# Assessing the Performance of LISFLOOD-FP and SWMM for a Small Watershed with Scarce Data Availability

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## Abstract

**:**

## 1. Introduction

## 2. Methodology

#### 2.1. Case Study

#### 2.2. Numerical Models Adopted

#### 2.2.1. SWMM

_{f}is friction slope, and g is the gravitational acceleration.

#### 2.2.2. LISFLOOD-FP

^{t}is flow from the previous time step Q

^{t}divided by cell width Δx, S is the water surface slope between cells, n is Manning’s roughness coefficient, and h

_{flow}is the depth between cells through which water can flow, defined by the water depths and cell elevations.

- The “acceleration” solver neglects the convective acceleration term;
- The “Roe” solver solves the full shallow-water equations. Despite being the most comprehensive solver, it has been reviewed only for a limited number of scenarios.

#### 2.3. Data Collection and Manipulation

## 3. Results

#### 3.1. Model Validation without Calibrating Data

#### 3.1.1. SWMM

#### 3.1.2. LISFLOOD-FP

- Using raw DEM as the surface grid: aiming to select the most suitable inputs based on reference map values;
- Using the “height-corrected DEM” (Figure 3b) as the surface grid: aiming to select the most suitable surface grid based on field observations.

#### Raw DEM

#### Height-Corrected DEM

#### 3.2. Model Performance

#### 3.2.1. SWMM

^{3}.

#### 3.2.2. LISFLOOD-FP

#### 3.3. Microscale Modeling

#### 3.3.1. SWMM

#### 3.3.2. LISFLOOD-FP

## 4. Discussion

#### 4.1. Are the Results of These Products Rational?

#### 4.2. Are the Input Parameters of these Products Adjustable to Achieve Realistic Results?

#### 4.3. Are the Models User-Friendly?

#### 4.4. Are These Products Time Consuming in Terms of Pre-Processing, Computational, and Post-Processing Time?

#### 4.5. Are These Products Suitable for Microscale Modeling?

#### 4.6. Are the Freeware Products Suitable for the Commercial Companies?

## 5. Conclusions

- Free numerical tools can compete with paid commercial ones; however, the pre- and post-processing times are typically longer, and it should also be mentioned that these products can be used for 2D models, without reaching the accuracy that more realistic and robust 1D/2D paid models are able to achieve. Freeware products make some simplifications in their modeling approach such as taking a uniform infiltration or precipitation into account, which can lead to inaccuracies; however, this study demonstrated that this limitation can be solved by discretizing the study area manually by the user;
- This work has shown that by putting extra attention and effort into collecting even limited, targeted and specific input data, reasonable results can still be generated, especially if what is needed should have been used for the calibration process;
- Finally, generating essential information about a specific area or at the microscale is dependent on the resolution of the provided input data, and this is valid for both freeware and commercial tools. However, this study demonstrated that, even with an initial lack of input datasets, freeware products are able to produce high-resolution results, even if not at a large scale but at local points.

## Author Contributions

## Funding

## Institutional Review Board Statement

## Informed Consent Statement

## Data Availability Statement

## Acknowledgments

## Conflicts of Interest

## References

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**Figure 4.**Manning’s n values: (

**a**) Roughness coefficient automatic generated by QGIS; (

**b**) roughness coefficient based on suggested values in the literature.

**Figure 5.**Predicted flood extent and depth after the precipitation: (

**a**) reference flood map [30]; (

**b**) simulation using the Roe solver with the suggested roughness values in the literature.

**Figure 6.**Predicted flood extent and depth after the precipitation: (

**a**) reference flood map [30]; (

**b**) simulation using Roe solver with the DEM-base roughness coefficient values.

**Figure 7.**Predicted flood extent and depth after the precipitation: (

**a**) reference flood map [30]; (

**b**) simulation using an acceleration solver, and the SWMM manual roughness coefficient values.

**Figure 8.**Predicted flood extent and depth after the precipitation: (

**a**) reference flood map [30]; (

**b**) simulation using acceleration solver, and the DEM-base roughness coefficient values.

**Figure 9.**Predicted flood extent and depth after the precipitation: (

**a**) flood map on the surface model which was used for the simulation; (

**b**) existing water in the artificial pond.

**Figure 10.**Predicted flood extent and depth after the rain event: (

**a**) reference flood map [30]; (

**b**) simulation using acceleration solver, and the DEM-base auto-generated roughness coefficient values on the height-corrected DEM.

