1. Water cycle prediction at the regional scale: on the importance of being consistent Vincent Fortin, Pierre Pellerin Meteorological Research Division Al Pietroniro, André Méthot Meteorological Service of Canada

2. Page 2 – July 3, 2015 Canadian Meteorological Centre: more than tomorrow's weather!

3. Page 3 – July 3, 2015 Applications of hydrological and hydrodynamic modelling Adaptive management of watersheds Optimization of hydropower production Flood warning Search and rescue Predicting impacts on habitat of changes in water level NWP and land-surface model verification

4. Page 4 – July 3, 2015 Coupled modelling system for hydrological prediction GEM atmospheric model

5. Page 5 – July 3, 2015 Coupled modelling system for hydrological prediction GEM atmospheric model 4DVAR/EnKF data assimilation 4DVAR/EnKF data assimilation

6. Page 6 – July 3, 2015 Coupled modelling system for hydrological prediction Land-surface scheme (CLASS, ISBA, SVS) GEM atmospheric model 4DVAR/EnKF data assimilation 4DVAR/EnKF data assimilation

7. Page 7 – July 3, 2015 Coupled modelling system for hydrological prediction Land-surface scheme (CLASS, ISBA, SVS) GEM atmospheric model WATROUTE routing model 4DVAR/EnKF data assimilation 4DVAR/EnKF data assimilation

8. Page 8 – July 3, 2015 CaLDAS: EnKF data assimilation CaLDAS: EnKF data assimilation Coupled modelling system for hydrological prediction Land-surface scheme (CLASS, ISBA, SVS) GEM atmospheric model WATROUTE routing model 4DVAR/EnKF data assimilation 4DVAR/EnKF data assimilation

9. Page 9 – July 3, 2015 Coupled modelling system for hydrological prediction Land-surface scheme (CLASS, ISBA, SVS) GEM atmospheric model WATROUTE routing model NEMO model for the ocean and large lakes 4DVAR/EnKF data assimilation 4DVAR/EnKF data assimilation CaLDAS: EnKF data assimilation CaLDAS: EnKF data assimilation

10. Page 10 – July 3, 2015 Coupled modelling system for hydrological prediction Components can be run either coupled or offline, with prescribed forcings Land-surface scheme (CLASS, ISBA, SVS) GEM atmospheric model WATROUTE routing model NEMO model for the ocean and large lakes MESH: Modélisation Environnementale de la Surface et de l'Hydrologie

11. Page 11 – July 3, 2015 Why not simply drive surface and hydrology models with observations? Required observations are generally not all available Forecasting becomes nearly impossible Accuracy of short-term forecasts can approach or even surpasses that of observations –snowfall observations Working within an integrated system makes it possible for hydrologists to actively contribute to the improvement of all components

12. Page 12 – July 3, 2015 Saskatchewan Northern Territories Toronto Central Quebec It works because weather forecasting is not so difficult Landscape Atmosphere Central Quebec Toronto Northern Territories Saskatchewan

13. Page 13 – July 3, 2015 Not only is weather forecasting easy, it is improving

14. Page 14 – July 3, 2015 Not only is weather forecasting easy, it is improving Major improvements to the data assimilation system The ISBA land-surface model replaces the force- restore scheme Major improvements to the data assimilation system The ISBA land-surface model replaces the force- restore scheme

15. Page 15 – July 3, 2015 GEM vs reanalysis products Many hydrologists already use reanalysis products (NCEP, NARR, MERRA, ERA-40, WATCH, Era-interim) For many applications where ~10 years or less of data is required, operational NWP outputs (e.g. GEM) provide higher resolution (up to 2.5 km for GEM HRDPS) and better skill (especially for surface variables) For short-term hydrological forecasting applications, past atmospheric forcings are used only to calibrate the hydrological model and obtain initial conditions –NWP forecasts are required to obtain streamflow forecasts –by using the same data source for model calibration and forecasting, we can bypass the NWP post-processing step

16. Page 16 – July 3, 2015 The Canadian Precipitation Analysis (CaPA) can be used to improve GEM precipitation Optimal interpolation technique used to merge gauges, radar and satellite data with a background provided by the GEM NWP model Fully automated quality control 6-h and 24-h accumulations North American domain 10 km resolution Early (T+1h) and late (T+7h) analyses Operational since April 2011 http://weather.gc.ca/analysis 24-h analysis valid 2014-08-15@12Z

17. Page 17 – July 3, 2015 Great Lakes / St. Lawrence testbed Demonstrate benefits of coupled numerical models WMO RFDP proposal in preparation Already included in: –Canada/Québec St. Lawrence Action Plan (SLAP): Environmental prediction working group –EC/NOAA MOU: close collaboration with the Great Lakes Environmental Research Laboratory Superior Michigan-Huron Erie Ontario

