Floods cause damage to people, buildings and infrastructures. Water distribution systems are particularly exposed, since water treatment plants are often located next to the rivers. Failure of the system leads to both direct losses, for instance damage to equipment and pipework contamination, and indirect impact, since it may lead to service disruption and thus affect populations far from the event through the functional dependencies of the network. In this work, we present an analysis of direct and indirect damages on a drinking water supply system, considering the hazard of riverine flooding as well as the exposure and vulnerability of active system components. The method is based on interweaving, through a semi-automated GIS procedure, a flood model and an EPANET-based pipe network model with a pressure-driven demand approach, which is needed when modelling water distribution networks in highly off-design conditions. Impact measures are defined and estimated so as to quantify service outage and potential pipe contamination. The method is applied to the water supply system of the city of Florence, Italy, serving approximately 380 000 inhabitants. The evaluation of flood impact on the water distribution network is carried out for different events with assigned recurrence intervals. Vulnerable elements exposed to the flood are identified and analysed in order to estimate their residual functionality and to simulate failure scenarios. Results show that in the worst failure scenario (no residual functionality of the lifting station and a 500-year flood), 420 km of pipework would require disinfection with an estimated cost of EUR 21 million, which is about 0.5 % of the direct flood losses evaluated for buildings and contents. Moreover, if flood impacts on the water distribution network are considered, the population affected by the flood is up to 3 times the population directly flooded.

Extreme weather events and major natural disasters are listed in the top five
global risks in terms of likelihood and impact

Flood damage to structures and infrastructures is classified into direct and
indirect, the former being caused by physical contact with floodwater and the
latter occurring far from the event in either space or time

The assessment of flood risk requires the evaluation of the three risk
components – i.e. hazard, vulnerability and exposure – for each subsystem
and the assessment of functional dependencies

Among safety critical infrastructures are freshwater supply systems (WSSs;
see Table A1 for a list of acronyms used in the paper) and
water treatment plants, which can be severely affected by floods since they
rely on electric power, mechanic devices and electronics. Water supply and
sanitation is widely considered to be a main factor in environmental
sustainability, human health, social services and resilience

The management of flood risk entails a combined approach comprising
mitigation, preparedness, response and recovery

In this work, a method is implemented as to evaluate flood impact on a WSS accounting for both direct and indirect damage on technological systems and inhabitants. Hazard, vulnerability and exposure of system components are assessed through a semi-automated procedure integrating the geographic information system (GIS) representation of flood scenarios with an hydraulic network model with pressure-driven demand (PDD). Failure scenarios are based on the analysis of exposure and vulnerability of critical network components, e.g. lifting stations. Two measures for the assessment of flood impact are introduced and the model is tested on a case study.

Main impacts associated with flooding for WSS based on surface water source.

Flood risk assessment scheme for WSS (ellipses stand for activities and rectangles represent data flow; shaded boxes represent activities that are not carried out in this work).

The assessment of flood risk on a WSS requires a comprehensive approach
including several scales of analysis (e.g. catchment area, riverbed,
distribution network) and models in order to capture the dependencies between
environmental forcing and WSS components and the inner dependencies of the
WSS itself. Figure

This work focuses on the evaluation through a numerical model of flood
impacts on the WDN, shown in the central panel of Fig.

The inundation model uses a river hydrograph (either recorded or calculated
for a hydro-meteorological scenario) to produce a raster map showing the
representative flood parameters, in particular water depth. In the literature
computation is commonly performed through simplified Navier–Stokes equations
with different numerical schemes and spatial resolutions of the computational
domain

