Object‐oriented soil erosion modelling: A possible paradigm shift from potential to actual risk assessments in agricultural environments

Borrelli, Pasquale; Meusburger, Katrin; Ballabio, Cristiano; Panagos, Panos; Alewell, Christine

doi:10.1002/ldr.2898

Cited by 51 publications

(32 citation statements)

References 45 publications

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“…Despite its shortcomings, the USLE and its successor RUSLE (Revised Universal Soil Loss Equation) is still the most common soil erosion model for data scarce catchments. It is reported that at least 90% of soil erosion studies conducted worldwide use RUSLE-based models [18]. According to Wischmeier and Smith, the USLE "is an erosion model designed to compute longtime average soil losses from sheet and rill erosion" [19].…”

Section: Universal Soil Loss Equationmentioning

confidence: 99%

Soil Erosion Modeling and Comparison Using Slope Units and Grid Cells in Shihmen Reservoir Watershed in Northern Taiwan

Liu

Chen

et al. 2018

Water

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Soil erosion is a global problem that will become worse as a result of climate change. While many parts of the world are speculating about the effect of increased rainfall intensity and frequency on soil erosion, Taiwan’s mountainous areas are already facing the power of rainfall erosivity more than six times the global average. To improve the modeling ability of extreme rainfall conditions on highly rugged terrains, we use two analysis units to simulate soil erosion at the Shihmen reservoir watershed in northern Taiwan. The first one is the grid cell method, which divides the study area into 10 m by 10 m grid cells. The second one is the slope unit method, which divides the study area using natural breaks in landform. We compared the modeling results with field measurements of erosion pins. To our surprise, the grid cell method is much more accurate in predicting soil erosion than the slope unit method, although the slope unit method resembles the real terrains much better than the grid cell method. The average erosion pin measurement is 6.5 mm in the Shihmen reservoir watershed, which is equivalent to 90.6 t ha−1 yr−1 of soil erosion.

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Section: Universal Soil Loss Equationmentioning

confidence: 99%

Soil Erosion Modeling and Comparison Using Slope Units and Grid Cells in Shihmen Reservoir Watershed in Northern Taiwan

Liu

Chen

et al. 2018

Water

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“…Particular for spatial resolution, Ding et al [26] reported that spatial resolution beyond 120 m would smother spatial heterogeneity in NDVI calculations. There is also limited information regarding the influence of the interactions of intra-annual variation of different crop cover types in relation to spatial heterogeneity on C value calculations [27].…”

Section: Introductionmentioning

confidence: 99%

“…[28,29]. In the process of quantifying the sensitivity of NDVI-derived C values, finely resolved and temporally dynamic land use information is imperative in order to identify plant cover to specific crop type level and accurately estimate C values for large agricultural landscapes [27]. In addition, topographical variations within a uniform land use type also affect the NDVI-derived C values.…”

Section: Introductionmentioning

confidence: 99%

Quantifying the Sensitivity of NDVI-Based C Factor Estimation and Potential Soil Erosion Prediction using Spaceborne Earth Observation Data

et al. 2020

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The Normalized Difference Vegetation Index (NDVI), has been increasingly used to capture spatiotemporal variations in cover factor (C) determination for erosion prediction on a larger landscape scale. However, NDVI-based C factor (Cndvi) estimation per se is sensitive to various biophysical variables, such as soil condition, topographic features, and vegetation phenology. As a result, Cndvi often results in incorrect values that affect the quality of soil erosion prediction. The aim of this study is to multi-temporally estimate Cndvi values and compare the values with those of literature values (Clit) in order to quantify discrepancies between C values obtained via NDVI and empirical-based methods. A further aim is to quantify the effect of biophysical variables such as slope shape, erodibility, and crop growth stage variation on Cndvi and soil erosion prediction on an agricultural landscape scale. Multi-temporal Landsat 7, Landsat 8, and Sentinel 2 data, from 2013 to 2016, were used in combination with high resolution agricultural land use data of the Integrated Administrative and Control System, from the Uckermark district of north-eastern Germany. Correlations between Cndvi and Clit improved in data from spring and summer seasons (up to r = 0.93); nonetheless, the Cndvi values were generally higher compared with Clit values. Consequently, modelling erosion using Cndvi resulted in two times higher rates than modelling with Clit. The Cndvi values were found to be sensitive to soil erodibility condition and slope shape of the landscape. Higher erodibility condition was associated with higher Cndvi values. Spring and summer taken images showed significant sensitivity to heterogeneous soil condition. The Cndvi estimation also showed varying sensitivity to slope shape variation; values on convex-shaped slopes were higher compared with flat slopes. Quantifying the sensitivity of Cndvi values to biophysical variables may help improve capturing spatiotemporal variability of C factor values in similar landscapes and conditions.

