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GeoloGy, ecoloGy, and landscapes, 2017 Vol . 1, no . 3, 167–172 https://doi.org/10.1080/24749508.2017.1361133 INWASCON OPEN ACCESS Experimental study on the impact of vegetation coverage on flow roughness coefficient and trapping of sediment a a a b Nadergoli Ebrahimi , Mohammadreza Gharibreza , Majid Hosseini and Muhammad Aqeel Ashraf s oil c onservation and Watershed Management Research Institute, a gricultural Research, education and extension organization ( aReeo ), Tehran, Iran; s chool of environmental s tudies, china University of Geosciences, Wuhan, p. R. china ARTICLE HISTORY ABSTRACT Received 8 February 2017 The roughness coefficient is function of physical characteristics of flow, such as water depth, a ccepted 8 July 2017 velocity, type and density of vegetation coverage. Impact assessment of vegetation coverage on flow roughness coefficient and trapping of sediment in experimental condition were the KEYWORDS research aims. The research tests were conducted in different water discharge, various slope of The Manning’s coefficient; substrate and three density of coverage using physical model. Relations for estimation of the the sediment hydraulic; Manning’s coefficient were developed using statistical analysis. The best performance of model physical model; vegetation coverage; and flume in estimation of the flow roughness was gained at 12% density of coverage and steady injection of sediments. Introduction depth in steady sediment transport condition. Sharp and James (2005) have studied trapping of sediments at er Th e is longtime that riverine hydraulic features have different density of vegetation converges at two physical been studied by researchers (Ciraolo, Ferreri, & Loggia, um fl es. They found that trapping of sediment will be 2006; Cook, 1938; Cox & Palmer, 1948). Further, river increased by rising of water level and density of stems. engineers have widely used hydraulic equations to Ebrahimi (2008) has developed new method for esti- gain the best design for river structures. Importance of mation of the flow roughness using artificial vegetation at hydraulic phenomenon in arid and semi-arid regions physical flume with variable substrate. The research vari- because of water shortage and need to gain maximum ables were the water discharge, type and density of vege- efficiency has been strengthened. Notable application tation and the bed slope. He stated that increase in density of this knowledge has been confirmed to solve environ- of artificial vegetation has resulted in increase of the flow mental issues, such as river bank erosion and sediment roughness. Reversely, rising in water level and velocity of transport. Besides, expectation of hydraulic behaviour of flow have led to low values of the Manning’s coefficient. rivers and agricultural water channels which are facing Mathematical model was set to estimate water level during to different cultivation patterns is known issue between experiments. Results showed that this model will expect river engineers. Identify of such problems will result in water level at a river using different roughness coefficients. gaining effective and economic patterns of cultivation Impact assessment of growing vegetation on the river and mitigation of flood risks. Nezo and Naot (1999) flow roughness was done by (Curran & Hession, 2013) at have introduced a model to assess impact of vegetation University of Virginia. He found how vegetation coverage coverage on the flow turbulence at rectangular cross sec - ae ff cts the dynamics of sediments. The riverbed rough- tions along the physical flume. They find out that high- ness ae ff cts water level, sediment transport, and morphol- est turbulence energy was at toe of vegetation coverage. ogy of the river. Stems with cylindrical shapes and natural Balochi and Shafai-Bajestan (2012) have studied effects grasses were used as vegetation coverage during tests. of sediment input from a stream into main waterway at Aliza, Tjahjanto, Ali, Shaylinda, and Asyraff (2009) junction point using physical model. They found that have studied effects of vegetation coverage on rough- downstream of junction point were eroded because of ness coefficient of open channels. As a result, they variation in rate of water and sediment discharge. u Th s, have reached a linear relation between roughness destructive effects on downstream river structures and coefficient and water depth. Similar research was changes in river morphology will be expected. e Th y also performed by (Fengfeng, Lin, & Dingman, 2011) at developed relevant relations for estimation of scouring hydraulic laboratory of Dalian University. They have CONTACT nadergoli ebrahimi firstname.lastname@example.org © 2017 The a uthor(s). published by Informa UK limited, trading as Taylor & Francis Group. This is an open a ccess article distributed under the terms of the creative c ommons a ttribution license (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. 