Spatial–temporal dynamic impact of changes in rainfall erosivity and vegetation coverage on soil erosion in the Eastern Mediterranean

Alsafadi, Karam; Abdo, Hazem Ghassan; Sayah, Mario J. Al; Rátonyi, Tamás; Harsányi, Endre; Mohammed, Safwan

doi:10.1007/s11356-022-24012-6

Cited by 9 publications

(8 citation statements)

References 73 publications

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“…It serves as a valuable tool for assessing the potential extent and spatial distribution of rainfall erosion, aiding in the formulation of appropriate conservation measures, such as constructing terraces, restoring vegetation, and establishing shelter belts to minimize soil erosion and safeguard soil resources [64,65]. Additionally, it facilitates rational farmland utilization, crop cultivation methods, and farming practices, consequently reducing the risk of soil erosion and enhancing farmland productivity and sustainability [66]. Furthermore, this index provides a foundation for identifying the ecological impacts of soil erosion, promoting vegetation growth, and restoring soil and water conservation functions within ecosystems affected by severe erosion [20,67].To effectively prevent and control soil erosion, several measures can be implemented.…”

Section: Discussionmentioning

confidence: 99%

Temporal and Spatial Variation in Rainfall Erosivity in the Rolling Hilly Region of Northeast China

Li,

Wang,

et al. 2023

Agronomy

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The Rolling Hilly Region of Northeast China (RHRNEC) is a critical grain production area in China, and soil erosion is a major problem in this region. To determine spatial and temporal changes in rainfall erosivity (RE) in the RHRNEC and generate information useful for soil and water conservation, agricultural management, and ecological protection efforts, a RE index consisting of nine erosivity indices based on normal and extreme precipitation was established. The normal RE index (NREI) comprised annual RE (ARE), wet season RE (WRE), dry season RE (DRE), typical wet-month RE (TWRE), and typical dry-month RE (TDRE), and the extreme RE index set (EREI) comprised maximum one-day RE (RE × 1 day), maximum five consecutive days RE (RE × 5 day), storm RE (RE50), and maximum continuous RE (CRE). ARE, WRE, and TWRE decreased at relative rates of 2.5%, 2.9%, and 4.1%, respectively. By comparison, DRE increased at a non-significant relative rate of 6.3%, and all extreme RE indices decreased at a non-significant rate relative to 1981–2015 mean values. The future trends for all RE indices were predicted to be opposite to historical trends. The future trends and historical trends of all indices exhibited opposite patterns. RE gradually increased from north to south, and WRE, DRE, and all extreme RE indices were significantly negatively correlated with longitude, latitude, and altitude (p < 0.05). ARE, WRE, and TWRE showed increasing trends in the north and south and decreasing trends in the center. The findings are useful for soil and water conservation, especially for agricultural management and ecological protection.

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Section: Discussionmentioning

confidence: 99%

Temporal and Spatial Variation in Rainfall Erosivity in the Rolling Hilly Region of Northeast China

Li,

Wang,

et al. 2023

Agronomy

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“…In this context, a rapid increase in air temperature and decreasing trend of precipitation will negatively affect crop production (Bal et al 2022a;Al-Bakri et al 2011). In Syria, the agricultural system is suffering from serious land degradation (Mohammed et al 2022a), such as soil erosion (Mohammed et al 2020a(Mohammed et al , 2020bAlsafadi et al 2022c), associated with miss management of land resources and climate change (Mohammed et al 2020d). However, climate change, especially drought, badly affected the agricultural sector in Syria and led to reduced crop yield all over the country.…”

