However, the mathematics used with these two concepts are very different. Determination of the recurrence interval is straight-forward when looking at past events.
Where there is no associated magnitude or a limited magnitude such as pumice-producing volcanic eruptions the recurrence interval T is number of years in the record N divided by the the number of events n. An example of an activity using these calculations is Determining Earthquake Probability and Recurrence.
When there is a magnitude associated with the data such as discharge with a flood or seismic moment with an earthquake the recurrence interval T is. Student activities using these calculations are Two streams, two stories Show example of recurrence interval with magnitude ranking Hide The Los Angeles River at Sepulveda Blvd had the following peak discharges between and data from the U. River at Sepulveda Blvd. The data can be reorganized, and a recurrence interval computed for each discharge.
In this case, n is 10, because we are using 10 years of data. Damage on the L. River from flooding in Note that these are not the true recurrence values for the L. River since only a selection of available data have been used. Once we start looking to the future, we are looking at forecasting which is governed by the mathematics of probability.
Rainfall recurrence intervals are based on both the magnitude and the duration of a rainfall event, whereas streamflow recurrence intervals are based solely on the magnitude of the annual peak flow. Ten or more years of data are required to perform a frequency analysis for the determination of recurrence intervals. More confidence can be placed in the results of a frequency analysis based on, for example, 30 years of record than on an analysis based on 10 years of record.
The rainfall recurrence intervals presented in this fact sheet were developed almost 40 years ago Hershfield, The USGS is currently collecting data and developing software to re-evaluate the rainfall recurrence intervals for Mecklenburg County by using more recent, locally collected data. These recurrence intervals may become better defined as more data become available for analysis. Recurrence intervals for the annual peak streamflow at a given location change if there are significant changes in the flow patterns at that location, possibly caused by an impoundment or diversion of flow.
Willapa Hills, December Two powerful storms brought extremely heavy rain and high winds to the Pacific Northwest during early December Orographic lifting caused copious amounts of rain to fall across western Oregon and Washington. Several rivers in northwestern Oregon and western Washington surpassed major flood stages. Rainfall intensities at and hours were particularly intense.
Figure 11 shows the maximum hour rainfall amounts during a five day period. Tropical storm Hermine moved ashore just south of Brownsville, Texas on September 6, Although the storm packed a light punch when it moved ashore, its moist, tropical air mass unleashed heavy rainfall across Texas.
The storm and its moist air mass slowly moved over San Antonio and into central Texas late on September 7th, then into Oklahoma on the 8 th and 9 th. Figure 12 shows a hour snapshot of rainfall and the associated ARI. Rainfall across central Texas reached 13 inches, which equated to an ARI of about years. September Northern Georgia Flood. The combination of low-level moist flow from the Atlantic Ocean and moist southwest flow above that from the Gulf of Mexico, coupled with terrain enhancement, caused slow moving thunderstorms to occur over northern Georgia for several days NOAA, The extreme rainfall caused catastrophic flooding across northern Georgia on September , The most intense rain occurred during a hour period from September when 10 to 20 inches of rain fell.
Figure 13 shows the distribution and magnitude of rainfall and the corresponding ARI for the hour period ending at UTC on September 21, Technical Paper 40 on provides ARI up to —years, but we extrapolated ARIs to years for purposes of gauging the severity of the rainfall. As noted earlier, Belanger calculated hour rainfall associated with this storm exceeded the 10,year ARI in places.
While similar descriptions of heavy rainstorms have not been as common, they provide an equally useful perspective on extreme rainfall events. Hampered by lack of known ARIs, rain gauge data and computational computing power, ARIs of specific events have not been routinely computed. Given the recent availability of real-time rainfall data and pre-existing rainfall frequency statistics, however, the natural evolution has been to create maps depicting the ARI of rainfall in real-time.
The information conveyed via an ARI map has been illustrated in post-storm studies, but real-time ARI maps of rainfall provide a new and powerful tool that benefits a number of disciplines. Having the capability of seeing ARIs of rainfall in near real-time is an important part of determining how to respond, and puts the rainfall into a perspective ARI, probability or percent chance that is understood by most people. Real-time ARI maps can help city and flood control district planners and engineers determine the intensity of downpours with which storm water drainage systems and other structures are designed to cope.
In addition, real-time ARI maps could provide forecasters with a critical alternative to stream gauge discharge frequency estimated during extreme events. This is especially significant because flooding can often wipe out or cause stream gauges to malfunction. In fact, the flooding in Georgia during September was so severe that 20 percent of the stream gauges failed NOAA Future Work Efforts are underway to expand ARI mapping to Puerto Rico and Hawaii, but the immediate focus is on providing additional durations and various area sizes.
However, in order to ascertain the ARI of areal rainfall amounts, it is necessary to know the actual areal rainfall depth and frequencies. The areal rainfall depth can be easily computed, but areal rainfall frequencies are more difficult to come by.
Thank you to Geoff Bonnin, Chief Hydrologic Science and Modeling Branch at National Weather Service, for the decade of inspiring and rewarding work at the Hydrometeorological Design Center where I gained my unique wealth of rainfall frequency knowledge.
The real-time ARI capability could not have evolved as it has without the confidence and support of Weather Decision Technologies, Inc. Thank you to Michael Eilts of WDT for the years of trust, collaborative support and gauge-adjusted radar-estimated precipitation data for testing, development and now an operational product. Belanger, Laura Griffith Bonnin, G. Goodridge, J. California Department of Water Resources, Sacramento, Hershfield, D.
Weather Bureau, Washington D. Hosking, J. Wallis Washington D. Hydrometeorological Design Studies Center Miller, J. National Institute of Water and Atmospheric Research Natural Hazards Centre
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