# Multitemporal SAR Data and 2D Hydrodynamic Model Flood Scenario Dynamics Assessment

^{1}

^{2}

^{3}

^{*}

## Abstract

**:**

## 1. Introduction

## 2. The Bradano River Case Study and the Available Dataset

^{2}in the Basilicata Region (Southern Italy), stretching for 8 km upstream of the mouth of the Bradano River (Figure 1). The surrounding land cover is mainly agricultural, characterized by cereal and vegetable crops or orchards, while an extended area near the river mouth and along the coast is occupied by pinewoods. Moreover, several residential buildings and agricultural enterprises are located in the floodplains, with quite a few tourist resorts near the basin outlet (i.e., along the coast). When considering that the mouth of the Bradano River is at high risk of flooding due to its intrinsic lithological and geomorphological properties [27], and that this location is advantageous for human population development in many perspectives, urban development occurred over time, along with construction of several structural mitigation measures (levees and banks, but also artificial hills and bridges), rendering the topography of the area extremely complex. Despite many efforts to protect against inundations in this area, several flood events have occurred recently, with increasing consequences for agriculture, industries and dwellings [28]. In this study, we focus on two such recent events, which took place, respectively, on 3–4 November 2010 and 2–3 December 2013.

^{3}/s. The considered event was triggered by an amount of rainfall that reached about 63 mm in one day (measured at the Matera gauging station upstream of the study area), causing damage to the residential and agricultural sectors, tourist resorts, and the main railway in the Taranto-Reggio Calabria stretch, which became unusable due to water and mud.

^{3}/s. The event considered was triggered by a significant amount of rainfall that reached about 100 mm in one day (measured at the Matera city gauging station upstream of the study area), while the total amount of rainfall reached about 120 mm. In particular, as officially reported by Basilicata Regional Civil Protection, the Bradano River flooded, impacting large areas that were concentrated along the riverbanks, leading to closure of the S.S. 106 Jonica A-road, namely one of the main highways in the area, and extensive damage to agriculture, to dwellings, and to the Greek archeological site of Tavole Palatine. In the days that followed, the phenomenon entered a recession stage and flood effects developed toward the coast, affecting smaller areas progressively farther away from the river.

## 3. Method

#### 3.1. SAR Image-Processing Algorithm for Flood Extent Extraction

#### 3.2. Water Depth Estimation Combining SAR Images and a Digital Elevation Model

^{2}. The resulting laser scanner points cloud had a vertical accuracy of about 0.15 m and horizontal accuracy of 0.30 m. When considering that the increase of DEM resolution corresponds to exponential computational time growth in hydraulic model simulation performance, it was considered appropriate to guarantee high altimetric detail of the hydraulic elements that have most influence on water flow (such as river reach dimension, cross-sections, banks, and levees, etc.), through manual manipulation rather than using an excessively detailed DEM grid resolution size [41]. Therefore, a square grid of 10 m resolution was used for input of the 2D hydraulic model, while the geometries of the most important hydraulic elements were entered in the grid to maintain good characterization of study area topography. Indeed, resolution should be chosen in relation to model structure and complexity, which always have limitations [42]. For instance, the FLORA-2D model cannot include inertial terms in governing equations, and therefore has low sensitivity to small-scale DEM features, meaning that close field flow processes are smoothed out, even when high-resolution grids are used [43].

#### 3.3. Flood Inundation Modeling

#### 3.4. Hydraulic Model Calibration Framework Using SAR- and DEM-Derived Flood Maps

_{j}and the SAR-derived map for a CSK revisit time of t

_{i}; $MSEmi{n}_{{t}_{i}}$ is the minimum value of all the MSE values, at the same t

_{i}temporal instant, associated to all FLORA-2D-simulation runs characterized by different values of the c

_{j}Manning coefficient. The equation outcome (Equation (3)) was used to judge the performance of the hydraulic model runs.

