Modelling flow and sediment trapping upstream and within grass buffer strips
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Yu, Bofu
Ghadiri, Hossein
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Abstract
Grass buffer strips impact the hydrology of flow and consequently the fate of sediment. A complex process-based model is developed to predict flow characteristics as well as sediment deposition and transport upstream, and within grass strips. The model is capable of estimating the proportion and amount of different sediment particle size classes in the outflow. The modified Green–Ampt equation was used to simulate infiltration. Gradually varied flow and kinematic wave approximation were used to model flow characteristics upstream and within grass strips. The GUEST model approach has been modified in order to use its basic approaches in sediment transport module in grass strips. Model predictions agree well with the results of two sets of controlled experiments. The bias, coefficient of model efficiency and the root mean squared error of the modelled efficiency of grass strips in reducing sediment concentration were 0.93–0.99, 0.58–0.99 and 8.9–12.7, respectively. The sensitivity analysis showed that the initial soil moisture and flow rate are the most sensitive parameters in predicting runoff loss. Increasing the slope steepness and flow rate dramatically decreases the efficiency of grass strips in reducing sediment concentration and mass. Copyright © 2015 John Wiley & Sons, Ltd.
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Hydrological Processes
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29
Issue
14
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© 2015 John Wiley & Sons, Ltd. This is the peer reviewed version of the following article: Modelling flow and sediment trapping upstream and within grass buffer strips, Hydrological Processes, Volume 29, Issue 14, Pages 3179–3192, 2015, which has been published in final form at 10.1002/hyp.10435. This article may be used for non-commercial purposes in accordance with Wiley Terms and Conditions for Self-Archiving (http://olabout.wiley.com/WileyCDA/Section/id-828039.html)
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Physical geography and environmental geoscience
Civil engineering
Environmental engineering
Environmental engineering not elsewhere classified