# Potential of Two SAR-Based Flood Mapping Approaches in Supporting an Integrated 1D/2D HEC-RAS Model

^{1}

^{2}

^{*}

## Abstract

**:**

## 1. Introduction

## 2. Materials and Methods

#### 2.1. Study Area

^{2}.

#### 2.2. Methodology

#### 2.2.1. Inundation Boundary Extraction through SAR Image Processing

#### 2.2.2. Hydrologic Simulation in HEC-HMS Software

^{2}, 49 km

^{2}, and 137 km

^{2}, respectively. The flow hydrograph generated at each of these outlets was exploited as upper boundary condition to the respective branch of the main river encompassed in the hydrodynamic simulation (Figure 1).

#### 2.2.3. Hydrodynamic Simulation in HEC-RAS Software

**Q**[L3T-1] is the total flow, A [L2] is the flow area, Φ [dimensionless] is the ratio of channel conveyance over the total conveyance, z [L] is the elevation of water surface, S

_{f}is the friction slope, and the subscripts c and f refer to the channel and floodplain, respectively. HEC-RAS uses an implicit finite difference scheme to approximate these equations and solves them numerically using the Newton–Raphson iteration methodology [67,68,69].

^{−1}], q is the source/sink flux term, g is the gravitational acceleration [LT

^{−2}], v

_{xx}and v

_{yy}are the horizontal eddy viscosity coefficients in the x and y directions [L

^{2}T

^{−1}], f

_{c}is the bottom friction coefficient [Τ

^{−1}], τ

_{s}is the surface wind stress [ML

^{−1}T

^{−2}], h is the water depth [L], ρ is the water density [M/L3], and f is the Coriolis parameter [T

^{−1}]. The left-hand side of Equations (7) and (8) contains the acceleration terms (unsteady and advective accelerations) whereas their right-hand side represents the internal or external forces acting on the fluid. An implicit finite volume algorithm is exploited for the solution of the 2D unsteady flow equations, allowing for larger time intervals compared to the explicit method.

^{−1}], R = R(H) is the hydraulic radius [L] as a function of the water surface elevation, ∇H is the water surface elevation gradient [L] and n is the Manning’s roughness coefficient [TL

^{−1/3}].

^{2}and consisted of 157,000 cells. Figure 5 presents a synoptic view of the area modeled and its main features.

#### 2.2.4. Comparison–Evaluation

_{mod}is the surface area predicted as flooded by the model, and S

_{obs}is that observed as flooded through the SAR image processing.

## 3. Results and Discussion

^{2}up to 17 km

^{2}, and the respective percentage of coincidence (CSI-index) with the SAR results ranging from 17.7–19.6% and 24.5–27% for each SAR methodology, depending on the selected coefficient value. Surprisingly, an even greater influence concerning CSI-index value is observed when the roughness coefficient within the main channel is varied (Figure 7f). Specifically, even though a more restricted value range than that applied for land use 4 was assumed, variation of roughness coefficient in the main channel between its minimum and maximum values leads to an alteration of index value from 23.4% to 27.5% for the FLOMPY approach (or from 17.3 to 19.4% for the simplified approach). Other recent studies that have exploited the CSI-index to compare HEC-RAS simulation results against satellite imagery-derived flood maps produced index scores of 64% and 66% (after calibration and validation, respectively) [77], between 74.2% and 76.6%, depending on the selected polarization (VV or VA) of the SAR products [78], and between 65% and 95% [79]. Moreover, Nguyen et al. [80], who assimilated SAR-derived flood maps to improve simulation results of a TELEMAC-2D model, reported CSI values ranging between 22% and 63.97% depending on the simulated scenario. Comparatively low index values were finally estimated by Ekeu-wei et al. [81], who calibrated a 2D Caesar-Lisflood hydrodynamic model by exploiting: (a) optical (MODIS) data alone and (b) in combination with SAR data in a vegetation-dominant region. The results demonstrated a low performance of the examined model, which attained a 23.5% and 27.3% CSI score for the optimum roughness coefficient, when the optical and the combined data were considered as reference, respectively. More importantly, Figure 7f reveals that the model predictive performance increases for greater values of the main channel roughness coefficient, contrary to the rest of the cases, i.e., land uses 4 and 5, for which better percentages of coincidence with SAR results occur for roughness coefficient values in the lower or middle areas of the selected value ranges. The greater effect of the main channel compared to the floodplain roughness coefficient was also verified by Lamichhane and Sharma [82], who found that the variation of roughness in the first case led to an 8.97% change in the extent of flooded area against a percentage of hardly 1.49% produced by the variation of roughness coefficient in the floodplains. Afzal et al. [77] also reported that a small increase in manning’s value in the main channel of a 2D HEC-RAS model caused a higher increase in the simulated inundated area, compared to a similar increase in the floodplains’ n values.

