PCRaster modelling platform

PCRaster research team, Derek Karssenberg

Department of Physical Geography, Faculty of Geosciences,

Utrecht University, the Netherlands (d.karssenberg@uu.nl)

Outline

1) Static Modelling

2) Dynamic Modelling

3) Visualisation

4) Technical information

Design considerations

People constructing models are domain specialists, not programmers

Tool provides ‘simple’ building blocks for models

Modellers construct models by combining the building blocks

Building blocks: standard functions on maps and blocks

point functions

direct neighbourhood

functions

entire neighbourhood

functions

neighbourhood defined by

topology

Static models (raster based GIS analysis)

All raster based operations

Particularly strong at analysis of digital terrain models and hydrology

Two scripting languages

PCRcalc: dedicated language

PCRaster Python: PCRaster functions as a Python module

Routines for prompt visualisation

Dynamic models

for each t

set of variables representing state of the model

at time index t

set of functionals representing processes over time step

represented by combining building blocks

xt = f xt -1( )

xt

f

Dynamic models

initial

# sequence of functions

dynamic (nrtimesteps=…)

# sequence of functions

Applications

Hydrological models (PCRGLOB-WB)

Ecological models

Land use change models

Landscape evolution models (erosional and denudational)

Geomorphology and degradation models (mass movements, debris flows)

River sediment

transport

Cellular automata

Catchment hydrology

Selection of other point

Interactive visualisation

Dynamic models

Raster based operations are building blocks

Frameworks in Python for:

Dynamic modelling (iterations over time)

Stochastic modelling / uncertainty (Monte Carlo simulation)

Routines for prompt visualisation of multi-dimensional data

Geographic dimension

Time dimension

Uncertainty dimension (Monte Carlo samples)

eWaterCycle project

• Create a realistic, high-resolution global hydrological model of all the

fresh water in the world

• Applications:

– Flood forecasting

– Groundwater depletion prediction

– Water protection measures

decision making support

eWaterCycle Model Requirements

• Ultra high resolution (100x100 meter)

• Data assimilation of remote sensing data

eWaterCylce model

‘Standard’ PCRaster model (PCRaster Python)

Dynamic model

Assimilation of remotely sensed soil moisture

Runs on a supercomputer (Cartesius at SURFSara)

Distributed computing (multiple nodes)

OpenCL (at a node)

eWaterCycle animation

eWaterCycle animation

eWaterCycle animation

PCRaster team & info

Cooperation between Utrecht University & Carthago Consultancy

Key partners:

ECMWF

Deltares

Joint Research Centre – European Commission

Dutch eScience Centre

Universities worldwide

Software engineers, modellers

Open source (GPL)

Programmed in C++

Info, courses and downloads at http://www.pcraster.eu

THANK YOU FOR YOUR ATTENTION