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Physical Geography

Projekte

Soil Erosion Risk Modeling in the Alps –

ERKBerg as a Prototype of ERK2 for mountain zones III, IV and summering grazing zones

 

S. SchmidtK. MeusburgerP. PanagosC. Alewell

 

Project funded by the Federal Office for the Environment (FOEN)

Grant numbers N° N222-0350 & N° P182-1535

Duration: 2015 – 2018

 

Soil Erosion on grassland is generally neglected due to the protective character of dense grass vegetation on soil loss. However, recent studies by Meusburger et al. (2010), Konz et al. (2012) and Alewell et al. (2013) show that large amounts of topsoil are mobilized also on grassland in the alpine areas (Fig. 1).

Soil Erosion in the Swiss Alps 

Figures 1: Soil Erosion in the Swiss Alps

 

A soil erosion modeling approach for arable land (Erosionsrisikokarte ERK2; Prasuhn et al. 2013) was already realized by the Centre for Development and Environment University Bern and Agroscope Reckenholz. To complement the ERK2-results and to create a nationwide soil erosion risk map, a risk assessment for the (alpine) grassland will be realized by geospatial modeling.

 

According to a comprehensive approach, the model is based on the Revised Universal Soil Loss Equation (RUSLE) by Wischmeier & Smith (1978). The general soil-loss-equation of RUSLE is a function of five factors:

A = R * K * LS * C * P

Where A is the mean long-time soil loss in t ha-1 yr-1, R the rain erosivity factor, K the soil erodibility and the parameters L and S describe the length and slope of the relief. C is the cover and management factor, P is a factor for protection and soil conservation.

 

The main task of the ongoing project is the adaptation of the model parameters which aren’t generally accepted for steep slopes and grassland in alpine areas. Furthermore, the project aims the investigation of spatial AND temporal soil erosion patterns by a dynamic soil erosion risk assessment.

 

The monthly rainfall erosivity of Switzerland, based on 87 automated gauging stations and a regression kriging approach, revealed spatial and temporal pattern with highest R-factors in summer (Fig.2; Schmidt et al. 2016). A proportion of 62% of the total annual sum of rainfall erosivity affects soils within a time period of 4 months (June, July, August, and September) (Fig. 3). 

 

Monthly rainfall erosivity maps for Switzerland derived by regression-kriging (SCHMIDT et al. 2016) 

Figure 2: Monthly rainfall erosivity maps for Switzerland derived by regression-kriging (Schmidt et al. 2016)

 

The increase of spatial and temporal resolution of remote sensing datasets enabled the mapping of C-factors on a monthly scale. We used an orthophoto with a spatial resolution of 0.25m (Swissimage FCIR) and a time-series of the fraction of green vegetation cover with a temporal resolution of 10-days (FCover) to calculate the dynamics of the C-factor on Swiss grassland. The annual average C-factor of all Swiss grassland is 0.012 (Schmidt et al. in review a). A national map of Swiss grassland and Swiss permanent grassland was presented for the first time (Schmidt et al. in review b).

 

The national soil erodibility map of Switzerland is based on digital soil mapping with 199 Land Use/Cover Area frame Survey (LUCAS) topsoil samples. The mean national K-factor for Switzerland is 0.033 t ha h ha-1 MJ-1 mm-1 (Schmidt et al. in prep.).

 

Cumulative daily rainfall erosivity proportion for Swiss biogeographic units, Switzerland and monthly erosivity for Europe (linear smoothed) (SCHMIDT et al. 2018) 

Figure 3: Cumulative daily rainfall erosivity proportion for Swiss biogeographic units, Switzerland and monthly erosivity for Europe (linear smoothed) (Schmidt et al. 2018)

 

Slope length and steepness for Switzerland is based on the Multiple Triangular Flow Direction (MTFD) approach (Seibert & McGlynn 2007) and was extensively tested on plot scale (Bircher et al. in prep.).

