Articles | Volume 22, issue 11
https://doi.org/10.5194/hess-22-6059-2018
© Author(s) 2018. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/hess-22-6059-2018
© Author(s) 2018. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Review article
| 
27 Nov 2018
Review article |
| 27 Nov 2018
| 27 Nov 2018
A review of the (Revised) Universal Soil Loss Equation ((R)USLE): with a view to increasing its global applicability and improving soil loss estimates
Rubianca Benavidez
CORRESPONDING AUTHOR
School of Geography, Environment, and Earth Sciences, Victoria
University of Wellington, Wellington, 6012, New Zealand
Bethanna Jackson
School of Geography, Environment, and Earth Sciences, Victoria
University of Wellington, Wellington, 6012, New Zealand
Deborah Maxwell
School of Geography, Environment, and Earth Sciences, Victoria
University of Wellington, Wellington, 6012, New Zealand
Kevin Norton
School of Geography, Environment, and Earth Sciences, Victoria
University of Wellington, Wellington, 6012, New Zealand
Related authors
Bethanna Jackson, Rubianca Benavidez, Keith Miller, and Deborah Maxwell
Geosci. Model Dev. Discuss., https://doi.org/10.5194/gmd-2020-261, https://doi.org/10.5194/gmd-2020-261, 2020
Publication in GMD not foreseen
Short summary
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There is an increasing will to preserve nature for its own sake and to protect its benefits for future generations. Various policies encourage more sustainable land management practices to protect rivers and lakes. Separating out broad scale from local impacts is difficult, but necessary for informed land management outcomes. We present tools automatically identifying flows of water, sediment and chemicals in and out of farms, forestry blocks, etc to enable smarter future management.
Rubianca Benavidez, Bethanna Jackson, Deborah Maxwell, and Enrico Paringit
Proc. IAHS, 373, 147–151, https://doi.org/10.5194/piahs-373-147-2016, https://doi.org/10.5194/piahs-373-147-2016, 2016
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Being within the typhoon belt, the Philippines is vulnerable to typhoons that can cause destructive floods. The Land Utilisation and Capability (LUCI) model is being applied to the Cagayan de Oro watershed to identify areas providing flood mitigation services and potential areas to target for improving flood mitigation. LUCI can complement the existing disaster risk framework by assessing land use plans under scenarios of extreme events and climate change, and the effect on flooding downstream.
Jamey Stutz, Andrew Mackintosh, Kevin Norton, Ross Whitmore, Carlo Baroni, Stewart S. R. Jamieson, Richard S. Jones, Greg Balco, Maria Cristina Salvatore, Stefano Casale, Jae Il Lee, Yeong Bae Seong, Robert McKay, Lauren J. Vargo, Daniel Lowry, Perry Spector, Marcus Christl, Susan Ivy Ochs, Luigia Di Nicola, Maria Iarossi, Finlay Stuart, and Tom Woodruff
The Cryosphere, 15, 5447–5471, https://doi.org/10.5194/tc-15-5447-2021, https://doi.org/10.5194/tc-15-5447-2021, 2021
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Understanding the long-term behaviour of ice sheets is essential to projecting future changes due to climate change. In this study, we use rocks deposited along the margin of the David Glacier, one of the largest glacier systems in the world, to reveal a rapid thinning event initiated over 7000 years ago and endured for ~ 2000 years. Using physical models, we show that subglacial topography and ocean heat are important drivers for change along this sector of the Antarctic Ice Sheet.
Bethanna Jackson, Rubianca Benavidez, Keith Miller, and Deborah Maxwell
Geosci. Model Dev. Discuss., https://doi.org/10.5194/gmd-2020-261, https://doi.org/10.5194/gmd-2020-261, 2020
Publication in GMD not foreseen
Short summary
Short summary
There is an increasing will to preserve nature for its own sake and to protect its benefits for future generations. Various policies encourage more sustainable land management practices to protect rivers and lakes. Separating out broad scale from local impacts is difficult, but necessary for informed land management outcomes. We present tools automatically identifying flows of water, sediment and chemicals in and out of farms, forestry blocks, etc to enable smarter future management.
Antoine Cogez, Frédéric Herman, Éric Pelt, Thierry Reuschlé, Gilles Morvan, Christopher M. Darvill, Kevin P. Norton, Marcus Christl, Lena Märki, and François Chabaux
Earth Surf. Dynam., 6, 121–140, https://doi.org/10.5194/esurf-6-121-2018, https://doi.org/10.5194/esurf-6-121-2018, 2018
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Sediments produced by glaciers are transported by rivers and wind toward the ocean. During their journey, these sediments are weathered, and we know that this has an impact on climate. One key factor is time, but the duration of this journey is largely unknown. We were able to measure the average time that sediment spends only in the glacial area. This time is 100–200 kyr, which is long and allows a lot of processes to act on sediments during their journey.
Rubianca Benavidez, Bethanna Jackson, Deborah Maxwell, and Enrico Paringit
Proc. IAHS, 373, 147–151, https://doi.org/10.5194/piahs-373-147-2016, https://doi.org/10.5194/piahs-373-147-2016, 2016
Short summary
Short summary
Being within the typhoon belt, the Philippines is vulnerable to typhoons that can cause destructive floods. The Land Utilisation and Capability (LUCI) model is being applied to the Cagayan de Oro watershed to identify areas providing flood mitigation services and potential areas to target for improving flood mitigation. LUCI can complement the existing disaster risk framework by assessing land use plans under scenarios of extreme events and climate change, and the effect on flooding downstream.
K. P. Norton, F. Schlunegger, and C. Litty
Earth Surf. Dynam., 4, 147–157, https://doi.org/10.5194/esurf-4-147-2016, https://doi.org/10.5194/esurf-4-147-2016, 2016
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Cut-fill terraces are common landforms throughout the world. Their distribution both in space and time is not clear-cut, as they can arise from numerous processes. We apply a climate-dependent regolith production algorithm to determine potential sediment loads during climate shifts. When combined with transport capacity, our results suggest that the cut-fill terraces of western Peru can result from transient stripping of hillslope sediment but not steady-state hillslope erosion.
M. Beyer, R. van der Raaij, U. Morgenstern, and B. Jackson
Hydrol. Earth Syst. Sci., 19, 2775–2789, https://doi.org/10.5194/hess-19-2775-2015, https://doi.org/10.5194/hess-19-2775-2015, 2015
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We assess the potential of Halon-1301 as a new groundwater age tracer, which had not been assessed in detail. We determine Halon-1301 and infer age in 17 New Zealand groundwater samples and various modern waters. Halon-1301 reliably inferred age in 71% of the sites within 1 SD of the ages inferred from tritium and SF6. The remaining (anoxic) waters show reduced concentrations of Halon-1301 along with even further reduced concentrations of CFCs. The reason(s) for this need to be further assessed.
P. Iribarren Anacona, K.P. Norton, and A. Mackintosh
Nat. Hazards Earth Syst. Sci., 14, 3243–3259, https://doi.org/10.5194/nhess-14-3243-2014, https://doi.org/10.5194/nhess-14-3243-2014, 2014
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In Patagonia at least 16 moraine-dammed lakes have failed in historical time. Commonly failed lakes were in contact with glaciers at the time of failure and had moderate (≥ 8°) to steep (≥15°) outlet slopes. Seven failed lakes are located in the Baker Basin, Chilean Patagonia, were hydro-electric generation plants are planned. We assessed the outburst susceptibility of moraine-dammed lakes in the Baker Basin and identified 28 lakes with high or very high outburst susceptibility.
