Unit of Soil and Water System Departement

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.

Plan Ø Context Ø Objectives Ø Modeling uncertainty Ø Some results Ø Conclusion 2

Context Consequences: Ø Cleanning cost: 11000 € Ø Soil loss economic impact for farmers Ø 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. 3

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

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

Modeling uncertainty Ø Test area ü Area: 12 ha ü Elevations: 159 -169 m ü Mean slope: 3. 67% 6

Modeling uncertainty Ø Digital Elevation Model (DEM) ü 14 stations 1 mx 1 m 2 mx 2 m 4 mx 4 m 3 DEMs 7

Modeling uncertainty Ø Monte Carlo simulation ü Purpose: Estimate original DEM errors , Generate equiprobable DEMs mean X : Y : , variance , semivariance ΔZ : 1098 GCPs 8

Modeling uncertainty Ø Purpose: Estimate original DEM errors and Generate equiprobable DEMs 1. Digital error model generation Ø Idea: visite each pixel of terrain model and generate error value Ø Generation uses kriging interpolation (mean, variance, semivariance) 2. Add error model to original DEM to obtain simulated DEM + Original DEM Digital error models Simulated DEMs 9

Modeling uncertainty Ø Apply runoff flow path extraction algorithms on simulated DEMs Ø Consider pixel as Bernoulli variable i. e. value=1 or 0 1 0 Ø Compute for each pixel the number of times (nb) it has been part of runoff fow path Ø Define probability P=nb/N (N is the number of simulated DEMs) 10

Modeling uncertainty Ø Define random variable D as distance from pixels (p>0) to extracted flow path Ø Compute cumulative distribution function i. e. P (D<=d) ü Objective: allow a user to define area which will contain flow path With a given probability 11

Modeling uncertainty Ø Tools for modeling uncertainty ü R : geo. R and gstat for DEMs simulations (1000) ü Whitebox GAT library for runoff flow path algorithms ü Programming automated tasks is done in Neatbeans 12

Some results Ø Pixels probability increases with DEM resolution Ø Runoff flow path position is more variable for 1 m x 1 m ü Certainly due to microtopography 13 1 mx 1 m 2 mx 2 m 4 mx 4 m

Some results Ø Cumulative distribution function of D 1 mx 1 m 14

Some results 15

Conclusion Ø Monte Carlo is powerfull Ø Usefull, specially for massive data collection tools Ø However, very difficult to be implemented Ø Limitation with commercial algorithms Ø Need to compute automated tasks Ø Computing time can be very long Ø Next step: compare the results of different algorithms 16

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