**Figure 11.**Significant differences between the reference map and the map generated by the LISFLOOD-FP: (

**a**) the area for which the DEM data were incorrect; (

**b**) the front view of the structure in the pond.

**Figure 12.**(

**a**) Calculation results after the model precipitation ends; (

**b**) calculation results 24 min after the precipitation starts.

**Figure 13.**(

**a**) Flood hazard map of understudy watershed for a 100-year rain event with a duration of one hour generated by LISFLOOD-FP; (

**b**) maximum predicted water velocities for a 100-year rain event.

**Figure 14.**The total runoff amount predicted by the original model compared to the total runoff amount predicted by the downsized model.

**Figure 15.**Comparison of the downsized model with the original model: (

**a**) flood hazard map of the downsized model for a 100-year rain event with a duration of one hour generated by LISFLOOD-FP; (

**b**) flood hazard map of the original model for a 100-year rain event with a duration of one hour generated by LISFLOOD-FP.

**Figure 16.**Downsizing the model without increasing the DEM resolution: (

**a**) DEM of the downsized watershed with a resolution of one meter; (

**b**) predicted hazard map for the downsized watershed with a DEM resolution of one meter.

Data | Provider |
---|---|

Sewerage system | site-specific surveying, Computer-Aided Design (CAD) maps |

Losses | engineering fluid mechanics [39] |

Depression storage depth | user’s guide to SWMM [40] |

Manning’s coefficient | user’s guide to SWMM [40], QGIS |

Infiltration parameter | user’s guide to SWMM [40] |

Precipitation | KOSTRA-DWD 2010R software [41] |

Soil characteristics | federal institute for geosciences and natural resources [42] |

Light Detection and Ranging (LiDAR) data | Cologne’s regional administration’s official website [43] |

Digital Elevation Model (DEM) | Cologne’s regional administration’s official website [44] |

Land use | Cologne’s regional administration’s official website [45] |

Surface Type | Depression Storage | Manning’s Coefficient | Maximum Infiltration Rate | Minimum Infiltration Rate | Decay Const |
---|---|---|---|---|---|

Rooftop | 1.30 mm | 0.012 | - | - | - |

Driveway | 1.30 mm | 0.011 | - | - | - |

Vegetation | 5.08 mm | 0.24 | 101.6 mm/h | 7.62 mm/h | 3 h^{−1} |

Forest | 7.62 mm | 0.40 | 203.2 mm/h | 7.62 mm/h | 3 h^{−1} |

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## Share and Cite

**MDPI and ACS Style**

Sadeghi, F.; Rubinato, M.; Goerke, M.; Hart, J.
Assessing the Performance of LISFLOOD-FP and SWMM for a Small Watershed with Scarce Data Availability. *Water* **2022**, *14*, 748.
https://doi.org/10.3390/w14050748

**AMA Style**

Sadeghi F, Rubinato M, Goerke M, Hart J.
Assessing the Performance of LISFLOOD-FP and SWMM for a Small Watershed with Scarce Data Availability. *Water*. 2022; 14(5):748.
https://doi.org/10.3390/w14050748

**Chicago/Turabian Style**

Sadeghi, Farzaneh, Matteo Rubinato, Marcel Goerke, and James Hart.
2022. "Assessing the Performance of LISFLOOD-FP and SWMM for a Small Watershed with Scarce Data Availability" *Water* 14, no. 5: 748.
https://doi.org/10.3390/w14050748