Calibrated methodology for assessing climate change adaptation costs for urban drainage systems

Abstract

Changes in precipitation patterns associated with climate change may pose significant challenges for storm water management systems across the U.S. In particular, adapting these systems to more intense rainfall events will require significant investment, though no method currently exists for estimating the costs of these investments on a national scale. To support assessment of these costs at the national level, this paper presents a reduced-form approach for estimating changes in normalized flood depth (the volume of node flooding normalized by the area of the catchment) and the associated costs of flood prevention. This reduced form approach is calibrated to results generated by the U.S. Environmental Protection Agency's Storm Water Management Model (SWMM) for city-wide or neighborhood-level catchments in seven cities across the U.S. Estimates derived from this approach represent a reasonable approximation of storm water management adaptation costs and exhibit no systematic bias relative to results derived from SWMM.

Keywords:

• climate change
• flood forecasting
• infrastructure

Acknowledgements

We are grateful to Paul Kirshen for helping lay the analytic groundwork for this paper and for providing insightful and thoughtful comments. We are also grateful to Jeremy Martinich, Jim Neumann, Brent Boehlert, and Matt Konopka for their helpful input, and also thank Miriam Fuchs, Benjamin Silton, and Nick Tyack for able research assistance.

Notes

^1. These cities were chosen in large part because fully populated SWMM models were readily available for each city.

^2. See IPCC (2000) for detailed information on the IPCC emissions scenarios.

^3. IGSM only provides changes in monthly precipitation. This approach assumes the number of daily storms remain the same but the daily storm intensities change uniformly for every storm at the monthly ratio provided by the IGSM.

^4. The runoff coefficient is a commonly used metric representing the portion of rainfall that becomes runoff in a given area (rather than infiltrating into the ground).

^5. This runoff coefficient equation is consistent with guidance published by the New York Department of Environmental Conservation (see Center for Watershed Protection, 2010) and has also been used by the U.S. EPA (see ENSR International, 2005). This equation is also a reasonable approximation of the nonlinear curve linking imperviousness to the runoff coefficient in Maidment (1993).

^6. Because micro-topography and other factors influence whether increased runoff will lead to the failure of a local urban drainage system (see Aronica et al., 2005), not all runoff will necessarily contribute to the exceedence of urban drainage network capacity. Several analytic options were explored in which only a fraction of runoff contributes to capacity exceedence, but the reduced form approach described here performs best relative to SWMM when all runoff is assumed to affect capacity exceedence.

^7. The Louisville, Kansas City, and Fort Collins SWMM models were provided by the U.S. EPA; the Aurora SWMM model was provided by the University of Colorado; the Haverhill and Lewiston models were provided by each city; and the Miami SWMM model was provided by Wayne Huber of Oregon State University.

^8. The signs of the reduced form and SWMM results are always consistent because both are based on the same projected changes in rainfall for a given frequency duration event.

^9. Other model specifications that included regional dummy variables and dummy variables for individual years were also considered. These were excluded from the analysis due to collinearity with the CSO dummy variable, which has a stronger explanatory basis.

^10. This value is based on contingency values that U.S. EPA (1999) cites from Wiegand et al. (1986) and Brown and Schueler (1997).

^11. For design life information, see Olson et al. (2010).

^12. The cost of land not already owned by a municipality is not included in these estimates.

Additional information

Funding

This work was supported by the U.S. Environmental Protection Agency's Climate Change Division under contract number EP-D-09-054.