1. HYPE model simulations for non- stationary conditions in European medium sized catchments Göran Lindström & Chantal Donnelly, SMHI, Sweden IAHS, 2013-07-23, Göteborg, Sweden. Hw15 - Testing simulation and forecasting models in non-stationary conditions After the Gudrun storm January 2005 Photo: H.Alexandesson

2. Outline Objectives  Simulate non-stationary conditions, for this workshop.  Evaluate effects of the Gudrun storm in 2005. Modeled basins  Garonne (France, increase in temperature, decrease in discharge)  Durance (France, increase in temperature, decrease of glacier)  Lissbro (Sweden, forest loss due to Gudrun storm)

3. HYPE model Hydrological Predictions for the Environment  Simulates daily fluxes and turn-over of water, Nitrogen & Phosphorus  Integrated soil- and groundwater, substances follow water flow paths  Developed for large-scale applications  Routing in rivers & lakes (incl. regulation)  Parameters are linked to soil type or land-use, and calibrated  Each combination of soil type and land-use is modeled separately  First version was developed in 2005-2007, and continuously developed  Potential evaporation by air temperature with seasonally varying factor

4. Soil typesLand useSLC+ Soil/Land Use classes (SLC) Most parameters coupled to soil or land-use

5. HYPE in the world

6. Durance and Garonne – -35000 subbasins -Median size 215 km 2 -Used for hindcasting, operational forecasting and future climate predictions, Q, Nitrogen and Phosphorous Taken from the E-HYPE pan-European application of the HYPE model For Durance and Garonne: Local model taken from E-HYPE (subbasin delineation, landuse, soil-type, lakes, glaciers, irrigation etc) Used the local forcing data (but with height adjustment to height of each subbasin in catchment for temperature) Calibrated to given Q data by adjusting ’super-parameters’ (also Precip correction where required)

7. Trends over data period: Observed Trends: Simulated vs Observed Trends: Modeled decrease slightly too weak

8. Durance Catchment area: 2170 km² P mean T mean Q mean NSERE (%) 1971-198016082.8530.670 1998 -200711504.1450.55+2

9. Garonne Catchment area: 9980 km²  HYPE underestimated the decrease in discharge  Temperature increase not the only cause of decreasing discharge?  Temperature increased by ~1.2 ºC (whole period)  Precipitation decreased by ~8% (whole period)  Data uncertainties?  Regulation, irrigation? P mean T mean Q mean NSERE (%) 1971-198012328.5222.60.78 1998-2007108610.0155.50.50+12

10. Does glacial melt in the Durance catchment explain non-stationarity? Glacier = 8 % of areaGlacier = 1 % of area

11. S-HYPE model for Sweden  For support to implementation of EU Water Framework Directive, forecasting etc.  ~ 35000 subbasins, ~15km2 subbasin resolution Cal/Eval at 400 stations Interpolation Runoff and discharge

12. Gudrun storm, January 2005  About 70 M m3 of trees were blown down.  18 people died (in Sweden)  Three worst storms in Sweden: 1902, 1969 and 2005  In a region affected by a summer flood in 2004  Worries about increased flood risk after loss of forest  Also known as Erwin storm

13. Gudrun storm January 2005  In the worst hit areas ~8 % of trees were blown down. Max wind speed (m/s) Loss of forest (m3/ha) Lissbro

14. Lissbro 97 km2, 81% forested, 1 % lakes 2004 summer flood The 10 years before Gudrun

15. Previous HBV study of clearfelling Brandt et al. (1988), small-scale experiments, central Sweden Discharge: +165-200 mm/yea r

16. Lissbro, Reference (no change in model) 4 key parameters adjusted to Lissbro data

17. Lissbro, Simulated clearfelling 8% of the forest converted to clearfelling Change in SLC classes

18. Lissbro, Decreased PotEvap Forest PET ~15% higher than open areas (8% forest loss) Change in PET parameter

19. Conclusions  Trends in discharge were fairly well captured by the HYPE model for the two French basins (but modeled discharge decrease was too weak in Garonne).  Glacier development had negligible effect in Durance.  The effects of the Gudrun storm on discharge in Lissbro were very small (within the uncertainty in the model calibration period).