Studying Intense Convective Rainfall in Turin’s Urban Area for Urban Flooding Early Warning System Implementation

Abstract

The effects of global warming, coupled with the continuing expansion of urbanization, have significantly increased vulnerability to urban flooding, widespread erosion risks, and related phenomena such as shallow landslides and mudflows. These challenges are particularly evident in both lowland and hill/foothill environments of urbanized regions. Improving resilience to urban flooding has emerged as a top priority at various levels of governance. This paper aims to perform an initial analysis with the goal of developing an early warning system to efficiently manage intense convective rainfall events in urban areas. To address this need, the paper emphasizes the importance of analyzing different hazard scenarios. This involves examining different hydro-meteorological conditions and exploring management alternatives, as a fundamental step in designing and evaluating interventions to improve urban flood resilience. The Turin Metropolitan Area (TMA), located in north-western Italy, represents a unique case due to its complex orography, with a mountainous sector in the west and a flat or hilly part in the east. During the warm season, this urban area is exposed to strong atmospheric convection, resulting in frequent hailstorms and high-intensity rainfall. These weather conditions pose a threat to urban infrastructure, such as drainage systems and road networks, and require effective management strategies to mitigate risks and losses. The TMA’s urban areas are monitored by polarimetric Doppler weather radars and a dense network of rain gauges. By examining various summer precipitation events leading to urban flooding between 2007 and 2021, this study assesses the practicability of deploying a weather-radar early-warning system. The focus is on identifying rainfall thresholds that distinguish urban flooding in lowland areas and runoff erosion phenomena in urbanized hills and foothills.

1. Introduction

One of the greatest challenges facing humanity is climate change because of the serious risks it poses to the environment and future generations and the need for immediate action. Handling this issue is, therefore, one of the most crucial global public policy challenges for countries today because the effects are already having an impact on the economy, society, and environment. The Mediterranean and Alpine regions of Europe will have to deal with the particularly detrimental effects of climate change, which, when coupled with the effects of human pressure on natural resources, make these regions two hotspots of climate change [1,2]. Given that over 55% of the world’s population lives in towns and cities, which account for approximately 75% of carbon dioxide (CO₂) emissions from global energy end-use, cities are more vulnerable than other areas in this context [3]. The most detrimental effects anticipated for cities over the next few decades are mostly linked to an extraordinary rise in summer maximum and average temperatures as well as a rise in the frequency of extreme weather events, such as heat waves, droughts, and periods of intense rainfall [4,5]. A warmer climate is expected to increase the severity of extremely wet and dry events, as well as water cycle variability and related extremes, faster than mean changes in most of the world’s areas [6]. The Mediterranean area is classified as a climate change hotspot, i.e., a region where the effects of climate change are amplified. Zittis et al. [7] investigated extreme precipitation in the Mediterranean region, showing statistically significant positive trends for 2001–2050 and 2051–2100 periods.

In this context, changes in land surfaces and water extraction for domestic, industrial, and agricultural uses are directly causing human activities to lead to greater shifts in the water cycle, as well as indirect effects through climate responses to greenhouse gas emissions and aerosol particles [8,9]. Urban settings, which are defined by high levels of surface sealing (i.e., buildings and other impermeable surfaces like roads, pavements, and parking lots), have poor soil infiltration capacities that cause runoff to occur quickly, intensifying the effects of storms, flash floods, and severe rainfall [10]. Cities can both contribute to the problem and the solution; as a result, the magnitude of the projected climate impacts needs a significant change in urban and spatial planning, as well as in the structure and functioning of cities [11]. This change is necessary to undertake mitigation and adaptation programs, which must be carried out in conjunction to strengthen one another [12]. Adaptation is essentially a local challenge; however, mitigation measures can be implemented through both local and global initiatives. Climate change impacts vary across regions, demanding tailored responses from local communities to prevent maladaptive and inequitable outcomes, making adaptation a social justice imperative [11]. The European Commission launched the Covenant of Mayors in 2008 to bring together local governments that voluntarily committed to achieving and exceeding the EU 2020 climate and energy targets. The Commission has highlighted the significant contribution that cities can make to the implementation of climate change strategies and is promoting this through major initiatives in which cities play a leading role. This entailed reducing CO₂ emissions via sustainable transportation, renewable energy, and energy efficiency. The program was updated in 2015 to align with the EU’s 2030 energy and climate goals. The time frame was extended to 2030, and the program’s scope was broadened to include access to sustainable energy sources and adaptation to climate change (incorporating the Mayors Adapt initiative [13,14]). It was established in 2016 as the principal pillar of the Global Covenant of Mayors for Climate and Energy campaign, which aims to expedite local climate action worldwide [13]. In this context, the city of Turin, located in north-western Italy, has also developed a document outlining a clear local adaptation strategy to reduce the vulnerability of the territory and the population while ensuring their health and well-being, the viability of the city, and the continuity of services, putting the most vulnerable people at the center of climate policy [15]. With the city council’s resolution of 28 July 2020, the Climate Resilience Plan was ratified by the local executive. This plan was developed with the participation of different local stakeholders, including (i) the Regional Agency for Environmental Protection of Piemonte (ARPAP); (ii) the Regione Piemonte; (iii) the Universities of Turin; and (iv) the water utility that manages the city’s integrated water service of the city (i.e., Societá Metropolitana Acque Torino, SMAT, Turin, Italy). This plan identifies the main vulnerabilities of the territory and proposes a series of short and long-term adaptation measures, defining a catalogue of actions aimed at reducing the impacts of the main risks of climate change to which the city is exposed: namely, heat waves and urban flooding. Urban flooding significantly impacts the entire urban infrastructure, including drainage systems and road networks, necessitating comprehensive management across all governance levels to effectively reduce risk and losses. Within this context, pluvial floods—triggered when heavy rainfall overwhelms a city’s drainage capacity—are particularly challenging to manage due to their rapid onset and localized nature. Pluvial floods occur when heavy rainfall exceeds the capacity of a city’s drainage system. Due to their fast onset and localized nature, such floods cause significant damage to urban environments and are difficult to manage.