Related subject area
Subject: Hillslope hydrology | Techniques and Approaches: Modelling approaches
Elucidating the role of soil hydraulic properties on aspect-dependent landslide initiation
Recession discharge from compartmentalized bedrock hillslopes
Frozen soil hydrological modeling for a mountainous catchment northeast of the Qinghai–Tibet Plateau
On the similarity of hillslope hydrologic function: a clustering approach based on groundwater changes
Spatiotemporal changes in flow hydraulic characteristics and soil loss during gully headcut erosion under controlled conditions
Estimation of rainfall erosivity based on WRF-derived raindrop size distributions
Physically based model for gully simulation: application to the Brazilian semiarid region
Assessing the perturbations of the hydrogeological regime in sloping fens due to roads
Hybridizing Bayesian and variational data assimilation for high-resolution hydrologic forecasting
Multi-source data assimilation for physically based hydrological modeling of an experimental hillslope
A new method, with application, for analysis of the impacts on flood risk of widely distributed enhanced hillslope storage
Towards improved parameterization of a macroscale hydrologic model in a discontinuous permafrost boreal forest ecosystem
Reconstructing long-term gully dynamics in Mediterranean agricultural areas
Evaluating performance of simplified physically based models for shallow landslide susceptibility
Multiresponse modeling of variably saturated flow and isotope tracer transport for a hillslope experiment at the Landscape Evolution Observatory
Determinants of modelling choices for 1-D free-surface flow and morphodynamics in hydrology and hydraulics: a review
Use of satellite and modeled soil moisture data for predicting event soil loss at plot scale
Quantification of the influence of preferential flow on slope stability using a numerical modelling approach
Hydrological hysteresis and its value for assessing process consistency in catchment conceptual models
Derivation and evaluation of landslide-triggering thresholds by a Monte Carlo approach
Stable water isotope tracing through hydrological models for disentangling runoff generation processes at the hillslope scale
Analysis of landslide triggering conditions in the Sarno area using a physically based model
The influence of grid resolution on the prediction of natural and road-related shallow landslides
Incipient subsurface heterogeneity and its effect on overland flow generation – insight from a modeling study of the first experiment at the Biosphere 2 Landscape Evolution Observatory
Coupled prediction of flood response and debris flow initiation during warm- and cold-season events in the Southern Appalachians, USA
Predicting subsurface stormflow response of a forested hillslope – the role of connected flow paths
Interplay of riparian forest and groundwater in the hillslope hydrology of Sudanian West Africa (northern Benin)
A model-based assessment of the potential use of compound-specific stable isotope analysis in river monitoring of diffuse pesticide pollution
A paradigm shift in stormflow predictions for active tectonic regions with large-magnitude storms: generalisation of catchment observations by hydraulic sensitivity analysis and insight into soil-layer evolution
Derivation of critical rainfall thresholds for shallow landslides as a tool for debris flow early warning systems
Statistical analysis and modelling of surface runoff from arable fields in central Europe
Hydrological modelling of a slope covered with shallow pyroclastic deposits from field monitoring data
Physically based modeling of rainfall-triggered landslides: a case study in the Luquillo forest, Puerto Rico
Characterization of groundwater dynamics in landslides in varved clays
A critical assessment of simple recharge models: application to the UK Chalk
The effect of spatial throughfall patterns on soil moisture patterns at the hillslope scale
Snow accumulation/melting model (SAMM) for integrated use in regional scale landslide early warning systems
Suspended sediment concentration–discharge relationships in the (sub-) humid Ethiopian highlands
A model of hydrological and mechanical feedbacks of preferential fissure flow in a slow-moving landslide
Scale effect on overland flow connectivity at the plot scale
Physical models for classroom teaching in hydrology
Coupling the modified SCS-CN and RUSLE models to simulate hydrological effects of restoring vegetation in the Loess Plateau of China
Effects of peatland drainage management on peak flows
A conceptual model of the hydrological influence of fissures on landslide activity
A structure generator for modelling the initial sediment distribution of an artificial hydrologic catchment
A novel explicit approach to model bromide and pesticide transport in connected soil structures
Quantifying spatial and temporal discharge dynamics of an event in a first order stream, using distributed temperature sensing
Effect of high-resolution spatial soil moisture variability on simulated runoff response using a distributed hydrologic model
A steady-state saturation model to determine the subsurface travel time (STT) in complex hillslopes
Comparison of algorithms and parameterisations for infiltration into organic-covered permafrost soils
Yanglin Guo and Chao Ma
Hydrol. Earth Syst. Sci., 27, 1667–1682, https://doi.org/10.5194/hess-27-1667-2023, https://doi.org/10.5194/hess-27-1667-2023, 2023
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In a localized area with the same vegetation, an overwhelming propensity of shallow landslides on the south-facing slope over the north-facing slope could not be attributed to plant roots. We provide new evidence from the pore water pressure of failing mass, unsaturated hydraulic conductivity, water storage, and drainage and the hillslope stability fluctuation to prove that the infinite slope model may be suitable for elucidating the aspect-dependent landslide distribution in the study area.
Clément Roques, David E. Rupp, Jean-Raynald de Dreuzy, Laurent Longuevergne, Elizabeth R. Jachens, Gordon Grant, Luc Aquilina, and John S. Selker
Hydrol. Earth Syst. Sci., 26, 4391–4405, https://doi.org/10.5194/hess-26-4391-2022, https://doi.org/10.5194/hess-26-4391-2022, 2022
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Streamflow dynamics are directly dependent on contributions from groundwater, with hillslope heterogeneity being a major driver in controlling both spatial and temporal variabilities in recession discharge behaviors. By analysing new model results, this paper identifies the major structural features of aquifers driving streamflow dynamics. It provides important guidance to inform catchment-to-regional-scale models, with key geological knowledge influencing groundwater–surface water interactions.
Hongkai Gao, Chuntan Han, Rensheng Chen, Zijing Feng, Kang Wang, Fabrizio Fenicia, and Hubert Savenije
Hydrol. Earth Syst. Sci., 26, 4187–4208, https://doi.org/10.5194/hess-26-4187-2022, https://doi.org/10.5194/hess-26-4187-2022, 2022
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Frozen soil hydrology is one of the 23 unsolved problems in hydrology (UPH). In this study, we developed a novel conceptual frozen soil hydrological model, FLEX-Topo-FS. The model successfully reproduced the soil freeze–thaw process, and its impacts on hydrologic connectivity, runoff generation, and groundwater. We believe this study is a breakthrough for the 23 UPH, giving us new insights on frozen soil hydrology, with broad implications for predicting cold region hydrology in future.
Fadji Z. Maina, Haruko M. Wainwright, Peter James Dennedy-Frank, and Erica R. Siirila-Woodburn
Hydrol. Earth Syst. Sci., 26, 3805–3823, https://doi.org/10.5194/hess-26-3805-2022, https://doi.org/10.5194/hess-26-3805-2022, 2022
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We propose a hillslope clustering approach based on the seasonal changes in groundwater levels and test its performance by comparing it to several common clustering approaches (aridity index, topographic wetness index, elevation, land cover, and machine-learning clustering). The proposed approach is robust as it reasonably categorizes hillslopes with similar elevation, land cover, hydroclimate, land surface processes, and subsurface hydrodynamics, hence a similar hydrologic function.
Mingming Guo, Zhuoxin Chen, Wenlong Wang, Tianchao Wang, Qianhua Shi, Hongliang Kang, Man Zhao, and Lanqian Feng
Hydrol. Earth Syst. Sci., 25, 4473–4494, https://doi.org/10.5194/hess-25-4473-2021, https://doi.org/10.5194/hess-25-4473-2021, 2021
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Gully headcut erosion is always a difficult issue in soil erosion, which hinders the revelation of gully erosion mechanisms and the establishment of a gully erosion model. This study clarified the spatiotemporal changes in flow properties, energy consumption, and soil loss, confirming that gully head consumed the most of flow energy (78 %) and can contribute 89 % of total soil loss. Critical energy consumption initiating soil erosion of the upstream area, gully head, and gully bed is confirmed.
Qiang Dai, Jingxuan Zhu, Shuliang Zhang, Shaonan Zhu, Dawei Han, and Guonian Lv
Hydrol. Earth Syst. Sci., 24, 5407–5422, https://doi.org/10.5194/hess-24-5407-2020, https://doi.org/10.5194/hess-24-5407-2020, 2020
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Rainfall is a driving force that accounts for a large proportion of soil loss around the world. Most previous studies used a fixed rainfall–energy relationship to estimate rainfall energy, ignoring the spatial and temporal changes of raindrop microphysical processes. This study proposes a novel method for large-scale and long-term rainfall energy and rainfall erosivity investigations based on rainfall microphysical parameterization schemes in the Weather Research and Forecasting (WRF) model.
Pedro Henrique Lima Alencar, José Carlos de Araújo, and Adunias dos Santos Teixeira
Hydrol. Earth Syst. Sci., 24, 4239–4255, https://doi.org/10.5194/hess-24-4239-2020, https://doi.org/10.5194/hess-24-4239-2020, 2020
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Soil erosion by water has been emphasized as a key problem to be faced in the 21st century. Thus, it is critical to understand land degradation and to answer fundamental questions regarding how and why such processes occur. Here, we present a model for gully erosion (channels carved by rainwater) based on existing equations, and we identify some major variables that influence the initiation and evolution of this process. The successful model can help in planning soil conservation practices.
