Soil maps as data input for soil erosion models Errors related to map scales Paul van Dijk Joëlle Sauter Elodie Hofstetter European Geosciences Union, General Assembly 2010, Vienna HS2.3/SSS47, May 6, 2010 EGU-2010, Vienna
Study objective Test the sensitivity of two soil erosion models to soil map scale Determine the incidence of soil map scale on the modelling results and on the conclusions drawn from the modelling study Ideally, when applying a distributed soil erosion model, the level of spatial detail contained in the input data should correspond to the study objective (scale) However, when the model is applied to answer rapidly to practical (urgent) questions, the model user often has to rely on existing data Which existing data might be unsuitable? Various technologies have improved the spatial resolution of some input data such as land use (high resolution satellite images) and elevation (i.e. LIDAR) The construction of soil maps is generally laborious and does not follow this trend: used soil maps often have lower spatial resolutions than the other input data EGU-2010, Vienna
Context: erosion problems in the Alsace In the Alsace: overland flow and soil erosion followed by muddy flows in villages seem to increase in frequency and amplitude In villages with arable land in their upstream area - loess-derived soils on hilly land - summer crops (mainly corn maize), hardly covering the soil during spring and early summer storms EGU-2010, Vienna
and in villages at the piemont, downstream of vineyards - steep slopes - not so much mud, but powerful runoff causing damage to infrastructure EGU-2010, Vienna
Why modelling? And which soil data? The ARAA does some modelling at the request of different stakeholders The regional scale: mapping erosion hazard to identify target areas (catchments, communes ) for the definition of control measures strategies: MESALES (a decision-tree expert model developed by Le Bissonnais et al.) (INRA-BRGM, 2006). The catchment scale: to help defining erosion control measures at strategic locations: LISEM (a physically-based model developed by De Roo et al, and later by Jetten) Very different scales, but the same soil data are used as model input: the 1/100 000 Soil Database of the ARAA! Is this a problem?
Method Apply the two models using the 1/100 000 soil map and more detailed soil maps where available, and compare the model results
The regional scale: MESALES Classification of Topographical information (slope and upstream area) Soil data: sensitivity to crusting and erodibility (as a function of texture, org. matter, gravel content) Land use Precipitation Classes are combined in a decision tree to give a erosion sensitivity of the terrain (without precipitation) and erosion hazard class (with precipitation) EGU-2010, Vienna
Crusting Erodibility
Sensitivity to erosion Initial raster (20 m) Various aggregations Example: commune Comparison of model results: 1/100 000 and 1/50 000 soil maps
Comparison of MESALES results depending on soil map scale EGU-2010, Vienna
Comparison of MESALES results for two soil map scales 100 All cells, including the Rhine plain toute la carte incluant la plaine 100 Only cells with sensitivity > 1 sensibilité > 1 (élimination plaine, forêts et prairies) Frequency (%) % de la surface totale 80 60 40 20 Frequency % de la surface totale (%) 80 60 40 20 0-4 -3-2 -1 0 1 2 3 4 0-4 -3-2 -1 0 1 2 3 4 Difference sensibilité 1/100000 in erosion - sensibilité class 1/50000 Difference in erosion class sensibilité 1/100000 - sensibilité 1/50000 Differences > 2 classes are quasi absent 90% of the cells have the same erosion class, but this is partly due to absence of slope (Rhine plain) and not to soil factors On 57 % of the cells: no difference in erosion sensitivity 1/100 000 leads to an overestimation on 25 % and to an underestimation on 18 % of the surface
Erosion sensitivity index per commune erosion sensitivity per commune (Haut-Rhin) 70 60 soil map 1/50 000 50 40 30 20 10 0 0 10 20 30 40 50 60 70 soil map 1/100 000
