In particular, when boulder or cobble with a large particle size is on the riverbed, it will be necessary to be able to describe a roughness layer (e.g. Nikora et al.
12th ISE 2018, Tokyo, Japan
DEVELOPMENT OF THE NUMERICAL SIMULATION MODEL AIMING AT DIRECT HABITAT EVALUATION OF RIVERBEDS OF GRAVEL-BED RIVERS MORIHIRO HARADA River Basin Research Center, Gifu University, 1-1 Yanagido Gifu, Gifu 501-1193, Japan
RAHMA YANDA River Basin Research Center, Gifu University, 1-1 Yanagido Gifu, Gifu 501-1193, Japan
This study focuses on "riverbed environment" which is the foundation of the stream ecosystem in the upper and middle reaches. This study aims to develop a numerical simulation model that can directly evaluate habitat of organisms dependent on riverbed at the same time as calculating flow and river bed variation in reach scale by changes in flow rate and sediment supply from upstream. First, the concept of the riverbed environment model is shown. Based on this, this study discusses what kind of function should be implemented in numerical calculation method. Next, the outline and results of numerical simulation model in development are shown. The Nays 2D solver of IRIC which is a general horizontal two - dimensional riverbed variation solver was improved, and the riverbed environment model coupled with the grain size distribution, flow resistance, porosity was implemented. As a result, it became possible to obtain not merely the necessary information for habitat evaluation of the riverbed, but also the precision of riverbed variation calculation itself improved. Then, the concept for evaluating the swimming fish habitat volume by estimating the flow profile including the roughness layer is introduced. By implementing this concept into the improved horizontal two - dimensional model, it will be possible to directly evaluate the volume of the swimming fish and benthic specie’s habitat.
1
INTRODUCTION
In the upstream region of the river, the terrestrial organic matters mainly fallen leaves support benthic organisms, the primary production by adherent algae and aquatic plants play a role in the middle streams. The place where organic matter such as fallen leaves is trapped, the place where adherent algae produces, the place where benthic organisms live are all "riverbeds", and the material and energy circulation in gravel-bed rivers is based on river beds. In addition, riverbeds are known to have multifaceted functions in river ecosystems such as usual habitats, feeding sites, spawning grounds, evacuation places of various organisms. The state of the riverbed that provides various ecological functions is diverse, the state is changed by erosion and accumulation of sediments by the flow. This study focuses on "riverbed environment" which is the foundation of the stream ecosystem in the upper and middle reaches. And this study aims to develop a numerical simulation model that can directly evaluate habitat of organisms dependent on riverbed at the same time as calculating flow and river bed variation in reach scale by changes in flow rate and sediment supply from up stream. 2
CONCEPT OF RIVERBED ENVIRONMENT MODELING
2.1 Concept of riverbed environment and its modeling The authors defines the physical condition of the habitat in the river bed, such as the existence state of sediment constituting the riverbed and the state of the corresponding water flow, as "riverbed environment". The authors reviewed the results of sediment hydrology, stream ecology, geomorphology, etc. on the riverbed environment in the upper and middle reaches, and organized the components of the riverbed environment in Figure 1. Factors related to the physical environmental elements constituting the riverbed environment were shown corresponding to the spatial scale of Frissell et al. (1986). According to the definition of Frissell et al., the riverbed environment is considered to belong to microhabitat, but its spatial distribution is dominated by the topography larger than the reach scale. Also, the riverbed environment
changes at the same point due to changes in flow rate and sediment transport. Therefore, in order to evaluate the riverbed environment by numerical simulation, it will be necessary to satisfy the following three requirements. That is, 1) it is possible to handle the reach scale simulation, 2) the local condition of sediment, local flow and sediment transport are calculated at the same time, and 3) the discharge fluctuations of a wide range can be handled with high accuracy. In particular, when boulder or cobble with a large particle size is on the riverbed, it will be necessary to be able to describe a roughness layer (e.g. Nikora et al. 2004) with a thickness that cannot be ignored with respect to the size of the aquatic life.