^{2}to 20.15 km

^{2}). These results are in agreement with our previous study [10] where the response of a simpler 1D hydraulic model as a function of different sources of uncertainty had been investigated. Another study that also confirms the dominant role of input hydrology compared to other components of uncertainty is that of Annis et al. [84], who obtained a 50–220% change in the derived inundation extent by varying input discharge against a 6–9% change by perturbing the roughness coefficient. Other studies that reported similar finding are those of Vojtek et al. [85] and Mosquera-Machado and Ahmad [86]. Specifically, considering the coincidence with FLOMPY results, inflow hydrograph variation produces a range of approximately 5.5% in HEC-RAS model performance, with better percentages emerging for upstream conditions close to the middle–upper inflow scenarios, thus demonstrating a slight underestimation of the initial inflow hydrographs as derived from the hydrologic simulation. In terms of the CSI-index value, it is apparent that the FLOMPY approach shows again a closer similarity with HEC-RAS-derived results, compared to the simplified approach, for all simulated scenarios. H and F indices have a response roughly analogous to the CSI-index, in that they both increase for greater upstream inflow conditions, with the exception that these continue rising even for inflows beyond the “mean plus standard deviation” scenario, where the highest CSI score was identified.

## 4. Conclusions

## Author Contributions

## Funding

## Institutional Review Board Statement

## Informed Consent Statement

## Data Availability Statement

## Acknowledgments

## Conflicts of Interest

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**Figure 1.**Spercheios river basin along with its main drainage network, the three simulated watersheds, and the precipitation gages considered. The river reaches which were included in the hydrodynamic model are also shown.

**Figure 2.**Schematic flow diagram of the methodological framework; (

**A**), (

**B**+

**C**), and (

**D**) correspond to the four individual parts of the methodology, i.e., SAR image processing, hydrologic and hydraulic simulation, and comparison–evaluation, respectively.

**Figure 3.**Standard deviation and mean value of the peak discharge of the hydrographs computed at the outlet of each of the three watersheds of interest for different numbers of samples: (

**a**) Upper Spercheios watershed; (

**b**) Asopos watershed, and (

**c**) Gorgopotamos watershed.

**Figure 4.**Flow hydrographs generated through the Monte Carlo analysis at the outlets of the three simulated watersheds and time of SAR image acquisition: (

**a**) Upper Spercheios watershed; (

**b**) Asopos watershed; (

**c**) Gorgopotamos watershed.

**Figure 6.**Inundation boundaries as extracted through the two different SAR processing methodologies.

**Figure 7.**Inundation area predicted by the model (primary vertical axis) and CSI-index value (%) for each SAR methodology applied (secondary vertical axis) as a function of variation in roughness coefficient value in: (

**a**) land use 2; (

**b**) land use 3; (

**c**) land use 4; (

**d**) land use 5; (

**e**) land use 6; and (

**f**) main channel.

**Figure 8.**Inundation area predicted by the model (primary vertical axis) and Hit Rate and False Alarm index values (%) for each SAR methodology applied (secondary vertical axis) as a function of variation in roughness coefficient value in: (

**a**) land use 2; (

**b**) land use 3; (

**c**) land use 4; (

**d**) land use 5; (

**e**) land use 6; and (

**f**) main channel.

**Figure 9.**SAR-observed (FLOMPY approach) and HEC-RAS-predicted inundated areas for the five indicative upstream inflow scenarios generated through the Monte Carlo uncertainty analysis: (

**a**) minimum hydrograph; (

**b**) mean minus standard deviation hydrograph; (

**c**) mean hydrograph; (

**d**) mean plus standard deviation hydrograph; and (

**e**) maximum hydrograph.

**Figure 10.**Vegetated areas next to the main channel where SAR processing techniques did not detect flood: (

**a**) area covered with high dense vegetation (trees); and (

**b**) area covered with high sparse vegetation (trees and crops).

**Table 1.**Initial and reclassified Land Use (LU) categories and selected roughness coefficient ranges according to the literature.

Corine LU Code | Corine LU Description | % of Total Model Area | % of Total Inundated Area | LU Reclassification According to 2D Modeling User’s Manual | Manning Roughness Coefficient Ranges (s/m^{1/3}) | |
---|---|---|---|---|---|---|

HEC-RAS LU Categorization | Reclassified LU Code | |||||

112 | Discontinuous urban fabric | 2.3 | 0.0 | Developed, Medium Intensity | 1 | 0.06–0.20 |

122 | Road and rail networks and associated land | 1.3 | 0.2 | Paved roads/car park/driveways | 2 | 0.03–0.05 |

133 | Construction sites | 2.2 | 1.3 | Construction sites | 3 | 0.10–0.14 |

211 | Non-irrigated arable land | 1.8 | 4.7 | Cultivated Crops | 4 | 0.03–0.30 |

212 | Permanently irrigated land | 48.7 | 76.3 | |||

223 | Olive groves | 5.6 | 0.0 | |||

242 | Complex cultivation patterns | 6.6 | 0.0 | |||

213 | Rice fields | 28.4 | 16.4 | Emergent Herbaceous Wetlands | 5 | 0.03–0.30 |

411 | Inland marshes | 0.1 | 0.0 | |||

421 | Salt marshes | 0.3 | 0.5 | |||

243 | Land principally occupied by agriculture | 0.6 | 0.6 | Pasture/grasslands | 6 | 0.03–0.40 |

