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 Assessment of Soil Erosion and Its Impact on Agricultural Productivity by Using the RMMF Model and Local Perception: A Case Study of Rangun Watershed of Mid-Hills, Nepal

2021 ◽  
Vol 2021 ◽  
pp. 1-10
Author(s):  
Dinesh Bhandari ◽  
Rajeev Joshi ◽  
Raju Raj Regmi ◽  
Nripesh Awasthi
Keyword(s):  
Soil Erosion ◽  
Soil Loss ◽  
Group Discussion ◽  
Agricultural Productivity ◽  
Weighted Mean ◽  
Risk Area ◽  
Barren Land ◽  
Erosion Risk ◽  
Risk Areas ◽  
The Impact

Soil erosion is a major concern for the environment and natural resources leading to a serious threat to agricultural productivity and one of the major causes of land degradation in the mid-hills region of Nepal. An accurate assessment of soil erosion is needed to reduce the problem of soil loss in highly fragile mountainous areas. The present study aimed to assess spatial soil loss rate and identified risk areas and their perceived impact on agricultural productivity by using the Revised Morgan–Morgan–Finney (RMMF) model and social survey in the Rangun watershed of Dadeldhura district, Nepal. Soil erosion was assessed by using data on soil, digital elevation model, rainfall, land use, and land cover visually interpreted from multitemporal satellite images, and ILWIS 3.3 academic software was used to perform the model. A household questionnaire survey (n = 120) and focus group discussion (n = 2) in identified risk areas were carried out to understand the people’s perception towards soil erosion and its impact on agricultural productivity. The predicted average soil erosions from the forest, agriculture, and barren land were 2.7 t ha−1 yr−1, 53.73 t ha−1 yr−1, and 462.59 t ha−1 yr−1, respectively. The erosion risk area under very low to low, moderate to moderately high, and high to very high covers 92.32%, 4.96%, and 2.73%, respectively. It indicates that the rate of soil erosion was lower in forest areas, whereas it was higher in the barren land. The cropped area of the watershed has been reduced by 2.96 ha−1 yr−1, and productivity has been decreased by 0.238 t ha−1 yr−1. The impacts such as removal of topsoil (weighted mean = 4.19) and gully formation (weighted mean = 3.56) were the highest perceived factors causing productivity decline due to erosion. People perceived the impact of erosion in agricultural productivity differently ( ∗ significant at P ≤ 0.05 ). The study concluded that, comparatively, barren and agricultural lands seem more susceptible to erosion, so the long-term conservation and management investment in susceptible areas for restoration, protection, and socioeconomic support contribute significantly to land rehabilitation in the Rangun watershed.

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 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 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.

Soil erosion hazard map for river basin managers: An example for the water bodies of the Loire river basin (France)

2020 ◽  
Author(s):  
Clément Chabert ◽  
Francesca Degan ◽  
Sébastien Salvador-Blanes ◽  
Olivier Evrard ◽  
Rosalie Vandromme ◽  
...  
Keyword(s):  
Water Quality ◽  
Soil Erosion ◽  
River Basin ◽  
Water Bodies ◽  
Rainfall Erosivity ◽  
Erosion Risk ◽  
Erosion Hazard ◽  
Management Plans ◽  
The Impact ◽  
Loire River

Abstract: Soil erosion on agricultural land is associated with deleterious off-site impacts including the siltation andthe pollution of the receiving water bodies. To better manage this situation, local/regional water agencies needspatially-distributed information to compare the sensitivity to erosion of the areas draining into these water bodiesand supplying the vast majority of sediment and associated pollutants leading to this water quality impairment.These soil erosion hazard maps are now often included in the latest versions of the basin management plans thatmust be designed to meet the water quality objectives of the EU Framework Directive. However, the resolution ofthese maps is often too coarse to meet the practical needs of these agencies. Accordingly, the current research usedthe latest input databases to improve the MESALES model outputs in one of the largest French River basins (Loire,117,000 km2), with the implementation of three main modifications. First, the seasonal variations of land coverwere incorporated into the model through a revised set of expert rules based on the agricultural census data. Second,the discrimination of the soil textures was improved within the infiltration and erodibility module of the model.Third, variations in rainfall erosivity across the study area were described taking into account the latest erosivitymap available at the European scale. Then, the model results obtained with the updated model version were comparedwith those generated by the previous version. Overall, the simulated soil erosion hazard changed for 35% ofthe pixels of the Loire River basin, with a significant increase of the lowest hazard classes during all seasons exceptsummer. When aggregating the results at the scale of water bodies, the simulated erosion hazard changed for 49%of these management units. Although 28% of these water bodies were associated with a lower hazard, 23% of theriver systems were attributed a higher erosion risk. The implications of these model/map revisions for the localdecision makers were discussed, taking the strategy of concentrating the management budget on those water bodiesassociated with the highest erosion risk as an example. In the future, this model could be used to compare the soilerosion hazard in contrasted regions of Europe and to simulate the impact of management plans designed todecrease this risk to support the decisions of water agencies