168 N. EBRAHIMI ET AL. used reeds as vegetation coverage which was natural Materials and methods obstacles at Chang River. Reeds have been introduced e r Th esearch methods were chosen to identify effective by several researchers as source of flow resistance. factors on the Manning’s coefficient. Ability of vege- Therefore, such capability of reeds has provided an tation coverage in sediment trapping and gaining the applied tool for management of natural waterways and best relation for estimation of the flow roughness was river mouths. tested using the physical model. Reviewed literatures Mir-Sadeghi (2014) have studied effects of grain have extracted several effective factors that are showed size of bed materials on flow characteristics and veg- at Table 1. A relation between such parameters was for- etation coverage using physical um fl e. The um fl e was mulated in Equation (1). composed of transparent walls, 9 m long, 0.4 m width, 0.7 m height, and changeable bed slope. Results showed f = (f , y , g, h, V, , , V , d50, ps, d, l , … , l )= 0 (1) 1 n ∗ 1 n that larger grain size was resulted in increased rough- Dimensional analyses were implemented for devel- ness coefficients and lower velocity of currents. He also oping relations between effective agents and dimen- stated that turbulence was occurred at separation points sionless parameters. The research method has not of flow. Meanwhile, turbulence of flow was dramati- considered effects of surface tension forces and water cally increased by rapid changes in grain size of bed viscosity because we assumed that these parame- materials. ters have slightly affected in open physical flume. Nehal, Hamimed, and Khaldi (2013) has investigated Therefore, effects of Reynolds number and Veber value effects of submerged vegetation on physical properties was ignored. On the other hand, for sub-critical flow of flow. Drowned vegetation has resulted in increasing conditions also, influence the slope of the flume was of water level and the Manning’s coefficient was remark - ignored. ably enhanced. Similar study was carried out by (Xia & Equation (2) was obtained by dimensional analysis Nehal, 2013) using Acorus Calamus species at coastal using Buckingham method. Therefore, three main fac- zone. He found that parts of stem and foliage of this spe- tors of velocity of flow (u), water depth (y ), and density cies showed different effects on hydrological properties of water (ρ) were assumed as time, length, and weight of flow. Manning’s coefficient was increased by increase units, respectively. of relation of water depth and height of stem (h/hs). The mean velocity of flow at foliage part was less than the l l u h 2 2 1 2 ∗ f = u y .f Fr, Re, , , , , R velocity at stem of plant. (2) 2 e u y y y ∗ n n Condition and terms of river flow with submerged vegetation at banks and the riverbed have studied by where Fr and Re were Froude and Reynolds numbers, (Bo, Hsun-Chuan, & You-Cheng, 2014). They stated l ∗ is ratio of mean velocity on shear velocity, is ratio of that drowned vegetation have played important role in l n length of vegetation on water depth, is ratio of space decreasing of flow velocity at riverbanks. Further, an interval of vegetation on depth of water, is ratio of increased in the flow velocity and depth of scouring hole vegetation height on water depth, and and R is shear was observed along the river centre. Reynolds. e r Th esearch aim was designated based on gap in Steady state of flow velocity and subcritical condition knowledge, where assessment of vegetation coverage in channels is dependent to Froude number. There is not impacts on the Manning’s coefficient have not been significant positive correlation between Reynolds num- studied in various concentration of sediment and slope ber and turbulent currents in channels with vegetation of the substrate. coverage. Therefore, the most effective parameters were u h identified as , , vegetation