Ice Cover in New York City Drinking Water Reservoirs: Modeling Simulations and Observations

NIHAR R. SAMAL, Institute for Sustainable Cities, City University of New York , NY, USA

DONALD C. PIERSON, MARK S. ZION, New York City Dept. of Environmental Protection, Kingston, NY, 12401, USA

KLAUS D. JOEHNK, CSIRO Land and Water, Black Mountain, CANBERRA, ACT 2601, Australia

2013 NYC WATERSHED/ TIFFT SCIENCE AND TECHNICAL SYMPOSIUMSEPTEMBER 18 &19, 2013

Thayer Hotel, West Point, NY

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Introduction and Objectives

• Measures of lake ice phenology provide an integrative description of the winter climate and the transition to the spring season.

• Date of ice on• Date of ice off• Duration of ice cover

• Here we test if lake ice phenology can be simulated using a simple ice model calibrated with regional data from one lake and driven by daily variations in

• Air Temperature• Wind Speed.

• Verification of modeling results by comparison with historical measurements of the onset and loss of lake ice

• The results presented here are an initial test of a simple ice model.

• The research question being addressed is: Can a simple model calibrated at a single site can make regional predictions at reasonable levels of accuracy?

Why are we interested in Ice Cover?• It is well documented that the timing of ice cover is changing as a

consequence of climate change

• The timing of ice on, ice off and the resulting ice cover duration in lakes and reservoirs will both modulate and reflect the impact of regional weather on lakes • Water column stability is greatly increased by the presence of ice cover.• Inverse stratification at the surface and near isothermal conditions below• In the presence of snow low light

• Short future ice cover can lead to a longer period of isothermal mixing prior to the onset of thermal stratification.• Nutrient uptake prior to stratification• Increased warming can lead to increased hypolimnion temperature

following stratification

• The presence of ice cover and inverse stratification can influence the transport of substances through the reservoir

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• New York City Water Supply Reservoirs– Ashokan Reservoir (West Basin)– Rondout Reservoir– Observed Ice Cover data 2004-2012

• Lake in the same region with large database on ice phenology- Otsego Lake (New York)

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Lakes and Reservoirs under investigation

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Lakes / Reservoirs Examined

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Lakes / Reservoirs ExaminedOtsego Lake

Rondout Reservoir

Ashokan Reservoir

Long Term Trends in Ice Cover – Otsego Lake

Data from SUNY Oneonta Biological Field Station

Date of Ice On Yearly and Decade Average

Date of Ice Off Yearly and Decade Average

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Methods and Modeling

Simple Ice model – SIM (developed by Klaus D. Joehnk, CSIRO, Australia)

• Sub model of the LAKEoneD lake stratification model

• Based on heat conduction equation in the ice cover• Simulates ice growth and decay

• No snow component (currently under development)

• Variations in lake water temperature not taken into account

• Driven by daily or hourly air temperature and wind speed

• Initial ice formation based on duration of time below temperature threshold • Ice off based on melting conditions and threshold thickness under wind load

• Output: ice-on & off and ice thickness

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Model parameters

Parameters used in the ice model

Meteorology 13 Temperature of frost day - TempFrostDay -1 °C 14 Minimum number of frost days - nMinFrostDay 2 (days) 15 Wind speed threshold for - WindBreakUp 1 (m/s) 16 Ice thickness to break up - WindMinIce 0.02 (m)

Ice parameter 21 Density of ice [kg/m3] - RhoIce 916.0 25 Latent heat of fusion [J/kg] - L 334000 26 Heat transfer freezing [W/(m2 K )] - Qf 12 27 melting [W/(m2 K )] - Qm 25 28 Thermal conductivity [W /(m K)] - TCond 2.24

Green: calibration parameters

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Model Calibration Otsego Lake

SIM is calibrated for Otsego lake for the period: 2005-2010

Same model parameters are used for testing other lakes and Reservoirs

10-Dec 9-Jan 8-Feb 10-Mar10-Dec

9-Jan

8-Feb

10-Mar

R² = 0.358391689292436

9-Jan 18-Feb 30-Mar 9-May9-Jan

18-Feb

30-Mar

9-May

R² = 0.933268305093628

Date of Ice On

Sim

ulat

ed

Measured

Date of Ice Off

Measured

Sim

ulat

ed

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Model Validation – Otsego LakeSIM is calibrated for Otsego lake for the period: 2005-2010

• Model validation: 1989-2004 • Same model parameters are used for testing other lakes and Reservoirs

5/3/1987 1/27/1990 10/23/1992 7/20/1995 4/15/1998 1/9/2001 10/6/2003 7/2/2006 3/28/2009 12/23/20110

0.1

0.2

0.3

0.4

0.5

0.6Mes_Ice_on Mes_Ice_off Hice (m)

10-Nov 20-Dec 29-Jan 10-Mar10-Nov

20-Dec

29-Jan

10-Mar

28-Feb 30-Mar 29-Apr28-Feb

20-Mar

9-Apr

29-Apr

Date of Ice On

Sim

ulat

ed

Measured

Date of Ice Off

Sim

ulat

ed

Measured

Sim

ulat

ed I

ce T

hick

ness

(m

)

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Model Performance NYC Reservoirs

Regional Simulations using calibrated parameters from Otsego Lake

1-Dec 22-Dec 12-Jan 2-Feb1-Dec

22-Dec

12-Jan

2-Feb

R² = 0.647731006222625

1-Dec 21-Dec 10-Jan 30-Jan1-Dec

22-Dec

12-Jan

2-Feb

R² = 0.581045934523641

Rondout ReservoirAshokan Reservoir

Date of Ice On

Sim

ulat

ed

Measured

Sim

ulat

ed

Measured

Good relationships between simulated and measured ice on dates. Simulated ice on dates are biased early compared to measured data

10-Mar 24-Mar 7-Apr 21-Apr10-Mar

24-Mar

7-Apr

21-Apr

R² = 0.358943543079186

10-Mar 17-Mar 24-Mar 31-Mar 7-Apr10-Mar

17-Mar

24-Mar

31-Mar

7-Apr

R² = 0.450848656294384

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Model Performance NYC Reservoirs

Regional Simulations using calibrated parameters from Otsego Lake

Rondout ReservoirAshokan Reservoir

Date of Ice Off

Sim

ulat

ed

Measured

Sim

ulat

ed

Measured

Moderate – weak relationship Modeled data have a tendency to predict a later than measured ice off date

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Summary

A simple model shows promise in allowing lake ice phenology to be simulated over broad geographical regions using readily available input data and a regional calibration.

Even though the simple model does not make detailed calculations of the ice cover energy budget, ice-on and off days are well reproduced for the NYC drinking water reservoirs and for Otsego lake in the same region.

Estimation of ice off dates in NYC reservoirs may be affected by differences in the direction of the major fetch between the regional calibration site (Otsego Lake) and the reservoirs

Long-term records of observed ice data in lakes and reservoirs are therefore related to the variability of local climate and also provide robust indications of climate change.

Further Improvements

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• Improve Otsego calibration to remove bias • Incorporate wind direction relative to lake fetch when simulating

ice loss• Simulate lake ice snow cover

Further Study

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• Relationship between the timing of ice off, and its relationship to the onset of thermal stratification and summer thermal structure is under investigation

• changing ice cover may ultimately influence phytoplankton succession and trophic status of a lake.

• Relationship of the dominating role of wind speed, air temperature and snow cover on ice formation and break up is under investigation

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Ice cover in Ashokan Reservoir

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Thanks for your

attention!!!