A GIS-based assessment of the potential soil erosion and flood hazard zones in Ekiti State, Southwestern Nigeria using integrated RUSLE and HAND models

Olorunfemi, Idowu Ezekiel; Komolafe, Akinola Adesuji; Fasinmirin, Johnson Toyin; Olufayo, Ayorinde A.; Akande, Samuel O.

doi:10.1016/j.catena.2020.104725

Cited by 80 publications

(53 citation statements)

References 53 publications

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“…Dynamics of the erosional process are greatly affected by the LS factor that controls runoff flow. Our results agree with the findings reported by [106] in which the altitude variance and topographic relief control the LS factor (Figure 6A); and higher value of P factor (Figure 8A) directly correlates with a higher rate of soil loss (Figure 8B). Interestingly, both of which are related to the steeper slopes (Figure 6A).…”

Section: Discussionsupporting

confidence: 92%

New Insight on Soil Loss Estimation in the Northwestern Region of the Zagros Fold and Thrust Belt

Othman¹,

Obaid

Ali

et al. 2021

IJGI

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Soil loss is one of the most important causes of land degradation. It is an inevitable environmental and socio-economic problem that exists in many physiographic regions of the world, which, besides other impacts, has a direct bearing on agricultural productivity. A reliable estimate of soil loss is critical for designing and implementing any mitigation measures. We applied the widely used Revised Universal Soil Loss Equation (RUSLE) in the Khabur River Basin (KhRB) within the NW part of the Zagros Fold and Thrust Belt (ZFTB). The areas such as the NW Zagros range, characterized by rugged topography, steep slope, high rainfall, and sparse vegetation, are most susceptible to soil erosion. We used the Digital Elevation Model (DEM) of the Shuttle Radar Topography Mission (SRTM), Tropical Rainfall Measuring Mission (TRMM), Harmonized World Soil Database (HWSD), and Landsat imagery to estimate annual soil loss using the RUSLE model. In addition, we estimated sediment yield (SY) at sub-basin scale, in the KhRB where a number of dams are planned, and where basic studies on soil erosion are lacking. Estimation of SY will be useful in mitigation of excessive sedimentation affecting dam performance and watershed management in this region. We determined the average annual soil loss and the SY in the KhRB to be 11.16 t.ha−1.y−1 and 57.79 t.ha−1.y−1, respectively. The rainfall and runoff erosivity (R factor), slope length (L factor), and slope steepness (S factor), are the three main factors controlling soil loss in the region. This is the first study to determine soil loss at the sub-basin scale along with identifying suitable locations for check dams to trap the sediment before it enters downstream reservoirs. The study provides valuable input data for design of the dams to prevent excessive siltation. This study also aims at offering a new approach in relating potential soil erosion to the actual erosion and hypsometric integrals.

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Section: Discussionsupporting

confidence: 92%

New Insight on Soil Loss Estimation in the Northwestern Region of the Zagros Fold and Thrust Belt

Othman¹,

Obaid

Ali

et al. 2021

IJGI

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“…In the present study, the RUSLE model integrated with GIS technology has been used to analyze the impact of LULC on soil erosion in the Temeji watershed. Similar studies have been conducted using RUSLE model and GIS technology to analyze the impact of LULC on soil erosion (Millward & Mersey 1999;Prasannakumar et al 2012;Galagay & Minale 2016;Ganasri & Ramesh 2016;Gashaw et al 2017;Ostovari et al 2017;Zerihun et al 2018;Kidane et al 2019;Mohammed et al 2020;Olorunfemi et al 2020). The RUSLE model combines various parameters, which were acquired from different sources (Table 1).…”

Section: Methodsmentioning

confidence: 99%

“…In the RUSLE model, the C-factor shows the effect of vegetation/crop cover and management practices on soil erosion rate (Renard et al 1997;Millward & Mersey 1999;Ostovari et al 2017). The C-factor ranges between 0 (no susceptibility to soil erosion due to well protected and managed land) to value 1, which depicts high susceptibility to erosion due to lack of protective cover (Ganasri & Ramesh 2016;Mohammed et al 2020;Olorunfemi et al 2020).…”

