A versatile method for groundwater vulnerability projections in future scenarios.
Water scarcity and associated risks are serious societal problems. A major challenge for the future will be to ensure the short-term and long-term provision of accessible and safe freshwater to meet the needs of the rapidly growing human population and changes in land cover and land use, where conservation and protection play a key role. Through a Bayesian spatial statistical method, a time-dependent approach for groundwater vulnerability assessment is developed to account for both the recent status of groundwater contamination and its evolution, as required by the European Union (Groundwater Directive, 2006/118/EC). This approach combines natural and anthropogenic factors to identify areas with a critical combination of high levels and increasing trends of nitrate concentrations, together with a quantitative evaluation of how different future scenarios would impact the quality of groundwater resources in a given area. In particular, the proposed approach can determine potential impacts on groundwater resources if policies are maintained at the status quo or if new measures are implemented for safeguarding groundwater quality, as natural factors are changing under climatic or anthropogenic stresses.
Dipartimento di Scienze della Terra "A. Desio", Università degli Studi di Milano, Via Luigi Mangiagalli, 34, Milan, 20133, Italy. Electronic address: stefania.stevenazzi@unimi.it.
Stefania
S
Stevenazzi
Dipartimento di Scienze della Terra "A. Desio", Università degli Studi di Milano, Via Luigi Mangiagalli, 34, Milan, 20133, Italy.
Marianna
M
Bonfanti
Dipartimento di Scienze della Terra "A. Desio", Università degli Studi di Milano, Via Luigi Mangiagalli, 34, Milan, 20133, Italy.
Marco
M
Masetti
Jet Propulsion Laboratory, California Institute of Technology, 4800 Oak Grove Drive, MS 300-235, Pasadena, CA 91109, USA.
Son V
SV
Nghiem
Geography and Environment, University of Southampton, Highfield Campus, Building 54, Southampton, SO17 1BJ, UK; Flowminder Foundation, Roslagsgatan 17, SE-11355, Stockholm, Sweden.
Alessandro
A
Sorichetta
D001499:Bayes Theorem; D003247:Conservation of Natural Resources; D004784:Environmental Monitoring / Q000379:methods*; D005544:Forecasting; D060587:Groundwater / Q000737:chemistry*; D006801:Humans; D007558:Italy; D062206:Spatial Analysis; D014874:Water Pollutants, Chemical / Q000737:chemistry*; D014881:Water Supply
D014874:Water Pollutants, Chemical
Groundwater vulnerability; Land use management; Statistical method; Time dimension