26-10-2010

The way forward: Better models, more data, better techniques?

There is either something highly productive in the number of aquatic ecosystem models that exist or…

A case study to demonstrate current limitations

there is something peculiarly unproductive about the effort going into multiple models around the world…

David Hamilton Waikato University, New Zealand Workshop on Lake Ecosystem Modelling Silkeborg, Denmark, 20-22 September 2010

A small subset of the models available: http://stommel.tamu.edu/~baum/ocean_models.html ACADIA ACOM BatTri BOM BRIOS DROG3D CLIO COHERENS DieCAST ECBILT ECOM-si ELCIRC FLAME FMS FRAM FUNDY GMODEL

There is a diversity of models also

Lake models:

Ocean models: GOTM HIM HOPE HYCOM LOAM LSM MICOM MITgcm MOM MOMA NCOM NLOM NUBBLE OCCAM OCCOMM OPA OSMOM

PEQMOD POCM POM POP POSEIDON POSUM QTCM QUODDY ROMS SCRUM SEA SELFE SEOM SPEM TOMS

Mooji et al. (2010): VOLLENWEIDER DYRESM-CAEDYM (1-DV) ELCOM-CAEDYM (3-D) CE-QUAL-W2 DELFT3D-ECO MYLAKE PCLake, SHIRA IPH-TRIM3D-PCLAKE PROTECH SALMO CHARISMA PISCATOR …and DHI DLM DYRESM-WQ BATHTUB MINLAKE

So how are our friends in the climate modelling community doing?

but despite the enormous funding and opportunity there are only c. 13 models

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26-10-2010

Timelines for water quality model applications years before present 103

102

101

100

10-1

10-2

Present

There is also quality control and evaluation

years into future 10-2

10-1

100

101

102

} } } } } } Hindcasting - past climate -past land-use

Hindcasting recent human disturbance: - eutrophication, - invasive species, - pollutant fate

Hindcasting for detailed temporal understanding: - material fluxes - transport - testing theory

Forecasting: Simulation: Prediction: - response to - lake - future climate weather forecastmanagement -future land use - algal bloom strategies likelihood/fate - extreme events (weather, storms)

- Hambright et al. 2004

- Hipsey et al. (2009) - Arhonditsis and Brett (2004)

- Robson and - Recknagel et al. Hamilton (2003, 2004) (2007) - Burger et al. (2007) - Wallace et al. (2000)

- Elliot et al. (2009a) - De Stasio et al. (1996) - Spillman et al. -Trolle et al. (2010) (2009) - Elliot et al. (2009b) - Hamilton et al. (1999)

…and vigorous scrutiny of these models!

An example of simulations using high frequency: DYRESM temperature simulations of Trout Bog, WI, USA years BP 10-1

}

}

Hindcasting recent human disturbance:

Hindcasting for detailed temporal understanding C Burger et al. (2007)

Outcome: the modeller is happy to have something that reproduces reality but little contribution to model improvement

30

10-2

25

Temperature (ºC)

100

Present

years BP 101

Present

An example of mid-duration simulations: DYRESM temperature simulations of Trout Bog, WI, USA

20

15

T0

T1

T2

T3

T4

T5

M0

M1

M2

M3

M4

M5

O0

O1

O2

O3

O4

O5

10

5

0 2006-08-22 2006-09-01 2006-09-11 2006-09-21 2006-10-01 2006-10-11 2006-10-21 2006-10-31

Outcome: identifying separation of model simulation data from measurements (quantitative values, frequencies) allows opportunities to improve model performance: a focus on process representations

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The current situation: data frequency, model time steps and outputs Minutes

Computational time-step

The future situation: data frequency, model time steps and outputs Minutes

Inflow/outflow temperature and conductivity

Hours

Hours

Meteorology

Days

Computational time-step

Inflow nutrients and lake water quality measurements

Inflow/outflow temperature and conductivity

Meteorology

Days Model time-span (3-D)

Weeks

Weeks

Inflow nutrients and lake water quality measurements

Model time-span (1-D)

Months

Model time-span (1-D)

Months

Years

Years

Decades

Decades

Space–time plot: Traditional monitoring and current sensor networks

Model time-span (3-D)

Spatial validation: Biofish measurements v ELCOM-CAEDYM simulation dissolved oxygen

temperature

100 km 27/09/2004

10 km

Spatial 1 km extent (horizontal)

existing sensor networks

Currently available towed/ 100 m autonomous instruments 10 m Fixed point sensors Traditional monthly profiles 1m

11/01/2005

10 cm Annual

Monthly

Weekly

Daily Hourly

Min. Sec.

random selection from ecology (2003)

Temperature (°C) DO (mg L-1) Chlorophyll a (μg L-1)

Frequency of measurement 05/04/2005

RMSE 0.895

R 0.984

n 5250

1.211

0.877

5250

5.031

0.664

5250

Data from Von Westernhagen

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Space–time plot: Traditional monitoring and current sensor networks

Landsat-derived chlorophyll a for Rotorua lakes

100 km

10 km

Spatial 1 km extent (horizontal)

Remote sensing

existing sensor networks

100 m 10 m

Allan et al. (2010). Int. J. Remote Sensing

Fixed point sensors Traditional monthly profiles

1m 10 cm Annual

Monthly

Weekly

Daily Hourly

Min. Sec.

random selection from ecology (2003)

Hamilton et al. (2010). Aquat. Sci.

Frequency of measurement Strong vertical and horizontal gradients necessitate the application of highly spatially resolved models

Landsat-derived and ELCOM-derived temperature for

Percentage of current Ohau Channel inflow versus cyanobacteria (as μg chl-a L-1) in Lake Rotoiti

Rotorua lakes and Lake Rotoehu Landsat T

ELCOM T ELCOM U dd 14 Model 0% Ohau

chlorophyll a [ug/L]

12

Model 5% Ohau

10

Model 10% Ohau Model 50% Ohau

8

Model 100% Ohau

6 4 2 0 Jul-01

Geothermal inflows provide a hightemperature end-member to assist remove sensing of temperature

Jan-02

Jul-02

Jan-03

Jul-03

Jan-04

Jul-04

Jan-05

Jul-05

Jan-06

But calibrated parameters used in the 1-D model did not appear to be directly applicable to the 3-D model; the latter model did not capture the effects of 0% Ohau (the inflow diversion) in a spatially realistic way without specific calibration separate to 1-D model Data from Mat Allan

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Potential to apply models to major lake ecosystem perturbations: Inflow diversion, Lake Rotoiti

Potential to apply models to major lake ecosystem perturbations: Modified zeolite application to Lake Okaro

Landsat image

Photo: Environment BOP

Thoughts on critical needs • How best to harness the collective expertise and motivation of the lake modelling community? ; • Can we engage ecologists using a modularised system for individual ecological components (benthos, sediment-water, macrophytes etc.)? ; • Should we be scrutinising and applying standards to the models that we use? ; • Investigate scale and time dependence of parameters to ensure they are applicable across field, lab and model applications? ; • Find major perturbations (biomanipulations, flocculents, inflow diversions, etc.) to robustly test model validity.

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