# Dynamic Roughness Modeling of Seasonal Vegetation Effect: Case Study of the Nanakita River

^{1}

^{2}

^{3}

^{*}

## Abstract

**:**

## 1. Introduction

## 2. Study Area

^{2}, the average annual discharge is 10 m

^{3}/s, and the flood discharge of a 100-year return period is 1650 m

^{3}/s. The bed slope of the Nanakita River is about 0.0016 in the relatively upstream reach and about 0.0003 in the lower reach [28].

^{3}, with the purpose of flood control, river flow maintenance, and water supply. According to the prefecture’s administration, the dam was not assigned the control of the river discharge at the time of Typhoon Hagibis.

## 3. Methodology

#### 3.1. UAV Observations

^{–2}.

#### 3.2. Hydrologic Model

#### 3.3. Two-Dimensional Hydraulic Model

#### 3.4. Dynamic Roughness

_{water}is the water depth of the section, and h

_{vegetation}is the vegetation height.

^{3}/s. Both simulations were validated by comparing the water level profile in the peak with the floodmark points at specific locations.

#### 3.5. Vegetation Characteristics

## 4. Results and Discussion

#### 4.1. Vegetation Conditions

#### 4.2. Hydrologic Simulation

^{3}s

^{–1}, lower than the 100-year return period of 1650 m

^{3}s

^{–1}affirmed by Tanaka et al. [26]. Figure 7 shows the discharge in each station, in the upstream section of the UAV-observed area and the outlet. The comparison between the observed and simulated water level in each station is shown in Figure 8. Nash–Sutcliffe efficiency and RMSE were calculated for each station to validate the model. Except for the Kawazaki station, all stations showed Nash–Sutcliffe values ranging from 0.62 to 0.88. Because the Nash–Sutcliffe efficiency of most of the gauge stations was close to 1, the model could be considered sufficiently accurate.

#### 4.3. Hydraulic Simulation

#### 4.4. Seasonal Effect of Vegetation on the Water Profile

^{2}) of vegetation average height versus water level were 0.91 and 0.83, respectively, confirming a stronger relationship than that of the vegetation area versus water level, with R of 0.79 and R

^{2}of 0.63. The parameters are dependent on each other, influencing the overall Manning roughness coefficient of the floodplains.

## 5. Conclusions

^{2}of 0.83. Therefore, considering only the vegetation area to estimate the water level, as does the static roughness model, would be less efficient. Considering the distributed vegetation height together with the vegetation area provides a stronger relationship between the riparian vegetation and the water level.

