Development of probability distributions for urban hydrologic model parameters and a Monte Carlo analysis of model sensitivity

Abstract

This paper proposes an approach to estimating the uncertainty related to EPA Storm Water Management Model model parameters, percentage routed (PR) and saturated hydraulic conductivity (Ksat), which are used to calculate stormwater runoff volumes. The methodology proposed in this paper addresses uncertainty through the development of probability distributions for urban hydrologic parameters through extensive calibration to observed flow data in the Philadelphia collection system. The established probability distributions are then applied to the Philadelphia Southeast district model through a Monte Carlo approach to estimate the uncertainty in prediction of combined sewer overflow volumes as related to hydrologic model parameter estimation. Understanding urban hydrology is critical to defining urban water resource problems. A variety of land use types within Philadelphia coupled with a history of cut and fill have resulted in a patchwork of urban fill and native soils. The complexity of urban hydrology can make model parameter estimation and defining model uncertainty a difficult task. The development of probability distributions for hydrologic parameters applied through Monte Carlo simulations provided a significant improvement in estimating model uncertainty over traditional model sensitivity analysis. Copyright © 2013 John Wiley & Sons, Ltd.

Estimating dominant runoff modes across the conterminous United States

“…It is likely that many of the contrary findings may be related to alterations in soil properties from anthropogenic land use practices that cause compaction, increased bulk density, and concomitant declines in K SAT . Indeed, numerous studies conducted in diverse geologic and geographic settings have demonstrated reductions in K SAT ranging from 1.8 to 100 times due to agricultural and urban land use practices (Chen, Day, Wick, & McGuire, 2014;Knighton, White, Lennon, & Rajan, 2014;Singleton & Addison, 1999;Wang, McKeague, & Switzer-Howse, 1985). Such declines in K SAT can have profound implications for runoff generating properties and by extension, flooding, soil erosion, and water quality.…”

Accelerating Bayesian inference in hydrological modeling with a mechanistic emulator

“…The discharge was measured during a single rain event, which occurred on May 28, 2013 and lasted for approximately 15 hours. Using a single rain event for SWMM calibration is not unusual (Knighton et al, 2014). For us, the main reason behind using a single event was to be able to calculate the exact parameter inference result with the full SWMM model and assess the accuracy of the approximate procedure.…”

Predictors of urban variable source area: a cross‐sectional analysis of urbanized catchments in the United States

“…Researchers have approached quantifying EIA from TIA in different ways, including using empirical conversion factors, field surveys and sensitivity analyzes, but there is general agreement that EIA, rather than TIA, more closely represents the physical process of hydrological impact on flow regimes (Alley and Veenhuis, 1983;Booth and Jackson, 1997;Brabec, 2002;Dinicola, 1990; Knighton et al, 2013;Palla and Gnecco, 2015;Shuster et al, 2005). Hydraulic connectivity has not only been shown to be one of the most sensitive parameters in urban hydrological modeling, resulting in modeled peak discharge variations of up to 265% in some cases (Lee and Heaney, 2003).…”

“…Hydraulic connectivity has not only been shown to be one of the most sensitive parameters in urban hydrological modeling, resulting in modeled peak discharge variations of up to 265% in some cases (Lee and Heaney, 2003). It is also among the parameters estimated with the most uncertainty in urban hydrological modeling (Knighton et al, 2013;Moglen and Kim, 2007). Others have suggested that overemphasis on connectivity of impervious area (EIA vs TIA) detracts from important changes to soil porosity, vegetation, imported water and other water infrastructure that urbanization has on hydrologic response and catchment water balance (Brandes et al, 2005;Hamel et al, 2013;Meierdiercks et al, 2010a).…”