11 TransAlta Inflow Forecast System - TIFS WISKI ESRD Conference – March 10, 2015 Lin Li, M.Sc.,...
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Transcript of 11 TransAlta Inflow Forecast System - TIFS WISKI ESRD Conference – March 10, 2015 Lin Li, M.Sc.,...
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
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Content
• Goal and Elements of a Forecast System
• Project Overview and Milestone
• TIFS: Data Acquisition System
• TIFS: GUI system
• TIFS: Inflow Forecast Models
33
The Goal of a Forecast System
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Elements of a Forecast System
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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
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TransAlta Watersheds – Location
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TAU Watersheds-Phase I Watersheds
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TAU Watersheds-Phase II Watersheds
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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
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TIFS - Data Flow
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TIFS - Modules
TransAlta Inflow Forecast System
Data Acquisition System GUI SystemInflow Forecast
Models
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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
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Weather Forecast Data - Processing
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Weather Forecast Data: GDPS
http://weather.gc.ca/grib/grib2_glb_25km_e.html
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Weather Forecast Data: RDPS
http://weather.gc.ca/grib/grib2_reg_10km_e.html
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Precipitation Analysis Data: CAPA
http://weather.gc.ca/grib/grib2_RDPA_ps10km_e.html
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GUI System – Launch Pad - Main
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GUI System - Launch Pad - Banff Model
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GUI System – run model with historical data
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GUI System – model results (graph)
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GUI System – model results (ensemble TS)
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GUI System – model results (statistic graphs)
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GUI System – model results (statistic report)
Scenario 1- run model with original inputs files
Scenario 2- change gauge_weights.txt file
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GUI System – run model with EC forecast data
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GUI System – Graph (model results-GDPS)
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GUI System – Graph (model results-RDPS)
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GUI System – Graph (model results-CAPA)
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GUI System – Reports
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GUI System – WISKI Map
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UBC Watershed Model
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++
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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.
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Raven Modeling Framework
• Spatial discretization – Banded • As per the UBCWM
• Based on hypsographic curves
• HRUs defined by land class – physiographic combinations within each band
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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)
Banff 10 0.748 0.885 1.633
Bighorn 7 0.991 0.601 1.592
Brazeau 10 0.701 0.716 1.417
Jumpingpound 11 1.048 0.717 1.765
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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
0 2 4 6 8 11 14 17 20 23 26 29 32 35
0.2
0.3
0.4
0.5
0.7
Generations
Ob
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Fu
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Setting Value
Number of generations 75
Swarm size 25
Constriction factor 1*
Cognitive parameter 2*
Social parameter 2*
Inertia weight 1.2*
Inertia reduction rate 0.1*
Convergence value 0.001
* represents default value
3535
Data Sources – Flow gauges
3636
Data Sources - Meteorological stations
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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
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DEM Processing
Merge DEM files into single fileCrop DEM to watershed extension
3939
Land Cover Harmonization
Original land cover Harmonized land cover
4040
Creation of RVH File
4141
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
4242
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 R2 of annual flow peaks and minimize monthly volume biases
4343
Model Calibration
4444
Performance Assessment
4545
Performance Assessment
4646
Performance Assessment
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Questions?