Fabien Cochand, Daniel Käser, Philippe Grosvernier, Daniel Hunkeler, and Philip Brunner
Hydrol. Earth Syst. Sci., 24, 213–226, https://doi.org/10.5194/hess-24-213-2020, https://doi.org/10.5194/hess-24-213-2020, 2020
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Roads in sloping fens constitute a hydraulic barrier for surface and subsurface flow. This can lead to the drying out of downslope areas of the fen as well as gully erosion. By combining fieldwork and numerical models, this study presents an assessment of the hydrogeological impact of three road structures especially designed to limit their impact. The study shows that the impact of roads on the hydrological regime in fens can be significantly reduced by using appropriate engineering measures.
Felipe Hernández and Xu Liang
Hydrol. Earth Syst. Sci., 22, 5759–5779, https://doi.org/10.5194/hess-22-5759-2018, https://doi.org/10.5194/hess-22-5759-2018, 2018
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Predicting floods requires first knowing the amount of water in the valleys, which is complicated because we cannot know for sure how much water there is in the soil. We created a unique system that combines the best methods to estimate these conditions accurately based on the observed water flow in the rivers and on detailed simulations of the valleys. Comparisons with popular methods show that our system can produce realistic predictions efficiently, even for very detailed river networks.
Anna Botto, Enrica Belluco, and Matteo Camporese
Hydrol. Earth Syst. Sci., 22, 4251–4266, https://doi.org/10.5194/hess-22-4251-2018, https://doi.org/10.5194/hess-22-4251-2018, 2018
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We present a multivariate application of the ensemble Kalman filter (EnKF) in hydrological modeling of a real-world hillslope test case with dominant unsaturated dynamics and strong nonlinearities. Overall, the EnKF is able to correctly update system state and soil parameters. However, multivariate data assimilation may lead to significant tradeoffs between model predictions of different variables, if the observation data are not high quality or representative.
Peter Metcalfe, Keith Beven, Barry Hankin, and Rob Lamb
Hydrol. Earth Syst. Sci., 22, 2589–2605, https://doi.org/10.5194/hess-22-2589-2018, https://doi.org/10.5194/hess-22-2589-2018, 2018
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Flooding is a significant hazard and extreme events in recent years have focused attention on effective means of reducing its risk. An approach known as natural flood management (NFM) seeks to increase flood resilience by a range of measures that work with natural processes. The paper develops a modelling approach to assess one type NFM of intervention – distributed additional hillslope storage features – and demonstrates that more strategic placement is required than has hitherto been applied.
Abraham Endalamaw, W. Robert Bolton, Jessica M. Young-Robertson, Don Morton, Larry Hinzman, and Bart Nijssen
Hydrol. Earth Syst. Sci., 21, 4663–4680, https://doi.org/10.5194/hess-21-4663-2017, https://doi.org/10.5194/hess-21-4663-2017, 2017
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This study applies plot-scale and hill-slope knowledge to a process-based mesoscale model to improve the skill of distributed hydrological models to simulate the spatially and basin-integrated hydrological processes of complex ecosystems in the sub-arctic boreal forest. We developed a sub-grid parameterization method to parameterize the surface heterogeneity of interior Alaskan discontinuous permafrost watersheds.
Antonio Hayas, Tom Vanwalleghem, Ana Laguna, Adolfo Peña, and Juan V. Giráldez
Hydrol. Earth Syst. Sci., 21, 235–249, https://doi.org/10.5194/hess-21-235-2017, https://doi.org/10.5194/hess-21-235-2017, 2017
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Gully erosion is one of the most important erosion processes. In this study, we provide new data on gully dynamics over long timescales with an unprecedented temporal resolution. We apply a new Monte Carlo based method for calculating gully volumes based on orthophotos and, especially, for constraining uncertainties of these estimations. Our results show that gully erosion rates are highly variable from year to year and significantly higher than other erosion processes.
Giuseppe Formetta, Giovanna Capparelli, and Pasquale Versace
Hydrol. Earth Syst. Sci., 20, 4585–4603, https://doi.org/10.5194/hess-20-4585-2016, https://doi.org/10.5194/hess-20-4585-2016, 2016
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This paper focuses on performance evaluation of simplified, physically based landslide susceptibility models. It presents a new methodology to systemically and objectively calibrate, verify, and compare different models and models performances indicators in order to individuate and select the models whose behavior is more reliable for a certain case study. The procedure was implemented in a package for landslide susceptibility analysis and integrated the open-source hydrological model NewAge.
Carlotta Scudeler, Luke Pangle, Damiano Pasetto, Guo-Yue Niu, Till Volkmann, Claudio Paniconi, Mario Putti, and Peter Troch
Hydrol. Earth Syst. Sci., 20, 4061–4078, https://doi.org/10.5194/hess-20-4061-2016, https://doi.org/10.5194/hess-20-4061-2016, 2016
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Very few studies have applied a physically based hydrological model with integrated and distributed multivariate observation data of both flow and transport phenomena. In this study we address this challenge for a hillslope-scale unsaturated zone isotope tracer experiment. The results show how model complexity evolves as the number and detail of simulated responses increases. Possible gaps in process representation for simulating solute transport phenomena in very dry soils are discussed.
Bruno Cheviron and Roger Moussa
Hydrol. Earth Syst. Sci., 20, 3799–3830, https://doi.org/10.5194/hess-20-3799-2016, https://doi.org/10.5194/hess-20-3799-2016, 2016
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This review paper investigates the determinants of modelling choices for numerous applications of 1-D free-surface flow and morphodynamics in hydrology and hydraulics. Each case study has a signature composed of given contexts (spatiotemporal scales, flow typology, and phenomenology) and chosen concepts (refinement and subscales of the flow model). This review proposes a normative procedure possibly enriched by the community for a larger, comprehensive and updated image of modelling strategies.
F. Todisco, L. Brocca, L. F. Termite, and W. Wagner
Hydrol. Earth Syst. Sci., 19, 3845–3856, https://doi.org/10.5194/hess-19-3845-2015, https://doi.org/10.5194/hess-19-3845-2015, 2015
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We developed a new formulation of USLE, named Soil Moisture for Erosion (SM4E), that directly incorporates soil moisture information. SM4E is applied here by using modeled data and satellite observations obtained from the Advanced SCATterometer (ASCAT). SM4E is found to outperform USLE and USLE-MM models in silty–clay soil in central Italy. Through satellite data, there is the potential of applying SM4E for large-scale monitoring and quantification of the soil erosion process.
W. Shao, T. A. Bogaard, M. Bakker, and R. Greco
Hydrol. Earth Syst. Sci., 19, 2197–2212, https://doi.org/10.5194/hess-19-2197-2015, https://doi.org/10.5194/hess-19-2197-2015, 2015
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The effect of preferential flow on the stability of landslides is studied through numerical simulation of two types of rainfall events on a hypothetical hillslope. A model is developed that consists of two parts. The first part is a model for combined saturated/unsaturated subsurface flow and is used to compute the spatial and temporal water pressure response to rainfall. Preferential flow is simulated with a dual-permeability continuum model consisting of a matrix/preferential flow domain.
O. Fovet, L. Ruiz, M. Hrachowitz, M. Faucheux, and C. Gascuel-Odoux
Hydrol. Earth Syst. Sci., 19, 105–123, https://doi.org/10.5194/hess-19-105-2015, https://doi.org/10.5194/hess-19-105-2015, 2015
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We studied the annual hysteretic patterns observed between stream flow and water storage in the saturated and unsaturated zones of a hillslope and a riparian zone. We described these signatures using a hysteresis index and then used this to assess conceptual hydrological models. This led us to identify four hydrological periods and a clearly distinct behaviour between riparian and hillslope groundwaters and to provide new information about the model performances.
D. J. Peres and A. Cancelliere
Hydrol. Earth Syst. Sci., 18, 4913–4931, https://doi.org/10.5194/hess-18-4913-2014, https://doi.org/10.5194/hess-18-4913-2014, 2014
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A Monte Carlo approach, combining rainfall-stochastic models and hydrological and slope stability physically based models, is used to derive rainfall thresholds of landslide triggering. The uncertainty in threshold assessment related to variability of rainfall intensity within events and to past rainfall (antecedent rainfall) is analyzed and measured via ROC-based indexes, with a specific focus dedicated to the widely used power-law rainfall intensity-duration (I-D) thresholds.