## 4. Results and Discussion

#### 4.1. Model Calibration

^{2}) for the simulation with variable Manning time-and-space coefficients in all of the considered time steps indicated that this simulated scenario was in substantial agreement with the SAR observations performed on the subsequent days and that water recession as observed by SAR was consistent from the physical point of view. The coefficient of determination (R

^{2}) was calculated using the following formulas:

_{i}and P

_{i}are the observed SAR and DEM derived water levels and predicted by the hydraulic model of the ith cell, respectively; $\overline{O}$ and $\overline{P}$ are the means of the observed and simulated value. R

^{2}ranges from 0 to 1, where higher values represent good model performance, and R

^{2}> 0.5 is generally considered satisfactory [60].

^{2}, Unsystematic RSME RSMEu, fraction bias FB, prediction within a factor of two of observation FA2, i.e., fraction of data contained in the interval $0.5\text{}{O}_{i}{P}_{i}2.0\text{}{O}_{i}$. Unsystematic RSME and fraction bias are calculated using the following formulas:

#### 4.2. Validation Performance Measures

## 5. Conclusions

## Acknowledgments

## Author Contributions

## Conflicts of Interest

## References

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**Figure 3.**Example of contingency map obtained by pixel-to-pixel comparison of the hydraulic simulation using variable spatio-temporal Manning coefficients, as in [45], and the Synthetic Aperture Radar (SAR)-derived flood extent map at the same time step, i.e., 4:47 a.m. of 3 November 2010.

**Figure 4.**Comparison between the 6 hydrodynamic simulations characterized by a different set of Manning coefficient values and the SAR- and DEM-derived water-depth map for 3 November 2010.

**Figure 5.**Comparison between the 6 hydrodynamic simulations characterized by a different set of Manning coefficient values and the SAR- and DEM-derived water depth map for 4 November 2010.

**Figure 6.**Comparison between the 6 hydrodynamic simulations characterized by a different set of Manning coefficient values and the SAR- and DEM-derived water depth map for 2 December 2013.

**Figure 7.**Comparison between the 6 hydrodynamics simulations characterized by a different set of Manning coefficient values and the SAR- and DEM-derived water-depth map for 3 December 2013.

**Table 1.**Characteristics of the Cosmo-SkyMed (CSK) observations provided by the Italian Space Agency (ASI).

Acquisition Time | Polarization | Orbit | |
---|---|---|---|

10 October 2010 | 4:47 a.m. | VV ^{1} | RA ^{1} |

3 November 2010 | 4:47 a.m. | VV | RA |

4 November 2010 | 4:47 a.m. | VV | RA |

16 November 2013 | 4:31 a.m. | HH ^{1} | RA |

2 December 2013 | 4:31 a.m. | HH | RA |

3 December 2013 | 4:31 a.m. | HH | RA |

^{1}VV = vertical polarization, HH = horizontal polarization, RA = right ascending. RD = right descending.

**Table 2.**Results of the calibration performance of the hydraulic simulation model for 3 November 2010 in terms of a flood extent map: the set of Manning coefficients c, false positive rate ${r}_{fp}$, true positive rate ${r}_{tp}$, false negative rate ${r}_{fn}$, minimized sum of errors (MSE), and accuracy, sensitivity, and specificity.

Statistical Performance Measurements of Flood-Extent Estimation 3 November 2010 | |||||||
---|---|---|---|---|---|---|---|

${\mathit{r}}_{\mathit{f}\mathit{p}}$ | ${\mathit{r}}_{\mathit{t}\mathit{p}}$ | ${\mathit{r}}_{\mathit{f}\mathit{n}}$ | MSE | Accuracy | Sensitivity | Specificity | |

c | |||||||

0.04 for channel and floodplains | 7.71 | 85.02 | 14.98 | 22.69 | 91.64 | 85.02 | 92.29 |

0.08 for channel and floodplains | 9.69 | 86.01 | 13.99 | 23.68 | 89.93 | 86.01 | 90.31 |

0.1 for channel and floodplains | 9.62 | 83.92 | 16.08 | 25.7 | 89.8 | 83.92 | 90.38 |

