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Teaching Innovation Project Modelling in the environmental sciences -

Teaching Innovation Project Modelling in the environmental sciences - Enhancing employability for the environmental sector Stefan Krause, Zoe Robinson School of Physical and Geographical Sciences. Modelling in Environmental Sciences. Modelling in Environmental Sciences. Rf. A. Int.

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Teaching Innovation Project Modelling in the environmental sciences -

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  1. Teaching Innovation Project Modelling in the environmental sciences - Enhancing employability for the environmental sector Stefan Krause, Zoe Robinson School of Physical and Geographical Sciences

  2. Modelling in Environmental Sciences

  3. Modelling in Environmental Sciences Rf A Int OVF1 Rf ET Ovf S1 OVF2 TF S2 C TF1 OVFn Sn P1 TF2 DTM Ro TFn etc. P2 Q Pn Q 2D distributed lumped 3D distributed Numerical Model Generation, Conceptualisation and Model Parameterisation, Data analysis, Geo-statistics, Calibration and Validation of Numerical Models, Scenario Development and Simulation, Model Testing and Prediction, Forecasting, Uncertainty Analysis….

  4. Motivation – Increasing Employability

  5. Motivation – The Status Quo

  6. Reasons – The Status Quo

  7. Reasons – The Status Quo

  8. The Project Applied Methods in the Environmental Sciences

  9. The Project Applied Methods in the Environmental Sciences

  10. Applied Methods in the Environmental Sciences • Environmental Statistics (Statistical Programming) • Environmental data • Introduction into statistics and time series analysis • Spatial statistics – Geo-statistics • Data analysis and presentation tools • Environmental (Geographical) Information Systems • Spatial data – types and structures • Spatial data bases and how to use them • Grid based digital terrain analysis • GIS for hydrological modelling • 3.Environmental Modelling • Modelling in an environmental context • Model types and model building • Model procedures, calibration and validation techniques • Scenario techniques • Model uncertainties

  11. How to make simulations and statistics exciting?

  12. Problems to consider:

  13. Types DEM : Digital Elevation Model DSM : Digital Surface Model DTM : Digital Terrain Model Data Structure Raster TIN Digital Surface Models Steve Kopp, Dean Djokic ( ESRI), Al Rea (USGS)

  14. Geographical data analyses Spatial Interpolation Ex: Interpolation of precipitation for weather forecasting

  15. Numerical Modelling of Groundwater Pollution

  16. Conceptual Model DevelopmentScenario Generation and SimulationCritical Analysis of Model Uncertainties

  17. Problems and Obstacles

  18. OUTLOOK

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