An Estimation of Soil Erosion Rate Hot Spots by Integrated USLE and GIS Methods: a Case Study of the Ghiss Dam and Basin in Northeastern Morocco

Taher, Morad; Mourabit, Taoufik; Bourjila, Abdelhak; Saadi, Omar; Errahmouni, Ali; Marzkioui, Fouad El; Mousaoui, Mhamed El

doi:10.7494/geom.2022.16.2.95

Cited by 9 publications

(4 citation statements)

References 26 publications

(44 reference statements)

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“…The characteristic lithology of this watershed might be another reason [97]. In addition, inappropriate agriculture practices and overgrazing also acted as other driving forces in gully erosion in this study area [62].…”

Section: Identification Of Gully Zonesmentioning

confidence: 83%

“…However, insufficient and poor sanitation facilities, as well as unsustainable agriculture in the study area, have resulted in deterioration of groundwater quality in this region [60,61]. Previously, a study [62] using the RUSLE method reported that more than 50% of the watershed is considered as moderately eroded. Thus, our research effort is directed towards a deep understanding of gully erosion in this study area.…”

Section: Study Areamentioning

confidence: 98%

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Hybrid Machine Learning Approach for Gully Erosion Mapping Susceptibility at a Watershed Scale

Hitouri

Varasano

Mohajane

et al. 2022

IJGI

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Gully erosion is a serious threat to the state of ecosystems all around the world. As a result, safeguarding the soil for our own benefit and from our own actions is a must for guaranteeing the long-term viability of a variety of ecosystem services. As a result, developing gully erosion susceptibility maps (GESM) is both suggested and necessary. In this study, we compared the effectiveness of three hybrid machine learning (ML) algorithms with the bivariate statistical index frequency ratio (FR), named random forest-frequency ratio (RF-FR), support vector machine-frequency ratio (SVM-FR), and naïve Bayes-frequency ratio (NB-FR), in mapping gully erosion in the GHISS watershed in the northern part of Morocco. The models were implemented based on the inventory mapping of a total number of 178 gully erosion points randomly divided into 2 groups (70% of points were used for training the models and 30% of points were used for the validation process), and 12 conditioning variables (i.e., elevation, slope, aspect, plane curvature, topographic moisture index (TWI), stream power index (SPI), precipitation, distance to road, distance to stream, drainage density, land use, and lithology). Using the equal interval reclassification method, the spatial distribution of gully erosion was categorized into five different classes, including very high, high, moderate, low, and very low. Our results showed that the very high susceptibility classes derived using RF-FR, SVM-FR, and NB-FR models covered 25.98%, 22.62%, and 27.10% of the total area, respectively. The area under the receiver (AUC) operating characteristic curve, precision, and accuracy were employed to evaluate the performance of these models. Based on the receiver operating characteristic (ROC), the results showed that the RF-FR achieved the best performance (AUC = 0.91), followed by SVM-FR (AUC = 0.87), and then NB-FR (AUC = 0.82), respectively. Our contribution, in line with the Sustainable Development Goals (SDGs), plays a crucial role for understanding and identifying the issue of “where and why” gully erosion occurs, and hence it can serve as a first pathway to reducing gully erosion in this particular area.

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Section: Identification Of Gully Zonesmentioning

confidence: 83%

Section: Study Areamentioning

confidence: 98%

Hybrid Machine Learning Approach for Gully Erosion Mapping Susceptibility at a Watershed Scale

Hitouri

Varasano

Mohajane

et al. 2022

IJGI

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“…Whether in agriculture, energy production or manufacturing, poor water quality can compromise the quality of end products, affect production costs and cause long-term environmental damage [16,17]. Faced with the multiple challenges posed by water quality management, it is imperative to have reliable and effective methods for its assessment [18][19][20][21]. This is where Water Quality Indices (WQIs) come into their own.…”

Section: Introductionmentioning

confidence: 99%

Water quality indices (WQIs): An in-depth analysis and overview

Moudou,

El Hammoudani,

Haboubi

et al. 2024

E3S Web Conf.

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A wide range of variables, both human and natural in origin, can negatively impact aquatic ecosystems, especially when they accumulate excessively. While some of these elements are vital for aquatic life, their high concentration poses a risk. Consequently, a monitoring and analysing water resource to assess the impact of these elements becomes essential. Commonly, water quality indices (WQIs) are used for this purpose. The continuous improvement, simplification, and adaptation of WQIs are necessary to meet the evolving needs of water management. This work focuses on developing a comprehensive model for assessing water quality in North of Moroccan watersheds. It aims to review and define the goals of water quality monitoring, classify the tools used for this purpose, and describe the process of developing WQIs.

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“…The Ghiss-Nekor aquifer serves as the primary groundwater storage in the Al-Hoceima province, a semi-arid Mediterranean area plagued by elevated groundwater salinity. This issue complicates drinking water supply management, worsened by reduced regulation capacity of the prinipal dam in the area (MBAK dam) as a result of silt accumulation [7]. Understanding the origins of salinization in this area is pivotal for managing the region's drinking water resources effectively.…”

Section: Introductionmentioning

confidence: 99%

Exploring salinity origins in the Ghiss-Nekor aquifer, northern Morocco: A multivariate statistical analysis

Bourjila,

Dimane,

Ghalit

et al. 2024

BIO Web Conf.

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The Ghiss-Nekor coastal aquifer is characterized by high salinity stemming from multiple sources, remains poorly investigated. This study aims to address this knowledge gap by employing both univariate (descriptive statistics) and multivariate statistical analyses, including correlation matrix and principal component analysis (PCA). Groundwater samples were collected from 52 sites across the study area and meticulously analyzed for pH, TDS, EC, and the ions such as Na+, K+, Mg2+, Ca2+, NH4+, HCO3-, NO3-, NO2-,Cl-, SO42-, PO43-, and SiO2. Descriptive statistics, notably standard deviation (SD), highlight the diverse sources contributing to salinization, among which seawater intrusion (SWI) emerges as a significant factor. Correlation matrix analysis underscores multiple pathways for salinization, implicating SWI, salt dissolution, chemical weathering, secondary salt leaching, and anthropogenic activities. PCA elucidates 81.05% of the total variance in physicochemical parameters, with strong loadings observed for EC, Na+, Cl-, and Mg2+, corroborating the influence of SWI and suggesting evaporation processes. Moreover, PCA reinforces the potential influence of both geogenic and anthropogenic factors in salinization within the study area. This comprehensive investigation provides valuable insights into the elevated salinity levels observed in the Ghiss-Nekor aquifer, contributing to a deeper understanding of its hydrogeochemical dynamics.

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An Estimation of Soil Erosion Rate Hot Spots by Integrated USLE and GIS Methods: a Case Study of the Ghiss Dam and Basin in Northeastern Morocco

Cited by 9 publications

References 26 publications

Hybrid Machine Learning Approach for Gully Erosion Mapping Susceptibility at a Watershed Scale

Hybrid Machine Learning Approach for Gully Erosion Mapping Susceptibility at a Watershed Scale

Water quality indices (WQIs): An in-depth analysis and overview

Exploring salinity origins in the Ghiss-Nekor aquifer, northern Morocco: A multivariate statistical analysis

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