In this framework, there is a renewed urgency for truly effective early-warning systems at the urban scale, which will enable better anticipation and preparedness and lead to better management practices for the effective design and evaluation of interventions to improve urban flooding resilience and give operational support for strategic planning. When long forecast horizons are necessary to provide early warnings, flood forecasting systems that integrate hydrological–hydraulic models with both deterministic and probabilistic meteorological forecasts from Numerical Weather Prediction (NWP) models prove to be highly useful [16,17]. However, the intensification of extreme precipitation events and the consequent flooding risk, driven by the accelerated hydrological cycle due to global warming [17], pose challenges in urbanized areas. These fast-evolving floods lead to substantial uncertainty in the modeling of non-linear processes within rainfall-runoff dynamics [18].

In this context, the uncertainty in rainfall-runoff modeling can be significant enough to discourage the use of hydrological models in the forecasting process. Instead, decisions based solely on the exceedance of predefined rainfall thresholds (RTs) may offer sufficient accuracy and timely responses despite the non-stationarity of hydraulic properties due to urbanization, which changes the definition of a consistent rainfall value needed to produce a certain level of risk [19]. Urbanization is known to alter the hydrological response to precipitation, resulting in higher flood peaks and volumes compared to rural basins [20].

A bottom-up approach for defining RTs is appropriate in this scenario. By considering the type of precipitation during extreme events, particularly those of a strongly convective nature (e.g., rain, hail), several case studies of urban flooding that caused damage or required operational management were analyzed. This analysis evaluated the feasibility of a meteorological radar early-warning system based on identifying rainfall thresholds and the precipitation characteristic of urban flooding in lowlands, as well as runoff erosion phenomena affecting urbanized hills and foothills.

Several cities in the world are in a complex orography, an environment favorable to convective precipitations during the warm season. Moreover, the rapid urban development combined with the compact enlargement of urban areas experienced at the beginning of this century amplified the effects of extreme precipitations.

The Turin Metropolitan Area (TMA) has some peculiar characteristics that make it a feasible and interesting case study: its geographical location makes the urban area prone to strong atmospheric convection causing frequent hailstorms and severe thunderstorms that can be monitored by polarimetric Doppler weather radars and by a dense rain gauge network. This work is a preliminary study aimed at designing an operational early-warning system based on real-time rainfall measurements and estimations. Investigating some case studies, the main purpose is to characterize principal factors associated with rainstorms that cause major impacts on the city (e.g., rainfall intensity, duration and distribution, or hail occurrence).

