scholarly journals Spatial and temporal variability of rainfall erosivity factor for Switzerland

2011 ◽  
Vol 8 (5) ◽  
pp. 8291-8314 ◽  
Author(s):  
K. Meusburger ◽  
A. Steel ◽  
P. Panagos ◽  
L. Montanarella ◽  
C. Alewell
Keyword(s):  
Soil Erosion ◽  
Western Alps ◽  
Annual Precipitation ◽  
Rainfall Erosivity ◽  
Spatial And Temporal Variability ◽  
Temporal And Spatial Distribution ◽  
Erosion Risk ◽  
Soil Erosion Risk ◽  
Increasing Trends ◽  
Average Annual Precipitation

Abstract. Rainfall erosivity, considering rainfall amount and intensity, is an important parameter for soil erosion risk assessment under future land use and climate change. Despite its importance, rainfall erosivity is usually implemented in models with a low spatial and temporal resolution. The purpose of this study is to assess the temporal- and spatial distribution of rainfall erosivity (R-factor) in Switzerland. Time series of 22 yr for rainfall (10 min resolution) and temperature (1 h resolution) data were analysed for 71 automatic gauging stations distributed throughout Switzerland. Multiple regression was used to interpolate the erosivity values of single stations and to generate a map for Switzerland. Latitude, longitude, average annual precipitation, biogeographic units (Jura, Midland, etc.), aspect and elevation were used as covariates, of which average annual precipitation, elevation and the biographic unit (Western Alps) were significant predictors. The mean value of long-term rainfall erosivity is 1323 MJ mm ha−1 h−1 yr−1 with a range of lowest values of 124 MJ mm ha−1 h−1 yr−1 at an elevated station in Grisons to highest values of 5611 MJ mm ha−1 h−1 yr−1 in Ticino. All stations have highest erosivity values from July to August and lowest values in the winter month. Swiss-wide the month May to October show significantly increasing trends of erosivity (p<0.005). Only in February a significantly decreasing trend of rainfall erosivity is found (p<0.01). The increasing trends of erosivity in May, September and October when vegetation cover is susceptible are likely to enhance soil erosion risk for certain agricultural crops and alpine grasslands in Switzerland.

scholarly journals Spatial and temporal variability of rainfall erosivity factor for Switzerland

2012 ◽  
Vol 16 (1) ◽  
pp. 167-177 ◽  
Author(s):  
K. Meusburger ◽  
A. Steel ◽  
P. Panagos ◽  
L. Montanarella ◽  
C. Alewell
Keyword(s):  
Soil Erosion ◽  
Mean Value ◽  
Annual Precipitation ◽  
Rainfall Erosivity ◽  
Spatial And Temporal Variability ◽  
Temporal And Spatial Distribution ◽  
Erosion Risk ◽  
Soil Erosion Risk ◽  
Increasing Trends ◽  
Average Annual Precipitation

Abstract. Rainfall erosivity, considering rainfall amount and intensity, is an important parameter for soil erosion risk assessment under future land use and climate change. Despite its importance, rainfall erosivity is usually implemented in models with a low spatial and temporal resolution. The purpose of this study is to assess the temporal- and spatial distribution of rainfall erosivity in form of the (Revised) Universal Soil Loss Equation R-factor for Switzerland. Time series of 22 yr for rainfall (10 min resolution) and temperature (1 h resolution) data were analysed for 71 automatic gauging stations distributed throughout Switzerland. Regression-kriging was used to interpolate the rainfall erosivity values of single stations and to generate a map for Switzerland. Latitude, longitude, average annual precipitation, biogeographic units (Jura, Midland, etc.), aspect and elevation were used as covariates, of which average annual precipitation, elevation and the biographic unit (Western Central Alps) were significant (p<0.01) predictors. The mean value of long-term rainfall erosivity is 1330 MJ mm ha−1 h−1 yr−1 with a range of lowest values of 124 MJ mm ha−1 h−1 yr−1 at an elevated station in Grisons to highest values of 5611 MJ mm ha−1 h−1 yr−1 in Ticino. All stations have highest erosivity values from July to August and lowest values in the winter months. Swiss-wide the month May to October show significantly increasing trends of rainfall erosivity for the observed period (p<0.005). Only in February a significantly decreasing trend of rainfall erosivity is found (p<0.01). The increasing trends of rainfall erosivity in May, September and October when vegetation cover is scarce are likely to enhance soil erosion risk for certain agricultural crops and alpine grasslands in Switzerland.

scholarly journals Predicting the Impact of Future Land Use and Climate Change on Potential Soil Erosion Risk in an Urban District of the Harare Metropolitan Province, Zimbabwe

2021 ◽  
Vol 13 (21) ◽  
pp. 4360
Author(s):  
Andrew K. Marondedze ◽  
Brigitta Schütt
Keyword(s):  
Climate Change ◽  
Land Use ◽  
Soil Erosion ◽  
Soil Loss ◽  
Rainfall Erosivity ◽  
Climate Scenarios ◽  
Residential Areas ◽  
Erosion Risk ◽  
Soil Erosion Risk ◽  
Loss Rates

