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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 Protection
2 The City University of New York – Hunter College
2
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
3
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
4
Irene in the Catskills: Schoharie Creek, Prattsville
Photo Immediately after Irene, Sep 1, 2011, photo by Danyell Davis, NYCDEP
5
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
6
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
7
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
9
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
10
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
11
Precipitation-Difference Contours and Stations
Irene minus Lee
Irene > LeeLee > Irene
Map by David Lounsbury, NYCDEP
12
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
13
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)
14
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
15
DecNovOctSepAugJulJunMayAprMarFebJan
25
20
15
10
5
0
Prec
ipita
tion
(cm
/day
)
a)
DecNovOctSepAugJulJunMayAprMarFebJan
900
800
700
600
500
400
300
200
100
0
Stre
amflo
w (
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
16
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:
17
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
18
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
19
Cold season Warm season
4-day Precipitation
1-day Streamflow
Number of 95%tile values per year: all stations
20
600
400
200
0
WarmColdAnnual
600
400
200
0WarmColdAnnual WarmColdAnnual
2-year
Season
Stre
amflo
w (
cu.m
/s)
5-year 10-year
25-year 50-year 100-year
b)
20
15
10
5
WarmColdAnnual
20
15
10
5
WarmColdAnnual WarmColdAnnual
2-Year
Season
Prec
ipita
tion
(cm
/day
)
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)
21
Return Period 10050251052
1600
1400
1200
1000
800
600
400
200
0
Str
eam
flow
(cu
.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
22
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
23
… and projecting an increasing intensity of EE (Kunkel et al 2013, GRL)
24
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.
25
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
26
Monier and Gao, 2013
27
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
Pre
cipip
tation (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
28
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”
29
Can be reflected by various changes such as in:
Regional air temperature
Water demand as a function of temperature
Patterns of rain and snow
Timing of snowmelt
Frequency and magnitude of hydrologic extreme events
Land use/cover
How does NYC address this issue ?
Nonstationarity
30
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
31
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
32
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)
33
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)
34
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!
35
Cold season Warm season
30-dayPrecipitation
30-dayStreamflow
Number of 95%tile values per year: all stations
36
Cold season Warm season
60-dayPrecipitation
60-dayStreamflow
Number of 95%tile values per year: all stations
37
Ashokan reservoir during a turbidity event. Turbid water moving from West to East basins (2006)
Extreme Events Can Affect Water Quality