Assessment of soil erosion models for predicting soil loss in cracked vegetated compacted surface layer

When physically based erosion models such as GUEST are used to determine soil erodibility parameters or to predict the rate of soil loss, data on runoff rates, as distinct from event runoff amount, are often needed. Data on runoff rates, however, are not widely available. This paper describes methods that can be used to overcome this lack of data on runoff rates. These methods require only rainfall rates and runoff amounts, which are usually available for sites set up primarily to test and validate the USLE technology. In addition, the paper summarises the data requirements for the erosion model GUEST and application procedures. In the accompanying paper, these methods are applied to 4 experimental sites in the ASIALAND Network.

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A process-based soil erosion model ensemble to reduce model uncertainty in climate change impact assessments

10.5194/egusphere-egu21-2464 ◽

2021 ◽

Author(s):

Joris Eekhout ◽

Agustín Millares-Valenzuela ◽

Alberto Martínez-Salvador ◽

Rafael García-Lorenzo ◽

Pedro Pérez-Cutillas ◽

...

Keyword(s):

Climate Change ◽

Soil Erosion ◽

Model Uncertainty ◽

Extreme Precipitation ◽

Climate Change Impact ◽

Soil Loss ◽

Model Ensemble ◽

Erosion Model ◽

Erosion Models ◽

Change Impact

<p>The impact of climate change on future soil loss is commonly assessed with soil erosion models, which are potentially an important source of uncertainty. Here we propose a soil erosion model ensemble, with the aim to reduce the model uncertainty in climate change impact assessments. The model ensemble consisted of five continuous process-based soil erosion models that run at a daily time step, i.e. DHSVM, HSPF, INCA, MMF and SHETRAN. All models simulate detachment by raindrop impact (interrill erosion), detachment by runoff (rill erosion) and immediate deposition of sediment within the cell of its origin. The models were implemented in the SPHY hydrological model. The soil erosion model ensemble was applied in a semi-arid catchment in the southeast of Spain. We applied three future climate scenarios based on global mean temperature rise (+1.5, +2 and +3 &#186;C). Data from two contrasting regional climate models were used to assess how an increase and a decrease in extreme precipitation affect model uncertainty. Soil loss is projected to increase and decrease under climate change, mostly reflecting the change in extreme precipitation. Model uncertainty is found to increase with increasing slope, extreme precipitation and runoff, which reveals some inherent differences in model assumptions among the five models. Moreover, the model uncertainty increases in all climate change scenarios, independent on the projected change in annual precipitation and extreme precipitation. This supports the importance to consider model uncertainty through model ensembles of climate, hydrology, and soil erosion in climate change impact assessments.</p><p>This research was funded by ERDF/Spanish Ministry of Science, Innovation and Universities - State Research Agency (AEI) /Project CGL2017-84625-C2-1-R; State Program for Research, Development and Innovation Focused on the Challenges of Society.</p>

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A review of the (Revised) Universal Soil Loss Equation ((R)USLE): with a view to increasing its global applicability and improving soil loss estimates

Hydrology and Earth System Sciences ◽

10.5194/hess-22-6059-2018 ◽

2018 ◽

Vol 22 (11) ◽

pp. 6059-6086 ◽

Cited By ~ 42

Author(s):

Rubianca Benavidez ◽

Bethanna Jackson ◽

Deborah Maxwell ◽

Kevin Norton

Keyword(s):

