Implications and Context for Effects of Climate Change on Urban Water Demands in the Mountain-West

Posted by Jason Lillywhite

Carly Hansen with Oakridge National Laboratory developed a modeling framework that incorporates multiple drivers of change, uncertainty, and system complexities allowing the user be to address water availability, management, and planning challenges. There are various drivers of change in future residential water use, some of which have clear compounding effects. Strategic planning (e.g. where to focus time and financial resources) requires an understanding of the relative contribution of each of these drivers. Exploring water use through a probabilistic GoldSim model helps address uncertainties inherent with human behavior and multiple scenarios allow for exploration of different drivers of change.

Using GoldSim to Simulate Projects

Posted by Jason Lillywhite

The planning and management of programs and large projects is inherently difficult, not only due to their complexity, but also because something almost always goes wrong (Murphy’s Law!). By combining the flexibility of a general-purpose and highly-graphical probabilistic simulation framework with specialized features to support financial modeling and scenario analysis, GoldSim is ideally suited as a high-level project planning tool suitable during the feasibility assessment and conceptual design phases, while the exact scope of the program is still in flux and it is critical to simulate the range of possible outcomes.

How Big Will Melbourne Australia be in 2035?

Posted by Jason Lillywhite

Many of you may be familiar with GoldSim models that incorporate simple population growth (e.g., in order to compute water demands in the future), but may not have seen complex demographic models that fully incorporate all the details required to accurately simulate the population growth for a large urban area. One of our recently showcased models on our website does just that. It probabilistically simulates the population of the City of Melbourne, Australia for the next 20 years. I found this model to be a highly effective population simulator, and believe the approach could potentially be applied to population forecasts for a variety of models.