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Teaching Innovation Project Modelling in the environmental sciences -

This project focuses on modelling in environmental sciences to enhance employability in the sector. Applied methods include environmental statistics, data analysis, GIS tools, and model validation techniques. Learn about spatial data, scenario development, and uncertainty analysis to make simulations and statistics engaging. Experts discuss critical aspects like digital elevation models and groundwater pollution modelling.

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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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