Upstate Freshwater Institute
Simulating multiple functional groups of phytoplankton in Cannonsville Reservoir
Markensten et al. NYWEA 2008
Hampus Markensten1, Don Pierson2, Emmet M. Owens1, Susan M. O'Donnell1 and Steven W. Effler1.
Email: hm@upstatefreshwater.org
1) Upstate Freshwater Institute Syracuse, USA
2) Department of Environmental Protection (DEP) NYC, USA
Cannonsville reservoir
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Markensten et al. NYWEA 2008
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Second largest reservoir serving New York City with drinking water. Mesotrophic with a retention time of 2.6 years and a storage capacity of 373*106 m3 of water (98.5 billion gallons).
Background
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In the past Cannonsville experienced high average chlorophyll concentrations and frequent phytoplankton blooms. Cyanobacteria often dominate in summer and autumn e.g. Aphanizomenon, Anabaena and Microcystis.
A one dimensional (1D) lake model that simulates temperature, hydrodynamics, nutrient dynamics and total phytoplankton biomass has previously been developed by the Upstate Freshwater Institute (UFI) (Doerr et al, 1998) and applied to the reservoir.
Markensten et al. NYWEA 2008
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DOERR, S.M., E.M. OWENS, R.K. GELDA, M.T. AUER & S.W. EFFLER. 1998. Development and testing of a nutrient-phytoplankton model for Cannonsville Reservoir. Lake and Reservoir Management 14.: 301-321.
Overview of UFI 1D reservoir water quality model
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Markensten et al. NYWEA 2008
Objectives
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• A need to predict the occurrence of bloom forming phytoplankton. • Solution: Merge the existing 1D reservoir water quality model, which includes a good description of hydrodynamic and chemistry, with a model focused on the dynamics of phytoplankton groups.
Markensten et al. NYWEA 2008
• Compare the model performance before and after merging the two models.
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PROTECH (phytoplankton response to environmental change) is a model developed by Colin Reynolds in UK that focuses on the phytoplankton biology. Phytoplankton can respond to changes in nutrient, light and temperature by vertical movements to reach the most favorable depth. Phytoplankton growth rates are calculated from size and volume relationships that affect nutrient uptake light harvesting and temperature dependence. Eight different functional groups of phytoplankton are simulated that differ in their surface area/volume, capability to fix nitrogen, use silica and regulate their buoyancy
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Markensten et al. NYWEA 2008
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Size and Shape Influences
• Growth Temperature adaptation Light absorption Grazing Passive movement (up or down) Nutrient uptake not explicitly affected
Markensten et al. NYWEA 2008
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Phytoplankton Growth Function
25000
Markensten et al. NYWEA 2008
20000
N t N 0e
rt
Cell Number
15000 10000 5000 0 0 2 4 6 Time (days) 8 10 12
What is different in PROTECH?
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• Morphological relationships describe growth: r20
Markensten et al. NYWEA 2008
Reynolds (1989)
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Temperature-sensitivity of growth rate (rθ) as a function of s/v
Markensten et al. NYWEA 2008
(Reynolds in Sommer 1989)
Light effect on phytoplankton growth
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Markensten et al. NYWEA 2008
(Reynolds in Sommer 1989)
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Overview of the hybrid 1D model including phytoplankton functional groups
Markensten et al. NYWEA 2008
Feature
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UFI ver 4.1
Carbon-based; constant stoichiometry; single (lumped) algae class N, P. Specified from measurements. FSS; VSS=Detritus + Algal material.
UFI PROTECH hybrid
Carbon-based; constant stoichiometry; multiple (eight) algae classes (PROTECH) N, P, Si. Modeled. VSS=Detritus + Algal material.
Representation of phytoplankton Nutrients Zooplankton
Markensten et al. NYWEA 2008
Suspended Solids
Phytoplankton Settling
Resuspension
Always downward at specified rate.
Wave-driven resuspension in littoral areas; flux computed from critical stress relationship.
Some phytoplankton may move up or down.
Complete resuspension of deposited algal particulates (Algal C) in mixed layer
Deposition
At rate determined by settling velocity.
Specified release rate of SRP (via TRP) and NH3; no transformation of deposited organic material.
Below mixed layer: determined by settling velocity. In mixed layer: no deposition.
Release of SRP, NH3 and Si from phytoplankton at same rate as in the water column (respiration).
Sediment Release, Diagenesis
Phytoplankton Functional Groups
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Markensten et al. NYWEA 2008
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Large Filamentous diatoms - Aulacoseira Small diatoms – Stephanodiscus Small Flagellates – Cryptomonas Rhodomonas Large Flagellates - Ceratium Large non N fixing cyanobacteria - Microcystis Large N fixing cyanobacteria - Anabaena Aphanizomenon
Model Calibration
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Markensten et al. NYWEA 2008
• The phytoplankton model was not tuned to local conditions – Alometric coefficients influencing phytoplankton growth are those given by Reynolds. • Minimal tuning of the sub-models describing hydrodynamics and nutrient kinetics.
Expectations
• To simulate realistic seasonal patterns of chlorophyll and functional group biomass. • To simulate inter annual variations in phytoplankton biomass
Results Comparison of Measured and Modeled Data 1998
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Hydrothermal Model Measured
Markensten et al. NYWEA 2008
PROTECH
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Measured
Markensten et al. NYWEA 2008
PROTECHhybrid1D
UFI 1D
Comparing hybrid model results with measurements
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Markensten et al. NYWEA 2008
Comparing hybrid model results with measurements
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Markensten et al. NYWEA 2008
Conclusions
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Both models perform well in predicting chlorophyll on an annual scale, and also realistically simulated seasonal patterns in chlorophyll. The PROTECH hybrid model successfully simulated occurrence of major functional groups in the Cannonsville Reservoir. The model is a valuable tool for predicting seasonal variability in chlorophyll and phytoplankton functional groups
Evaluation of the model using other reservoirs and longer time series of data is underway.
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Cannonsville Isopleths 1966 - 1990
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Markensten et al. NYWEA 2008
Water Temperature (C)
Delta Change based on Chlorophyll a (mg m-3) ECAM A2 2081-2300 Baseline
Chlorophyll a (mg m-3)
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Markensten et al. NYWEA 2008