Constraining a Flow Model with Field Measurements to Assess Water Transit Time Through a Vadose Zone

Chloride-salinity as indicator of the chemical composition of groundwater: empirical predictive model based on aquifers in Southern Quebec, Canada

The present study first describes the variations in concentrations of 12 chemical elements in groundwater relative to salinity levels in Southern Quebec (Canada) groundwater systems, and then uses this data to develop an empirical predictive model for evaluating groundwater chemical composition relative to salinity levels. Data is drawn from a large groundwater chemistry database containing 2608 samples. Eight salinity classes were established from lowest to highest chloride (Cl) concentrations. Graphical analyses were applied to describe variations in major, minor, and trace element concentrations relative to salinity levels. Results show that the major elements were found to be dominant in the lower salinity classes, whereas Cl becomes dominant at the highest salinity classes. For each of the major elements, a transitional state was identified between domination of the major elements and domination of Cl. This transition occurred at a different level of salinity for each of the major elements. Except for Si, the minor elements Ba, B, and Sr generally increase relative to the increase of Cl. The highest Mn concentrations were found to be associated with only the highest levels of Cl, whereas F was observed to be more abundant than Mn. Based on this analysis of the data, a correlation table was established between salinity level and concentrations of the chemical constituents. We thus propose a predictive empirical model, identifying a profile of the chemical composition of groundwater relative to salinity levels, to help homeowners and groundwater managers evaluate groundwater quality before resorting to laborious and costly laboratory analyses.

Novel Water Retention and Nutrient Management Technologies and Strategies Supporting Agricultural Water Management in Continental, Pannonian and Boreal Regions

Urgent water and food security challenges, particularly in continental and boreal regions, need to be addressed by initiatives such as the Horizon 2020-funded project WATer retention and nutrient recycling in soils and streams for improved AGRIcultural production (WATERAGRI). A new methodological framework for the sustainable management of various solutions resilient to climate change has been developed. The results indicate that the effect of the climate scenario is significantly different for peatlands and constructed wetlands. The findings also highlight that remote-sensing-based yield prediction models developed from vegetation indices have the potential to provide quantitative and timely information on crops for large regions or even at the local farm scale. Verification of remotely sensed data is one of the prerequisites for the proper utilization and understanding of data. Research shows that current serious game applications fall short due to challenges such as not clarifying the decision problem, the lack of use of decision quality indicators and limited use of gaming. Overall, WATERAGRI solutions improve water and food security by adapting agriculture to climate change, recycling nutrients and providing educational tools to the farming community. Farmers in small agricultural catchments benefit directly from WATERAGRI, but over the long-term, the general public does as well.