scholarly journals Assessing Soil Loss by Water Erosion in a Typical Mediterranean Ecosystem of Northern Greece under Current and Future Rainfall Erosivity

Water ◽  
2021 ◽  
Vol 13 (15) ◽  
pp. 2002
Author(s):  
Stefanos Stefanidis ◽  
Vasileios Alexandridis ◽  
Chrysoula Chatzichristaki ◽  
Panagiotis Stefanidis
Keyword(s):  
Open Source ◽  
Soil Loss ◽  
Renewable Resource ◽  
Mitigation Strategies ◽  
Mitigation Measures ◽  
Mediterranean Ecosystem ◽  
Rainfall Erosivity ◽  
Negative Consequences ◽  
Northern Greece ◽  
Erosion Risk

Soil is a non-renewable resource essential for life existence. During the last decades it has been threatened by accelerating erosion with negative consequences for the environment and the economy. The aim of the current study was to assess soil loss changes in a typical Mediterranean ecosystem of Northern Greece, under climate change. To this end, freely available geospatial data was collected and processed using open-source software package. The widespread RUSLE empirical erosion model was applied to estimate soil loss. Current and future rainfall erosivity were derived from a national scale study considering average weather conditions and RCMs outputs for the medium Representative Concentration Pathway scenario (RCP4.5). Results showed that average rainfall erosivity (R-Factor) was 508.85 MJ mm ha h−1 y−1 while the K-factor ranged from 0.0008 to 0.05 t ha h ha−1 MJ−1 mm−1 and LS-factor reached 60.51. Respectively, C-factor ranged from 0.01 to 0.91 and P-factor ranged from 0.42 to 1. The estimated potential soil loss rates will remain stable for the near future period (2021–2050), while an increase of approximately 9% is expected by the end of the 21th century (2071–2100). The results suggest that appropriate erosion mitigation strategies should be applied to reduce erosion risk. Subsequently, appropriate mitigation measures per Land Use/Land Cover (LULC) categories are proposed. It is worth noting that the proposed methodology has a high degree of transferability as it is based on open-source data.

GIS estimation of annual average soil loss rate from Hangar River watershed using RUSLE

2019 ◽  
Vol 11 (2) ◽  
pp. 529-539 ◽  
Author(s):  
Mahmud Mustefa ◽  
Fekadu Fufa ◽  
Wakjira Takala
Keyword(s):  
Soil Erosion ◽  
Soil Loss ◽  
Loss Rate ◽  
Study Finding ◽  
Mitigation Measures ◽  
Rainfall Erosivity ◽  
Vegetative Cover ◽  
Annual Average ◽  
Slope Length ◽  
Soil Loss Rate

Abstract Currently, soil erosion is the major environmental problem in the Blue Nile, Hangar watershed in particular. This study aimed to estimate the spatially distributed mean annual soil erosion and map the most vulnerable areas in Hangar watershed using the revised universal soil loss equation. In this model, rainfall erosivity (R-factor), soil erodibility (K-factor), slope steepness and slope length (LS-factor), vegetative cover (C-factor), and conservation practice (P-factor) were considered as the influencing factors. Maps of these factors were generated and integrated in ArcGIS and then the annual average soil erosion rate was determined. The result of the analysis showed that the amount of soil loss from the study area ranges from 1 to 500 tha−1 yr−1 with an average annual soil loss rate of 32 tha−1 yr−1. Considering contour ploughing with terracing as a fully developed watershed management, the resulting soil loss rate was reduced from 32 to 19.2 tha−1 yr−1. Hence, applying contour ploughing with terracing effectively reduces the vulnerability of the watershed by 40%. Based on the spatial vulnerability of the watershed, most critical soil erosion areas were situated in the steepest part of the watershed. The result of the study finding is helpful for stakeholders to take appropriate mitigation measures.

scholarly journals Modelling soil erosion at European scale: towards harmonization and reproducibility

