Hydrological Impact of Hurricane Irene and Tropical Storm Lee in Historical Context:

Is the Frequency and Magnitude of Extreme Hydrological Events Changing in Southern New York State?

Adao Matonse1, Allan Frei2, David Lounsbury1 Donald Pierson1, Mark Zion1, and Elliot Schneiderman1

1 New York City Environmental Protection2 The City University of New York – Hunter College

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Presentation Outline

Introduction

Study Area

Data and Methods

How Unique were Irene and Lee?

The Frequency of Extreme Events in the Study Region

The Magnitude of Extreme Events

Implications for Hydrology

Conclusions

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Tannersville, photo by Sean Mahoneyhttp://www.wnyc.org/blogs/wnyc-newsblog/2011/aug/31/focus-shifts-people-stranded-catskills-flood-waters-recede/

Main Street, Margaretvillehttp://www.buzzfeed.com/mjs538/frightening-hurricane-irene-destruction-photos

Windhamhttp://www.buzzfeed.com/mjs538/frightening-hurricane-irene-destruction-photos

New Windsorhttp://en.wikipedia.org/wiki/File:Forge_Hill_Road_bridge_washout_after_Hurricane_Irene,_New_Windsor,_NY.jpg

Irene in the Catskills

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Irene in the Catskills: Schoharie Creek, Prattsville

Photo Immediately after Irene, Sep 1, 2011, photo by Danyell Davis, NYCDEP

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Changes over Time in Frequency of Extreme Events

Warner Creek, Near Stony Clove, Catskills, May 2012

Headcut incised during storms of 2011 exposing glacial till that has probably not been exposed in 15-20K yrs.

Course sediment is being recruited from upstream, but channel bed now at a lower base level than prior to Irene.

These sorts of fluvial geomorphological changes are related to the frequency and magnitude of extreme events, as well as human impact on channel conditions. Human impacts up- or down-stream can also play a role.

Photos and geomorphological information from Danyell Davis, NYCDEP

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NYSERDA funded study :Hydrology, Vulnerability and Adaptation Implications

of Hurricane Irene and Tropical Storm Lee:

Case Study of the Mid-Hudson Valley and Greater Catskills Regions

1. Hydrological study (this talk)2. Impacts, Costs, and vulnerabilities3. Adaptation and Needs Assessment

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Collaborators

NYCDEP: Don Pierson, Elliot Schneiderman, Mark Zion, Soni Pradhanang, Don Kent, Rajith Mukundan, Nihar Samal, Yongtai Hwang, David Lounsbury, Danyell Davis, Terry Spies, Dom Thongs, Jim Porter

NYSERDA funded project on Hurricane Irene and Tropical Storm Lee: William Solecki, Simon Gruber, Robin Leichenko, Lesley Patrick, Susan Sofranko, Michael Brady

8Map by David Lounsbury - NYCDEP

Study Area and Station Locations

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Data hourly-radar-based gridded daily 4km precipitation (NRCC)

(DeGaetano and Wilks 2009) 12 precipitation gauge records (NCDC)

>= 30 yrs continuous records 9 stream gauge records (USGS)

>= 30 yrs continuous records, unregulated

Methods Parametric (standard recurrence interval analysis) non-parametric (define “extreme” as >=95th Percentile Event) Seasonal (warm season = 1 June through 31 October) Antecedent conditions (30 day precipitation prior to an extreme

event) Proximity in space and time of two extreme events

Data and Methods

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4-Day Precipitation Contours and Recurrence Intervals

Hurricane Irene (26-29 Aug., 2011) Tropical Storm Lee (5-8 Sep. 2011)

Maps by David Lounsbury, NYCDEP

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Precipitation-Difference Contours and Stations Irene minus Lee

Irene > LeeLee > Irene

Map by David Lounsbury, NYCDEP

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Irene and Lee in context of historical extreme precipitation events

The events of Fall 2011 were unprecedented in this region due to a combination of

the magnitude of precipitation during Irene and Lee

the proximity of these two events

Antecedent conditions and subsequent precipitation

The occurrence of two events of comparable magnitude to Irene and Lee within only 11 days of each other is highly unusual in this region

30-day total precipitation prior to Irene was large compared to other extreme events

If one considers all 60-day precipitation totals on record, the fall of 2011 stands out as an unprecedented event.

