Water Resources Management Seasonality of roughness - the indicator of annual river flow resistance condition in lowland catchment --Manuscript Draft-Manuscript Number:
WARM-D-16-01020
Full Title:
Seasonality of roughness - the indicator of annual river flow resistance condition in lowland catchment
Article Type:
General paper
Keywords:
Manning's n, seasonal roughness factor, hydraulic model, German lowland river network
Corresponding Author:
Song Song, Ph.D. Nanjing University CHINA
Corresponding Author Secondary Information: Corresponding Author's Institution:
Nanjing University
Corresponding Author's Secondary Institution: First Author:
Song Song, Ph.D.
First Author Secondary Information: Order of Authors:
Song Song, Ph.D. Britta Schmalz Youpeng Xu Nicola Fohrer
Order of Authors Secondary Information: Funding Information:
China Sponsorship Council (2010619019)
Abstract:
Accurate estimation of flow resistance restricts the quality of the hydraulic model performance. In this study, we try to investigate the seasonal dynamic of the Manning's roughness coefficient (n) based on one-dimensional hydraulic model HEC-RAS in German lowland area. Four river section models were set up based on the 1 m digital elevation model and field measurement, in which the seasonal roughness factor were calibrated and validated with the gauge record. The results revealed that: 1) the Manning's n varied from 46% to 135% from the basic value in Autumn; 2) the seasonal roughness factor improved the quality of the model output. 3) the vegetation condition and water elevation dominated the Manning's n in summer and winter half year respectively. Water temperature was another influencing factor in winter half year; 4) the peak value of Manning's n appeared in late summer due to the highest biomass, while the minimum roughness occurred in early-spring because of the combined influence of low biomass, high water level and relatively higher temperature. The involvement of seasonal roughness factor improved the model performance and the results are comparable to the earlier research of the same area. The current study provided reference for the roughness research.
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Seasonality of roughness - the indicator of annual river flow resistance condition in lowland catchment S. Song1,3, B. Schmalz2,3, Y.P. Xu1, N. Fohrer3 1
School of Geographic and Oceanographic Science, Nanjing University, Nanjing, China
2
Institute of Hydraulic and Water Resources Engineering, Technical University of Darmstadt,
Darmstadt, Germany 3
Dept. of Hydrology and Water Resources Management, University of Kiel, Kiel, Germany
[email protected]
Abstract: Accurate estimation of flow resistance restricts the quality of the hydraulic model performance. In this study, we try to investigate the seasonal dynamic of the Manning’s roughness coefficient (n) based on one-dimensional hydraulic model HEC-RAS in German lowland area. Four river section models were set up based on the 1 m digital elevation model and field measurement, in which the seasonal roughness factor were calibrated and validated with the gauge record. The results revealed that: 1) the Manning’s n varied from 46% to 135% from the basic value in Autumn; 2) the seasonal roughness factor improved the quality of the model output. 3) the vegetation condition and water elevation dominated the Manning’s n in summer and winter half year respectively. Water temperature was another influencing factor in winter half year; 4) the peak value of Manning’s n appeared in late summer due to the highest biomass, while the minimum roughness occurred in early-spring because of the combined influence of low biomass, high water level and relatively higher temperature. The involvement of seasonal roughness factor improved the model performance and the results are comparable to the earlier research of the same area. The current study provided reference for the roughness research.
