Calibrating a Complex Environmental Model to Historic Data San Francisco, October 25-26, Goldsim User Conference Presented By: Alan Keizur, P.E. Golder Associates

Agenda  Background and brief overview of example application  Discuss calibration approach and case study  Issues and considerations  Question and answer period

Golder Associates  International engineering and earth sciences consulting firm with over 5,000 employees

Tailings Pond

Fresh Water Reservoir

Open Channel with Staff Gage

SAG Mill

Model Overview  Model is used to evaluate the potential impact of operational changes (e.g., increase production) or different climate profiles  Forecasts are developed based on specific scenarios:  Dam raise projections  Forecast water levels in storage reservoirs under different conditions  Exceed discharge limits  Anticipate riparian flow violations  Forecasts typically 3 months to 5 years  Primarily deterministic simulations (most uncertain variables are addressed via scenarios)  The model does support Monte Carlo analysis

Model Overview (1)

Model Overview (2) Legend here

Example Model Logic

Water Balance Concept Pond (Storage) Direct Precipitation Runoff Transfers from Other Facilities Discharge from Gates or Valves Spillway Pumping Seepage Evaporation

Calibration Approach  How do we know how well the model works?  One approach is to begin model simulations some time in the past and compare with measured data.  Enter actual values for key model inputs (time histories)  Adjust “calibration variables” as necessary to improve fit (within reasonable ranges)

Model Calibration  A calibration dashboard was developed to compare model projection against measured data for backward-looking simulation  Calibration points were defined at several key locations  Water levels in reservoirs  Flow rates in diversion ditches and receiving waters  Primary calibration variable is runoff  GoldSim optimization capability was utilized

Calibration Dashboard

Calibration Run – Reservoir Water Level

Calibration Run – Stream Flow

Prerequisites  A reliable record of measured data at key locations (generally at least one year, preferably longer)  Ability to minimize the number of “unknown” variables (ideally limit to one)  Reasonable conceptual model for the behavior of the unknown variable (with appropriate bounds)  e.g., empirical equation with one or more coefficients

Use of Optimization Capability  Optimization was used as a starting point to determine what combinations of input variables perform well  Credit goes to Golder’s Brisbane office for the idea  Steps  Extract individual submodels into standalone files for computational efficiency New submodel feature may eliminate need to do this  Run optimization on key input variables (within reasonable bounds), minimizing difference between predicted and actual  Determine if resulting combinations make physical sense consistent with what is known about the site  Make manual adjustments as necessary

Optimization Settings Dialog

Model Maintenance Thoughts  Model calibration is updated periodically to incorporate newly-collected data  Enables longer comparison period to be utilized  Increases confidence in model forecasts  Becomes complicated if the system has changed over time  Helps to identify additional data collection needs  Helps to identify errors in conceptual model or input data  if good match cannot be made without selecting “unreasonable” values for calibration variables  We are developing a spreadsheet to store QA’d site data for use as data source for GoldSim model

Example Disconnect

Questions and Answers Muchas Gracias!