2014 ◽  
Vol 2 (4) ◽  
pp. 2639-2680 ◽  
Author(s):  
C. Bosco ◽  
D. de Rigo ◽  
O. Dewitte ◽  
J. Poesen ◽  
P. Panagos
Keyword(s):  
Soil Erosion ◽  
Soil Loss ◽  
Computational Modelling ◽  
Mitigation Strategies ◽  
Rainfall Erosivity ◽  
Large Spatial Scale ◽  
Nutrient Contents ◽  
Continental Scale ◽  
Country Level ◽  
Soil Erosion By Water

Abstract. Soil erosion by water is one of the most widespread forms of soil degradation. The loss of soil as a result of erosion can lead to decline in organic matter and nutrient contents, breakdown of soil structure and reduction of the water holding capacity. Measuring soil loss across the whole landscape is impractical and thus research is needed to improve methods of estimating soil erosion with computational modelling, upon which integrated assessment and mitigation strategies may be based. Despite the efforts, the prediction value of existing models is still limited, especially at regional and continental scale. A new approach for modelling soil erosion at large spatial scale is here proposed. It is based on the joint use of low data demanding models and innovative techniques for better estimating model inputs. The proposed modelling architecture has at its basis the semantic array programming paradigm and a strong effort towards computational reproducibility. An extended version of the Revised Universal Soil Loss Equation (RUSLE) has been implemented merging different empirical rainfall-erosivity equations within a climatic ensemble model and adding a new factor for a better consideration of soil stoniness within the model. Pan-European soil erosion rates by water have been estimated through the use of publicly available datasets and locally reliable empirical relationships. The accuracy of the results is corroborated by a visual plausibility check (63% of a random sample of grid cells are accurate, 83% at least moderately accurate, bootstrap p ≤ 0.05). A comparison with country level statistics of pre-existing European maps of soil erosion by water is also provided.

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 Recent and Future Changes in Rainfall Erosivity and Implications for the Soil Erosion Risk in Brandenburg, NE Germany

Water ◽  
2019 ◽  
Vol 11 (5) ◽  
pp. 904 ◽  
Author(s):  
Gericke ◽  
Kiesel ◽  
Deumlich ◽  
Venohr
Keyword(s):  
Soil Loss ◽  
Heavy Rainfall ◽  
Climate Models ◽  
Climate Model ◽  
Arable Land ◽  
Rainfall Erosivity ◽  
Erosion Risk ◽  
Ne Germany ◽  
The Impact ◽  
R Factors

The universal soil loss equation (USLE) is widely used to identify areas of erosion risk at regional scales. In Brandenburg, USLE R factors are usually estimated from summer rainfall, based on a relationship from the 1990s. We compared estimated and calculated factors of 22 stations with 10-minutes rainfall data. To obtain more realistic estimations, we regressed the latter to three rainfall indices (total and heavy-rainfall sums). These models were applied to estimate future R factors of 188 climate stations. To assess uncertainties, we derived eight scenarios from 15 climate models and two representative concentration pathways (RCP), and compared the effects of index choice to the choices of climate model, RCP, and bias correction. The existing regression model underestimated the calculated R factors by 40%. Moreover, using heavy-rainfall sums instead of total sums explained the variability of current R factors better, increased their future changes, and reduced the model uncertainty. The impact of index choice on future R factors was similar to the other choices. Despite all uncertainties, the results indicate that average R factors will remain above past values. Instead, the extent of arable land experiencing excessive soil loss might double until the mid-century with RCP 8.5 and unchanged land management.

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.

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 Detailed Analysis of Spatial–Temporal Variability of Rainfall Erosivity and Erosivity Density in the Central and Southern Pannonian Basin

2021 ◽  
Vol 13 (23) ◽  
pp. 13355
Author(s):  
Tanja Micić Ponjiger ◽  
Tin Lukić ◽  
Biljana Basarin ◽  
Maja Jokić ◽  
Robert L. Wilby ◽  
...  
Keyword(s):  
Temporal Variability ◽  
Large Scale ◽  
Pannonian Basin ◽  
Temporal Trends ◽  
Mitigation Measures ◽  
Annual Rainfall ◽  
Rainfall Erosivity ◽  
Erosion Risk ◽  
Erosion Processes ◽  
Starting Point

