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Helmut Fischer, Volker Kirchesch, Katrin Quiel, Andreas Schöl Bundesanstalt für Gewässerkunde

Influence of global change on the water quality of the Elbe/Labe Einfluss des globalen Wandels auf die Gewässergüte der Elbe. Helmut Fischer, Volker Kirchesch, Katrin Quiel, Andreas Schöl Bundesanstalt für Gewässerkunde. Contents. 1. Introduction Position in the model network

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Helmut Fischer, Volker Kirchesch, Katrin Quiel, Andreas Schöl Bundesanstalt für Gewässerkunde

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  1. Influence of global change on the water quality of the Elbe/Labe Einfluss des globalen Wandels auf die Gewässergüte der Elbe Helmut Fischer, Volker Kirchesch, Katrin Quiel, Andreas Schöl Bundesanstalt für Gewässerkunde

  2. Contents 1. Introduction Position in the model network Factors influencing water quality The water quality model QSim 2. Results Longitudinal development of water quality Model results (validation) Temporal development of water quality 3. Conclusions and outlook

  3. Einleitung: Gliederung X13 X4 X1 X3 Z5 X15 X2 Y3 Y5 Y2 Z4 Z3 Y8 Z2 Y1 Y4 Z1 Y7 Z1 Y6 Z9 Z7 Z6 Z8 Introduction - position of the project in the model network Land use and regional water balance Nutrient load (MONERIS) Water management (WBALMO) Hydrological cycle and crop yields (SWIM) Point source: Industry Inflow Land use (LAND USE SCANNER) Point source: Sewage plant Regionalization of global change Evaporation Diffuse source: Sealed surfaces Future climate (STAR) Wetlands Diffuse source: Erosion Water suppliers Diffuse source: Atmospheric Deposition Development of agricultural sector (RAUMIS) Industry Diffuse source: Drainage Water use Diffuse source: Surface denudation Mining Economics and demography (REGE) Wetlands (MODAM) Diffuse source: Groundwater Power plants Diffuse source / Sink: Wetlands Households /business (HAUSHALT WASSER) Irrigation Development of energy sector (KASIM) Industry (INDUSTRIE WASSER) Nutrient concentr.PhytoplanktonOxygen Minimal flow for conservation Energy / Mining (KASIM) Water quality (QSim) Transport on inland waterways Agriculture / Irrigation Development of water technologies Transport on inland waterways

  4. X1 X15 X2 Y3 Y5 Y2 Y8 Y1 Y7 Y6 Introduction - position of the project in the model network Land use and regional water balance Nutrient load (MONERIS) Hydrological cycle and crop yields (SWIM) Point source: dir. ind. discharges Point discharges: WWTP Regionalization of global change Land use (LAND USE SCANNER) Diffuse Source: imp. surfaces Future climate (STAR) Diffuse Source: Erosion Diffuse Source: Atm. Deposition Development of agricultural sector (WATSIM-RAUMIS) Diffuse Source: Drainage Water use Diffuse Source: Surface runoff Economics and demography (REGE) Wetlands (MODAM) Diffuse Source: Groundwater Households / business (HAUSHALT WASSER) Sinks: Surface waters/Wetlands Industry (INDUSTRIE WASSER) Agriculture / Irrigation Water quality (QSim) Development of water technologies Nutrient concentr.PhytoplanktonOxygen

  5. Introduction Factors influencing algal biomass and nutrient budget and Models providing input data for QSim  Climate(temperature, light regime, precipitation)  model “STAR“, PIK  Discharge (water depth, retention times)  model “SWIM“, PIK Nutrients (inputs, concentrations)  model “MONERIS“, IGB nutrient budget  Algal growth and biomass

  6. phytoplankton diatoms, green and bluegreen algae dissolved nutrients O2 NH4, NO2,NO3, P, Si organic carbon POC/DOC heterotrophic bacteria, nitrifying bacteria zooplankton rotifers mussels generating segments planktonic modules benthic modules heat- balance discharge hydraulics Introduction – the model QSim Schöl et al. 1999, 2002

  7. Longitudinal („Lagrangian“) sampling campaigns: chlorophyll a 200 250 200 200 150 150 150 100 100 100 50 50 50 0 0 0 0 100 200 300 400 500 600 0 100 200 300 400 500 600 0 100 200 300 400 500 600 Results Summer2000 26.6. – 5.7. (9 days) low discharge Summer2005 24.7. – 1.8. (7 days) medium discharge Spring2006 8.5. – 15.5. (6 days) high discharge Chlorophyll a [µg/l] Elbe-km 18:00 n = 3 High phytoplankton biomass leads to oxygen supersaturation in the Middle Elbe (up to 200% in summer) and and heavy loads of organic matter in the tidal area. Elbe 6:00 18:00 tributaries 6:00