2. Study Area

The city of Turin is the capital of the Piedmont region in north-western Italy. With a population of 842,612 inhabitants, Turin is the fourth-largest Italian municipality in terms of population. The Turin Metropolitan Area (TMA) is in an area with a complex orography in the center of a mountainous amphitheater. The city, crossed by four rivers (the Po, the Dora Riparia, the Stura di Lanzo, and the Sangone rivers), rises on the plain at the outlet of Susa, Lanzo, and Sangone Alpine valleys, and the river Po separates the hilly part of the city from the plain (Figure 1). The study area experiences precipitation that varies significantly in both space and time, with a tendency towards rainfall induced by topography, exposure to humid air from the Mediterranean Sea, and relatively prolonged periods of dryness [21,22,23,24]. These unique characteristics make the area particularly prone, during the warm seasons, to strongly convective precipitation phenomena, which have rapid development and high impacts. The study area is monitored by two polarimetric Doppler weather radars and by a rain gauge network with a density of one rain gauge every 60 km², managed by ARPAP and described in Section 3.1. The ARPAP rain gauges considered in this study, together with the location of the weather radar near the boundaries of the TMA, are shown in Figure 1.

In this context, the process of land consumption, which follows the expansion of urbanized areas, exacerbates the impacts of heavy rainfall events over the urban area. According to the latest monitoring from the Italian System for Environmental Protection [25], land consumption in the City of Turin was settled at 65.11% in 2022, corresponding to an surface area of about 8472 hectares. Given the hydro-geological, weather–climatic, and anthropogenic characteristics of the area, heavy convective precipitation events are often associated with additional hazards, including floods and landslides, with their potentially damaging consequences for society, agriculture, industry, ecosystem services, and essential infrastructure. In the heavily sealed area of the city, street flooding, damage to the water distribution network (soil erosion phenomena can lead to subsidence and consequent pipe breaks), and sewer overflows (with potential contamination of the surrounding environment as well as failure to properly dispose of rainwater, exacerbating flooding in streets and underpasses) are the main problems faced by the stakeholders involved in urban management.

The drainage network of the city of Turin consists of 112 main collectors, with an egg-shaped cross-section made of reinforced concrete, having a minimum equivalent diameter of 500 mm and a maximum equivalent diameter of 1200 mm. These collectors gather drainage water from a sub-network of smaller pipelines made of plastic material, spanning a total length of approximately 1300 km over an area of about 100 km². The roughness has been assumed with a Manning coefficient of 0.013 s/m^(1/3) for the reinforced concrete pipes and 0.011 s/m^(1/3) for the plastic pipes (PVC, PEAD). The main framework of the drainage network in the city of Turin has been built since 1893, and it is composed of two separate networks, one dedicated to rainwater and the other to sewage and human waste. This approach allows for more efficient wastewater treatment by avoiding dilution, which can lead to increased treatment costs and prevent an unnecessary contamination of the less polluted rainwater. The urban drainage network of Turin, as well as the whole integrated water cycle of the TMA, is managed by SMAT.

The system’s design parameters did not consider the impact of climate change and may be at risk of frequent breakdowns during extreme rainfall events. In this context, to build a climate-smart water service with proactive emergency response capability, SMAT has equipped itself with a numerical model of the stormwater drainage network (based on the digital twin of the network). The results of the numerical simulations (carried out using the US Environmental Protection Agency’s Storm Water Management Model (EPA SWMM) software Version 5.2 [26]) highlight the most stressed points in relation to two different rainfall thresholds used for this type of characterization: hourly and 10 min events associated with a 5-year return period. A constant intensity precipitation was applied throughout the entire rainfall period to a dynamic rainfall-runoff simulation model, aimed at identifying the most stressed sections of the drainage network.

The most significant flood vulnerabilities emerged when using the 1 h duration rainfall, with the model identifying three areas where these floods concentrate (Corso Cosenza, Corso Lecce, and Via Tirreno), as shown in Figure 2. The numerical model has not yet been calibrated using field-measured data, but the initial results are nevertheless valuable for identifying areas most susceptible to flooding phenomena.

