored nitrate concentrations in groundwaters in the area. The predictive accuracy of the model developed reached 86% in the training samples, 74% in the overall sample and 71% in the test samples. This indicates that this methodology is promising to describe the spatial pattern of nitrate pollution. Copyright © 2008 John Wiley & Sons, Ltd.

This paper presents the development of a regional flow simulation model of the stream–aquifer system of Ismarida plain, northeastern Greece. It quantifies the water budget for this aquifer system and describes the components of groundwater and the characteristics of this system on the basis of results of a 3-year field study. The semiconfined aquifer system of Ismarida Lake plain consists of unconsolidated deltaic clastic sediments, is hydraulically connected with Vosvozis River, and covers an area of 46.75 km2. The annual precipitation ranges in the study area from 270 to 876 mm. Eighty-seven irrigation wells are densely located and have been widely used for agricultural development. Groundwater flow in this aquifer was simulated with MODFLOW. Model calibration was done with observed water levels, and match was excellent. To evaluate the impacts of the current pumping schedule and propose solutions, four management scenarios were formulated and tested with the model. Based on model results, the simulated groundwater budget indicates that there must be approximately 33% decrease of withdrawals to stop the dramatic decline of groundwater levels. The application of these scenarios shows that aquifer discharge to the nearby river would be very low after a 20-year period.

Groundwater vulnerability has been subject of much research due to the valuable information it provides concerning groundwater protection and exploitation potential. Up to now, most groundwater vulnerability studies adopt subjective systems of rating the various factors and subsequently, their results are often ambiguous and contradicting. Within the present study a methodology for the estimation of intrinsic groundwater vulnerability at the aquifer scale is presented. The methodology is based on travel time estimation from specified sources of pollution to the aquifer. Besides the deterministic calculation of travel times, the methodology provides a rating system for each pollution source, based on its relative severity and the estimated threat that it poses to the aquifer. Therefore, it can be regarded as a hybrid method that couples the advantages provided by the physically based methods with those of the subjective rating systems. The methodology is applied to the Neon Sidirochorion aquifer, Northeastern Greece, an overexploited aquifer where river waters, sea waters and lake waters interact, causing groundwater quality deterioration to the aquifer. The results indicated that the proposed groundwater vulnerability assessment methodology is well capturing pollution related to saltwater intrusion and agricultural activities, while it is concluded that the conceptual model is significantly affecting the vulnerability assessment results and therefore has to be previously developed.

The assessment of groundwater vulnerability to pollution aims at highlighting areas at a high risk of being polluted. This study presents a methodology, to estimate the risk of an aquifer to be polluted from concentrated and/or dispersed sources, which applies an overlay and index method involving several parameters. The parameters are categorized into three factor groups: factor group 1 includes parameters relevant to the internal aquifer system’s properties, thus determining the intrinsic aquifer vulnerability to pollution; factor group 2 comprises parameters relevant to the external stresses to the system, such as human activities and rainfall effects; factor group 3 incorporates specific geological settings, such as the presence of geothermal fields or salt intrusion zones, into the computation process. Geographical information systems have been used for data acquisition and processing, coupled with a multicriteria evaluation technique enhanced with fuzzy factor standardization. Moreover, besides assigning weights to factors, a second set of weights, i.e., order weights, has been applied to factors on a pixel by pixel basis, thus allowing control of the level of risk in the vulnerability determination and the enhancement of local site characteristics. Individual analysis of each factor group resulted in three intermediate groundwater vulnerability to pollution maps, which were combined in order to produce the final composite groundwater vulnerability map for the study area. The method has been applied in the region of Eastern Macedonia and Thrace (Northern Greece), an area of approximately 14,000 km2. The methodology has been tested and calibrated against the measured nitrate concentration in wells, in the northwest part of the study area, providing results related to the aggregation and weighting procedure.

