Background and Motivation
- Total Rescues: 74,463
- Rip Related: 40,810
- Surf Related: 8,449
- Total Drownings: 109
- Rip Related: 53
- Surf Related: 11
USLA 2007 Life Saving Statistics
Rip currents are the number 1 cause for drownings and rescues at the beach in the United States (USLA). Often referred to incorrectly as "rip tides", they have increasingly been of interest to the beach going public due to their hazardous nature. Along with this increased interest, scientific study of these nearshore events has also peaked in recent years, as scientists attempt to better understand the dynamics of rip currents and their relationship with other nearshore physical processes.
As our understanding of rip currents has increased, a desire to predict or forecast times in which rip activity is especially hazardous has developed. By considering various marine and weather conditions nearshore, several rip forecasting schemes have been created to predict the likelihood of hazardous rip conditions (Engle et al., 2002; Lascody, 1998; etc). The National Weather Service now provides rip current warnings on days when they are deemed necessary in many coastal areas.
Observational Studies of Rip Currents
There have been numerous observational studies regarding rip currents since the mid 1990's. These studies have been focused on the variability of rip current velocities temporally and spatially (Aagaard et al., 1997; etc) as well as on the morphodynamics of rip current systems (Brander, 1999; Brander and Short, 2000). In 2005 an observational study RIPEX (MacMahan et al.) provided the most complete field analysis of a rip current system to date, utilizing 15 instruments to measure velocity and bathymetry data over the course of 44 days off the California coast. Through these observational studies a strong relationship was found between the nearshore bathymetry, wave conditions, the tide and rip current velocities. These relationships provide the main background needed to formulate any sort of rip current predictive model. However, all of the rip current observational studies performed so far have focused on a singular rip current system (100-400m of coast) and most studies, with the exception of RIPEX, lasted only a few days. Thus, there remains an interest in a long term observational study (~3 months) that provides information regarding the alongshore variability (~ 5 miles of coast) of rip currents and surf zone dynamics.
Rip Current Forecasts and Prediction
The first rip current forecast model LURCS (LUshine Rip Current Scale) was developed in 1991 (Lushine) and based its prediction on the local wind speed and direction, wave height and the tide. Although the simple model showed promise when back tested on the south Florida coast, it began to break down when used elsewhere. In 1997 (Lascody) a variation to this original model was developed, ECFL (East Central Florida) LURCS, for use specifically on the east central Florida coast. The modified scale added swell period (> 7 seconds) and swell height to the original version and made some slight changes to the weight of the various factors. It was also back tested and found to be an improvement over the original. In 2002 the rip current forecast model was again modified (Engle et al.). In this iteration Engle et al. found that wind speed and direction were nearly a non factor and thus were removed completely. Instead the only factors used were wave period, wave height, wave direction and the tide. This model was back tested, again in Florida, and showed some improvement over the last iteration yet still showed weaknesses, especially when forecasting days of relatively medium rip activity.
The current state of rip forecasting uses some variation of the most recent (Engle et al.) model and varies by location as each NWS forecast center is responsible for creating its own model. The main flaw of any rip forecast model as they presently exist is the lack of any consideration of the nearshore bathymetry data. This is mostly due to the inherent difficulties in obtaining such data, however as recent observational studies have found (Brander, 1999; Brander and Short, 2000; MacMahan et al., 2005; etc.), nearshore bathymetry data is essential in understanding any rip current generation. Thus, an important part of this study is to find a simple and accurate way to measure the nearshore bathymetry and to analyze how spatial and temporal variability in the bathymetry influences rip likelihood and strength.