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ICG-EMO Workshop – Brussels 2009. TBNT analysis in the North Sea with GEM model Xavier Desmit, Hans Los, Meinte Blaas Jan van Beek, Vincent Mastalerz, Erika Martini. Part 1. Objectives and Model Setup. 1.1 Objectives. Calculate fluxes across agreed boundaries in OSPAR maritime area.
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ICG-EMO Workshop – Brussels 2009 TBNT analysis in the North Sea with GEM model Xavier Desmit, Hans Los, Meinte Blaas Jan van Beek, Vincent Mastalerz, Erika Martini
Part 1 • Objectives and Model Setup
1.1 Objectives • Calculate fluxes across agreed boundaries • in OSPAR maritime area • Calculate nutrient budgets • Determine consequences in terms of eutrophication status • in sensitive areas (target and national areas)
1.2 Model Setup • GEM grid (ZUNO-3D), 3D hydrodynamic Curvilinear grid Resolution 1 to 20 km Nr of segments 10 x 4350
1.2 Model Setup • GEM grid (ZUNO-3D), 3D hydrodynamic • Traditional BC and forcings • River loads from CEFAS • GEM validation in : • Los, Villars and Van der Tol (2008) • Los and Blaas (2008) Curvilinear grid Resolution 1 to 20 km Nr of segments 10 x 4350
Atlantic Atm Dep UK2 GM NL2 UK1 NL1 Channel BE FR 1.2 Model Setup • GEM grid (ZUNO-3D), 3D hydrodynamic • Traditional BC and forcings • River loads from CEFAS • GEM validation in : • Los, Villars and Van der Tol (2008) • Los and Blaas (2008) Curvilinear grid Resolution 1 to 20 km Nr of segments 10 x 4350 • TBNT using tagging technique • Spin up 1996 + 1997-2001 with tagging • 10 cases (7 agg. Rivers + 2 Boundaries + 1 Atm Dep)
1.2 Model Setup National Areas Target Areas All daily gross fluxes are stored !!!
flux shadow flux NH4 NH4r 1.3 Tagging Technique • Principle of Tagging Method (N & P) • 1) New state variables are added • NH4-r • NO3-r PO4-r • all organic N species -r all organic P species –r • 2) Tagged variables are inputed in specific sources (e.g. RNL1) • 3) Shadow fluxes are computed on the basis of • - Real fluxes for corresponding untagged state variables • - Proportion of tagged to untagged variable (e.g. NH4-r/NH4) NH4r / NH4 x flux
Part 2 • Patterns of transport for N & P • (Movies of N & P)
2.1 Spreading from NL1 rivers TotN tagged TotP tagged Depth averaged
2.2 Spreading from UK2 rivers TotN tagged TotP tagged
Part 3 • Fluxes of Riverine N & P • (Fluxes of N & P through national areas)
Source / Sink Processing per km2 (removal of TotN) 3.1 Yearly fluxes of TotN TotN - Gross fluxes (kTon . yr-1) TotN - Net fluxes (kTon . yr-1)
From NL1 TotNr (kTon . yr-1) 3.3 Tagged N from UK2 & NL1 From UK2 Higher Residence time TotNr (kTon . yr-1)
From NL1 TotPr - Gross (kTon . yr-1) 3.4 Tagged P from UK2 & NL1 From UK2 TotPr - Gross (kTon . yr-1)
Source / Sink Processing per km2 (removal of TotP) 3.2 Yearly fluxes of TotP TotP - Gross fluxes (kTon . yr-1) TotP - Net fluxes (kTon . yr-1)
Part 4 • Contribution of Sources • (Seasonal Timeseries for N & P)
DIN OrgN TotN NLC2 NLO2 GC1 4.1 Seasonal ts for Nitrogen
DIP OrgP TotP NLC2 NLO2 GC1 4.2 Seasonal ts for Phosphorus
Part 5 • Impact on Phytoplankton Production • (Seasonal Maps of tagged Biomass)
2002 Spring (Feb16-Jun30) Summer (Jul01-Sep15) Autumn (Sep16-Oct31) 5.1 N-tagged phytoplankton RNL1 Units: gN m-3 RUK2
2002 Spring (Feb16-Jun30) Summer (Jul01-Sep15) Autumn (Sep16-Oct31) 5.2 P-tagged phytoplankton RNL1 Units: gN m-3 RUK2
Part 6 • Conclusions
Most of nutrient-limited areas are still under major influence of boundaries ( ) 6.0 Conclusions • Mass Balances • High residence times in combination with high loads make BE-NL-GM-DK areas efficient removers of TotN (and TotP in a lesser extent) • BE, NL and GM areas are zones of transit ; exogeneous material is processed • Yearly net boundary import happens at Channel bnd; Atlantic bnd net flux ≈ 0 • North Sea is a net sink for N & P in 2002; as are all national areas, except NL • Transport patterns show that fluxes of nutrients between two areas are sometimes concentrated in small “corridors” (case of Oysterground) • Timeseries • Only GM natnl area is dominated by rivers more than by boundaries (for TotN) • East coast is less under bound. influence than west coast (30-80% vs 95%) • TotP from UK (9 %) and TotN from NL (5 %) are contributors in Oysterground ; TotN from AD (3 %) is bigger than TotN from RUK