Forecasting flash floods some hours in advance
is still a challenge, especially in environments made up of many small
catchments. Hydrometeorological forecasting systems generally allow for
predicting the possibility of having very intense rainfall events on quite
large areas with good performances, even with 12–24 h of anticipation.
However, they are not able to predict the exact rainfall location if we
consider portions of a territory of 10 to 1000 km

The models used to achieve the goal are essentially (i) a probabilistic rainfall nowcasting model able to extrapolate the rainfall evolution from observations, (ii) a non-hydrostatic high-resolution numerical weather prediction (NWP) model and (iii) a distributed hydrological model able to provide a streamflow prediction in each pixel of the studied domain. These tools are used, together with radar observations, in a synergistic way, exploiting the information of each element in order to complement each other. For this purpose observations are used in a frequently updated data assimilation framework to drive the NWP system, whose output is in turn used to improve the information as input to the nowcasting technique in terms of a predicted rainfall volume trend; finally nowcasting and NWP outputs are blended, generating an ensemble of rainfall scenarios used to feed the hydrological model and produce a prediction in terms of streamflow.

The flood prediction system is applied to three major events that occurred in the Liguria region (Italy) first to produce a standard analysis on predefined basin control sections and then using a distributed approach that exploits the capabilities of the employed hydrological model. The results obtained for these three analysed events show that the use of the present approach is promising. Even if not in all the cases, the blending technique clearly enhances the prediction capacity of the hydrological nowcasting chain with respect to the use of input coming only from the nowcasting technique; moreover, a worsening of the performance is observed less, and it is nevertheless ascribable to the critical transition between the nowcasting and the NWP model rainfall field.

The Liguria region, located in north-western Italy, has been recurrently affected in the last century by severe rainfall events that produced flash floods and landslides (Acquaotta et al., 2018). This type of event causes severe damage to the coastal urbanized areas and sometimes also human casualties, not only in the Liguria region (Faccini et al., 2009, 2012; Silvestro et al., 2012, 2016; Davolio et al., 2015) but also in other areas of the Mediterranean (Drobinski, 2014; Delrieu et al., 2004; Ducrocq et al., 2008). All the aforementioned events were associated with well-organized, very intense and localized convective systems affecting the same area of a few square kilometres for several hours (Parodi et al., 2012; Rebora et al., 2013; Fiori et al., 2014; Buzzi et al., 2014). A forecast suitable for the small spatial and short temporal scales of these events is needed, but these scales are hardly predictable by meteorological models. Nowcasting models can predict the evolution of the rainfall pattern at a regional scale, starting from the last observed radar rainfall images (i.e. radar-based rainfall nowcasting) and merging different sources of information to provide a short-term forecast (usually a few hours).

Radar-based rainfall nowcasting can be achieved by extrapolating the future rainfall distribution from a sequence of radar images; it has been found that the radar-based rainfall nowcast has good skill for forecasting with a short lead time. The basic techniques to produce quantitative precipitation forecasts (QPFs) from radar echoes are based on cross-correlation or individual radar echo-tracking (Collier, 1981). Progress in the nowcasting procedure was the development of nowcasting methods operating in the Fourier domain (Seed, 2003; Xu and Chandrasekar, 2005) to take into account the fact that the predictability of rainfall depends on the spatial scale of the precipitation structures (Wilson et al., 1998; Germann and Zawadzki, 2002). Among the techniques that follow a probabilistic approach, Metta et al. (2009) developed a stochastic spectral-based nowcasting technique that predicts the future rainfall scenarios starting from the rainfall fields observed by radar and evolving them through Fourier decomposition. Also, Berenguer et al. (2011) presented a method based on the String of Beads model, while Foresti et al. (2016) analysed the performance of a nowcasting algorithm that accounts stochastically for the process of growth and decay of rainfall cells.

The main limit of such nowcasting procedures is that the accuracy of rainfall prediction is quite high for very short lead times (20–120 min), but, since it is based only on the extrapolation of the observed rainfall field, it rapidly decreases for longer ranges. As a consequence, the hydrological forecast is also useful from a hydrological nowcasting perspective for limited lead times (Silvestro et al., 2015a), with applications also to urban hydrology (Thorndahl et al., 2017). One of the reasons why the accuracy rapidly decreases with increasing lead times is that radar nowcasting techniques do not model (or models them stochastically) processes such as growth and decay of precipitation (Golding, 1998) that become important for longer lead times. So, for a very short range (0–3 h), radar nowcasting performs best, whereas for longer lead times, forecasts based on NWP are better (Kilambi and Zawadzki, 2005; Lin et al., 2005; Kober et al., 2012; Wang et al., 2015). On the other hand, NWP does not allow rainfall predictions with sufficient spatial and time detail (Davolio et al., 2015; Silvestro et al., 2016).

