ice cover in new york city drinking water reservoirs: modeling simulations and observations
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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
2
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
3
• 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)
4
Lakes and Reservoirs under investigation
5
Lakes / Reservoirs Examined
6
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
8
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
9
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
11
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
)
12
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
13
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
14
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
15
• 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!!!
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