The implemented hydraulic model is comprised of two parts. Firstly, the river
is represented with a 1-D unsteady flow model and the urban flood-prone area
is modelled as a system of interconnected quasi-2-D storage cells. A digital
surface model with resolution of 1 m and vertical accuracy of 0.25 m, derived from
lidar surveys, is used for the detailed representation of the flow domain at
street-building scale; buildings are, by default, considered to be waterproof
blocks. The computation of flood propagation is performed through an implicit
1-D finite-difference scheme of the general equation of unsteady flow
(i.e. mass and momentum conservation equations). The quasi-2-D hydraulic
model for the floodplain consists of several storage areas (cells) connected
to the riverbanks through a set of lateral weirs, whose geometry is
extracted from a topographic survey. When the inundation starts, the
quasi-2-D module – governed by mass conservation and stage-storage
relationships – calculates water levels from the volume stored in the cell.
Flow between adjacent cells is described by a weir equation accounting for
backwater effects. The details of the model construct and equations adopted
in the HEC-RAS framework (for both 1-D and quasi-2-D modules) are described
in

Exposure analysis requires matching of data from inundation maps and
information on location of assets, usually performed by means of GIS. All the components of the WDN, both active and
passive, must therefore be geo-referenced to be compared with inundation maps
for assigned scenarios. For the risk assessment of the WDS, exposure analysis
is conducted on active components based on the maximum water depth occurring
during the flood event. Maximum water depth is also used to assess the
potential contamination at nodes. The selection of a suitable inundation
model giving accurate flood depths depends on the characteristics of the
domain, i.e. area and topography, although a spatial resolution of the
order of 1 m (e.g. lidar-derived products) should be preferred in urban
areas to represent the street-building pattern. Exposure analysis consists
of four steps. First, the coordinates of the WSS point components (nodes,
reservoirs, lifting stations, etc.) are exported from the WSS model to the
GIS environment so that a new vector is created whose coordinate reference
system is assigned in the shapefile properties. Afterwards, the raster
inundation map is imported into the GIS workspace and converted if necessary
to a compatible reference system. The raster cell information (i.e. water
depth) is then extracted over the point feature and added as attribute
(e.g. with the “point sampling tool” plug-in available for QGIS). For each
failure-prone point component belonging to an exposed asset, the water depth
attribute is compared to a threshold depth which takes into account local
geometry and functional dependencies. If calculated depth exceeds the
threshold, the component is marked as failed, added to the list of exposed
asset and its properties modified in the WSS model (Sect.

The model is based on the freely available EPANET libraries, which calculate time-varying pressures at the nodes given a set of initial tank levels, pump switching criteria, base nodal demands and demand patterns. In particular, EPANET can be launched by other software through a set of DLL libraries.

One drawback of the standard EPANET implementation is its strict
demand-driven approach, which stems from the primary goal of simulating
correctly operated networks. In such networks, pressure at each node is
sufficient so as to allow withdrawal of required flow rate from each node, so
that demands can be assumed as defined input data. However, when simulating
strongly off-design networks, nodes featuring a reduced pressure are quite
common, so that a PDD approach is needed

EPANET allows two types of nodes:

Overall, the model works as follows: for each time step, a first trial simulation is run with all nodes in state 2 in order to get the expected pressures. Afterwards, each node is checked to assess whether its pressure is in the pressure range corresponding to the current flow regimen and, if this is not the case, its state is accordingly raised or lowered by one unit (namely, it is not possible to jump from state 2 to state 0 and vice versa). After node states have been changed, simulation is repeated until no more state change is necessary. Calculated flow rates and pressures are considered to represent network operation during the subsequent time step. In particular, flow rates are used to calculate the time to the next event (tank being filled or drained), and the first event affecting network topology is considered (e.g. demand change, pump setting toggling due to time pattern, tank becoming empty or full). Tank levels are thus updated and simulation proceeds to the next time step. The described procedure allows for calculating pressure and supplied demand at each node for each time step, therefore fully estimating the network state in each moment.

Diagram of PDD model implementation.

