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Open AccessArticle

Development of a Non-Parametric Stationary Synthetic Rainfall Generator for Use in Hourly Water Resource Simulations

1
Department of Agricultural and Biological Engineering, University of Florida, Gainesville, FL 32603, USA
2
National Snow and Ice Data Center (NSIDC), University of Colorado, Boulder, CO 80309, USA
3
Department of Civil, Architectural and Environmental Engineering, Drexel University, Philadelphia, PA 19104, USA
4
Department of Earth and Environmental Engineering, Columbia University, New York, NY 10027, USA
*
Author to whom correspondence should be addressed.
Water 2019, 11(8), 1728; https://doi.org/10.3390/w11081728
Received: 6 June 2019 / Revised: 26 July 2019 / Accepted: 26 July 2019 / Published: 20 August 2019
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. View Full-Text
Keywords: precipitation; stochastic process; K nearest neighbor; bootstrapping precipitation; stochastic process; K nearest neighbor; bootstrapping
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MDPI and ACS Style

Yu, Z.; Miller, S.; Montalto, F.; Lall, U. Development of a Non-Parametric Stationary Synthetic Rainfall Generator for Use in Hourly Water Resource Simulations. Water 2019, 11, 1728.

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