The slopes of the hilly part of the city of Turin are particularly vulnerable to landslides in the most degraded parts of the forest, which are already affected by slope instability, and in the vicinity of anthropogenic barriers (bridges, siphons, weirs, and canalized sections of streams), where the speed of runoff increases. The common landslide types affecting Turin Hill (Figure 2) are mainly caused by rainfall, and they can be subdivided into two main groups based on the depth of detachment surfaces and landslide size. From the triggering mechanisms point of view, landslide types can be also subdivided into gravitational phenomena caused by short and intense rainfall (rainstorms or heavy rainfall of a duration <12 h), heavy rainfall lasting more than 12 h, and landslides that are influenced by antecedent precipitation (rainfall and snow melt values characterizing past weeks or months). Phenomena linked to short-duration intense rainfall includes disintegrating soil slips (DSSs) and mudflows (MFs). DSSs affect a few cm of soil (including grass cover) and are triggered by intense runoff causing sheet (for DSSs) and rill (for MFs, resulting from the coalescence of DSSs that converge in the impluviums) erosion starting from brief and intense rainfall, with a rainfall intensity triggering threshold of about 20 mm/h [27]. Shallow landslides (SLs) have a surficial detachment surface (commonly from 50 cm to 2 m depth) and are characterized by low volumes of mobilized material (usually <500 m³). SLs develop on slopes with a slope between 18° and 45°. SLs are triggered by rainfall of a duration greater than 12 h, with a triggering threshold of cumulative rainfall of 60 mm for a duration of 12 h, 70 mm for a duration of 24 h, and 80 mm for a duration of 36 h [28]. The second landslide group includes landslides characterized by the deepest detachment surface (>2 m), such as translational slides (TSs), rotational slides (RSs), earth flows (EFs), and slope debris flows (SDFs). EFs, SDFs, and small RSs are triggered by heavy rainfall with a duration longer than 12 h with the same triggering threshold values of SLs. Unlike other types of landslides, the TSs do not develop in the debris-colluvial covers but in the sedimentary bedrock of Turin Hill causing the sliding of rock-blocks along stratification or structural surfaces. They are less common than other landslide types affecting the Turin Hill and, like large RSs and deep landslides characterized by complex kinematics, their triggering is strongly dependent on the antecedent precipitation amount over a variable time-lapse, different from case to case [29].

3. Materials and Methods

Thunderstorms, which are characterized by violent precipitation and dangerous phenomena like lightning, hail, strong winds, and possible tornadoes, are among the most dangerous weather events found at the mid-latitudes. In the study area, they can be classified into two categories: heat thunderstorms and frontal/pre-frontal thunderstorms. These storms typically occur during spring, summer, and early autumn. Heat thunderstorms, which are prevalent in Alpine and Alpine-foothill regions, occur when the humid air becomes unstable during the day due to intense solar radiation, resulting in the development of cumuliform clouds and rising air along the mountains. They usually take place in the afternoon during summer. Frontal and pre-frontal thunderstorms, on the other hand, are the most intense and long-lasting. They are associated with high instability, sudden drops in pressure, the influx of cold air after a warm period, strong convergence in the lower layers, intense convective motion, and the absence of strong winds, which hinder the development of vertical motion. From the period between 2007 and 2021, the analysis focused on summer precipitation events impacting the urban infrastructure network. These events either resulted in flooding in areas specified in Figure 2, needed intervention from SMAT operational teams, or were documented in event reports by ARPAP. The choice of the analysis period is tied to the homogeneity of the rain gauge network (described in Section 3.1) and the available data on severe weather impacts. By using a bottom-up approach, it becomes feasible to track the geographical extent, initiation time, and duration of these pivotal events. Furthermore, this method helps detect the maximum amount of rainfall associated with these events by accumulating precipitation data over specific time frames such as 10, 20, 30 min, and 1 h. Precipitation maxima are taken between one of the eight rain gauges shown in Figure 1. If it is not viable to extract the ground precipitation data gathered by rain gauges, it is still possible to achieve this estimation by using polarimetric Doppler weather radar data, which is also used to monitor the study area and which can distinguish both the type of precipitation (rain or hail) and, in the case of hail, the size of the hailstones. From this analysis, correlated to the precipitation type, it is possible to trace the significant intensities back to hydrological maxima. From these maxima, the return times can be estimated.

3.1. Precipitation Observational Network

Several sources of observations are available for the quantitative monitoring of precipitation in the study area: radar measurements and rain gauges data managed by ARPAP (Figure 1). Weather radars provide indirect estimations of the rainfall rate, while rain gauge data provide direct measurements of the precipitation at ground level. The rain gauge monitoring network of the study area is part of the real-time meteo-hydrographic network of the Piedmont region. It reaches an average density of one station per 100 km² and can define the synoptic framework of the region from a meteorological point of view. The rain gauge network consists of tipping-bucket sensors (bascule with calibrated 1000 cm² orifice) under the World Meteorological Organization recommendations with a measuring range between 0 and 300 mm/h of precipitation and a resolution of 0.2 mm of rainfall. One-minute rainfall observations are corrected for underestimations at high rain rates according to [30,31].

