The paper investigates the mechanism of seawater intrusion and the performance of free and open-source codes for the simulation of variable density flow problems in coastal aquifers. For this purpose, the research focused on the Marathon Watershed, located in the northeastern tip of Attica, Greece. For the simulation of the groundwater system, MODFLOW, MT3DMS and SEAWAT codes were implemented, while sensitivity analysis and calibration processes were carried out with UCODE. Hydraulic head calibration was performed on the MODFLOW model, and TDS concentration was validated in the SEAWAT model. The calibrated parameters of the MODFLOW model were obtained for the variable density flow simulation with SEAWAT. The MODFLOW and SEAWAT hydraulic head outputs were analyzed and compared to one another. The outcome of this analysis is that SEAWAT produced slightly better results in terms of the hydraulic heads, concluding that parameter transferability can take place between the two models. For the purpose of the seawater intrusion assessment, the use of the SEAWAT code revealed that the aquifer is subjected to passive and passive–active seawater intrusion during wet and dry seasons, respectively. Finally, an irregular shape of a saltwater wedge is developed at a specific area associated with the hydraulic parameters of the aquifer.

The present work aims to compare two different subsurface hydrological models, namely HYDRUS and MODFLOW UZF package, in terms of groundwater recharge; thus, both models were coupled with MODFLOW. The study area is an experimental kiwifruit orchard located in the Arta plain in the Epirus region of Greece. A novel conceptual framework is introduced in order to (i) use in situ and laboratory measurements to estimate parameter values for both sub-surface flow models; (ii) couple the developed models with MODFLOW to estimate groundwater recharge; and (iii) compare and evaluate the performance of both approaches, with differences stemming from the distinctive equations describing the flow in the unsaturated zone. Detailed soil investigation was conducted in two soil horizons in the research field to identify soil texture zones, along with infiltration experiments implementing both double-ring and single-ring infiltrometers. The results of the field measurements indicate that fine-textured soils are predominant within the field, affecting several hydrological processes, such as infiltration, drainage, and root water uptake. Field measurements were incorporated in unsaturated zone flow modeling and the infiltration fluxes were simulated with the application of both the UZF package of MODFLOW and the HYDRUS code. The two codes presented acceptable agreement between the simulated and observed hydraulic head values with a similar performance in terms of statistics; however, they produced different results regarding recharge rates in the aquifer as simulated by MODFLOW. HYDRUS produced higher hydraulic head values in the aquifer throughout the simulation, related to higher recharge rates arising from the root water uptake and the capillary effects that are computed by HYDRUS but neglected by the UZF package of MODFLOW.

Soil structure is the predominant factor of water, gases, and nutrients movement within the unsaturated zone. Despite the growing interest in the influence of structured soil media on soil-water dynamics, there is a gap in explicit technical documentation on monitoring soil hydraulic properties in undisturbed soil samples. This study proposes a new framework to measure and monitor soil hydraulic properties in undisturbed soil samples, in parallel optimizing falling head and centrifuge method for structured soil samples. Both unimodal and bimodal soil hydraulic functions are used to describe Soil Water Retention Curve (SWRC) data derived from laboratory experiments, and a modified one is introduced to integrate all the possible distinct pore domains of the soil matrix. The description SWRC data by the modified soil hydraulic equation coincides with Durner equation, while provides important insights on the saturated water content of each sub-pore domain. Water flow models in the unsaturated zone have been developed in Hydrus 1D, applying hydraulic parameters that correspond to soil hydraulic properties obtained from experimental data and the Rosetta Pedotransfer function. The combined used of optimized methods on undisturbed soil samples provided an insightful description of soil hydraulic properties, while water flow models with conceptualized soil hydraulic properties and data assimilation (DA) outperformed every other method. Water flow models using bimodal hydraulic function achieved the greater accuracy in each observation node of the soil column (10 cm: 0.074 cm³cm⁻³, 50 cm: 0.0756 cm³cm⁻³).

