Defining N Reductions based on Mechanistic Biogeochemical

Defining N Reductions based on Mechanistic Biogeochemical Models

Anders Chr. Erichsen, Hanne Kaas, Trine Cecilie Larsen & Flemming Møhlenberg

Background

•  Since the late 80’ties Denmark has been reducing nutrient loadings significantly and as part of the European Water Frame Work Directive, Denmark has increased the reductions targets once more aiming to obtain “Good Environmental Status” in coastal areas.

•  The last reductions (1. generation action plans) towards 2015 goals did, however, not get through parliament silently. The reductions were based on relatively simple empiric models. Hence, the agricultural & aqua cultural society, NGO’s (nature preserving, anglers etc.) as well as the scientific community all agreed on the need for much more science based ecosystem tools for future environmental management.

22 January 2016 © DHI #2

•  The simple empiric model includes all water bodies

•  No temporal differences included

•  Depth limited of eelgrass is used as the only indicator for monitoring the environmental progress

1. Generation of River Basin Management Plans


'Laurentius-‐ligningen'Ålegræs dybde = 214 * (kvælstof) -‐0.68

R² = 0.42

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0 200 400 600 800 1000 1200 1400

Ålegræ

s dybdegræ

nse (m)

Kvælstof (mg N/m3)

Eel

gras

s D

epth

Lim

it (m

)

Total Nitrogen (mg/m3)

Photo: Peter Bondo Christensen

Recommendations: ”Eelgrass and marine quality elements” Recommendations from the working group: ”Eelgrass and marine quality elements” (from the Conference on the further development of the scientific foundation of the Danish River Basin Management Plans, September, 2012): •  There is a need for mechanistic modeling tools, covering the Danish coastal waters and

selected fjords, as well as more simple statistical tools for other, smaller fjords •  Model tools should include dynamic considerations regarding eelgrass (spatial coverage)

and include eelgrass buffer effect and feedback mechanisms in terms of nutrients •  Model tools shall include other quality elements in addition to eelgrass, ie. phytoplankton,

macro algae and benthic fauna - as far as possible •  Sediment shall be included in the models (re-suspension and pools of nutrients) •  The tool shall be able to assess the impact of climate change •  And if possible, the tool shall be used to assess the effects of bottom trawling, and mussel dredging,

as well as marine measures as establishment of stone reefs, mussel farming and seaweed production


Objective of the development supporting the 2. generation

•  Improve the tools for setting differentiated actions and estimate specific nitrogen target loads within each of the 119 Danish water bodies

•  Describe more inter-calibrated quality elements:

•  Summer chlorophyll-a •  Eelgrass depth limit proxy Kd

•  Danish Quality Index – not a part of the model results •  Provide spatial and temporal estimates of the status •  Include loadings from neighboring catchments

Project done in close cooperation between DHI, Aarhus University and the Danish EPA


Mechanistic Models

•  Four biogeochemical models •  The inner Danish Waters –

including the Baltic Sea •  The Odense Fjord •  The Roskilde Fjord •  The Limfjord


© DHI

Chemicals 12 nm

WFD areas

Nutrient input

•  More than 400 sources (national and regional)

•  Based on Danish national estimates •  Based on HELCOM PLC

•  Estimated retentions from local models included in the regional model


Mechanistic Models

Setup and Validation


ECO Lab template

Biogeochemical Model (Inner Danish waters) Pelagic phase: •  Phytoplankton (C, N, P) – 3 groups •  Chlorophyll-a •  Zooplankton (C) – groups of micro & meso •  Detritus (C, N, P) - POM •  Ammonia (NH4) •  Nitrate and Nitrite (NOx) •  Inorganic P (PO4) •  Dissolved Oxygen •  DOM (C, N, P) – refractory and labile

Sediment phase: •  Sediment organic material (C, N, P) •  Sediment inorganic nutrients (NH4, NOx, PO4) •  Iron bound P •  Reduced substance •  Sediment inorganic material Benthic primary production: •  Macro algae •  Sea grass •  Micro benthic algae

Validation – 10 years of modelling


•  Day-to-day variations •  Gradients •  Year-to-year variations

Focus on •  Quality indicators •  N and P supporting

parameters

Validation – last 5 years data


5 categories: •  Sluice fiords •  Closed eutrophied

fiords •  Closed semi-

eutrophied fiords •  Open fiords and

bays •  Open waters Orange: Measurements Blue: Model


Eelgrass Biomass



Validating Chlorophyll-a


•  Inner Danish Waters: •  BIAS: 0,3 µg/l •  R2=0.71

•  The Limfjord: •  BIAS: 1.9 µg/l (~25%) •  R2=0.48

•  The Odense Fjord: •  BIAS: 0,4 µg/l (<10%) •  R2=0,53.

•  The Roskilde Fjord: •  BIAS: 0,1 µg/l (<10%) •  R2=0,49.

