14 activity in Ho Chi Minh City to estimate ranges of future exposure. The project used simple depth-damage curves to estimate future vulnerability. As shown in Figure 4.1, we linked these components using a simple model built in the Analytica modeling environment.<sup>10</sup> This Analytica model also allowed comparison of the impacts of alternative adaptation and retreat measures as part of alternative integrated flood risk management strategies.

# **Measures of Risk (M)**

Based on our workshops and discussions with Steering Center for Flood Control, this project employed three measures of risk to compare the potential consequences of alternative flood risk management strategies in our study area. Two measures focus on population risk and one on economic risk. These are:

1. Risk to the poor, measured as the expected number of people affected annually by flooding;

2. Risk to the non-poor, measured as the expected number of people affected annually by flooding; and 3. Risk to economic value, measured in percent GDP lost annually to flooding.

The analysis employs two measures of population risk because equity issues are important to Steering Center for Flood Control. The population risk measures distinguish between a cohort called “poor” and those of higher income in the Nhieu LocThi Nghe catchment identified as “non-poor.” Many definitions exist for “low-income” and the literature lacks any consensus about the distinction between the poor and nonpoor even in the present. In this future this distinction becomes even less certain. As discussed in the uncertainty discussion below, this project treats the number of poor and non-poor in Ho Chi Minh City as fundamentally uncertain, drawing upon a range of data on poverty rates from the Vietnamese government and other sources to inform the range of possible future poverty rates.<sup>11</sup> Figure 4.2 shows risk to the poor population in what is described below as benchmark conditions.

The project employs a single measure of economic risk because the available data did not support any useful disaggregation. Note that we measure economic risk as a fraction of GDP rather than in absolute terms because Ho Chi Minh City’s economic exposure and risk will almost certainly grow as Vietnam’s economy expands. Thus, a more meaningful metric is whether or not risk grows faster or slower than the economy (Hallegatte 2012). In addition, this measure facilitates comparison of policies over many cases that vary in their assumptions about future economic growth rates.

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> 10 Analytica, a visual modeling platform for quantitative risk and uncertainty analysis, allows analysts create influence diagrams that define how factors in analysis relate to each other and to quickly add or modify elements of the model during the course of the analysis and in response to input from stakeholders. Analytica is well suited for RDM because it can be easily configured to run many cases and save those cases to a database. See www.lumina.com.

> 11 Steering Center for Flood Control was particularly interested in comparing risk to the poor and non-poor, and sufficient data were available to explore this formulation. However, one could also usefully segment the population along other dimensions, such as geography, age, and gender.