Arkville 1195 n/a 3 56 n/a n/a 146 4 4-Oct 77 4 2-OctDelhi 1577 17 9 74 3 2 185 1 8-Sep 96 1 2-OctDeposit 1004 20 3 49 4 3 116 2 4-Oct 67 1 30-SepEllenville 1253 n/a 4 68 n/a 15 159 3 4-Oct 77 2 2-OctLiberty 1203 11 10 63 9 3 148 2 8-Sep 73 2 2-OctMiddletown 1148 n/a 2 54 n/a 53 141 2 5-Oct 75 2 30-OctMohonk 2214 3 8 110 9 2 283 1 8-Sep 148 1 2-OctPort Jervis 2304 9 33 112 10 2 292 3 8-Sep 152 1 5-OctRosendale 993 1 22 48 6 1 133 1 8-Sep 67 1 30-SepSlide Mtn 1298 3 10 57 22 2 150 1 8-Sep 69 1 2-OctWalton 1161 11 2 64 16 4 129 2 8-Sep 69 2 2-OctWest Point 2174 5 15 111 2 1 286 2 7-Sep 131 1 3-Oct

Lee 30DA warm

season rank

Warm season 60-day precipitation

Total number on record

Rank of 2011

highest

End date of 2011 highest

Total number

on record

Rank of 2011

highest

End date of 2011 highestStation Name

4-day precipitation (4DP)30-day antecedent precipitation (30DA) prior to a 4DP extreme

Total number of warm

season 4DP events

Irene warm season rank

Lee warm season rank

Number of warm season 4DP extreme

events

Irene 30DA warm

season rank

Warm season 30-day precipitation

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Values are ranges across all stations.

Percentage of Seasons During WhichExtreme 4-day Precipitation EventsOccur Within 12 days of each other

(prior to Irene and Lee)

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Frequency of Extreme Events: Non-Parametric Analysis

Example from one station Ellenville 4-day Precipitation

Cold Season Warm Season

5 highest values

95 %tile value

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DecNovOctSepAugJulJunMayAprMarFebJan

25

20

15

10

5

0

Prec

ipitat

ion (c

m/da

y)

a)

DecNovOctSepAugJulJunMayAprMarFebJan

900800700600500400300200100

0

Strea

mflow

(cu.m

/s)

b)

Non-parametric data analysis Extreme Events(All Events >= 95 percentile)

Daily Precipitation

Daily Streamflow

Unimodal (peak in Fall)

Bimodal (peaks in Spring & Fall)

warmseason

warmseason

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Number of 95%tile values per yearExample from Ellenville 4-day Precipitation

Cold Season

max smooth value

Smooth (11-yr mean)

Warm Season

Changes over Time in Frequency of Extreme Events:

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Cold Season Warm Season

Smooth (11-yr mean)

max smooth value

1985, 2006

Changes over Time in Frequency of Extreme Events

Number of 95%tile 4-d precipitation values per year all stations

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Smooth (11-yr mean)

max smooth value

1985, 2006

Cold Season Warm Season

Changes over Time in Frequency of Extreme Events

Number of 95%tile daily streamflow values per year all stations

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Cold season Warm season

4-day Precipitation

1-day Streamflow

Number of 95%tile values per year: all stations

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600400

2000

WarmColdAnnual

600400200

0WarmColdAnnual WarmColdAnnual

2-year

Season

Strea

mflow

(cu.m

/s)

5-year 10-year

25-year 50-year 100-year

b)

20

15

10

5

WarmColdAnnual

20

15

10

5WarmColdAnnual WarmColdAnnual

2-Year

Season

Prec

ipitat

ion (c

m/da

y)

5-Year 10-Year

25-Year 50-Year 100-Year

a)

Frequency analysis

Daily Precipitation

Daily Streamflow

Warm > Cold(all events)

