# Remote Sensing Methodology for Roughness Estimation in Ungauged Streams for Different Hydraulic/Hydrodynamic Modeling Approaches

^{1}

^{2}

^{3}

^{*}

## Abstract

**:**

## 1. Introduction

## 2. Materials and Methods

#### 2.1. Study Area

^{2}. The altitude of the specific watershed ranges from 52 to 1600 m, and the mean slope of the area is 28%. The largest part of the specific watershed is covered mainly by forest and semi-natural areas (80.47%) and agricultural areas (19.54%). The selection of the stream reach is based on the existence of typical bed material usually observed in mountainous and semi-mountainous streams and the complexity of the river topography (Figure 2b,c). In particular, Xerias is a gravel- and cobble-bed stream, which is a typical characteristic of intermittent flow streams experiencing flash flood events. The torrential character of the specific stream reach and the river bed is unchanged in the last decades and without severe manmade interventions. Moreover, the Xerias stream, draining through the city of Volos, has experienced frequent flood episodes due to intense storms. In October of 2006, the city of Volos in Magnesia, Greece, was impacted by an extreme flash flood event that damaged several technical infrastructures, transportation networks, and agricultural areas throughout the Xerias River watershed. During this flash flood event, the heavy rainfall, equal to 232 mm, that lasted approximately 12 h caused extended fluvial flooding [41]. It is important to note that the railway bridge, which is located within the study area, collapsed during this flood event. Details of the observed historical flood event of 9 October 2006 and the watershed characteristics can be found in recent studies [30,41,44,45].

#### 2.2. Data, Ground Truth, and Tools Applied

^{2}[8]. In order to examine the bed material in a sufficient way, 11 sampling grids were equally distributed in the entire stream reach of 2.2. km (Figure 2) in locations representative for the under-study stream. In order to minimize the sampling time, the more oversized bed materials with minimum size 10 to 15 cm were measured within the stream and were picked off the stream surface (pebble count; Figure 3a–c) while the bed material of smaller size was collected and measured at the laboratory facilities (Figure 3d). The three mutually perpendicular particle axes that were measured are: the longest (a-axis), the intermediate (b-axis), and the shortest (c-axis) axis. All length measurements were accomplished using a digital caliper (Figure 3d). Finally, the grid count data were classified based on the Wentworth scale (Table 1; [8]) depending on the actual b-axis length (in units of cm), the longest intermediate axis perpendicular to the a-axis as performed in the Canadian guidelines [48].

#### 2.3. Dominant Grain Size Classification via Airborne Image in Streams: Tested Methods

#### 2.4. Manning’s n Roughness Coefficient Estimation Methodology

_{50}, D

_{65}, D

_{84}, and D

_{94}were estimated based on the grid sampling methodology [8]. All empirical relationships (Table 3) were used to determine the river bed Manning’s roughness values. Therefore, boulder, cobble, and gravel Manning’s roughness values were estimated based on the combination of the grid sampling results with all empirical relationships. Thus, the riverbed roughness coefficient was defined for each category using the minimum, median, maximum values, and the maximum value increased by 20% derived from the process mentioned above.

**Table 3.**Empirical relationships proposed by the international literature for assessing Manning’s roughness coefficient (n) values.

A/A | Equation | Reference |
---|---|---|

1 | $\mathrm{n}=0.0431{\mathrm{D}}_{90}^{1/6}$ | [56] |

2 | $\mathrm{n}=0.0439{\mathrm{D}}_{90}^{1/6}$ | [56] |

3 | $\mathrm{n}=0.0593{\mathrm{D}}_{50}^{0.179}$ | [57] |

4 | $\mathrm{n}=0.0561{\mathrm{D}}_{65}^{0.179}$ | [57] |

5 | $\mathrm{n}=0.0495{\mathrm{D}}_{90}^{0.16}$ | [57] |

6 | $\mathrm{n}=\frac{{\mathrm{D}}_{90}^{1/6}}{15.29}$ | [58] |

7 | $\mathrm{n}=\frac{{\mathrm{D}}_{90}^{1/6}}{16}$ | [58] |

8 | Gravel, n = 0.028–0.035 Cobble, n = 0.03–0.05 Boulder, n = 0.04–0.07 | [59] |

9 | $\mathrm{n}=\frac{0.1129{\mathrm{R}}^{1/6}}{1.16+2\mathrm{log}\left(\raisebox{1ex}{$\mathrm{R}$}\!\left/ \!\raisebox{-1ex}{${\mathrm{D}}_{84}$}\right.\right)}$ | [29] |

10 | $\mathrm{n}=\left[0.183+\mathrm{ln}\left(\frac{1.762{\mathrm{S}}_{\mathrm{f}}^{0.1581}}{{\mathrm{Fr}}^{0.2631}}\right)\right]\left(\frac{{\mathrm{D}}_{84}^{0.167}}{\sqrt{\mathrm{g}}}\right)$ | [60] |

