Anders Grimvall Department of Mathematics Linköping University, Sweden

1. Model-based and statistical methods for assessment of goal achievementEEA, Copenhagen 21-22 February, 2005 Anders Grimvall Department of Mathematics Linköping University, Sweden

2. Outline of presentation • Statistical methods for separating human impact from natural fluctuations • Ensemble runs of process-based models • Validation of models for scenario analyses EEA, 21-22 Feb 2005

3. Nitrogen load and water dischargeat Lobith on the Rhine EEA, 21-22 Feb 2005

4. Observed and normalised nitrogen load at Lobith on the Rhine EEA, 21-22 Feb 2005

5. Simple flow-normalisation of the riverine load of phosphorus at Brunsbüttel on the Elbe River EEA, 21-22 Feb 2005

6. Normalisation of the load of phosporuscarried by the Elbe River Normalisation with respect to water discharge, salinity and load of suspended particulate matter EEA, 21-22 Feb 2005

7. Conclusions- statistical normalisation • The combined effect of all past interventions in the drainage area can be clarified with a temporal resolution that is satisfactory for decision-making • The attribution of anthropogenic trends to specific interventions may require other tools EEA, 21-22 Feb 2005

8. General structure of process-based models of the flow of water and substances through a catchment The riverine load y at given time t and site z is a function of all inputs at all occasions up to time t and all sites upstream of z • Different types of model inputs: • initial conditions (state of the system at the onset of the simulation) • anthropogenic forcing of the system • meteorological forcing of the system • model parameters EEA, 21-22 Feb 2005

9. 120 120 100 100 80 80 60 60 40 40 20 20 1 11 21 1 11 21 120 120 100 100 80 80 60 60 40 40 20 20 1 11 21 1 11 21 120 120 100 100 80 80 60 60 40 40 20 20 1 11 21 1 11 21 Ensemble runs for meteorological normalisationof riverine loads Simulated Real weather data weather data Weather Step 1: generator The natural variation is suppressed Anthropogenic forcing Simulated Model output 1 weather data 1 Physics-based model Average output for each time t Step 2: Anthropogenic forcing Simulated Model output k weather data k Physics-based model

10. Meteorologically normalised outputs of the Integrated Nitrogen in Catchments (INCA-N) model A single sub-basin comprising only arable land and receiving a constant level of ammonium and nitrate fertiliser (combined total 156 kg N/ha/yr) EEA, 21-22 Feb 2005

11. Ensemble runs clarifying the response to changes in the fertilisation scheme • Run the model with (i) a given fertilisation scheme (ii) a slightly adjusted scheme and compute the difference between the two model runs. • Repeat such pairs of runs for a representative distribution of meteorological forcings and compute the mean output for each time t. • If the adjustment is zero for the second year and onwards, we can summarise the results in impulse response functions for the impact of fertiliser application. EEA, 21-22 Feb 2005

12. Predicted response of riverine loads of inorganic nitrogen to an impulse in fertiliser application A single sub-basin comprising only arable land Base-flow index 0 Ratio of the cumulated increase in riverine loads to the increase in fertiliser application Travel time of the extra nitrogen added

13. Predicted response of riverine loads of inorganic nitrogen to an impulse in fertiliser application A single sub-basin comprising only arable land Base-flow index 1 Ratio of the cumulated increase in riverine loads to the increase in fertiliser application Travel time of the extra nitrogen added

14. Ensemble runs clarifying water travel times The response to changes in the input of an inert substance can be clarified by performing ensemble runs in which: all processes involving transformation or immobilisation of nitrogen are switched off An inert substance moves like the water in which it is dissolved EEA, 21-22 Feb 2005

15. Distributions of travel times for inorganic nitrogen and water Preferential removal of nitrogen taking long pathways Base-flow index 1 Base-flow index 0 EEA, 21-22 Feb 2005

16. Conclusions- ensemble runs Ensemble runs involving artificially generated meteorological inputs can be employed to: • Extract model features that might otherwise be hidden by the total variation in the model output • Compute meteorologically normalised model outputs for retrospective or scenario analyses of riverine loads EEA, 21-22 Feb 2005

17. Conventional model validation Proper validation requires data sets having a substantial variation in the input under consideration EEA, 21-22 Feb 2005

18. Conclusions- validation of process-based models • Proper validation of models for scenario analyses can only be done in catchments where substantial interventions have been undertaken • It can be questioned whether the INCA-N model, and many other models, are able to predict long lags in the water quality response to interventions in the drainage area EEA, 21-22 Feb 2005

19. Overall conclusions • Statistical normalisation can give added value to observed data • Ensemble runs can give added value to process-based models • The currently used model validation techniques can be questioned EEA, 21-22 Feb 2005

20. Further reading • Hussian, M., Grimvall, A., and Petersen, W. 2004. Estimation of the human impact on nutrient loads carried by the Elbe River. Environmental Monitoring and Assessment96:15-33. • Wahlin, K., Shahsavani, D., Grimvall, A., Wade, A. and Butterfield, D. 2004. Reduced models of the retention of nitrogen in catchments. In C. Pahl-Wostl, S. Schmidt, A.E. Rizzoli, and A. J. Jakeman (eds.) Complexity and Integrated Resources Management, Transactions of the 2nd Biennial Meeting of the International Environmental Modelling and Software Society, iEMSs: Manno, Switzerland, 2004. EEA, 21-22 Feb 2005

21. Flow normalisation of the nitrogen load carried by the Göta River - data from Trollhättan Normalisation with respect to water discharge EEA, 21-22 Feb 2005