Uncertainty of runoff flow path on a small agricultural watershed

1. Unit of Soil and Water System Departement of Environment Science and Technology Gembloux Agro-Bio Tech – University of Liege Uncertainty of runoff flow path on a small agricultural watershed Ouédraogo M.

2. Plan • Context • Objectives • Modeling uncertainty • Some results • Conclusion

3. Context • Consequences: • Cleanningcost: 11000 € • Soilloss economic impact for farmers • Stressfull for population Frequency of muddy floods over a 10-year period in all municipalities of the study area; data for Wallonia (1991–2000) taken from Bielders et al. (2003), data for Flanders (1995–2004) derived from a questionnaire sent to all municipalities in 2005. O. Evrard, C. Bielders, K. Vandaele, B. van Wesemael, Spatial and temporal variation of muddy floods in central Belgium, off-site impacts and potential control measures, CATENA, Volume 70, Issue 3, 1 August 2007, Pages 443-454, ISSN 0341-8162, 10.1016/j.catena.2006.11.011.

4. Context GPS, Topographic cards, Aerial and Terrestrial scanning, Aerial Photogrammetry… Elevation data DEM Errors • How can we model the impact of errors?

5. Objectives • Analyze uncertainty of runoff flow path extraction on small agricultural watershed • Determine how uncertainty is depending on DEM resolution • Determine wether uncertainty is depending on the algorithm

6. Modeling uncertainty • Test area • Area:12 ha • Elevations:159 -169 m • Mean slope: 3.67%

7. Modeling uncertainty • Digital Elevation Model (DEM) • 14 stations 1 m x 1 m 2 m x 2 m 4 m x 4 m 3 DEMs

8. Modeling uncertainty • Monte Carlo simulation • Purpose: Estimate original DEM errors , GenerateequiprobableDEMs , semivariance mean , variance 1098 GCPs

9. Modeling uncertainty • Purpose: Estimate original DEM errors and GenerateequiprobableDEMs • Digital error model generation • Idea: visite each pixel of terrain model and generateerror value • Generation uses kriging interpolation (mean, variance, semivariance) • Adderror model to original DEM to obtainsimulated DEM + Original DEM Digital error models Simulated DEMs

10. Modeling uncertainty • Applyrunoff flow path extraction algorithms on simulatedDEMs • Consider pixel as Bernoulli variable i.e. value=1 or 0 • Compute for each pixel the number of times (nb) it has been part of runofffowpath • Defineprobability P=nb/N (N is the number of simulatedDEMs) 1 0

11. Modeling uncertainty • Definerandom variable D as distance from pixels (p>0) to extracted flow path • Compute cumulative distribution function i.e. P (D<=d) • Objective: allow a user to define area whichwillcontain flow path • With a givenprobability

12. Modeling uncertainty • Tools for modeling uncertainty • Whitebox GAT library for runoff flow pathalgorithms • Programmingautomatedtasksisdone in Neatbeans • R : geoR and gstat • for DEMs simulations (1000)

13. Some results • Pixels probabilityincreaseswith DEM resolution • Runoff flow path position is more variable for 1 m x 1 m • Certainly due to microtopography 1 m x 1 m 2 m x 2 m 4 m x 4 m

14. Some results • Cumulative distribution function of D 1 m x 1 m

15. Some results

16. Conclusion • Monte Carlo ispowerfull • Usefull, specially for massive data collection tools • However, verydifficult to beimplemented • Limitation with commercial algorithms • Need to computeautomatedtasks • Computing time canbevery long • Nextstep: compare the results of differentalgorithms

17. Thank you