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Keywords: Manning’s n, seasonal roughness factor, hydraulic model, German lowland river network
1. Introduction Hydraulic and hydrological model are essential tools in instant simulation and future scenario prediction, in perspective of water resource management, flood control and mitigation (De Doncker et al., 2009; Shahrokhnia and Javan, 2007). Adequate and precise data of discharge, water level, geometry, resistance etc. is the basis of the model setup and calibration (Bakry, 1996; Horritt and Bates, 2002a; Song et al., 2014). Among all these parameters, the resistance of the river channel and the banks is immeasurable, and mainly dependent on estimation in the field (De Doncker et al., 2011; Mahmoudi et al., 2013; Pappenberger et al., 2005). As obstructions to flow, the vegetation increase channel resistance and water stage, while reducing average flow velocities (Pitlo and Dawson, 1989). It can even dominate the local hydraulic condition within the streams they occupy, especially in lowland stream, which contains extensive stands of aquatic vegetation (Green, 2006; Preston and Croft, 1997). Although the attempt to determine the mean value of resistance for vegetated streams can be traced back as early as 1869 (Rouse and Ince, 1957), research into the effects of vegetation on stream resistance has distinctly lagged behind studies of resistance in open channels (V. T. Chow, 1959; Watson, 1987). One widespread method for Manning roughness determination is the field estimation according to the empirical table provided half century ago (V. T. Chow, 1959). For one thing, the estimation quality depends on the temporal vegetation 2
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coverage and submerged ratio; for another, the experience of the researcher brings uncontrollable subjective error. Manning equation is another widely accepted methodology in stream resistance evaluation, especially in vegetated area. The metric version of the Manning equation is as follows (V. T. Chow, 1959; Yen, 1992): k
V = n ∗ R2/3 ∗ S1/2
(1)
where V (m/s) is the averaged river velocity, n means the Manning´s roughness coefficient, R refers the hydraulic radius of the cross section, S indicates the channel slope, and k represents a conversion coefficient, internationally accepted as 1. The Manning formula has embedded into most numerical models and its applicability has been tested by worldwide study under various hydraulic conditions (Grimaldi et al., 2010; Meléndez Robledillo et al., 2006; Retsinis et al., 2013). The variation of k value and the modified formation of the equation according to the diverse water environment improved the accuracy of the Manning roughness, but also made the Manning roughness determination more complicate (Ruf, 1988; Yu and Lim, 2003; Yu et al., 2009). Based on the difficulty in the availability of the reliable vegetated Manning roughness, the ability of the model to simulate the behavior of vegetated streams is severely lacking, especially streams with randomly distributed vegetation (Fisher, 1992; Green, 2005a; Murray and Paola, 2003). Therefore, there is urgent need to improve the Manning roughness parameter to optimize the model performance. Several empirical studies focused on the effects of vegetation quantity to their hydraulic resistance have been carried out (Bakry et al., 1992; Champion and Tanner, 3
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2000; Watson, 1987). The results demonstrated that the multi-cross-sectional vegetation proportion is the most appropriate predictor of manning roughness on both theoretical and empirical grounds (Green, 2005b, 2005c). Based on the fact that the vegetation proportion is closely associated with the season, it would be efficient to optimize the model performance, in respect of the seasonality of the manning roughness. Due to the roughness application in 2D and 3D models is limited, 1D simulation of the open rivers would be particularly informative for the resistance condition in cross sectional or reachwise scale (Yen, 2002). Improve the flow resistance estimation and simulation quality in lowland area is especially important due to its sensitivity to hydrological extremes and fluctuations. Based on these consideration, in this paper, we mainly focused on estimating the Manning roughness of the lowland river networks based on field observation, outlining the seasonal variation of the Manning roughness with well calibrated and validated 1D hydraulic model, presenting the effects of the Manning roughness to the hydraulic models, and finally revealing the influence factors of the roughness variations.
2. Procedures 2.1 Study area The Upper Stör catchment is characterized by the extensive low plain area, located in the center of Schleswig-Holstein/Northern Germany (Fig. 1). The drainage area is 468 km2 and the discharge gauged at the outlet ranges from 1.26 m3/s to 44 m3/s (LKN-SH, 2012). The elevation of this area falls from 60 m to 2 m.a.s.l at the outlet in the study area, and the gradients are usually smaller than 1° in most of the catchment 4
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(LVermA, 1995). This area is dominated by the temperate marine climate and demonstrates four distinct seasons. The annual mean precipitation is 875 mm. Weather statistic from Schleswig-Holstein showed that the averaged minimum temperature is –2 °C in January and the maximum monthly average temperature is 17 °C in July, respectively (Climatemps, 2013). The climate condition leads to the high coverage of vegetation both in streams and on the floodplain in summer and autumn, but with low biomass in winter and spring. The vegetation cover for the side bank is normally grass and shrubs, while on stream bed is submerged aquatic plants. The land use in the Upper Stör catchment is dominated by agricultural land (48.1%). Pasture, grassland and forest account for 41% of the catchment area (DLR, 1995; EEA, 2000).
