Conference PaperPDF Available

Identification of Spatiotemporal Patterns of Rainfall Erosivity as Decision Support to Erosion Control in Switzerland

Authors:
  • Dr. Simon Scheper - Research | Consulting | Teaching
  • Swiss Federal Institute for Forest, Snow and Landscape Research - WSL

Abstract and Figures

Rainfall has direct impacts on soil mobilization by rapid wetting or splash and runoff effects and is one of the main driving forces of water erosion. A combination of rainfall amount and intensity is expressed as rainfall erosivity (R-factor) which is one of the five soil erosion risk factors (rainfall erosivity, soil erodibility, slope steepness and length, cover management, and support practices) in the Revised Universal Soil Loss Equation (RUSLE) (Renard et al., 1997; Foster et al., 2008). As Switzerland has a high spatial and seasonal variation of climate parameters, the correlated rainfall erosivity can be expected to have a regional characteristic and seasonal dynamic throughout the year. The study of these spatiotemporal patterns is decisive in combination with the dynamics of the vegetation cover (C-factor) in order to allow for an accurate soil erosion risk assessment and thus a target-oriented management of agricultural practices and hazard controls. The intra-annual variation of the R-factor was mapped by a monthly modeling approach to assess simultaneously spatial and monthly patterns of rainfall erosivity (Schmidt et al., 2016). We used a network of 87 precipitation gauging stations with a 10 min temporal resolution to extract the long-term (19.5 years) monthly mean rainfall erosivity (R mo). Subsequently, the spatial and temporal pattern of R mo were explained based on a stepwise generalized linear regression (GLM) and significant spatial covariates like snow depth, a combination product of hourly precipitation measurements and radar observations (CombiPrecip), daily Alpine precipitation (EURO4M-APGD), monthly precipitation sums (RhiresM), and topographic parameters (elevation, slope, aspect derived from SwissAlti3D). The monthly R-factor for each month is mapped by summarizing the predicted R-factors of the regression equation and the corresponding residues which are interpolated by ordinary kriging (regression-kriging). Furthermore, we investigated the cumulative percentage of the daily R-factor within a year to assess the annual time period in which rainfall erosivity has its highest proportion. Monthly rainfall erosivity maps of Switzerland confirm the high seasonality of rainfall erosivity with lowest national means in January (10.5 MJ mm ha-1 h-1 month-1) and highest in August (263,5 MJ mm ha-1 h-1 month-1). Likewise, a high spatial variability can be observed for Switzerland. The cumulative daily rainfall erosivity revealed on a national scale a share of 62% of the total annual rainfall erosivity within the period from June to September. Within the canton Ticino, even 70% was reached within the same period. The high percentage of rainfall erosivity within a short period of time (4 months) is likely to have a large impact on the soil erosion susceptibility since it may coincide with the lowest (after harvesting or grass cutting/ pasturing) and/or most unstable vegetation cover (after late sowing) (Hartwig and Ammon, 2002; Wellinger et al., 2006; Torriani et al., 2007; Prasuhn, 2011). Our research highlight that rainfall erosivity in Switzerland has a very high variability within months and regions. The findings are of relevance for soil conservation planning and might be a basis for selective erosion control measures, such as a change in crop or crop rotation to weaken the rainfalls impact on soils and vegetation by increasing soil cover or stabilizing topsoil during these susceptible months.
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BGS JAHRESTAGUNG 9./10. FEBRUAR 2017 UNIVERSITÄT B E R N
BODENWISSENSCHAFT UND
BODENSCHUTZ: EINE
GRENZÜBERSCHREITENDE
HERAUSFORDERUNG
SSP CONGRÈS ANNUEL LES 9/10 FÉVRIER 2017 UNIVERSI T É D E B E R N E
LA SCIENCE ET LA PROTECTION DU SOL:
UN DÉFI À TRAVERS LES FRONTIÈRES
SSP CONGRESSO ANNUALE 9/10 FEBBRAIO 2017 UNIVERSITÀ DI BERNA
SCIENZA E PROTEZIONE DEL SUOLO:
UNA SFIDA SENZA CONFINI
SSSS ANNUAL MEETING FEBRUARY 9th/10th 2017 UNI V ERSITY OF BERN
SOIL SCIENCE AND SOIL PROTECTION:
A CHALLENGE ACROSS BOUNDARIES
1
IMPRESSUM
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Wächter Daniel
Wiggenhauser Matthias
36
Identification of spatiotemporal patterns of rainfall erosivity as decision support to erosion control
in Switzerland
Simon Schmidt1, Christine Alewell1, Panos Panagos2 & Katrin Meusburger1
1 Environmental Geosciences, University Basel, Bernoullistrasse 30, 4056 Basel, Switzerland, si.schmidt@unibas.ch
2 Joint Research Centre of the European Commission, Institute for Environment and Sustainability, Via E. Fermi 2749, 21027 Ispra, Italy
Rainfall has direct impacts on soil mobilization by rapid wetting or splash and runoff effects and is one of the main
driving forces of water erosion. A combination of rainfall amount and intensity is expressed as rainfall erosivity (R-
factor) which is one of the five soil erosion risk factors (rainfall erosivity, soil erodibility, slope steepness and length,
cover management, and support practices) in the Revised Universal Soil Loss Equation (RUSLE) (Renard et al.,
1997; Foster et al., 2008).
