Understanding water resources at basin scale by regionalisation

1. Understanding water resources at basin scale by regionalisation David Love, Manuel Magombeyi, IlyasMasih, ShreedharMaskey, Akpofure Taigbenu, Stefan Uhlenbrook, Pieter van der Zaag

2. Introduction • Regionalisation • transferring small-scale (or meso-scale) measurements to a large scale (or basin or regional) model • Transferring from gauged catchments to ungauged catchments • Calibrate model against control catchments or long time series • Catchment classification for regionalisation

3. Objectives (i) To regionalise three selected models in three semi-arid secondary river basins (ii) To compare the benefits and weaknesses of the selected models in the semi-arid environment

4. Study areas

5. Catchment classification • Flow duration curves • Compare groups of FDCs using non-parametric Kolmogorov-Smirnov test • Catchment characteristics • multivariate statistical classification of catchments based on biophysical descriptors (discriminant analysis) • determine relationships between biophysical descriptors and hydrological parameters

6. Models used

7. HBVx Regionalisation, Mzingwane

8. Results – Catchment Classification, Mzingwane • Discriminant analysis • Confirmed statistically differences between Region IV and Region V • Flow duration curves • Confirmed statistically differences between sub-perennial and (sub-)ephemeral catchment groups • Did not confirm independence of ephemeral catchment groups

9. Results – Catchment Classification, Mzingwane

10. Results – HBVx Regionalisation, Mzingwane

11. Results – HBVx Regionalisation, Mzingwane • Calibration: 60% CNS > 0.3 • Regionalisation partially successful: • 40% CNS > 0.3 • Model performance best at 10 d

12. Challenges of daily data 22 km2

13. Results – HBVx Regionalisation, Mzingwane • Parameter sets that performed best: slow infiltration and percolation, moderate to fast “overland” flow, less “baseflow” • Mutangi • Sub-perennial catchments simulated well • Successfully calibrated HDAM • Degraded catchments simulated poorly • Proportion degraded land correlated to K0, fast “overland” flow component

14. SWAT calibration, B72 of Olifants Catchment B72H B72A

15. Results – SWAT calibration, B72 of Olifants Catchment • Calibrated in B72E, B72F, B72G • Critical parameters were • baseflow alpha factor • curve number • soil evaporation compensation • soil available water capacity • Correlation coefficient 0.56

16. Results – SWAT regionalisation, B72 of Olifants Catchment • Correlation coefficient for calibration of whole sub-catchment B72H: 0.51 • Application to ungauged B72A

17. Results – Catchment Classification, Karkheh • Based on • geographical area • spatial proximity • shape of the flow duration curve (FDC)

18. Results – HBV Regionalisation, Karkheh

19. Way forward • SWAT regionalisation, Olifants Basin • SWAT application, Karkheh & Mzingwane • HBV application, Mzingwane & Olifants • HBVx application, Karkheh & Olifants

20. Acknowledgements • Challenge Program on Water and Food: PN17 and PN57 • International Foundation for Science (DL) • Echel Eau Foundation (MM) • International Water Management Institute (IM) • Rand Water (MM)

21. Acknowledgements • Ministry of Energy, Meteorological Organization and Agricultural Research and Extension Organization of Iran • Department of Environment and Water Affairs of South Africa • Department of Meteorological Services and Zimbabwe National Water Authority of Zimbabwe

22. Thank-you