0.33 for channel and 0.06 for floodplains | 9.39 | 85.97 | 14.03 | 23.42 | 90.19 | 85.97 | 90.61 |

0.025 for channel and 0.01 for floodplains | 7.59 | 84.76 | 15.24 | 22.83 | 91.72 | 84.76 | 92.41 |

variable in time and space | 9.24 | 86.91 | 13.09 | 22.33 | 90.41 | 86.91 | 90.76 |

**Table 3.**Results of the hydraulic simulation model calibration performance for 4 November 2010 in terms of the flood extent map: the set of Manning coefficients c, false positive rate ${r}_{fp}$, true positive rate ${r}_{tp}$, false negative rate ${r}_{fn}$, minimized sum of errors (MSE), and accuracy, sensitivity, and specificity.

Statistical Performance Measurement of Flood-Extent Estimation 4 November 2010 | |||||||
---|---|---|---|---|---|---|---|

${\mathit{r}}_{\mathit{f}\mathit{p}}$ | ${\mathit{r}}_{\mathit{t}\mathit{p}}$ | ${\mathit{r}}_{\mathit{f}\mathit{n}}$ | MSE | Accuracy | Sensitivity | Specificity | |

c | |||||||

0.04 for channels and floodplains | 7.08 | 72.11 | 27.89 | 34.97 | 92.13 | 72.11 | 92.92 |

0.08 for channel and floodplains | 14.04 | 81.76 | 18.24 | 32.28 | 85.8 | 81.76 | 85.96 |

0.1 for channel and floodplains | 21.28 | 86.18 | 13.82 | 35.1 | 79.011 | 86.18 | 78.72 |

0.33 for channel and 0.06 for floodplains | 9.96 | 78.45 | 24.55 | 34.51 | 89.6 | 78.45 | 90.04 |

0.025 for channel and 0.01 for floodplains | 6.96 | 71.63 | 28.37 | 35.33 | 92.22 | 71.63 | 93.04 |

variable in time and space | 16.88 | 87.6 | 12.4 | 29.28 | 83.29 | 87.6 | 83.12 |

**Table 4.**Results of the calibration performance of the hydraulic simulation model for 3 November 2010 in terms of water depth: the relative value of Manning coefficients c, Root Mean Square Error RMSE, Nash- Sutcliffe Efficiency Coefficient (NSE), coefficient of determination R

^{2}, Unsystematic RSME RSMEu, fraction bias FB, prediction within a factor of two of observation FA2.

Statistical Performance Measurements of Water-Depth Estimation 3 November 2010 | ||||||
---|---|---|---|---|---|---|

RSME (m) | NSE | R^{2} | RSMEu (m) | FB | FA2 | |

c | ||||||

0.04 for channel and floodplains | 0.39 | 0.53 | 0.64 | 0.46 | 0.21 | 0.84 |

0.08 for channel and floodplains | 0.50 | 0.22 | 0.51 | 0.45 | −0.26 | 0.78 |

0.1 for channel and floodplains | 0.68 | −0.41 | 0.24 | 0.47 | −0.34 | 0.66 |

0.033 for channel and 0.06 for floodplains | 0.36 | 0.61 | 0.63 | 0.45 | −0.76 | 0.88 |

0.025 for channel and 0.01 for floodplains | 0.40 | 0.50 | 0.64 | 0.45 | 0.23 | 0.82 |

variable in time and space | 0.32 | 0.69 | 0.7 | 0.45 | 0.06 | 0.91 |

**Table 5.**Results of the calibration performance of the hydraulic simulation model for 3 November 2010: the relative value of Manning coefficients c, Root Mean Square Error RMSE, Nash- Sutcliffe Efficiency Coefficient (NSE), coefficient of determination R

^{2}, Unsystematic RSME RSMEu, fraction bias FB, prediction within a factor of two of observation FA2.