Fig 1.The location of Upper Stör catchment and study points
2.2 Data and techniques
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2.2.1 Hydraulic model Hydrologic Engineering Centers River Analysis System (HEC–RAS) is an integrated 1–D hydraulic model for interactive use developed by the US Army Corps of Engineers (Brunner, 1995). The river discharge and velocity simulation of HEC-RAS is based on formula (1). The model is mainly physically-based system to analyze steady/unsteady river dynamics, sediment transportation and water quality dynamics (Hicks and Peacock, 2005; Horritt and Bates, 2002b; Yang et al., 2006). In this paper the steady/unsteady flow module was adopted to simulate the long-term flow dynamics affected by the roughness condition. The aim of the steady flow analysis is to calibrate and validate the river geometry data. The validated parameters are then used for the unsteady flow simulation. Steady flow calculation is based on the conservation of energy. The energy equation is written as follows (Brunner, 1995),
WS1 +
α2 V22 2g
= WS2 +
α1 V21 2g
+ he
(2)
Here: WS1, WS2 is the water surface of cross sections in the same channel; V1, V2 indicates the averaged cross-sectional velocity; α1, α2 refer to the velocity weighting coefficients; g means acceleration due to gravity; he is energy head loss; S1 presents the river bed slope; S2 means the energy slope; 6
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Unsteady flow calculations are based on the continuity principle. It states that the change of the amount of an incompressible fluid must always equal to the difference between incoming and out-flowing fluid during a given time interval on a given river section, which is called Saint-Venant equations in calculation (Eq. (3) and (4)) (Brunner, 1995; USACE, 2010).
∂A ∂t ∂Q ∂t
+
∂(Q2 ⁄A) ∂x
∂Q
+ ∂x = 0 ∂H
+ gA ∂x gA(S1 − S2 ) = 0
(3) (4)
Where: A is cross-sectional area perpendicular to the flow direction; Q is the discharge; g is acceleration due to gravity; H is the elevation of the water surface above a specified datum, also called stage; t is the temporal coordinate (given time interval); x is the longitudinal coordinate (given river section).
2.2.2 Data collection Four sites, Brachenfeld (A), Padenstedt (B), and Sarlhusen (C) and Willenscharen (D), were chosen as study sites from the Upper Stör river networks (Fig. 1, Tab. 1). Seven cross sections evenly distributed along a length of 300 m of each river section were 7
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surveyed to collect the topographic and hydraulic data. The river geometry, flow discharge, velocity, water surface width, depth and slope were collected from each cross section, during a moderate water level period (September 2011) (Fig. 2). All the measurement except river geometry at the same cross section were repeated in January, 2012 and April, 2012 respectively. FlowSens device (SEBA Hydrometrie, Germany) an Acoustic Doppler Qliner (ADQ, OTT Company, Kempten/Germany) were used in the river velocity and discharge measurement. Both equipment have been proven to be accurate and applicable in the field survey (Song et al., 2012). The Manning roughness was estimated in the field according to the conversion relationship (V. Chow, 1959) and the averaged value were shown in the Tab. 1. Additional topographic data on the adjacent floodplain of the river section was derived from the Digital Elevation Model (DEM) with 1 m resolution in HECgeo-RAS and Arc GIS 10.0.
