32 analysis considered a wide range of possible increases and identified the threshold increase that would cause various strategies to fail to reduce risk. Second, there was a lack of reliable depth-damage curves representing the vulnerability of various Ho Chi Minh City populations to flooding. The RDM analysis considered a wide range of depthdamage curves and found that this relationship (whatever it may be) was less important than other factors in determining the performance of the different strategies considered.

This project does suggest, however, one way in which RDM analyses can prove more demanding of data. Traditional analyses are often usefully confined to providing hazard or exposure maps for one or a few scenarios. However, in identifying vulnerabilities of proposed plans and identifying robust responses to those vulnerabilities, RDM tends towards more integrative, system-wide analysis. For instance, the SWMM model at the core of this project was originally used to evaluate how new infrastructure investments would reduce flooding in a best-estimate future scenario. To understand the range of climate and socio-economic conditions for which these investments would reduce risk, this project needed to link the existing SWMM model with an Analytica model of the entire system. Building this system model consumed much of the effort in this current project. Once built, this system model also allowed us to analyze how combinations of adaptation and retreat options, coupled with the infrastructure investments, could reduce risk over a wider range of uncertain future conditions.

In sum, for any given policy question, RDM analyses will have less demanding data requirements than traditional analyses, and RDM’s greater computational and configuration challenges can be overcome. But RDM analyses may tend to lead decision makers to ask broader questions. These broader questions may lead to better decisions, but may also increase the demands for data and model development.

# **RDM Involves New Ways of Thinking about the Future**

In meetings with decision makers in Vietnam, a number of questions arose regarding the ability to apply RDM methods locally and build local capacity. One concern stems from the fact that local decision making in developing countries often relies heavily on external analytical expertise, and sometimes also external funding. In these cases, the role of local agencies may be to frame the terms of reference for externally-performed engineering analyses to use an RDM approach, emphasizing running models many times and identifying robust decisions. Alternatively, with some training, local agencies and universities may be able to lead the RDM analysis, while still potentially relying on engineering consultants to run models. This is to some degree the arrangement within our project team. RAND analysts were responsible for specifying the SWMM model runs and strategies and analyzing the resulting database of results. SCE modeled the strategies and performed the runs. In this situation, local agencies would play RAND’s role in framing the analysis.

A second concern stakeholders cited was that the complex and sometimes contentious political process in developing countries may not allow for a complex analysis. However, RDM is designed to facilitate collaboration among stakeholders