Over the Winter and Spring, I collaborated with WestWater Research and ESA Sitka on a tool to visualize future municipal water demands to 2050 across the Colorado Front Range. As local water supplies are already stressed, population growth in 13 counties may exacerbate the issue and reduce the ability for water service providers (WSP) to meet the demands of new customers with new sources of supply. In the face of water scarcity, a variety of strategies and practices can be taken by WSPs to accommodate new growth. One current practice is referred to as “buy and dry”, where municipalities acquire water rights from agricultural producers to guarantee water for other uses. This is a controversial issue for communities around the West, and the future magnitude and spatial location of this practice are uncertain. To help address the lack of information about the issue, we developed a scenario-based tool (Fig. 1) to evaluate the effect of three broad strategies on the overall municipal WSP water balance. The three strategies targeted: (1) population growth, (2) housing density, and (3) per-person water use rates. Using a logical set of rules and rates of change, we applied the tool to 70 different WSPs across the Front Range to spatially-allocate future development pressure.
Figure 1: Scenario-based tool to estimate the water impacts of growth on the Colorado Front Range to 2050.
To project land use development patterns into the future, we created an algorithm called the Hungry Algorithm (Eq. 1). Land conversion occurs at the highest development intensity (DI, Fig. 2), first. DI can be thought of as the pressure or likelihood of certain open lands to flip to developed. It was created by passing a moving-window, kernel filter across the currently developed lands in all 13 counties. To approximate the likely size of growth in the future, the kernel was the average patch size of any and all developed lands (i.e. roads, residential, commercial, etc.) as classified from remote sensing. In its selection routine, the algorithm first prioritizes the development for in-fill as those areas have the highest DI. Once opportunities for in-fill were gone, the algorithm then prioritizes growth proximal to commercial zones and roads between towns and cities (Fig. 2).
Equation 1: Formulation of the Hungry Algorithm. DI is development intensity, and the three Scenarios are (1) population growth, (2) housing density, and (3) per-person water use rates.
This ruleset is rudimentary, yet logical, as it constrains growth in and around previously developed sites and corridors. With those rules in place, we used population growth estimates from the Colorado State Demographer to distribute spatially-explicit demand for land development out to 2050. Furthermore, we included ranges in both population density, and per-person water usage. The three scenarios (i) are expressed at the scale of the individual water service districts (j). If lands are no longer available for development as the stock is exhausted, then the algorithm stops and demand in that district remains unsatisfied. Finally, with locations of future housing development established, we calculated the potential water supply created by converting existing irrigated lands to developed housing—the “buy and dry” practice previously mentioned. In the graphs of the tool (Fig. 1), the retired irrigation demand creates the “additional” water supply, which is then considered “new” water supply available for municipal use.
Figure 2: Spatially-explicit development pressure across the Colorado Front Range. Inset map shows that development potential (or intensity) includes land outside both existing development and water service boundaries.
The Hungry Algorithm is flexible. We have the capability to expand to additional spatial opportunities and constraints based on the needs of individual clients. We can add any number of measured or modeled spatially-explicit phenomena to the algorithm’s formulation as additional lines of code (i.e. subject to (s.t.), minimization or maximization functions, Eq. 1). For example, we considered add-ons such as alternate patterns of growth (e.g. ex-urban, ranchettes, essential habitat conservation), changes in water service district boundaries, drought indices, stressors to the water balance, access to groundwater resources, soil and slope characteristics, improvements in water efficiencies from development and agricultural practices, and other factors that will influence the future scenarios of water resource management across the Front Range. We have received positive responses from an existing, non-profit client who’s pursuing additional financial support from Foundations. As this was a proof-of-concept, our next step will be to contact other, new clients about what they would like to see in the tool, and how they would like to be able to use it. We look forward to hearing about the challenges our clients face in this space, and to brainstorm how our tool could help to address those challenges.