The model, featuring non-memoryless elements (tanks), needs to be correctly initialised. In normal operation, tank levels undergo a daily pattern of filling and emptying, according to demands and water availability. In order to appropriately initialise tank levels, a warm-up simulation is run by randomly initialising tank levels and checking their value every 24 h. If the calculated levels differ from those corresponding to 24 h before by less than a tolerance parameter, the model is considered to be in steady state and water level for each tank and time value are saved in a matrix, which can thereafter be used to initialise the values for the forthcoming simulations.

Two measures have been defined in order to evaluate the global impact of the flood on network operativeness and integrity.

First, impact of the flood on network operation is assessed through
evaluation of the number of inhabitants experiencing lack of service. To this
aim, data about population density in the area made available by the Italian
Institute of Statistics

Flooded area for the four recurrence intervals and exposure of vulnerable components. Areas with water depth above 0.01 m are considered to be flooded). Reference coordinate system is EPSG:3003.

As a second measure, network damage due to pipe contamination is evaluated by
calculating the total length of pipework to be decontaminated. A pipe is
considered to be contaminated if at any point in time the head inside the pipe is
lower than the floodwater head outside or below zero, i.e.

The study area is the municipality of Florence, Italy, with an areal extent
of 102 km

The metre-scale DTM used for the hydraulic model is freely available in the regional cartographic repository (dati.toscana.it/dataset/lidar). The hydraulic data (hydrographs and river water profiles) are made available by the catchment authority (Autorità di Bacino del Fiume Arno), which is in charge of flood risk management and water resource planning.

Four flood scenarios with different recurrence intervals (RIs) are considered
when applying the method described in Sect.

The studied WSS features one main treatment facility, 17 tanks and the pipework to supply drinking water for domestic and industrial use.

Freshwater supply is ensured by the river, which flows westbound amidst the
urban area. Water is abstracted from the river by three 373 kW pumps
in the treatment plant “Anconella”, which is located in the left bank and
designed to process 4 m

An EPANET model of the WSS is provided by the utility operator Publiacqua SpA. The model is barely skeletonised and consists of 4863 nodes and 12 436 pipes for a total length of the modelled piping network of 619 km.

The WSS elements most vulnerable to floods are the lifting stations and the pumps feeding the storage tanks, because they rely on electrical power and are affected by power outage. Water depth at the location of vulnerable WDS components is compared to a threshold depth to define the operation state of each of them. In this work, a threshold of 0.5 m is defined, so components experiencing greater depths are considered failed and switched off in the water distribution model. The 0.5 m threshold has been identified based on the judgement of experts who undertook a “what-if” analysis to evaluate the vulnerability of active components. This threshold has been considered conservative with respect to the mean position of electric and electronic devices observed in the plants.

In this section, results of the analyses are shown. The section is divided
into three subsections. Firstly, flood hazard scenarios are
illustrated and the exposure analysis of the WSS components is described; two
failure scenarios with different residual functionality of the exposed
lifting station are selected (Sect.

Figure

Table

Summary of exposed components.

Results are shown relative to the 200- and 500-year recurrence intervals, those for which failure of the DWTP is expected. In particular, two scenarios are considered: in scenario 1, it is assumed that the DWTP completely stops providing freshwater to the system; in scenario 2, some backup system is assumed to keep one of the three main pumps feeding the network in operation.

Inundation map and nodal heads for the 500-year recurrence interval,
120 min after lifting station failure, for

The nodal heads 120 min after the lifting station failure are shown in
Fig.

Evolution of aggregate service metrics in time is calculated for the two
aforementioned failure scenarios.