Figure 1. Physical properties and factors of riverbed environment. Modified from Harada & Kayaba (2015).
2.2 Functions required for models that can directly evaluate riverbed habitat Next, numerical simulation model satisfying the three requirements described in the previous section are considered. Calculation of riverbed variation due to flooding is generally done by horizontal two - dimensional model in many cases (left side of Figure 2). This kind of model is suitable for solving the riverbed variation during large flood, applicable from reach scale to segment scale. However, when the flow discharge is small (relative submergence is small), the calculation precision of the flow resistance decreases with using fixed roughness coefficient. Also, the physical properties on riverbed environment is hardly expressed.
Figure 2. Comparison of model concepts which can handle the riverbed environment. The center of the three is the proposed one.
Meanwhile, three dimensional two - phase model have been developed as a method that can calculate the interaction between three - dimensional flow and particles (right side of Figure 2). This kind of models are so powerful, that it is expected not only to clarify the sediment transport phenomenon but also to elucidate the mechanism of habitat of aquatic life. On the other hand, since it requires a considerable amount of computation, it seems to be difficult to handle space larger than the reach scale. The schematic image of the model the authors are developing is shown in the center of Figure 2. Features of the model under development are as follows: 1) the characteristics of the flow field including the roughness layer are considered, 2) flow resistance is counted by considering the roughness height and relative submergence, 3) surface topography is considered with surface grain size distribution (GSD), and 4) porosity of riverbed is treated as a variable with GSD. With these features, this new model can solve flow and riverbed variation from usual water level to large flood, applicable from reach scale to segment scale. The information necessary for habitat evaluation of riverbed environment is included in the model, and habitat evaluation is possible directly from analysis results. The authors continue to develop this model and partial improvements have already been made. 3
IMPROVEMENT OF HORIZONTAL TWO-DIMENSIONAL MODEL
3.1 Base model and improvements Next, the outline and results of numerical simulation model in development are shown. The Nays 2D solver of IRIC which is a general horizontal two - dimensional riverbed variation solver was improved. Nays 2D solver is numerical simulation model which was developed by Shimizu et al. (Jang & Shimizu 2005). The model can handle the flow and riverbed variation in shallow channel with high accuracy, it was widely introduced through the IRIC Project community (URL: http://i-ric.org/en/) and used throughout the world. The authors attempted to expand the Nays 2D model in order to realize a model suitable for evaluating the riverbed environment, as shown in the previous section. At the present time, the flow resistance and the porosity have been improved, quasi three dimensionalization has not been completed yet. 3.1.1 Improvement of calculation method of flow resistance In the upper and middle streams, the sediment particles are larger relative to the flow depth when the flow rate is low. In order to evaluate usual habitat, it is necessary to be able to evaluate the flow condition at the usual flow rate with high accuracy. In such a situation where the relative water depth is small, Manning's law is known to underestimate resistance. This issue was already confirmed by the high discrepancy between calculated and measured velocity ratios, which was carried out over 2890 field- measurement data sets (Rickenmann & Recking 2010). In Nays 2D, the Manning’s coefficient is used as a constant to evaluate flow resistance and bed sheer stress. It is common to treat coefficients related to flow resistance as constants. Therefore, several models were tried as expressions that can accurately evaluate flow resistance at wide range of relative water depth. Rickenmann & Recking showed that Hey’s formula (1979) showed high accuracy in a wide range of dataset, despite being a very old formula. Therefore, the source code was modified so that Hey's and some expressions could be selected. Although the original Nays 2D model treats Manning coefficient as a constant, we add the Manning-Strickler formula (eq.1) and Hey’s formula (eq.2) so that Manning coefficient can be calculated as a variable with GSD in each grid. U/u* is calculated from water depth and GSD information at each grid, each time step, it is converted to Manning coefficient and used for calculation. ∗
8.3
∗
6.25
(1) 5.75
.
