Post on 23-Jun-2020
MODELLING THE IMPACT OF FINFISH AQUACULTURE ON SEDIMENT
BIOGEOCHEMISTRYDaniele Brigolin, University of Venice
ECEM 2007, Trieste November 27-30
Pastres Roberto University of Venice, Italy
EU FP6 ECASA projectwww.ecasa.org.uk
Chris Cromey, T.D. Nickell SAMS, Oban, Scotland (UK)
D.R. Aguilera, Pierre Regnier University of Utrecht, The Netherlands
Outline
• Impacts of finfish farming on the sediment geochemistry;
• Description of the model;
• Study site and field data description;
• Results of model calibration;
• Response of model output to the establishment of a new salmon farm;
• Perspectives.
Finfish farming impacts on the sediment
Potential impacts on the sediment (e.g. Hargrave et al., 1997; Pearson & Black, 2000)
currents
Wasted food
sedimentation
burial
Oxic and anoxic mineralization
Faeces
S=, NH4+, DIP
Increase in the superficial OC concentration
Decrease of the oxygen penetration depth
Increase in dissolved nutrient concentrations
Increase in S= concentrations
Changes in macrofaunal community
Changes in the fluxes at the sediment-water interface
Changes in the chemical properties of the sediments provide a measure of the impact caused by the fish farm which is integrated over time.
Benthic impacts are proportional to:
Exposure of the site (currents);
Husbandry practices (feeding strategy and quality of the food)
Modelling the impacts of finfish farming on the sediments
OC flux reaching the sediment-water interface
Bioticindexes (ITI, AMBI)
Simple geochemicalindicators (TOC)
Current approach
OC flux reaching the sediment-water interface
Ecologicalindexes
Approach proposed in this work
More complex geochemical indicators (e.g. NH4
+, S=, O2..)
Conceptual model: Deposition + Early diagenesis
WATERCOLUMN
Food wastage and Faeces production
Deposition model(DEPOMODTM)
Organic carbon flux
Current velocity
O2 demand, nutrient fluxes
EDM: Early Diagenetic ModelEDM: Early Diagenetic Model(developed in BRNS(developed in BRNSTM TM environment)environment)
SedimenttemperatureSEDIMENT
DEPOMOD – particle tracking model (Cromey et al., 2002 Aquaculture)
(validated for Scottish sealochs)
Objectives of this work
1) evaluating the applicability of a Reactive-Transport Model of early diagenesis (EDM) in combination with the DEPOMOD;
2) testing DEPOMOD+EDM integrated model at sites which are exposed to high organic carbon fluxes from aquaculture activities, under transient conditions.
The potential use of the integrated model is discussed in relation to benthic biogeochemical indicators for cost-effective EIA and monitoring practices.
Surficial sediment
layer
Deep sediment layer
Water column
Temp. Fluxes:
OC, Fe(III), Mn(IV)
Lower boundary condition: null gradient
t: time z: depthΣR variation in the concentration due to biogeochemical processesDB diffusion coefficientω : sedimentation rateφ: sediment porosity.
The modeller specifies the fluxes at the upper boundary for the solid species, and the concentrations for the dissolved speciesConcentr.:
O2, NH4+, HPO4
2-, NO3-,
SO42-, Mn2+, Fe2+
( ) ( ) ( ) ( )( )t,,CRzCD
zzC
tC
ssBss βϕϕωϕϕ −+
∂−∂
∂∂+
∂−∂−=
∂−∂ 1111
( ) ( )t,,CRz
Cln/DzCD
zzC
tC
wwmwBww βϕϕϕϕϕ ωϕ +
∂−∂+
∂∂
∂∂+
∂∂−=
∂∂ 21
Organic matter degradation and early diagenesis processes: conceptual model
Surficial sediment
layer
Deep sediment layer
Water column
Temp. Fluxes:
OC, Fe(III), Mn(IV)
Lower boundary condition: null gradient
t: time z: depthΣR variation in the concentration due to biogeochemical processesDB diffusion coefficientω : sedimentation rateφ: sediment porosity.