Soil Erosion ◽

Soil Loss ◽

Gully Erosion ◽

Data Availability ◽

Universal Soil Loss Equation ◽

Soil Productivity ◽

Temporal Scales ◽

Erosion Models ◽

The World ◽

Soil Loss Equation

Abstract. Soil erosion is a major problem around the world because of its effects on soil productivity, nutrient loss, siltation in water bodies, and degradation of water quality. By understanding the driving forces behind soil erosion, we can more easily identify erosion-prone areas within a landscape to address the problem strategically. Soil erosion models have been used to assist in this task. One of the most commonly used soil erosion models is the Universal Soil Loss Equation (USLE) and its family of models: the Revised Universal Soil Loss Equation (RUSLE), the Revised Universal Soil Loss Equation version 2 (RUSLE2), and the Modified Universal Soil Loss Equation (MUSLE). This paper reviews the different sub-factors of USLE and RUSLE, and analyses how different studies around the world have adapted the equations to local conditions. We compiled these studies and equations to serve as a reference for other researchers working with (R)USLE and related approaches. Within each sub-factor section, the strengths and limitations of the different equations are discussed, and guidance is given as to which equations may be most appropriate for particular climate types, spatial resolution, and temporal scale. We investigate some of the limitations of existing (R)USLE formulations, such as uncertainty issues given the simple empirical nature of the model and many of its sub-components; uncertainty issues around data availability; and its inability to account for soil loss from gully erosion, mass wasting events, or predicting potential sediment yields to streams. Recommendations on how to overcome some of the uncertainties associated with the model are given. Several key future directions to refine it are outlined: e.g. incorporating soil loss from other types of soil erosion, estimating soil loss at sub-annual temporal scales, and compiling consistent units for the future literature to reduce confusion and errors caused by mismatching units. The potential of combining (R)USLE with the Compound Topographic Index (CTI) and sediment delivery ratio (SDR) to account for gully erosion and sediment yield to streams respectively is discussed. Overall, the aim of this paper is to review the (R)USLE and its sub-factors, and to elucidate the caveats, limitations, and recommendations for future applications of these soil erosion models. We hope these recommendations will help researchers more robustly apply (R)USLE in a range of geoclimatic regions with varying data availability, and modelling different land cover scenarios at finer spatial and temporal scales (e.g. at the field scale with different cropping options).

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Evaluation of three soil erosion models for clay soils

Soil Research ◽

10.1071/sr9890199 ◽

1989 ◽

Vol 27 (1) ◽

pp. 199 ◽

Cited By ~ 10

Author(s):

DM Freebairn ◽

DM Silburn ◽

RJ Loch

Keyword(s):

Experimental Data ◽

Soil Erosion ◽

Soil Loss ◽

Universal Soil Loss Equation ◽

Clay Soils ◽

Single Model ◽

Erosion Models ◽

Soil Loss Equation

The Universal Soil Loss Equation (USLE) and two modified USLE models were assessed for their ability to predict soil erosion on contour bay catchments on the Darling Downs, Queensland. The models were applied using USLE handbook values as well as optimized values determined by fitting the models to the experimental data. All three models explained greater than 80% of the variance in measured soil loss with no single model being consistently superior to the others. Cover reduced erosion more than that predicted by the USLE.

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Comparison of the Applicability of Different Soil Erosion Models to Predict Soil Erodibility Factor and Event Soil Losses on Loess Slopes in Hungary

Water ◽

10.3390/w13243517 ◽

2021 ◽

Vol 13 (24) ◽

pp. 3517

Author(s):

Boglárka Keller ◽

Csaba Centeri ◽

Judit Alexandra Szabó ◽

Zoltán Szalai ◽

Gergely Jakab

Keyword(s):

Soil Erosion ◽

Soil Loss ◽

Coefficient Of Determination ◽

Soil Erodibility ◽

The European Union ◽

Extreme Precipitation Events ◽

Erosion Models ◽

Physically Based ◽

Physically Based Models ◽

Soil Losses

Climate change induces more extreme precipitation events, which increase the amount of soil loss. There are continuous requests from the decision-makers in the European Union to provide data on soil loss; the question is, which ones should we use? The paper presents the results of USLE (Universal Soil Loss Equation), RUSLE (Revised USLE), USLE-M (USLE-Modified) and EPIC (Erosion-Productivity Impact Calculator) modelling, based on rainfall simulations performed in the Koppány Valley, Hungary. Soil losses were measured during low-, moderate- and high-intensity rainfalls on cultivated soils formed on loess. The soil erodibility values were calculated by the equations of the applied soil erosion models and ranged from 0.0028 to 0.0087 t ha h ha−1 MJ−1 mm−1 for the USLE-related models. EPIC produced larger values. The coefficient of determination resulted in an acceptable correlation between the measured and calculated values only in the case of USLE-M. Based on other statistical indicators (e.g., NSEI, RMSE, PBIAS and relative error), RUSLE, USLE and USLE-M resulted in the best performance. Overall, regardless of being non-physically based models, USLE-type models seem to produce accurate soil erodibility values, thus modelling outputs.

