Modelling event scale rainfall erosivity across European climate regions 

2021 ◽  
Author(s):  
Francis Matthews ◽  
Panos Panagos ◽  
Gert Verstraeten
Keyword(s):  
Soil Erosion ◽  
Kinetic Energy ◽  
Power Law ◽  
Soil Loss ◽  
Process Models ◽  
Rainfall Erosivity ◽  
Annual Average ◽  
Event Scale ◽  
Model Efficiency

<p>The characteristics (magnitude and timing) of individual rainfall erosivity (RE) events in Europe strongly control soil loss at timescales from the individual event to long term annual average. While annual averages of soil erosion encompass the long-term variability of the event-based drivers of soil erosion (soil condition, water kinetic energy, vegetation properties), they provide both little direct information on the timing of soil loss or capacity to fully understand future erosion. Across the spectrum of empirical to physically based process models, event-scale estimates of rainfall energy are vital. The (R)USLE EI<sub>30</sub> index is a popular description of the combined effect of rainfall kinetic energy and the maximum 30-minute intensity of a rainfall event on soil loss. Modelling RE from daily or event rainfall accumulation seeks to capture the intra-annual meteorological controls on the EI30 index, with the goal of utilising rainfall data with higher abundance (eg daily) than conventional but less common hyetograph data. To date, no systematic study has provided model parameter surfaces for Europe’s climatic regions and investigated their spatial configuration. For each of 74 relevant environmental strata (EnS) within 13 broader environmental zones, we calibrate and validate 5 power-law based models with monthly and annual parameter sets using the REDES dataset, composed of over 300,000 RE events from national gauge networks.</p><p>We demonstrate the applicability of delineated environmental strata for subsampling and modelling event rainfall erosivity with heterogeneous national gauge data coverage and extent. Power-law model fits with 12 individual monthly parameter sets outperformed annual models with periodic cosine functions. The power-law α and β parameters are generally correlated through space (r = 0.66) and follow the general European trend of long-term annual average RE, increasing from North-West to South-East. The average annual Nash-Sutcliffe model efficiency for all strata increased from 0.427 (max: 0.76, min: 0.21) to 0.437 when the top 1 percentile of events were removed, which contribute between 8 and 27% of the total RE per stratum. The prediction capacity was higher in autumn and winter than in spring and summer when rainfall holds generally higher unit kinetic energy. Average model efficiency per environmental zone depended on both the rainfall stochasticity and size of the national data sample within each stratum, highlighting the importance of ample data extents for predicting event rainfall erosivity in Europe.</p>

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 Soil erosion by snow gliding – a first quantification attempt in a subalpine area in Switzerland

2014 ◽  
Vol 18 (9) ◽  
pp. 3763-3775 ◽  
Author(s):  
K. Meusburger ◽  
G. Leitinger ◽  
L. Mabit ◽  
M. H. Mueller ◽  
A. Walter ◽  
...  
Keyword(s):  
Land Use ◽  
Soil Erosion ◽  
Land Cover ◽  
Sediment Yield ◽  
Soil Loss ◽  
Water Erosion ◽  
Erosion Rates ◽  
The Difference ◽  
Snow Gliding