KdPAR as Eelgrass Depth Limit Proxy

•  Why is light important? •  Light is a prerequisite for eelgrass growth –

however, other pressures are also important, but without light no growth

•  Is light alone enough? •  Changes in light is impacted by changes in

eelgrass – feedback! •  The models includes feedback mechanisms,

and especially the initial change in light as a result of changed re-suspension


Photo: Peter Bondo Christensen

N Reductions

Model scenarios


Mechanistic models - Scenarios

Status situation: Estimation of summer chlorophyll-a (Maj-September) and summer Kd values (Marts-September): •  Danish N load: Present load •  Baltic Sea N load: Present Load •  Atmosphere N load: Present load Scenarios: Estimation of summer chlorophyll-a (Maj-September) and summer Kd values (Marts-September): •  Danish N load : 15%, 30% or 60% reductions in DK N-load combined with

10-20% reductions in P-load •  Baltic Sea N load : BSAP •  Atmosphere N load : Göteborg Protocol Reference situation: Estimation of summer chlorophyll-a (Maj-September) and summer Kd values (Marts-September) corresponding to a period approx. 100 years ago: •  Danish N load : Background load of N og P •  Baltic Sea N load : Loads corresponding to the period 1890-1910 based on

BNI data •  Atmosphere N load : Modelled deposition corresponding to year 1900 (DCE) 22 January 2016 © DHI #19

Background data

• Collection of key-data from water body N and P loads

Model runs

• Validated models used for 6 predefined N and P scenarios

Relations

• Relations between status and N and P loads established

N & P

• Screenings tool applied for estimating reduction targets


Danish N-load

Status: Present load, DK Present load, Baltic Sea Present load, Atmosphere

Indicator Chlorophyll/Kd

Slope based on Danish N

Reference- load

2007-2011 load

Screening Method

Reference: Background load (~30% of present load), DK Reference load, Baltic Sea, BNI Reference load, Atmosphere, DCE

Scenarios: DK reductions (15%, 30% & 60%) BSAP, Baltic Sea Göteborg protocol, Atmosphere

Effects of BSAP & GP

Examples



Indicator Chlorophyll/Kd

Max. effect of DK N

Effects from other sources

and factors

Screening Method

Slope based on Danish N

Status: Present load, DK Present load, Baltic Sea Present load, Atmosphere

Reference: Background load (~30% of present load), DK Reference load, Baltic Sea, BNI Reference load, Atmosphere, DCE

Scenarios: DK reductions (15%, 30% & 60%) BSAP, Baltic Sea Göteborg protocol, Atmosphere

Danish N-load

Reference- load

2007-2011 load

Average part of chlorophyll explained by DK N (water body based data)


Labelling N

•  Danish Catchments run-off N

•  North Sea originating N (some Danish N included)

•  Baltic Sea catchment run-of and atmospheric N depositions (Danish N included)


Dansk N’s andel af overflade sommer klorofyl (avg 2007-2011 summer CH)



Method – Step by step


Status

• Estimating status: Observations integrated with the model results to calculate status of the water body (and not for the single monitoring station)

Need for reductions

• Determination of the distance between status and target values

Regional effects

• Potential effects from BSAP and Göteborg Protocol subtracted

Danish part

• The absolute distance (corrected for BSAP and GP) between status and target is recalculated to take the Danish part into account

Reduction targets

• Calculating the reduction targets for each of the two indicators • Calculating the reduction targets for the different water bodies or groups of water bodies

Reductions - examples


LIMFJORDEN Water body

Based on chlorophyll

Based on Kd

Average Group

Nissum Bredning, Thisted Bredning, Kås Bredning, Løgstør Bredning, Nibe Bredning, Langerak

156 46% 28% 37% 32%

Bjørnholms Bugt, Riisgårde Bredning, Skive Fjord, Lovns Bredning

157 66% 39% 52% 48%

Hjarbæk Fjord 158 77% 34% 56% 56%

KATTEGAT

Kattegat, Læsø 154 0% 8% 4% 7%

Kattegat, Aalborg Bugt 222 0% 29% 14%

Nordlige Kattegat - Ålbæk Bugt 225 0% 0% 0%

Hevring Bugt 138 0% 22% 11%

Djursland Øst 140 0% 18% 9%

Anholt 139 0% 11% 5%

Kattegat, Nordsjælland 200 0% 26% 13%

Kattegat, Nordsjælland > 20m 205 0% 0% 0%

Mechanistic Modelling & MSFD

•  WFD vs. MSFD •  Nutrient interactions and turnover is depending on shallow water environment and retention •  WFD impacts MSFD but targets are not aligned

•  Choice of indicators - The indicator should react to the parameters regulated •  Indicators for regulation •  Ecosystem indicators – eelgrass, hypoxia, etc. Important but response is slow (decades) •  Recover?

•  Descripter 5: Eutrophication •  Mechanistic model results as input to other


Status

COMMUNICATION FROM THE COMMISSION TO THE EUROPEAN PARLIAMENT AND THE COUNCIL: (Free translation) …… It is necessary that the Member States increases their efforts to base their action plans on a thorough assessment of pressures and impacts on the aquatic ecosystem and a reliable assessment of the water body status. If the evaluation of status is inadequate, all river basin management plans will indeed be ill-founded, and there will be a risk that Member States do not act where the need is greatest, and in a cost effective manner. Hence, monitoring should be maintained and/or improved.

Integration of measurement as we have done or by DA (but DA in hindcast and not for forecast purpose!)


Uncertainties – and what to do about them?

•  Status •  Point measurements vs. water body average •  Targets values •  Uncertainties in pressures (loadings etc) •  Slope (sensitivity to pressures) •  Implementations on regional treaties

•  All the above will impact the reduction targets and hence the socio-economic impact

•  ….. So how do we define and handle uncertainties?


Thank you for your attention … Anders Chr. Erichsen ([email protected])


Documents

Defining N Reductions based on Mechanistic Biogeochemical