1. Geographical & Environmental Modelling Dr Nigel Trodd Coventry University

2. In this lecture you will: identify reasons for geographical modelling define a ‘(geo-)information envelope’ using data quality parameters exemplify the process of geographical modelling apply a terrain-based model to analyse a natural hazard

3. Models

4. Why Model ? There is a growing demand for models that can simulate and predict environmental processes. A range of computer models have been developed that simulate processes (e.g. debris flow, pollutant dispersal, flooding) at scales between 1:50 000 and 1:500 000. Results of these models provide important information for decision makers and planners - allowing better implementation of appropriate land management measures.

5. ‘I’m a celebrity get me …a risk map’ In the past one map might exist qualitative

6. select & combine digital geospatial information quantitative – statistical model – process model Modern risk assessment In the past one map might exist qualitative

7. Developing a model ?

8. Quantitative modelling

10. What (geospatial) info do we need? the ‘information envelope’ identify variables of interest which ones? prioritise the mission-critical data requirements for effective decision-making how? – sensitivity analysis we need some criteria - data quality e.g. thematic, positional & temporal character

11. Elements of spatial data quality Accuracy precision coverage

12. Case study: regional soil erosion It is suggested that soil erosion reaches its maximum in areas with an effective mean annual precipitation of 300mm. This largely affects semi-arid and semi- humid regions. The problem of soil erosion in these areas is also compounded by the need for water conservation, and the ecological sensitivity of the environment - removal of natural vegetation for cultivation can have a major effect.

13. Mediterranean regions experience some of the highest erosion rates Map of desertification hazard Major causes: human activity

14. Factors influencing soil erosion

15. Factors Influencing Soil Erosion Rate Rainfall Run-off Wind Soil Slope Plant Cover Conservation Measures Erosivity Erodibility Protection

16. Modelling soil erosion

17. Model pre-requisites the model should be based on the concepts of the erosion process the model should reliably simulate the distribution character of the erosion process the model should be validated under a range of natural conditions the scale at which the model operates should match the spatial resolution of the EO data and DEM.

18. Physically-Based Models Physically based models are based on the knowledge of the fundamental erosion processes and incorporate laws of conservation of mass and energy. Most of them use a statement of the conservation of matter at it moves in time and space, and can be applied to soil erosion on a small segment of a hill slope.

19. Empirical Models A simple empirical model can be of the type: Qs = aQw b Qs =sediment discharge Qw = water discharge This is a simple model that does not explain why the erosion takes place. In order to do this, more complex models expressing the relationship between soil loss and a number of variables can be constructed.

20. Physically-Based – CREAMS - Chemicals, Runoff and Erosion from Agricultural Management Systems – WEPP - Water Erosion Prediction Project – GUSS - Griffith University Erosion Sedimentation Systems – EUROSEM - European Soil Erosion Model Empirical – USLE - Universal Soil Loss Equation – RUSLE - Revised USLE – SLEMSA -Soil Loss Estimator for SA – Morgan, Morgan and Finney Method Soil Erosion Models

21. The Universal Soil Loss Equation

22. The USLE The USLE, developed by W. Wischmeier and D. Smith (1978), has been the most widely accepted and utilised soil loss equation for over 30 years. Designed as a method to predict average annual soil loss caused by sheet and rill erosion. it can estimate long - term annual soil loss and guide conservationists on proper cropping, management, and conservation practices, it should not be applied to a specific year or a specific storm. The USLE is mature technology and enhancements to it are limited by the simple equation structure.

23. The USLE A = R.K.L.S.C.P  A = average annual soil loss in t/a (tons per acre)  R = rainfall erosivity index  K = soil erodibility factor  LS = topographic factor (L is for slope length & S is for slope)  C = crop management factor  P = conservation practice factor

24. Remote sensing & GIS

25. The role of RS and GIS Modelling environmental processes such as soil erosion requires the spatial and temporal assessment of process controlling variables for the entire area under investigation. EO and GIS technologies enable the extraction of information from imagery and DEM’s, and to process vast amounts of data for this purpose.

26. USLE FactorDerivation RClimate Data KField work or soil maps LSDEM CRemote Sensing PRemote Sensing or Field Observation

27. Source: Mongkolsawat et al. (1994)

28. Source: Scilands GmbH

29. In this lecture you have: identified reasons for modelling terrain defined a ‘(geo-)information envelope’ exemplified the process of terrain modelling to analyse natural hazards