## Author Contributions

## Funding

## Institutional Review Board Statement

## Informed Consent Statement

## Data Availability Statement

## Acknowledgments

## Conflicts of Interest

## References

- Cowan, W.L. Estimating hydraulic roughness coefficients. Agric. Eng.
**1956**, 37, 473–475. [Google Scholar] - Ebrahimi, N.G.; Fathi-Moghadam, M.; Kashefipour, S.M.; Saneie, M.; Ebrahimi, K. Effects of flow and vegetation states on river roughness coefficients. J. Appl. Sci.
**2008**, 8, 2118–2123. [Google Scholar] [CrossRef][Green Version] - Darby, S.E. Effect of riparian vegetation on flow resistance and flood potential. J. Hydraul. Eng.
**1999**, 125, 443–454. [Google Scholar] [CrossRef] - Sun, X.; Shiono, K.; Rameshwaran, P.; Chandler, J.H. Modelling vegetation effects in irregular meandering river. J. Hydraul. Res.
**2010**, 48, 775–783. [Google Scholar] [CrossRef] - Nikora, V.; Larned, S.; Nikora, N.; Debnath, K.; Cooper, G.; Reid, M. Hydraulic resistance due to aquatic vegetation in small streams: Field study. J. Hydraul. Eng.
**2008**, 134, 1326–1332. [Google Scholar] [CrossRef] - Wang, P.; Wang, C.; Zhu, D.Z. Hydraulic resistance of submerged vegetation related to effective height. J. Hydrodynam.
**2010**, 22, 265–273. [Google Scholar] [CrossRef] - Wilson, C.A.M.E. Flow resistance models for flexible submerged vegetation. J. Hydrol.
**2007**, 342, 213–222. [Google Scholar] [CrossRef] - Wu, F.-C.; Shen, H.W.; Chou, Y.-J. Variation of roughness coefficients for unsubmerged and submerged vegetation. J. Hydraul. Eng.
**1999**, 125, 934–942. [Google Scholar] [CrossRef][Green Version] - Devi, T.B.; Kumar, B. Experimentation on submerged flow over flexible vegetation patches with downward seepage. Ecol. Eng.
**2016**, 91, 158–168. [Google Scholar] [CrossRef] - Jalonen, J.; Järvelä, J.; Aberle, J. Leaf area index as vegetation density measure for hydraulic analyses. J. Hydraul. Eng.
**2013**, 139, 461–469. [Google Scholar] [CrossRef] - Schoneboom, T.; Aberle, J.; Dittrich, A. Hydraulic resistance of vegetated flows: Contribution of bed shear stress and vegetative drag to total hydraulic resistance. In River Flow; Dittrich, A., Koll, K., Aberle, J., Geisenhainer, P., Eds.; Bundesanstalt für Wasserbau: Karlsruhe, German, 2010; pp. 269–276. ISBN 978-3-939230-00-7. [Google Scholar]
- Caroppi, G.; Järvelä, J. Shear layer over floodplain vegetation with a view on bending and streamlining effects. Environ. Fluid Mech.
**2022**, 22, 587–618. [Google Scholar] [CrossRef] - Mohammadi, S.; Kashefipour, S.M. Numerical modeling of flow in riverine basins using an improved dynamic roughness coefficient. Water Resour.
**2014**, 41, 412–420. [Google Scholar] [CrossRef] - Yoshida, K.; Maeno, S.; Ogawa, S.; Mano, K.; Nigo, S. Estimation of distributed flow resistance in vegetated rivers using airborne topo-bathymetric LiDAR and its application to risk management tasks for Asahi River flooding. J. Flood Risk Manag.
**2020**, 13, e12584. [Google Scholar] [CrossRef] - Song, S.; Schmalz, B.; Xu, Y.P.; Fohrer, N. Seasonality of roughness—The indicator of annual river flow resistance condition in a lowland catchment. Water Resour. Manag.
**2017**, 31, 3299–3312. [Google Scholar] [CrossRef] - Carbonell-Rivera, J.P.; Estornell, J.; Ruiz, L.A.; Torralba, J.; Crespo-Peremarch, P. Classification of UAV-based photogrammetric point clouds of riverine species using machine learning algorithms: A case study in the Palancia River, Spain. Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci.
**2020**, XLIII-B2-2020, 659–666. [Google Scholar] [CrossRef] - Casado, M.R.; Gonzalez, R.B.; Kriechbaumer, T.; Veal, A. Automated identification of river hydromorphological features using UAV high resolution aerial imagery. Sensors
**2015**, 15, 27969–27989. [Google Scholar] [CrossRef][Green Version] - Javernick, L.; Brasington, J.; Caruso, B. Modeling the topography of shallow braided rivers using structure-from-motion photogrammetry. Geomorphology
**2014**, 213, 166–182. [Google Scholar] [CrossRef] - van Iersel, W.; Straatsma, M.; Addink, E.; Middelkoop, H. Monitoring height and greenness of non-woody floodplain vegetation with UAV time series. ISPRS J. Photogramm. Remote Sens.
**2018**, 141, 112–123. [Google Scholar] [CrossRef] - Forlani, G.; Dall’Asta, E.; Diotri, F.; di Cella, U.M.; Roncella, R.; Santise, M. Quality assessment of DSMs produced from UAV flights georeferenced with on-board RTK positioning. Remote Sens.
**2018**, 10, 311. [Google Scholar] [CrossRef][Green Version] - Zahidi, I.; Yusuf, B.; Cope, M. Vegetative roughness estimation for hydraulic modelling: A review. Res. Civ. Environ. Eng.
**2014**, 2, 1–10. [Google Scholar] - Shakti, P.C.; Hirano, K.; Iizuka, S. Flood inundation mapping of the Hitachi region in the Kuji River basin, Japan, during the October 11–13, 2019 extreme rain event. J. Disaster Res.
**2020**, 15, 712–725. [Google Scholar] [CrossRef] - MLITT. National Land Data Information. Ministry of Land, Infrastructure, Transport and Tourism. Available online: https://nlftp.mlit.go.jp/ksj/gml/datalist/KsjTmplt-L03-b_r.html (accessed on 7 August 2022).
- Moriguchi, S.; Matsugi, H.; Ochiai, T.; Yoshikawa, S.; Inagaki, H.; Ueno, S.; Suzuki, M.; Tobita, Y.; Chida, T.; Takahashi, K.; et al. Survey report on damage caused by 2019 Typhoon Hagibis in Marumori Town, Miyagi Prefecture, Japan. Soils Found.