(2)
3.1.2 Implementation of porosity calculation In Nays 2D solver, porosity is also treated as a constant. A value of about 0.4 is often used as the porosity, it is a value under uniform particle size condition. Fujita et al. (2008) developed the method which can evaluate the porosity in the mixed grain size condition. This model classify the grain size distribution into typical distribution type such as log-normal and Talbot distribution, and the functions to evaluate the porosity value are given for each
typical distributions. Source code was added so that the porosity of each grid can be calculated from GSD using Fujita's model. 3.2 Validation by experiment results on meandering flume In order to verify the improved model, we compared it with experimental results done in past research. The results of the flume experiment under mixed particle size condition performed in the flume whose planar shape is represented by a sine generated curve were extracted from the past literature (Ashida et al.1990) and the experiment was reproduced by simulation. The phenomenon confirmed by the experiment was a complicated result that river bed variation and sorting effect. For the improved model, it was expected to improve the calculation accuracy of the flow velocity and the water depth. Comparing Manning's coefficient as a constant, Manning-Strickler formula and Hey's formula, it seems that the result using Hey's formula seems to be closest to the experiment (Figure. 3). Furthermore, the calculation result of surface GSD was greatly improved than the flow velocity and water depth (Figure. 4).
Experiment
(A) Normal model (constant)
(B) Manning-Strickler
(C) Hey
Figure 3. Velocity distribution of the experimental result and model simulations.
Experiment
(A) Normal model (constant)
(B) Manning-Strickler
(C) Hey
Figure 4. Mean diameter distribution of the experimental result and model simulations.
The porosity changed in the range of 0.26 to 0.39 by introducing the Fujita model. Since the porosity was not measured in the experiment, it is not possible to verify the simulation result. But since the porosity has a physically close relationship with the GSD, it is considered that a more correct value is obtained by using Fujita model than handling the porosity as a constant value. As a result of improvement, the riverbed environment model coupled with the GSD, flow resistance, porosity was implemented. Although this model is under development, it became possible to obtain the information necessary for habitat evaluation of the riverbed, but also the precision of riverbed variation calculation itself improved.
4
MODELING OF FLOW VELOCITY PROFILES INCLUDING ROUGHNESS LAYER
4.1 Flow characteristics of roughness layer and estimation of velocity profile By improving the horizontal two – dimensional model, the grain size distribution, flow resistance and porosity were combined. With this improvement, the volume of space available to Benthos is directly expressed. It may be possible to evaluate clogging of river beds due to finer sediments (sediment pollution) etc. It is also expected to be able to express the places suitable for spawning grounds of fishes and refreshing river beds due to floods. In addition, by correcting the flow resistance evaluation formula, the reproducibility of the flow near the shoreline part when the flow rate is low will be improved. Therefore, it is expected to improve accuracy when using a model such as PHABSIM that uses flow velocity and water depth as indicators (Waddle 2001). However, the flow velocity distribution of the flow field with large roughness has a wide flow velocity and complicated turbulence near the river bed, as characterized by the roughness layer. Rahma et al. (2016) showed that the reducing of spatial variance of spatially averaged mean velocity within the roughness layer associated with decreasing of bed roughness as the sediment fills the voids among cobbles. It was also observed that the decreasing of roughness layer thickness as the bed roughness reduced. What these facts show is that the change in the surface topography of the river bed not only changes the average flow velocity but also significantly changes the quality of habitat for aquatic lives. The authors believe that the diversity of roughness layer should be noticed in habitat evaluation. In connection with the above discussion, several studies aimed at estimating the flow velocity profile including the roughness layer have also been conducted. The authors have tried a method to estimate a flow velocity profile including a roughness layer by a model as simple as possible. There are problems with the method of defining the thickness of the roughness layer and the method of determining the Karman constant, so it has not reached the practical level.
zc
zc
zm
zm
zt
zt
(a) Cobble only
(b) Cobble with gravels
zc zm zt
(c) Cobble almost covered with gravels
Figure.5 Spatial-averaging of mean flow velocity for case A, B and C (σV is spatial variance of mean flow velocity ). z is distance from the flume bed. Modified from Rahma et al. (2016).
