Target 6.4 of the recently adopted Sustainable Development Goals (SDGs) deals with the reduction of water scarcity. To monitor progress towards this target, two indicators are used: Indicator 6.4.1 measuring water use efficiency and 6.4.2 measuring the level of water stress (WS). This paper aims to identify whether the currently proposed indicator 6.4.2 considers the different elements that need to be accounted for in a WS indicator. WS indicators compare water use with water availability. We identify seven essential elements: 1) both gross and net water abstraction (or withdrawal) provide important information to understand WS; 2) WS indicators need to incorporate environmental flow requirements (EFR); 3) temporal and 4) spatial disaggregation is required in a WS assessment; 5) both renewable surface water and groundwater resources, including their interaction, need to be accounted for as renewable water availability; 6) alternative available water resources need to be accounted for as well, like fossil groundwater and desalinated water; 7) WS indicators need to account for water storage in reservoirs, water recycling and managed aquifer recharge. Indicator 6.4.2 considers many of these elements, but there is need for improvement. It is recommended that WS is measured based on net abstraction as well, in addition to currently only measuring WS based on gross abstraction. It does incorporate EFR. Temporal and spatial disaggregation is indeed defined as a goal in more advanced monitoring levels, in which it is also called for a differentiation between surface and groundwater resources. However, regarding element 6 and 7 there are some shortcomings for which we provide recommendations. In addition, indicator 6.4.2 is only one indicator, which monitors blue WS, but does not give information on green or green-blue water scarcity or on water quality. Within the SDG indicator framework, some of these topics are covered with other indicators.

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Abstract. The aim of this paper is the application of the KULTURisk regional risk assessment (KR-RRA) methodology, presented in the companion paper (Part 1, Ronco et al., 2014), to the Sihl River basin, in northern Switzerland. Flood-related risks have been assessed for different receptors lying on the Sihl River valley including Zurich, which represents a typical case of river flooding in an urban area, by calibrating the methodology to the site-specific context and features. Risk maps and statistics have been developed using a 300-year return period scenario for six relevant targets exposed to flood risk: people; economic activities: buildings, infrastructure and agriculture; natural and semi-natural systems; and cultural heritage. Finally, the total risk index map has been produced to visualize the spatial pattern of flood risk within the target area and, therefore, to identify and rank areas and hotspots at risk by means of multi-criteria decision analysis (MCDA) tools. Through a tailored participatory approach, risk maps supplement the consideration of technical experts with the (essential) point of view of relevant stakeholders for the appraisal of the specific scores weighting for the different receptor-relative risks. The total risk maps obtained for the Sihl River case study are associated with the lower classes of risk. In general, higher (relative) risk scores are spatially concentrated in the deeply urbanized city centre and areas that lie just above to river course. Here, predicted injuries and potential fatalities are mainly due to high population density and to the presence of vulnerable people; flooded buildings are mainly classified as continuous and discontinuous urban fabric; flooded roads, pathways and railways, most of them in regards to the Zurich central station (Hauptbahnhof) are at high risk of inundation, causing severe indirect damage. Moreover, the risk pattern for agriculture, natural and semi-natural systems and cultural heritage is relatively less important mainly because the scattered presence of these assets. Finally, the application of the KR-RRA methodology to the Sihl River case study, as well as to several other sites across Europe (not presented here), has demonstrated its flexibility and the possible adaptation of it to different geographical and socioeconomic contexts, depending on data availability and particulars of the sites, and for other (hazard) scenarios.

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