Tab. 1. Characteristics of the ten selected sub-catchments Land use & (Roughness Height) Slope (%) Site Catchment Width Depth Vel No. size [km2] [m] [m] [m/s] LOB CH ROB LOB CH ROB A 70.09 0.40 0.21 0.55 C (0.10) 0.05 P (0.07) 2.15 0.40 0.24 B 196.05 0.20 0.43 0.10 C (0.09) 0.06 P (0.07) 4.50 0.69 0.39 C 203.02 0.10 0.19 0.13 P (0.08) 0.06 A (0.08) 5.65 0.75 0.31 D 461.74 0.17 0.10 -0.2 P (0.08) 0.06 P (0.08) 8.50 1.44 0.28 Note: LOB is the left bank of the channel; CH is the channel; ROB is the right bank of the channel. The roughness is the averaged value from the three field estimations. C refers to the construction land use; F means Forest; P is pasture land; A is arable land. Vel is the abbreviation of velocity.
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Fig. 2. How to collect data at each cross section
2.2.3 Methodology framework The methodology framework of our study is shown in Fig. 3. Four river sectional HEC-RAS models were set up, calibrated and validated. The data input for the steady models including the river geometry, water surface elevation, Manning roughness coefficient (n) and river slope, which were collected from the field survey and 1 m DEM (Digital Elevation Model). The measured data from three field campaigns were mainly used to set up and calibrate the steady flow models, based on which the unsteady flow models were processed. Gauged discharge and water surface elevation data from the four river sections were collected from 1990 to 2010 for unsteady flow simulation. The calibration period for the seasonal roughness variation is 2010, while the validation period is 1990-2009.
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Fig. 3. The methodology of the our study
3. Analysis and results 3.1 Steady river section models Well calibrated and validated steady flow models are the curial step for unsteady flow simulation.
At least two data sets from two different water levels are required to adequately calibrate and validate a numerical model (Wagner and Mueller, 2002). The procedure adopted in our study was first to set up the HEC-RAS model of each river section with the field data set from September and then calibrated the river geometry with the other two data sets. During the calibration and validation, the water surface elevations, maximum depth, hydraulic depth and mean cross-sectional velocity were taken into consideration to achieve the minimum error for all three seasons. The HEC-RAS output and field surveyed data were positively correlated in the validation model. All the correlation coefficients were higher than 0.9. The averaged absolute
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relative error of cross-sectional velocity, top width, maximum depth and hydraulic depth is given in Tab. 2. This indicates the reliable performance of the steady model, and provided reliable basis for the unsteady flow models. Tab. 2. Relative error of steady models Error (%) cs7 cs6 cs5 cs4 cs3 cs2 cs1 Mean error
WSE 14.78 9.20 11.53 10.76 8.90 5.05 3.20 9.06
April, 2012 V September, 2011 C XD HD MV WSE XD HD MV 3.43 9.12 19.53 0.31 3.37 1.01 3.75 15.95 3.85 5.15 6.78 5.14 1.18 1.66 5.69 23.84 6.39 1.71 1.50 1.15 1.65 10.37 11.73 7.90 2.04 2.51 0.27 1.58 7.37 15.37 8.78 8.59 0.70 1.72 0.55 8.92 20.48 19.01 1.70 0.37 0.44 0.32 6.28 5.51 7.89 0.87 1.02 1.35 0.57 8.29 12.84 10.66 3.14 2.09 1.02 1.44 10.21 1.92
WSE 4.43 7.24 2.31 4.24 5.98 3.47 3.10 4.39
January, 2012 V XD HD MV 8.10 5.44 10.20 4.27 1.38 8.90 5.81 4.48 1.86 3.04 2.90 2.02 8.39 7.58 1.67 0.82 2.70 2.33 1.54 5.37 1.64 4.57 4.26 4.09 4.33
WSE: water surface elevation; XD: Maximum depth; HD: hydraulic depth; MV: mean cross-sectional velocity; C and V refer to the calibrated and validated model respectively.