The modeller specifies the fluxes at the upper boundary for the solid species, and the concentrations for the dissolved speciesConcentr.:
O2, NH4+, HPO4
2-, NO3-,
SO42-, Mn2+, Fe2+
( ) ( ) ( ) ( )( )t,,CRzCD
zzC
tC
ssBss βϕϕωϕϕ −+
∂−∂
∂∂+
∂−∂−=
∂−∂ 1111
( ) ( )t,,CRz
Cln/DzCD
zzC
tC
wwmwBww βϕϕϕϕϕ ωϕ +
∂−∂+
∂∂
∂∂+
∂∂−=
∂∂ 21
Organic matter degradation and early diagenesis processes: conceptual model
Operator splitting methodImplicit Numerical scheme
Regnier et al. (2002) Appl. Math. Model.
Organic Matter
Organic matter Degradation
redox reactions
Electron acceptors:O2, NO3
-, Fe(III)Mn(IV), SO42-
Re-oxidationreactions
Reduced compoundsFe2+, Mn2+, NH4
+, S=
Electron acceptorsO2, Fe(III),
Mn(IV)
FeS & Carbonates Precipitations
Carbonates equilibria
Fluxes of NH4+, O2, DIP
BRNS Early diagenesis model Reaction network: Primary & Secondary reactions
Sediment Water Interface
A purposely-designed field campaign was carried out in August 2006, in order to apply the integrated model
The fish-farm wasmoved to the actualsite in Feb 2006
Study site – Loch Creran, West coast of Scotland
Study site – Loch Creran, West coast of Scotland
3 stations : 10m and 40m from the cages, and control;
3 replicates per station using a megacorer (Φ =100 mm);
Analysis were performed at each 1 cm on the top 10 cm + surnatant waters.
Parameters sampled:
- Porosity;- Organic Carbon in the sediment;- Fe2+ and Mn2+ in pore waters;- NH4
+ and HPO42- in pore waters;
- SO42- in pore waters.
Application of the model at Loch Creran - methodology
Step 1: the Early Diagenesis Model (EDM) was calibrated, by comparing the steady-state model outputs with the field data collected at station BC (control);
Step 2: the DEPOMOD was run, in order to obtain a prediction of the farm originated Organic Carbon flux at stations B10 and B40;
Step 3: organic carbon fluxes predicted by DEPOMOD are added to the background OC fluxes, moving the EDM from a steady-state to a transient-state;
Step 4: transient profiles predicted by the model are compared with a set of sediment chemistry data purposely collected at stations B10 and B40.
Step 1, results: EDM calibration – station Bc
- All the parameters of the model were fixed on the basis of literature references;
- The fluxes of solids OC, Fe(III) and Mn(IV) at the upper boundary were calibrated by minimizing a goal function which quantifies the deviation between model predictions and field data.
0 1 2 3 4OC [%]
0
5
10
dept
h [c
m]
a
0 100 200 300 400NH+
4 [µ mol L-1]
0
5
10
dept
h [c
m]
b
0 40 80DIP [µ mol L-1]
0
5
10
dept
h [c
m]
c
0 20 40SO4 [mmol L-1]
0
5
10
dept
h [c
m]
d
0 40 80 120 160 200Fe2+
[µ mol L-1]
0
5
10
dept
h [c
m]
e
0 40 80 120Mn2+
[µ mol L-1]
0
5
10
dept
h [c
m]
f
Step 2: DEPOMOD output – organic carbon flux at the S.W.I.
500
1000
1500
2000
Salmon cagesSampling stations
g C m-2 yr-1
B40
B10
50m
50m 500
1000
1500
2000
Salmon cagesSampling stations
g C m-2 yr-1
B40
B10
500
1000
1500
2000
Salmon cagesSampling stations
g C m-2 yr-1
B40
B10
50m
50m
At the time of the survey,the fish-farm has beenoperating for 6 months.
