1. Robust Simulation of Future Hydrologic Extremes in BC under Climate Change Arelia T. Werner (wernera@uvic.ca), Markus A. Schnorbus and Rajesh R. Shrestha CWRA BC Branch Conference: Session 7 Richmond BC Canada - November 18 2015

2. Principles of Robust Hydrologic Projections under Climate Change Apply a process-based model Calibrate to many sub-basins Statistically downscale with quantile based bias correction Select multiple climate models to get a wide range of possible futures

3. Impacts of climate change in three hydrologic regimes in British Columbia, Canada ~Schnorbus, Werner and Bennett, 2014 Hydrologic Processes

4. 23 Future Projections (CMIP3)

5. Focus on: Peak and Low Flows

6. Projecting nonstationary streamflow extremes for the Fraser River, Canada: a climate covariate-dependent generalized extreme value model ~Shrestha, Schnorbus and Cannon 2015 (in preparation) CMIP3 hydrologic simulations were used with CMIP5 climate models and a Generalized Extreme Value conditional density network model to project future floods in the Fraser River

7. Nonstationary Extremes Modelling GCMs Statistical Downscaling Hydrologic Model Nonstationary GEV Model Seasonal prec., seasonal temp. Streamflow Quantiles Streamflow peaks

8. Projected Change in Flow Quantiles Q100 Q200 RCP 4.5RCP 8.5 Q100 Q200 Flow [m 3 /s]

9. Factors Influencing Projected Changes to Floods and Low-Flows Gridded Observations

10. Factors Influencing Projected Changes to Floods and Low-Flows Gridded Observations

11. Evaluation of Three Gridded Climate Datasets for Western North America ~Cannon, Schnorbus, Shrestha and Werner (in preparation) Validation for climate and extremes against ARDA network Evaluation and comparison of climatic trends by sub-basin of the Fraser River Accessing need for precipitation adjustment with calibration in hydrologic model Diagnosing utility of pre- versus post- precipitation adjustment of climate datasets

12. Factors Influencing Projected Changes to Floods and Low-Flows Gridded Observations

13. Hydrologic extremes – an intercomparison of multiple gridded statistical downscaling methods ~Werner and Cannon, 2015 HESSD Methods Four reanalyses Two gridded climate datasets Seven statistical downscaling methods Climate extremes Hydrologic extremes Results Skill of the downscaling methods generally depended on reanalysis and gridded observational dataset CI failed to reproduce the distribution and BCSD and BCSDX the timing of winter 7- day low flow events, regardless of reanalysis or observational dataset Overall, DBCCA passed the greatest number of tests for the ClimDEX indices, while BCCAQ, which is designed to more accurately resolve event-scale spatial gradients, passed the greatest number of tests for hydrologic extremes

14. Reducing Uncertainties in Winter Low-Flows

15. Reducing Uncertainties in 3-Day Peak Flows

16. Factors Influencing Projected Changes to Floods and Low-Flows Gridded Observations

17. Selecting GCM Scenarios that Span the Range of Changes in a Multimodel Ensemble: Application to CMIP5 Climate Extreme Indices ~Cannon 2015 Journal of Climate Clustering algorithm to perform automated, objective GCM scenario selection Selected models are ordered (i.e., the 6- member solution simply adds a scenario the 5-member solution) Rapidly captures the overall range of the ensemble Requires few models than other clustering algorithms

18. “The ensemble of 12 climate models selected for downscaling is provided in the table below. The ordering, which differs by region (see map of Giorgi regions, Giorgi and Francisco, 2000), is selected to provide the widest spread in projected future climate for smaller subsets of the full ensemble following Cannon (2015).”

19. Downscaled with BCSD and BCCAQ

20. Factors Influencing Projected Changes to Floods and Low-Flows Gridded Observations

21. Adding Glacier Processes to VIC Upgrading the VIC model to better represent glacier dynamics and runoff Adjusting Hydrologic Response Units

22. Ongoing / Future Work Validate gridded climate dataset Calibrate VIC with Glaciers in the Columbia Select global climate models (GCMs) Downscale selected GCMs with BCSD and BCCAQ Project changes to streamflow out to 2100 – Climatology (30 year mean monthly conditions) – Extremes (Floods, Droughts) Make data available via PCIC’s data portal

23. PCIC’s Data Portal www.pacificclimate.org

24. MethodProCon BCSD Most widely applied Efficient Monthly large-scale Questionable daily stats BCSDX Potentially better at diurnal temperature Untested BCCA Daily large-scale driven Drizzle and other biases from linear combination DBCCA Potentially corrects biases in BCCA Untested CI Daily large-scale driven No bias correction BCCI Bias corrected, daily large-scale driven Untested for hydrology BCCAQ Daily large-scale driven BCCA corrected with BCCI Untested Gridded Statistical Downscaling Methods

25. Projected Change in Flow Quantiles Q100 Q200 A1B 25 A2 Q100 Q200 Flow [m 3 /s]