Extreme Floods and their Relationship with Tropical Cyclones in Puerto Rico

AMS Hurricanes San Juan, PR 2016

José Javier Hernández Ayala, PhD

Department of Geography

University of Florida

Objectives

• TCs bring significant rainfall to PR, yet little is known about their overall relationship with extreme floods.

• Understand the role that TCs play in extreme floods of the island.

• Identify areas were TCs have stronger or weaker influences on the extreme

flood distribution.

Literature• Puerto Rico has a high frequency of extreme unit discharge

flood peaks relative to other locations in the United States (O’Connor and Costa, 2004).

• Many record peaks in Puerto Rico were associated with tropical cyclones like Hurricane Hortense of (1996) and Hurricane Georges of (1998) (Larsen and Santiago-Roman, 2001).

• Tropical cyclones that were at least 230 km from the island’s coast and embedded in moisture environments of 44.5 mm or more produced mean rainfall values of 50 mm or more(Hernandez and Matyas, 2015).

• Rainfall associated with TCs tends to be concentrated in the southeast region of the island and the south is the most dependent region to TC rainfall for August and September (Hernandez and Matyas, 2016 in review).

Puerto Rico

Data

• Tracks of TCs from (IBTrACS) that were at least tropical depressions and spent 12 hours or more within a 500 km radius around the island.

• Daily mean discharge data for twelve stations with complete data (14,975 observations) for the time period of the 1970-2010 for the island of Puerto Rico.

Discharge Stations and TC Tracks

Flood Definition• No practical physical threshold that could be applied to all the twelve stations in the

different basins.

• This study defines a flood as a discharge value in the 99th percentile of the entire data distribution.

• A flood peak is associated to a TC if the center of the storm is within 500 km of the island’s coast during a time frame of two days before and seven days after its closest approach (Villarini and Smith, 2013).

• Declustering was done to satisfy statistical requirement, if discharge values went below threshold for more than three days the next peak is a separate flood event.

Mean Discharge Series

Extreme Values Analysis Point Process Approach

• Combines classical models to extremes like the annual maximum series (AMS) and

partial duration series (PDS).

• Formulated in terms of the Generalized Extreme Value (GEV) distribution parameters

Location (Central Tendency), Scale (Variance) and Shape (Shape).

• Those GEV parameters tell us about the behavior of the extreme values in the tails

of the distribution.

• In this study the Point Process model was fitted to the entire discharge series (TCs)

and to a series with TCs removed.

• Comparisons between the GEV parameters of both series were made in order to

examine if removing TCs affects the parameter estimates (distribution) of extremes.

Asymmetry of projected increases in extreme

temperature distributions (Kodra and Ganguly, 2014)

Percentage of Floods Associated with TCs

EVA Point Process Diagnostic Plots East

West

GEV Location (Central Tendency)

! Hydro_Stations_Pro_12_Manu_1_Results

Location

4.85 - 15.71

15.72 - 33.10

33.11 - 94.45

94.46 - 175.72

175.73 - 365.85

Parameter % Change

-6.790 - -9.710

-9.711 - -18.520

-18.521 - -25.120

-25.121 - -55.160

-55.161 - -72.610

GEV Scale (Variance)

! Hydro_Stations_Pro_12_Manu_1_Results

Scale

3.05 - 23.46

23.47 - 60.24

60.25 - 141.12

141.13 - 233.06

233.07 - 542.15

Parameter % Change

-13.390

-13.391 - -24.880

-24.881 - -39.310

-39.311 - -62.450

-62.451 - -89.030

GEV Shape (Skewness)

! Hydro_Stations_Pro_12_Manu_1_Results

Shape

0.09 - 0.14

0.15 - 0.40

0.41 - 0.63

0.64 - 0.93

0.94 - 1.58

Parameter % Change

70.90

70.89 - 0.00

-0.01 - -47.05

-47.06 - -67.74

-67.75 - -88.43

Top Flood Producing TCs

TCs Year Month/Days Cat Floods Max Precip Mean Precip TC Distance Moisture

Klaus 1984 11/06-11/08 TS 34 179.50 72.01 4.43 52.85

David 1979 08/29-08/31 H5 30 382.60 237.56 125.00 46.52

Jeanne* 2004 09/15-09/17 TS 29 370.80 190.35 0.00 51.39

Isabel 1985 10/06-10/08 TD 28 690.10 186.72 221.20 48.07

Lenny 1999 11/17-11/19 H3 28 235.50 99.37 123.80 53.74

Georges* 1998 09/21-09/23 H3 24 577.80 271.43 0.00 49.56

Ike 2008 09/25-09/27 H3 23 111.30 28.67 376.10 48.10

Hortense* 1996 09/09-09/11 H1 22 552.20 209.74 0.00 49.72

Hugo* 1989 09/17-09/19 H4 21 285.80 84.14 0.00 52.88

Eloise 1975 09/15-09/17 TS 18 591.80 279.15 68.00 43.58

Frederic* 1979 08/30-09/01 TS 17 360.20 106.28 0.00 49.48

Chris 1988 08/24-08/26 TD 15 304.50 158.91 31.70 43.65

Olga* 2007 12/10-12/12 TS 14 209.80 99.89 0.00 40.00

Debby 1982 09/13-09/14 TD 13 212.10 94.86 50.00 47.19

Debby 2000 08/22-08/24 H1 12 235.00 85.96 287.41 52.85

Means TS 21.87 353.27 147.00 85.84 48.64

Conclusion

• Percent changes between the GEV parameters that include location (mean), scale (variance) and shape (skewness) between the TC and Non-TC data exhibited a decrease at the majority of stations.

• Stations in the eastern interior and the northcentral region showed the largest decrease in all parameters when TCs were removed from the series.

• The effect of TCs on the upper tails of the flood distribution seem to be minimum as we move to the west.

• High rainfall values in the southeast explain the roles that TCs play in the flood hydrology of the eastern interior.

Questions?