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Department of Physical Geography |
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Department of Physical Geography |
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This page lists some projects which are more or less related to the PCRaster project. This list is not meant to be complete. Currently we are busy adding more information about current and recent projects to this page. We hope that this information will be useful to people who are working on similar kinds of problems.
Aguila is the current generation of visualisation software for PCRaster. PCRaster used to contain the Display application for 2D visualisation of raster maps and the Timeplot application for visualisation of timeseries data. And Victor Jetten created the Drape application for 2½D visualisation of raster maps. Display and Timeplot are still distributed in the current PCRaster release but are largely obsoleted by Aguila. This is one application for the visualisation of raster maps and timeseries data.
See the Aguila page for more information.
APMoSPHERE is a thematic project, funded under the Global Monitoring for Environment and Security initiative, as part of the European Union's Fifth Research Framework Programme. Its aim is to compile high resolution maps of air pollution across the EU, as a basis for scientific research and policy support.
Contributions of the PCRaster group to the APMoSPHERE project involve (i) coordination of the various geostatistical approaches to air quality mapping, and (ii) development of visualisation module that enables analysis and comparison of maps with probabilistic outputs (prediction probability density functions, or results of batches of Monte Carlo simulations). Details are described in apm.pdf.
Most of gstat's functionality has been re-implemented in the gstat R-package of S library, making it directly available as function calls from within S (R or S-Plus). Current development includes (i) further integration of S graphics engines for enabling multivariable spatio-temporal geostatistical analysis, (ii) development of S classes for spatial data (points, grids, polygons) to further integrate GIS data and/or functionality with S platforms.
From the GDAL website:
GDAL (Geospatial Data Abstraction Library) is a translator library for raster geospatial data formats that is released under an X/MIT style Open Source license. As a library, it presents a single abstract data model to the calling application for all supported formats. The related OGR library (which lives within the GDAL source tree) provides a similar capability for simple features vector data.
It is now possible to read and write PCRaster raster files using the GDAL library. This is relevant for software developers writing software which has to read and/or write raster files. When an application uses GDAL for raster I/O it automatically supports all (>40!) file formats GDAL supports. Check the GDAL website for more information.
The Influence tool calculates the so called enrichment factor, which is defined as the occurrence of a land use type in the neighbourhood of a location relative to the occurrence of this land use type in the study area as a whole. See the article by Peter H. Verburg, Ton C. M. de Nijs, Jan Ritsema van Eck, Hans Visser and Kor de Jong: A method to analyse neighbourhood characteristics of land use patterns in Computers, Environment and Urban Systems (in press), for principles and application of Influence.
See the Influence page for more information.
Raster-based models may suffer from numerical dispersion in the transport routine. To simulate transport of material without introducing numerical dispersion, we developed a model linked to PCRaster in which we implemented the particle tracking method Method of Characteristics. Konikow & Bredehoeft (1978) have developed this model to simulate groundwater flow and contaminant transport. We use the model link to simulate sediment dispersal over floodplains in The Netherlands. For this purpose we use input data such as initial concentrations, water levels, flow velocities and dispersivities. In the future, we will use this model as part of a floodplain sedimentation model. Nevertheless, the model can also be used for other purposes, e.g., the modelling of debris flow run outs or dispersing overland flow. See the MoC page for more information.
The PCRaster Python Extension is a Python extension which enables you to write PCRaster models in Python. This way, you have two options regarding the language you can to use to express your model: the PCRaster environmental modelling language and the Python programming language.
See the PCRaster Python Extension page for more information.
The RHINEFLOW family is a series of models for simulating climate change in the Rhine catchment. RHINEFLOW has been developed inside PCRaster by Jaap Kwadijk and Willem van Deursen. The parent of the family, RHINEFLOW-1 has been one of the first models to be developed in PCRaster, and the current version 3 implementation is still used within the Dutch water management planning. RHINEFLOW implements a one-layer water balance on top of a Local Drain Direction network for the entire Rhine basin. It uses a geographical database with a spatial resolution of 1x1 km, and a meteorological database with precipitation, temperature and evaporation rates with a timestep of 10 days for a large number of stations in the basin. More information can be found at www.carthago.nl/RhineflowIntro.htm.
Spark is an implementation of the spatial reclassification kernel as described in the article by M.J. Barnsley and S.L. Barr in Photogrammetric Engineering & Remote Sensing in 1996: Inferring Urban Land Use from Satellite Sensor Images Using Kernel-Based Spatial Reclassification. It is a command line tool which reclassifies a classified input raster based on the frequency and the spatial arrangement of input classes within a square kernel. See Sluiter, R., De Jong, S.M., Van der Kwast, J. and Walstra, J. (2004) A Contextual Approach to Classify Mediterranean Heterogeneous Vegetation using the Spatial Re-classification Kernel and DAIS7915 Imagery. Remote Sensing Image Analysis: Including the Spatial Domain. S. M. De Jong and F. D. Van der Meer, Kluwer. pp. (in press), for a description and an applicatation of Spark.
See the Spark page for more information.
PCRaster comes with several functions for modelling surface water flow. This project aims at using PCRaster to simulate surface water flow in a several 100 km2 area in the North East of the Netherlands. Groundwater is simulated using MODFLOW while the output of the river package is imported to a model built with PCRaster for surface water routing.
This is a small pilot project in cooperation with the Netherlands Institute of Applied Geoscience TNO - National Geological Survey.
See the Surface water routing of leakage from MODFLOW page for more information.
In Bangladesh, WARPO (Water Resources Planning Organisation) is the national organisation responsible for water management planning. One of WARPO's main tasks is to prepare periodic updates of the National Water Management Plan (NWMP). Updating of a NWMP with the intention to review and adapt national water management strategies is a major task, which involves intensive studies in coordination with national and international experts and institutes. For the preparation of the next round NWMP, a structured planning procedures had to be developed to analyze alternative national water management strategies. The aim of such a framework is to provide information for decision making. To make optimal use of experiences and avoid duplication, the presented outline accounts for the existing models and experiences.
PCRaster is chosen as platform for the integration of detailed hydraulic model results and water balance studies for regions in Bangladesh. PCRaster was mainly chosen because of the flexibility of the tools, the open structure towards integration of existing models and the capability of developing own models and modules.
See the Supporting water management in Bangladesh page for more information.
The WEPS in PCRaster model simulates wind-blown sediment transport at a field at the temporal scale of an event. The concept and equations of this PCRaster model are based on the WEPS (Wind Erosion Prediction System) model. The version of WEPS that is written in PCRaster, only covers the EROSION sub-model. Translating the original FORTRAN code into the dynamic modeling language of PCRaster resulted in a script with an open structure allowing the user to make changes in the model code. Furthermore in WEPS in PCRaster it is possible to incorporate spatial variation in input variables, which was not yet possible in the current version of WEPS (version 1.0).
The manual can be downloaded in pdf format: weps.pdf. Software and scientific papers on the model can be obtained by contacting Saskia Faye-Visser: saskia.faye-visser@wur.nl
The extension integrates the groundwater model MODFLOW-2000 in the PCRaster modelling environment.
See the PCRasterModflow Extension page for more information.
The Automatic Model Optimization Reference Implementation, AMORI, is a framework that integrates the modelling and the optimization processes by providing a plug-in interface for both. A genetic algorithm and Markov simulations are currently implemented.
See the AMORI Extension page for more information.
The framework combines the tasks of model construction and assimilation with observational data within a single environement. It supports deterministic modelling as well as stochastic modelling like Monte Carlo simulations, Particle filtering or Ensemble Kalman filtering.
See the framework page for more information.