Abstract. Snow processes might be one important driver of soil erosion in Alpine grasslands and thus the unknown variable when erosion modelling is attempted. The aim of this study is to assess the importance of snow gliding as a soil erosion agent for four different land use/land cover types in a subalpine area in Switzerland. We used three different approaches to estimate soil erosion rates: sediment yield measurements in snow glide depositions, the fallout radionuclide 137Cs and modelling with the Revised Universal Soil Loss Equation (RUSLE). RUSLE permits the evaluation of soil loss by water erosion, the 137Cs method integrates soil loss due to all erosion agents involved, and the measurement of snow glide deposition sediment yield can be directly related to snow-glide-induced erosion. Further, cumulative snow glide distance was measured for the sites in the winter of 2009/2010 and modelled for the surrounding area and long-term average winter precipitation (1959–2010) with the spatial snow glide model (SSGM). Measured snow glide distance confirmed the presence of snow gliding and ranged from 2 to 189 cm, with lower values on the north-facing slopes. We observed a reduction of snow glide distance with increasing surface roughness of the vegetation, which is an important information with respect to conservation planning and expected and ongoing land use changes in the Alps. Snow glide erosion estimated from the snow glide depositions was highly variable with values ranging from 0.03 to 22.9 t ha−1 yr−1 in the winter of 2012/2013. For sites affected by snow glide deposition, a mean erosion rate of 8.4 t ha−1 yr−1 was found. The difference in long-term erosion rates determined with RUSLE and 137Cs confirms the constant influence of snow-glide-induced erosion, since a large difference (lower proportion of water erosion compared to total net erosion) was observed for sites with high snow glide rates and vice versa. Moreover, the difference between RUSLE and 137Cs erosion rates was related to the measured snow glide distance (R2 = 0.64; p < 0.005) and to the snow deposition sediment yields (R2 = 0.39; p = 0.13). The SSGM reproduced the relative difference of the measured snow glide values under different land uses and land cover types. The resulting map highlighted the relevance of snow gliding for large parts of the investigated area. Based on these results, we conclude that snow gliding appears to be a crucial and non-negligible process impacting soil erosion patterns and magnitude in subalpine areas with similar topographic and climatic conditions.

scholarly journals Assessment of land cover on soil erosion in Lam Phra Phloeng watershed by USLE model

2018 ◽  
Vol 192 ◽  
pp. 02017 ◽  
Author(s):  
Jatuwat Wattanasetpong ◽  
Uma Seeboonruang ◽  
Uba Sirikaew ◽  
Walter Chen
Keyword(s):  
Land Use ◽  
Soil Erosion ◽  
Land Cover ◽  
Soil Loss ◽  
Land Development ◽  
Developed Countries ◽  
Surface Erosion ◽  
Agricultural Field ◽  
Land Cover Data

Soil loss due to surface erosion has been a global problem not just for developing countries but also for developed countries. One of the factors that have greatest impact on soil erosion is land cover. The purpose of this study is to estimate the long-term average annual soil erosion in the Lam Phra Phloeng watershed, Nakhon Ratchasima, Thailand with different source of land cover by using the Universal Soil Loss Equation (USLE) and GIS (30 m grid cells) to calculate the six erosion factors (R, K, L, S, C, and P) of USLE. Land use data are from Land Development Department (LDD) and ESA Climate Change Initiative (ESA/CCI) in 2015. The result of this study show that mean soil erosion by using land cover from ESA/CCI is less than LDD (29.16 and 64.29 ton/ha/year respectively) because soil erosion mostly occurred in the agricultural field and LDD is a local department that survey land use in Thailand thus land cover data from this department have more details than ESA/CCI.

scholarly journals Estimation of annual soil erosion dynamics (2005 - 2015) in Pakistan using Revised Universal Soil Loss Equation (RUSLE)

2021 ◽  
Author(s):  
Hammad Gilani ◽  
Adeel Ahmad ◽  
Isma Younes ◽  
Sawaid Abbas
Keyword(s):  
Soil Erosion ◽  
Soil Loss ◽  
Management Practices ◽  
Climatic Factors ◽  
Universal Soil Loss Equation ◽  
Rainfall Erosivity ◽  
Slope Length ◽  
Conservation Practice ◽  
Administrative Units ◽  
Soil Loss Equation

Abrupt changes in climatic factors, exploitation of natural resources, and land degradation contribute to soil erosion. This study provides the first comprehensive analysis of annual soil erosion dynamics in Pakistan for 2005 and 2015 using publically available climatic, topographic, soil type, and land cover geospatial datasets at 1 km spatial resolution. A well-accepted and widely applied Revised Universal Soil Loss Equation (RUSLE) was implemented for the annual soil erosion estimations and mapping by incorporating six factors; rainfall erosivity (R), soil erodibility (K), slope-length (L), slope-steepness (S), cover management (C) and conservation practice (P). We used a cross tabular or change matrix method to assess the annual soil erosion (ton/ha/year) changes (2005-2015) in terms of areas and spatial distriburtions in four soil erosion classes; i.e. Low (<1), Medium (1–5], High (5-20], and Very high (>20). Major findings of this paper indicated that, at the national scale, an estimated annual soil erosion of 1.79 ± 11.52 ton/ha/year (mean ± standard deviation) was observed in 2005, which increased to 2.47 ±18.14 ton/ha/year in 2015. Among seven administrative units of Pakistan, in Azad Jammu & Kashmir, the average soil erosion doubled from 14.44 ± 35.70 ton/ha/year in 2005 to 28.03 ± 68.24 ton/ha/year in 2015. Spatially explicit and temporal annual analysis of soil erosion provided in this study is essential for various purposes, including the soil conservation and management practices, environmental impact assessment studies, among others.

scholarly journals RUSLE Model Based Annual Soil Loss Quantification for Soil Erosion Protection in Fincha Catchment, Abay River Basin, Ethiopia.

