On Evaluating Flow Resistance of Rigid Vegetation Using Classic Hydraulic Roughness at High Submergence Levels: An Experimental Work

Pasquino, Vittorio; Gualtieri, Paola; Doria, Guelfo Pulci

doi:10.1007/978-3-319-27750-9_22

Cited by 9 publications

(8 citation statements)

References 13 publications

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“…Feng [8] pointed out that due to land use and cover changes (LUCC) on the Loess Plateau, its water yield may decline. Besides, with the large-scale vegetation restoration on the Loess Plateau, the flow process is significantly affected, and its impact depends on the vegetation type [9,10]. However, Shi [11] found that the contribution rate of climate change to the reduction of runoff in the middle reaches of the Yellow River accounted for more than 40%.…”

Section: Introductionmentioning

confidence: 99%

Response of LUCC on Runoff Generation Process in Middle Yellow River Basin: The Gushanchuan Basin

Zhang

et al. 2020

Water

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Runoff reduction in most river basins in China has become a hotpot in recent years. The Gushanchuan river, a primary tributary of the middle Yellow river, Northern China, showed a significant downward trend in the last century. Little is known regarding the relative contributions of changing environment to the observed hydrological trends and response on the runoff generation process in its watershed. On the basis of observed hydrological and meteorological data from 1965–2010, the Mann-Kendall trend test and climate elasticity method were used to distinguish the effects of climate change and human activities on runoff in the Gushanchuan basin. The results indicate that the runoff in the Gushanchuan Basin has experienced significant declines as large as 77% from 1965 to 2010, and a mutation point occurred around 1997; the contribution rate of climate change to runoff change is 12.9–15.1%, and the contribution rate of human activities to runoff change is 84.9–87.1%. Then we divided long-term data sequence into two stages around the mutation point, and analyzed runoff generation mechanisms based on land use and cover changes (LUCC). We found that the floods in the Gushanchuan Basin were still dominated by Excess-infiltration runoff, but the proportion in 1965–1997 and 1998–2010 decreased gradually (68.46% and 45.83% in turn). The proportion of Excess-storage runoff and Mixed runoff has increased, which means that the runoff is made up of more runoff components. The variation law of the LUCC indicates that the forest area increased by 49.61%, the confluence time increased by 50.42%, and the water storage capacity of the watershed increased by 30.35%.

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Section: Introductionmentioning

confidence: 99%

Response of LUCC on Runoff Generation Process in Middle Yellow River Basin: The Gushanchuan Basin

Zhang

et al. 2020

Water

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“…Cassan, Belaud, Baume, and Dejean (2015) could show the good correspondence between local shear stress and reach scale resistance, but vegetation properties could be estimated only indirectly through their effect on flow characteristics. Flows with emergent vegetation typical of intermittent channels were much less investigated, notably because of the difficulty to explore velocity fields within the vegetation (Pasquino et al 2016), despite some advances in this field using numerical experiments (Boothroyd, Hardy, Warburton, & Marjoribanks, 2016 (Fisher, 1992). Blockage factor covers different vegetation size metrics and scales.…”

Section: Introductionmentioning

confidence: 99%

From 3D grassy vegetation point cloud to hydraulic resistance: Application to close‐range estimation of Manning coefficients for intermittent open channels

Vinatier¹,

Bailly

Belaud

2017

Ecohydrology

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The understanding of interrelations between biotic and abiotic processes in intermittent open-channels is currently of primary importance to better assess the services and disservices they provide. A large body of literature attempts to characterize vegetation functional traits affecting hydraulic rugosity, through the introduction of the blockage factor of flow by vegetation. However, this factor has multiple definitions and is still difficult to assess in the fields with actual and diverse vegetation covers, especially for grassy plants of ditches. Our study aims at predicting flow resistance from 3D vegetation characteristics using a close-range laser scanner. Flow resistance and vegetation 3D characteristics were defined using Manning coefficient and blockage factors, respectively. We tested combined effects of flow discharge against plant species and densities characterizing intermittent channels in a channel flume. Our results showed a variability of Manning coefficient describing flow rugosity against species and densities, with a highest rugosity for sclerophyllous species than herbaceous ones. Different blockage factors were calculated on the basis of scan clouds linked to Manning coefficients using non linear equation. The best relationship (R 2 = 0.9) were found for non linear equation relating Manning coefficients to a simplified blockage factor figuring the mean vegetation height deduced from the projection of the scan point cloud to the channel frontal area. The introduction of a coefficient to correct underestimated values issued from herbaceous species considering their reconfiguration under hydrodynamic loading is thus discussed. and estimate several blockage factor induced by vegeta-7 tion cover. 8• The blockage factor explained 90% of flow resistance de-9 duced from the total head loss at the channel scale. 10• The results we found allow the objective pre-determination

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“…As these expressions highlight, two major parameters that affect flow resistance are submergence and non-dimensional vegetation density. Huthoff et al [8], Augustijn et al [14], Nepf [15], and Pasquino et al [16] reported that the shear layer was similar as a result of the increase in the submergence ratio (H/H V > 5; H¯Total flow height and H v ¯Height of vegetation). Belcher et al [17] have described three distinct flow regimes based on the density of vegetation (λ << 0.1, λ = 0.1, and λ > 0.23, where λ is the density of vegetation).…”

Section: Introductionmentioning

confidence: 99%

Predicting Bulk Average Velocity with Rigid Vegetation in Open Channels Using Tree-Based Machine Learning: A Novel Approach Using Explainable Artificial Intelligence

Meddage

Ekanayake

Herath

et al. 2022

Sensors

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Predicting the bulk-average velocity (UB) in open channels with rigid vegetation is complicated due to the non-linear nature of the parameters. Despite their higher accuracy, existing regression models fail to highlight the feature importance or causality of the respective predictions. Therefore, we propose a method to predict UB and the friction factor in the surface layer (fS) using tree-based machine learning (ML) models (decision tree, extra tree, and XGBoost). Further, Shapley Additive exPlanation (SHAP) was used to interpret the ML predictions. The comparison emphasized that the XGBoost model is superior in predicting UB (R = 0.984) and fS (R = 0.92) relative to the existing regression models. SHAP revealed the underlying reasoning behind predictions, the dependence of predictions, and feature importance. Interestingly, SHAP adheres to what is generally observed in complex flow behavior, thus, improving trust in predictions.

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On Evaluating Flow Resistance of Rigid Vegetation Using Classic Hydraulic Roughness at High Submergence Levels: An Experimental Work

Cited by 9 publications

References 13 publications

Response of LUCC on Runoff Generation Process in Middle Yellow River Basin: The Gushanchuan Basin

Response of LUCC on Runoff Generation Process in Middle Yellow River Basin: The Gushanchuan Basin

From 3D grassy vegetation point cloud to hydraulic resistance: Application to close‐range estimation of Manning coefficients for intermittent open channels

Predicting Bulk Average Velocity with Rigid Vegetation in Open Channels Using Tree-Based Machine Learning: A Novel Approach Using Explainable Artificial Intelligence

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