scholarly journals Geospatial approach in mapping soil erodibility using CartoDEM – A case study in hilly watershed of Lower Himalayan Range

2016 ◽  
Vol 125 (7) ◽  
pp. 1463-1472 ◽  
Author(s):  
Suresh Kumar ◽  
Surya Gupta
Atmosphere ◽  
2018 ◽  
Vol 9 (2) ◽  
pp. 68 ◽  
Author(s):  
Rim Zitouna-Chebbi ◽  
Laurent Prévot ◽  
Amal Chakhar ◽  
Manel Marniche-Ben Abdallah ◽  
Frederic Jacob

Author(s):  
Hideki Shimada ◽  
Sri Maryati ◽  
Akihiro Hamanaka ◽  
Takashi Sasaoka ◽  
Kikuo Matsui

2018 ◽  
Author(s):  
Wenwu Zhao ◽  
Hui Wei ◽  
Lizhi Jia ◽  
Stefani Daryanto ◽  
Yanxu Liu

Abstract. The objectives of this work were to select the possible best texture-based method to estimate K and understand possible indirect environmental factors of soil erodibility. In this study, 151 soil samples were collected during soil surveys in Ansai watershed. Five methods of estimating K value were used to estimate soil erodibility, including the erosion-productivity impact model (EPIC), the nomograph equation (NOMO), the modified nomograph equation (M-NOMO), the Torri model and the Shirazi model. The K values in Ansai watershed ranged between 0.009 and 0.092 t hm2 hr/(MJ mm hm2). The K values based on Torri, NOMO, and Shirazi models were similar and were located close to each other in the Taylor diagrams. By combining the measured soil erodibility, we suggested Shirazi and Torri model as the optimal models for Ansai watershed. The correlations between soil erodibility and the selected environmental variables changed for different vegetation type. For native grasslands, soil erodibility had significant correlations with terrain factors. For most artificially managed vegetation types (e.g., apple orchards) and artificially restored vegetation types (e.g., sea buckthorn), the soil erodibility had significant correlations with the growing conditions of vegetation. The dominant factors that influenced soil erodibility differed with different vegetation types. Soil erodibility had indirect relationship with not only environmental factors (e.g., elevation and slope), but also human activities which potentially altered soil erodibility.


2018 ◽  
Vol 7 (2.1) ◽  
pp. 41
Author(s):  
Chander Kant ◽  
Mrinmoy Majumder ◽  
Dharmendra Kumar Tyagi ◽  
Ashish Prabhat Singh

Soil erosion has become a major deterrent in any watershed management program. The erodibility of the soil from the river banks has degraded watersheds all over the world. That is why in any watershed development programmers’ erodibility of soil becomes a significant design parameter. However, there is lack of efficient simulation model for estimation of soil erosion. The existing models are location sensitive and mostly empirical nature in the present investigation, the authors tried to estimate the soil erodibility factor of the USLE method with the help of Enhanced PSO. The data for development of model is generated by Normalized Design of Experiment method which assumes that maximum and minimum value can be represented by I and O respectively. The same model was developed with the help of GMDH also. As per the model matrices of GMDH model shows better reliability. The selected model was applied to predict soil erodibility factor for 21 no’s of location in west Tripura region. From the prediction and comparison with the actual data it was found that the selected models have an accuracy of 99.8% in predicted model and 89.8% in case study.


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