Introduction to STORM

This section will summarize and extend the motivation and key aspects that lead to the de-velopment of STORM. Water utility managers and stakeholders are required by regulations, e.g., the water safety plan (Davison et al., 2005), to perform risk analysis, prioritize hazards and thus control risk within the catchment. Risk management within the catchment is often performed in a forward mode, taking the complex hazard properties into account and as-sessing their impact to the receptor. Two key challenges arise.

First, the hazards within the catchment have properties that differ depending on the hazard type (e.g., chemical properties, spatial and temporal release pattern). For example, hazards may include natural risk phenomena or human activities, such as industrial, agricultural,

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transport-related hazards and geogenic hazards (see Fig. 7.4). These risk sources are spa-tially distributed across the catchment, fail at different times, lead to different temporal im-pact distributions at the receptor and are of different contaminant type. Still, in the sense of cumulative risk assessment, their individual impacts have to be considered to measure overall risk. Therefore, smart aggregation techniques beyond simple risk summation are in demand. More details are presented in Section 7.1.1.

Second, different risk management objectives with different underlying risk definitions may lead to contradicting management actions. For example, MacGillivray et al. (2006) state that the overarching public health goal may be in conflict with goals regarding economically efficient water supply. The German guideline DVGW (2009) introduces a risk-based and process-oriented management for technical, economical and health-related hazards. Water managers and stakeholders have different interests in groundwater and thus follow activi-ties that may be conflicting within the well catchment. The hazard ranking, risk perception and derived risk management strategies depend on the stakeholder-objective view for a spe-cific situation, such that human-health risk assessment needs different risk measures than assessing the technical performance. More details are presented in Section 7.1.2.

Due to these two above mentioned reasons,cumulative impact assessmentandmulti-objective stakeholder views, a flexible risk quantification and management framework is in demand.

STORM aims to prioritize hazards within a cumulative risk setting in ten modules and in-vestigates the factors that influence this ranking. These factors may include different risk perception, different critical cut-off levels or different risk objectives. Chapter 10 demon-strates the influence of these three factors in an application.

7.1.1. Mass-Discharge-based Risk Aggregation

Most aspects in risk aggregation, such as summation or worst-case scenarios, have been discussed in Section 3.6. As a summary of that section, aggregation has to be based on the mass discharge at the receptor. It accounts for hazards that are distributed across the whole catchment and for simultaneous contaminant well arrival, although hazards have failed independently of each other at different times. These two aspects have been already investigated in literature (see Tait et al., 2004; Troldborg et al., 2008).

Aggregation across contaminant types, such as chlorinated solvents and BTEX compounds, is less profound and only available by introducing the concept of utility theory (e.g., Fish-burn, 1970). The basic principle is to unify the consequence unit, such as expressing the severity scale in terms of costs, disability-adjusted life years (DALY), customer minutes lost (e.g., Lindhe et al., 2009), cancer risk (e.g., Freeze and McWhorter, 1997), hazard quotient (e.g., US EPA, 2007) or other risk estimates. Here, I introduce four mass-discharge-based well vulnerability criteria as utility values that can serve asintermediate riskestimates. Inter-mediate risk estimates allow the quantification of transport-based risk measures that comply with stakeholder objectives (see next Section 7.1.2). These risk estimates host aggregation in-formation only on mass-discharge level (module 8). Summation of these vulnerability-based risk levels (utility values) across hazard types is prohibitive in a non-linear risk situation.

Thus, threshold-based risk values can only be aggregated by statistical aggregation of the underlying mass fluxes (module 9).

Tait et al. (2004) state that not the time of failure, but the temporal arrival of contaminant at the well is important for risk assessment. In fact, this is true as long as hazards only fail once. In all other cases, the spatial and temporal arrival at the well and the arrival due to periodic failure have to be considered. Therefore, the novelty in STORM is aggregation of recurring hazard failures over time, such as combined sewage overflow due to heavy rain-fall events, regular fertilizing over a well catchment lifetime (e.g., Kourakos et al., 2012) and fecal transport (e.g., Page et al., 2012) from deer feeding places or cow pastures. The concept of recurring events is commonly accepted in flood risk and reliability engineering. In flood risk management, the severity is plotted versus the recurring interval. Thus, risk managers are able to install mitigation measures or emergency plans based on their risk perception and accept system failure once in a prescribed (large) recurrence period. Risk managers using tools of reliability engineering, such as fault-tree analysis, calculate the expected number of failure events for a given time period. Lindhe et al. (2009) estimated the expected failure in minutes within a one hundred year time period. Here, I’ll introduce a mean annual impact measure, where the impact measure is defined by a corresponding stakeholder objective (Section 7.1.2).