[EN] The agricultural sector faces the challenge of ensuring food security without an excessive burden on the environment. Simulationmodels provide excellent instruments for researchers to gainmore insight into relevant processes and best agricultural practices and provide tools for planners for decision making support. The extent to which models are capable of reliable extrapolation and prediction is important for exploring new farming systems or assessing the impacts of future land and climate changes. A performance assessmentwas conducted by testing six detailed state-of-the-artmodels for simulation of nitrate leaching (ARMOSA, COUPMODEL, DAISY, EPIC, SIMWASER/STOTRASIM, SWAP/ANIMO) for lysimeter data of the Wagna experimental field station in Eastern Austria, where the soil is highly vulnerable to nitrate leaching. Three consecutive phases were distinguished to gain insight in the predictive power of themodels: 1) a blind test for 2005 2008 in which only soil hydraulic characteristics, meteorological data and information about the agricultural management were accessible; 2) a calibration for the same period in which essential information on field observations was additionally available to the modellers; and 3) a validation for 2009 2011 with the corresponding type of data available as for the blind test. A set of statistical metrics (mean absolute error, root mean squared error, index of agreement,model efficiency, root relative squared error, Pearson's linear correlation coefficient) was applied for testing the results and comparing the models. None of the models performed good for all of the statistical metrics. Models designed for nitrate leaching in high-input farming systems had difficulties in accurately predicting leaching in low-input farming systems that are strongly influenced by the retention of nitrogen in catch crops and nitrogen fixation by legumes. An accurate calibration does not guarantee a good predictive power of the model. Nevertheless all models were able to identify years and crops with high- and low-leaching rates., This research was made possible by the GENESIS project of the EU 7th Framework Programme (Project No. 226536; FP7-ENV-2008-1). We are grateful for the experimental data provided by Joanneum Raum (Graz, Austria). The modelling team of Democritus University of Thrace would like to thank Per-Erik Jansson (Royal Institute of Technology, Stockholm, Sweden) for his valuable help during the application of Coup Model.

A groundwater monitoring and modeling program in two aquifers within the lower Nestos river basin in Northern Greece is presented. A monitoring network of 54 wells was developed in the two study areas, and groundwater level measurements and water quality sample analyses were conducted for a period of 2.5 years, from March 2007 to October 2009. The field data were used for the calibration and verification of the mathematical model MODFLOW in the two aquifers. The validated model was used to examine ten alternative management scenarios regarding groundwater abstraction in the two aquifers. The study showed that MODFLOW, if properly validated, is a useful and flexible tool in groundwater resources management.

The conceptual model and the hydrochemical characteristics of the aquifer system of Vistonis plain in Northeastern Greece are presented. The study area is a rural region whose irrigation needs are met by the semi-confined aquifer system. Studies were undertaken in years 2003-2005 which utilized geological data, pumping test data and groundwater level measurements, in order to investigate the hydrogeological conditions of the semi-confined aquifer system of the study area. Based on this data a FLOWPATH II simulation was developed. The hydrochemical characteristics of the aquifer system of the study area were analyzed using basic statistics, Principal Component Analysis (PCA) and the expanded Durov diagram. The hydrogeological characteristics of the aquifer system vary significantly across the study area and are largely the result of contrasts in depositional environments. FLOWPATH II showed a clear tendency for groundwater to flow from a NW to a SE or E direction. The groundwater chemical analyses results showed that, the aquifer is locally affected by a geothermal field. PCA rendered seven significant PCs (eigenvalue > 1), explaining 76.8% of the total variance of the dataset. PCA revealed factors influenced by anthropogenic activities, such as fertilizer application and waste disposal, and also naturally induced hydrochemical processes.

The results of an investigation of the quantitative and qualitative characteristics of Vosvozis river in Northern Greece is presented. For the purposes of this study, three gaging stations were installed along Vosvozis river, where water quantity and quality measurements were conducted for the period August 2005 to November 2006. Water discharge, temperature, pH, dissolved oxygen (DO) and electrical conductivity (EC) were measured in situ using appropriate equipment. The collected water samples were analyzed in the laboratory for the determination of nitrate, nitrite and ammonium nitrogen, total Kjeldalh nitrogen (TKN), orthophosphate (OP), total phosphorus (TP), COD, and BOD. Agricultural diffuse sources provided the major source of nitrate nitrogen loads during the wet period. During the dry period (from June to October), the major nutrient (N, P) and COD, BOD sources were point sources. The trophic status of Vosvozis river during the monitoring period was determined as eutrophic, based on Dodds classification scheme. Moreover, the SWAT model was used to simulate hydrographs and nutrient loads. SWAT was validated with the measured data. Predicted hydrographs and pollutographs were plotted against observed values and showed good agreement. The validated model was used to test eight alternative scenarios concerning different cropping management approaches. The results of these scenarios indicate that nonpoint source pollution is the prevailing type of pollution in the study area. The SWAT model was found to satisfactorily simulate processes in ephemeral river basins and is an effective tool in water resources management.

It has come to Springer’s attention that two paragraphs in the paper Soil salinization in the agricultural lands of Rhodope District, northeastern Greece, by Vassilios Pisinaras, Vassilios A. Tsihrintzis, Christos Petalas, Konstantinos Ouzounis (DOI 10.1007/s10661-009-0986-6) published in Environmental Monitoring and Assessment (EMAS), were copied virtually intact from the paper The agricultural impacts of irrigation induced waterlogging and soil salinity in