11 | $\mathrm{n}=\left[0.183+\mathrm{ln}\left(\frac{1.7462{\mathrm{S}}_{\mathrm{f}}^{0.1581}}{{\mathrm{Fr}}^{0.2631}}\right)\right]\frac{{\left({\mathrm{D}}_{84}\right)}^{1/6}}{\sqrt{\mathrm{g}}}$ | [61] |

12 | $\mathrm{n}=\left[0.183+\mathrm{ln}\left(\frac{1.3014{\mathrm{S}}_{\mathrm{f}}^{0.0785}{\left(\frac{\mathrm{R}}{{\mathrm{D}}_{84}}\right)}^{0.0211}}{{\mathrm{Fr}}^{0.1705}}\right)\right]\frac{{\left({\mathrm{D}}_{84}\right)}^{1/6}}{\sqrt{\mathrm{g}}}$ | [61] |

13 | $\mathrm{n}=\left[0.219+\mathrm{ln}\left(\frac{1.3259{\mathrm{S}}_{\mathrm{f}}^{0.0932}{\left(\frac{\mathrm{R}}{{\mathrm{D}}_{50}}\right)}^{0.026}}{{\mathrm{Fr}}^{0.2054}}\right)\right]\frac{{\left({\mathrm{D}}_{50}\right)}^{1/6}}{\sqrt{\mathrm{g}}}$ | [61] |

^{3}/s), R = hydraulic radius (m), D

_{i}= characteristic size of bed material, which is larger than i% of particles (m), S

_{f}= energy slope (m/m), Fr = Froude number, g = acceleration due to gravity (m/s

^{2}).

#### 2.5. Hydrodynamic Modeling Configuration

^{3}/sec. Several other configurations and model parameters, such as the downstream boundary conditions, were determined in agreement with HEC-RAS standards [53,74].

## 3. Results

#### 3.1. Sediment Grain Size Analysis and Classification

#### 3.1.1. Sediment Grain-Size Distributions (Field Measurements)

#### 3.1.2. Sediment Grain Size Classification via Image Analysis

#### 3.2. Manning’s Roughness Coefficient Estimation

#### 3.3. Hydraulic Simulation Results

## 4. Discussion

## 5. Conclusions

## Author Contributions

## Funding

## Data Availability Statement

## Conflicts of Interest

## References

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**Figure 1.**Flowchart of research methodology steps. Different shades of grey color represent the distinct methodological approaches (the three grey shades ranging from light to dark grey represent the grain size classification based on entropy values, the generation of the spatially distributed roughness coefficient maps, and the estimation of the flood extent, respectively).

**Figure 2.**Xerias stream reach (red dots represent the location of the sampling grids) (

**a**) and inhomogeneous bed composition (

**b**,

**c**).

**Figure 3.**Grid sampling method of 1 m

^{2}[8] applied in grids of inhomogeneous bed composition (

**a**–

**c**) and axes measurements at laboratory facilities (

**d**).

**Figure 4.**The rainfall hyetograph of the 9 October 2006 and the estimated flood hydrograph of the event by applying the Clark Instantaneous Unit Hydrograph (CIUH) method (Adapted from [27]).

**Figure 6.**Cumulative grain-size distributions obtained by grid sampling of 11 grids concerning: all samples count (

**a**), only boulder count (

**b**), only cobble count (

**c**), only gravel count (

**d**).

**Figure 7.**Orthophoto mosaic: (

**a**) Simple Haralick Texture (entropy band) (

**b**) and Stream-bed substrate classification (

**c**) of a northern part along Xerias stream.

**Figure 8.**Grain shape assessment results (with blue dots) and the calculated average shape (marked with black +).

**Figure 10.**Graph with values of the validation indices F1 and F2 for all examined scenarios and modeling approaches: (

**a**) F1 scores, (

**b**) F2 scores.

**Figure 11.**Optimum solutions of simulated maximum water depth and flood extent based on F1 scores: (

**a**) 1D—steady-state modeling approach using debris roughness scenario; (

**b**) 1D—unsteady state modeling approach using debris roughness scenario; (

**c**) Coupled (1D/2D) modeling approach using high roughness scenario; (

**d**) 2D—modeling approach using high roughness scenario.

**Table 1.**Grid count data classification based on Wentworth scale [8].

Substrate Type | Size (cm) |
---|---|

Sand-mud | 0.0062–0.2 |

Gravel | 0.2–6.4 |

Cobble | 6.4–25.6 |

Boulder | 25.6–409.6 |

Bedrock | >409.6 |

**Table 2.**Tested methods and approaches for dominant grain size classification applied to the lower part of Xerias stream.