density, respectively. u y The flume setup Table 1. showing effective factors on the Manning’s coefficient and trapping of sediments. e fl Th ume setup was designated based on measurements No. Facto and condition of the tests. Therefore, the flume scale was 1 darsy and Isbakh friction coefficient (ƒ) estimated based on dimensionless limits and ability of 2 Water depth, earth gravity (g) equipment for providing proper water discharge. Two 3 Height of vegetation 4 The mean velocity of flow (u) important preconditions for the flume setup were ability 5 d ensity of water (ρ) of the flume substrate for changing of slope and injec- 6 The dynamic viscosity of water (μ) tion of sediments during tests. Accordingly, the flume 7 The shear velocity of flow (u) 8 non-dimensional values of area, length, and distance interval was made using transparent walls (Plexiglas), with 10 m between each plan length and 0.25 m width. Further, pattern of vegetation 9 d ensity of sediments (ρs) 10 The mean size of sediments (d) coverage for installation at flume were included bunches 11 s tatic angle (φ) −2 of plastic trees with 400 cm area, 7 cm height, and 12 Geometric standard deviation of sediments (σ) GEOLOGY, ECOLOGY, AND LANDSCAPES 169 Figure 1. pattern of vegetation coverage during tests. Table 2. showing effects of 50% coverage of vegetation on the Manning’s coefficient. Observational rough- Density of coverage (%) Water discharge Slope ness Calculated roughness 50 4 0.0037 0.032 0.066 0.069 50 4 0.0017 0.055 0.038 0.037 50 4 0.0011 0.071 0.031 0.028 50 6 0.0021 0.051 0.062 0.047 50 6 0.0011 0.078 0.026 0.029 50 6 0.0012 0.078 0.026 0.028 50 8 0.0016 0.069 0.030 0.036 50 8 0.0011 0.088 0.024 0.027 50 8 0.0012 0.093 0.022 0.025 2 cm space between each plant (Figure 1). The sediment injection equipment and Triangular weir also were made based on the tests conditions. The tests conditions The tests conditions were defined based on sediment concentration, velocity of flow, water depth, vege- tation density, and slope of substrate, respectively. Figure 2. c orrelation in roughness coefficient in 50% vegetation Therefore, impact of vegetation coverage on the coverage. Manning’s coefficient was studied in three conditions −1 of water flow (4, 6, and 8 li.s ). Simultaneously, the B a flume slope has changed in three statuses (0.002, n = ∗ u∕u ∗ R S (3) 0.004, and 0.006) and three density of coverage (12, 25, and 50%). The water discharge was set by steady where S is the substrate slope, R is the hydraulic radius, water pumping and measuring of output water from and α, β, and γ are exponents which were calculated by triangular weir. regression lines. e Th exponential relation was optimized e fl Th ume substrate was covered by sand particles by reducing the least mean square using Solver sowa ft re. (D50 = 1.9 mm). e t Th est conditions were completed by Such mathematical relations were gained based on cor- −1 steady injection of 400 g.s of fine grained sediments relation between observational and calculated results. To (0.15 mm). In addition, the flow velocity, changes in select the best mathematical equation, adequacy of models morphology of substrate and rate of sediment loss were (Me), and dimensionless error (NE ) were used. The Me measured at five cross sections using the electromagnetic values defined between 0 to −∞, which the highest accu- current metre (OSK 14077 model), the bottom profiler, racy of model is assumed to be Me = 1. The NE values and the sediment trap, respectively. Table 2, for instance, indicate frequency of errors by “n” coefficient. Adequacy represents test conditions for the test condition with 50% of regressive models was obtained by Equation (4). density of vegetation coverage. (k − P ) i i i=1 Me = 1 − ∑ (4) (k − k ) i=1 i mean The test analysis where n is number of observations, k mean is averages Analysis of tests was carried out based on determination of observations, and k is observation values. Besides, of the energy line. e Th exponential relation Equation (3 ) the predicted value (P ) was calculated using six math- was set in the Excel and Solver softwares to calculate the ematical relations. Therefore, the highest adequacy was Manning’s coefficient. 