Per- and polyfluoroalkyl substances (PFAS) are chemical compounds containing carbon-fluorine bonds of high toxicity related with several concerning effects to human health and to the environment. Since PFAS are widely used in everyday life with numerous industrial uses as well as in fire training sites, airports and military areas, these substances have already penetrated to the soil and the aquatic environment. PFAS movement in surface water, soil and groundwater is a field of high interest during the last few years among the scientific community with much recent research focusing on PFAS movement, sorption and travel time through modeling. The aim of the present work is to examine the movement of Perfluorobutanesulfonic acid (PFBS) in the subsurface through numerical modeling. Different hydraulic models were utilized to simulate water and solute movement in the unsaturated zone and the groundwater. More specifically, HYDRUS 1-D and PHREEQC codes for unsaturated zone flow and MODFLOW 6 code for groundwater flow were implemented. The proposed framework was applied in Kifissos basin, Athens, Greece. The unsaturated zone flow model was constructed for a pilot area where Kifissos riverbed is natural (not channelized). To conceptualize the unsaturated zone column under the natural riverbed in the pilot site, several lithostratigraphic data were employed. Sensor data of the river stage were utilized for model inflow. Mass transport within the unsaturated zone was simulated using Hydrus 1D code for the convection and dispersion of chemical species in the liquid phase of the unsaturated zone. PFBS initial concentration was obtained from a grab sampling campaign. Reactive sorption onto the solid phase and adsorption onto the air-Air-Water Interface (AWI) of the unsaturated zone is simulated with PHREEQC 3, given the initial concentrations from Hydrus-1D mass transport simulation. For the simulation of air-water interfacial adsorption the predefined mathematical rate expressions have been scripted into RATES data block of PHREEQC 3. A regional groundwater model was constructed for the case study. The model includes two convertible (phreatic) aquifer layers. The complex lithology of Athens was configured through hydraulic properties zonation. Groundwater flow model performance was validated with existing measurements. Further discretization was applied to model grid at the vicinity of the pilot area. The Groundwater Transport Process (GWT) of MODFLOW 6 was utilized to simulate advection and dispersion processes. Mobile Storage Transfer (MST) Package was utilized to simulate solute storage, sorption, and decay on the mobile domain. Finally, the coupling of the unsaturated flow and solute transport results was accomplished through Mass Source Loading package (SRC). The coupling of several subsurface hydrological models revealed that PFBS can be characterized as threatening substance for groundwater due to its mobility, the minimal sorption at the AWI and the competitive displacement from solid surfaces with the introduction of longer-chain PFAS. Groundwater numerical modeling suggested that PFBS plume movement and concentration is affected by regional groundwater flow and sorption processes.Acknowledgements: This reasearch is part of the project UPWATER (Understanding groundwater Pollution to protect and enhance WATERquality) that has received funding from the European Union under grant agreement No 101081807

While the observations from earth-observing satellites and in-situ weather meteorological and monitoring stations continue to expand, researchers are to deal with an abundance of data and, consequently, with a long modeling procedure. Machine learning algorithms, as universal nonlinear function approximation tools, are effective in analyzing and modeling spatio-temporal environmental data, more efficiently, either in time or in terms of the availability of various variables, than physically-based models.Soil moisture is an essential climatic parameter, especially for understanding and forecasting variations in surface temperature, precipitation, drought, flood, and the effects of climate change. As a parameter with high spatial and temporal variability, it is a strong necessity for predictive models that embed spatially irregular measurements, which stand for spatially distributed weather meteorological and monitoring stations. To date, most approaches, that have been documented in the literature, model environmental data only at the discrete locations of the monitoring stations.This research aims to employ a recently proposed methodology, for spatio-temporal prediction of environmental data (Amato et al., 2020), and to propose a new methodology for spatio-temporal prediction of soil moisture in lowland areas, making use of the basic hydrologic premise that precipitation and temperature strongly hinge on topography. The features of the machine learning models used to predict soil moisture within the research area are the meteorological parameters of several agro-meteorological weather stations that have been installed at the site.The study area is the plain of Arta, located in the Epirus region (NW Greece), and includes the lower part of the watersheds of rivers Aracthos and Louros. The final receptor for upstream surface water and groundwater is a sensitive and complex system of wetlands, the marine ecosystem of Amvrakikos. The research area is receiving a lot of attention because of its agricultural characteristics and water infrastructures (extensive irrigation and drainage network, pumping stations, and hydroelectric dam).Acknowledgments: This research has been co-financed by the European Union and Greek national funds through the Operational Program Competitiveness, Entrepreneurship and Innovation, under the call SUPPORT OF REGIONAL EXCELLENCE (project code MIS: 5047059).Amato, F., Guignard, F., Robert, S., & Kanevski, M. (2020). A novel framework for spatio temporal prediction of environmental data using deep learning. Scientific Reports, 10(1), Article 1. https://doi.org/10.1038/s41598-020-79148-7