**2021**, 61, 586–599. [Google Scholar] [CrossRef] - Tali, M.G.; Tavakolinia, J.; Heravi, A.M. Flood vulnerability assessment in northwestern areas of Tehran. J. Disaster Res.
**2016**, 11, 699–706. [Google Scholar] [CrossRef] - Tanaka, H.; Adityawan, M.B.; Mano, A. Morphological changes at the Nanakita River mouth after the Great East Japan Tsunami of 2011. Coast. Eng.
**2014**, 86, 14–26. [Google Scholar] [CrossRef][Green Version] - Viet, N.T.; Tanaka, H.; Nakayama, D.; Yamaji, H. Effect of morphological changes and waves on salinity intrusion in the Nanakita River mouth. Proc. Hydraul. Eng.
**2006**, 50, 139–144. [Google Scholar] [CrossRef][Green Version] - Pilailar, S.; Sakamaki, T.; Izumi, N.; Tanaka, H.; Nishimura, O. The characteristic change of fine particulate organic matter due to a flood in the Nanakita River. Proc. Hydraul. Eng.
**2003**, 47, 1033–1038. [Google Scholar] [CrossRef] - Japan Dam Foundation. Nanakita Dam [Miyagi Pref.]—Dams in Japan. Available online: http://damnet.or.jp/cgi-bin/binranA/enAll.cgi?db4=0302 (accessed on 7 August 2022).
- Sato, S.; Kure, S.; Moriguchi, S.; Udo, K.; Imamura, F. Online information as real-time big data about heavy rain disasters and its limitations: Case study of Miyagi Prefecture, Japan, during Typhoons 17 and 18 in 2015. J. Disaster Res.
**2017**, 12, 335–346. [Google Scholar] [CrossRef] - Nastiti, K.D.; Kim, Y.; Jung, K.; An, H. The application of rainfall-runoff-inundation (RRI) model for inundation case in upper Citarum watershed, West Java-Indonesia. Procedia Eng.
**2015**, 125, 166–172. [Google Scholar] [CrossRef][Green Version] - Bhagabati, S.; Kawasaki, A. Consideration of the rainfall-runoff-inundation (RRI) model for flood mapping in a deltaic area of Myanmar. Hydrol. Res. Lett.
**2017**, 11, 155–160. [Google Scholar] [CrossRef][Green Version] - San, Z.M.L.T.; Zin, W.W.; Kawasaki, A.; Acierto, R.A.; Oo, T.Z. Developing flood inundation map using RRI and SOBEK models: A case study of the Bago River basin, Myanmar. J. Disaster Res.
**2020**, 15, 277–287. [Google Scholar] [CrossRef] - Sayama, T.; Ozawa, G.; Kawakami, T.; Nabesaka, S.; Fukami, K. Rainfall–runoff–inundation analysis of the 2010 Pakistan flood in the Kabul River basin. Hydrol. Sci. J.
**2012**, 57, 298–312. [Google Scholar] [CrossRef] - Ishizaki, H.; Matsuyama, H. Distribution of the annual precipitation ratio of radar/raingauge-analyzed precipitation to AMeDAS across Japan. SOLA
**2018**, 14, 192–196. [Google Scholar] [CrossRef] - Yamazaki, D.; Ikeshima, D.; Sosa, J.; Bates, P.D.; Allen, G.H.; Pavelsky, T.M. MERIT hydro: A high-resolution global hydrography map based on latest topography dataset. Water Resour. Res.
**2019**, 55, 5053–5073. [Google Scholar] [CrossRef][Green Version] - Iwasa, Y.; Inoue, K. Mathematical simulations of channel and overland flood flows in view of flood disaster engineering. J. Nat. Disaster Sci.
**1982**, 4, 1–30. [Google Scholar] - Inoue, K.; Nakagawa, H.; Toda, K. Numerical analysis of overland flood flows by means of one-and two-dimensional models. In Proceedings of the 5th JSPS-VCC Seminar on Integrated Engineering, Engineering Achievement and Challenges, Johor Bahru, Malaysia, 12–14 November 1994; pp. 388–397. [Google Scholar]
- Hashimoto, M.; Yoneyama, N.; Kawaike, K.; Deguchi, T.; Hossain, M.A.; Nakagawa, H. Flood and substance transportation analysis using satellite elevation data: A case study in Dhaka City, Bangladesh. J. Disaster Res.
**2018**, 13, 967–977. [Google Scholar] [CrossRef] - Japan Institute of Country-ology and Engineering. Manual of plans for river channel (in Japanese). In Manual of Plans for River Channel; Sankaidou: Tokyo, Japan, 2002. [Google Scholar]
- Arcement, G.J.; Schneider, V.R. Guide for Selecting Manning’s Roughness Coefficients for Natural Channels and Flood Plains; Water Supply Paper 2339; United States Federal Highway Administration: Washington, DC, USA, 1989; pp. 1–44. [CrossRef][Green Version]
- Weidner, U.; Förstner, W. Towards automatic building extraction from high-resolution digital elevation models. ISPRS J. Photogramm. Remote Sens.
**1995**, 50, 38–49. [Google Scholar] [CrossRef] - de Doncker, L.; Troch, P.; Verhoeven, R.; Bal, K.; Meire, P.; Quintelier, J. Determination of the Manning roughness coefficient influenced by vegetation in the river Aa and Biebrza river. Environ. Fluid Mech.
**2009**, 9, 549–567. [Google Scholar] [CrossRef]

