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Uncertainty of runoff flow path on a small agricultural watershed
Unit of Soil and Water SystemDepartement of Environment Science and Technology
Gembloux Agro-Bio Tech – University of Liege
Ouédraogo M.
Plan
ContextObjectivesModeling uncertaintySome resultsConclusion
2
Context
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.
Consequences:
Cleanning cost: 11000 €
Soil loss economic impact for farmers
Stressfull for population
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Context
DEM
GPS, Topographic cards, Aerial and Terrestrial scanning, Aerial
Photogrammetry…
Elevation data
Errors
How can we model the impact of errors?
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
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Modeling uncertainty Test area
Area:12 ha
Elevations:159 -169 m
Mean slope: 3.67%
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Modeling uncertainty Digital Elevation Model (DEM)
14 stations
3 DEMs
1 m x 1 m2 m x 2 m
4 m x 4 m
Modeling uncertainty
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Monte Carlo simulation
Purpose: Estimate original DEM errors , Generate equiprobable DEMs
X Y ΔZ : : :
2mean , variance , semivariance
1098 GCPs
Modeling uncertainty
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Purpose: Estimate original DEM errors and Generate equiprobable DEMs
1. Digital error model generation
Idea: visite each pixel of terrain model and generate error value Generation uses kriging interpolation (mean, variance,
semivariance)2. Add error model to original DEM to obtain simulated DEM
+
Original DEM Digital error modelsSimulated DEMs
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Modeling uncertainty Apply runoff flow path extraction algorithms on simulated DEMs
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 runoff fow path
Define probability P=nb/N (N is the number of simulated DEMs)
0
1
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Modeling uncertainty
Define random 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 which will contain flow path With a given probability
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Modeling uncertainty
R : geoR and gstat for DEMs simulations (1000)
Whitebox GAT library for runoff flow path algorithmsProgramming automated tasks is done in Neatbeans
Tools for modeling uncertainty
Some results
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1 m x 1 m 2 m x 2 m 4 m x 4 m
Pixels probability increases with DEM resolution
Runoff flow path position is more variable for 1 m x 1 m
Certainly due to microtopography
14
Some results
Cumulative distribution function of D
1 m x 1 m
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Some results
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Conclusion Monte Carlo is powerfull
Usefull, specially for massive data collection tools
However, very difficult to be implemented
Limitation with commercial algorithms
Need to compute automated tasks
Computing time can be very long
Next step: compare the results of different algorithms
Thank you
17