3.2 Unsteady river section models 3.2.1 Calibrated seasonal roughness The daily discharge and water surface elevation data in 2010 of the four river sections were collected from the gauge stations. The discharge series were adopted as input for the unsteady models, and the water surface elevation series were used to evaluate the quality of the HEC-RAS output. The principle of the seasonal roughness calibration is to achieve the minimum water surface elevation error by modifying the Manning’s n of every month manually. In our study, we standardized the seasonal roughness factor according to the basic Manning roughness we got from the first field campaign in September. The seasonal roughness for all the river sections is shown in Tab. 3. Tab. 3. Seasonal roughness factors of the models A B C
JAN 0.88 0.67 0.63
FEB MAR APR MAY JUN JUL AUG SEP OCT NOV DEC 0.94 0.44 0.44 0.88 0.88 1.24 1.30 1.00 0.94 0.44 0.50 0.83 0.50 0.58 0.83 0.83 1.42 1.50 1.00 0.83 0.50 0.50 0.67 0.37 0.47 0.68 0.75 1.19 1.27 1.00 0.95 0.58 0.63
D 0.80 0.87 Mean 0.74 0.83
0.53 0.46
0.60 0.52
0.80 0.87 1.20 0.80 0.83 1.26
1.33 1.00 1.35 1.00
1.00 0.93
0.53 0.51
0.60 0.56
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Fig. 4 shows the daily modeled water surface elevation and the gauged records in 2010 under seasonal roughness condition. High agreement between two data series can be identified. Both the coefficient of determination (R2) and the Nash-Sutcliffe (Nash) at all four study sites were higher than 0.90. The mean absolute error of each point ranges from 2.47 cm to 7.33 cm. All these data proved the high efficiency and reliability of the models.
Note: Mae, mean absolute error of the water surface elevation (cm).
Fig. 4. The calibrated HEC-RAS modeled and real measured water surface elevation
The averaged absolute error of the output from the single roughness model and the seasonal roughness modified model were compared, and the results were give in Fig. 5. The adoption of seasonal roughness factor distinctly improved the performance of the unsteady model. As shown in Fig. 5, the under estimation was as high as 30cm in 12
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single roughness simulation, especially in July and August, when the vegetation density and coverage is under peak status. In general, the deviation from the modeled data to real measured data decreased around 1cm in site A and B, while the decrease reached 7 cm in point C and D respectively, when the single roughness was substituted by the seasonal roughness in the 2010 simulation.
Note: Water stage error_1 – model error under general mean roughness condition; Water stage error_2 – model error under seasonal roughness conditions
Fig 5. Model errors under different roughness conditions
3.2.2 Seasonal roughness validation The validation of the seasonal roughness factor were made with gauged data from 1990 to 2009. As shown in Fig. 6, in general the under estimation appeared mainly in the low water level period. In flood season the inconsistency were especially high for the peak flow simulations. Although the mean absolute error increased by around 3 cm compared with the calibrated model, the validated model output fit the real
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measured data well. The determination coefficients (R2) of the 4 models were higher than 0.90, and the Nash-Sutcliffe (Nash) varied from 0.75 to 0.86. This suggested the seasonal roughness factor is applicable in the long-term hydraulic simulation.
Note: Mae, mean absolute error of the water surface elevation (cm).
Fig. 6. The validated HEC-RAS modeled and real measured water surface elevation
3.2.3 Seasonal roughness coefficient The Fig. 7 gives the seasonal roughness factor and the averaged value of the four study sites. Late summer was the season with highest roughness value due to the high amount of vegetation, especially from July to August (Fig. 7). In addition to early winter (November to December), the lowest roughness occurred also in mid-spring (March to April) (Fig.7). The variation of the seasonal Manning’s n revealed the 14
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growing trend of the vegetation, from March till August.
Fig. 7. Seasonal roughness factors of four river sections
The modified monthly Manning’s n of every study sites were mapped in Fig.8. It clearly showed that the roughness of point D was the lowest in all the seasons, with a variation from 0.02 to 0.05. The roughness for the other three river sections was higher and had a value range from 0.02 to 0.10. Besides, the monthly roughness of section A, B and C, were more identical both in changing trends and absolute values. Temporarily, the roughness coefficient reached the peak value in August, while in March, the monthly roughness coefficient of the four study points was relatively lower, 0.02 – 0.03. The roughness condition of every site was with higher variation in summer and early autumn, from June to October (Fig. 8).