2021 ◽  
Author(s):  
Habtamu Tamiru ◽  
Meseret Wagari
Keyword(s):  
Soil Erosion ◽  
River Basin ◽  
Soil Loss ◽  
Annual Rainfall ◽  
Rainfall Erosivity ◽  
Landsat 8 ◽  
Land Resources ◽  
Rusle Model ◽  
Factor C ◽  
Significant Factors

Abstract Background: The quantity of soil loss as a result of soil erosion is dramatically increasing in catchment where land resources management is very weak. The annual dramatic increment of the depletion of very important soil nutrients exposes the residents of this catchment to high expenses of money to use artificial fertilizers to increase the yield. This paper was conducted in Fincha Catchment where the soil is highly vulnerable to erosion, however, where such studies are not undertaken. This study uses Fincha catchment in Abay river basin as the study area to quantify the annual soil loss, where such studies are not undertaken, by implementing Revised Universal Soil Loss Equation (RUSLE) model developed in ArcGIS version 10.4. Results: Digital Elevation Model (12.5 x 12.5), LANDSAT 8 of Operational Land Imager (OLI) and Thermal Infrared Sensor (TIRS), Annual Rainfall of 10 stations (2010-2019) and soil maps of the catchment were used as input parameters to generate the significant factors. Rainfall erosivity factor (R), soil erodibility factor (K), cover and management factor (C), slope length and steepness factor (LS) and support practice factor (P) were used as soil loss quantification significant factors. It was found that the quantified average annual soil loss ranges from 0.0 to 76.5 t ha-1 yr-1 was obtained in the catchment. The area coverage of soil erosion severity with 55%, 35% and 10% as low to moderate, high and very high respectively were identified. Conclusion: Finally, it was concluded that having information about the spatial variability of soil loss severity map generated in the RUSLE model has a paramount role to alert land resources managers and all stakeholders in controlling the effects via the implementation of both structural and non-structural mitigations. The results of the RUSLE model can also be further considered along with the catchment for practical soil loss quantification that can help for protection practices.

scholarly journals Estimation of rainfall erosivity based on WRF-derived raindrop size distributions

2020 ◽  
Vol 24 (11) ◽  
pp. 5407-5422
Author(s):  
Qiang Dai ◽  
Jingxuan Zhu ◽  
Shuliang Zhang ◽  
Shaonan Zhu ◽  
Dawei Han ◽  
...  
Keyword(s):  
Soil Erosion ◽  
Kinetic Energy ◽  
Water Conservation ◽  
Large Scale ◽  
Weather Prediction ◽  
Wrf Model ◽  
Analysis Data ◽  
Rainfall Erosivity ◽  
Size Distributions ◽  
Raindrop Size

Abstract. Soil erosion can cause various ecological problems, such as land degradation, soil fertility loss, and river siltation. Rainfall is the primary water-driven force for soil erosion, and its potential effect on soil erosion is reflected by rainfall erosivity that relates to the raindrop kinetic energy. As it is difficult to observe large-scale dynamic characteristics of raindrops, all the current rainfall erosivity models use the function based on rainfall amount to represent the raindrops' kinetic energy. With the development of global atmospheric re-analysis data, numerical weather prediction techniques become a promising way to estimate rainfall kinetic energy directly at regional and global scales with high spatial and temporal resolutions. This study proposed a novel method for large-scale and long-term rainfall erosivity investigations based on the Weather Research and Forecasting (WRF) model, avoiding errors caused by inappropriate rainfall–energy relationships and large-scale interpolation. We adopted three microphysical parameterizations schemes (Morrison, WDM6, and Thompson aerosol-aware) to obtain raindrop size distributions, rainfall kinetic energy, and rainfall erosivity, with validation by two disdrometers and 304 rain gauges around the United Kingdom. Among the three WRF schemes, Thompson aerosol-aware had the best performance compared with the disdrometers at a monthly scale. The results revealed that high rainfall erosivity occurred in the west coast area at the whole country scale during 2013–2017. The proposed methodology makes a significant contribution to improving large-scale soil erosion estimation and for better understanding microphysical rainfall–soil interactions to support the rational formulation of soil and water conservation planning.