Contaminated pipe length in failure scenario 1

If a pipe has been contaminated it needs to be disinfected before being put
in service again. Disinfection is usually achieved by flushing:
trailer-mounted equipment pumps a disinfecting solution (e.g. liquid chlorine
or sodium hypochlorite) through a closed piping loop. Firstly, service
laterals are closed and customers are connected to bypass piping.
Subsequently, the cleaning solution is pumped from a tank on the equipment
trailer into the pipe to be cleaned. After cleaning, the solution is
neutralised and pumped to a sanitary sewer. The entire system is then flushed
(including laterals) to eliminate sediments and completely remove the
disinfecting fluid. From the operational point of view, discharge is
monitored during the flushing to assure a sufficient contact time and
chlorine residuals after disinfection are recorded to meet the sanitary
standards. An order-of-magnitude estimation of the cost of the
disinfection–flushing operation is EUR 50 per metre of cleaned pipe

Volume stored in tanks as a function of time since failure.

Figure

In case of power shutdown, the transient behaviour of the system is
determined by the amount of water stored in tanks. In order to better
understand the relevance of each storage tank in the system, a sensitivity
analysis has been performed. In particular, a sensitivity matrix is
calculated by numerically computing the derivative of head of each node with
respect on the level of each tank in a quasi-static assumption. By examining
the resulting data, two types of tanks are identified, according to their
altitude. On the one hand, variations of water levels in low-altitude tanks
strongly impact most network nodes, as shown in Fig.

Digital elevation model of the study area and sensitivity to tank
level for a lower tank (VCMantigna)

The impact of extreme weather events and natural disasters on urban structures and technological infrastructures, as well as in the perspective of climate change, is causing rising interest of citizens and institutions. In particular, the estimation of damage to network infrastructures poses an additional challenge due to the highly connected physical and functional topology by which the detrimental effects spread to areas farther from the event location and lead to indirect losses.

In this work, a comprehensive methodology to assess the impact of a flood on a WSS is defined and implemented in a semi-automated fashion. In particular, two main submodels are used: (i) an inundation model, which uses hydro-meteorological data and a DTM to compute flood depth given the flood recurrence interval (hazard analysis); and (ii) a WSS hydraulic model, used to simulate fluid-dynamic behaviour of the network from topology, functional and demand data. Initially, a flood scenario is calculated by the inundation model, and water depths near the active WSS components (pumps, electrically operated valve, etc.) are extracted. The failure of active components is linked to a selected safety threshold for flood depth, here assumed equal to 0.5 m. If water depth near an active component exceeds the given safety threshold, the component is considered failed (exposure analysis) and its state is modified accordingly in the WSS model. Thereafter, the WSS model is run and nodal pressures are calculated. In this phase, users experiencing lack of service are identified as a function of time. Moreover, by comparing water pressure in the network with local flood depth, areas affected by backflow are identified. Finally, calculated data are aggregated to compute two time-dependent measures which quantify the global lack of service (through the number of affected users) and global contamination extent (through the total length of pipes undergoing backflow).

The described method is applied to a case study. The study area hosts about
380 000 inhabitants on an area of 102 km

The implemented methodology uses flood data (WSS topology and characteristics) and water demand data to compute WSS contamination risk maps and service maps at various time moments after the event. The model is automated and lightweight, the analysis being completed in few minutes, and can be effectively used in the strategic planning of disaster recovery procedures or in comparing network strengthening solutions in budget allocation activities.

Future developments may include studying the effect of first-intervention procedures (e.g. subzoning of the network to select specific areas to be contaminated while preserving operation in others) and extending the model to simulate recovery procedures so that recovery times and transient network behaviour can be estimated based on scheduling and available resources.

The data underlying the research are available as a Supplement
(

CA conceived the impact assessment methodology and was responsible of flood hazard, exposure assessment, GIS operations and mapping. FT implemented the PDD code, simulated the piping network and evaluated the impact metrics. EV supervised the network modelling and FC promoted the research and supervised the flood risk aspects.

The authors declare that they have no conflict of interest.

We acknowledge Publiacqua SpA for providing the sample network data and for the advice given as stakeholder.

This research was financially supported by Fondazione Ente Cassa di Risparmio di Firenze under the research programme “ECRFI 2014”. Edited by: Bruno Merz Reviewed by: two anonymous referees