311 | Broad-leaved forest | 0.3 | 0.0 | Mixed forests (either deciduous or evergreen) | 7 | 0.07–0.40 |

313 | Mixed forest | 0.0 | 0.0 | |||

323 | Sclerophyllous vegetation | 0.2 | 0.0 | |||

324 | Transitional woodland-shrub | 1.2 | 0.0 | Shrub/scrub | 8 | 0.05–0.40 |

331 | Beaches, dunes, sands | 0.6 | 0.1 | Barren Land (Rock/Sand/Clay) | 9 | 0.03–0.10 |

**Table 2.**Representative roughness coefficient values per land use (for the 2D flow areas) and river segment (for the 1D river) applied during the sensitivity analysis.

LU Code | Established Roughness Coefficient Range | Mean Roughness Coefficient Value | Max Roughness Coefficient Value | Min Roughness Coefficient Value | 25% of Total Range | 75% of Total Range |
---|---|---|---|---|---|---|

2D Flow Areas | ||||||

1 | 0.06–0.20 | 0.13 | 0.20 | 0.06 | 0.10 | 0.17 |

2 | 0.03–0.05 | 0.04 | 0.05 | 0.03 | 0.035 | 0.045 |

3 | 0.10–0.14 | 0.12 | 0.14 | 0.10 | 0.11 | 0.13 |

4 | 0.03–0.30 | 0.17 | 0.30 | 0.03 | 0.10 | 0.23 |

5 | 0.03–0.30 | 0.17 | 0.30 | 0.03 | 0.10 | 0.23 |

6 | 0.03–0.40 | 0.22 | 0.40 | 0.03 | 0.12 | 0.31 |

7 | 0.07–0.40 | 0.24 | 0.40 | 0.07 | 0.15 | 0.32 |

8 | 0.05–0.40 | 0.23 | 0.40 | 0.05 | 0.14 | 0.31 |

9 | 0.03–0.10 | 0.07 | 0.10 | 0.03 | 0.05 | 0.08 |

River segment | 1D river | |||||

Lower river reaches (4, 5, 7) | 0.03–0.05 | 0.04 | 0.05 | 0.03 | 0.035 | 0.045 |

Middle river reaches (2, 6) | 0.04–0.06 | 0.05 | 0.06 | 0.04 | 0.045 | 0.055 |

Upper river reaches (1, 3) | 0.05–0.07 | 0.06 | 0.07 | 0.05 | 0.055 | 0.065 |

**Table 3.**Total area of inundation derived from HEC-RAS simulation and percentage of coincidence between SAR-observed and model-predicted inundation results for each SAR processing approach and each input hydrograph utilized.

Inflow Hydrograph Derived from MCA Analysis | Model-Predicted Inundation Area (km^{2}) | Index (%) | |||||
---|---|---|---|---|---|---|---|

1st Approach (Simplified) | 2nd Approach (FLOMPY) | ||||||

CSI | HR | FAR | CSI | HR | FAR | ||

Minimum | 11.13 | 16.74 | 41.9 | 78.2 | 21.95 | 36.9 | 64.9 |

Mean minus standard deviation | 13.27 | 17.80 | 49.8 | 78.3 | 24.36 | 44.1 | 64.8 |

Mean | 15.06 | 18.95 | 57.4 | 77.9 | 26.88 | 51.3 | 63.9 |

Mean plus standard deviation | 16.71 | 18.89 | 61.8 | 78.6 | 27.21 | 55.1 | 65.0 |

Maximum | 20.15 | 17.60 | 67.1 | 80.7 | 26.31 | 60.4 | 68.2 |

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**MDPI and ACS Style**

Zotou, I.; Karamvasis, K.; Karathanassi, V.; Tsihrintzis, V.A.
Potential of Two SAR-Based Flood Mapping Approaches in Supporting an Integrated 1D/2D HEC-RAS Model. *Water* **2022**, *14*, 4020.
https://doi.org/10.3390/w14244020

**AMA Style**

Zotou I, Karamvasis K, Karathanassi V, Tsihrintzis VA.
Potential of Two SAR-Based Flood Mapping Approaches in Supporting an Integrated 1D/2D HEC-RAS Model. *Water*. 2022; 14(24):4020.
https://doi.org/10.3390/w14244020

**Chicago/Turabian Style**

Zotou, Ioanna, Kleanthis Karamvasis, Vassilia Karathanassi, and Vassilios A. Tsihrintzis.
2022. "Potential of Two SAR-Based Flood Mapping Approaches in Supporting an Integrated 1D/2D HEC-RAS Model" *Water* 14, no. 24: 4020.
https://doi.org/10.3390/w14244020