Applied comparison of the erosion risk models EROSION 3D and LISEM for a small catchment in Norway

Starkloff, Torsten; Stolte, Jannes

doi:10.1016/j.catena.2014.02.004

Cited by 40 publications

(33 citation statements)

References 24 publications

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“…The variables were reclassified into five classes of soil erosion risk, as follows: 1 (very low), 2 (low), 3 (moderate), 4 (high) and 5 (very high). The variables were combined using a weighted ratio, obtained from expert judgment by means of a multi-criteria evaluation (Starkloff and Stolte, 2014;Vulević et al, 2015), and using software for the analysis and interpretation of satellite images and GIS, specifically those related to map algebra. The weighting process of the factors determined the following results: 20% (0.2) for the USLE R factor, 30% (0.3) for the topographic slope, 30% (0.3) for the NDVI, 10% (0.1) for land use, and 10% (0.1) for soil texture, expressed as follows:…”

Section: Land Use (Lu)mentioning

confidence: 99%

Soil erosion risk zoning in the Ecuadorian coastal region using geo-technological tools

2019

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The activation of erosive processes on the slopes of the drainage catchments in the Ecuadorian coastal region represents a serious environmental problem that results in a loss of soil resources with the consequent reduction in agricultural productivity, an increasing lack of areas with vegetation, and decreased water quality. These issues increase, among other things, the probability of flooding. The purpose of this research is to zone soil erosion risk in the Manabí province, which is located on the Ecuadorian coast. The methodology is supported by the employment of Geographic Information Technologies (GIT) in order to access and process information of interest, such as: precipitation data with which to calculate the R Factor of the Universal Soil Loss Equation (USLE); topography, which was employed to create a slope map from the Digital Elevation Model (DEM) ASTER; vegetation cover, by applying the Standard Difference Vegetation Index (NDVI); land use, through the interpretation of orthophotos and a field survey; and infiltration capacity, obtained by considering the texture of the soil. The factors were combined by means a weighted relationship (map algebra) and exploiting the potential of software for the design of Geographic Information Systems (GIS). The results indicate the presence of soils with little or without vegetal protection, in areas with steep slopes, moderate rainfall and extensive farming activities. These characteristics determine conditions of high soil erodability, and propitiate the triggering of erosive processes. This scenario shows the need for autonomous governments to implement policies, strategies and actions so as to promote the strengthening of institutional and community capacities that will promote environmental sustainability.

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Section: Land Use (Lu)mentioning

confidence: 99%

Soil erosion risk zoning in the Ecuadorian coastal region using geo-technological tools

2019

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“…This catalogue contains a progressively updated compilation of empirically obtained parameter values for different soils and crops, considering seasonal variations and management practices (Schmidt et al, 1999;Schindewolf & Schmidt, 2012). The parameter soil moisture is most sensitive and highly variable in time and space (Schmidt, 1992;Starkloff & Stolte, 2014). Thus, we initially used the average soil moisture values from the parameter catalogue.…”

Section: Modelling Sediment Reallocationmentioning

confidence: 99%

Sediment Reallocations due to Erosive Rainfall Events in the Three Gorges Reservoir Area, Central China

et al. 2016

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Soil erosion by water outlines a major threat to the Three Gorges Reservoir Area in China. A detailed assessment of soil conservation measures requires a tool that spatially identifies sediment reallocations due to rainfall-runoff events in catchments. We applied EROSION 3D as a physically based soil erosion and deposition model in a small mountainous catchment. Generally, we aim to provide a methodological frame that facilitates the model parametrization in a data scarce environment and to identify sediment sources and deposits. We used digital soil mapping techniques to generate spatially distributed soil property information for parametrization. For model calibration and validation, we continuously monitored the catchment on rainfall, runoff and sediment yield for a period of 12 months. The model performed well for large events (sediment yield > 1 Mg) with an averaged individual model error of 7.5%, while small events showed an average error of 36.2%. We focused on the large events to evaluate reallocation patterns. Erosion occurred in 11.1% of the study area with an average erosion rate of 49.9 Mg ha À1 . Erosion mainly occurred on crop rotation areas with a spatial proportion of 69.2% for 'corn-rapeseed' and 69.1% for 'potato-cabbage'. Deposition occurred on 11.0%. Forested areas (9.7%), infrastructure (41.0%), cropland (corn-rapeseed: 13.6%, potatocabbage: 11.3%) and grassland (18.4%) were affected by deposition. Because the vast majority of annual sediment yields (80.3%) were associated to a few large erosive events, the modelling approach provides a useful tool to spatially assess soil erosion control and conservation measures.

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“…On the contrary, the strength of the event‐based models is their ability to address runoff‐infiltration and soil erosion processes on a physical basis at the catchment scale at high spatial and temporal resolution (Pandey et al . , ; Starkloff & Stolte, ).…”

Section: Introductionmentioning

confidence: 99%

“…They are generally not suitable to describe soil erosion with sufficient detail within small catchments (Pandey, Himanshu, Mishra, & Singh, 2016). On the contrary, the strength of the eventbased models is their ability to address runoff-infiltration and soil erosion processes on a physical basis at the catchment scale at high spatial and temporal resolution (Pandey et al, 2016;Starkloff & Stolte, 2014).…”

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confidence: 99%

Improving runoff prediction using agronomical information in a cropped, loess covered catchment

Lefrancq

Dijk²,

Jetten

et al. 2017

Hydrol. Process.

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Predicting runoff hot spots and hot‐moments within a headwater crop‐catchment is of the utmost importance to reduce adverse effects on aquatic ecosystems by adapting land use management to control runoff. Reliable predictions of runoff patterns during a crop growing season remain challenging. This is mainly due to the large spatial and temporal variations of topsoil hydraulic properties controlled by complex interactions between weather, growing vegetation, and cropping operations. This interaction can significantly modify runoff patterns and few process‐based models can integrate this evolution of topsoil properties during a crop growing season at the catchment scale. Therefore, the purpose of this study was to better constrain the event‐based hydrological model Limburg Soil Erosion Model by incorporating temporal constraints for input topsoil properties during a crop growing season (LISEM). The results of the temporal constraint strategy (TCS) were compared with a classical event per event calibration strategy (EES) using multi‐scale runoff information (from plot to catchment). The EES and TCS approaches were applied in a loess catchment of 47 ha located 30 km northeast of Strasbourg (Alsace, France). A slight decrease of the Nash–Sutcliffe efficiency criterion on runoff discharge for TCS compared to EES was counterbalanced by a clear improvement of the spatial runoff patterns within the catchment. This study showed that limited agronomical and climatic information added during the calibration step improved the spatial runoff predictions of an event‐based model. Reliable prediction of runoff source, connectivity, and dynamics can then be derived and discussed with stakeholders to identify runoff hot spots and hot‐moments for subsequent land use and crop management modifications.

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Applied comparison of the erosion risk models EROSION 3D and LISEM for a small catchment in Norway

Cited by 40 publications

References 24 publications

Soil erosion risk zoning in the Ecuadorian coastal region using geo-technological tools

Soil erosion risk zoning in the Ecuadorian coastal region using geo-technological tools

Sediment Reallocations due to Erosive Rainfall Events in the Three Gorges Reservoir Area, Central China

Improving runoff prediction using agronomical information in a cropped, loess covered catchment

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