Statistical Performance Measurements of Water-Depths Estimation 4 November 2010 | ||||||
---|---|---|---|---|---|---|

RSME (m) | NSE | R^{2} | RSMEu (m) | FB | FA2 | |

c | ||||||

0.04 for channel and floodplains | 0.30 | 0.28 | 0.70 | 0.25 | 0.37 | 0.81 |

0.08 for channel and floodplains | 0.27 | 0.50 | 0.68 | 0.44 | −0.16 | 0.93 |

0.1 for channel and floodplains | 0.37 | 0.07 | 0.64 | 0.46 | −0.32 | 0.84 |

0.033 for channel and 0.06 for floodplains | 0.26 | 0.53 | 0.57 | 0.34 | 0.08 | 0.91 |

0.025 for channel and 0.01 for floodplains | 0.31 | 0.22 | 0.69 | 0.24 | 0.4 | 0.8 |

variable in time and space | 0.19 | 0.76 | 0.76 | 0.33 | 0.01 | 0.95 |

**Table 6.**Results of hydraulic simulation model calibration performance for 2 December 2013 in terms of a flood extent map: the set of Manning coefficients c, false positive rate ${r}_{fp}$, true positive rate ${r}_{tp}$, negative rate ${r}_{fn}$, minimized sum of errors (MSE), and accuracy, sensitivity, and specificity.

Statistical Performance Measurements of Flood-Extent Estimation 2 December 2013 | |||||||
---|---|---|---|---|---|---|---|

${\mathit{r}}_{\mathit{f}\mathit{p}}$ | ${\mathit{r}}_{\mathit{t}\mathit{p}}$ | ${\mathit{r}}_{\mathit{f}\mathit{n}}$ | MSE | Accuracy | Sensitivity | Specificity | |

c | |||||||

0.04 for channel and floodplains | 10.35 | 66.07 | 33.93 | 44.28 | 85.17 | 66.07 | 89.65 |

0.08 for channel and floodplains | 12.71 | 76.17 | 23.83 | 36.54 | 85.17 | 76.17 | 87.29 |

0.1 for channel and floodplains | 30.52 | 92.55 | 7.45 | 37.97 | 73.86 | 92.55 | 64.48 |

0.033 for channel and 0.06 for floodplains | 13.57 | 72.95 | 27.05 | 40.62 | 83.87 | 72.95 | 86.43 |

0.025 for channel and 0.01 for floodplains | 10.17 | 65.88 | 34.12 | 44.29 | 85.28 | 65.88 | 89.89 |

variable in time and space | 24.17 | 98.53 | 1.47 | 25.64 | 80.15 | 98.53 | 75.83 |

**Table 7.**Results of hydraulic simulation model calibration performance for 3 December 2013 in terms of a flood extent map: the set of Manning coefficients c, false positive rate ${r}_{fp}$, true positive rate ${r}_{tp}$, false negative rate ${r}_{fn}$, minimized sum of errors (MSE), and accuracy, sensitivity, and specificity.

Statistical Performance Measurements of Flood-Extent Estimation 3 December 2013 | |||||||
---|---|---|---|---|---|---|---|

${\mathit{r}}_{\mathit{f}\mathit{p}}$ | ${\mathit{r}}_{\mathit{t}\mathit{p}}$ | ${\mathit{r}}_{\mathit{f}\mathit{n}}$ | MSE | Accuracy | Sensitivity | Specificity | |

c | |||||||

0.04 for channel and floodplains | 9.24 | 45.12 | 54.88 | 64.12 | 87.01 | 45.12 | 90.76 |

0.08 for channel and floodplains | 20.7 | 70.37 | 29.63 | 50.33 | 78.57 | 70.37 | 79.3 |

0.1 for channel and floodplains | 32.13 | 91.93 | 8.07 | 40.2 | 69.85 | 91.93 | 67.87 |

0.033 for channel and 0.06 for floodplains | 14.05 | 52.74 | 47.26 | 61.31 | 83.22 | 52.74 | 85.95 |

0.025 for channel and 0.01 for floodplains | 8.97 | 44.69 | 55.31 | 64.28 | 87.22 | 44.69 | 91.03 |

variable in time and space | 18.16 | 81.27 | 18.73 | 36.89 | 81.79 | 81.27 | 81.84 |

**Table 8.**Results of hydraulic simulation model calibration performance for 2 December 2013 in terms of water depth: the relative value of the Manning coefficients c, Root Mean Square Error RMSE, Nash- Sutcliffe Efficiency Coefficient (NSE), coefficient of determination R

^{2}, Unsystematic RSME RSMEu, fraction bias FB, prediction within a factor of two of observation FA2.