  8. Chlorophyll a – measured and modelled data Results Sommer2000 26.6. – 5.7. low discharge Chlorophyll a [µg/l] 18:00 Elbe 6:00 18:00 tributaries 6:00 Elbe-km

  9. Longitudinal sampling campaigns: dissolved phosphorus (ortho-phosphate) Results Summer2000 26.6. – 5.7. (9 days) low discharge Summer2005 24.7. – 1.8. (7 days) medium discharge Spring2006 8.5. – 15.5. (6 days) high discharge ortho-PO4-P [mg/l] Elbe-km Elbe n = 6 Tributaries

  10. Results • Phosphate – measured and modelled data Sommer2000 26.6. – 5.7. low discharge ortho-PO4-P [mg/l] Elbe tributaries Elbe-km

  11. Longitudinal sampling campaigns: silica Results Summer2000 26.6. – 5.7. (9 days) low discharge Summer2005 24.7. – 1.8. (7 days) medium discharge Spring2006 8.5. – 15.5. (6 days) high discharge SiO2-Si [mg/l] Elbe-km Elbe n = 6 Tributaries

  12. Results • Silica – measured and modelled data Sommer2000 26.6. – 5.7. low discharge SiO2-Si [mg/l] Elbe tributaries Elbe-km

  13. Longitudinal sampling campaigns: nitrogen (nitrate and total nitrogen) 4 7 4 6 3 3 5 4 2 2 3 2 1 1 1 0 0 0 0 100 200 300 400 500 600 600 0 100 200 300 400 500 0 100 200 300 400 500 600 Results Summer2000 26.6. – 5.7. (9 days) low discharge Summer2005 24.7. – 1.8. (7 days) medium discharge Spring2006 8.5. – 15.5. (6 days) high discharge NO3-N, total N [mg/l] Elbe-km Total-N n = 6 Elbe NO3-N Total-N Tributaries NO3-N

  14. Results • Nitrate – measured and modelled data Sommer2000 26.6. – 5.7. low discharge NO3-N [mg/l] Elbe tributaries Elbe-km

  15. Chlorophyll a [µg/l] 300 modelled Chlorophyll a km 3.9 250 Dec measured 240 200 230 150 Nov 220 100 210 50 Oct 200 0 Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec 190 300 Sep 180 km 172.6 250 170 200 Aug 160 150 [µg/l] 150 100 140 Jul 50 a 130 0 Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec 120 Jun 300 110 left km 318.1 250 mean (left, right) 100 Chlorophyll May 200 90 150 80 100 Apr 70 50 60 0 Mar Jan Feb Mar Apr May Jun Jul Aug Sep Okt Nov Dec 50 300 40 km 474.5 250 Feb 30 200 20 150 Jan 10 100 0 50 0 100 200 300 400 500 0 Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec Elbe-km 1998 Results Chlorophyll a (1998) measured and modelled (Schöl et al. 2006; measured data by ARGE Elbe) modelled

  16. Results Comparison of phytoplankton composition in the years 1998 and 2003 2003 a warm and dry summer  development of cyanobacteria (data by ARGE Elbe)

  17. Results 2003Measured phytoplankton composition (data ARGE Elbe) 2003Modelled phytoplankton composition

  18. Results In which direction will the system change if - temperature increases - summer discharges decrease Model runs with database from years 1998 and 2003 Model results • slightly more zooplankton • only minor change in algal biomass • change in phytoplankton composition (increase of cyanobacteria)

  19. Conclusions and Outlook • Current state • strong phytoplankton growth (factor ~4) • intense turnover of nutrients (biologically controlled) • heavy organic load („secondary pollution“) impairs the oxygen budget of the Elbe estuary • high nutrient loads are problematic for coastal areas • Future state • intensification of biological processes- better growth conditions (light, temperature) • stronger nutrient limitation is possible • better conditions for cyanobacteria • open system for invading species (e.g. mussels) + - Phytoplankton development + -

  20. Conclusions and Outlook • Further work within GLOWA-Elbe II • Modelling nutrient dynamics and phytoplankton growth in the Czech Elbe section (below Vltava/Moldau). • Calculation of 5 climate scenarios x 4 socio-economic scenarios • Future state • intensification of biological processes- better growth conditions (light, temperature) • stronger nutrient limitation is possible • better conditions for cyanobacteria • open system for invading species (e.g. mussels) + - Phytoplankton development + -

  21. Thank You for your attention!

  22. Longitudinal sampling campaigns: (ortho-phosphate) and total phosphorus Results Summer2000 26.6. – 5.7. (9 days) low discharge Summer2005 24.7. – 1.8. (7 days) medium discharge Spring2006 8.5. – 15.5. (6 days) high discharge ortho-PO4-P, total P [mg/l] Elbe-km Total-P n = 6 Elbe ortho-PO4-P Total-P Tributaries ortho-PO4-P

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