ARPAP manages two polarimetric Doppler C-band weather radars (f = 5.6 GHz, λ ≈ 5 cm) capable of providing precipitation estimates in the study area with an operating range of 150–200 km, 800 m resolution: Bric della Croce (located near Turin at 736 m a.s.l., latitude 45.03° N, longitude 7.73° E) and, in collaboration with the Liguria region, Monte Settepani (located at 1390 m a.s.l., latitude 44.25° N, longitude 8.20° E). Both systems are Doppler polarimetric radars and perform a volumetric scan every five minutes, collecting data on conical surfaces corresponding to predetermined heights between approximately 0 and 30 degrees [32,33]. The Piedmont weather radar can also estimate the probability of hail (POD) according to [34]: the operational POD product has been used to evaluate the chances of hail during the severe thunderstorms that hit downtown. The integration of radar measurements with the rainfall network is necessary to bridge the observation gap in the lower layers of the atmosphere and to ensure a good agreement between the direct measurements of the precipitation in the soil and the radar estimate.

3.2. Case Studies and Ground Effects

Urban floodings are typically brought on by locally heavy rains that exceed a pipe’s capability and pressurize it, causing surface flooding. According to [31], the severity of floods is primarily influenced by the spatial–temporal variability of rainfall, the size of the catchment, and the properties of the soil. Severe convective storm typically causes local floods, transportation damage, hail accumulations, and tree fall. High environmental and social costs from floods might result in significant financial losses. Some of these effects involve harm to public infrastructures, individual properties, materials, and occasionally they directly impact the population, necessitating the analysis and mitigation of their causes and effects. The first part of the study has been devoted to identifying the occurrence of severe weather conditions over Turin between 2007 and 2021. Three sources of information have been used:

• Event reporting on severe thunderstorms by ARPAP;
• News on severe weather that hit the city collected from websites and social media (e.g., Twitter);
• Damages and impacts on the city reported by SMAT.

Table 1. Severe storms that hit Turin, Italy, from 2007 to 2021.

Date	Impact Area	Impacts
2007-06-20	North TMA	fallen trees, belt road floods, shallow landslides, disintegrating soil slips, mudflows, river flood
2010-08-11	Central TMA	belt road floods
2012-06-12	North-Central TMA	belt road floods and fallen trees, significant hail
2013-07-29	Central TMA	belt road floods and fallen trees, wind gusts
2016-08-29	North TMA	fallen trees and belt road floods
2017-06-05	West TMA	subways and belt road floods
2017-06-27	Central-South TMA	flooded roads, fallen trees, hail >2 cm
2019-08-11	whole TMA	roof blow-offs from high winds, fallen trees, hail
2020-06-03	whole TMA	hail accumulation
2020-06-07	whole TMA	subways floods, hail accumulation
2020-06-15	North TMA	overflow of small streams, disintegrating soil slips, mudflows, falling trees, hail
2020-06-17	West TMA	subways and roads floods, high winds, large hailstones and hail accumulation
2020-08-17	whole TMA	diffuse road floods, public transportation failure, falling trees, roof damages
2020-08-28	Central TMA	flooding, falling trees
2021-06-22	North TMA	subways and floods
2021-07-08	North TMA	flooding, falling trees
2021-07-13	whole TMA	large hailstones, falling trees
2021-08-01	whole TMA	hail, high winds

lists the summer events selected for this study, indicating the area of the city of Turin that was most affected and the type of ground effect.

From 2007 to 2021, eighteen severe storms affected the urban infrastructure network, causing damages or requiring the intervention of operational teams. Most of the events involved the whole of the TMA, with flooding on the roads and affecting public transportation, fallen trees, and hailstorms. For the 15 years considered in this study, only two events caused landslides because of the location of the most intense peak of the rainstorm that has mainly concerned Turin Hill, contrary to the other thunderstorms that have affected downtown areas. Turin is a very old town, founded during the Roman Age; a combination of inconsistent public design standards over time and constrained private design standards play a main role in its urban flooding. In the past, there were no minimum first-floor elevation requirements for developments, and the city had minimal control over design. This led to numerous flooding problems that are still today challenging to resolve.

4. Results

Return periods for several rainfall durations are the most common variables used for rainfall analysis. It could be challenging to predict the return period of urban flooding at a single defined site based on the return period of the rain. The complexity of a flood, where water runs through the drainage system, surcharges the drainage system, and flows on the surface to depressions in the topography may cause a non-monotonic growing relationship between the intensity of the rain and the highest water level at a particular location. These non-monotonicities will be made worse by additional hydraulic features like pumps, weirs, gates, retention basins, etc., in the drainage system, preferential flow routes, and ponding on the surface [35]. Figure 3 summarizes the results of the statistical analysis of annual rainfall maxima computed from rain gauges in the TMA.

