An overview of soil erosion modelling compatible with RUSLE approach

被引:0
|
作者
Massimo Fagnano
Nazzareno Diodato
Ines Alberico
Nunzio Fiorentino
机构
[1] Università degli Studi “Federico II” di Napoli,Dipartimento Ingegneria Agraria e Agronomia del Territorio
[2] GEWEX-CEOP Network,Met European Research Observatory
[3] World Climate Research Programme,Centro Interdipartimentale di Ricerca Ambiente
[4] Università degli Studi “Federico II” di Napoli,undefined
来源
Rendiconti Lincei | 2012年 / 23卷
关键词
Soil erosion; Modelling; GIS; RUSLE;
D O I
暂无
中图分类号
学科分类号
摘要
Different approaches were used to model soil losses in the Sele River basin (southern Italy) characterized by data scarcity. The suitability of models interpolating different sources of data was evaluated with the aim to suggest similar methodologies in other regions where data availability is not sufficient to use the more complex and detailed models. The first approach is based on the concept of the balance between driving and resisting forces. Rainfall is considered as both a driving and resisting factor: the rain erosivity not only increases with its amount and intensity but also enhances the protective effect of vegetation. The long-term erosion rate of the basin resulted mainly affected by local land-cover conditions that showed a more dramatic effect than the variability of rain erosivity. In the period during which soils were protected by natural woodlands, net erosion rates were extremely low, while the elimination of forest (AD 1780–1810) increased erosion that reached annual rates from 20 to 300 Mg km−2. The second approach is a revised and scale-adapted Foster–Meyer–Onstad model suitable for scarce input data (CliFEM = Climate Forcing and Erosion Modelling). This new idea was addressed to develop a monthly Net Erosion model (NER) and gross erosion was estimated from the sediment delivery ratio (SDR). From this approach it is clear that the erosion regime was clearly autumnal with a mean rate of 8 Mg ha−1 per month. The long-term average soil erosion highlighted, since 1990, a more irregular temporal pattern, with the highest annual erosion (200 Mg ha−1) in 2002. The third approach combines the revised universal soil loss equation (RUSLE) with GIS–geospatial technology. Regression Ordinary Kriging (ROK)-based maps of erosive rainfall were made on annual and monthly basis. The months following soil tillage (from August to November) have become even more hazardous for soil erosion, with values higher than 80% of total yearly soil losses, because in this period the highest rainfall erosivity is coupled to the lowest soil cover due to soil tillage at the end of summer. In these conditions soil can be protected only by the agro-environmental measures aimed at reducing soil erodibility and at increasing soil cover, such as conservative soil tillage (i.e. sod seeding) and perennial cover crops in orchards and vineyards.
引用
收藏
页码:69 / 80
页数:11
相关论文
共 50 条
  • [1] An overview of soil erosion modelling compatible with RUSLE approach
    Fagnano, Massimo
    Diodato, Nazzareno
    Alberico, Ines
    Fiorentino, Nunzio
    [J]. RENDICONTI LINCEI-SCIENZE FISICHE E NATURALI, 2012, 23 (01) : 69 - 80
  • [2] A comparative study of soil erosion modelling by MMF, USLE and RUSLE
    Mondal, Arun
    Khare, Deepak
    Kundu, Sananda
    [J]. GEOCARTO INTERNATIONAL, 2018, 33 (01) : 89 - 103
  • [3] Application of a RUSLE-based soil erosion modelling on Mauritius Island
    Nigel, Rody
    Rughooputh, Soonil D. D. V.
    [J]. SOIL RESEARCH, 2012, 50 (08) : 645 - 651
  • [4] Soil erosion modelling for NSW coastal catchments using RUSLE in a GIS environment
    Yang, Xihua
    Chapman, Greg
    [J]. GEOINFORMATICS 2006: GEOSPATIAL INFORMATION SCIENCE, 2006, 6420
  • [5] Uncertainty Assessment in Soil Erosion Modelling Using RUSLE, Multisource and Multiresolution DEMs
    Pandey, Ashish
    Gautam, Amar Kant
    Chowdary, V. M.
    Jha, C. S.
    Cerda, Artemi
    [J]. JOURNAL OF THE INDIAN SOCIETY OF REMOTE SENSING, 2021, 49 (07) : 1689 - 1707
  • [6] Uncertainty Assessment in Soil Erosion Modelling Using RUSLE, Multisource and Multiresolution DEMs
    Ashish Pandey
    Amar Kant Gautam
    V. M. Chowdary
    C. S. Jha
    Artemi Cerdà
    [J]. Journal of the Indian Society of Remote Sensing, 2021, 49 : 1689 - 1707
  • [7] Soil Erosion Prediction Using RUSLE with GIS
    Lin, Qinghui
    Wang, Xiaoyan
    [J]. 2006 IEEE INTERNATIONAL GEOSCIENCE AND REMOTE SENSING SYMPOSIUM, VOLS 1-8, 2006, : 1086 - +
  • [8] SEMMED: A distributed approach to soil erosion modelling
    de Jong, SM
    Riezebos, HT
    [J]. REMOTE SENSING '96: INTEGRATED APPLICATIONS FOR RISK ASSESSMENT AND DISASTER PREVENTION FOR THE MEDITERRANEAN, 1997, : 199 - 204
  • [9] Soil erosion modelling using GIS-integrated RUSLE of Urpash watershed in Lesser Himalayas
    Mohmmad Idrees Attar
    Yogesh Pandey
    Sameena Naseer
    Shabir Ahmad Bangroo
    [J]. Arabian Journal of Geosciences, 2024, 17 (3)
  • [10] Inherent relationship of the USLE, RUSLE topographic factor algorithms and its impact on soil erosion modelling
    Efthimiou, Nikolaos
    Lykoudi, Evdoxia
    Psomiadis, Emmanouil
    [J]. HYDROLOGICAL SCIENCES JOURNAL, 2020, 65 (11) : 1879 - 1893