 

Due to the excellent database of Switzerland, the model could be used as a prototype for risk assessment in the European alpine regions. It is envisaged to present the soil erosion risk map for grassland by the end of the year 2018.

Literature

Alewell, C.; Meusburger, K.; Juretzko, G.; Mabit, L.; and Ketterer, M. (2013): Suitability of 239+240Pu as a tracer for soil erosion in alpine grasslands, Chemosphere, doi: 10.1016/j.chemosphere.2013. 12.016.

 

Bircher, P., Prasuhn, V., Liniger, H.-P. (in prep.): Comparison of different multiple flow algorithms for RUSLE slope-length factor (L) and slope steepness factor (S) calculation in Switzerland.

 

Konz, N.; Prasuhn, V.; and Alewell, C. (2012): On the measurement of alpine soil erosion, CATENA, 91, 63-71, 10.1016/j.catena.2011.09.010.

 

Meusburger, K.; Konz, N.; Schaub, M.; and Alewell, C. (2010): Soil erosion modelled with USLE and PESERA using QuickBird derived vegetation parameters in an alpine catchment, International Journal of Applied Earth Observation and Geoinformation, 12, 208-215, 10.1016/j.jag.2010.02.004.

 

Prasuhn, V., Liniger, H., Gisler, S., Herweg, K., Candinas, A., Clément, J.-P. (2013): A high-resolution soil erosion risk map of Switzerland as strategic policy support system, Land Use Policy 32, 281–291. 10.1016/j.landusepol.2012. 11.006.

 

Seibert, J., McGlynn, B.L. (2007): A New Triangular Multiple Flow Direction Algorithm for Computing Upslope Areas from Gridded Digital Elevation Models, Water Resources Research 43, 10.1029/2006WR005128.

 

Schmidt, S., Alewell, C., Meusburger, K. (in review a): Mapping Spatio-Temporal Dynamics of the Cover and Management Factor (C-Factor) for Grasslands in Switzerland.

 

Schmidt, S., Alewell, C., Meusburger, K. (in review b): Swiss National Grassland Map and Change (1996-2015) of Permanent Grasslands Extent in Switzerland.

 

Schmidt, S., Alewell, C., Panagos, P., Meusburger, K. (2016): Regionalization of monthly rainfall erosivity patterns in Switzerland, Hydrol. Earth Syst. Sci. 20, 4359–4373. 10.5194/hess-20-4359-2016.

 

Schmidt, S., Alewell, C., Panagos, P., Meusburger, K. (2017): Saisonale und räumliche Variabilität der Niederschlagserosivität in der Schweiz, BGS Bulletin 38, 37-46.

 

Schmidt, S., Ballabio, C., Alewell, C., Panagos, P., Meusburger, K. (in review): Filling the European blank spot – Swiss Soil Erodibility Assessment with Topsoil Samples.

 

Wischmeier, W. H.; and Smith, D. D. (1978): Predicting Rainfall Erosion Losses – A Guide to Conservation Planning, U.S. Dep. Of. Agriculture, Handbook No. 537.

Soil loss by wind (SoLoWind): a new GIS-based model to identify risk areas 

 

S. SchmidtK. MeusburgerC. Alewell

 

No funding

Duration: 2013 – 2017

Logo SoLoWind 

 

The focus of wind erosion studies in Germany is located in the Northern and Eastern parts of the country, where wind erosion is a major soil threat and environmental concern. One of the most susceptible regions not only within Germany, but also within Europe (1, 2) is Western Saxony even though no high resolution erosion risk map exists for that region yet.

