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

1. 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 SYMPOSIUM SEPTEMBER 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?

3. 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

4. Lakes and Reservoirs under investigation • 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)

5. Lakes / Reservoirs Examined

6. Lakes / Reservoirs Examined Otsego Lake Ashokan Reservoir Rondout Reservoir

7. Long Term Trends in Ice Cover – Otsego Lake Date of Ice On Yearly and Decade Average Date of Ice Off Yearly and Decade Average Data from SUNY Oneonta Biological Field Station

8. Methods and Modeling • Simple Ice model – SIM (developed by Klaus D. Joehnk, CSIRO, Australia) • Sub model of the LAKEoneD lake stratification model • Based onheat 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] - RhoIce916.0 25 Latent heat of fusion [J/kg] - L 334000 26 Heat transfer freezing [W/(m2K )] - Qf12 27 melting [W/(m2K )] - Qm25 28 Thermal conductivity [W /(m K)] - TCond2.24 Green: calibration parameters

10. 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 Date of Ice On Date of Ice Off Simulated Simulated Measured Measured

11. Model Validation – Otsego Lake • SIM 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 Simulated Ice Thickness (m) Date of Ice Off Date of Ice On Simulated Simulated Measured Measured

12. Model Performance NYC Reservoirs Regional Simulations using calibrated parameters from Otsego Lake Date of Ice On Rondout Reservoir Ashokan Reservoir Simulated Simulated Measured Measured Good relationships between simulated and measured ice on dates. Simulated ice on dates are biased early compared to measured data

13. Model Performance NYC Reservoirs Regional Simulations using calibrated parameters from Otsego Lake Date of Ice Off Ashokan Reservoir Rondout Reservoir Simulated Simulated Measured 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.

15. Further Improvements • Improve Otsego calibration to remove bias • Incorporate wind direction relative to lake fetch when simulating ice loss • Simulate lake ice snow cover

16. Further Study • 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

17. Ice cover in Ashokan Reservoir

18. Thanks for your attention!!!