# Sensitivity analysis of temporal variability of convective rainfall on urban surface water floods

Akshay Kowlesser, Olivier Payrastre, Pierre Nicolle, Eric Gaume

<figure><img src="image_1.png" /></figure>

## To cite this version:

Akshay Kowlesser, Olivier Payrastre, Pierre Nicolle, Eric Gaume. Sensitivity analysis of temporal variability of convective rainfall on urban surface water floods. Journées Doctorales en Hydrologie Urbaine - JDHU 2024, Oct 2024, Nantes, France. . hal-04978901

HAL Id: hal-04978901

https://univ-eiffel.hal.science/hal-04978901v1

Submitted on 6 Mar 2025

HAL is a multi-disciplinary open access
archive for the deposit and dissemination of sci-
entific research documents, whether they are pub-
lished or not. The documents may come from
teaching and research institutions in France or
abroad, or from public or private research centers.

L'archive ouverte pluridisciplinaire HAL, est
destinée au dépôt et à la diffusion de documents
scientifiques de niveau recherche, publiés ou non,
émanant des établissements d'enseignement et de
recherche français ou étrangers, des laboratoires
publics ou privés.

<figure><img src="image_2.png" /></figure>

Distributed under a Creative Commons Attribution 4.0 International License
# Sensitivity analysis of temporal variability of convective rainfall on urban surface water floods

KOWLESSER Akshay (1)*, PAYRASTRE Olivier (1), NICOLLE Pierre (1), GAUME Éric (1)

(1) GERS-LEE, Univ. Gustave Eiffel, IFSTTAR, 44344 Bouguenais, France

*akshay.kowlesser@univ-eiffel.fr

## 1.0 INTRODUCTION

### 1.1 PROBLEM STATEMENT

<figure><img src="image_3.png" /></figure>

Heavy precipitation **more frequent** and **more intense** with additional **global warming** (IPCC, 2023).

<figure><img src="image_4.png" /></figure>

The share of **urban population** was projected to have increased to **56.9 % in 2022** (UNDESA, 2023).

<figure><img src="image_5.png" /></figure>

Heavy rainfall often cause **surface water floods** in urban areas. **Surface water floods** represent a **significant** part of the total flood damages in France (Moncoulon et al., 2020).

### 1.2 SITE

<figure><img src="image_6.png" /></figure>

### 1.3 OBJECTIVES

i. Investigate short term extreme rainfall and their occurrence based on precise rainfall data.

ii. Categorize rainfall events based on shapes of the hyetographs.

iii. Assess the impacts of the shapes on flood dynamics in small urban catchment areas.

## 2.0 METHODOLOGY

Rainfall Event Extraction with Rainmaker (USGS-R, 2012)

* Spatial resolution : **1km**

* Temporal resolution : **15 min**

* Time Period : **2013 -2023**

<figure><img src="image_7.png" /></figure>

Categorize 2-hour events in 4 shapes :

* Short triangle

* Long Triangle

* Rectangular

* Double Peaks

<figure><img src="image_8.png" /></figure>

2D Hydrodynamic - CARTINO 2D

* CN = 100

* Strickler = 25

* Mesh size = 3m

<figure><img src="image_9.png" /></figure>

## 3.0 RESULTS

### 3.1 EVENT EXTRACTION & CATEGORIZATION

Categorization criteria based on :

i. The **maximum-to-mean rainfall intensity ratio** ($I_{max}/I_{mean}$).

a) low ratio = **homogeneity**, characteristic of a **rectangular shape**,

b) high ratio = **peakiness**, associated with **short or long triangle shapes**.

ii. The **Duration-Rank**$_{50\%}$ and **Duration-Rank**$_{90\%}$ criteria, measure the duration, in minutes, after which **50% and 90% of total rainfall** occurred based on a distribution of **descending order intensities**.

<figure><img src="image_10.png" /></figure>

* Almost all rainfall events can be classified in 4 shapes : Double Peak, Long triangle, Rectangular, Short Triangle

* Long Triangle consist > 50 % of events

* Mean and Median of normalized shapes represent the shape categories well.