170 N. EBRAHIMI ET AL. obtained for test condition with 50% of vegetation cov- Further, the lowest amount of the Manning’s coefficient erage and without injection of sediment. In contrast, was observed for test condition with 25% density of veg- the lowest adequacy was observed in the test condition etation coverage. with 25% of vegetation coverage without injection of Relations between calculated and observational val- sediment. ues of the Manning’s coefficient in various statuses of e dim Th ensionless error (NE ) was calculated using vegetation coverage densities have been illustrated at Equation (5). Figures 2–4. Signic fi ant correlation ( R = 0.927) between calculated and observational values was gained for test NE = 100(n − n )∕n (5) n c m m condition of 12% density of vegetation (Figure 4). This where n and n are calculated and observed values of result has considerably confirmed statistical results. m c n, respectively. Variations in the Manning’s coefficient against different values of flow velocity were plotted (Figure 5). Present study has indicated the notable role of the Eddy flow in Results and discussion decrease of correlations. e t Th ests results can be implemented at the real scale, is Present research and previous studies (Curran & noticeable result of present research. Multiconditions Hession, 2013; Sharp & James, 2005) have highlighted of tests has resulted in various range of data which was role of vegetation coverage in concentration of sedi- evaluated by mathematical equations. As a result, rela- ments, roughness coefficient, the flow regime, water tions, coefficient of determination and dimensionless depth and the channel morphology. The sediment error for the test conditions is presented at Table 3. behaviour against type and density of vegetation cov- According to NE amounts, −0.002 and −5.31 have erage in mountainous Alpine streams was studied by indicated the best and the worst relations for calculation (Rey, Isselin-Nondedeu, & Bédécarrats, 2007). This of the Manning’s coefficient. Therefore, the most reliable study which was implemented at steep river channels relations for impact assessment of vegetation coverage also showed importance of vegetation coverage in trap- on the roughness coefficient were obtained for the test ping of sediments. Therefore, present study has simu- condition with 12% density of vegetation. Besides, the lated similar condition using of the variable flume bed. test status was associated with steady injection of sed- Although, most of previous works have evaluated role iments. On the other hand, the highest coefficient of of vegetation coverage in trapping of sediments, while determination (0.99) was gained for flow without sedi- present study has successfully achieved the best relation ment injection and 12% density of vegetation coverage. for estimation of the Manning’s coefficient. Table 3. showing relations and coefficient of determinations in different test conditions. Density of vegetation (%) Coefficient of determination Me NE Sediment injection The Manning’s coefficient relations −0.99 50 0.86 0.86 3.55 yes 0.03 n = 0.00046 ∗ ∗ R ∗ S −0.4987 50 0.98 0.97 −0.28 no −0.7062 0.533 n = 0.924 ∗ ∗ R ∗ S −0.91 25 0.81 0.78 1.8 yes u −0.67 −0.5 n = 0.00035 ∗ ∗ R ∗ S −1.29 25 0.76 0.64 1.32 no −5 u −0.5482 −0.6048 n = 4.25 ∗ 10 ∗ ∗ R ∗ S −0.8785 12 0.92 0.91 −0.002 yes u −0.091 −0.274 n = 0.0056 ∗ ∗ R ∗ S −1.61 12 0.99 0.7 −5.31 no u −0.0134 −0.1325 n = 0.00057 ∗ ∗ R ∗ S Figure 3. c orrelation in roughness coefficient in 25% vegetation coverage. GEOLOGY, ECOLOGY, AND LANDSCAPES 171 application of vegetation coverage in river engineer- ing brought benefits, such as areas for recreation and tourism. Acknowledgement e a Th uthor oer ff s sincere gratitude to academic staff of SCWMI for their kind technical supports during the research tests. Figure 4. c orrelation in roughness coefficient in 12% vegetation coverage. Disclosure statement No potential conflict of interest was reported by the authors. Funding This study is supported by Soil Conservation and Watershed Management Research Institute, Tehran, Iran (SCWMRI) [grant number 2-29-29-94110]. ORCID Mohammadreza Gharibreza http://orcid.org/0000-0001- 6599-1480 Figure 5. c orrelation between roughness coefficient and the flow velocity in different density of vegetation coverage. References Aliza, N., Tjahjanto, D., Ali, Z. M., Shaylinda, N., & Asyra, ff Conclusion M. (2009). 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Geology Ecology and Landscapes – Taylor & Francis
Published: Jul 3, 2017
Keywords: The Manning’s coefficient; the sediment hydraulic; physical model; vegetation coverage; and flume
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