Warm < Cold(for low return interval only)

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Return Period 10050251052

1600

1400

1200

1000

800

600

400

200

0

Stre

amflo

w (c

u.m

/s)

All Hist.1940-19701950-19801960-19901970-20001980-2011

Data Used

Tremper Kill

Changes over Time in Magnitude of Flood Flows

Each color is a different overlapping 30-year period starting 10 years apart

One example using multiple 30-year periods annual peak flow

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NOAA Climate Extremes Index (CEI) (= arithmetic average of six indicators)http://www.ncdc.noaa.gov/extremes/cei/graph/ne/cei/03-05 (accessed Sep 9, 2013)

Other studies on extreme climatic events (EE)Showing increasing percentage CEI in recent years

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… and projecting an increasing intensity of EE (Kunkel et al 2013, GRL)

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Intergovernmental Panel on Climate ChangeSpecial Reports: SREX (2012)

Climate Extreme (extreme weather or climate event):

The occurrence of a value of a weather or climate variable above (or below) a threshold value near the upper (or lower) ends of the range of observed values of the variable.

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Intergovernmental Panel on Climate ChangeSpecial Reports: SREX (2012)

Projected decrease in recurrence intervals for daily precipitation equal to historical 20-year recurrence during the late 20th Century.

Different colors = different scenarios

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Monier and Gao, 2013

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Of changing frequency and magnitude of extreme events Bulletin 17B recommends 25-year record for flood

frequency analysis Based on assumption of stationarity of climate Results show this assumption is questionable due to:

Trends Hydrologic regime shifts (mean, variance) Need for a “new normal”

Implications for Hydrology

199819841970195619421928

6

5

4

3

2

1

Year

Prec

ipip

tatio

n (in

)

MAPE 28.4503MAD 0.6392MSD 0.8281

Accuracy Measures

ActualFits

Variable

Annual_Trend Analysis_ArkvilleLinear Trend Model

Yt = 1.308 + 0.0211*tShifts in mean Winter-flow

USGS-1350000, 1929-2010

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Not new in water resources planning and management Is usually considered when supported by a strong

scientific basis (Hirsch 2011)Basins urbanization lead to change in FF distributions Groundwater depletion affecting baseflow and low

flow regime And these are not related to climate change

Nonstationarity or “changing normal”

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Can be reflected by various changes such as in:Regional air temperature Water demand as a function of temperaturePatterns of rain and snowTiming of snowmeltFrequency and magnitude of hydrologic extreme

eventsLand use/cover

How does NYC address this issue ?

Nonstationarity

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Simulates water supply by routing water between reservoirs, while accounting for various rules and competitive goals

Supports water supply operations and planning needs

High flexibility, improved inflow forecast

NYC Operations Support Tool

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The events of Fall 2011 were unprecedented in this region due to a combination of: the magnitude of precipitation in each event the proximity in space and time of the two

events antecedent and subsequent precipitation

As a result, Fall 2011 had the highest 60-day precipitation total on record in this region

Conclusions

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The frequencies of extreme warm season hydrologic events have risen to their highest values on record during the last two decades

While this study does not address potential for these patterns to continue into the future, these results are consistent with other recent empirical studies, and with 21st century projections based on climate models

Conclusions (cont’d)

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Nonstationarity is a concern in hydrology and needs to be addressed

It need to be accounted for in water resources planning and management

The NYC OST is a good example for a flexible management tool to face today’s challenges

Conclusions (cont’d)

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Deep gorge created in Frost Valley (Ulster County Route 47) when floods after Hurricane Irene blew out a culvert below the road in Oliverea, NY. http://en.wikipedia.org/wiki/Effects_of_Hurricane_Irene_in_New_York#Orange_County

Thank You!

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Cold season Warm season

30-dayPrecipitation

30-dayStreamflow

Number of 95%tile values per year: all stations

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Cold season Warm season

60-dayPrecipitation

60-dayStreamflow

Number of 95%tile values per year: all stations

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Ashokan reservoir during a turbidity event. Turbid water moving from West to East basins (2006)

Extreme Events Can Affect Water Quality