* **Double Peak** – Flatter shape on average - **Random Shape selected**

* 16 scenarios chosen for **hydrodynamic simulations** - 4 shapes and 4 return periods (10, 50, 100, 1000 years) for the **2 hours** cumulated rainfall.

### 3.2 FLOOD DYNAMICS - CARTINO 2D

<figure><img src="image_11.png" /></figure>

* Short triangles hyetograph yield highest peak flows.

* Rectangular hyetograph yield lowest peak flows

<figure><img src="image_12.png" /></figure>

Scan here for the abstract

## 3.0 RESULTS

### 3.3 EXAMPLE : 100 YEAR RETURN PERIOD - SHORT TRIANGLE & RECTANGULAR

Relative Difference in Q (m³/s)
Short Triangle - Rectangular

<figure><img src="image_13.png" /></figure>

Relative Difference in H (m)
Short Triangle - Rectangular

<figure><img src="image_14.png" /></figure>

Difference in H (m)
Short Triangle - Rectangular

<figure><img src="image_15.png" /></figure>

### 3.4 DISTRIBUTION OF DIFFERENCE IN MAX HEIGHTS PER RESPONSE TIME

0 - 30 min

<figure><img src="image_16.png" /></figure>

30 - 60 min

<figure><img src="image_17.png" /><figcaption>Max difference in height</figcaption></figure>

60 - 90 min

<figure><img src="image_18.png" /></figure>

90 - 120 min

<figure><img src="image_19.png" /></figure>

120 - 180 min

<figure><img src="image_20.png" /></figure>

180 - 240 min

<figure><img src="image_21.png" /></figure>

* **Highest Inundation** occurs with the **short triangle shape**; **lowest inundation** with **rectangular shape**.

* Relative difference in **discharge** >> Relative difference in **inundation heights**.

* Influence of shapes more pronounced in **upper and middle reaches** of the catchment.

* Maximum difference in the **inundation heights** occur at **60 - 120 min** response times.

## 4.0 CONCLUSION

* **Four distinct types** of rainfall shapes can be automatically identified over a 2-hour storm duration.

* Temporal variation **influence upstream and middle reaches of basin** in decreasing order (Short Triangles, Long Triangles, Double Peaks, Rectangular) & occur between **60 - 120 mins**.

## 5.0 REFERENCES

IPCC (2023) : Sections. In: Climate Change 2023: Synthesis Report. Contribution of Working Groups I, II and III to the Sixth Assessment Report of the Intergovernmental Panel on Climate Change [Core Writing Team, H. Lee and J. Romero (eds.)]. IPCC, Geneva, Switzerland, pp. 35-115, doi: 10.59327/IPCC/AR6-9789291691647.

Moncoulon, D., Marchal, R., & Onfroy, T. (2020). Les risques liés au ruissellement, l'apport des données de sinistralité. La Houille Blanche, 106(6), 56-65. https://doi.org/10.1051/lhb/2020058

UNDRR. (2020). The Human Cost of Disasters: An Overview of the Last 20 Years (2000-2019). Available at: https://www.undrr.org/publication/human-cost-disasters-overview-last-20-years-.

USGS-R (2012) USGS-R/rainmaker: Rainfall event processing version 1.0.2 from github, version 1.0.2 from GitHub. Available at: https://rdrr.io/github/USGS-R/Rainmaker (Accessed: 15 April 2024).

## 6.0 ACKNOWLEDGEMENTS

This project has received funding from the European Union's Horizon 2020 research and innovation programme under the Marie Skłodowska-Curie COFUND grant agreement No 101034248.

The authors thank Météo France and INRAE for providing the rainfall data, to IGN for supplying the digital terrain model data and to the CEREMA for the CARTINO 2D tool.

CLEAR
DOC

CITY LAB EDUCATION
AND RESEARCH
DOCTORAL PROGRAMME

<figure><img src="image_22.png" /></figure>

Université
Gustave
Eiffel

LABORATOIRE LEE
LABORATOIRE EAU
ET ENVIRONNEMENT

<figure><img src="image_23.png" /></figure>

Co-funded by
the European Union