1. Institut für Wasser- und Umweltsystemmodellierung Lehrstuhl für Hydrologie und Geohydrologie Prof. Dr. rer. nat. Dr.-Ing. András Bárdossy Pfaffenwaldring 61, 70569 Stuttgart, Deutschland www.iws.uni-stuttgart.de Universität Stuttgart Simultaneous calibration of hydrological models to capture non-stationary conditions András Bárdossy & Yingchun Huang

2. 2 Introduction Non stationary conditions: –Input: Weather – Climate –Properties: Land use How to cope: –Temporal (limited in extent) –Spatial (limited in similarity) Similarity vs. Self similarity

3. Modelling Model calibration – parameter estimation –Known input and output Select a model Select performance criteria (NS, GK, Multiobjective) Optimization principle –Search a single optimum –Search a set of optima (equifinality) Model „Validation“ –Known input and output Test calibration parameters on a different case

4. Goals To capture the essential features –Transferable to different conditions –Modelling is not repeating what was observed

5. The tools Models –HBV –Hymod –Xianjiang Performance –NS –KG –NS+LogNS Calibration method –ROPE – depth based calibration (Half space depth) with a set of optima (represented by 10000 pars.)

6. ROPE MC step Selection step (Best 15%)

7. Monte Carlo of the deep in the selected

8. Select best 15%...

9. Location of the study area 15 selected catchments (300-1800 km 2 )

10. Weather is not stationary (1950-2000) 10 year mean values –0.5C difference between the mean values –Up to 40 % difference in precipitation Investigate transfer from one time period to the other –10 years intervals starting 1950

13. Model performance –Strongly dependent of the application period –Weakly dependent on the calibration period Bad news – we can only modify calibration For 8 catchments all calibrations transfer well for all periods good guys For 3 catchments many transfers are problematic bad guys

14. Optimal performance –For the given „distribution“ of weather Same weather different frequencies  different parameters –Parameter estimation „for any weather distribution“ Distribution from other time periods (Time) Distribution from other catchments (Space)

15. Method 1 -Time Adjust weather of the calibration period –Emphasize years with weather similar to target Known for observed periods „Given“ for climate change –Reshuffling not possible due to discharge observations –Weighting

16. Weighted objective function

17. Simple trick Little but positive effect –Better transfer of the model parameters Mean – most cases Minimum nearly always –Catchment 4 1960 applied for 1970s NS 0.508  0.526

18. Method 2 - Space There are other catchments which experienced different (target) weather (German weather will be like Italian) –Take a similar catchment and use it –What is similar? –How to use it?

19. Similar – if common parameters work well in the calibration period Common parameters obtained via common calibration Parameters which are good for all catchments

20. Pairwise application C=2 –All good for all bad –Similarity over the calibration periods –If common calibration does not deteriorate calibration quality then similar

21. Transfer Calibration 1970-1979 application 1950-1959

22. Gain in the average for catchment 4

23. Gain in the average for catchment 9

24. Gain in the minimum for catchment 4

25. Gain in the minimum for catchment 11

26. Method 2 results Common calibration improves transfer quality if similar catchments are used Similarity can be recognized Minimum performance is strongly improved –low risk of failure

27. Other possibilities Common calibration with C>2 Filtering observation errors –Bias –Random errors Common calibration for land use change –Using implicit assumptions –Assigning parameter(s) to land use and calibrate individually

28. Summary A good model should work under all conditions  transferability Transferability is mainly receiver dependent Transferability can be improved –Using a weather mix (weights) –Using other catchments via common calibration

29. Does this matter at all?

30. 10 years + 1 C o scenario

31. Thank you!