Estimation of rainfall erosivity (RE) and erosivity density (ED) is essential for understanding the complex relationships between hydrological and soil erosion processes. The main objective of this study is to assess the spatial–temporal trends and variability of the RE and ED in the central and southern Pannonian Basin by using station observations and gridded datasets. To assess RE and ED, precipitation data for 14 meteorological stations, 225 grid points. and an erosion model consisting of daily, monthly, seasonal, and annual rainfall for the period of 1961–2014 were used. Annual RE and ED based on station data match spatially variable patterns of precipitation, with higher values in the southwest (2100 MJ·mm·ha−1·h−1) and southeast (1650 MJ·mm·ha−1·h−1) of the study area, but minimal values in the northern part (700 MJ·mm·ha−1·h−1). On the other hand, gridded datasets display more detailed RE and ED spatial–temporal variability, with the values ranging from 250 to 2800 MJ·mm·ha−1·h−1. The identified trends are showing increasing values of RE (ranging between 0.20 and 21.17 MJ·mm·ha−1·h−1) and ED (ranging between 0.01 and 0.03 MJ·ha−1·h−1) at the annual level. This tendency is also observed for autumn RE (from 5.55 to 0.37 MJ·mm·ha−1·h−1) and ED (from 0.05 to 0.01 MJ·ha−1·h−1), as for spring RE (from 1.00 to 0.01 MJ·mm·ha−1·h−1) and ED (from 0.04 to 0.01 MJ·ha−1·h−1), due to the influence of the large-scale processes of climate variability, with North Atlantic Oscillation (NAO) being the most prominent. These increases may cause a transition to a higher erosive class in the future, thus raising concerns about this type of hydro-meteorological hazard in this part of the Pannonian Basin. The present analysis identifies seasons and places of greatest erosion risk, which is the starting point for implementing suitable mitigation measures at local to regional scales.

scholarly journals ESTIMATION OF ANNUAL AVERAGE SOIL LOSS, BASED ON RUSLE MODEL IN KALLAR WATERSHED, BHAVANI BASIN, TAMIL NADU, INDIA

2015 ◽  
Vol II-2/W2 ◽  
pp. 207-214 ◽  
Author(s):  
S. Abdul Rahaman ◽  
S. Aruchamy ◽  
R. Jegankumar ◽  
S. Abdul Ajeez
Keyword(s):  
Soil Erosion ◽  
Soil Loss ◽  
Tamil Nadu ◽  
Rainfall Erosivity ◽  
Storm Events ◽  
Rill Erosion ◽  
Slope Length ◽  
Conservation Practice ◽  
Erosion Risk ◽  
Wide Range

Soil erosion is a widespread environmental challenge faced in Kallar watershed nowadays. Erosion is defined as the movement of soil by water and wind, and it occurs in Kallar watershed under a wide range of land uses. Erosion by water can be dramatic during storm events, resulting in wash-outs and gullies. It can also be insidious, occurring as sheet and rill erosion during heavy rains. Most of the soil lost by water erosion is by the processes of sheet and rill erosion. Land degradation and subsequent soil erosion and sedimentation play a significant role in impairing water resources within sub watersheds, watersheds and basins. Using conventional methods to assess soil erosion risk is expensive and time consuming. A comprehensive methodology that integrates Remote sensing and Geographic Information Systems (GIS), coupled with the use of an empirical model (Revised Universal Soil Loss Equation- RUSLE) to assess risk, can identify and assess soil erosion potential and estimate the value of soil loss. GIS data layers including, rainfall erosivity (R), soil erodability (K), slope length and steepness (LS), cover management (C) and conservation practice (P) factors were computed to determine their effects on average annual soil loss in the study area. The final map of annual soil erosion shows a maximum soil loss of 398.58 t/ h<sup>-1</sup>/ y<sup>-1</sup>. Based on the result soil erosion was classified in to soil erosion severity map with five classes, very low, low, moderate, high and critical respectively. Further RUSLE factors has been broken into two categories, soil erosion susceptibility (A=RKLS), and soil erosion hazard (A=RKLSCP) have been computed. It is understood that functions of C and P are factors that can be controlled and thus can greatly reduce soil loss through management and conservational measures.

