Analysis of the dependence of the optimal parameter set on climate characteristics

1. Analysis of the dependence of the optimal parameter set on climate characteristics Marzena Osuch, RenataRomanowicz, Emilia Karamuz Institute of Geophysics Polish Academy of Sciences, POLAND

2. Aims • Cross-validation of a conceptual rainfall-runoff model (HBV) • Analysis of the temporal variability of the HBV model parameters • Dependence of model parameters on climate characteristics • Assessment of influence of climate characteristics on identifiability of model parameters

3. Study areas • Selected catchments from the proposed database : • Allier River atVieille-Brioude, France, area 2267 km2 • AxeCreekatLonglea, Australia, area 236.9 km2 • Bani River atDouna, Mali, Ivory Coast and Burkina Faso, 103 391.032 km2 • Durance River at La Clapiere, France, 2170.0 km2 • Garonne River atPortet-sur-Garonne, France, 9980 km2 • Kamp River atZwettl, Austria, 621.8 km2 • Wimmera River atGlenorchyWeirTail, Australia, 2000 km2 • and an additional catchment located in Poland • Wieprz River atKosmin, Poland, area 10231 km2 Wieprz River sourcepanoramio.com

4. Methods: HBV Model • Conceptual lumped rainfall-runoff model • Inputs: precipitation, potential evapotranspiration and discharges at daily time step • Objective function: the Nash–Sutcliffe efficiency criterion • Optimization algorithm: Simplex Nealder-Mead

5. Methods: calibration • 5-year sliding windowcalibration • Different number of periods for each catchment due to different size of dataset

6. Calibration and validation Bani catchment • NS coefficients for calibration vary from 0.91 to 0.99 • the y-axis shows start of the period for which the model is calibrated, the x-axis shows the beginning of the validation period • Models calibrated to the data from the 60s poorly verified on the data from the 80s

7. Temporalvariability of theHBV model parameters: Bani catchment Analysis of significance of linearregressionat 0.05 level • An increase of FC, KS, PERCvalues • A decrease of CFLUXvalues

8. Temporal variability of the HBV model parameters The direction of changes and intensity of trend vary between the HBV model parameters and catchments.

9. Hydro-climatic characteristics We analysed the following climate characteristics: • Sum of precipitation over 5 year period • Maximum daily precipitation • Sum of flows over 5 year period, • Maximum daily flows • Sum of PET over 5 year period • Maximum daily PET • Sum of air temperature over 5 year period • Maximum daily air temperature • Aridity index (PET/P) Water-related Temp-related

10. Variability of climatic conditions, Bani catchment • Decline in sum of precipitation • decrease in flow • Increase of maximum air temperature and PET • Increase of aridity index

11. Dependence of model parameters on climate characteristics Bani catchment • Pearson correlation coefficient • Bold red values are significant at 0.05level • FC, LP, KS, PERC and CFLUX parametersare correlated with climatic characteristics • The highest correlation 0.89 is between KS parameter and sum of flows

12. Influence of climate characteristics on identifiability of model parameters • We applied a sensitivity analysis (SA) by Sobol method to assess the identifiability of theHBVmodel parameters • The Sobolmethod is a well recognized variance-based method • SA aims at establishing effect of model parameters on model output • The identifiability was assessed by • First order sensitivity index – quantifies influence of parameter i on the NS criterion • Total order sensitivity index -quantifies influence of parameter ion the NS criteriontakingintoaccount its interactions with the other parameters

13. Influence of climate characteristics on Sobol first order sensitivityindex: Bani Water-related Temp-related Water-related Air temperature-related

14. Influence of sum of precipitationon identifiability of model parameters: Bani • Two groups of parameters • First group (water-related): FC, α, KF and PERC –their identiliability increases with an increase of theamount of water • Second group (air temperature-related): β, LP and CFLUX -theiridentifiability decreases with an increase of theamount of water

15. Influence of aridityindexon identifiability of model parameters: Bani • Two groups of parameters • First group(water-related) : FC, α, KF and PERC –their identiliabilitydecreses with an increase of aridity index (and sum of PET, sum of air temp, maximum PET, maximum air temp) • Second group(air temperature-related): β, LP and CFLUX - theiridentifiability increases with an increase of aridity index

16. Summary (1) • The HBV model was calibrated on a series of 5 year periods and validated on other periods in 8 catchments. The results of calibration are very good. Validation of models shows two different patterns: a combination of good and bad years (Allier, Durance, Garonne) or poor validation of almost every model for the last periods (Axe, Wimmera) • We analysed the temporal variabilityof the HBV model parameters in 8 catchments by linear trend analysis. In most catchments (except Wieprz) there was a statistically significant linear trend. The direction of changes and intensity of trend vary between the HBV model parameters and catchments.

17. Summary (2) • In the next step we estimated a dependence of model parameters on climate characteristics (sum and maximum values of precipitation, air temperature, PET, flow and aridity index). Derived regressions are statistically significant at 0.05 level. The direction of changes and intensity vary between catchments and model parameters. • Influence of climate characteristics on identifiability of model parameters was assessed by Sobol sensitivity analysis. Results indicate strong dependency between first order Sobol sensitivity index and climatic characteristics. The HBV model parameters were classified into two groups: water-related and air temperature-related.