Fig. 8. The calibrated Manning roughness value at each study site
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4. Discussion 4.1 The roughness calibration and validation The quality of the calibrated models was better than the validated ones, according to the determination coefficient, Nash-Sutcliffe and mean absolute error. The main reason might be the variation of the geometry in the long-term flow process. Earlier research in this area suggested an averaged 2.85 cm sediment deposition trend in the study time (Song et al., 2015). Although the sediment depth was not much compared with the river depth and water surface elevation, but due to the sensitivity of the river bed gradient in lowland area, the hydraulic condition would be affected. In this study, we carried out the field measurement to collect the detail geometry information of river section in September, 2011, which would more precisely reflect the geometry condition in 2010 than that in 1990. Furthermore, the calibration period is only 1 year, much less than the validation period, which was 19 year. Long term simulation involved more uncontrollable factors, such as the affects of climate change, human activity, etc.. Therefore, the performance of the calibrated model was more precise.
4.2 Estimated and modified Manning roughness values One of the main findings in the current study is that a very wide range of Manning roughness coefficient within one year cycle was identified at all the study sites. The Manning roughness coefficient estimated from field campaign ranged from 0.04 to 0.07, while the modified Manning roughness value ranged from 0.02 to 0.10, as shown in Fig. 7. The mean Manning roughness value was 0.05 in sections A, B and C, 0.03 in section D. It is equivalent to the mean Manning roughness under high to very 16
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high vegetation condition according to the look-up table (V. Chow, 1959). Based on the field observation, the vegetation coverage of the sites in this study was around 90% and 70% on the side bank in summer and winter respectively, while the channel bed was covered around 30% in summer and nearly no vegetation in winter. According to the river categorization, rivers with vegetation coverage higher than 40% is an representative of high weedy reach (O’Hare et al., 2010). This means the four study sections are typically high vegetated rivers, especially in summer. The estimated temporal and calibrated seasonal roughness mainly reflects the real vegetation condition, and the model performance is applicable and reasonable.
4.3 Seasonal roughness in similar area The analysis of the continuous stage records of 25 years from River Test, Hampshire (USACE) found that the maximum roughness value occurred in late summer, and the lowest value appeared in winter (Gurnell and Midgley, 1994). A positive linear relationship between plant coverage and n was set up for the shallow reaches in New Zealand (Champion and Tanner, 2000). The results showed that n increased from 0.05 to 0.5 with the vegetation coverage increased from 0 to 100%. The Manning roughness value in some river reaches in England and Scotland varied ±50% from the annually mean values (O’Hare et al., 2010). Measurement in Egypt lowland area revealed a clearly season pattern of the n value (Bakry et al., 1992), while the temporal monthly averaged n estimated within a year ranged from 0.017 to 0.062 for canals with emergent ditch-bank vegetation, the value range is from 0.028 to 0.074 for the canals with submerged vegetation. The flume experiment indicated that the 17
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roughness is controlled by the density of the plant cover in the channel (Watson, 1987). The Manning’s n varied from 0.005 to 0.045 with biomass 0-5 kg/m2. The field survey in River Ebble recorded the monthly average from February to August, and revealed the highest Manning’s n in July and August, and the lowest value in February. In our study, the maximum roughness values occurred in late summer, which confirmed the results of earlier research in similar area. Nevertheless, the minimum roughness period showed in early spring was not evidenced before.
4.4 Manning roughness, water depth and temperature According to Fig. 7 and 8, the lowest seasonal roughness coefficient appeared in middle Spring (March to April), instead of Winter months (December to February) in our study, despite the similar vegetation condition. The main reason lies in the impact of water depth on roughness factor. The roughness coefficient inversely correlated to the water depth in the same river section in winter half year (Fig. 9). According to a laboratory study, the flow resistance are composed of bed resistance and stem resistance, which are caused by the river bed geometry and aquatic plants respectively (Stone and Shen, 2002). They further set up the calculation formula of bed resistance, and pointed out the inverse proportional relation between bed resistance and flow depth. Owing to the very little aquatic plants in winter half year, the Manning roughness of the four study reaches from November to April can be representative by bed resistance. Therefore, the Manning roughness from dry months (January to February) was relatively lower compared that of the other four months in winter half year. Although the vegetation is the dominate factor in hydraulic condition in the 18
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lowland river networks, it is not the only factor. Both field and laboratory study suggested that the manning roughness is proportional to the vegetation density but inversely proportional to the water depth (Shih and Rahi, 1982; Tsihrintzis and Madiedo, 2000; Wu et al., 1999). The integrated affects of vegetation and water level can be evidenced by the Manning’s roughness coefficient of river section D. With similar vegetation condition, but much higher averaged water depth, the monthly Manning coefficient of section D was much lower than that of the other three sections all year around (Fig. 8, 9).