 

A new wind erosion model for modeling soil loss by wind called SoLoWind was developed and tested for Western Saxony (3). SoLoWind extends the existing DIN model (DIN standard 19706) applied by the public authorities in Germany to a multidirectional model with new causal factors. The new factors are combined by fuzzy logic with the original DIN factors into four modules. The “Natural Wind Erosions Susceptibility” (SUS) module determines the regional soil erodibility with respect to soil texture, soil organic content, soil moisture and wind speeds. A “Soil Cover” (COV) module distinguishes between bare soil and covered soil in satellite images. Furthermore, the modules “Mean Field Length” (MFL) and “Mean Protection Zones” (MPZ) are parameters for the wind erosions avalanching effect and sheltering of windbreaks. Both modules are weighted according to the frequency of wind directions.

 

Datasets and framework of soil loss by wind (SoLoWind) and its modules natural wind erosion susceptibility (SUS), soil cover (COV), mean field length (MFL) and mean protection zones (MPZ) (Schmidt et al. 2017) 

Figure 1: Datasets and framework of soil loss by wind (SoLoWind) and its modules natural wind erosion susceptibility (SUS), soil cover (COV), mean field length (MFL) and mean protection zones (MPZ) (Schmidt et al. 2017)

 

The application showed that about one-third of all arable land in Western Saxony have either high (26.9%) or very high soil erosion risk (3.6%) by wind. As such, wind erosion is a serious land degradation threat for the region as it is in the adjacent federal states. According to the modeled off-site effects of wind erosion, a potential danger of reduced visibility by windblown dust to sections of the highway A72 could clearly be identified which calls for immediate protection measures.

 

Mean wind erosion risk on arable land in Western Saxony modelled by soil loss by wind (SoLoWind) (Schmidt et al. 2017) 

Figure 2: Mean wind erosion risk on arable land in Western Saxony modelled by soil loss by wind (SoLoWind) (Schmidt et al. 2017)

 

The transparency, adaptability, and user-friendliness of the model suggest that SoLoWind might serve as a planning tool for soil conservation strategies not merely in Western Saxony, but also in other regions.

Literature

Schmidt, S., Meusburger, K., de Figueiredo, T., and Alewell, C. (2017): Modelling Hot Spots of Soil Loss by Wind Erosion (SoLoWind) in Western Saxony, Germany, Land Degradation & Development, 28, 3, 1100-1112. doi:10.1002/ldr.2652.

Risk by water erosion for highways in Saxony-Anhalt

 

 H. Helbig; S. Schmidt; R. Köthe

 

Project for/with the State Office of Geology and Mining

Duration: 2013

 

Sediment on the highway (Helbig et al. 2015) 

 

Thunderstorms with heavy rainfall induced flooding of motorways in Saxony-Anhalt repeatedly. Such events are usually connected with soil erosion on fields near the roads and lead, apart from loss of soil fertility, to damage on infrastructure and expose traffic to danger. Endangered street sections are present, where surface runoff as a result of heavy rain could be able to trespass a lane. The aim of the investigation were the detection of both prospective trespassing points and street sections of highest risk at the motorways A 2, A 38, A 14, A 9, A143 and the national highway B 6n (up to Güsten). Under usage of digital elevation data and modeled runoff pathways 215 prospective trespassing points have been detected.

 

Potential trespassing spots and corresponding catchments (Helbig et al. 2015) 

Figure 1: Potential trespassing spots and corresponding catchments (Helbig et al. 2015)

 

Data of land use, approximated erosion risk and content of surface runoff are used to find the street sections of highest risk. The approach was exemplarily validated using field observations. It shows that the approach is useful for a first priority assessment of endangered street sections. Limitations mainly result from spatial resolution of digital elevation data. Before planning real measures against flooding and erosion the results have to be checked by field investigations and use of higher resolution digital elevation data.

 

Trespassing spot next to the highway with sediment fan (Helbig et al. 2015) 

Figure 2: Trespassing spot next to the highway with sediment fan (Helbig et al. 2015)

Literature

Helbig, H., Schmidt, S., and Köthe, R. (2015): Gefährdung von Autobahnen durch Wassererosion in Sachsen-Anhalt, Straße & Autobahn 12/2015, 851-860.

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