Production: 1500 tonn y-1
6 x 22m Φ circular net cages reaching 14m depth
5% of feed waste was assumed;
Farm details:
Step 3: EDM transient simulation
OC (food+faeces) flux at Station B10
Depomod output
These fluxes were added on the top of the background OC flux, which was estimated by calibrating the EDM.
This additional OC flux was imposed for 6 months (age of the farm), perturbing the steady-state profiles and driving the model to a transient-state.
Step 4. EDM model output vs field data at station B10
- Due to the mineralization of elevated quantities of fish farm-derived labile organic matter, nutrient concentrations at station B10 are greatly enhanced compared with the non-impacted station BC, reaching values approximately 10 times higher
- A sub-surface maximum in nutrient concentration is localized around 5 cm depth
0 4 8OC [%]
0
5
10
dept
h [c
m]
a
0 1000 2000 3000 4000NH+
4 [µ mol L-1]
0
5
10
dept
h [c
m]
b
0 400 800DIP [µ mol L-1]
0
5
10
dept
h [c
m]
c
0 20 40SO4 [mmol L-1]
0
5
10
dept
h [c
m]
d
0 40 80 120 160 200Fe2+
[µ mol L-1]
0
5
10
dept
h [c
m]
e
0 40 80 120Mn2+
[µ mol L-1]
0
5
10
dept
h [c
m]
f
Model predicted fluxes at the sediment-water interface
NH4 efflux mmol m-2 d-1
0.00
5.00
10.00
15.00
Bc B10
The model predicts a relevant enhancement in NH4+ efflux at the station
localized underneath the farm, with respect to the control station.
NH4 efflux and sediment oxygen demand, are predicted by the model.
0.0 4.0 8.0 12.0 16.0 20.0S(-II) [mmol L-1]
0
5
10
dept
h [c
m]
5% waste
2% waste
0 1000 2000 3000 4000NH+
4 [µ mol L-1]
0
5
10
dept
h [c
m]
5% waste
2% waste
Perspectives – Farm management practices
In order to test the integrated model sensitivity to boundary conditions, a scenario was run by assuming a lower percentage of fish food wasted, 2% instead of 5%,
These results indicate that the integrated model may represent an useful tool for studying the level of impact on sediment geochemistry associated with different management practices.
Perspectives - Prediction of cost-effective indicators
0.0 4.0 8.0 12.0 16.0 20.0S(-II) [mmol L-1]
0
5
10
dept
h [c
m]
The future aim of this class of models might typically be to focus on the prediction of cost-effective indicators of organic enrichment.
According to Hargrave et al. (1997) S = concentration and sediment oxygen demand are among the most sensitive indicators of fish-farm organic enrichement.
The current efforts to provide in situ monitoring of sediment ecosystems (e.g. www.cobo.org.uk) are improving the suitability of this class of geochemical-based measures.
Wildish et al. (2001) concluded that monitoring programs based on geochemical measures are more cost-effective than the ones based on macrofaunal community measures.
Concluding remarks
Two numerical models, simulating fish-farm waste deposition and early diagenesis processes in the sediment, were applied at a Fjordic Sealoch, for studying the impacts of finfish aquaculture on sedimentary redox dynamics.
The early diagenesis model (EDM) was calibrated by using a set of original field data of selected pore water and solid-state chemical species measured in the field at a pristine site.
The EDM response to an increase in OC rain due to the installation of the fish farm was studied by comparing model predictions with field data.
The study of the EDM behaviour under transient conditions and the potential use of the two integrated models for aquaculture site-selection and monitoring purposes were the most relevant aspects discussed in this work.
Thank you !
The authors gratefully acknowledge the assistance of Ms Susan McKinlay with core slicing, Mrs Heather Orr with CHN analysis, Ms Cheryl Haidon with metals analysis, Mr Tim Brand for phosphate analysis and Mr Martyn Harvey with sulphate analysis