Contribution of phytoecological data to spatialize soil erosion: Application of the RUSLE model in the Algerian atlas

Boussadia-Omari, Lynda; Ouillon, Sylvain; Hirche, Aziz; Salamani, Mustapha; Guettouche, Mohamed Saïd; Ihaddaden, Akli; Nedjraoui, Dalila

doi:10.1016/j.iswcr.2021.05.004

Cited by 14 publications

(9 citation statements)

References 58 publications

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“…The erodibility factor (K) expresses the effect of water flow and the soil resistance to particle movement. In previous research, the erodibility factor values varied between 0 to 1 Mg•h/MJ•mm [40][41][42][43][44][45][46][47][48][49][50][51][52][53][54][55][56][57]. The soil properties, such as chemistry, texture, organic matter content, and calcium carbonate contents, are very important in the determination of soil erodibility [58].…”

Section: Methodsmentioning

confidence: 99%

Analyzing the Benefit-Cost Ratio of Sediment Resources by Remote Sensing Data in the Ping River Basin, Thailand

Rangsiwanichpong

Melesse

2022

Water

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Sediment is one of the important natural resources on the Earth. Information on sediment resources is key to making decisions regarding soil resources management and mitigation during sediment hazard events. Thus, this research analyzed and mapped the benefit-cost ratio (BCR) of sediment in the Ping River using a Geographic Information System (GIS). Furthermore, the benefit of sediment was analyzed using a new application of the Revised Universal Soil Loss Equation (RUSLE) with a spatial resolution of 1 km2. The results reveal that the potential of annual soil loss and sediment deposition in the Ping River Basin (PRB) were approximately 825 and 530 m3/km2·y, respectively. In addition, the results indicated that there was a higher BCR in the upstream area of the PRB where there was greater sediment deposition. The average benefit of sediment in the PRB is USD 4280/km2·y. It is expected that the BCR of the sediment resources map analyzed in this research will help policy-makers for decision-making on the benefits of sediment resources in Thailand.

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Section: Methodsmentioning

confidence: 99%

Analyzing the Benefit-Cost Ratio of Sediment Resources by Remote Sensing Data in the Ping River Basin, Thailand

Rangsiwanichpong

Melesse

2022

Water

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“…The AHP method was then used to assess the contributions of several elements to soil erosion risk and potential. In the AHP, decisions are made based on a pairwise comparison of the elements of a problem with respect to their relative impact ("weight" or "intensity") on a property they share; i.e., the factors are compared with one another to determine the relative preference of each factor, which is expressed as a numeric value and The elements of the matrix of pair-wise comparisons may be represented as follows [7][8][9]:…”

Section: Revised Universal Soil Loss Equation (Rusle)mentioning

confidence: 99%

“…The Eigenvector corresponding to the biggest Eigenvalue of the matrix (i.e., the primary Eigenvector) indicates the relative importance of the factors. To evaluate the level of consistency of an n-by-n matrix, the consistency index (CI) was developed as follows [6,7]: (10) where max is the matrix's greatest Eigenvalue. max will equal n if there are no inconsistencies in the pairwise comparisons, resulting in a CI of zero.…”

Section: Revised Universal Soil Loss Equation (Rusle)mentioning

confidence: 99%

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Forecasting Soil Erosion Risk Using GIS and Remote Sensing for the Nam Un Basin, Sakon Nakhon Province, Thailand

Ruksajai¹,

Konyai²,

Sriboonlue³

2023

Pol. J. Environ. Stud.

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Geohazard mapping using remote sensing and GIS is effective. Nam UN classic terrain with soil erosion and other geohazards. The Nam UN Basin's yearly soil loss and high erosion potential are estimated using RUSLE, remote sensing, and GIS. 14.26 t/ha/year of soil erosion is seen on the map. Soil erosion zones are also shown on the map. According to the study, 33.40 percent of the whole area (457.07 kilometers) is prone to severe soil erosion, while 7.72 percent (105.76 kilometers) is prone to high erosion. To decrease soil erosion, decision-makers use soil erosion prognosis analysis. The Analytical Hierarchy Process (AHP) was utilized to identify key soil erosion prone locations by incorporating geo-environmental variables such land use/land cover, geomorphology, Dem, drainage density, slope, elevation, LS factor, rainfall, soil texture, and soil depth. 33.40% of the region is highly prone to soil erosion.

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“…In recent decades, soil erosion and flood occurrences in many parts of the world have increased due to population growth, deforestation, and other human manipulation of the landscape (Food and Agriculture Organization, 2016). Additionally, runoff and soil erosion causes excessive sedimentation downstream of rivers that introduce pollution to aquatic environments and reduce reservoirs' storage capacity (Hu et al, 2020;Boussadia-Omari et al, 2021). Studies have shown that runoff rates depend on various factors, including rainfall (rainfall value and intensity), land cover and land use, topography, and antecedent soil moisture (A.M.C) and soil properties (Poesen and Hooke, 1997;Pickup and Marks, 2000).…”

Section: Introductionmentioning

confidence: 99%

Evaluating the effects of vegetation and land management on runoff control using field plots and machine learning models

Gholami

Sahour

Khaleghi

et al. 2022

Environ Sci Pollut Res

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Excess surface water after heavy rainfalls leads to soil erosion and flash floods, resulting in human and financial losses. Reducing runoff is an essential management tool to protect water and soil resources. This study aimed to evaluate the effects of vegetation and land management methods on runoff control and to provide a model to predict runoff values. Filed plot data and three machine learning methods, including artificial neural network (ANN), coactive neuro-fuzzy inference system (CANFIS), and extreme gradient boosting (EGB), were used in a test site in the north of Iran. In this regard, plots with various vegetation and land management treatments including bare soil treatment, rangeland cover treatment, forest litter treatment, rangeland litter treatment, tillage treatment in the direction of slope, tillage treatment perpendicular to the slope, and repetition of treatments under forest canopy were constructed on a hillslope. After each rainfall event, the amount of rainfall and corresponding runoff generated in each plot was recorded. Three machine learning models (ANN, CANFIS, and EGB) were used to establish relationships between amounts of recorded runoff and its controlling factors (rainfall, antecedent soil moisture (A.M.C), shrub canopy percentage and height, tree canopy percentage and height, soil texture (clay, silt, and sand percent), slope degree, leaf litter percentage of soil, and tillage interval). These data were normalized, randomized, and divided into training and testing subsets. Results showed that the ANN performed better than the other two 2 models in predicting runoff in training (R-sqr=0.98; MSE=0.004) and the test stages (R-sqr=0.90; MSE=0.95). Statistical analysis and sensitivity analysis of inputs factors showed that rainfall, rangeland cover, and A.M.C are the three most important factors controlling runoff generation. The adopted method can be used to predict the effect of different vegetation and land management scenarios on runoff generation in the study area and the areas with similar settings elsewhere.

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Contribution of phytoecological data to spatialize soil erosion: Application of the RUSLE model in the Algerian atlas

Cited by 14 publications

References 58 publications

Analyzing the Benefit-Cost Ratio of Sediment Resources by Remote Sensing Data in the Ping River Basin, Thailand

Analyzing the Benefit-Cost Ratio of Sediment Resources by Remote Sensing Data in the Ping River Basin, Thailand

Forecasting Soil Erosion Risk Using GIS and Remote Sensing for the Nam Un Basin, Sakon Nakhon Province, Thailand

Evaluating the effects of vegetation and land management on runoff control using field plots and machine learning models

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