A probabilistic model for turbidity and temperature in the schoharie reservoir withdrawal
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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

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A Probabilistic Model for Turbidity and Temperature in the Schoharie Reservoir Withdrawal

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A probabilistic model for turbidity and temperature in the schoharie reservoir withdrawal

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 14th-15th,

Thayer Hotel, West Point, New York


Schoharie reservoir water supply withdrawal and esopus creek

Schoharie ReservoirWater Supply Withdrawal and Esopus Creek

  • water quality issues for withdrawal

    • temperature (T)

    • turbidity (Tn)

withdrawal depth

when full = 18 m

Shandaken

Tunnel ~ 29 km

Esopus Creek

Ashokan Reservoir


Variations in water quality of withdrawal and thresholds of concern

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


Development of modeling strategy

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


Design of probabilistic model framework for schoharie reservoir

Design of Probabilistic Model Framework for Schoharie Reservoir

Stream Temperature

(empirical model)

Stream Turbidity Loading

(empirical model)

Stream Flow

(USGS)

Reservoir Operations

(NYC DEP)

  • Water Quality Model

  • transport/hydrothermal sub model

  • turbidity submodel with resuspension

Met. Data (NOAA)

MLI Optimization Algorithm

Wave Model

Withdrawal Temperature and Turbidity

long-term records

independent emp. models

multi-level opt. algo.


Water quality model w2tn

Water Quality Model (W2Tn)

  • transport/hydrothermal submodel (W2/T)

    • mechanistic, dynamic, two-dimensional from CE-QUAL-W2 (USACE)see Gelda and Effler 2007. 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 settlingsee Gelda and Effler 2007. J. Environ. Eng. Div. ASCE133:139-148


Water quality model w2tn segmentation and a simulation

Water Quality Model (W2Tn) Segmentation and a Simulation

intake


Long term records to specify inputs for probabilistic model

Long-Term Records to SpecifyInputs for Probabilistic Model


Independent empirical models to specify inputs for probabilistic model

Independent Empirical Models to Specify Inputs for Probabilistic Model

Stream temperature (plunging)

Ts,i = a0 + a1 Tair,i-3 + a2 log (Qi)

  • long-term stream T predicted from Tair and Q records

Turbidity-Flow Relationship (external loads)

Tn = 2.5 C660

- long-term stream Tn loads predicted from Q records


Performance of probabilistic model in representing variability of withdrawal t

Performance of Probabilistic Model in Representing Variability of Withdrawal T

  • observations: 1987-2004

  • prediction bounds: for driving conditions of 1987-2004

  • probabilistic model succeeds in representing range of observations


Performance of probabilistic model in representing variability of withdrawal turbidity

Performance of Probabilistic Model in Representing Variability of Withdrawal Turbidity

  • observations: 1987-2004

  • prediction bounds: for driving conditions of 1987-2004

  • probabilistic model succeeds in representing range of observations


Performance of probabilistic model in simulating water quality in the withdrawal

Performance of Probabilistic Model in Simulating Water Quality in the Withdrawal

generally good performance


Example application of the probabilistic model scenario description

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?


Projections for mli scenario with probabilistic model site 3 versus site 1 5

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

Schoharie Cr.

- no noteworthy benefit for MLI at site 1.5 versus site 3


Projections for mli scenarios with probabilistic model comparisons to existing withdrawal case

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


Summary

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


Related professional journal citation

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, 2008. Probabilistic model for temperature and turbidity in a reservoir withdrawal. Lake and Reserv. Manage. 24: 219-230.


Investigation of model and input updates upgrades 2009

Investigation of Model and Input Updates/Upgrades (2009)

  • turbidity submodel and stream turbidity loading model


Investigation of model and input updates upgrades 20091

Investigation of Model and Input Updates/Upgrades (2009)

  • turbidity submodel and stream turbidity loading model

  • Updates based on

  • new particle characterizations (Peng et al. 2009)

  • resuspension studies (Cornell) and modeling (Owens et al. 2009)

  • expansion of model testing for additional years of detailed monitoring (Owens et al. 2009)

  • correction of coding error for resuspension

based on additional stream monitoring data


Effects of updates upgrades on probabilistic model projections

Effects of Updates/Upgrades on Probabilistic Model Projections

  • an example

Schoharie Cr.

  • management perspectives on MLI/location alternatives remain unchanged


Summary1

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


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