scholarly journals Assessment of Intra-Annual and Inter-Annual Variabilities of Soil Erosion in Crete Island (Greece) by Incorporating the Dynamic “Nature” of R and C-Factors in RUSLE Modeling

2020 ◽  
Vol 12 (15) ◽  
pp. 2439 ◽  
Author(s):  
Christos Polykretis ◽  
Dimitrios D. Alexakis ◽  
Manolis G. Grillakis ◽  
Stelios Manoudakis
Keyword(s):  
Land Use ◽  
Soil Erosion ◽  
Temporal Resolution ◽  
Soil Loss ◽  
High Temporal Resolution ◽  
Rainfall Erosivity ◽  
Management Interventions ◽  
Crete Island ◽  
The Impact ◽  
Loss Rates

Under the continuously changing conditions of the environment, the exploration of spatial variability of soil erosion at a sub-annual temporal resolution, as well as the identification of high-soil loss time periods and areas, are crucial for implementing mitigation and land management interventions. The main objective of this study was to estimate the monthly and seasonal soil loss rates by water-induced soil erosion in Greek island of Crete for two recent hydrologically contrasting years, 2016 (dry) and 2019 (wet), as a result of Revised Universal Soil Loss Equation (RUSLE) modeling. The impact of temporal variability of the two dynamic RUSLE factors, namely rainfall erosivity (R) and cover management (C), was explored by using rainfall and remotely sensed vegetation data time-series of high temporal resolution. Soil, topographical, and land use/cover data were exploited to represent the other three static RUSLE factors, namely soil erodibility (K), slope length and steepness (LS) and support practice (P). The estimated rates were mapped presenting the spatio-temporal distribution of soil loss for the study area on a both intra-annual and inter-annual basis. The identification of high-loss months/seasons and areas in the island was achieved by these maps. Autumn (about 35 t ha−1) with October (about 61 t ha−1) in 2016, and winter (about 96 t ha−1) with February (146 t ha−1) in 2019 presented the highest mean soil loss rates on a seasonal and monthly, respectively, basis. Summer (0.22–0.25 t ha−1), with its including months, showed the lowest rates in both examined years. The intense monthly fluctuations of R-factor were found to be more influential on water-induced soil erosion than the more stabilized tendency of C-factor. In both years, olive groves in terms of agricultural land use and Chania prefecture in terms of administrative division, were detected as the most prone spatial units to erosion.

scholarly journals Impact of Land Use and Land Cover Change on Soil Erosion Using Rusle Model And Gis: A Case of Temeji Watershed, Western Ethiopia

2020 ◽  
Author(s):  
Mitiku Badasa Moisa ◽  
Daniel Assefa Negash ◽  
Biratu Bobo Merga ◽  
Dessalegn Obsi Gemeda
Keyword(s):  
Land Use ◽  
Soil Erosion ◽  
Land Cover ◽  
Water Conservation ◽  
Soil Loss ◽  
Mitigation Strategies ◽  
Cultivated Land ◽  
Rusle Model ◽  
Erosion Risk ◽  
The Impact

Abstract BackgroundThe impact of Land Use/Land Cover (LULC) conversion on soil resources is getting global attention. Soil erosion is one the critical environmental problems worldwide with high severity in developing countries due to land degradation. This study integrates the Revised Universal Soil Loss Equation (RUSLE) model with a Geographic Information Systems (GIS) to estimate the impacts of LU/LC conversion on the mean annual soil loss in Temeji watershed. In this study, LU/LC change of Temeji watershed were assessed from 2000 to 2020 by using 2000 Landsat ETM+ and 2020 Landsat OLI/TIRS images and classified using supervised maximum likelihood classification algorithms. ResultsResults indicates that majority of the LU/LC in the study area is vulnerable to soil erosion. Our findings show that cultivated land had the highest average soil loss rate in Temeji watershed. High soil loss is observed when grass and forest land were converted into cultivated land with mean soil loss of 88.8t/ha/yr and 86.9t/ha/yr in 2020. Results revealed that about 6608.5ha (42.8%) and 8391.8ha (54.4%) were categorized under severe classes in 2000 and 2020, respectively.ConclusionsThe results can definitely support policy makers and environmental managers in implementation of soil and water conservation practices and erosion risk prevention and mitigation strategies in Temeji watershed.