Approach | Method | Tool | Anticipated Result |
---|---|---|---|

Pixel-based image analysis | Supervised/Unsupervised classification | ISODATA, K-means, Maximum Likelihood Classifier (MLC) | Pixel-based classification map |

Object-based image analysis (OBIA) | ENVI feature extraction module (ENVI v.5.5) | Example and Segment-only approaches | Object-based classification map |

Local image texture analysis (QGIS v.3.18) | Grey-Level Co-occurrence Matrix (GLCM) | Reclassified entropy values (Band 2) interpreting grain size classes |

Predefined Diameters | ds, (m) | Predefined Diameters | ds, (m) | Predefined Diameters | ds, (m) | |||
---|---|---|---|---|---|---|---|---|

Gravel | D_{50} | 0.028 | Cobble | D_{50} | 0.062 | Boulder | D_{50} | 0.219 |

D_{65} | 0.031 | D_{65} | 0.073 | D_{65} | 0.230 | |||

D_{84} | 0.039 | D_{84} | 0.100 | D_{84} | 0.244 | |||

D_{90} | 0.041 | D_{90} | 0.115 | D_{90} | 0.249 |

**Table 5.**Pixel- and object-based image analysis methods that indicated better classification results.

Approach/Method | Bands | Results and Set Parameters |
---|---|---|

Pixel-based image analysis/ Maximum Likelihood Classifier | RGB | Riparian vegetation and the vegetation detected in the middle of the stream-bed |

Boulders and Bedrock classes | ||

Object-based analysis (OBIA)/ ENVI feature extraction module | RGB | Boulders (full lambda; 35 scale value; 50 merge value; SVM) |

Cobbles (polynomial kernel type/ radial basis function) | ||

Red and Intensity | Gravels (SVM; radial or polynomial kernel type) | |

Object-based analysis (OBIA)/GLCM | Haralick Texture file (Entropy band) | Cobbles, Gravels, and Sand-mud |

CODE | Classification Category | Manning’s n Scenarios | |||
---|---|---|---|---|---|

Low | Median | High | Debris | ||

1 | * Bedrock | 0.013 | 0.013 | 0.013 | 0.013 |

2 | Boulder | 0.0301 | 0.0414 | 0.082 | 0.0984 |

3 | Cobble | 0.0265 | 0.035 | 0.073 | 0.0876 |

4 | Gravel | 0.0223 | 0.0297 | 0.068 | 0.0816 |

5 | Sand-Mud | 0.026 | 0.0305 | 0.035 | 0.042 |

6 | Medium size vegetation | 0.04 | 0.06 | 0.08 | 0.096 |

7 | Low vegetation | 0.025 | 0.0375 | 0.05 | 0.06 |

8 | River banks (cobble and gravel) | 0.0223 | 0.0341 | 0.0731 | 0.08772 |

9 | Bare land with low vegetation | 0.03 | 0.035 | 0.05 | 0.06 |

10 | Cobble with low vegetation in some places | 0.0318 | 0.042 | 0.0878 | 0.10536 |

11 | * Continuous urban fabric | 0.06 | 0.09 | 0.12 | 0.12 |

12 | Discontinuous urban fabric | 0.03 | 0.04 | 0.05 | 0.06 |

13 | * Road and rail networks and associated land | 0.013 | 0.013 | 0.013 | 0.013 |

14 | Green urban areas | 0.017 | 0.025 | 0.033 | 0.0396 |

15 | Non-irrigated arable land | 0.025 | 0.035 | 0.045 | 0.054 |

16 | * Olive groves | 0.06 | 0.08 | 0.1 | 0.1 |

17 | Light brush and trees | 0.035 | 0.05 | 0.06 | 0.072 |

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## Share and Cite

**MDPI and ACS Style**

Papaioannou, G.; Markogianni, V.; Loukas, A.; Dimitriou, E.
Remote Sensing Methodology for Roughness Estimation in Ungauged Streams for Different Hydraulic/Hydrodynamic Modeling Approaches. *Water* **2022**, *14*, 1076.
https://doi.org/10.3390/w14071076

**AMA Style**

Papaioannou G, Markogianni V, Loukas A, Dimitriou E.
Remote Sensing Methodology for Roughness Estimation in Ungauged Streams for Different Hydraulic/Hydrodynamic Modeling Approaches. *Water*. 2022; 14(7):1076.
https://doi.org/10.3390/w14071076

**Chicago/Turabian Style**

Papaioannou, George, Vassiliki Markogianni, Athanasios Loukas, and Elias Dimitriou.
2022. "Remote Sensing Methodology for Roughness Estimation in Ungauged Streams for Different Hydraulic/Hydrodynamic Modeling Approaches" *Water* 14, no. 7: 1076.
https://doi.org/10.3390/w14071076