1. 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

2. 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.

3. 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).

4. Puerto Rico

5. 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.

6. Discharge Stations and TC Tracks

7. 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.

8. Mean Discharge Series

9. 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)

10. Percentage of Floods Associated with TCs

11. EVA Point Process Diagnostic Plots
EastWest

12. GEV Location (Central Tendency)
! Hydro_Stations_Pro_12_Ma
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

13. GEV Scale (Variance)
! Hydro_Stations_Pro_12_Manu_
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

14. GEV Shape (Skewness)
! Hydro_Stations_Pro_12_Manu
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

15. 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

16. 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.

17. Questions?