Evaluating spatial and temporal variability in soil erosion risk—rainfall erosivity and soil loss ratios in Andalusia, Spain

CATENA ◽  
1999 ◽  
Vol 34 (3-4) ◽  
pp. 209-225 ◽  
Author(s):  
C.S. Renschler ◽  
C. Mannaerts ◽  
B. Diekkrüger
Keyword(s):  
Soil Erosion ◽  
Temporal Variability ◽  
Soil Loss ◽  
Rainfall Erosivity ◽  
Spatial And Temporal Variability ◽  
Erosion Risk ◽  
Soil Erosion Risk

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.

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 Time Scale Effects and Interactions of Rainfall Erosivity and Cover Management Factors on Vineyard Soil Loss Erosion in the Semi-Arid Area of Southern Sicily

Water ◽  
2019 ◽  
Vol 11 (5) ◽  
pp. 978 ◽  
Author(s):  
Giorgio Baiamonte ◽  
Mario Minacapilli ◽  
Agata Novara ◽  
Luciano Gristina
Keyword(s):  
Soil Erosion ◽  
Soil Loss ◽  
Scale Effects ◽  
Management Practice ◽  
Temporal Analysis ◽  
Temporal Scale ◽  
Rainfall Erosivity ◽  
Erosion Risk ◽  
Best Management ◽  
The Impact

Several authors describe the effectiveness of cover crop management practice as an important tool to prevent soil erosion, but at the same time, they stress on the high soil loss variability due to the interaction of several factors characterized by large uncertainty. In this paper the Revised Universal Soil Loss Equation (RUSLE) model is applied to two Sicilian vineyards that are characterized by different topographic factors; one is subjected to Conventional Practice (CP) and the other to Best Management Practice (BMP). By using climatic input data at a high temporal scale resolution for the rainfall erosivity (R) factor, and remotely sensed imagery for the cover and management (C) factor, the importance of an appropriate R and C factor assessment and their inter and intra-annual interactions in determining soil erosion variability are showed. Different temporal analysis at ten-year, seasonal, monthly and event scales showed that results at events scales allow evidencing the interacting factors that determine erosion risk features which at other temporal scales of resolution can be hidden. The impact of BMP in preventing soil erosion is described in terms of average saved soil loss over the 10-year period of observation. The evaluation of soil erosion at a different temporal scale and its implications can help stakeholders and scientists formulate better soil conservation practices and agricultural management, and also consider that erosivity rates are expected to raise for the increase of rainfall intensity linked to climate change.

Soil erosion risk in Korean watersheds, assessed using the revised universal soil loss equation

2011 ◽  
Vol 399 (3-4) ◽  
pp. 263-273 ◽  
Author(s):  
Soyoung Park ◽  
Cheyoung Oh ◽  
Seongwoo Jeon ◽  
Huicheul Jung ◽  
Chuluong Choi
Keyword(s):  
Soil Erosion ◽  
Soil Loss ◽  
Universal Soil Loss Equation ◽  
Erosion Risk ◽  
Soil Erosion Risk ◽  
Soil Loss Equation

scholarly journals ESTIMATE THE ANNUAL SOIL LOSS IN KUMMATTIPATTI NADI WATERSHED USING RUSLE MODEL THROUGH GEOSPATIAL TECHNOLOGY

2020 ◽  
Vol 46 (2) ◽  
pp. 75-82
Author(s):  
Suraj Shaikh ◽  
Masilamani Palanisamy ◽  
Abdul Rahaman Sheik Mohideen
Keyword(s):  
Soil Erosion ◽  
Soil Loss ◽  
Tamil Nadu ◽  
Management Strategies ◽  
Geographical Information ◽  
Rainfall Erosivity ◽  
Agricultural Activity ◽  
Erosion Risk ◽  
Factor C ◽  
Conservation And Management

Soil erosion and soil loss is one of the common problems threatening the environment. This degrading phenomenon declines the soil fertility and significantly affects the agricultural activity. As a consequence, the productivity of soil is affected unquestionably. In this reason, there is a basic need to take up conservation and management measures which can be applied to check further soil erosion. Even though, soil erosion is a mass process spread cross the watershed, it is not economically viable to implement conservation techniques to the entire watershed. However, a method is a pre-requisite to identify the most vulnerable areas and quantify the soil erosion. In this study, Revised Universal Soil Loss Equation (RUSLE) has been accepted to estimate soil erosion in the Kummattipatti Nadi watershed part of the Coimbatore district of Tamil Nadu, India. This model has several parameters including runoff-rainfall erosivity factor (R), soil erodability Factor (K), topographic factor (LS), cropping management factor (C), and support practice factor (P). All these layers are prepared through geographical information system (GIS) by using various data sources and data preparation methods. The results of the study shows that the annual average soil loss within the watershed is about 6 t/ha/yr (metric ton per hectare per year). Higher soil erosion is observed in the land use classes of gullied wasteland, open scrub forest and degraded plantation. The soil erosion risk is extremely higher on the steep slopes and adjoining foothills. The proper conservation and management strategies has to be implement in this watershed for the development.

scholarly journals Terrain Characterization and Soil Erosion Risk Assessment for Watershed Prioritization Using Remote Sensing and GIS (A case study of Nawagaon Maskara Rao Watershed, Saharanpur, India)

2009 ◽  
Vol 23 (1) ◽  
pp. 86
Author(s):  
Beny Harjadi
Keyword(s):  
Remote Sensing ◽  
Risk Assessment ◽  
Land Use ◽  
Soil Erosion ◽  
Soil Conservation ◽  
Soil Loss ◽  
Remote Sensing And Gis ◽  
Erosion Risk ◽  
Soil Erosion Risk ◽  
Conservation Measures

Soil erosion is crucial problem in India where more than 70% of land in degraded. This study is to establish conservation priorities of the sub watersheds across the entire terrain, and suggest suitable conservation measures. Soil conservation practices are not only from erosion data both qualitative SES (Soil Erosion Status) model and quantitative MMF (Morgan, Morgan and Finney) model erosion, but we have to consider LCC (Land Capability Classification) and LULC (Land Use Land Cover). Study demonstrated the use of RS (Remote Sensing) and GIS (Geographic Information System) in soil erosion risk assessment by deriving soil and vegetation parameters in the erosion models. Sub-watersheds were prioritized based on average soil loss and the area falls under various erosion risk classes for conservation planning. The annual rate of soil loss based on MMF model was classified into five soil erosion risk classes for soil conservation measures. From 11 sub watersheds, for the first priority of the watershed is catchment with the small area and the steep slope. Recommendation for steep areas (classes VI, VII, and VIII) land use allocation should be made to maintain forest functions.

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