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
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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….
www.jobs.ac.uk/environment:
…. 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
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 1st semester from this year
Demonstrator for computer based classes requested
More problem based “surgeries” on selected real data
Course expectations and content – employer evaluation