Estimating annual maxima rainfall accumulations is challenging, due to both the high variability of precipitations and the limited temporal and spatial coverage of rain gauges. In regional rainfall frequency analysis, the Hosking–Wallis heterogeneity test [36] is commonly used to determine the homogenous rainfall zones. Each station in a homogeneous area should ideally have the same L-moment ratios, but each station’s L-moments ratio varies because of variations in its measurements. However, it can be deemed to be homogeneous if there are variations in the meteorological L-moment ratios. After having verified the homogeneity hypothesis, the optimal three-parameter theoretical distribution was found using a goodness-of-fit test statistic [37]. A comparison of the L-Ck sample and the L-Ck population for various distributions serves as the foundation for the goodness-of-fit test. A ZDIST 1.64 value is what a suitable distribution function should aim for. For all durations of 10, 20, 30, and 60 min, the GEV distribution is found suitable (

Table 2. Rainfall maxima of the selected case studies over 10, 20, 30, and 60 min time frame, together with return time estimation. Hail greater than 1 cm is also indicated by “*”.

Date	Total (mm)	Rain Maxima (mm)				Return Period (yr)				Hail
		10′	20′	30′	60′	10′	20′	30′	60′
2007-06-20	63.8	15.6	29.5	39.1	61.6	2	2	10	10
2010-08-11	81.0	28.6	41.7	52.3	83.4	20	10	>100	50
2012-06-12	67.0	19.9	41.7	44.7	66.8	2	10	20	10	*
2013-07-29	34.6	21.3	34.4	35.8	38.0	2	5	5	2
2016-08-29	64.6	33.5	52.8	63.9	69.5	>100	>100	>100	10	*
2017-06-27	57.1	21.2	37.9	44.4	57.1	2	5	20	5	*
2019-08-11	35.0	19.8	26.8	27.6	27.6	2	2	2	2	*
2020-06-03	27.4	18.8	24.0	24.0	24.0	2	2	2	2	*
2020-06-07	37.4	16.0	23.5	29.7	30.9	2	2	2	2	*
2020-06-15	57.8	23.1	30.9	35.6	55.4	2	2	5	5
2020-06-17	48.6	25.8	38.6	43.6	48.2	5	10	20	2	*
2020-08-17	80.0	23.6	38.7	54.0	80.0	5	10	>100	20	*
2020-08-28	45.8	33.4	43.4	44.0	44.0	>100	20	20	2
2021-06-22	95.8	26.9	40.8	52.6	75.0	10	10	>100	20
2021-07-08	56.0	12.6	19.2	26.0	47.7	2	2	2	2
2021-07-13	48.2	17.3	26.0	30.7	40.2	2	2	2	2	*
2021-08-31	45.6	19.1	32.5	40.4	45.6	5	5	5	2	*

).

For the above-mentioned case studies, rainfall maxima at 10, 20, 30, and 60 min have been calculated from the eight automatic tipping-bucket rain gauges in the Turin area (Table 2). Given the rainfall maxima for the different intervals, the return periods corresponding to these values have been derived from the Atlas of Intense rainfall [38]. The last column reports for each event the hail presence, derived from POD products by weather radar and from news or social media reports: one asterisk means some chances of hail, meanwhile two asterisks mean diffuse hail, probably even large, with accumulation. All the case studies show return periods equal to or greater than two years and the presence of hail. Some events stand out:

• 5 June 2017, 13 July 2021, and 1 August 2021, with low return periods. Here, the hailstorm probably played a role in occluding stormwater drains;
• 17 August 2020 and 22 June 2021, with very high return periods and corresponding large impacts.

Generally, it can be stated that rainfall intensities that are lower than a 2-year return period are improbable to impact the city. In contrast, a return period greater than 10 years gives a high probability of urban flooding. To check this hypothesis, the occurrences of rainfall intensities for the given durations and 2, 5, and 10-year return periods have been calculated from the rain gauges for 2007–2021 (

Table 3. Number of days with accumulated rainfall, recorded in one of the eight rain gauges, with greater than thresholds relative to return period of 2, 5, and 10 years during 2007–2021.

Accumulation	Return Period (Years)
(min)	2	5	10
10	32	6	5
20	26	7	4
30	24	9	7
60	43	17	6

).

