Spatial Variability of Soil Erodibility at the Rhirane Catchment Using Geostatistical Analysis

被引:7
|
作者
Othmani, Ouafa [1 ]
Khanchoul, Kamel [1 ]
Boubehziz, Sana [1 ]
Bouguerra, Hamza [2 ]
Benslama, Abderraouf [3 ,4 ]
Navarro-Pedreno, Jose [4 ]
机构
[1] Badji Mokhtar Univ Annaba, Dept Biol, Soil & Sustainable Dev Lab, Annaba 23000, Algeria
[2] Badji Mokhtar Annaba Univ, Fac Earth Sci, Dept Geol, Water Resources & Sustainable Dev Lab, POB 12, Annaba 23000, Algeria
[3] Univ Ghardaia, Lab Valorisat & Conservat Ecosyst Arides LVCEA, Ghardaia 47000, Algeria
[4] Univ Miguel Hernandez Elche, Dept Agrochem & Environm, Elche 03202, Spain
关键词
GIS; K-USLE; Kriging; land cover; soil erosion; USLE NOMOGRAPH; EROSION; STABILITY; QUALITY;
D O I
10.3390/soilsystems7020032
中图分类号
S15 [土壤学];
学科分类号
0903 ; 090301 ;
摘要
Soil erodibility is one of the most crucial factors used to estimate soil erosion by applying modeling techniques. Soil data from soil maps are commonly used to create maps of soil erodibility for soil conservation planning. This study analyzed the spatial variability of soil erodibility by using a digital elevation model (DTM) and surface soil sample data at the Rhirane catchment (Algeria). A total of 132 soil samples were collected of up to 20 cm in depth. The spatial distributions of the K-value and soil physical properties (permeability, organic matter, and texture) were used to elaborate ordinary Kriging interpolation maps. Results showed that mean values of soil organic matter content were statistically different between Chromic Cambisols (M = 3.4%) vs. Calcic Cambisols (M = 2.2%). The analysis of variance of the organic matter provided a tool for identifying significant differences when comparing means between the soil types. The soil granulometry is mainly composed of silt and fine sand. The soil erodibility showed values varying between 0.012 and 0.077 with an average of 0.034, which was greater in soils with calcic horizons. Statistical evaluation by using Pearson's correlation revealed positive correlations between erodibility and silt (0.63%), and negative correlations with sand (-0.16%), clay (-0.56%), organic matter (-0.32%), permeability (-0.41%), soil structure (-0.40%), and the soil stability index (-0.26%). The variability analysis of the K-factor showed moderate spatial dependency with the soil erodibility map indicating moderate to highly erodible risk in cropland and sparse grassland land uses. Overall, the study provides scientific support for soil conservation management and appropriate agricultural food practices for food supply.
引用
收藏
页数:17
相关论文
共 50 条
  • [1] Catchment scale geostatistical simulation and uncertainty of soil erodibility using sequential Gaussian simulation
    Reza Jamshidi
    Deirdre Dragovich
    Ashley A. Webb
    Environmental Earth Sciences, 2014, 71 : 4965 - 4976
  • [2] Catchment scale geostatistical simulation and uncertainty of soil erodibility using sequential Gaussian simulation
    Jamshidi, Reza
    Dragovich, Deirdre
    Webb, Ashley A.
    ENVIRONMENTAL EARTH SCIENCES, 2014, 71 (12) : 4965 - 4976
  • [3] MULTIVARIATE ANALYSIS AND SPATIAL VARIABILITY TO ESTIMATE SOIL ERODIBILITY OF AN ANFISOL
    Miqueloni, Daniela Popim
    Paes Bueno, Celia Regina
    REVISTA BRASILEIRA DE CIENCIA DO SOLO, 2011, 35 (06): : 2175 - 2182
  • [4] Analysis of Large Scale Spatial Variability of Soil Moisture Using a Geostatistical Method
    Lakhankar, Tarendra
    Jones, Andrew S.
    Combs, Cynthia L.
    Sengupta, Manajit
    Haar, Thomas H. Vonder
    Khanbilvardi, Reza
    SENSORS, 2010, 10 (01): : 913 - 932
  • [5] Assessing spatial uncertainty in mapping soil erodibility factor using geostatistical stochastic simulation
    Buttafuoco, G.
    Conforti, M.
    Aucelli, P. P. C.
    Robustelli, G.
    Scarciglia, F.
    ENVIRONMENTAL EARTH SCIENCES, 2012, 66 (04) : 1111 - 1125
  • [6] Assessing spatial uncertainty in mapping soil erodibility factor using geostatistical stochastic simulation
    G. Buttafuoco
    M. Conforti
    P. P. C. Aucelli
    G. Robustelli
    F. Scarciglia
    Environmental Earth Sciences, 2012, 66 : 1111 - 1125
  • [7] Describing spatial variability using geostatistical analysis
    Srivastava, RM
    GEOSTATISTICS FOR ENVIRONMENTAL AND GEOTECHNICAL APPLICATIONS, 1996, 1283 : 13 - 19
  • [8] Estimating spatial variability of soil salinity using geostatistical methods
    Pozdnyakova, L
    Zhang, RD
    PROCEEDINGS OF THE FOURTH INTERNATIONAL CONFERENCE ON PRECISION AGRICULTURE, PTS A AND B, 1999, : 79 - 89
  • [9] Geostatistical analysis of soil moisture variability on Da Nangou catchment of the loess plateau, China
    Wang, J
    Fu, BJ
    Qiu, Y
    Chen, LD
    Wang, Z
    ENVIRONMENTAL GEOLOGY, 2001, 41 (1-2): : 113 - 120
  • [10] Spatial variability assessment of soil available phosphorus using geostatistical approach
    Mondal, Bhabani Prasad
    Sekhon, Bharpoor Singh
    Sadhukhan, Rahul
    Singh, Rajiv Kumar
    Hasanain, Mohammad
    Mridha, Nilimesh
    Das, Bappa
    Chattopadhyay, Arghya
    Banerjee, Koushik
    INDIAN JOURNAL OF AGRICULTURAL SCIENCES, 2020, 90 (06): : 1170 - 1175