Influence of disdrometer type on rainfall kinetic energy measurement

2020 ◽  
Author(s):  
Lisbeth Lolk Johannsen ◽  
Nives Zambon ◽  
Peter Strauss ◽  
Tomas Dostal ◽  
Martin Neumann ◽  
...  
Keyword(s):  
Kinetic Energy ◽  
Velocity Distribution ◽  
Soil Loss ◽  
Drop Size ◽  
Spatial Separation ◽  
Rain Gauge ◽  
Rainfall Erosivity ◽  
Important Indicator ◽  
Erosion Risk ◽  
Different Types

&lt;p&gt;Rainfall kinetic energy (KE) is an important indicator for the potential soil loss due to rainfall in erosion risk assessment. Kinetic energy-intensity (I) relationships have been developed as a means to calculate the KE of rainfall, when only the rainfall intensity is known. The direct measurement of KE has been enabled due to the use of disdrometers, which measure the size and velocity of raindrops. Previous measurements have shown that rainfall measurements for the same site differed among disdrometer types. Therefore, the best fitting KE-I relationship is likely dependent on the type of disdrometer. In this study, the influence of the disdrometer-specific drop size and velocity measurements on the formulation of new KE-I relationships as well as the fit of existing equations from literature was investigated. Disdrometer rainfall data was collected in 1-minute intervals from six laser-based disdrometers. Two disdrometers of each of the following three types were compared: the PWS100 Present Weather Sensor from Campbell Scientific, the Laser Precipitation Monitor from Thies Clima and the first generation Parsivel from OTT Hydromet. The disdrometers were set up individually at sites in Austria, Czech Republic and New Zealand. Rainfall was measured between 2014 and 2019 with varying amounts of collected data for each site. The results revealed the inherent differences in drop size and velocity distribution estimation between different types of devices. The same pattern of rainfall drop size and velocity distribution could be seen for disdrometers of the same type despite spatial separation. This indicates that actual spatial differences in rainfall characteristics may be difficult to discern when comparing data from different types of disdrometers. New exponential KE-I relationships based on disdrometer data were formulated for each site and device. To confirm the use of the new KE-I equations, one of the equations was validated using rain gauge data from the same site. The best fit of literature KE-I equation varied among sites and devices. The relationship employed in the Revised Universal Soil Loss Equation (RUSLE) always underestimated KE with a percent bias ranging from -2 to -30 %. This study highlights the differences in disdrometer rainfall kinetic energy measurements and how these influence the formulation and evaluation of KE-I relationships, which are important in rainfall erosivity studies.&lt;/p&gt;

Near real-time monitoring of post-fire erosion after storm events: a case study in Warrumbungle National Park, Australia

2018 ◽  
Vol 27 (6) ◽  
pp. 413 ◽  
Author(s):  
Xihua Yang ◽  
Qinggaozi Zhu ◽  
Mitch Tulau ◽  
Sally McInnes-Clarke ◽  
Liying Sun ◽  
...  
Keyword(s):  
Real Time ◽  
National Parks ◽  
Spatial Data ◽  
Soil Loss ◽  
National Park ◽  
Field Measurements ◽  
Geographical Information ◽  
Rainfall Erosivity ◽  
Storm Events ◽  
Erosion Risk

Wildfires in national parks can lead to severe damage to property and infrastructure, and adverse impacts on the environment. This is especially pronounced if wildfires are followed by intense storms, such as the fire in Warrumbungle National Park in New South Wales, Australia, in early 2013. The aims of this study were to develop and validate a methodology to predict erosion risk at near real-time after storm events, and to provide timely information for monitoring of the extent, magnitude and impact of hillslope erosion to assist park management. We integrated weather radar-based estimates of rainfall erosivity with the revised universal soil loss equation (RUSLE) and remote sensing to predict soil loss from individual storm events after the fire. Other RUSLE factors were estimated from high resolution digital elevation models (LS factor), satellite data (C factor) and recent digital soil maps (K factor). The accuracy was assessed against field measurements at twelve soil plots across the Park and regular field survey during the 5-year period after the fire (2013–17). Automated scripts in a geographical information system have been developed to process large quantity spatial data and produce time-series erosion risk maps which show spatial and temporal changes in hillslope erosion and groundcover across the Park at near real time.

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