4.A Geostatistical simulations
4.A Geostatistical simulations and uncertainty propagation
Outline:- mathematical foundations
- numerical techniques
- applications
- open source tools: coupling GRASS GIS and R
- calibration and validation of models
Lecture
Slides Supplemental materials:- Jimenez: A (Non) Conditional Gaussian Simulation Example Using Python
- Geostatistical_conditional_simulation in petroleum industry
- Geostatistics session 5 conditional simulation video by Jef Caers, Stanford
- Conditional simulation in Geospatial Analysis book
- Geostatistical simulation in ArcGIS
- UCAR
- C. Lantuejoul, 2013, Geostatistical simulation: models and algorithms, Springer
- Handbook of Mathematical Geosciences Springer Open, 2018
Assignment 4.A
Use geostatistical simulation to map selected landscape feature or phenomenon and evaluate the uncertainty of your result- Derive a probability map of stream networks using geostatistical simulation and error propagation techniques, following the methodology described in the Chapter 10, Hengl, T.: A practical guide to geostatistical mapping (theory is in the chapter 2.4) and the references. Suggested workflow in GRASS and R
- Optional: apply the stream probability mapping method to our small rural watershed using the points in the layer "elev_lid792_randpts" in our data set or any elevation data set of your choice.
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Optional: evaluate the uncertainty of inundation extent prediction due to errors in a DEM
Learn how to Run GRASS from R