FloodRISE Mapping Methods for the Tijuana River Valley and Goat Canyon Watershed

The FloodRISE hazard maps produced for the Tijuana River Valley and the Goat Canyon Watershed resulted from three distinct tasks: flood frequency analysis (FFA), hydrologic and hydraulic modeling, and post-processing of model output. Generally speaking, FFA estimates the recurrence interval of rare flooding events, while hydrologic and hydraulic modeling predicts the hazards associated with simulated floods (depths, velocities, extents, etc.). Several of the produced maps required post-processing methods to combine the results of multiple simulations into a single mapping product. This document outlines the FFA, hydraulic modeling, and post-processing methods that were applied by FloodRISE to produce the Tijuana River Valley and Los Laureles flood hazard maps. The sources of the qualitative legend descriptions are also provided. 1. Flood Frequency Analysis FFA is complicated in the coastal zone due to the multiple causes or “drivers” of flooding. Figure 1 shows the broader geographic context of the Tijuana River Estuary and demonstrates the susceptibility of estuarine environments to multiple flood drivers. In this study, we mapped flooding caused by extreme ocean levels, stream flow from the Tijuana (TJ) River, and precipitation over Goat Canyon and Smuggler’s gulch watersheds. The presence of multiple flood drivers often warrants a multivariate approach for FFA [13]. Under this approach, multivariate extreme value analysis (EVA) is used to estimate the probability of scenarios where multiple extremes occur simultaneously. However, we did not conduct multivariate EVA in this study because of the low correlation between flood drivers and the lack of emergent flood hazards caused by the joint occurrence of extremes. Table 1 presents the Pearson’s correlation coefficient matrix between the flood drivers considered herein. The relatively low correlation is somewhat surprising but understandable. Extended periods of above average rainfall in the upper TJ River Watershed cause large stream flow events, whereas relatively short-lived coastal storm systems can elevate ocean water levels and lead to intense precipitation. The low correlation between flood drivers demonstrates that the simultaneous occurrence of extreme events would be especially rare. Perhaps more importantly, hydraulic model sensitivity analysis revealed that predicted flood depths, extents, and velocities are insensitive to the joint-occurrence of extremes in this system. For example, flood depths predicted by the hydraulic model are not sensitive to the downstream ocean level during large TJ River floods. The lack of “sufficient” correlation between drivers and the hydraulic model’s insensitivity to the joint occurrence of extremes ∗Department of Civil and Environmental Engineering, University of California, Irvine Email addresses: [email protected] (Adam Luke), [email protected] (Brett Sanders) allows us to consider the flood drivers independently and use univariate EVA for frequency analysis. 1.1. Tijuana River Flood Frequency Analysis FFA of TJ River flows was based on a Pearson Type III (PIII) distribution fitted to the historic record of log-transformed annual maximum discharges. This approach is consistent with the recommended FFA methodology in the US [17]. The data record originated from TJ River flow measurements at the US/ME border reported by the International Boundary and Water Commission. To infer the parameters of the PIII distribution, we used the Bayesian parameter estimation technique described by Luke et al. [7] where an informative prior was used to incorporate regional information about the skewness of the PIII distribution. For the TJ River, parameter estimation was complicated by signs of nonstationarity in the historic record, or time variant statistical properties of the annual maximum discharge data. Figure 2A shows the full data record at the US/ME border. At the time of this study, data was not available after 2006. The black line in Figure 2A denotes the year when the TJ River channelization was completed, which appeared to alter the mean and standard deviation of the flood peaks. Indeed, the pre-channelization distribution is different from the postchannelization distribution at the 0.05 significance level according to the two-sample Kolmogorov-Smirnov test [9]. Due to the apparent change in the distribution of flood peaks following channelization, we did not use data prior to 1979 for estimation of the PIII parameters. The choice to omit data prior to channelization creates a relatively small sample size for parameter estimation and leads to large variance in the estimated return periods (Figure 2B). Assuming stationarity following the channelization, the return periods in Figure 2B are simply the inverse of the annual exceedance probabilities associated with the return levels on the y axis. If the pre-channelization flood peaks are included in the frequency analysis, we risk bias in the parameter estimates and resulting return periods. Notice also that the empirical frequency curve shown in In support of the National Science Foundation FloodRISE project July 27, 2017 United States Mexico Tijuana, MX San Diego, CA Oceanside Rosarito B) Tijuana River Watershed