4.2 Application to habitat evaluation By implementing the estimation of the flow velocity profile (discussed in 4.1) into the improved horizontal two dimensional model (shown in 3.), it will be possible to directly evaluate the volume of the swimming fish habitat focusing on the flow velocity. Harada et al. (2017) proposed the method of habitat evaluation for water-column fish. The method focuses on the velocity profile, which is spatially heterogeneous in the roughness layer. The method uses the local velocity as index of swimming fish habitat compared with cruising speed of each fish species, so that the method can evaluate the volume which is able to be used as swimming fish. The most important point of this method is to use the flow velocity profile instead of the bulk flow velocity or the depth averaged velocity as an index, because the living fishes choose the area where they can swim, for example, form induced sublayer slowed down by the cobbles or boulders, etc. This method can provide a quantitative estimation for fish habitats with changes in river bed conditions.
5
CONCLUSION
In this paper, the outline and results of numerical simulation model aiming at evaluating habitat of riverbed environment currently being developed are reported. The Nays 2D solver of IRIC was improved, and the riverbed environment model coupled with the grain size distribution, flow resistance, porosity was implemented. As a result, it became possible to obtain not merely the necessary information for habitat evaluation of the riverbed, but also the precision of riverbed variation calculation itself improved. There are still problems in estimating the flow velocity profile including the roughness layer, we will continue to develop the simulation model which include the information necessary for habitat evaluation of riverbed environment, and habitat evaluation is possible directly from analysis results. ACKNOWLEDGMENTS This work was partly supported by JSPS KAKENHI Grant Number JP16K21074. We wish to thank Professor Yasuyuki Shimizu Hokkaido University for lending the source code of IRIC Nays 2D solver, Dr. Kazutake Asahi who supported setting up the development environment. REFERENCES [1] Frissell C. A., Liss W. J., Warren C. E. and Hurley M. D. “A hierarchical framework for stream habitat classification: Viewing streams in a watershed context”, Environmental Management, 10(2), (1986), pp 199-214. [2] Nikora V., Koll K., McEwan I., McLean S. and Dittrich A. “Velocity Distribution in the Roughness Layer of RoughBed Flows”, Journal of Hydraulic Engineering, 130, 10, (2004), pp1036-1042. [3] Harada M. and Kayaba Y., “Perspective on study of riverbed environment in the upper and middle reaches”, Ecology and Civil Engineering, Vol. 18-1, (2015), pp 3-18. [4] Jang, C. and Y. Shimizu “Numerical simulation of relatively wide, shallow channels with erodible banks”, Journal of Hydraulic Engineering, ASCE, Vol. 131, No.7, (2005), pp 565-575. [5] Rickenmann, D. and Recking, A. “Evaluation of flow resistance in gravel-bed rivers through a large field data set”, Water Resources Research, Vol. 47, (2011) p 22. [6] Hey R. D. “Flow resistance in gravel-bed rivers”, Journal of the Hydraulic Division, 91(HY4), (1979), pp 365-379. [7] Fujita M., Sulaiman M., Ikhsan J. and Tsutsumi D, “A bed-porosity variation model and its application”, Advances in River Engineering, JSCE,vol.14, (2008), pp 13-18. [8] Ashida K., Egashira S., Liu B. and Umemoto M., “Sorting and bed topography in meander channels”, Annuals. Disas. Prev. Res. Inst. Kyoto Univ., No.33 B-2, (1990), pp 261-279. [9] Waddle, T., “PHABSIM for Windows user's manual and exercises” (No. 2001-340), (2001), pp 288. [10] Rahma Y., Harada M. and Tamagawa I. “The effect of sediment supply on hydraulic characteristics of flow over the imbricated cobbles”, Journal of JSCE, Ser.B1(Hydraulic Engineering), Vol. 72, No. 4, (2016), pp I_613-I_618. [11] Harada M., Rahma Y., Onoda Y. and Kayaba Y. “Swimming fish habitat evaluation concept focusing on flow characteristics around the roughness layer in streams”, E-proceedings of the 37th IAHR World Congress, (2017), pp 2596-2601.