D. Windhorst, P. Kraft, E. Timbe, H.-G. Frede, and L. Breuer
Hydrol. Earth Syst. Sci., 18, 4113–4127, https://doi.org/10.5194/hess-18-4113-2014, https://doi.org/10.5194/hess-18-4113-2014, 2014
G. Capparelli and P. Versace
Hydrol. Earth Syst. Sci., 18, 3225–3237, https://doi.org/10.5194/hess-18-3225-2014, https://doi.org/10.5194/hess-18-3225-2014, 2014
D. Penna, M. Borga, G. T. Aronica, G. Brigandì, and P. Tarolli
Hydrol. Earth Syst. Sci., 18, 2127–2139, https://doi.org/10.5194/hess-18-2127-2014, https://doi.org/10.5194/hess-18-2127-2014, 2014
G.-Y. Niu, D. Pasetto, C. Scudeler, C. Paniconi, M. Putti, P. A. Troch, S. B. DeLong, K. Dontsova, L. Pangle, D. D. Breshears, J. Chorover, T. E. Huxman, J. Pelletier, S. R. Saleska, and X. Zeng
Hydrol. Earth Syst. Sci., 18, 1873–1883, https://doi.org/10.5194/hess-18-1873-2014, https://doi.org/10.5194/hess-18-1873-2014, 2014
J. Tao and A. P. Barros
Hydrol. Earth Syst. Sci., 18, 367–388, https://doi.org/10.5194/hess-18-367-2014, https://doi.org/10.5194/hess-18-367-2014, 2014
J. Wienhöfer and E. Zehe
Hydrol. Earth Syst. Sci., 18, 121–138, https://doi.org/10.5194/hess-18-121-2014, https://doi.org/10.5194/hess-18-121-2014, 2014
A. Richard, S. Galle, M. Descloitres, J.-M. Cohard, J.-P. Vandervaere, L. Séguis, and C. Peugeot
Hydrol. Earth Syst. Sci., 17, 5079–5096, https://doi.org/10.5194/hess-17-5079-2013, https://doi.org/10.5194/hess-17-5079-2013, 2013
S. R. Lutz, H. J. van Meerveld, M. J. Waterloo, H. P. Broers, and B. M. van Breukelen
Hydrol. Earth Syst. Sci., 17, 4505–4524, https://doi.org/10.5194/hess-17-4505-2013, https://doi.org/10.5194/hess-17-4505-2013, 2013
Makoto Tani
Hydrol. Earth Syst. Sci., 17, 4453–4470, https://doi.org/10.5194/hess-17-4453-2013, https://doi.org/10.5194/hess-17-4453-2013, 2013
M. N. Papa, V. Medina, F. Ciervo, and A. Bateman
Hydrol. Earth Syst. Sci., 17, 4095–4107, https://doi.org/10.5194/hess-17-4095-2013, https://doi.org/10.5194/hess-17-4095-2013, 2013
P. Fiener, K. Auerswald, F. Winter, and M. Disse
Hydrol. Earth Syst. Sci., 17, 4121–4132, https://doi.org/10.5194/hess-17-4121-2013, https://doi.org/10.5194/hess-17-4121-2013, 2013
R. Greco, L. Comegna, E. Damiano, A. Guida, L. Olivares, and L. Picarelli
Hydrol. Earth Syst. Sci., 17, 4001–4013, https://doi.org/10.5194/hess-17-4001-2013, https://doi.org/10.5194/hess-17-4001-2013, 2013
C. Lepore, E. Arnone, L. V. Noto, G. Sivandran, and R. L. Bras
Hydrol. Earth Syst. Sci., 17, 3371–3387, https://doi.org/10.5194/hess-17-3371-2013, https://doi.org/10.5194/hess-17-3371-2013, 2013
J. E. van der Spek, T. A. Bogaard, and M. Bakker
Hydrol. Earth Syst. Sci., 17, 2171–2183, https://doi.org/10.5194/hess-17-2171-2013, https://doi.org/10.5194/hess-17-2171-2013, 2013
A. M. Ireson and A. P. Butler
Hydrol. Earth Syst. Sci., 17, 2083–2096, https://doi.org/10.5194/hess-17-2083-2013, https://doi.org/10.5194/hess-17-2083-2013, 2013
A. M. J. Coenders-Gerrits, L. Hopp, H. H. G. Savenije, and L. Pfister
Hydrol. Earth Syst. Sci., 17, 1749–1763, https://doi.org/10.5194/hess-17-1749-2013, https://doi.org/10.5194/hess-17-1749-2013, 2013
G. Martelloni, S. Segoni, D. Lagomarsino, R. Fanti, and F. Catani
Hydrol. Earth Syst. Sci., 17, 1229–1240, https://doi.org/10.5194/hess-17-1229-2013, https://doi.org/10.5194/hess-17-1229-2013, 2013
C. D. Guzman, S. A. Tilahun, A. D. Zegeye, and T. S. Steenhuis
Hydrol. Earth Syst. Sci., 17, 1067–1077, https://doi.org/10.5194/hess-17-1067-2013, https://doi.org/10.5194/hess-17-1067-2013, 2013
D. M. Krzeminska, T. A. Bogaard, J.-P. Malet, and L. P. H. van Beek
Hydrol. Earth Syst. Sci., 17, 947–959, https://doi.org/10.5194/hess-17-947-2013, https://doi.org/10.5194/hess-17-947-2013, 2013
A. Peñuela, M. Javaux, and C. L. Bielders
Hydrol. Earth Syst. Sci., 17, 87–101, https://doi.org/10.5194/hess-17-87-2013, https://doi.org/10.5194/hess-17-87-2013, 2013
A. Rodhe
Hydrol. Earth Syst. Sci., 16, 3075–3082, https://doi.org/10.5194/hess-16-3075-2012, https://doi.org/10.5194/hess-16-3075-2012, 2012
G. Y. Gao, B. J. Fu, Y. H. Lü, Y. Liu, S. Wang, and J. Zhou
Hydrol. Earth Syst. Sci., 16, 2347–2364, https://doi.org/10.5194/hess-16-2347-2012, https://doi.org/10.5194/hess-16-2347-2012, 2012
C. E. Ballard, N. McIntyre, and H. S. Wheater
Hydrol. Earth Syst. Sci., 16, 2299–2310, https://doi.org/10.5194/hess-16-2299-2012, https://doi.org/10.5194/hess-16-2299-2012, 2012
D. M. Krzeminska, T. A. Bogaard, Th. W. J. van Asch, and L. P. H. van Beek
Hydrol. Earth Syst. Sci., 16, 1561–1576, https://doi.org/10.5194/hess-16-1561-2012, https://doi.org/10.5194/hess-16-1561-2012, 2012
T. Maurer, A. Schneider, and H. H. Gerke
Hydrol. Earth Syst. Sci., 15, 3617–3638, https://doi.org/10.5194/hess-15-3617-2011, https://doi.org/10.5194/hess-15-3617-2011, 2011
J. Klaus and E. Zehe
Hydrol. Earth Syst. Sci., 15, 2127–2144, https://doi.org/10.5194/hess-15-2127-2011, https://doi.org/10.5194/hess-15-2127-2011, 2011
M. C. Westhoff, T. A. Bogaard, and H. H. G. Savenije
Hydrol. Earth Syst. Sci., 15, 1945–1957, https://doi.org/10.5194/hess-15-1945-2011, https://doi.org/10.5194/hess-15-1945-2011, 2011
J. Minet, E. Laloy, S. Lambot, and M. Vanclooster
Hydrol. Earth Syst. Sci., 15, 1323–1338, https://doi.org/10.5194/hess-15-1323-2011, https://doi.org/10.5194/hess-15-1323-2011, 2011
T. Sabzevari, A. Talebi, R. Ardakanian, and A. Shamsai
Hydrol. Earth Syst. Sci., 14, 891–900, https://doi.org/10.5194/hess-14-891-2010, https://doi.org/10.5194/hess-14-891-2010, 2010
Y. Zhang, S. K. Carey, W. L. Quinton, J. R. Janowicz, J. W. Pomeroy, and G. N. Flerchinger
Hydrol. Earth Syst. Sci., 14, 729–750, https://doi.org/10.5194/hess-14-729-2010, https://doi.org/10.5194/hess-14-729-2010, 2010
Cited articles
Abu Hammad, A., Lundekvam, H., and Børresen, T.: Adaptation of RUSLE in the
eastern part of the Mediterranean region, Environ. Manage., 34,
829–841, https://doi.org/10.1007/s00267-003-0296-7, 2005.
Adornado, H. A. and Yoshida, M: Assessing the Adverse Impacts of Climate
Change: A Case Study in the Philippines, J. Dev. Sus. Agr.,
5, 141–146, 2010.
Adornado, H. A., Yoshida, M., and Apolinares, H.: Erosion Vulnerability
Assessment in REINA, Quezon Province, Philippines with Raster-based Tool
Built within GIS Environment, J. Agric. Res. , 18,
24–31, https://doi.org/10.3173/air.18.24, 2009.
Aksoy, H. and Kavvas, M. L.: A review of hillslope and watershed scale
erosion and sediment transport models, Catena, 64,
247–271, https://doi.org/10.1016/j.catena.2005.08.008, 2005.
