A Probabilistic Model for Turbidity and Temperature in the Schoharie Reservoir Withdrawal Steven W. Effler and Rakesh K. Gelda Upstate Freshwater Institute, Syracuse, NY Donald C. Pierson New York City Department of Environmental Protection 2009 Watershed Science & Technical Conference September 14 th -15 th, Thayer Hotel, West Point, New York

2 Schoharie Reservoir Water Supply Withdrawal and Esopus Creek water quality issues for withdrawal –temperature (T) –turbidity (Tn) Ashokan Reservoir Esopus Creek Shandaken Tunnel ~ 29 km withdrawal depth when full = 18 m

3 Variations in Water Quality of Withdrawal and Thresholds of Concern threshold: 21.1 °C drivers of variability: meteorology reservoir drawdown threshold: ~ 15 NTU drivers of variability: runoff reservoir drawdown meteorology related management modeling goal for Schoharie Reservoir: develop and implement a modeling strategy to represent this variability in model applications

4 Development of Modeling Strategy a “probabilistic” framework is desired to represent variability long-term records of environmental and operational drivers (model inputs), together with tested water quality models, offer opportunity to represent variability these historic conditions are inherently representative of the system

5 Design of Probabilistic Model Framework for Schoharie Reservoir Water Quality Model transport/hydrothermal sub model turbidity submodel with resuspension Stream Temperature (empirical model) Stream Turbidity Loading (empirical model) Stream Flow (USGS) Reservoir Operations (NYC DEP) Met. Data (NOAA) Withdrawal Temperature and Turbidity Wave Model MLI Optimization Algorithm long-term recordsindependent emp. modelsmulti-level opt. algo.

6 Water Quality Model (W2Tn) transport/hydrothermal submodel (W2/T) –mechanistic, dynamic, two-dimensional from CE- QUAL-W2 (USACE) see Gelda and Effler J. Environ. Eng. Sci. 6:73-84 turbidity submodel –three particle sizes of turbidity –sources – external loads (primarily Scoharie Creek), resuspension (circulation and wave-driven) –sinks – export and settling see Gelda and Effler J. Environ. Eng. Div. ASCE 133:

7 Water Quality Model (W2Tn) Segmentation and a Simulation intake

8 Long-Term Records to Specify Inputs for Probabilistic Model Model Driver/InputSpecifications meteorology (57 years); off-site Albany (NOAA) since 1948, on-site since 1997 inflows (gaged) Schoharie Creek since 1948; others more recent (USGS) outflows (operations) , NYCDEP; , USGS

9 Independent Empirical Models to Specify Inputs for Probabilistic Model Stream temperature (plunging) T s,i = a 0 + a 1 T air,i-3 + a 2 log (Q i ) - long-term stream T predicted from T air and Q records Turbidity-Flow Relationship (external loads) Tn = 2.5 C long-term stream Tn loads predicted from Q records

10 Performance of Probabilistic Model in Representing Variability of Withdrawal T observations: prediction bounds: for driving conditions of probabilistic model succeeds in representing range of observations

11 Performance of Probabilistic Model in Representing Variability of Withdrawal Turbidity observations: prediction bounds: for driving conditions of probabilistic model succeeds in representing range of observations

12 Performance of Probabilistic Model in Simulating Water Quality in the Withdrawal generally good performance

13 Example Application of the Probabilistic Model: Scenario Description potential benefits of multi-level intakes (MLI) and location in the reservoir is there a benefit to “spatial avoidance” of turbid plumes?

14 Projections for MLI Scenario with Probabilistic Model: Site 3 versus Site 1.5 for 57 years of historic conditions summary statistic of number of days withdrawal Tn > 15 NTU, for individual years of record - no noteworthy benefit for MLI at site 1.5 versus site 3 Schoharie Cr.

15 Projections for MLI Scenarios with Probabilistic Model, Comparisons to existing Withdrawal Case for 57 years of historic conditions cumulative distribution format for presentation of results - modest benefit of MLI; exceedences decrease from 27 to 16% of days

16 Summary probabilistic modeling framework for temperature and turbidity for Schoharie Reservoir developed, tested and preliminarily applied –key components: tested mechanistic water quality models, long- term records for drivers, and empirical models insights from preliminary applications concerning multi- level intake alternatives broad utility of approach –other issues and systems (Ashokan, Kensico) –flexibility to accept upgrades/updates –coupling with hydrologic model (OASIS) to integrate water quantity needs of overall system

17 Related Professional Journal Citation a more complete treatment of material addressed in this presentation can be found in the following peer-reviewed journal paper Gelda, R. K. and S. W. Effler, Probabilistic model for temperature and turbidity in a reservoir withdrawal. Lake and Reserv. Manage. 24:

18 Investigation of Model and Input Updates/Upgrades (2009) turbidity submodel and stream turbidity loading model

19 Investigation of Model and Input Updates/Upgrades (2009) turbidity submodel and stream turbidity loading model Updates based on 1.new particle characterizations (Peng et al. 2009) 2.resuspension studies (Cornell) and modeling (Owens et al. 2009) 3.expansion of model testing for additional years of detailed monitoring (Owens et al. 2009) 4.correction of coding error for resuspension based on additional stream monitoring data

20 Effects of Updates/Upgrades on Probabilistic Model Projections an example Schoharie Cr. management perspectives on MLI/location alternatives remain unchanged

21 Summary probabilistic modeling framework for temperature and turbidity for Schoharie Reservoir developed, tested and preliminarily applied –key components: tested mechanistic water quality models, long- term records for drivers, and empirical models insights from preliminary applications concerning multi- level intake alternatives broad utility of approach –other issues and systems (Ashokan, Kensico) –flexibility to accept upgrades/updates –coupling with hydrologic model (OASIS) to integrate water quantity needs of overall system