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“…Extensive databases are required to store the information needed to deal with the effects associated with the numerous agricultural practices that exist in the USA and elsewhere. Frequently, in modeling erosion using USLE-based models with Geographic Information Systems in catchments or watersheds, the intra-annual variability of soil cover conditions in arable land is neglected [47].…”

Section: The C Factormentioning

confidence: 99%

A Review of the Science and Logic Associated with Approach Used in the Universal Soil Loss Equation Family of Models

Kinnell

2019

Soil Systems

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Soil erosion caused by rain is a major factor in degrading agricultural land, and agricultural practices that conserve soil should be used to maintain the long-term sustainability of agricultural land. The Universal Soil Loss Equation (USLE) was developed in the 1960s and 1970s to predict the long-term average annual soil loss from sheet and rill erosion on field-sized areas as an aid to making management decisions to conserve soil. The USLE uses six factors to take account of the effects of climate, soil, topography, crops, and crop management, and specific actions designed to conserve soil. Although initially developed as an empirical model based on data from more than 10,000 plot years of data collected in plot experiments in the USA, the selection of the independent factors used in the model was made taking account of scientific understanding of the drivers involved in rainfall erosion. In addition, assumptions and approximations were needed to make an operational model that met the needs of the decision makers at that time. Those needs have changed over time, leading to the development of the Revised USLE (RUSLE) and a second version of that, the Revised USLE, Version 2 (RUSLE2). While the original USLE model was not designed to predict short-term variations in erosion well, these developments have involved more use of conceptualization in order to deal with the time-variant impacts of the drivers involved in rainfall erosion. The USLE family of models is based on the concept that the “unit” plot, a bare fallow area 22.1 m long on a 9% slope gradient with cultivation up and down the slope, provides a physical situation where the effect of climate and soil on rainfall erosion can be determined without the need to consider the impact of the four other factors. The science and logic associated with this approach is reviewed. The manner by which the soil erodibility factor is determined from plot data ensures that the long-term average annual soil loss for the unit plot is predicted well, even when the assumption that event soil loss is directly related to the product of event rainfall energy, and the maximum 30-min intensity is not wholly appropriate. RUSLE2 has a capacity to use CLIGEN, the weather generator used in WEPP, and so can predict soil losses based on individual storms in a similar way to WEPP. Including a direct consideration of runoff in determining event erosivity enhances the ability to predict event soil losses when runoff is known or predicted well, but similar to more process-based models, this ability is offset by the difficulty in predicting runoff well.

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Object‐oriented soil erosion modelling: A possible paradigm shift from potential to actual risk assessments in agricultural environments

Cited by 51 publications

References 45 publications

Soil Erosion Modeling and Comparison Using Slope Units and Grid Cells in Shihmen Reservoir Watershed in Northern Taiwan

Soil Erosion Modeling and Comparison Using Slope Units and Grid Cells in Shihmen Reservoir Watershed in Northern Taiwan

Quantifying the Sensitivity of NDVI-Based C Factor Estimation and Potential Soil Erosion Prediction using Spaceborne Earth Observation Data

A Review of the Science and Logic Associated with Approach Used in the Universal Soil Loss Equation Family of Models

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