Statistical Performance Measurements of Water-Depths Estimation 2 December 2013 | ||||||
---|---|---|---|---|---|---|

RSME (m) | NSE | R^{2} | RSMEu (m) | FB | FA2 | |

c | ||||||

0.04 for channel and floodplains | 0.40 | 0.19 | 0.51 | 0.44 | 0.26 | 0.79 |

0.08 for channel and floodplains | 0.46 | −0.16 | 0.52 | 0.42 | 0.43 | 0.63 |

0.1 for channel and floodplains | 0.61 | −0.89 | 0.58 | 0.68 | −0.41 | 0.77 |

0.033 for channel and 0.06 for floodplains | 0.34 | 0.42 | 0.57 | 0.50 | −0.05 | 0.84 |

0.025 for channel and 0.01 for floodplains | 0.40 | 0.15 | 0.52 | 0.43 | 0.28 | 0.78 |

variable in time and space | 0.26 | 0.65 | 0.71 | 0.45 | −0.09 | 0.93 |

**Table 9.**Results of hydraulic simulation model calibration performance for 3 December 2013: the relative value of the Manning coefficients c, Root Mean Square Error RMSE, Nash- Sutcliffe Efficiency Coefficient (NSE), coefficient of determination R

^{2}, Unsystematic RSME RSMEu, fraction bias FB, prediction within a factor of two of observation FA2.

Statistical Performance Measurements of Water Depths Estimation 3 December 2013 | ||||||
---|---|---|---|---|---|---|

RSME (m) | NSE | R^{2} | RSMEu (m) | FB | FA2 | |

c | ||||||

0.04 for channel and floodplains | 0.32 | 0.55 | 0.6 | 0.38 | 0.16 | 0.79 |

0.08 for channel and floodplains | 0.33 | 0.42 | 0.7 | 0.48 | −0.30 | 0.74 |

0.1 for channel and floodplains | 0.43 | −0.15 | 0.6 | 0.51 | −0.42 | 0.58 |

0.033 for channel and 0.06 for floodplains | 0.21 | 0.70 | 0.74 | 0.43 | −0.10 | 0.81 |

0.025 for channel and 0.01 for floodplains | 0.33 | 0.51 | 0.51 | 0.36 | 0.18 | 0.77 |

variable in time and space | 0.22 | 0.72 | 0.8 | 0.41 | 0.04 | 0.83 |

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## Share and Cite

**MDPI and ACS Style**

Scarpino, S.; Albano, R.; Cantisani, A.; Mancusi, L.; Sole, A.; Milillo, G.
Multitemporal SAR Data and 2D Hydrodynamic Model Flood Scenario Dynamics Assessment. *ISPRS Int. J. Geo-Inf.* **2018**, *7*, 105.
https://doi.org/10.3390/ijgi7030105

**AMA Style**

Scarpino S, Albano R, Cantisani A, Mancusi L, Sole A, Milillo G.
Multitemporal SAR Data and 2D Hydrodynamic Model Flood Scenario Dynamics Assessment. *ISPRS International Journal of Geo-Information*. 2018; 7(3):105.
https://doi.org/10.3390/ijgi7030105

**Chicago/Turabian Style**

Scarpino, Santina, Raffaele Albano, Andrea Cantisani, Leonardo Mancusi, Aurelia Sole, and Giovanni Milillo.
2018. "Multitemporal SAR Data and 2D Hydrodynamic Model Flood Scenario Dynamics Assessment" *ISPRS International Journal of Geo-Information* 7, no. 3: 105.
https://doi.org/10.3390/ijgi7030105