Soil Erosion Vulnerability Mapping in Selected Rural Communities of uThukela Catchment, South Africa, Using the Analytic Hierarchy Process

Ebhuoma, Osadolor; Gebreslasie, Michael; Ngetar, Njoya Silas; Phinzi, Kwanele; Bhattacharjee, Shwarnali

doi:10.1007/s41748-022-00308-y

Cited by 6 publications

(3 citation statements)

References 76 publications

(89 reference statements)

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“…The dominance of extreme precipitation in Free State, KwaZulu-Natal, Mpumalanga, and partly Eastern Cape provinces demonstrated that these regions were prone to flooding and water erosion. As reported by Ebhuoma et al 88 , it was estimated that 35% of the upper uThukela catchment in KwaZulu-Natal was at risk to soil degradation caused by water. Besides the degree of slope and vegetation cover, the precipitation in KwaZulu-Natal influenced soil erosion with a weight factor of 18%.…”

Section: Discussionmentioning

confidence: 82%

Impact of climate extreme events and their causality on maize yield in South Africa

Simanjuntak,

Gaiser,

Ahrends

et al. 2023

Sci Rep

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Extreme climate events can have a significant negative impact on maize productivity, resulting in food scarcity and socioeconomic losses. Thus, quantifying their effect is needed for developing future adaptation and mitigation strategies, especially for countries relying on maize as a staple crop, such as South Africa. While several studies have analyzed the impact of climate extremes on maize yields in South Africa, little is known on the quantitative contribution of combined extreme events to maize yield variability and the causality link of extreme events. This study uses existing stress indices to investigate temporal and spatial patterns of heatwaves, drought, and extreme precipitation during maize growing season between 1986/87 and 2015/16 for South Africa provinces and at national level and quantifies their contribution to yield variability. A causal discovery algorithm was applied to investigate the causal relationship among extreme events. At the province and national levels, heatwaves and extreme precipitation showed no significant trend. However, drought severity increased in several provinces. The modified Combined Stress Index (CSIm) model showed that the maize yield nationwide was associated with drought events (explaining 25% of maize yield variability). Heatwaves has significant influence on maize yield variability (35%) in Free State. In North West province, the maize yield variability (46%) was sensitive to the combination of drought and extreme precipitation. The causal analysis suggests that the occurrence of heatwaves intensified drought, while a causal link between heatwaves and extreme precipitation was not detected. The presented findings provide a deeper insight into the sensitivity of yield data to climate extremes and serve as a basis for future studies on maize yield anomalies.

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Section: Discussionmentioning

confidence: 82%

Impact of climate extreme events and their causality on maize yield in South Africa

Simanjuntak,

Gaiser,

Ahrends

et al. 2023

Sci Rep

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“…The AHP is a suitable technique for identifying and mapping erosion-prone areas (Belkendil, A. et al 2018;Belloula, M. et al 2020;Aslam, B. et al 2021;Sinshaw, B.G. et al 2021;Ebhuoma, O. et al 2022). AHP is a method that compares qualitative factors and expresses them as numerical values.…”

Section: Analytic Hierarchy Processmentioning

confidence: 99%

“…where Sl is the slope, As is the aspect, Li is the lithology, El is the elevation, Pr is the precipitation, Drd is the drainage density, and W were reclassified into six categories: insignificant, very low, low, moderate, high, and very high (Ebhuoma, O. et al 2022). It is noteworthy that, owing to the seasonal variation in the impact of each factor, the erosion susceptibility maps encompass different categories.…”

Section: The Winter Seasonmentioning

confidence: 99%

Erosion susceptibility mapping of a loess-covered region using Analytic Hierarchy Process – A case study: Kalat-e-Naderi, northeast Iran

Nooshin Nokhandan,

Ghahraman,

Horváth

2024

HunGeoBull

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In this study, the Analytic Hierarchy Process (AHP) is applied to generate erosion susceptibility maps in four basins of Kalat-e-Naderi county, namely Archangan, Kalat, Qaratigan, and Chahchaheh basins, situated in northeast Iran. The Kalat-e-Naderi region is characterized by a partial coverage of loess. Given the agricultural significance of loess and its susceptibility to erosion, this research focuses specifically on regions covered by loess. Geographic Information System (GIS) tools, including ArcMap and Quantum Geographic Information System (QGIS), were utilized to facilitate the creation of erosion susceptibility maps. Seven factors, including slope, aspect, elevation, drainage density, lithology, the Normalized Difference Vegetation Index (NDVI), and precipitation were selected for consideration. Recognizing the variability of precipitation and vegetation cover across different seasons, seasonal data for the specified factors were employed. Consequently, erosion susceptibility maps were generated on a seasonal basis. Pairwise comparison tables revealed that precipitation, lithology, and slope emerged as the dominant factors contributing to erosion susceptibility in this region. The resultant maps distinctly delineate basins with higher precipitation values, unresistant lithology (such as loess, characterized by high porosity and permeability), and steeper slopes, exhibiting heightened susceptibility to erosion (Archangan and Kalat basins). The credibility of the research findings was examined through on-site observations. The outcomes of this study may provide pertinent insights for decision-makers and planners. This information can be effectively employed in formulating strategies aimed at conserving soil quality in areas vulnerable to erosion hazards.

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Predictive machine learning for gully susceptibility modeling with geo-environmental covariates: main drivers, model performance, and computational efficiency

Phinzi,

Szabó

2024

Nat Hazards

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Currently, machine learning (ML) based gully susceptibility prediction is a rapidly expanding research area. However, when assessing the predictive performance of ML models, previous research frequently overlooked the critical component of computational efficiency in favor of accuracy. This study aimed to evaluate and compare the predictive performance of six commonly used algorithms in gully susceptibility modeling. Artificial neural networks (ANN), partial least squares, regularized discriminant analysis, random forest (RF), stochastic gradient boosting, and support vector machine (SVM) were applied. The comparison was conducted under three scenarios of input feature set sizes: small (six features), medium (twelve features), and large (sixteen features). Results indicated that SVM was the most efficient algorithm with a medium-sized feature set, outperforming other algorithms across all overall accuracy (OA) metrics (OA = 0.898, F1-score = 0.897) and required a relatively short computation time (< 1 min). Conversely, ensemble-based algorithms, mainly RF, required a larger feature set to reach optimal accuracy and were computationally demanding, taking about 15 min to compute. ANN also showed sensitivity to the number of input features, but unlike RF, its accuracy consistently decreased with larger feature sets. Among geo-environmental covariates, NDVI, followed by elevation, TWI, population density, SPI, and LULC, were critical for gully susceptibility modeling. Therefore, using SVM and involving these covariates in gully susceptibility modeling in similar environmental settings is strongly suggested to ensure higher accuracy and minimal computation time.

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Soil Erosion Vulnerability Mapping in Selected Rural Communities of uThukela Catchment, South Africa, Using the Analytic Hierarchy Process

Cited by 6 publications

References 76 publications

Impact of climate extreme events and their causality on maize yield in South Africa

Impact of climate extreme events and their causality on maize yield in South Africa

Erosion susceptibility mapping of a loess-covered region using Analytic Hierarchy Process – A case study: Kalat-e-Naderi, northeast Iran

Predictive machine learning for gully susceptibility modeling with geo-environmental covariates: main drivers, model performance, and computational efficiency

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