18. Page 18 – July 3, 2015 Coupled modelling system for the Great Lakes Configuration used for recently published results Land-surface schemes CLASS or ISBA at 15 km GEM RDPS 15 km atmospheric model 2 integrations per day WATROUTE routing model at 15 km 2 km NEMO model for the Great Lakes UU,VV,TT,HU P0,FB,FI,PR Q,TQ RFF,RCH MESH:

19. Page 19 – July 3, 2015 Coupled modelling system for the Great Lakes Configuration to be implemented operationnally (sorry, no results to show yet): Land-surface scheme SVS at 2 km GEM HRDPS 2.5 km atmospheric model 4 integrations per day WATROUTE routing model at 1 km 2 km NEMO model for the Great Lakes UU,VV,TT,HU P0,FB,FI,PR Q,TQ RFF,RCH MESH:

20. Page 20 – July 3, 2015 Predicting net basin supplies to Lake Superior with GEM+ISBA Overlake evaporation (-E) Net precipitation (P-E) Net basin supplies (NBS=P-E+R) Resid: residual calculation of NBS from lake levels obs. and lake outflow Deacu et al. (2012) J. Hydromet. World's largest lake by area: - Lake area: 82 000 km² - Watershed: 128 000 km²

21. Page 21 – July 3, 2015 Predicting net basin supplies to the Great Lakes with GEM+ISBA REGN: from GEM model outputs at 15km GLERL LakeP: assessment by NOAA/GLERL from near- shore obs. of precip., temperature, humidity, wind and streamflow Resid: residual calculation from lake levels obs. Deacu et al. (2012) J. Hydromet.

22. Page 22 – July 3, 2015 Simulating Great Lakes physical behaviour using GEM+NEMO Water level change [m] Surface temperature [C] Ice fraction Surface currrents [m/s] Surface temperature [C] Dupont et al. (2012) WQRJC

23. Page 23 – July 3, 2015 Streamflow simulation for subwatersheds (CLASS LSS) Haghnegabar et al. (2014), Atmosphere-Ocean Grand River at Iona, MI (4571 km 2 ) Black River at Watertown, NY (3000 km 2 ) (b) (a)

24. Page 24 – July 3, 2015 How did we get there? Monitoring activities dedicated to improving the model Parsimonious landscape parameterizations Coordinated model development

25. Page 25 – July 3, 2015 Monitoring activities dedicated to improving the model Research basins Flux towers

26. Page 26 – July 3, 2015 Parsimonious landscape parameterizations, calibrated parameters Grouped Response Units (Kouwen et al., 1993) –identify important landscape features –within a grid cell, only keep track of areal coverage of each GRU –assign one parameter set to each GRU WATDRAIN hillslope model (Soulis et al., 2011) –takes slope into account in land-surface, hydrology and atmospheric models –influences runoff but also soil moisture and evaporation

27. Page 27 – July 3, 2015 Coordinated model development Working as an integrated team on atmospheric, hydrologic and ocean model development by sharing key components: –land-surface model –turbulent flux calculations –computing infrastructure Using streamflow and water level observations for atmospheric prediction: –to verify NWP forecasts –to tune the water balance of land-surface schemes –eventually, to estimate deep soil moisture Assessing the impacts of improvements to one component on the environmental prediction system as a whole

28. Page 28 – July 3, 2015 Overlake evaporation prediction Deacu, Fortin et al. (2012), Journal of Hydrometeorology Average latent heat flux, winter 2011 (W/m²) GEM 15kmGEM 10kmOAFlux Lake Superior supplies 200 150 100 50 0 W/m²

29. Page 29 – July 3, 2015 Conclusions At the regional scale, feedbacks to the atmosphere cannot be ignored: if you are using an atmospheric model product for precipitation and you want to close the water balance using a hydrological model, then you should worry about evapotranspiration computed by the atmospheric model as well Hydrologists and meteorologists have much to gain by collaborating –high-resolution land-surface modelling and data assimilation systems developed by the NWP community are evolving and improving quickly –land-surface models used by the NWP community often lack some basic hydrological processes and need to be calibrated Be prepared: –NWP systems already provide forecasts of sufficient quality to drive hydrological models for both hindcasting and forecasting at the regional scale –NWP systems will soon provide gridded runoff fields of comparable quality –running coupled models is becoming more and more affordable: water resources engineers will soon be running such systems from their basement! Systems like MESH offer a good starting point