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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

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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

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Generations

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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

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Data Sources – Flow gauges

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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

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Land Cover Harmonization

Original land cover Harmonized land cover

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Creation of RVH File

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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

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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

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Model Calibration

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Performance Assessment

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Performance Assessment

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Performance Assessment

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Questions?