3. Stochastic Modelling

Download this website as pdf.

Download this website as epub (for e-readers).

To subscribe to our courses visit http://www.pcraster.eu

• 3.1. Visualisation of stochastic data
  • 3.1.1. Realizations
  • 3.1.2. Probability distributions and exceedance probabilities
  • 3.1.3. Confidence intervals
• 3.2. The stochastic modelling framework
  • 3.2.1. The static stochastic modelling class
  • 3.2.2. The dynamic stochastic modelling class
• 3.3. Writing to disk, drawing realizations
  • 3.3.1. Writing realizations to disk
  • 3.3.2. Drawing realizations
• 3.4. Functions calculating statistics over realizations
  • 3.4.1. Mean and variance, static data
  • 3.4.2. Percentiles, static data
  • 3.4.3. Dynamic data
• 3.5. Static modelling with point operations: forest fire
  • 3.5.1. Introduction and gradient calculation
  • 3.5.2. Point operation to calculate fire front velocity
• 3.6. Static stochastic modelling: neighbourhood operations
  • 3.6.1. Drawing realizations of a stochastic digital elevation model
  • 3.6.2. Propagate DEM uncertainty through slope calculation
  • 3.6.3. Neighbourhood defined by topology: relative distance calculation
• 3.7. Dynamic stochastic modelling: snow melt model
  • 3.7.1. Snow melt as a stochastic model
  • 3.7.2. Stochastic dynamic input: precipitation
  • 3.7.3. Stochastic parameters: temperature lapse rate
• 3.8. Dynamic stochastic modelling: infiltration model
  • 3.8.1. Introduction
  • 3.8.2. Model structure and field data
  • 3.8.3. Deterministic modelling using mean K
  • 3.8.4. The stochastic model
  • 3.8.5. Evaluating individual realizations of the model
  • 3.8.6. Evaluating the stochastic output
  • 3.8.7. The effect of the distribution of saturated conductivity on discharge