Table 3 reports the days when one of the eight rain gauges exceeds the threshold for a given duration and return period from 2007 to 2021. Recalling that only 17 events impacted the city, it is worth noting that the 2-year return level overestimates the number of relevant events. When it moves for return periods between 5 and 10 years, the number of occurrences agrees with the number of events in the same period. Considering the 30-min accumulation rainfall, the number of days overpassing the threshold of the 2-year return period is greatly increased at the end of the period: 14 days since 2017 over 24 days since 2007.

Analysis of the Most Significant Case History: Thunderstorms of 20 June 2007, 17 August 2020, and 13 July 2021

On 20 June 2007, a high-intensity storm hit the city of Turin. A storm cell originated around 15:00 UTC, initially affecting northern Turin and rapidly extending across a large part of the city. Starting from 16:00 UTC, the city downtown and the hilly neighborhoods were affected by hail, strong wind gusts, and an intense downpour. The “Giardini Reali” rain gauge recorded 58.4 mm of rainfall in one hour. The persistence of an African anticyclone over the Mediterranean in the period from 17 to 20 June maintained conditions of widespread atmospheric instability in Piedmont, with high moisture in all layers of the atmosphere and very high temperatures over the region (29/32 °C on the Piedmont plains on 19 June 2007). Since 19 June 2007, a deep depression circulation of Atlantic origin has moved towards western Europe, influencing Piedmont with humid southwestern currents and strong conditions of atmospheric instability. Between 14:30 and 15:00 UTC, a first storm cell was triggered over the city of Turin, with the first hail phenomena and the first very localized rainfall of low intensity. This cell subsequently caused the triggering of another storm cell, through a regeneration mechanism typical of multicells but much more energetic, which caused exceptional rainfall (over 50 mm/h) in the Turin area, with hail episodes that were also very intense. From a meteorological point of view, this event can, therefore, be defined as a multicellular storm with an oblique axis (or “V” shaped storm); there are no typical characteristics of supercells, such as long temporal duration or mesocyclonic circulation inside the cell, and, of the cells themselves, they were very spatially limited. There was no evidence of tornadoes or tornado phenomena, although notable wind reinforcements were recorded in the presence of higher rainfall. The rainfall event was also confirmed in the analysis of the maps provided by the Regional Weather Radar Detection System of Arpa Piedmont, which detected the development of the storm cell over Turin and constantly followed its evolution: the development of the cell over northern Turin occurred around 15:20 UTC, with precipitation values above 30 mm/h; in the following 10 min, the generation of a new convective nucleus occurred over downtown of Turin, which then intensified and remained almost stationary compared to the flows in the middle troposphere, with a very slow movement towards the hilly areas. Only after 16:30 UTC did the cell begin to cross the Turin Hill, running out completely around 17:20 UTC (Figure 4).

It is precisely the characteristic of almost stationarity for more than an hour (15:30–16:30 UTC) that made the event take on the typical characteristics of the main cause of the urban flooding phenomenon, with the consequences in terms of cumulative rainfall, visible in Figure 4 (cumulative precipitation, estimated from Bric della Croce weather radar, over 6 h, between 12:00 and 18:00 UTC). Figure 4 shows the maximum precipitation in the area between foothills and hills of around 100 mm, well above the 63.8 mm recorded by the “Giardini Reali” rain gauge, and, in Figure 5, the area affected by flooding, surface runoff, and shallow landslides is summarized. In other parts of the city, widespread pluvial flooding affecting the urban road network, underpasses, and underground structures due to the heavy rainfall intensity overwhelming the capacity of drainage systems was observed. Moreover, many phenomena of dancing manhole covers [39,40] and the formation of potholes, with size normally not exceeding 1.5 m in diameter and a depth of about 1 m, that have widely affected urban roads occurred.

On 17 August 2020, an Atlantic low pressure has affected western Europe since mid-August. The center of the low pressure gradually retreated toward the north-northwest over the day, moving up toward the British Isles and thus away from the continent. At the same time, however, the drop in the geopotential aloft in the mid-latitudes advanced slowly eastward, extending from Piedmont to the rest of northern Italy. The atmosphere remained unstable due to cold air aloft, associated with the Atlantic low, which in its eastward motion continued to overlook much of the Piedmont area. The CAPE recorded by Cuneo Levaldigi airport sounding at 12 UTC was 1857 J/kg. There was a temporary rise in temperature during the middle hours of the day, followed by a new partial cold intrusion from the north over Piedmont in the evening. In the afternoon of 17 August 2020, several particularly severe thunderstorms, moving eastward, developed from the western pre-alpine areas. A thunderstorm system around 13:30–14:00 UTC hit Turin with hail and violent showers (Figure 6).