Aiello, A., Adamo, M., and Canora, F.: Modelling Spatially–Distributed Soil
Erosion through Remotely–Sensed Data and GIS, in: Computational Science and Its
Applications – ICCSA 2014, edited by: Murgante, B., Misra, S.,
Rocha, A. M. A. C., Torre, C. M., Rocha, J. G., Falcão, M. I., Taniar, D.,
Apduhan, B. O., and Gervasi, O., 372–385, Springer, Cham, https://doi.org/10.1007/978-3-319-09147-1_27, 2014.
Alexandridis, T. K., Sotiropoulou, A. M., Bilas, G., Karapetsas, N., and
Silleos, N. G.: The Effects of Seasonality in Estimating the C-Factor of Soil
Erosion Studies, Land Degrad. Develop., 26, 596–603, https://doi.org/10.1002/ldr.2223, 2015.
Amore, E., Modica, C., Nearing, M. A., and Santoro, V. C.: Scale effect in
USLE and WEPP application for soil erosion computation from three Sicilian
basins, J. Hydrol., 293, 100–114, https://doi.org/10.1016/j.jhydrol.2004.01.018, 2004.
Arnoldus, H. M. J.: Methodology used to determine the maximum potential
average annual soil loss due to sheet and rill erosion in Morocco,
FAO Soils Bulletin, 34, 39–51, 1977.
Arnoldus, H. M. J.: An approximation of the rainfall factor in the Universal
Soil Loss Equation, in: De Boodt, M. and Gabriels, D., Assessment
of Erosion, 127–131, Chichester, UK, Wiley, 1980.
Bagherzadeh, A.: Estimation of soil losses by USLE model using GIS at Mashhad
plain, Northeast of Iran, Arab. J. Geosci., 7,
211–220, https://doi.org/10.1007/s12517-012-0730-3, 2014.
Bantayan, N. C. and Bishop, I. D.: Linking objective and subjective modelling
for landuse decision-making, Landsc. Urban Plan., 43,
35–48, https://doi.org/10.1016/S0169-2046(98)00101-7, 1998.
Basher, L., Douglas, G., Elliott, S., Hughes, A., Jones, H., McIvor, I.,
Page, M., Rosser, B., and Tait, A.: Impacts of climate change on erosion and
erosion control methods – A critical review (Vol. 4), available at:
https://www.mpi.govt.nz/document-vault/4074 (last access:
12 November 2018), 2012.
Benavidez, R. A.: Understanding the effect of changing land use on
floods and soil erosion in the Cagayan de Oro catchment, (Unpublished
doctoral dissertation), Victoria University of Wellington, New Zealand, 2018.
Beskow, S., Mello, C. R., Norton, L. D., Curi, N., Viola, M. R., and Avanzi,
J. C.: Soil erosion prediction in the Grande River Basin, Brazil using
distributed modeling, Catena, 79, 49–59,
https://doi.org/10.1016/j.catena.2009.05.010, 2009.
Bonilla, C. A. and Vidal, K. L.: Rainfall erosivity in Central Chile,
J. Hydrol., 410, 126–133, https://doi.org/10.1016/j.jhydrol.2011.09.022, 2011.
Borrelli, P., Robinson, D. A., Fleischer, L. R., Lugato, E., Ballabio, C.,
Alewell, C., Meusburger, K., Modugno, S., Schütt, B., Ferro, V.,
Bagarello, V., Van Oost, K., Montanarella, L., and Panagos, P.: An assessment
of the global impact of 21st century land use change on soil erosion, Nat.
Commun., 8, 1–13, https://doi.org/10.1038/s41467-017-02142-7, 2017.
Boyle, J. F., Plater, A. J., Mayers, C., Turner, S. D., Stroud, R. W., and
Weber, J. E.: Land use, soil erosion, and sediment yield at Pinto Lake,
California: Comparison of a simplified USLE model with the lake sediment
record, J. Paleolimnol., 45, 199–212, https://doi.org/10.1007/s10933-010-9491-8, 2011.
Bridges, E. and Oldeman, L.: Global Assessment of Human-Induced Soil
Degradation, Arid Soil Res. Rehabil., 13, 319–325,
https://doi.org/10.1080/089030699263212, 1999 (data available at:
http://data.isric.org/geonetwork/srv/eng/catalog.search\#/metadata/9e84c15e-cb46-45e2-9126-1ca38bd5cd22,
last access: 23 November 2018).
Carroll, M. L., DiMiceli, C. M., Sohlberg, R. A., and Townshend, J. R. G.:
250 m MODIS Normalized Difference Vegetation Index, Collection 4, University
of Maryland, College Park, Maryland, 2004 (data available at:
http://glcf.umd.edu/data/ndvi/, last access: 23 November 2018).
Chen, L., Qian, X., and Shi, Y.: Critical Area Identification of Potential
Soil Loss in a Typical Watershed of the Three Gorges Reservoir Region,
Water. Resour. Manage., 25, 3445–3463, https://doi.org/10.1007/s11269-011-9864-4, 2011.
Dabney, S. M., Yoder, D. C., and Vieira, D. A. N.: The application of the
Revised Universal Soil Loss Equation, Version 2, to evaluate the impacts of
alternative climate change scenarios on runoff and sediment yield, J.
Soil Water Conserv., 67, 343–353, https://doi.org/10.2489/jswc.67.5.343, 2012.
da Silva, A. M.: Rainfall erosivity map for Brazil, Catena,
57, 251–259, https://doi.org/10.1016/j.catena.2003.11.006, 2004.
David, W. P.: Soil and Water Conservation Planning: Policy Issues and
Recommendations, J. Philipp. Dev., 15, 47–84,
1988.
de Asis, A. M. and Omasa, K.: Estimation of vegetation parameter for modeling
soil erosion using linear Spectral Mixture Analysis of Landsat ETM data,
ISPRS J. Photogramm. Remote. Sens., 62, 309–324, https://doi.org/10.1016/j.isprsjprs.2007.05.013, 2007.
de Mello, C. R., Norton, L. D., Pinto, L. C., Beskow, S., and Curi, N.:
Agricultural watershed modeling: a review for hydrology and soil erosion
processes, Ciencia E Agrotecnologia, 40, 7–25, https://doi.org/10.1590/S1413-70542016000100001, 2016.
de Vente, J. and Poesen, J.: Predicting soil erosion and sediment yield at
the basin scale: Scale issues and semi-quantitative models, Earth
Sci. Rev., 71, 95–125, https://doi.org/10.1016/j.earscirev.2005.02.002, 2005.
Delgado, M. E. M. and Canters, F:. Modeling the impacts of agroforestry
systems on the spatial patterns of soil erosion risk in three catchments of
Claveria, the Philippines, Agroforestry Syst., 85,
411–423, https://doi.org/10.1007/s10457-011-9442-z, 2012.
Demirci, A. and Karaburun, A.: Estimation of soil erosion using RUSLE in a
GIS framework: A case study in the Buyukcekmece Lake watershed, northwest
Turkey, Environ. Earth. Sci., 66, 903–913, https://doi.org/10.1007/s12665-011-1300-9, 2012.
Desmet, P. J. J. and Govers, G.: A GIS procedure for automatically
calculating the USLE LS factor on topographically complex landscape units,
J. Soil Water Conserv., 51, 427–433,
1996.
Diodato, N.: Estimating RUSLE's rainfall factor in the part of Italy with a
Mediterranean rainfall regime, Hydrol. Earth Syst. Sci., 8, 103–107,
https://doi.org/10.5194/hess-8-103-2004, 2004.
Duarte, L., Teodoro, A., Gonçalves, J., Soares, D., and Cunha, M.:
Assessing soil erosion risk using RUSLE through a GIS open source desktop and
web application, Environ. Monit. Assess., 188, 1–16,
https://doi.org/10.1007/s10661-016-5349-5, 2016.
Dumas, P. and Fossey, M.: Mapping potential soil erosion in the Pacific
Islands: A case study of Efate Island (Vanuatu), 11th Pacific Science
Inter-Congress: Pacific Countries and Their Ocean, Facing Local and Global
Changes, 2009.
Durigon, V. L., Carvalho, D. F., Antunes, M. A. H., Oliveira, P. T. S., and
Fernandes, M. M.: NDVI time series for monitoring RUSLE cover management
factor in a tropical watershed, Int. J. Remote Sens., 35, 441–453,
https://doi.org/10.1080/01431161.2013.871081, 2014.
