Frequency Analysis for Precipitation Events and Dry Durations of Virginia

Abstract

Stormwater Control Measures (SCMs) are widely used to control and treat stormwater runoff pollution. The first step in SCM design is to evaluate the precipitation patterns at a site. SCMs are normally designed using a storm with a specific return period. A robust design process that uses frequency distribution of precipitation and monitoring of performance could improve our understanding of the behavior and limitations of a particular design. This is not the current norm in the design of SCMs. In this research, frequency analyses (FA) of precipitation events was conducted using hourly precipitation data from 1948 to 2010 for eight sites representing the four major physiographic regions of Virginia. The available data were treated using an inverse distance method to eliminate missing gaps before processing into events determined by minimum inter-event times. FA was at each site to develop frequency plots of precipitation and dry duration. FA of the eight locations indicates a range of rainfall depths from 22.9 mm in Bristol to 35.6 mm in Montebello, compared to the nominal “water quality storm” of 25.4 mm (i.e., 1 in.). Similarly, for dry duration, for a 10 % exceedance probability, the range is from 16.8 days in Richmond and Norfolk to 19.5 days in Montebello. Dry duration provides guidance for vegetation selection, which is important for some SCMs. The degree of variability in both parameters argues for consideration of site-specific information in design. FA was also used to provide guidance to improve monitoring programs. Monte Carlo simulations demonstrated that performance monitoring programs applied in different regions would likely encounter more than 30 % of precipitation events less than 6.35 mm, and 10 % over 25.4 mm under various sampling regimes. The percentages of precipitation events encountered in the Coastal Plain and Piedmont regions are not impacted by sampling regimes, however the Blue Ridge Mountains and Valley and Ridge regions are likely impacted. Anticipating event occurrences improves the chances of implementing a successful monitoring program. The use of these results could enhance the performance of SCMs with consideration of local conditions for both monitoring SCMs and their design basis.