As Switzerland has a high spatial and seasonal variation of climate parameters, the correlated rainfall erosivity can
be expected to have a regional characteristic and seasonal dynamic throughout the year. The study of these spatio-
temporal patterns is decisive in combination with the dynamics of the vegetation cover (C-factor) in order to allow for
an accurate soil erosion risk assessment and thus a target-oriented management of agricultural practices and haz-
ard controls.
The intra-annual variation of the R-factor was mapped by a monthly modeling approach to assess simultaneously
spatial and monthly patterns of rainfall erosivity (Schmidt et al., 2016). We used a network of 87 precipitation gaug-
ing stations with a 10 min temporal resolution to extract the long-term (19.5 years) monthly mean rainfall erosivity
(Rmo). Subsequently, the spatial and temporal pattern of Rmo were explained based on a stepwise generalized linear
regression (GLM) and significant spatial covariates like snow depth, a combination product of hourly precipitation
measurements and radar observations (CombiPrecip), daily Alpine precipitation (EURO4M-APGD), monthly precipi-
tation sums (RhiresM), and topographic parameters (elevation, slope, aspect derived from SwissAlti3D). The month-
ly R-factor for each month is mapped by summarizing the predicted R-factors of the regression equation and the
corresponding residues which are interpolated by ordinary kriging (regression-kriging). Furthermore, we investigated
the cumulative percentage of the daily R-factor within a year to assess the annual time period in which rainfall ero-
sivity has its highest proportion.
Monthly rainfall erosivity maps of Switzerland confirm the high seasonality of rainfall erosivity with lowest national
means in January (10.5 MJ mm ha-1 h-1 month-1) and highest in August (263,5 MJ mm ha-1 h-1 month-1). Likewise, a
high spatial variability can be observed for Switzerland. The cumulative daily rainfall erosivity revealed on a national
scale a share of 62% of the total annual rainfall erosivity within the period from June to September. Within the canton
Ticino, even 70% was reached within the same period. The high percentage of rainfall erosivity within a short period
of time (4 months) is likely to have a large impact on the soil erosion susceptibility since it may coincide with the
lowest (after harvesting or grass cutting/ pasturing) and/or most unstable vegetation cover (after late sowing) (Hart-
wig and Ammon, 2002; Wellinger et al., 2006; Torriani et al., 2007; Prasuhn, 2011).
Our research highlight that rainfall erosivity in Switzerland has a very high variability within months and regions. The
findings are of relevance for soil conservation planning and might be a basis for selective erosion control measures,
such as a change in crop or crop rotation to weaken the rainfalls impact on soils and vegetation by increasing soil
cover or stabilizing topsoil during these susceptible months.
65
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Article
Full-text available
One major controlling factor of water erosion is rainfall erosivity, which is quantified as the product of total storm energy and a maximum 30 min intensity (I30). Rainfall erosivity is often expressed as R-factor in soil erosion risk models like the Universal Soil Loss Equation (USLE) and its revised version (RUSLE). As rainfall erosivity is closely correlated with rainfall amount and intensity, the rainfall erosivity of Switzerland can be expected to have a regional characteristic and seasonal dynamic throughout the year. This intra-annual variability was mapped by a monthly modeling approach to assess simultaneously spatial and monthly patterns of rainfall erosivity. So far only national seasonal means and regional annual means exist for Switzerland. We used a network of 87 precipitation gauging stations with a 10 min temporal resolution to calculate long-term monthly mean R-factors. Stepwise generalized linear regression (GLM) and leave-one-out cross-validation (LOOCV) were used to select spatial covariates which explain the spatial and temporal patterns of the R-factor for each month across Switzerland. The monthly R-factor is mapped by summarizing the predicted R-factor of the regression equation and the corresponding residues of the regression, which are interpolated by ordinary kriging (regression–kriging). As spatial covariates, a variety of precipitation indicator data has been included such as snow depths, a combination product of hourly precipitation measurements and radar observations (CombiPrecip), daily Alpine precipitation (EURO4M-APGD), and monthly precipitation sums (RhiresM). Topographic parameters (elevation, slope) were also significant explanatory variables for single months. The comparison of the 12 monthly rainfall erosivity maps showed a distinct seasonality with the highest rainfall erosivity in summer (June, July, and August) influenced by intense rainfall events. Winter months have the lowest rainfall erosivity. A proportion of 62 % of the total annual rainfall erosivity is identified within four months only (June–September). The highest erosion risk can be expected in July, where not only rainfall erosivity but also erosivity density is high. In addition to the intra-annual temporal regime, a spatial variability of this seasonality was detectable between different regions of Switzerland. The assessment of the dynamic behavior of the R-factor is valuable for the identification of susceptible seasons and regions.
