Furthering the precision of RUSLE soil erosion with PSInSAR data: an innovative model

Aswathi, J.; Sajinkumar, K.S.; Rajaneesh, A.; Oommen, Thomas; Bouali, El Hachemi; Binojkumar, R.B.; Rani, V. R.; Thomas, Jobin; Thrivikramji, K. P.; Ajin, R. S.; Abioui, Mohamed

doi:10.1080/10106049.2022.2105407

Cited by 31 publications

(12 citation statements)

References 64 publications

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“…However, the USLE model is applicable to slopes with slopes ranging from 0°to 7°. To improve the unreasonable analysis method of USLE and increase the accuracy and scope of the model, based on USLE, Renard [21] developed the revised universal soil loss equation (RUSLE) [22] model. However, it still follows the basic framework of USLE, and it is still an empirical statistical model and only applicable to plain and gently sloping areas.…”

Section: Related Workmentioning

confidence: 99%

Applying Convolutional Neural Network to Predict Soil Erosion: A Case Study of Coastal Areas

Liu

et al. 2023

IJERPH

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The development of ecological restoration projects is unsatisfactory, and soil erosion is still a problem in ecologically restored areas. Traditional soil erosion studies are mostly based on satellite remote sensing data and traditional soil erosion models, which cannot accurately characterize the soil erosion conditions in ecological restoration areas (mainly plantation forests). This paper uses high-resolution unmanned aerial vehicle (UAV) images as the base data, which could improve the accuracy of the study. Considering that traditional soil erosion models cannot accurately express the complex relationships between erosion factors, this paper applies convolutional neural network (CNN) models to identify the soil erosion intensity in ecological restoration areas, which can solve the problem of nonlinear mapping of soil erosion. In this study area, compared with the traditional method, the accuracy of soil erosion identification by applying the CNN model improved by 25.57%, which is better than baseline methods. In addition, based on research results, this paper analyses the relationship between land use type, vegetation cover, and slope and soil erosion. This study makes five recommendations for the prevention and control of soil erosion in the ecological restoration area, which provides a scientific basis and decision reference for subsequent ecological restoration decisions.

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Section: Related Workmentioning

confidence: 99%

Applying Convolutional Neural Network to Predict Soil Erosion: A Case Study of Coastal Areas

Liu

et al. 2023

IJERPH

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“…The U.S. Department of Agriculture Soil Conservation Service (USDA-SCS) model [60] was selected as a production function to estimate the precipitation amounts that contribute to the runoff generation. This function was extensively used in successively applied in a semi-arid environment, including the Moroccan basins [19,20,33,61,62].…”

Section: Production Function "Scs"mentioning

confidence: 99%

Streamflow Simulation in Semiarid Data-Scarce Regions: A Comparative Study of Distributed and Lumped Models at Aguenza Watershed (Morocco)

Bouadila

Bouizrou

Aqnouy

et al. 2023

Water

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In semi-arid regions such as the southwestern zone of Morocco, better management of water resources is crucial due to the frequent flooding phenomena. In this context, the use of hydrological models is becoming increasingly important, specifically in the Aguenza watershed. A multitude of hydrological models are available to make very efficient modeling, and from this perspective, a comparative approach was adopted using two models with different characteristics. Streamflow simulations were carried out continuously at daily time steps using GR4J and ATHYS (2002–2011). The latter was used also to simulate rainfall-runoff events (1984–2014). Simulation results using the distributed model are very efficient compared to those obtained by the lumped model “GR4J”, which shows the disadvantages of neglecting the hydrological processes during a hydrological study. However, a remarkable improvement was observed in the general appearance of the resulting hydrographs and the performance parameters after using the distributed model ((Calibration: NSE, RSR, and PBIAS increased successively by 8%, 6%, and 45.2%); (Validation: NSE, RSR, and PBIAS increased successively by 6%, 4%, and 8.9%)). In terms of flood event simulations, a good concordance between observed and simulated discharge was observed (NSEmedian = 0.7), indicating its great reliability for simulating rainfall-runoff events in semi-arid and data-scarce regions. This research highlights the importance of using hydrological models, specifically the distributed model ATHYS, for the better management of water resources in semi-arid regions with frequent flooding events.

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“…A sensitivity analysis is essential to identify the most effective vulnerability parameters for planning sustainable aquifer crisis management [29,30]. In addition, it avoids subjectivity and will allow the identification of important and influential model parameters [31,32].…”

Section: Sensibility Analysismentioning

confidence: 99%

Groundwater Vulnerability and Potentially Toxic Elements Associated with the Iron Mining District of Ouixane (Northeast of Morocco)

Khafouri

Talbi

Abdelouas

et al. 2022

Water

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This study aims to investigate the groundwater vulnerability concerning potentially toxic elements in the vicinity of the abandoned iron mine of Ouixane (Morocco). A modified DRASTIC method (DRSTI) is proposed with satisfactory results. High vulnerability zones represent 40% of the study area, while medium and low vulnerability represent, respectively, 42% and 18% of the study area. These results have been validated by groundwater geochemical analyses of potentially toxic elements carried out in the framework of previous studies in the same area. Thus, the superposition of the waste rock and tailings map with the vulnerability map showed that the latter is located in areas of high to medium vulnerability and therefore constitutes the main cause of the deterioration of the geochemical quality of groundwater. Otherwise, the vulnerability method showed that the main parameters that significantly affect the vulnerability are: the depth of water (D), net recharge (R), and the unsaturated zone (I), while the other parameters do not significantly affect the model used and do not have much influence on the results of the vulnerability assessment. The method used allowed us to locate the most vulnerable areas to potentially metallic toxic elements pollution resulting from the abandoned iron mine of Ouixane, and it constitutes a tool for decision support and for developing effective action plans to mitigate and monitor the effects of the transfer of potentially toxic elements pollution to groundwater.

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Furthering the precision of RUSLE soil erosion with PSInSAR data: an innovative model

Cited by 31 publications

References 64 publications

Applying Convolutional Neural Network to Predict Soil Erosion: A Case Study of Coastal Areas

Applying Convolutional Neural Network to Predict Soil Erosion: A Case Study of Coastal Areas

Streamflow Simulation in Semiarid Data-Scarce Regions: A Comparative Study of Distributed and Lumped Models at Aguenza Watershed (Morocco)

Groundwater Vulnerability and Potentially Toxic Elements Associated with the Iron Mining District of Ouixane (Northeast of Morocco)

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