scholarly journals Soil erosion modelling: A global review and statistical analysis

2021 ◽  
pp. 146494
Author(s):  
Pasquale Borrelli ◽  
Christine Alewell ◽  
Pablo Alvarez ◽  
Jamil Alexandre Ayach Anache ◽  
Jantiene Baartman ◽  
...  
Keyword(s):  
Statistical Analysis ◽  
Soil Erosion ◽  
Erosion Modelling

scholarly journals Soil erosion modelling: A global review and statistical analysis

2020 ◽  
Author(s):  
Pasquale Borrelli ◽  
Christine Alewell ◽  
Pablo Alvarez ◽  
Jamil Alexandre Ayach Anache ◽  
Jantiene Baartman ◽  
...  
Keyword(s):  
Statistical Analysis ◽  
Soil Erosion ◽  
Erosion Modelling

Uncertainty Assessment in Soil Erosion Modelling Using RUSLE, Multisource and Multiresolution DEMs

2021 ◽  
Author(s):  
Ashish Pandey ◽  
Amar Kant Gautam ◽  
V. M. Chowdary ◽  
C. S. Jha ◽  
Artemi Cerdà
Keyword(s):  
Soil Erosion ◽  
Uncertainty Assessment ◽  
Erosion Modelling

Inherent relationship of the USLE, RUSLE topographic factor algorithms and its impact on soil erosion modelling

2020 ◽  
Vol 65 (11) ◽  
pp. 1879-1893
Author(s):  
Nikolaos Efthimiou ◽  
Evdoxia Lykoudi ◽  
Emmanouil Psomiadis
Keyword(s):  
Soil Erosion ◽  
Topographic Factor ◽  
Erosion Modelling ◽  
Relationship Of

scholarly journals A global bibliometric perspective on soil erosion modelling

2020 ◽  
Author(s):  
Nejc Bezak ◽  
Keyword(s):  
Conference Proceedings ◽  
Soil Erosion ◽  
Regression Tree ◽  
Tree Model ◽  
Boosted Regression Tree ◽  
Field Activity ◽  
Erosion Modelling ◽  
Meta Information ◽  
Number Of Citations ◽  
Regression Tree Model

<p>Systematic bibliometric investigations are useful to evaluate and compare the scientific impact of journal papers, book chapters and conference proceedings. Such studies allow the detection of emerging research topics, the analyses of cooperation networks, and the collection of in-depth insights into a specific research topic. In the presented work, we carried out a bibliometric study in order to obtain an in-depth knowledge on soil erosion modelling applications worldwide.</p><p>As a starting point, we used the soil erosion modelling meta-analysis data collection generated by the authors of this abstract in a joint community effort. This database contains meta-information of more than 3,000 documents published between 1994 and 2018 that are indexed in the SCOPUS database. The documents were reviewed and database entries verified. The database contains various types of meta-information about the modelling studies (e.g., model used, study area, input data, calibration, etc.). The bibliometric information was also included in the database (e.g., number of citations, type of publication, Scopus category, etc.). We investigated differences among publication types and differences between papers published in journals that are part of various Scopus categories. Moreover, relationships between publication CiteScore, number of authors, and number of citations were analyzed. A boosted regression tree model was used to detect the relative impact of the selected meta-information such as erosion model used, spatial modelling scale, study period, field activity on the total number of citations. Detailed investigation of the most cited papers was also conducted. The VOSviewer software was used to analyze citations, co-citations, bibliographic coupling, and co-authorship networks of the database entries.  </p><p>Our bibliometric investigations demonstrated that journal publications, on average, receive more citations than book series or conference proceedings. There were differences among the erosion models used, and some specific models such as the WaTEM/SEDEM model, on average, receive more citations than other models (e.g., USLE). It should also be noted that self-citation rates in case of most frequently used models were similar. Global studies, on average, receive more citations than studies dealing with plot, regional, or national scales. According to the boosted regression tree model, model calibration, validation, or field activity do not have significant impact on the obtained publication citations. Co-citation investigation revealed some interesting patterns. Our results also indicate that papers about soil erosion modeling also attract citations from different fields and better international cooperation is needed to advance this field of research with regard to its visibility and impact on human societies.    </p>

scholarly journals Soil erosion modelling: The new challenges as the result of policy developments in Europe

2019 ◽  
Vol 172 ◽  
pp. 470-474 ◽  
Author(s):  
Panos Panagos ◽  
Athanasios Katsoyiannis
Keyword(s):  
Soil Erosion ◽  
Erosion Modelling ◽  
New Challenges

scholarly journals What is wrong with post‐fire soil erosion modelling? A meta‐analysis on current approaches, research gaps, and future directions