Article
Full-text available
Climate change is expected to affect both the average level and the variability of crop yields. In this modelling study, we quantified mean and inter-annual variability of grain yield for maize Zea mays L., winter wheat Triticum spp. L. and winter canola Brassica napus L. for climatic conditions corresponding to current and doubled atmospheric CO2 concentrations. Climate scenarios with and without taking into account changes in the inter-annual variability of climate were developed from the output of a regional climate model for the time window 2071 to 210.0. Climate change effects on the mean yield of maize and canola were consistently negative, but a positive impact was simulated for mean yield of winter wheat for elevated CO2 concentration. The coefficient of yield variation increased in the scenarios for maize and canola, but decreased for wheat. Higher thermal time requirements increased mean yield and reduced yield variability for all crops. Shifts in the sowing dates had a beneficial impact on the yield of maize, but not on the yield of canola and wheat. It is concluded that in the Alpine region, the potential effect of climate change is crop-specific. However, the introduction of new cultivars may provide means by which to maintain or even increase current productivity levels for most of the crops.
Article
Cover crops and living mulches bring many benefits to crop production. Interest in winter annual cover crops such as winter rye and hairy vetch for ground cover and soil erosion control has been increasing in the last 30 yr in some areas. The integration of cover crops into a cropping system by relay cropping, overseeding, interseeding, and double cropping may serve to provide and conserve nitrogen for grain crops, reduce soil erosion, reduce weed pressure, and increase soil organic matter content (Hartwig and Hoffman 1975). Hairy vetch has increased availability of nitrogen to succeeding crops, increased soil organic matter, improved soil structure and water infiltration, decreased water runoff, reduced surface soil temperature and water evaporation, improved weed control, and increased soil productivity (Frye et al. 1988). More recent research with perennial living mulches, such as crownvetch (Hartwig 1983), flatpea, birdsfoot trefoil, and white clover (Ammon et al. 1995), has added a new dimension to the use of ground covers that eliminates the need to reseed each year. Cropping systems with the use of ground covers have been worked out for vineyards, orchards, and common agronomic crops, such as corn, small grains, and forages. Legume cover crops have the potential for fixing nitrogen, a portion of which will be available for high-nitrogen-requiring crops such as corn. In areas where excess nitrogen is already a problem, the use of ground covers may provide a sink to tie up some of this excess nitrogen and hold it until the next growing season, when a crop that can make use of it might be planted (Hooda et al. 1998). Even legumes tend to use soil nitrogen rather than fixing their own, if it is available. It is these possibilities that provide the incentive for looking at the effect of various kinds of cover crops on soil erosion, nitrogen budgets, weed control, and other pest management and environmental problems.
Article
Long-term field monitoring of soil erosion by water was conducted on arable land in the Swiss midlands. All visible erosion features in 203 fields were continuously mapped and quantified over 10 years. The eroded soil volume associated with linear erosion features was calculated by measuring the length and cross-sectional area in rills at representative positions and the extent of interrill erosion was estimated. Averaged across the 10 study years, just under one-third (32.2%) of the fields exhibited erosion. With 0.75 t ha−1 yr−1 (mean) and 0.56 t ha−1 yr−1 (median), the average annual soil loss of the region was relatively small. The year-to-year variation in soil loss of the region was great and ranged from 0.16 to 1.83 t ha−1 yr−1. The maximum annual soil erosion in a single field was 96 t yr−1 or 58 t ha−1 yr−1, thus demonstrating that only a few erosion events on a few fields may decisively contribute to the total extent of soil erosion in a region. Linear and interrill erosion accounted for 75% and 25% of total soil loss, respectively. Wheel tracks, furrows, headlands, and slope depressions were important on-site accelerators of erosion. Run-on from adjacent upslope areas was an important trigger of erosion. Of the soil moved by erosion, 52% was deposited within the field of origin. A high proportion (72%) of the linear erosion features caused off-site damage. Part of the total eroded soil (20%) was transported into water, thereby contributing to their contamination. The long-term field assessment of soil erosion helps to fill existing knowledge gaps concerning temporal and spatial variability of soil erosion on arable land, the extent and severity of soil erosion and its sources and causes, as well as subsequent off-site damage.
Potential effects of changes in mean climate and climate variability on the yield of winter and spring crops in Switzerland
  • D S Torriani
  • P Calanca
  • S Schmid
  • M Beniston
  • J Fuhrer
Torriani, D. S., Calanca, P., Schmid, S., Beniston, M., and Fuhrer, J.: Potential effects of changes in mean climate and climate variability on the yield of winter and spring crops in Switzerland, Clim. Res., 34, 59–69, doi:10.3354/cr034059, 2007.
Draft User's Guide, Revised Universal Soil Loss Equation Version 2 (RUSLE-2)
  • G R Foster
  • D C Yoder
  • G A Weesies
  • D K Mccool
  • K C Mcgregor
  • R Bingner
Foster, G. R., Yoder, D. C., Weesies, G. A., McCool, D. K., McGregor, K. C., and Bingner, R.: Draft User's Guide, Revised Universal Soil Loss Equation Version 2 (RUSLE-2), Washington, DC, 431 pp., 2008.