**Figure 1.**Study area: (

**a**) Location of the Nanakita River basin in Miyagi prefecture; (

**b**) basin map with the topography used as calculation area for the rainfall-runoff inundation model; (

**c**) the 2 km stretch of the Nanakita River obtained from the UAV observation of September 2019 used as calculation area for the 2D hydraulic model.

**Figure 3.**Relationship between the Manning roughness coefficient and the degree of submergence for different types of vegetation hardness and the regression curve.

**Figure 5.**Image of the DEM (

**left**), DSM (

**center**), and vegetation height (

**right**); vegetation height is the result of DSM–DEM for the vegetated grid cells.

**Figure 6.**Vegetated area and vegetation average height in the UAV-observed stretch shown in Figure 1c from April 2020 to March 2021.

**Figure 7.**RRI simulated discharges in all the water level gauge stations shown in Figure 1b, in the UAV upstream section, and in the outlet.

**Figure 9.**RRI-simulated peak inundation caused by the typhoon event in the river basin shown in Figure 1b.

**Figure 10.**Simulated water level profiles of the simple Manning roughness coefficient setting simulation and the dynamic Manning roughness coefficient calculation simulation.

**Figure 11.**Comparison of simulated and observed water level values from the static roughness model using several Manning values.

**Figure 12.**Pixel-level behavior of the Manning roughness coefficient estimation along the simulation time for: (

**a**) the dynamic roughness model in September 2019 at point 1 from Figure 1c, (

**b**) the dynamic roughness model in September 2019 at point 2 from Figure 1c, (

**c**) the static roughness model at point 1 in Figure 1c, and (

**d**) the static roughness model at point 2 in Figure 1c.

**Figure 13.**Water level profiles simulated by the 2D hydraulic model using the dynamic roughness model with: (

**a**) the vegetation average height and (

**b**) vegetation area from April 2020 to March 2021.

**Figure 14.**Linear relationship between the water level in the middle section of the UAV observed area and the vegetation parameters: (

**a**) area; (

**b**) average height.

Parameter | Group 1 | Group 2 |
---|---|---|

Accuracy | 0.99 | 0.96 |

Precision | 0.93 | 0.84 |

Misclassification | 0.01 | 0.04 |

Recall | 0.98 | 0.74 |

Manning | RMSE |
---|---|

0.022 | 0.931 |

0.038 | 0.671 |

0.040 | 0.641 |

0.050 | 0.522 |

0.060 | 0.421 |

0.068 | 0.354 |

0.070 | 0.340 |

Publisher’s Note: MDPI stays neutral with regard to jurisdictional claims in published maps and institutional affiliations. |

© 2022 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/).

## Share and Cite

**MDPI and ACS Style**

Araújo Fortes, A.; Hashimoto, M.; Udo, K.; Ichikawa, K.; Sato, S.
Dynamic Roughness Modeling of Seasonal Vegetation Effect: Case Study of the Nanakita River. *Water* **2022**, *14*, 3649.
https://doi.org/10.3390/w14223649

**AMA Style**

Araújo Fortes A, Hashimoto M, Udo K, Ichikawa K, Sato S.
Dynamic Roughness Modeling of Seasonal Vegetation Effect: Case Study of the Nanakita River. *Water*. 2022; 14(22):3649.
https://doi.org/10.3390/w14223649

**Chicago/Turabian Style**

Araújo Fortes, André, Masakazu Hashimoto, Keiko Udo, Ken Ichikawa, and Shosuke Sato.
2022. "Dynamic Roughness Modeling of Seasonal Vegetation Effect: Case Study of the Nanakita River" *Water* 14, no. 22: 3649.
https://doi.org/10.3390/w14223649