scholarly journals Mapping erosion prone areas in the Bouhamdane watershed (Algeria) using the Revised Universal Soil Loss Equation through GIS

2017 ◽  
Vol 32 (1) ◽  
pp. 13-23 ◽  
Author(s):  
Hamza Bouguerra ◽  
Abderrazak Bouanani ◽  
Kamel Khanchoul ◽  
Oussama Derdous ◽  
Salah Eddine Tachi
Keyword(s):  
Soil Erosion ◽  
Soil Loss ◽  
Erosion Control ◽  
Water Erosion ◽  
Universal Soil Loss Equation ◽  
Rainfall Erosivity ◽  
Vegetative Cover ◽  
Erosion Risk ◽  
Very High ◽  
Soil Loss Equation

Abstract Soil erosion by water is a major problem that the Northern part of Algeria witnesses nowadays; it reduces: the productivity of agricultural areas due to the loss of lands, and leads to the loss of storage capacity in reservoirs, the deterioration of water quality etc. The aim of this study is to evaluate the soil losses due to water erosion, and to identify the sectors which are potentially sensitive to water erosion in the Bouhamdane watershed, that is located in the northeastern part of Algeria. To this end, the Revised Universal Soil Loss Equation (RUSLE) was used. The application of this equation takes into account five parameters, namely the rainfall erosivity, topography, soil erodibility, vegetative cover and erosion control practices. The product of these parameters under GIS using the RUSLE mathematical equation has enabled evaluating an annual average erosion rate for the Bouhamdane watershed of 11.18 t·ha-1·y-1. Based on the estimates of soil loss in each grid cell, a soil erosion risk map with five risk classes was elaborated. The spatial distribution of risk classes was 16% very low, 41% low, 28% moderate, 12% high and 3% very high. Most areas showing high and very high erosion risk occurred in the lower Bouhamdane watershed around Hammam Debagh dam. These areas require adequate erosion control practices to be implemented on a priority basis in order to conserve soil resources and reduce siltation in the reservoir.

scholarly journals Predicting soil erosion using Rusle and NDVI time series from TM Landsat 5

2014 ◽  
Vol 49 (3) ◽  
pp. 215-224 ◽  
Author(s):  
Daniel Fonseca de Carvalho ◽  
Valdemir Lucio Durigon ◽  
Mauro Antonio Homem Antunes ◽  
Wilk Sampaio de Almeida ◽  
Paulo Tarso Sanches de Oliveira
Keyword(s):  
Soil Erosion ◽  
Soil Loss ◽  
Central Area ◽  
Remote Sensing Data ◽  
High Water ◽  
Rainfall Erosivity ◽  
Erosion Risk ◽  
R Factor ◽  
Factor C ◽  
Soil Losses

The objective of this work was to evaluate the seasonal variation of soil cover and rainfall erosivity, and their influences on the revised universal soil loss equation (Rusle), in order to estimate watershed soil losses in a temporal scale. Twenty-two TM Landsat 5 images from 1986 to 2009 were used to estimate soil use and management factor (C factor). A corresponding rainfall erosivity factor (R factor) was considered for each image, and the other factors were obtained using the standard Rusle method. Estimated soil losses were grouped into classes and ranged from 0.13 Mg ha-1 on May 24, 2009 (dry season) to 62.0 Mg ha-1 on March 11, 2007 (rainy season). In these dates, maximum losses in the watershed were 2.2 and 781.5 Mg ha-1 , respectively. Mean annual soil loss in the watershed was 109.5 Mg ha-1 , but the central area, with a loss of nearly 300.0 Mg ha-1 , was characterized as a site of high water-erosion risk. The use of C factor obtained from remote sensing data, associated to corresponding R factor, was fundamental to evaluate the soil erosion estimated by the Rusle in different seasons, unlike of other studies which keep these factors constant throughout time.

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