2022
DOI: 10.1016/j.ijsrc.2021.04.002
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Impacts of land use and land cover changes on hydrological processes and sediment yield determined using the SWAT model

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Cited by 73 publications
(25 citation statements)
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“…However, the statistical summary of the model's performance evaluation in the Dinya reach was highly satisfactory with NS of 0.91, r 2 of 0.93 and p-factor of 1.00 (Table 3) [26,47]. The sediments validation results show statistical summary for the Kaduna sub-basin with NS (0.47), r 2 (0.82), p-factor (0.63) and r 2 (1.49) values that are suitable for modelling (Table 3) [47], and somewhat contrasting to the non-satisfactory dataset of the Gutalu sub-basin with NS of −0.11, r 2 of 0.06, p-factor of 0.88, and r-factor of 2.96. The documented high r-factor of the flow and sediments calibration/validation resulted from model uncertainties, which may be in the form of (i) conceptual uncertainties (i.e., portioning method of SCS curve number), (ii) processes occurring within the watershed that were not included in the model (wetland, erosion mining, etc.)…”
Section: Sensitivity Analysis Calibration and Validation Datasetsmentioning
confidence: 90%
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“…However, the statistical summary of the model's performance evaluation in the Dinya reach was highly satisfactory with NS of 0.91, r 2 of 0.93 and p-factor of 1.00 (Table 3) [26,47]. The sediments validation results show statistical summary for the Kaduna sub-basin with NS (0.47), r 2 (0.82), p-factor (0.63) and r 2 (1.49) values that are suitable for modelling (Table 3) [47], and somewhat contrasting to the non-satisfactory dataset of the Gutalu sub-basin with NS of −0.11, r 2 of 0.06, p-factor of 0.88, and r-factor of 2.96. The documented high r-factor of the flow and sediments calibration/validation resulted from model uncertainties, which may be in the form of (i) conceptual uncertainties (i.e., portioning method of SCS curve number), (ii) processes occurring within the watershed that were not included in the model (wetland, erosion mining, etc.)…”
Section: Sensitivity Analysis Calibration and Validation Datasetsmentioning
confidence: 90%
“…Land-use land cover (LULC) information is an essential component in watershed modelling with regards to hydrology, sediment yield, and water quality within the basin area, because changes in LULC may result in significant modification of sediment yield pattern within the watershed. Additionally, it could lead to variation in the hydrological response of watersheds, thus impacting river flows [1,2]. Given constant hydrological variables, change in sediment yield can be attributed to the corresponding alteration in land use in the upstream catchment, as it gave rise to detrimental sedimentation in the Ethiopian Haramaya Lake [3].…”
Section: Introductionmentioning
confidence: 99%
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“…LULC variations have been studied at various geographical and temporal ranges for their impact on hydrology and soil loss (Assfaw, 2020; Baig et al, 2022; Chaemiso et al, 2021; Choto & Fetene, 2019; Gebremichael, 2021; Wang et al, 2022). Furthermore, evaluations of multiple research papers indicate that LULC modification might modify streamflow, hence altering a basin’s hydrological components (HCs) and sediment yield (SED) (Afonso de Oliveira Serrão et al, 2022; Kumar et al, 2018; Negese, 2021).…”
Section: Introductionmentioning
confidence: 99%
“…Unlike the previously mentioned hydrological models, the soil and water assessment tool (SWAT) model requires only a tiny direct calibration to produce accurate hydrological predictions. [ 22 , 25 ] Furthermore, [ 26 ] indicated that the SWAT model is a good estimator of hydro‐sedimentological processes that decision‐makers can use to manage water and environmental resources. To this effect, the SWAT model was applied in this investigation.…”
Section: Introductionmentioning
confidence: 99%