scholarly journals Effects of Climate and Land-Cover Changes on Soil Erosion in Brazilian Pantanal

2019 ◽  
Vol 11 (24) ◽  
pp. 7053 ◽  
Author(s):  
Carina Colman ◽  
Paulo Oliveira ◽  
André Almagro ◽  
Britaldo Soares-Filho ◽  
Dulce Rodrigues
Keyword(s):  
Climate Change ◽  
Land Use ◽  
Soil Erosion ◽  
Land Cover ◽  
Ecosystem Service ◽  
Soil Loss ◽  
Land Cover Changes ◽  
Rainfall Erosivity ◽  
Comprehensive Planning ◽  
Brazilian Pantanal

The Pantanal biome integrates the lowlands of the Upper Paraguay Basin (UPB), which is hydrologically connected to the biomes of the Cerrado and Amazon (the highlands of the UPB). The effects of recent land-cover and land-use (LCLU) changes in the highlands, combined with climate change, are still poorly understood in this region. Here, we investigate the effects of soil erosion in the Brazilian Pantanal under climate and LCLU changes by combining different scenarios of projected rainfall erosivity and land-cover management. We compute the average annual soil erosion for the baseline (2012) and projected scenarios for 2020, 2035, and 2050. For the worst scenario, we noted an increase in soil loss of up to 100% from 2012 to 2050, associated with cropland expansion in some parts of the highlands. Furthermore, for the same period, our results indicated an increase of 20 to 40% in soil loss in parts of the Pantanal biome, which was associated with farmland increase (mainly for livestock) in the lowlands. Therefore, to ensure water, food, energy, and ecosystem service security over the next decades in the whole UPB, robust and comprehensive planning measures need to be developed, especially for the most impacted areas found in our study.

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 Geospatial Analysis of Soil Erosion Susceptibility and Causative Factors in Anambra State, South East, Nigeria

2021 ◽  
pp. 5-32
Author(s):  
Romanus Udegbunam Ayadiuno ◽  
Dominic Chukwuka Ndulue ◽  
Chinemelu Cosmas Ndichie ◽  
Arinze Tagbo Mozie ◽  
Philip O. Phil-Eze ◽  
...  
Keyword(s):  
Soil Erosion ◽  
Surface Temperature ◽  
Land Surface Temperature ◽  
Land Surface ◽  
Soil Loss ◽  
Vegetation Index ◽  
Anthropogenic Activities ◽  
Rainfall Erosivity ◽  
Anambra State ◽  
Causative Factors

Land degradation is a function of soil erosion leading to soil loss and reduction in crop productivity as well as other socio-economic activities. The menace of soil erosion is challenging due to diverse factors including advertent and inadvertent anthropogenic activities. This study looks at soil erosion susceptibility and causative factors in Anambra State, both static and dynamic with the intent of identifying them, investigating spatial variability of soil loss, relate erodibility to soil properties and causative factors to soil erosion. Eight (8) prominent causative factors (CFs), were identified. These causative factors (CFs) were analyzed using ArcGIS 10.2. Sixty (60) soil samples were extracted randomly, analyzed, and tested. The study identified CFs such as Drainage Density, Erosion Density, Lineament Density, Slope Length, Land Surface Temperature, and Rainfall Erosivity, which contribute to Soil Erodibility (K - Factor). Land Surface Temperature, Soil Moisture Index, Rainfall Erosivity, and Normalized Difference Vegetation Index contributed to the loss of 8.97 ton/ha/yr, 9.1288 ton/ha/yr, 1,1134.7 ton/ha/yr, and 0.245 ton/ha/yr respectively to erosion in Anambra State. Conclusively, the dynamic causative factors influence soil susceptibility and trigger erosion in the State.

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.

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