Nitrogen (and phosphorus?) surface water retention in the Baltic Sea drainage basin

Transcription

1 Nitrogen (and phosphorus?) surface water retention in the Baltic Sea drainage basin Per Stålnacke Bioforsk

2 Retention processes (Howarth et al. 1996): storage in catchment biomass as for instance in aggrading forests; storage or denitrification in catchment soils; storage or denitrification in groundwater; denitrification or storage in wetlands or riparian zones, i.e. at the interface between ground- and surface water; in-stream processes of retention, either by benthic or planktonic denitrification or by storage in sediments.

3 Phosphorus retention in lakes as a function of water renewal time 0% retention 100% retention

4 Nitrogen retention as a function of hydrology (Behrendt and Opitz, 2000) 0% retention 100% retenion

5 Stream depth (D in meters) was established from the following relationship (R2=0.83) with water-flow (m3 s-1): D= S Retention (L) was expressed as a fraction of external inputs, a loss rate (per unit water travel time) according to R= - t-1 ln(1 - L), where t is the water time of travel and ln is the natural logarithm(alexander et al, 2000)

6 Artificial drainage reduces the general riparian nitrate retention of the affected watersheds (Garnier and Billen, 199) % riparian retention Saulx Marne Oise Upper Seine Surmelin Petit Morin Eure Grand Morin % watershed drained

7 Mean nitrate concentrations at the outlet of the tile drains and at main channel in outlet of two agricultural stream in Estonia and Latvia (Stålnacke et al., 1999) mg / L nitrate-n Tile drain outlets Mouth of catchment Kahametsa Berze

8 RECOCA project BONUS-project with aim to update CSIM (BNI for BSAP)

9 Input data (divided into 117 basins) Time-average annual load ( ) for 87 river basins (Helcom 77 basins, NERI 10 basins) WWTP emissions (HYDE data base on population and assumptions of treatment efficiency) Land cover on 4 classes: cultivated, wetland, lake area, other land (CORINE) Industrial point sources (Helcom)

10 NUTRET software tool (EURO-HARPproject)

11 The MESAW model Statistical model concept developed by Grimvall & Stålnacke (1996) Uses relationships between riverine loads and land-use statistics or other explanatory variables (soil, weatherdata) Simultaneous estimates of source strenght (e.g export coeff.) and retention coefficients

12 MESAW uses these kind of relationships 10 9 Subbasin load (output-input) vs. open land Area-specific losses (kg/ha N) Open land (%)

13 Originally developed for evaluation of outputs from several sub-basins Output from a given sub-basin = Input from upstream sub-basins + Emissions - Retention B1 B1 B2 B3 B2 B3 B4 B4 Outlet

14 Load at the outlet of an arbitrary subbasin can be estimated from: n L ( 1 R ) L ( 1 R ) S ( 1 R ) P ( 1 R ) D i i, j j j 1 L i = load at outlet of subbasin i, L j = load at outlet of nearest upstream subbasin j, R i,j = retention on the way from outlet of subbasin j till outlet of subbasin i, n = number of subbasins located nearest upstream, S i = total losses from soil to water in subbasin i, P i = point source discharges to waters in subbasin i, D i = atmospheric deposition on surface waters in subbasin i, R 1-3 = retention for the source emissions S, P and D, respectively, e i = statistical error term. 1 i 2 i 3 i i

15 Retention from outlet of subbasins to river mouth in Daugava with the MESAW statistcial model (unpublished from GoR-project)

16 Nitrogen concentrations vs. land use in 107 Baltic Sea rivers (Stålnacke et al. 2009) y = 6,0525x + 0,3748 R 2 = 0,7475 Danish streams TN (mg/l) Lielupe German rivers Daugava Pregel Narva 0,00 0,10 0,20 0,30 0,40 0,50 0,60 0,70 0,80 0,90 % cultivated land

17 1 model run: No statistically significant parameters

18 MESAW calibration (Stålnacke et al. 2015)

19 MESAW calibration (Stålnacke et al. 2015)

20 4th model run: many statistically significant parameters (p<0.10)

21 Retention: lakes: lake area /drainage basin area Rivers: sqrt drainage area Statistically significant parameters (p<0.10)

22

23 Realistic export coefficients Multiplicative diffuse export coeffiecients

24 Observed load vs MESAW predicted load

25 Retention (%) Model-fit (Stålnacke et al. 2015) y = x R² = Coast DK and the Sound Coast North of Northern Kattegat Lake area (%)

26

27 Nitrogen surface water retention in 117 Baltic Sea river basins with MESAW (Stålnacke et al. 2015) 5000 tonnes N/yr 6900 tonnes N/yr retained = 56%

28 Summary of results: Nitrogen around 380,000 tons of nitrogen is annually retained in surface waters. In comparison, the total riverine load to the Baltic Sea has been estimated to 570,000 tons N/yr from these 117 basins. This means that the retention is around 40%. In terms of absolute retention values, three river basins account for 50% of the total retention in the 117 basins; i.e., tons in Neva, tons in Vistula and tons in Oder. Most of the retention occurs in lakes. In Göta älv we estimated a total retention of 72%, whereof 67% occurred in the lakes of that drainage area (Vänern primarily). Other river basins with high retention were Kymijoki (70%), Motalaström (73%) and Neva (74%). All these basins are characterized by a high percentage of lakes. Low retention was estimated for lake-poor basins, e.g., Aurajoki (2%), Kasari (4%) and Kelia (3%).

29 Phosphorus retention estimates with MESAW with much worse results

30 Summary of results: Phosphorus Very uncertain results around 12,000 tons of phosphorus is annually retained in surface waters in the 76 Baltic Sea basins with measured P loads. In comparison, the total riverine load to the Baltic Sea from these 76 basins was estimated to 17,000 tons P/yr, giving a phosphorus retention of 41%.

31 Previous estimates of river system retention in the Baltic Sea 30% of TP and TN (Helcom) Mörth et al. (2007), reported a mean N retention of 15% in the Baltic Sea rivers MESAW retention results: N: 40% or 380,000 tonnes (117 basins) P: 40% or 12,000 tonnes (76 basins)

32 Conclusions MESAW used uniform input data and estimation of retention in all 117 BS basins MESAW also gives uncertainty estimates We found the highest retention in basins featured by huge lake area MESAW retention results: N: 40% or 380,000 tonnes (117 basins). 95% CI: 85,00-523,000 tonnes Phosphorus retention is very uncertain: (40% or 12,000 tonnes (76 basins))

33 Further applications MESAW presently applied in Estonia (EEA/Norwegian Funding Mechanism) using data from some 50 sampling sites (subbasins) in addition to physicallybased models (see presentation by Ennet).