Ranking of 260 communes ranking of "communes" according to erosion sensitivity (Haut-Rhin) 300 soil map 1/50 000 250 200 150 100 50 HIGH MIDDLE LOW 0 0 50 100 150 200 250 300 soil map 1/100 000 The selection of priority Ranks communes : 1 = highest for sensitivity action is difficult when using the 1/100 000 soil map: classifying 260 = lowest them sensitivity in three or four groups is the best solution EGU-2010, Vienna
Sensitivity of LISEM to soil map scale Pedotransfert functions (SPAW) were used to derive LISEM soil-related parameters (Ks, Theta_s, psi) from the soil map attribute data Comparison of LISEM results (infiltration, erosion and sedimentation) using the 1/100 000 soil map with a more detailed map (1/50 000 or finer) on 1000 gridcells randomly chosen in the catchment
LISEM output Overland flow and routing
LISEM output Erosion
LISEM output Deposition
Time series at selected points débit (m3/s) 25 20 15 10 5 Q m3/s P mm/h Conc g/l 200 160 120 80 40 P (mm/h) ; Sed.conc (g/l) 0 0 0 50 100 150 200 250 300 350 temps (min) EGU-2010, Vienna
Two catchments were tested EGU-2010, Vienna Ergersheim 250 ha, slopes 2-5% variable soil types and textures (loess soils, clayey-calcareous soils and alluvial soils) arable land and vineyards Stetten-Brinckheim 365 ha, slopes 5-10% loess-derived soils and small textural variations dominated by arable land
The soil maps tetten-brinckheim loess-derived soils) Ergersheim (contrasted soils) 1/100 000 1/100 000 1/100 000 1/50 000 1/100 000 1/25 000
Catchment Bassin versant with aux loess- sols derived homogènes soils (small textural Stetten-Brinckheim variations) Catchment Bassin versant with aux strongly sols contrasted contrastés soils (high textural Ergersheim variations) 1/100 000 soil map Infiltration 0 0 20 40 60 80 donnees sols aux 1/50 000 Detailed soil map same with both soil Erosion maps 80 60 40 20 60 40 20 infiltration (mm) 1 : 1 Results are essentially the 1/100 000 soil map 1/100 000 soil map erosion (ton/ha) 1 : 1 0 0 20 40 60 donnees sols aux 1/50 000 30 20 10 Detailed soil map Dépôts depot (ton/ha) 1 : 1 0 0 10 20 30 A B C 80 60 40 20 Infiltration infiltration (mm) 1 : 1 0 0 20 40 60 80 donnees sols aux 1/25 000 Detailed soil map differences: Erosion the 1/100 000 60 40 20 erosion (ton/ha) 1 : 1 0 0 20 40 60 donnees sols aux 1/25 000 30 20 Detailed soil map Dépôts 10 depot (ton/ha) 1 : 1 0 0 10 20 30 Infiltration Results shows significant map leads to underestimated infiltration, Erosion and overestimated erosion and deposition Deposition EGU-2010, Vienna
Catchment with loessderived soils (small textural variations) Bassin versant : Stetten-Brinckheim Différence infiltration entre échelle 1/50 000 et 1/100 000 (mm) Catchment with strongly contrasted soils (high textural variations) Bassin versant : Ergersheim Différence infiltration entre échelle 1/25 000 et 1/100 000 (mm) Infiltration Erosion The selection of hot spots within the catchment for reduced tillage or other erosion control measures is is difficult when using the Différence érosion entre échelle 1/50 000 et 1/100 000 (ton/ha) 1/100 000 soil map for the Ergersheim catchment! 0 à 1 1 à 5 5 à 10 10 à 20 20 à 30 0 à 0.25 0.25 à 0.5 0.5 à 1 1 à 5 > 5 Différence érosion entre échelle 1/25 000 et 1/100 000 (ton/ha) 0 à 1 1 à 5 5 à 10 10 à 20 20 à 30 0 à 0.25 0.25 à 0.5 0.5 à 1 1 à 5 > 5
Conclusions Inappropriate soil map scale can be a significant error source for erosion model output, and being aware of this can avoid misuse of modelling results In such cases, possible solutions are: Refine soil (textural) information for the study area Generalize (through classification or spatial aggregation) the model output There is not one single appropriate soil map scale per model Higher textural variability in the study area requires higher resolutions of soil maps Such variability can be present at the regional scale, but as well in small catchments EGU-2010, Vienna
Acknowledgements Financing Risque, décision, territoire Model support Medales: Yves Le Bissonnais, Olivier Cerdan LISEM: Victor Jetten EGU-2010, Vienna
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