Dymond, J. R.: Soil erosion in New Zealand is a net sink of CO2,
Earth Surf. Proc. Land., 35, 1763–1772, https://doi.org/10.1002/esp.2014, 2010.
Eiumnoh, A.: Integration of Geographic Information Systems (GIS) and
Satellite Remote Sensing (SRS) for Watershed Management, Technical
Bulletin 150, 2000.
El-Swaify, S. A., Gramier, C. L., and Lo, A.: Recent advances in soil
conservation in steepland in humid tropics, in: Proceedings of the
International Conference on Steepland Agriculture in the Humid Tropics,
87–100, Kuala Lumpur, MADI, 1987.
El-Swaify, S. A. and Dangler, E. W: Erodibilities of selected tropical soils
in relation to structural and hydrologic parameters, Soil Conserv. Soc. Am.,
Ankeny, Iowa, 1976.
Eltaif, N., Gharaibeh, M., Al-Zaitawi, F., and Alhamad, M.: Approximation of
rainfall erosivity factors in north Jordan, Pedosphere, 20, 711–717,
https://doi.org/10.1016/S1002-0160(10)60061-6, 2010.
Farhan, Y. and Nawaiseh, S.: Spatial assessment of soil erosion risk using
RUSLE and GIS techniques, Environ. Earth. Sci., 74, 4649–4669,
https://doi.org/10.1007/s12665-015-4430-7, 2015.
Fernandez, C., Wu, J., McCool, D., and Stoeckle, C.: Estimating water erosion
and sediment yield with GIS, RUSLE, and SEDD, J. Soil Water Conserv., 58,
128–136, 2003.
Fernandez, M. A. and Daigneault, A.: Erosion mitigation in the Waikato
District, New Zealand: economic implications for agriculture, Int. J. Food
Agric. Econ., 48, 1–21, https://doi.org/10.1111/agec.12338, 2016.
Ferreira, V. and Panagopoulos, T.: Seasonality of soil erosion under
Mediterranean conditions at the Alqueva dam watershed, Environ. Manage.,
54, 67–83, https://doi.org/10.1007/s00267-014-0281-3, 2014.
Ferro, V., Giordano, G., and Iovino, M.: Isoerosivity and erosion risk map
for Sicily, Hydrol. Sci. J., 36, 549–564, https://doi.org/10.1080/02626669109492543,
1991.
Flabouris, K.: Study of rainfall factor R on the RUSLE law, (Doctoral
dissertation), Aristotle University of Thessaloniki, Greece, 2008.
Foster, G. R., Toy, T. E., and Renard, K. G.: Comparison of the USLE,
RUSLE1.06c, and RUSLE2 for Application to Highly Disturbed Lands, in: First
Interagency Conference on Research in Watersheds, 27–30 October, 154–160,
United States Department of Agriculture, 2003.
Fu, G., Chen, S., and McCool, D.: Modeling the Impacts of no-till practice on
Soil Erosion and Sediment Yield with RUSLE, SEDD, and ArcView GIS, Soil Till.
Res., 85, 38–49, https://doi.org/10.1016/j.still.2004.11.009, 2006.
Gaubi, I., Chaabani, A., Ben Mammou, A., and Hamza, M. H.: A GIS-based soil
erosion prediction using the Revised Universal Soil Loss Equation (RUSLE)
(Lebna watershed, Cap Bon, Tunisia), Nat. Hazards, 86,
219–239, https://doi.org/10.1007/s11069-016-2684-3, 2017.
Hernandez, E. C., Henderson, A., and Oliver, D. P.: Effects of changing land
use in the Pagsanjan–Lumban catchment on suspended sediment loads to Laguna
de Bay, Philippines, Agric. Water Manag., 106, 8–16,
https://doi.org/10.1016/j.agwat.2011.08.012, 2012.
Hudson, N. W.: Soil Conservation, Ithaca, New York, Cornell University
Press, 1971.
Institute of Water Research: RUSLE – An online soil erosion assessment tool,
available at: http://www.iwr.msu.edu/rusle/ (last access:
19 November 2018), 2015.
İrvem, A., Topaloğlu, F., and Uygur, V.: Estimating spatial
distribution of soil loss over Seyhan River Basin in Turkey, J. Hydrol.,
336, 30–37, https://doi.org/10.1016/j.jhydrol.2006.12.009, 2007.
Jahun, B. G., Ibrahim, R., Dlamini, N. S., and Musa, S. M.: Review of Soil
Erosion Assessment using RUSLE Model and GIS, J. Biol. Agric. Healthc.,
5, 36–47, 2015.
Jain, M. K. and Das, D.: Estimation of sediment yield and areas of soil
erosion and deposition for watershed prioritization using GIS and remote
sensing, Water Resour. Manage., 24, 2091–2112,
https://doi.org/10.1007/s11269-009-9540-0, 2010.
Jayasinghe, P. K. S. C., Adornado, H. A., Yoshida, M., and Leelamanie, D. A.
L.: A Web-Based GIS and Remote Sensing Framework for Spatial Information
System (SIS): A Case Study in Nuwaraeliya, Sri Lanka, Agricultural Information Research, 19, 106–116, https://doi.org/10.3173/air.19.106, 2010.
Kavian, A., Fathollah Nejad, Y., Habibnejad, M., and Soleimani, K.: Modeling
seasonal rainfall erosivity on a regional scale: A case study from
Northeastern Iran, Int. J. Environ. Res., 5, 939–950,
2011.
Kim, J. B., Saunders, P., and Finn, J. T.: Rapid assessment of soil erosion in
the Rio Lempa Basin, Central America, using the universal soil loss equation
and geographic information systems, Environ. Manage.,
36, 872–885, https://doi.org/10.1007/s00267-002-0065-z, 2005.
Kinnell, P. I. A.: Event soil loss, runoff and the Universal Soil Loss
Equation family of models: A review, J. Hydrol., 385,
384–397, https://doi.org/10.1016/j.jhydrol.2010.01.024, 2010.
Klik, A., Haas, K., Dvorackova, A., and Fuller, I. C.: Spatial and temporal
distribution of rainfall erosivity in New Zealand, Soil Res.,
53, 815–825, 2015.
Kulikov, M., Schickhoff, U., and Borchardt, P.: Spatial and seasonal dynamics
of soil loss ratio in mountain rangelands of south-western Kyrgyzstan, J.
Mt. Sci., 13, 316–329, https://doi.org/10.1007/S11629-014-3393-6, 2016.
Land Development Department: Assessment of Soil Loss using the
Equation of Soil Loss, Bangkok, Thailand, 2002.
Le Roux, J., Morgenthal, T., Malherbe, J., Pretorius, D., and Sumner, P.:
Water erosion prediction at a national scale for South Africa, Water SA, 34,
available at:
http://www.scielo.org.za/scielo.php?script=sci_arttext&pid=S1816-79502008000300003
(last access: 19 November 2018), 2005.
Li, L., Wang, Y., and Liu, C.: Effects of land use changes on soil erosion in
a fast developing area, Int. J. Environ. Sci. Technol.,
11, 1549–1562, https://doi.org/10.1007/s13762-013-0341-x, 2014.
López-Vicente, M., Navas, A., and Machín, J.: Identifying erosive
periods by using RUSLE factors in mountain fields of the Central Spanish
Pyrenees, Hydrol. Earth Syst. Sci., 12, 523–535,
https://doi.org/10.5194/hess-12-523-2008, 2008.
Loureiro, N. D. S. and Coutinho, M. D. A.: A new procedure to estimate the
RUSLE EI30 index, based on monthly rainfall data and applied to the Algarve
region, Portugal, J. Hydrol., 250, 12–18,
https://doi.org/10.1016/S0022-1694(01)00387-0, 2001.
Lu, H., Prosser, I. P., Moran, C. J., Gallant, J. C., Priestley, G., and
Stevenson, J. G.: Predicting sheetwash and rill erosion over the Australian
continent, Soil Res., 41, 1037–1062, https://doi.org/10.1071/SR02157, 2003.
Lu, H. and Yu, B.: Spatial and seasonal distribution of rainfall erosivity in
Australia, Soil Res., 40, 887, https://doi.org/10.1071/SR01117, 2002.
Ma, H. L., Wang, Z. L., and Zhou, X.: Estimation of soil loss based on RUSLE
in Zengcheng, Guangdong Province, Yangtze River, 41,
90–93, 2010.
Madarcos, B. S.: Soil Erosion in Four Cashew-based Crops Under Two Slope
Categories, University of the Philippines at Los Baños Library, 1985.
Merritt, W. S., Letcher, R. A., and Jakeman, A. J.: A review of erosion and
sediment transport models, Environ. Model Softw., 18, 761–799,
https://doi.org/10.1016/S1364-8152(03)00078-1, 2003.