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A review of the (Revised) Universal Soil Loss Equation (R/USLE): with a view to increasing its global applicability and improving soil loss estimates

10.5194/hess-2018-68 ◽

2018 ◽

Cited By ~ 3

Author(s):

Rubianca Benavidez ◽

Bethanna Jackson ◽

Deborah Maxwell ◽

Kevin Norton

Keyword(s):

Soil Erosion ◽

Soil Loss ◽

Data Availability ◽

Water Bodies ◽

Universal Soil Loss Equation ◽

Soil Productivity ◽

Temporal Scales ◽

Erosion Models ◽

The World ◽

Soil Loss Equation

Abstract. Soil erosion is a major problem around the world because of its effects on soil productivity, nutrient loss, siltation in water bodies, and degradation of water quality. By understanding the driving forces behind soil erosion, we can more easily identify erosion-prone areas within a landscape and use land management and other strategies to effectively manage the problem. Soil erosion models have been used to assist in this task. One of the most commonly used soil erosion models is the Universal Soil Loss Equation (USLE) and its family of models: the Revised Universal Soil Loss Equation (RUSLE), the Revised Universal Soil Loss Equation version 2 (RUSLE2), and the Modified Universal Soil Loss Equation (MUSLE). This paper reviewed the different components of USLE and RUSLE etc., and analysed how different studies around the world have adapted the equations to local conditions. We compiled these studies and equations to serve as a reference for other researchers working with R/USLE and related approaches. We investigate some of the limitations of R/USLE, such as issues in data-sparse regions, its inability to account for soil loss from gully erosion or mass wasting events, and that it does not predict sediment pathways from hillslopes to water bodies. These limitations point to several future directions for R/USLE studies: incorporating soil loss from other types of soil erosion, estimating soil loss at sub-annual temporal scales, and using consistent units for future literature. These recommendations help to improve the applicability of the R/USLE in a range of geoclimatic regions with varying data availability, and at finer spatial and temporal scales for scenario analysis.

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ASSESSMENT OF SOIL EROSION IN VINH LINH, QUANG TRI USING RMMF MODEL (REVISED MORGAN - MORGAN - FINNEY)

HUE UNIVERSITY JOURNAL OF SCIENCE AGRICULTURE AND RURAL DEVELOPMENT ◽

10.26459/jard.v83i5.3083 ◽

2013 ◽

Vol 83 (5) ◽

Author(s):

Nguyễn Quang Việt ◽

Trương Đình Trọng ◽

Hồ Thị Nga

Keyword(s):

Soil Erosion ◽

Land Cover ◽

Soil Loss ◽

Soil Particle ◽

Transport Capacity ◽

Gis Technology ◽

Particle Detachment ◽

Sediment Transport Capacity ◽

Northern District ◽

Runoff And Soil Loss

Vinh Linh, the northern district of Quang Tri province is characterized by a diversified topography with a large variety of elevations, high rainfall, and decreasing land cover due to forest exploiting for cultivation land. Thus, there is a high risk of erosion, soil fertility washout. With the support of GIS technology, the authors used the rMMF model to measure soil erosion. The input data of model including 15 coefficients related to topography, soil properties, climate and land cover. The simulations of rMMF include estimates of rainfall energy, runoff, soil particle detachment by raindrop, soil particle detachment by runoff, sediment transport capacity of runoff and soil loss. The result showed that amount of soil loss in year is estimated to vary between 0 kg/m2 minimum and 149 kg/m2 maximum and is divided into 4-classes of erosion. Light class almost covers the region researched (75.9% of total area), while moderate class occupies 8.1% of total area, strong classes only hold small area (16% of total area). Therefore, protection of the forest floor in sloping areas is one of the most effective methods to reduce soil erosion.

