Development of a Stochastic Storm Generator Using High-Resolution Precipitation Records

Abstract

Abstract.Sophisticated field and watershed scale environmental models for runoff, erosion control, environmental, and global-change investigations require detailed continuous temporal and spatial inputs of precipitation to drive the hydrologic processes. For accurate estimates of these processes, the resolution of the input data must allow the representation of the variability of precipitation as it represents a major source of variability in the model outputs. Currently, the use of stochastic weather generators is widespread to generate continuous series of meteorological data at gauged and ungauged locations. These weather simulators are designed to replicate the statistical properties of real weather data at monthly or daily time resolutions. However, daily values of precipitation do not represent the variability of storm parameters within a day, which is assumed to significantly influence the predictions of environmental or agricultural models where processes are sensitive to sub-daily values. This research proposes a parsimonious stochastic storm generator based on 5-min time resolution and correlated non-normal Monte Carlo-based numerical simulation. The model considers correlated non-normal random rainstorm characteristics such as time between storms, duration, and amount of precipitation, as well as the storm intensity structure. The accuracy of the model was verified by comparing the generated rainfall with rainfall data from a randomly selected 5-min weather station in North Carolina. Current results have shown that the proposed storm generator can capture the essential statistical features of rainstorms as well as their patterns followed by their intensities, preserving the first four moments of monthly storm events, good annual extreme event correspondence, and the correlation structure within each storm. Finally, as the proposed model depends on statistical properties at a site, this may allow the use of the synthetic storms in ungauged locations provided relevant information from a regional analysis is available. Keywords: Monte Carlo, Stochastic storm generator, Storm distribution.