Section: Cover Management Factormentioning

confidence: 99%

“…The support practices (P) factor is the ratio of soil loss with specific support practice to the corresponding soil loss with up and down cultivation (Wischmeier & Smith 1978;Millward & Mersey 1999). Similar to C-values, the P-values ranges from 0 to 1, whereby the value 0 indicates a good conservation practice and erosion resistance facility and the vale 1 indicates poor conservation practice and no manmade erosion resistance facility (Renard et al 1997;Ganasri & Ramesh 2016;Olorunfemi et al 2020). Because of lack of conservation practice-related data in the study watershed, the P-factor values were taken from the literature review, which varies between 0.53 and 0.9 (Figure 6).…”

Section: Support Practices Factormentioning

confidence: 99%

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Impact of land-use and land-cover change on soil erosion using the RUSLE model and the geographic information system: a case of Temeji watershed, Western Ethiopia

Moisa

Negash

Merga³

et al. 2021

Journal of Water and Climate Change

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The impact of land-use land-cover (LULC) change on soil resources is getting global attention. Soil erosion is one of the critical environmental problems worldwide with high severity in developing countries. This study integrates the Revised Universal Soil Loss Equation model with a geographic information system to estimate the impacts of LULC conversion on the mean annual soil loss in the Temeji watershed. In this study, LULC change of Temeji watershed was assessed from 2000 to 2020 by using 2000 Landsat ETM+ and 2020 Landsat OLI/TIRS images and classified using supervised maximum likelihood classification algorithms. Results indicate that the majority of the LULC in the study area is vulnerable to soil erosion. High soil loss is observed when grassland and forest land were converted into cultivated land with a mean soil loss of 88.8 and 86.9 t/ha/year in 2020. Results revealed that about 6,608.5 ha (42.8%) and 8,391.8 ha (54.4%) were categorized under severe classes in 2000 and 2020, respectively. Accordingly, the soil loss severity class is directly correlated with the over-exploitation of forest resources and grasslands for agricultural purposes. These results can be useful for advocacy to enhance local people and stakeholder's participation toward soil and water conservation practices.

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“…Borrelli et al [20] estimated the RUSLE C-factor to reflect the effect of the Common Agricultural Policy in reducing soil erosion. Olorunfemi et al [21] successfully assessed the potential soil erosion using RUSLE model in Southwestern Nigeria. Teng et al [2] used RUSLE to estimate both current and potential soil erosion by water on the Tibetan Plateau.…”

Section: Introductionmentioning

confidence: 99%

Examining Soil Erosion Responses to Grassland Conversation Policy in Three-River Headwaters, China

Zhao

Lin

et al. 2021

Sustainability

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Soil erosion in the Three-River Headwaters (TRH) region has continued to intensify in recent decades due to human activities and climate change. To reverse this situation, the Chinese central government has launched the Subsidy and Incentive System for Grassland Conservation (SISGC). As a sign of the effectiveness of SISGC implementation, the dynamic changes of soil erosion can provide timely feedback for decision makers and managers. The Revised Universal Soil Loss Equation (RUSLE) model was used to simulate the spatial distribution of soil erosion before and after SISGC implementation, and Mann–Kendall (MK) test to reveal the effect of policy implementation. The results showed that: (1) the soil erosion in the TRH was mainly mild (83.83% of the total eroded area), and the average soil erosion rate and the total erosion were 13.63 t ha−1 y−1 and 323.58 × 106 t y−1 respectively before SISGC implementation; (2) SISGC implementation has curbed soil erosion. After SISGC implementation, the total soil erosion decreased by 3.80%, which showed obvious differences between grassland types; (3) The influences of SISGC were mainly because it has increased vegetation cover, further decreasing soil erosion. However, soil erosion in Alpine grassland has deteriorated, indicating direct targeted policymaking should be on the agenda. Furthermore, SISGC should be continued and grassland-type-oriented to restore the grassland ecosystem.

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A GIS-based assessment of the potential soil erosion and flood hazard zones in Ekiti State, Southwestern Nigeria using integrated RUSLE and HAND models

Cited by 80 publications

References 53 publications

New Insight on Soil Loss Estimation in the Northwestern Region of the Zagros Fold and Thrust Belt

New Insight on Soil Loss Estimation in the Northwestern Region of the Zagros Fold and Thrust Belt

Impact of land-use and land-cover change on soil erosion using the RUSLE model and the geographic information system: a case of Temeji watershed, Western Ethiopia

Examining Soil Erosion Responses to Grassland Conversation Policy in Three-River Headwaters, China

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