Merritt, W. S., Croke, B. F. W., Jakeman, A. J., Letcher, R. A., and Perez,
P.: A Biophysical Toolbox for assessment and management of land and water
resources in rural catchments in Northern Thailand, Ecol. Modell., 171,
279–300, https://doi.org/10.1016/j.ecolmodel.2003.08.010, 2004.
Merzouk, A.: Relative erodibility of nine selected Moroccan soils as related
to their physical, chemical, and mineralogical properties, (Doctoral
dissertation), University of Minnesota, St. Paul, United States of America,
1985.
Mihara, H.: Raindrop and Soil Erosion, National Institute of Agricultural
Science Bulletin Series A, 1, 1–59, National Institute of Agricultural
Science, Tokyo, 1951.
Mitasova, H., Hofierka, J., Zlocha, M., and Iverson, L.: Modelling
topographic potential for erosion and deposition using GIS, Int. J. Geogr.
Inf. Sci., 10, 629–641, https://doi.org/10.1080/02693799608902101, 1996.
Mitasova, H., Barton, M., Ullah, I., Hofierka, J., and Harmon, R.: GIS-Based
Soil Erosion Modeling, in: Treatise on Geomorphology, 3, 228–258, Academic
Press, San Diego, Califronia, https://doi.org/10.1016/B978-0-12-374739-6.00052-X, 2013.
Momm, H. G., Bingner, R. L., Wells, R. R., and Wilcox, D.: Agnps GIS-based
tool for watershed-scale identification and mapping of cropland potential
ephemeral gullies, Appl. Eng. Agric., 28,
17–29, 2012.
Moore, I. D. and Burch, G. J.: Physical basis of the length-slope factor in
the Universal Soil Loss Equation, Soil Sci. Soc. Am. J., 50, 1294–1298,
1986.
Moore, I. D., Grayson, R. B., and Ladson, A. R.: Digital Terrain Modeling?: A
Review of Hydrological Geomorphological and Biological Applications, Hydrol.
Process., 5, 3–30, https://doi.org/10.1002/hyp.3360050103, 1991.
Morgan, R. P. C.: Estimating regional variations in soil erosion hazard in
Peninsular Malaysia, Malayan Nature Journal, 28, 94–106,
1974.
Morgan, R. P. C.: Soil Erosion and Conservation, Longman Group Ltd., Essex,
1986.
Morgan, R. P. C.: Soil Erosion and Conservation, National Soil Resources
Institute, Cranfield University, https://doi.org/10.1002/9781118351475.ch22, 2005.
Nagle, G. N., Fahey, T. J., and Lassoie, J. P.: Management of sedimentation
in tropical watersheds, Environ. Manage., 23, 441–452,
https://doi.org/10.1007/s002679900199, 1999.
Naipal, V., Reick, C., Pongratz, J., and Van Oost, K.: Improving the global
applicability of the RUSLE model – adjustment of the topographical and
rainfall erosivity factors, Geosci. Model Dev., 8, 2893–2913,
https://doi.org/10.5194/gmd-8-2893-2015, 2015.
Nakil, M.: Analysis of parameters causing water induced soil erosion,
Unpublished Fifth Annual Progress Seminar, Indian Institute of Technology,
Bombay, 2014.
Nakil, M. and Khire, M.: Effect of slope steepness parameter computations on
soil loss estimation: review of methods using GIS, Geocarto Int., 31,
1078–1093, https://doi.org/10.1080/10106049.2015.1120349, 2016.
NASA: Normalized Difference Vegetation Index, New York City, USA, NASA
Goddard Institute for Space Studies, available at:
https://data.giss.nasa.gov/landuse/ndvi.html, last access:
23 November 2018.
Nigel, R. and Rughooputh, S. D. D. V.: Application of a RUSLE-based soil
erosion modelling on Mauritius Island, Soil Res., 50, 645–651, 2012.
Nontananandh, S. and Changnoi, B.: Internet GIS, based on USLE modeling, for
assessment of soil erosion in Songkhram watershed, Northeastern of Thailand,
Kasetsart Journal – Natural Science, 46, 272–282, 2012.
Ochoa-Cueva, P., Fries, A., Montesinos, P., Rodríguez-Díaz, J. A.,
and Boll, J.: Spatial Estimation of Soil Erosion Risk by Land-cover Change in
the Andes of Southern Ecuador, Land Degrad. Dev., 26, 565–573,
https://doi.org/10.1002/ldr.2219, 2015.
Ozsoy, G., Aksoy, E., Dirim, M. S., and Tumsavas, Z.: Determination of soil
erosion risk in the mustafakemalpasa river basin, Turkey, using the revised
universal soil loss equation, geographic information system, and remote
sensing, Environ. Manage., 50, 679–694, https://doi.org/10.1007/s00267-012-9904-8,
2012.
Panagos, P., Karydas, C. G., Gitas, I. Z., and Montanarella, L.: Monthly soil
erosion monitoring based on remotely sensed biophysical parameters: a case
study in Strymonas river basin towards a functional pan-European service,
Int. J. Digit. Earth, 5, 461–487, https://doi.org/10.1080/17538947.2011.587897,
2012.
Panagos, P., Meusburger, K., Ballabio, C., Borrelli, P., and Alewell, C.: Soil
erodibility in Europe: A high-resolution dataset based on LUCAS, Sci.
Total Environ., 479–480, 189–200, https://doi.org/10.1016/j.scitotenv.2014.02.010, 2014.
Panagos, P., Borrelli, P., and Meusburger, K.: A New European Slope Length
and Steepness Factor (LS-Factor) for Modeling Soil Erosion by Water,
Geosciences, 5, 117–126, https://doi.org/10.3390/geosciences5020117, 2015a.
Panagos, P., Borrelli, P., Meusburger, K., Alewell, C., Lugato, E., and
Montanarella, L.: Estimating the soil erosion cover-management factor at the
European scale, Land Use Policy, 48, 38–50, https://doi.org/10.1016/j.landusepol.2015.05.021, 2015b.
Panagos, P., Borrelli, P., Meusburger, K., Van Der Zanden, E. H., Poesen, J.,
and Alewell, C.: Modelling the effect of support practices (P-factor) on
the reduction of soil erosion by water at European scale, Environ. Sci.
Policy, 51, 23–34, https://doi.org/10.1016/j.envsci.2015.03.012, 2015c.
Panagos, P., Ballabio, C., Borrelli, P., Meusburger, K., Klik, A., Rousseva,
S., and Alewell, C.: Rainfall erosivity in Europe, Sci. Total
Environ., 511, 801–814, https://doi.org/10.1016/j.scitotenv.2015.01.008,
2015d (data available at: https://esdac.jrc.ec.europa.eu/content/rainfall-erosivity-european-union-and-switzerland,
last access: 23 November 2018).
Panagos, P., Borrelli, P., Poesen, J., Ballabio, C., Lugato, E., Meusburger,
K., and Alewell, C.: The new assessment of soil loss by water erosion in
Europe, Environ. Sci. Policy, 54, 438–447, https://doi.org/10.1016/j.envsci.2015.08.012, 2015e.
Panagos, P., Borrelli, P., Meusburger, K., Yu, B., Klik, A., Jae Lim, K., and
Ballabio, C.: Global rainfall erosivity assessment based on high-temporal
resolution rainfall records, Sci. Rep., 7, 4175, https://doi.org/10.1038/s41598-017-04282-8, 2017
(data available at: https://esdac.jrc.ec.europa.eu/content/global-rainfall-erosivity,
last access: 23 November 2018).
Pimentel, D., Harvey, C., Resosudarmo, P., Sinclair, K., Kurz, D., McNair,
M., and Blair, R.: Environmental and Economic Costs of Soil Erosion and
Conservation Benefits, Science, 267, 1117–1123,
https://doi.org/10.1126/science.267.5201.1117, 1995.
Post, D. A. and Hartcher, M. G.: Evaluating uncertainty in modelled sediment
delivery in data-sparse environments: application to the Mae Chaem Catchment,
Thailand, in: Proceedings of symposium S7 held during the Seventh IAHS
Scientific Assembly, 80–89, Foz do Iguaçu, 2005.
Raissouni, A., Issa, L., Lech-Hab, K., and El Arrim, A.: Water Erosion Risk
Mapping and Materials Transfer in the Smir Dam Watershed (Northwestern
Morocco), JGEESI, 5, 1–17, https://doi.org/10.9734/JGEESI/2016/20061, 2016.
Ram, B., Dhyani, B. L., and Kumar, N.: Assessment of erodibility status and
refined Iso-Erodent Map of India, Indian Journal of Soil Conservation,
32, 171–177, 2004.
