Natural hazard risk analysis in the framework of water safety plans.

The available literature on natural hazard risk analysis focused on the implementation of water safety plans (WSPs) is surprisingly quite poor, despite the significant increase in the number and severity of disasters and adverse effects on drinking water supply systems generated by natural hazards. At the same time, WSPs that conveniently account for natural hazards with a comprehensive approach 'from source to tap' are still scarce as they typically occur at larger spatial scales and adequate prevention, mitigation and adaptation require efficient inter-institutional collaborations. The aim of this paper is to highlight the main bottlenecks for water utilities to include natural hazards in the development of their WSPs. The research adopted a stakeholders-oriented approach, involving a considerable number of water utilities (168), water sectoral agencies (15) and institutions (68) across the Adriatic-Ionian Region through a stepwise process that generated joint SWOT analysis, the development of a decision support system (DSS) focused on WSPs procedures and tabletop exercises. The final outcomes generated strategic documents (REWAS - Adrion Road map for resilient water supply) that highlighted the necessity for efficient cross-sectoral and inter-institutional cooperation in the development of well-founded and robust WSPs to address natural hazard risk analysis for water supply systems (DWSS).

Assessment of Groundwater Quality by Chemometrics.

Chemometric methods were used to analyze large data sets of groundwater quality from 18 wells supplying the central drinking water system of Larissa city (Greece) during the period 2001 to 2007 (8.064 observations) to determine temporal and spatial variations in groundwater quality and to identify pollution sources. Cluster analysis grouped each year into three temporal periods (January-April (first), May-August (second) and September-December (third). Furthermore, spatial cluster analysis was conducted for each period and for all samples, and grouped the 28 monitoring Units HJI (HJI=represent the observations of the monitoring site H, the J-year and the period I) into three groups (A, B and C). Discriminant Analysis used only 16 from the 24 parameters to correctly assign 97.3% of the cases. In addition, Factor Analysis identified 7, 9 and 8 latent factors for groups A, B and C, respectively.