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

Hydrology and Earth System Sciences ◽

10.5194/hess-18-3763-2014 ◽

2014 ◽

Vol 18 (9) ◽

pp. 3763-3775 ◽

Cited By ~ 9

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.

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Topographic Position, Land Use and Soil Management Effects on Soil Organic Carbon (Vineyard Region of Niš, Serbia)

Agronomy ◽

10.3390/agronomy11071438 ◽

2021 ◽

Vol 11 (7) ◽

pp. 1438

Author(s):

Snežana Jakšić ◽

Jordana Ninkov ◽

Stanko Milić ◽

Jovica Vasin ◽

Milorad Živanov ◽

...

Keyword(s):

Land Use ◽

Surface Layer ◽

Spatial Distribution ◽

Soil Erosion ◽

Organic Carbon ◽

Soil Organic Carbon ◽

Soil Compaction ◽

Soil Management ◽

Forest Land ◽

Topographic Position

Spatial distribution of soil organic carbon (SOC) is the result of a combination of various factors related to both the natural environment and anthropogenic activities. The aim of this study was to examine (i) the state of SOC in topsoil and subsoil of vineyards compared to the nearest forest, (ii) the influence of soil management on SOC, (iii) the variation in SOC content with topographic position, (iv) the intensity of soil erosion in order to estimate the leaching of SOC from upper to lower topographic positions, and (v) the significance of SOC for the reduction of soil’s susceptibility to compaction. The study area was the vineyard region of Niš, which represents a medium-sized vineyard region in Serbia. About 32% of the total land area is affected, to some degree, by soil erosion. However, according to the mean annual soil loss rate, the total area is classified as having tolerable erosion risk. Land use was shown to be an important factor that controls SOC content. The vineyards contained less SOC than forest land. The SOC content was affected by topographic position. The interactive effect of topographic position and land use on SOC was significant. The SOC of forest land was significantly higher at the upper position than at the middle and lower positions. Spatial distribution of organic carbon in vineyards was not influenced by altitude, but occurred as a consequence of different soil management practices. The deep tillage at 60–80 cm, along with application of organic amendments, showed the potential to preserve SOC in the subsoil and prevent carbon loss from the surface layer. Penetrometric resistance values indicated optimum soil compaction in the surface layer of the soil, while low permeability was observed in deeper layers. Increases in SOC content reduce soil compaction and thus the risk of erosion and landslides. Knowledge of soil carbon distribution as a function of topographic position, land use and soil management is important for sustainable production and climate change mitigation.

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Analysis of Ownership Data from Consolidated Land Threatened by Water Erosion in the Vlára Basin, Slovakia

Sustainability ◽

10.3390/su13010051 ◽

2020 ◽

Vol 13 (1) ◽

pp. 51

Author(s):

Alexandra Pagáč Mokrá ◽

Jakub Pagáč ◽

Zlatica Muchová ◽

František Petrovič

Keyword(s):

Soil Erosion ◽

Soil Loss ◽

Agricultural Land ◽

Water Erosion ◽

Land Consolidation ◽

Land Fragmentation ◽

Erosion Risk ◽

Definition Of ◽

The Relationship ◽

Soil Loss Equation

Water erosion is a phenomenon that significantly damages agricultural land. The current land fragmentation in Slovakia and the complete ambiguity of who owns it leads to a lack of responsibility to care for the land in its current condition, which could affect its sustainability in the future. The reason so much soil has eroded is obvious when looking at current land management, with large fields, a lack of windbreaks between them, and no barriers to prevent soil runoff. Land consolidation might be the solution. This paper seeks to evaluate redistributed land and, based on modeling by the Universal Soil Loss Equation (USLE) method, to assess the degree of soil erosion risk. Ownership data provided information on how many owners and what amount of area to consider, while taking into account new conditions regarding water erosion. The results indicate that 2488 plots of 1607 owners which represent 12% of the model area are still endangered by water erosion, even after the completion of the land consolidation project. The results also presented a way of evaluating the territory and aims to trigger a discussion regarding an unambiguous definition of responsibility in the relationship between owner and user.

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