Rango, A. and Arnoldus, H.: Aménagement des bassins versants, Cahiers
techniques de la FAO, Food and Agriculture Organization of the United
Nations, 1987.
Renard, K. and Freimund, J. R.: Using monthy precipitation data to estimate
R-factor in the revised USLE, J. Hydrol., 157, 287–306, 1994.
Renard, K., Foster, G., Weesies, G., McCool, D., and Yoder, D.: Predicting
soil erosion by water: a guide to conservation planning with the Revised
Universal Soil Loss Equation (RUSLE), Agricultural Handbook No. 703,
65–100, https://doi.org/10.1201/9780203739358-5, 1997.
Rodda, H. J. E., Stroud, M. J., Shankar, U., and Thorrold, B. S.: A GIS based
approach to modelling the effects of land-use change on soil erosion in New
Zealand, Soil Use Manag., 17, 30–40,
https://doi.org/10.1111/j.1475-2743.2001.tb00005.x, 2001.
Roose, E. J.: Erosion et ruissellement en Afrique de l'ouest: vingt
années de mesures en petites parcelles expérimentales,
Adiopodoumé, Ivory Coast, 1975.
Rosewell, C.: Potential Sources of Sediments and Nutrients: Sheet and Rill
Erosion and Phosphorous Sources, in: State of the Environment Technical Paper
Series (Inland Waters), Department of the Environment, Sport and Territories,
Canberra, Australia, 1997.
Rosewell, C. and Turner, J.: Rainfall Erosivity in New South Wales, Issue 20
of Technical Report, Department of Conservation and Land Management, Sydney,
1992.
Roslee, R., Bidin, K., Musta, B., Tahir, S., Tongkul, F., and Norhisham, M.
N.: GIS Application for Comprehensive Spatial Soil Erosion Analysis with
MUSLE Model in Sandakan Town Area, Sabah, Malaysia, Geological Behavior,
1, 1–5, 2017.
Rozos, D., Skilodimou, H. D., Loupasakis, C., and Bathrellos, G. D.:
Application of the revised universal soil loss equation model on landslide
prevention. An example from N. Euboea (Evia) Island, Greece, Environ. Earth.
Sci., 70, 3255–3266, https://doi.org/10.1007/s12665-013-2390-3, 2013.
Russo, A:. Applying the Revised Universal Soil Loss Equation model to land
use planning for erosion risk in Brunei Darussalam, Australian Planner,
3682, 1–17, https://doi.org/10.1080/07293682.2014.957332, 2015.
Sadeghi, S. H. R., Gholami, L., Khaledi Darvishan, A., and Saeidi, P.: A
review of the application of the MUSLE model worldwide, Hydrolog. Sci. J.,
59, 365–375, https://doi.org/10.1080/02626667.2013.866239, 2014.
Sadeghifard, D., Jabari, E., and Ghayasian, H.: Rainfall erosivity zonation
in Iran, in: The First National Conference on Civil Engineering, Sharif
University of Technology, Iran, available at:
https://www.civilica.com/Paper-NCCE01-226_2417394703.html (last access:
19 November 2018), 2004.
Schmitt, L. K.: Developing and applying a soil erosion model in a data-poor
context to an island in the rural Philippines, Environ. Dev.
Sustain., 11, 19–42, https://doi.org/10.1007/s10668-007-9096-1, 2009.
Shamshad, A., Azhari, M. N., Isa, M. H., Hussin, W. M. A. W., and Parida, B.
P.: Development of an appropriate procedure for estimation of RUSLE EI30
index and preparation of erosivity maps for Pulau Penang in Peninsular
Malaysia, Catena, 72, 423–432, https://doi.org/10.1016/j.catena.2007.08.002, 2008.
Sholagberu, A. T., Ul Mustafa, M. R., Wan Yusof, K., and Ahmad, M. H.:
Evaluation of rainfall-runoff erosivity factor for Cameron highlands, Pahang,
Malaysia, J. Ecol. Eng., 17, 1–8, https://doi.org/10.12911/22998993/63338, 2016.
Sinha, D. and Joshi, V. U.: Application of universal soil loss equation
(USLE) to recently reclaimed badlands along the Adula and Mahalungi Rivers,
Pravara Basin, Maharashtra, J. Geol. Soc. India, 80, 341–350,
https://doi.org/10.1007/s12594-012-0152-6, 2012.
Smith, D. D. and Whitt, D. M.: Estimating Soil Loss from Field Area of Clay
Pan Soils, Soil Sci. Soc. Am. J., 12, 485–490, 1947.
Stewart, B., Woolhiser, D., Wischmeier, W., Caro, J., and Frere, M. H.:
Control of water pollution from cropland, United States Department of
Agriculture, United States, 1975.
Thorne, C. R., Zevenbergen, L. W., Grissinger, E. H., and Murphey, J. B.:
Calculator programe and nomograph for on-site predictions of ephemeral gully
erosion, United States Department of Agriculture Soil Conservation Service,
United States, 1985.
Torri, D., Poesen, J., and Borselli, L.: Predictability and uncertainty of
the soil erodibility factor using a global dataset, CATENA, 46, 1–22,
https://doi.org/10.1016/S0341-8162(97)00036-2, 1997.
USDA-ARS (U.S. Department of Agriculture – Agricultural Research Service):
Revised Universal Soil Loss Equation, available at:
https://www.ars.usda.gov/southeast-area/oxford-ms/national-sedimentation-laboratory/watershed-physical-processes-research/docs/revised-universal-soil-loss-equation-rusle-welcome-to-rusle-1
(last access: 19 November 2018), 2002.
van der Knijff, J. M., Jones, R. J. A., and Montanarella, L.: Soil Erosion
Risk Assessment in Europe, available at:
https://www.preventionweb.net/files/1581_ereurnew2.pdf (last access:
21 November 2018), 2000.
Williams, R. J. and Renard, K. G.: EPIC – a new method for assessing
erosions effect on soil productivity, J. Soil Water Conserv., 38,
381–383, 1983.
Wilson, J. P. and Gallant, J. C.: Digital Terrain Analysis, edited by:
Wilson, J. P. and Gallant, J. C., in: Terrain Analysis: Principles and
Applications,
1–28, 2000.
Wischmeier, W. H. and Mannering, J. V.: Relation of Soil Properties to its
Erodibility, Soil and Water Management and Conservation, 15, 131–137,
https://doi.org/10.2136/sssaj1969.03615995003300010035x, 1969.
Wischmeier, W. H.
and Smith, D. D.: Predicting rainfall erosion losses,
Agriculture Handbook No. 537, 537, 285–291, https://doi.org/10.1029/TR039i002p00285, 1978.
Wu, S., Li, J., and Huang, G.: An evaluation of grid size uncertainty in
empirical soil loss modeling with digital elevation models, Environ. Model.
Assess., 10, 33–42, https://doi.org/10.1007/s10666-004-6595-4, 2005.
Yoon, K. S., Kim, C., and Woo, H.: Application of RUSLE for Erosion Estimation
of Construction Sites in Coastal Catchments, J. Coastal Res., 2,
1696–1700, 2009.
Yang, X., Chapman, G. A., Gray, J. M., and Young, M. A.: Delineating soil
landscape facets from digital elevation models using compound topographic
index in a geopgraphic information system, Soil Res., 45, 569–576, 2007.
Zakerinejad, R. and Maerker, M.: An integrated assessment of soil erosion
dynamics with special emphasis on gully erosion in the Mazayjan basin,
southwestern Iran, Nat. Hazards, 79, 25–50, https://doi.org/10.1007/s11069-015-1700-3, 2015.
Zhou, F. J., Chen, M. H., Lin, F. X., Huang, Y. H., and Lu, C. L.: The
rainfall erosivity index in Fujian Province, J. Soil Water Conserv., 9, 13–18, 1995.
Životić, L., Perović, V., Jaramaz, D., Dordević, A.,
Petrović, R., and Todorović, M.: Application of USLE, GIS, and remote
sensing in the assessment of soil erosion rates in Southeastern Serbia,
Pol. J. Environ. Stud., 21, 1929–1935, 2012.
Short summary
Soil erosion is a global problem and models identify vulnerable areas for management. One such model is the Revised Universal Soil Loss Equation. We review its different sub-factors and compile studies and equations that modified it for local conditions. The limitations of RUSLE include its data requirements and exclusion of gullying and landslides. Future directions include accounting for these erosion types. This paper serves as a reference for others working with RUSLE and related approaches.
Soil erosion is a global problem and models identify vulnerable areas for management. One such...