Monitoring urban area expansion through multispectral remotely sensed data and other geomatics techniques is fundamental for sustainable urban planning. Forecasting of future land use land cover (LULC) change for the years 2034 and 2050 was performed using the Cellular Automata Markov model for the current fast-growing Epworth district of the Harare Metropolitan Province, Zimbabwe. The stochastic CA–Markov modelling procedure validation yielded kappa statistics above 80%, ascertaining good agreement. The spatial distribution of the LULC classes CBD/Industrial area, water and irrigated croplands as projected for 2034 and 2050 show slight notable changes. For projected scenarios in 2034 and 2050, low–medium-density residential areas are predicted to increase from 11.1 km2 to 12.3 km2 between 2018 and 2050. Similarly, high-density residential areas are predicted to increase from 18.6 km2 to 22.4 km2 between 2018 and 2050. Assessment of the effects of future climate change on potential soil erosion risk for Epworth district were undertaken by applying the representative concentration pathways (RCP4.5 and RCP8.5) climate scenarios, and model ensemble averages from multiple general circulation models (GCMs) were used to derive the rainfall erosivity factor for the RUSLE model. Average soil loss rates for both climate scenarios, RCP4.5 and RCP8.5, were predicted to be high in 2034 due to the large spatial area extent of croplands and disturbed green spaces exposed to soil erosion processes, therefore increasing potential soil erosion risk, with RCP4.5 having more impact than RCP8.5 due to a higher applied rainfall erosivity. For 2050, the predicted wide area average soil loss rates declined for both climate scenarios RCP4.5 and RCP8.5, following the predicted decline in rainfall erosivity and vulnerable areas that are erodible. Overall, high potential soil erosion risk was predicted along the flanks of the drainage network for both RCP4.5 and RCP8.5 climate scenarios in 2050.

Global water soil erosion risk associated with climate change

2020 ◽  
Author(s):  
Muqi Xiong
Keyword(s):  
Climate Change ◽  
Soil Erosion ◽  
Average Rate ◽  
Water Erosion ◽  
Cold Climate ◽  
Erosion Risk ◽  
Soil Erosion Risk ◽  
Forest Lands ◽  
Increasing Trends ◽  
Global Water

&lt;p&gt;Water-driven soil erosion is the most widespread form of soil degradation worldwide, which threatens to the sustainability of agriculture. Climate change may aggravate the threat of erosion. On the basis of the Revised Universal Soil Loss Equation, combined with Geographic Information Systems (GIS), we assessed spatiotemporal variances in global water erosion risk trends during the period 1992&amp;#8211;2015 using the linear regression model. The research objective was to explore the spatial pattern of global water erosion risk change in recent decades and to identify the driving factors. The results show that the global water erosion risk increased over 54% of the surface during 1992&amp;#8211;2015, with an average rate of 0.17 t&amp;#183;ha&lt;sup&gt;-1&lt;/sup&gt;&amp;#183;yr&lt;sup&gt;-2&lt;/sup&gt;. The lands with significant increasing trends (p &lt; 0.05) accounted for 12% of global lands, with an average rate of 0.27 t&amp;#183;ha&lt;sup&gt;-1&lt;/sup&gt;&amp;#183;yr&lt;sup&gt;-2&lt;/sup&gt;. In which, over 75% regions with significant increasing trends were croplands and forest lands in the cold climate zone as the rainfall intensity increased. However, the increasing rates of soil erosion risk on bare lands and croplands were extremely larger than that on lands with natural vegetation, which means that water erosion on natural lands had much lower sensitive to rainfall changes. &lt;span&gt;These results suggest that improving vegetation conditions in the region with sensitive climate change could reduce the erosion threat.&lt;/span&gt;&lt;/p&gt;

Soil erosion risk and its spatial pattern in upstream area of Guanting reservoir

2011 ◽  
Vol 65 (1) ◽  
pp. 221-229 ◽  
Author(s):  
Xi Wang Zhang ◽  
Bing Fang Wu ◽  
Xiao Song Li ◽  
Shan Long Lu
Keyword(s):  
Soil Erosion ◽  
Spatial Pattern ◽  
Erosion Risk ◽  
Guanting Reservoir ◽  
Soil Erosion Risk

scholarly journals Soil Erosion Estimation Using Remote Sensing Techniques in Wadi Yalamlam Basin, Saudi Arabia

2016 ◽  
Vol 2016 ◽  
pp. 1-8 ◽  
Author(s):  
Jarbou A. Bahrawi ◽  
Mohamed Elhag ◽  
Amal Y. Aldhebiani ◽  
Hanaa K. Galal ◽  
Ahmad K. Hegazy ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Saudi Arabia ◽  
Soil Erosion ◽  
Decision Support Tool ◽  
Data Availability ◽  
Productive Capacity ◽  
Support Tool ◽  
Erosion Risk ◽  
Soil Erosion Risk ◽  
Key Aspects

Soil erosion is one of the major environmental problems in terms of soil degradation in Saudi Arabia. Soil erosion leads to significant on- and off-site impacts such as significant decrease in the productive capacity of the land and sedimentation. The key aspects influencing the quantity of soil erosion mainly rely on the vegetation cover, topography, soil type, and climate. This research studies the quantification of soil erosion under different levels of data availability in Wadi Yalamlam. Remote Sensing (RS) and Geographic Information Systems (GIS) techniques have been implemented for the assessment of the data, applying the Revised Universal Soil Loss Equation (RUSLE) for the calculation of the risk of erosion. Thirty-four soil samples were randomly selected for the calculation of the erodibility factor, based on calculating theK-factor values derived from soil property surfaces after interpolating soil sampling points. Soil erosion risk map was reclassified into five erosion risk classes and 19.3% of the Wadi Yalamlam is under very severe risk (37,740 ha). GIS and RS proved to be powerful instruments for mapping soil erosion risk, providing sufficient tools for the analytical part of this research. The mapping results certified the role of RUSLE as a decision support tool.

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