Digitale Lehre
For information on e-learning during the 2020/21 winter semester please check the respective Moodle course!
Lehrinhalte
Advanced Groundwater Modeling: Sensitivity Analysis and Model Calibration
Advanced groundwater modeling class with specific focus on calibration of models with multiple parameters using observed data on model outputs. Calibration is presented via the public domain universal inversion codes, UCODE and PEST. Basic concepts covered include:
- Analyze data to be used for model development. Based on knowledge of the system, decide how to define parameters.
- Using the initially constructed model, identify parameters important to observations, parameters important to predictions, and observations important to predictions. We will question whether these simulated relations are consistent with real-world conditions.
- Determine parameter values that provide a best fit to observations.
- Calculate predictions.
- Obtain measures of prediction uncertainty.
Exercises will be provided and used throughout the course. These techniques will be discussed using a groundwater model example, but can be applied to every model and example of applications in different fields will be provided.
Literatur
Main Textbook:
Effective Groundwater Model Calibration, M. Hill and C. Tiedeman, Wiley Interscience (2007).
Other references:
Inverse Problems in Groundwater Modeling, N.-Z. Sun, Kluwer (1994).
Inverse Problem Theory and Methods for Model Parameter Estimation, A. Tarantola online free.
Online-Angebote
Moodle
For information on e-learning during the 2020/21 winter semester please check the respective Moodle course!
Lehrinhalte
Advanced Groundwater Modeling: Sensitivity Analysis and Model Calibration
Advanced groundwater modeling class with specific focus on calibration of models with multiple parameters using observed data on model outputs. Calibration is presented via the public domain universal inversion codes, UCODE and PEST. Basic concepts covered include:
- Analyze data to be used for model development. Based on knowledge of the system, decide how to define parameters.
- Using the initially constructed model, identify parameters important to observations, parameters important to predictions, and observations important to predictions. We will question whether these simulated relations are consistent with real-world conditions.
- Determine parameter values that provide a best fit to observations.
- Calculate predictions.
- Obtain measures of prediction uncertainty.
Exercises will be provided and used throughout the course. These techniques will be discussed using a groundwater model example, but can be applied to every model and example of applications in different fields will be provided.
Literatur
Main Textbook:
Effective Groundwater Model Calibration, M. Hill and C. Tiedeman, Wiley Interscience (2007).
Other references:
Inverse Problems in Groundwater Modeling, N.-Z. Sun, Kluwer (1994).
Inverse Problem Theory and Methods for Model Parameter Estimation, A. Tarantola online free.
Online-Angebote
Moodle
- Lehrende: Stephan Schulz
Semester: WT 2020/21