Figure 6 shows the reflectivity map (800 m × 800 m resolution) from the Piedmont weather radar composite on 17 August 2020 between 13:30 and 14:00 UTC. The map provides insights into precipitation distribution and how fast the event was. The severe thunderstorm was also accompanied by hail (Figure 7).

Figure 7 shows POD product for 17 August 2020 in the TMA. The cyan color is in areas hit by hail with a size greater than 2 cm. Wind gusts were also significant, with wind speed exceeding 50 km/h in the Turin area.

On 13 July 2021, the severe North Atlantic storm, which caused widespread flooding and heavy damage in Germany, Holland, and Belgium was also responsible for widespread instability over Piedmont, which affected almost the entire region at various times during the day. The arrival of very cold air at high altitude easily destabilized the hot humid summer atmosphere, which thus showed a significant lapse rate over northwest Italy, i.e., a significant temperature difference between the various vertical atmospheric layers, easily higher in the central (hottest) hours of the day. Consequently, the convective instability indices were very high over Piedmont, especially in the first part of the day, with high values of convective energy available during the day, and also ready for the initiation of thunderstorms in the afternoon and evening. The Convective Available Potential Energy (CAPE) recorded by Cuneo Levaldigi airport sounding (44.541 N 7.620 E) at 12 UTC was 2700 J/kg. Figure 8 shows the rainfall accumulations and hail probability with an indication of large hail, according to [41].

This case study is characterized by low rainfall return periods for all durations, but the showers interested a large part of downtown (Figure 8) with diffuse hail. In the part of the hit area, hail sizes greater than 4 cm have been reported. Hailstorms cause flushes of leaves that can accumulate on sidewalks and roadways into storm drains during and after storms. Large numbers of leaves can be problematic for homeowners, stormwater drainage systems, and water quality. Water can back up on the street and possibly into surrounding basements as a result of the leaves matting across the storm drain and obstructing water movement.

5. Discussion

Urban flooding phenomena are serious threats to big cities, causing economic losses and sometimes casualties. This preliminary study investigates the impacts of thunderstorms on Turin, Italy. The aim is to design an operational early-warning system based on ground observations and weather radar data. Critical events from 2007 to 2021 have been analyzed and described regarding return periods at sub-hourly and hourly rainfall maxima. The hail accumulation and hail size greater than 1 cm has also been reported. The impacts of these storms on the urban area of Turin have been evaluated, considering the SMAT reports of damages, the ARPAP reports, and information gathered from newspapers, broadcasters, and social media. The analysis shows that precipitation accumulations with return periods of less than 2 years are unlikely to impact the urban area of Turin, while return periods of 10 years or more have a high probability of causing damage. Precipitation accumulations with a return period of fewer than 2 years have impacts when associated with hail accumulations or hail size greater than 1 cm like for cases on 3 and 7 June 2020. Summarizing the main findings of this study of urban flooding in Turin, Italy, the following can be stated:

• Rainfall accumulations with return periods less than 2 years generally cause negligible impacts on city roads, public transportation, or storm-water drainage systems;
• Rainfall accumulation with return periods greater than 10 years most probably cause impacts to the urban area;
• Hailstorm accumulation and large hail have impacts on the urban area, regardless of rainfall accumulation.

6. Conclusions

Analyzing 15 years of stormy events that affected the TMA, this study identified significant return periods for hourly and sub-hourly rainfall accumulations. Nevertheless, this analysis of return periods may need updates in the next few years due to the rapid rate of increase in global climate change. These results are the first step to outline an effective decision support system to face intense precipitation of short duration in urban areas. An increased density of rain gauges in urban areas, together with non-conventional precipitation sensors and radar-estimated areal data, is essential to obtain high-resolution precipitation fields that are reliable and accurate over the territory. To this aim, an accurate merging of precipitation data must be carried out in depth, together with quantitative precipitation estimates (QPEs), to develop operational nowcasting systems that are capable of improving the lead time of warnings for urban flooding hazards. The precipitation field is highly localized, requiring spatial quantification of the amount of precipitation on the ground, highlighting the great benefit of weather radar monitoring. Consideration is being given to further improving it by integrating the two C-band polarimetric Doppler weather radars with a polarimetric X-band weather radar. The improved spatial and temporal estimation of rainfall fields in the TMA by the new observation system and the effective early-warning system will also be considered as input for the hydraulic numerical modeling of the drainage network that SMAT is already implementing. This will allow for improved responsiveness in the urban area and a proactive management of water infrastructures and networks.