2020 ◽  
Author(s):  
A.R. Lopes ◽  
A. Girona‐García ◽  
S. Corticeiro ◽  
R. Martins ◽  
J.J. Keizer ◽  
...  
Keyword(s):  
Soil Erosion ◽  
Meta Analysis ◽  
Research Gaps ◽  
Future Directions ◽  
Erosion Modelling

scholarly journals Mr4Soil: A MapReduce-Based Framework Integrated with GIS for Soil Erosion Modelling

2019 ◽  
Vol 8 (3) ◽  
pp. 103
Author(s):  
Zhigang Han ◽  
Fen Qin ◽  
Caihui Cui ◽  
Yannan Liu ◽  
Lingling Wang ◽  
...  
Keyword(s):  
Parallel Computing ◽  
Soil Erosion ◽  
Laser Scanning ◽  
Three Dimensional ◽  
Large Data ◽  
Data Sets ◽  
Precision Data ◽  
Model Calculations ◽  
Erosion Modelling ◽  
Computing Framework

A soil erosion model is used to evaluate the conditions of soil erosion and guide agricultural production. Recently, high spatial resolution data have been collected in new ways, such as three-dimensional laser scanning, providing the foundation for refined soil erosion modelling. However, serial computing cannot fully meet the computational requirements of massive data sets. Therefore, it is necessary to perform soil erosion modelling under a parallel computing framework. This paper focuses on a parallel computing framework for soil erosion modelling based on the Hadoop platform. The framework includes three layers: the methodology, algorithm, and application layers. In the methodology layer, two types of parallel strategies for data splitting are defined as row-oriented and sub-basin-oriented methods. The algorithms for six parallel calculation operators for local, focal and zonal computing tasks are designed in detail. These operators can be called to calculate the model factors and perform model calculations. We defined the key-value data structure of GeoCSV format for vector, row-based and cell-based rasters as the inputs for the algorithms. A geoprocessing toolbox is developed and integrated with the geographic information system (GIS) platform in the application layer. The performance of the framework is examined by taking the Gushanchuan basin as an example. The results show that the framework can perform calculations involving large data sets with high computational efficiency and GIS integration. This approach is easy to extend and use and provides essential support for applying high-precision data to refine soil erosion modelling.

Sediment origins across the terrestrial-aquatic continuum: climate threat mitigation and promotion of water quality

2020 ◽  
Author(s):  
Katy Wiltshire ◽  
Toby Waine ◽  
Bob Grabowski ◽  
Miriam Glendell ◽  
Steve Addy ◽  
...  
Keyword(s):  
Water Quality ◽  
Soil Erosion ◽  
Soil Loss ◽  
River Channel ◽  
River Channels ◽  
Catchment Scale ◽  
Transport Pathways ◽  
Fine Grained ◽  
Erosion Rates ◽  
Erosion Modelling

<p>Although fine-grained sediment (FGS) is a natural component of river systems, increased fluxes can impact FGS levels to such an extent they cause detrimental, irreversible changes in the way rivers function intensifying flood risk and negatively affecting water quality.</p><p>Previous catchment scale studies indicate there is no simple link between areas of sediment loss and the organic carbon (OC) load in waterways; areas with a high soil loss rate may not contribute most sediment to the rivers and areas that contribute the most sediment may not contribute the most OC. Anthropogenic and climate changes can accelerate soil erosion and the role of soil OC transported by erosional processes in the fluxes of C between land, water and atmosphere is still debated. Tracing sediment pathways, likely depositional areas and connections to streams leads to better assumptions about control processes and better estimation of OC fluxes.</p><p>In this innovative study OC fingerprinting of sediment reaching a catchment’s waterbodies is combined with OC stock and erosion modelling of the terrestrial catchment. Initial results show disconnect between catchment OC loss erosion modelling and fingerprinting results, which could be due to failure to model connectivity between the land and river channel. The current soil erosion model RUSLE (Revised Universal Soil Loss Equation) calculates only the spatial pattern of mean annual soil erosion rates. Using the WaTEM SEDEM model, which in includes routing (and possible en route deposition) of eroded sediments to river channels, we aim to determine the dominant source of OC within catchment streams by identification of both the land-use specific areas with the highest OC loss and the transport pathways between the sources and river channel.</p>

Sign in / Sign up

Export Citation Format

Share Document