UC Santa Barbara

Stormwater creates flood hazards and induces water quality deterioration in urban regions. The cumulative impacts of flood hazards posed by urban stormwater and its frequently degraded water quality are environmental concerns worldwide. This work introduces a nonlinear programming (NLP) method for the optimal selection and sizing of stormwater control measures (SCMs) in urban regions. The NLP method meets water-quantity and chemical balances, achieves cost effectiveness of stormwater control expenditures, considers the capacity of SCMs to capture and retain stormwater, and accounts for the pollutant removal efficiency of SCMs. The NLP method produces a selection of SCMs that meets stormwater management criteria effectively and compatibly with green engineering principles. Applications of this paper's methodology provides examples of SCM implementation in the City of Los Angeles, California.