1. 11 TransAlta Inflow Forecast System - TIFS WISKI ESRD Conference – March 10, 2015 Lin Li, M.Sc., P.Eng., Engineer, Water Management German Mojica, P.Eng., Sr. Engineer, Water Management

2. 22 Content Goal and Elements of a Forecast System Project Overview and Milestone TIFS: Data Acquisition System TIFS: GUI system TIFS: Inflow Forecast Models

3. 33 The Goal of a Forecast System

4. 44 Elements of a Forecast System

5. 55 Project Overview Objective: To implement an Inflow Forecasting System for the Bow and North Saskatchewan Rivers basins Project Scope: Develop 9 Inflow forecast models (Raven UBCWM) in 2 phases 7 models for Bow sub-basins: Banff, Cascade, Kananaskis River, Spray Lake, Spray River, Waiparous Creek, Jumpingpound 2 models for North SASK: Bighorn, Brazeau Tools and Scripts: Green Kenue, ArcGIS, Python, Rscripts, Ostrich, Raven Project team: National Research Council Canada - NRC, Kisters North America, TransAlta

6. 66 TransAlta Watersheds – Location

7. 77 TAU Watersheds-Phase I Watersheds

8. 88 TAU Watersheds-Phase II Watersheds

9. 99 Project Overview Develop GUI system: WISKI Launch Pad WISKI Launch Pad Model Run application (VB.net) WISKI Standard Graphs WISKI Reports (Kister’s Kiscripts) WISKI Map KiDAT application KiDSM application Data Integration: Alberta Environment (AEnv) NRT data Environment Canada (EC) weather forecast data Data preprocessing in WISKI Missing data processing: continuous daily Met and flow data required Noise and error data processing

10. 10 TIFS - Data Flow

11. 11 TIFS - Modules TransAlta Inflow Forecast System Data Acquisition System GUI System Inflow Forecast Models AEnv NRT Data receiving and processing EC Forecast Data Decoding and Processing TAC - IP21 Data receivingand processing Report subsystem Graph subsystem Parameter manualcalibrationAutomatic forecastingManual forecasting

12. 12 Data Acquisition System EC Weather Forecast Data Decoding from EC Grib2 format to WISKI TS format GDPS: Global Deterministic Prediction System Precipitation and Temperature RDPS: Regional Deterministic Prediction System Precipitation and Temperature CAPA: Regional Deterministic Prediction Analysis Precipitation

13. 13 Weather Forecast Data - Processing

14. 14 Weather Forecast Data: GDPS  http://weather.gc.ca/grib/grib2_glb_25km_e.html

15. 15 Weather Forecast Data: RDPS  http://weather.gc.ca/grib/grib2_reg_10km_e.html

16. 16 Precipitation Analysis Data: CAPA  http://weather.gc.ca/grib/grib2_RDPA_ps10km_e.html

17. 17 GUI System – Launch Pad - Main

18. 18 GUI System - Launch Pad - Banff Model

19. 19 GUI System – run model with historical data

20. 20 GUI System – model results (graph)

21. 21 GUI System – model results (ensemble TS)

22. 22 GUI System – model results (statistic graphs)

23. 23 GUI System – model results (statistic report) Scenario 1- run model with original inputs files Scenario 2- change gauge_weights.txt file

24. 24 GUI System – run model with EC forecast data

25. 25 GUI System – Graph (model results-GDPS)

26. 26 GUI System – Graph (model results-RDPS)

27. 27 GUI System – Graph (model results-CAPA)

28. 28 GUI System – Reports

29. 29 GUI System – WISKI Map

30. 30 UBC Watershed Model Lumped Model Designed for mountainous regions “Banded” model – spatial discretization by elevation band Designed for daily time step interpolation techniques Linear routing Written in C++

31. 31 Raven Modeling Framework Raven is not a model but a “framework” Has variable: Discretization Routing algorithms Snowmelt algorithms Time step Etc. Can “emulate” other models if the processes are properly defined. Has “emulated” HBV-EC model, UBCWM, etc.

32. 32 Raven Modeling Framework Spatial discretization – Banded As per the UBCWM Based on hypsographic curves HRUs defined by land class – physiographic combinations within each band

33. 33 Raven Modeling Framework Advantages: High performance C++ code (10 year execution time < 2 seconds) UBCWM emulation accomplished (BC Hydro project) Simulation Period (years) Initialization (seconds) Simulation (seconds) Total Execution Time (seconds) Banff100.7480.8851.633 Bighorn70.9910.6011.592 Brazeau100.7010.7161.417 Jumpingpound111.0480.7171.765

34. 34 Model Optimization - OSTRICH OSTRICH – Model independent calibration tool. Employs a number of optimization routines For this project: Employed R to calculate the objective function Used the particle swarm optimization routine 25x75 simulations = 1875 iterations; 10 year simulation ~ 75 minutes execution SettingValue Number of generations75 Swarm size25 Constriction factor1* Cognitive parameter2* Social parameter2* Inertia weight1.2* Inertia reduction rate0.1* Convergence value0.001 * represents default value

35. 35 Data Sources – Flow gauges

36. 36 Data Sources - Meteorological stations

37. 37 Modelling Approach Watershed setup Use ArcGIS and Green Kenue Basin discretization – DEM data processing Elevation bands/zones Land cover data processing Land cover harmonization Use Python script Creation of HRUs (.rvh file) Creation of Raven Input files.rvp,.rvt, rvc, rvi, and gauges weights file

38. 38 DEM Processing Merge DEM files into single file Crop DEM to watershed extension

39. 39 Land Cover Harmonization Original land cover Harmonized land cover

40. 40 Creation of RVH File

41. 41 Model Calibration The objective is to estimate the model parameters by minimizing differences between observed and simulated hydrographs Semi-automatic some intuitive/perceptive parameters are adopted based on experience Tool: Ostrich Particle Swarm Optimization (PSO) algorithm

42. 42 Model Calibration Employed objective function NSE: Nash-Sutcliff Efficiency Criterion (commonly used) AAMFVB: Absolute value of the Mean Monthly Flow Volume Bias Goal: Maximize NSE and R 2 of annual flow peaks and minimize monthly volume biases

43. 43 Model Calibration

44. 44 Performance Assessment

45. 45 Performance Assessment

46. 46 Performance Assessment

47. 47 Questions?