Abstract
This paper presents a new non-parametric, synthetic rainfall generator for use in hourly water resource simulations. Historic continuous precipitation time series are discretized into sequences of dry and wet events separated by an inter-event dry period at least equal to four hours. A first-order Markov Chain model is then used to generate synthetic sequences of alternating wet and dry events. Sequential events in the synthetic series are selected based on couplings of historic wet and dry events, using nearest neighbor and moving window methods. The new generator is used to generate synthetic sequences of rainfall for New York (NY), Syracuse (NY), and Miami (FL) using over 50 years of observations. Monthly precipitation differences (e.g., seasonality) are well represented in the synthetic series generated for all three cities. The synthetic New York results are also shown to reproduce realistic event sequences proved by a deep event-based analysis.
Highlights
Management of runoff is one of the most important water quality goals in urbanized watersheds.Accurate estimation of runoff from urban catchments requires hourly or sub-hourly precipitation time series
While historical precipitation time series at this temporal resolution can be used to drive any of the hydrologic and hydraulic models (e.g., USEPA SWMM, SUSTAIN, WinSLAMM, etc.) commonly used for urban simulations, their runoff predictions are directly determined by the particular sequence of precipitation in the historical record that is input
Once the daily state has been determined, researchers use a variety of methods, including bootstrapping or distributed sampling, to generate precipitation amounts for each wet day derived from historic observations
Summary
Management of runoff is one of the most important water quality goals in urbanized watersheds. Accurate estimation of runoff from urban catchments requires hourly or sub-hourly precipitation time series. While historical precipitation time series at this temporal resolution can be used to drive any of the hydrologic and hydraulic models (e.g., USEPA SWMM, SUSTAIN, WinSLAMM, etc.) commonly used for urban simulations, their runoff predictions are directly determined by the particular sequence of precipitation in the historical record that is input. Without uploading additional precipitation time series one by one, none of the existing urban hydrologic models can be used to efficiently investigate the role that alternative patterns of precipitation could have on runoff predictions. Risk-based hydrologic investigations, for example those focusing on agricultural water use, reservoir/watershed management, and climate change impact assessments, typically use synthetic precipitation series. The precipitation sequences used in these kinds of studies typically have a coarser temporal resolution (e.g., daily, monthly) due to the relatively long time scales under consideration in such investigations [1,2,3]
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