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

lumped 2D distributed 3D distributed A C Q RfET Ro OVF2 TF1 TF2 TFn OVF1 OVFn S1 S2 Sn P1 P2 Pn etc. Rf Int Ovf TF DTM Q 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….

…. An understanding of open channel hydraulics and the principles of hydraulic modelling, as well as the ability to use river modelling software, would be advantageous. Experience in topographic surveying and use of GIS would also be beneficial …. Experience in characterizing and simulating fate, transport, of organic and inorganic contaminants …. GIS, map preparation, data analysis, modelling, and report preparation …. Working experience with ArcGIS type mapping and geostatistical methodologies …. Working experience with mixing models or flow path simulators such as MT3D, Bioplume, SEAWAT, Visual MODFLOW, etc. Motivation – Increasing Employability

Descriptive approaches instead of strategies for problem solving and analytical methods Strong opinions but weak knowledge backgrounds Insufficient methodological knowledge – lack of tools Misunderstood ‘Problem Based Learning’ talking about problems (exciting) – methods to analyse or mitigate (boring, difficult) Motivation – The Status Quo

Significant methodological background knowledge required before PBL-application in Environ. Science Perception of maths, statistics (difficult, boring….) Unexciting teaching strategies! Reasons – The Status Quo

The Project Applied Methods in the Environmental Sciences

The Project Applied Methods in the Environmental Sciences

Applied Methods in the Environmental Sciences 1.Environmental Statistics (Statistical Programming) Environmental data Introduction into statistics and time series analysis Spatial statistics – Geo-statistics Data analysis and presentation tools 2.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

How to make simulations and statistics exciting? Problem based projects – use of own data – field courses, dissertations Focussing on the controversies Uncertainties in model simulations and scenario assumptions

Degree of sophistication: - How much complexity can we afford? - Complex, fully integrated system solutions vs. simple and basic approaches Commercial vs. open source software - Integration of supporting partners (Un-conventional?) teaching styles – permanent alteration of lecture – computer based practical – tutorial Problems to consider:

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

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

Numerical Modelling of Groundwater Pollution

Conceptual Model Development Scenario Generation and Simulation Critical Analysis of Model Uncertainties

Problems and Obstacles High demand on supervision, especially during computer based classes “Unexpected” content, style - course expectations Attention deficits To late for being really beneficial for dissertation data analysis

OUTLOOK Module going to run in 1 st semester from this year Demonstrator for computer based classes requested More problem based “surgeries” on selected real data Course expectations and content – employer evaluation