Section: Introductionmentioning

confidence: 99%

Modeling the impacts of projected climate change on wheat crop suitability in semi-arid regions using the AHP-based weighted climatic suitability index and CMIP6

et al. 2023

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Due to rapid population growth and the limitation of land resources, the sustainability of agricultural ecosystems has attracted more attention all over the world. Human activities will alter the components of the atmosphere and lead to climate change, which consequently affects crop production badly. In this context, wheat is considered an important crop and ranks as one of the top strategic crops globally. The main objective of this research is to develop a new approach (a weighted climatic suitability index) for evaluating the climate suitability for wheat production. The specific objectives are to project the impact of future climate change on wheat suitability using three models based on WCSI and CMIP6-based projections and to identify the most vulnerable area to climate change and productivity reduction. The climatic criteria for wheat production were selected and classified into eight indicators based on the Sys' scheme and the FAO framework, and then the weighted overlay approach was used in conjunction with the analytic hierarchy process. To confirm the reliability of the integrated WCSI, we determined the nonlinear curve fitting of integrated WCSI-induced wheat yields by the exponential growth equation. Finally, the CMIP6-GCMs projected from three shared socioeconomic pathways were used for WCSI mapping and predicting wheat yields in the short and long term (Southern Syria was selected as a case study). The results show that the nonlinear correlation between wheat yields and the integrated WCSI was 0.78 (R2 = 0.61) confirming the integrated WCSI's reliability in reflecting yield variation caused by climate suitability. The results indicated that WCSI for wheat will be lower over the study area during 2080–2100 compared to the current climate. During 2080–2100, the wheat yield is projected to decrease by 0.2–0.8 t. ha−1 in the western parts of the study area. The findings of this study could be used to plan and develop adaptation strategies for sustainable wheat production in the face of projected climate change. The results of the study will also help in the strategic planning of wheat production in Syria under the projected climate. The results of this research are limited to small areas as a case study, although they are not relevant to similar regions worldwide. However, the study employs novel analytical methods that can be used broadly.

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“…Despite the challenges posed by soil erosion in the Mediterranean region, only a few studies have examined the question. In addition, the few studies focusing on soil erosion mostly adopted the revised universal soil loss equation (RUSLE) in a geographic information system (GIS) environment or remote sensing techniques (Abdo, 2021; Abdo & Salloum, 2017; Almohamad, 2020; Alsafadi et al, 2022). However, it is essential to use field‐based observations to validate the model outputs.…”

Section: Introductionmentioning

confidence: 99%

Performance evaluation of machine learning algorithms to assess soil erosion in Mediterranean farmland: A case‐study in Syria

et al. 2023

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The development of new techniques, such as machine learning (ML), can provide better insight into the processes and drivers of soil erosion and runoff. However, the performance of these techniques to assess soil erosion in agricultural landscapes is poorly understood. The aim of this study was to evaluate the performance of four machine learning algorithms, generalized linear model (GLM), Random Forest (RF), elastic net regression (EN) and multiple adaptive regression splines (MARS), in predicting soil erosion and runoff in Syria. Soil erosion and runoff were measured on three experimental plots (2.25 m Â 1.50 m Â 0.50 m, 0.10 m depth in the soil), combined with three different slopes and land use types: RS (8%, olive), SS (12%, citrus), KS (20%, pomegranate). Both erosion and runoff were determined after rainfall events of >10 mm between October 2019 and April 2020. Based on 24 effective rainfall events, the average soil erosion was 0.18 ± 0.14 kg m À2 per event in KS, 0.14 ± 0.11 kg m À2 per event in SS, and 0.12 ± 0.10 kg/m 2 per event in RS. Regression analysis indicated strong relationship between the rainfalls and the runoff, the highest connection was recorded in the KS plot (r 2 = 0.85; p < 0.05 n = 24). The analysis of covariance indicated that only the runoff had a significant impact on soil erosion

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Spatial–temporal dynamic impact of changes in rainfall erosivity and vegetation coverage on soil erosion in the Eastern Mediterranean

Cited by 9 publications

References 73 publications

Temporal and Spatial Variation in Rainfall Erosivity in the Rolling Hilly Region of Northeast China

Temporal and Spatial Variation in Rainfall Erosivity in the Rolling Hilly Region of Northeast China

Modeling the impacts of projected climate change on wheat crop suitability in semi-arid regions using the AHP-based weighted climatic suitability index and CMIP6

Performance evaluation of machine learning algorithms to assess soil erosion in Mediterranean farmland: A case‐study in Syria

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