Section 7.5 introduces module 8, convoluting mass discharges of hazards at the receptor level (space, time and frequency aggregation). Module 9 (annuality and annual mean risk

measure) is presented in Section 7.6, showing statistical mass-discharge-based aggregation across contaminant types. Module 10 (see Section 7.6) introduces prioritization of individual hazards within a cumulative risk situation.

7.1.2. Vulnerability-based Risk Objectives

Stakeholders that participate in risk assessment for drinking water catchments have differ-ent opinions and interests, such that also their objectives differ. ¨Oberg and Bergb¨ack (2005) state that there exist two risk analysis interests in environmental engineering, ecological (e.g., McKnight et al., 2012) and human-health risk (e.g., Freeze and McWhorter, 1997). As previously discussed, technical and economic risk analysis is as relevant as the former two.

These different stakeholder views may possibly lead to different rankings of risk sources within the catchment and depend on the impact unit used to estimate risk.

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Figure 7.2.: Adverse effects triggered by type of contaminant breakthrough in the well water, leading to stakeholder-objective-risk measures.

A similar situation occurs, if one stakeholder follows more than one suggested risk objective.

This is often tacitly assumed, when asking for a unified vulnerability-based risk measure or when different risk measures are compared to each other (e.g., qualitative with quantitative ones). The resulting dilemma becomes very obvious, even when prioritizing only individual hazards without any aggregation. For example, human-health related questions may focus on concentration levels, assessing the acceptable or chronic daily intake rate (see Eq. 3.1) as proposed by the US EPA (1989). Hazards that lead to a high and extended concentra-tion dose in the supplied water are prioritized. Nevertheless, in an early-alert system, these hazards may be less prioritized due to longer reaction times,t_react. In this case, hazards that threaten the supply safety with shorter arrival time, even at lower peak concentration levels, are prioritized (see Fig. 7.2). Therefore, I claim that hazard prioritization is only meaningful, if it consistently considers only one pre-defined risk objective (see Section 3.2). The

” unify-ing“ idea is proposed for further research, by casting the different stakeholder views into a

multi-objective optimization framework.

To allow a flexible analysis, to address the needs of many stakeholders, and to allow for multi-objective approaches in later research, it is necessary to provide a framework that is flexible to adapt to the risk objectives demanded by different stakeholders. A similar idea has been followed by de Barros et al. (2012). They introduced environmental performance metrics, accounting for many objectives separately in one model. Here, the mass-discharge-based well vulnerability criteria provide the necessary information to derive all transport-related risk measures (see Fig. 7.2) and account for the multiple stakeholder objectives. For example, from the intermediate risk level of well down-time (W ET), the performance of risk measure”customer minutes lost“ (CML, e.g., Lindhe et al., 2009) can be derived. Can-cer risk (e.g., Rodak and Silliman, 2012), human-health-related hazard quotients (HQ, US EPA, 1989) or DALY can be derived from the cumulative contaminant load for a certain time period (Contaminant Load Exposure,CLE). Toxic units (TU, e.g., McKnight et al., 2012) can be derived from the maximum concentration load (M CR) and so forth.

As mentioned before, another advantage of using well vulnerability criteria in the STORM context is that they allow aggregation of risk sources across different contaminant types (see Section 7.5). Contrary, the information value of a chosen STORM (e.g., well exposure time, maximum concentration ratio) strongly depends on the hazard types (e.g., long-term or pulse source) that may pose a risk to water supply in the catchment. Tab. C.1 provides a brief overview on STORM and which metrics are suitable to assess the risk of certain hazard types. More details will be provided in Section 7.5.