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Watershed Planning within a Quantitative Scenario Analysis Framework
12:44

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Published on: July 24, 2016

A comparison of data-driven groundwater vulnerability assessment methods.

Alessandro Sorichetta1, Cristiano Ballabio, Marco Masetti

  • 1European Commission, Joint Research Centre, Institute for Environment and Sustainability, Via E. Fermi 2749, I-21027 Ispra (VA), Italy.Dipartimento di Scienze della Terra "A. Desio," Università degli Studi di Milano, Via Mangiagalli 34, 20133 Milan, Italy.United States Geological Survey, 954 National Center, Reston, VA 20192.Istituto per la Dinamica dei Processi Ambientali, Consiglio Nazionale delle Ricerche (CNR-IDPA), Piazza della Scienza 1, 20126 Milan, Italy.

Ground Water
|January 8, 2013
PubMed
Summary

Statistical models identified population density and groundwater depth as key factors for nitrate contamination in the Milan District. These findings help map groundwater vulnerability to nitrate pollution.

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Area of Science:

  • Environmental Science
  • Hydrogeology
  • Geostatistics

Background:

  • Growing availability of geo-environmental data enables statistical assessment of groundwater vulnerability.
  • Nitrate is a common groundwater contaminant, indicating aquifer susceptibility.
  • Understanding nitrate occurrence is crucial for managing water resources.

Purpose of the Study:

  • To evaluate the role of explanatory variables in nitrate occurrence using statistical models.
  • To map spatial variations in groundwater vulnerability to nitrate in the Milan District.
  • To compare the performance and results of Weights of Evidence (WofE) and Logistic Regression (LR) models.

Main Methods:

  • Application of multivariate Weights of Evidence (WofE) and Logistic Regression (LR) models.
  • Modification of the WofE method for enhanced comparability with LR.
  • Extension of LR analysis using a nonlinear Generalized Additive Model.
  • Spatial mapping of groundwater vulnerability probability.

Main Results:

  • Both WofE and LR models showed similar spatial patterns in groundwater vulnerability.
  • Population density (proxy for sewage/septic sources) and groundwater depth were identified as significant explanatory variables.
  • Modified WofE and extended LR improved model discrimination (c-statistic).

Conclusions:

  • The study successfully mapped groundwater vulnerability to nitrate in the Milan District.
  • Population density and groundwater depth are critical indicators for nitrate contamination risk.
  • The applied statistical methods provide a robust framework for assessing groundwater vulnerability.