Fig. 9. The calibrated Manning roughness coefficients against water depth of each section in winter half year (from November to April)
With the increasing of air temperature, form winter to spring, the viscosity of water decreased. The resistance between water body and river bed, as well as between flow layers decreased correspondingly. The combination of low biomass, higher water
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level and higher water temperature of early spring in the winter half year, leaded to the minimum Manning roughness.
4.5 Manning roughness and hydraulic model The HEC-RAS model showed high sensitivity to the channel and floodplain friction, varying from 20% to 100% in the simulation (Horritt and Bates, 2002b; Shahrokhnia and Javan, 2007). One of the main source of the model uncertainty attribute to the calibration of the effective roughness parameter (Pappenberger et al., 2005). Due to the effects of the vegetation and its seasonality, the vegetation resistance has been pointed out to increase the uncertainty of the hydraulic models. Earlier research about roughness variation mainly carried out in field measurement, and setting up roughness model to evaluate the affection of biomass (De Doncker et al., 2011; Mahmoudi et al., 2013; Meléndez Robledillo et al., 2006; O’Hare et al., 2010; Parhi et al., 2012; Wiberg and Smith, 1991). The hydraulic model research paid more attention in seeking accurate roughness for the single flood simulation or roughness for special condition. The current research embedded seasonal roughness variation into long-term unsteady flow analysis was not found in the earlier publication yet. This study is trying to merge the gap between seasonal roughness and hydraulic model to improve the prediction accuracy. Due to the zonality of the climate, the vegetation and precipitation has local disciplinarian in each catchment, which in turn leads to the various annual patterns of the vegetation biomass and water level. Therefore, the seasonal roughness factor we proposed in this study provides reference for hydrology and hydraulic research, but also has limitation to be transferred to catchments under 20
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different climate condition.
5. Conclusion and outlook In this study, we collected the 1 m elevation model, 20 years gauged discharge and water surface elevation series, together with the geometry, roughness information of river cross section of four river reaches in Germany lowland catchment. Based on these data, the one dimension hydraulic models were set up, calibrated and validated, to evaluate the seasonal variation of roughness and its affect to the model performance. The main findings show that, 1) the Manning’s n varied from 46% to 135% from the basic value in Autumn; 2) the quality of the modeled water surface elevation improved after the adoption of seasonal roughness factor, the error decreased by 1 cm in section A/B and 7 cm in section C/D; 3) the roughness condition was controlled by vegetation, water level and temperature. The bed vegetation dominated the roughness condition in summer half year (May October), while the water elevation has more influence in winter half year; 4) the highest value of Manning’s n occurred in late summer, July to August, and the minimum roughness was in early spring, March to April.
With the growing and withering of the plants, the resistance they made to the river flow varies. Apart from that, the roughness condition is influenced by flow depth and water viscosity, which is dominated by precipitation and temperature. The three factors, plant, precipitation and temperature, constitute various climate region, each of 21
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which presents instinct seasonality. This is the source of the Manning’s roughness seasonality. The current paper presents a methodology and set up a hydraulic model incorporated the seasonal roughness to reveal the affects of the vegetation to the flow process. The improvement of model performance caused by the seasonal roughness factor would support the high precision models, especially for flood simulation. The modelers must have also in mind that the combination of side bank vegetation, river bed vegetation, as well as water level should be paid enough attention during roughness estimation and calibration.
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