Therefore, after the very first hours (usually 0–3 h) of radar-based nowcasting, NWP forecasts can be merged to generate a seamless 0–6 h prediction with higher skill. This procedure requires an accurate QPF in the very short term from the meteorological model, at a high resolution of a few kilometres, since the tolerance for timing or location errors is very limited, especially in case of severe storms (Sun et al., 2014). In fact, a correct forecast allows a smooth transition from radar extrapolation to model prediction. To meet the nowcasting requirements, NWP models have to be run at convection-permitting resolution (1-4 km; Kain et al., 2006; Weisman et al., 2008), starting from a better initial condition that also reduces the spin-up period. Therefore, several methodologies for rapid data assimilation have been developed in order to be suitable for nowcasting application. rapid update cycle procedures have been widely used in an operational framework (Wilson and Roberts, 2006; Benjamin et al., 2004) to provide a “warm start” and, hence, reduce the model spin-up. Also, radar reflectivity has been employed to improve the initial condition, e.g. exploiting the information on latent heating through the application of the nudging technique (Sokol and Zacharov, 2012; Dow and Macpherson, 2013; Bick et al., 2016; Davolio et al., 2017a), eventually reducing the intensity or position error in rainfall prediction. Also other studies attempted to improve the hydrological forecast by improving the rainfall prediction using both observations and models. Rossa et al. (2010) assimilated radar data in an NWP system; Davolio et al. (2017a) assimilated the rainfall field derived from both radar and gauge observations (Sinclair and Pegram, 2005) in a convection-permitting NWP model and used the rainfall prediction in a probabilistic hydrometeorological forecasting chain. Liechti et al. (2013) and Liechti and Zappa (2019) explored the impact on hydrological prediction of different techniques of the rainfall forecast based on both NWP and radar data (Panziera et al., 2011). Kyznarova et al. (2012, 2013) tried to use the Integrated Nowcasting through Comprehensive Analysis (INCA) system (Haiden et al., 2011) precipitation products as input to a hydrological model, evaluating the benefit with respect to extrapolation techniques.

Within this framework, the present study attempts to use, in an integrated way, a nowcasting model (PhaSt), a high-resolution NWP model (MOLOCH) and rainfall observations. Rainfall estimates derived from both radar and rain gauges (Sinclair and Pegram, 2005) are used for frequent (60 min) data assimilation by the NWP model in order to better reproduce observations in terms of the spatial and time location, on the one hand, and to improve QPF (Davolio et al., 2017a), on the other hand. A probabilistic nowcasting model is adapted in order to use the QPF provided by NWP models. A blending technique based on previous studies (Kilambi and Zawadzki, 2005; Kober et al., 2012; Bowler et al., 2006) is finally applied to smoothly mix nowcasting rainfall scenarios and the NWP QPF. The use of forecast rainfall fields in nowcasting can effectively extend the lead time by several hours, making it suitable for issuing flood and flash flood warnings. The possibility to predict, with more accuracy, the rainfall fields as input to the hydrological model can improve significantly the accuracy of the hydrological forecast applied with short lead times (Berenguer et al., 2005; Silvestro and Rebora, 2012). The last module of the present nowcasting chain is represented by the distributed hydrological model Continuum (Silvestro et al., 2013; Silvestro et al., 2015b; Cenci et al., 2016), which is used to transform QPF in streamflow predictions in a frequently updated flood forecasting system (20 min). Using the forecast rainfall as described above as input, it provides a probabilistic output, with many predicted discharge scenarios, which useful in real-time management operations in an emergency.

Although the elements of the chain are not innovative by themselves, some new elements are introduced within this study. First, the blending is performed not only by combining the rainfall fields forecasted by the nowcasting and the NWP model in its spatial distribution, as in more standard approaches (Kilambi and Zawadzki, 2005), but also by modifying the nowcasting rainfall fields along the forecast window according to the information related to the time variation in rainfall volume derived from the NWP model, and this could be considered to be a matter of fact, a sort of blending of the rainfall volume. Second, the NWP model forecast is frequently “updated” with observations through a data assimilation technique before its information content is used to trigger the nowcasting model. Finally, verification is done in a hydrological perspective following both a point approach and a distributed approach in order to enhance the sample of data. The system is applied to three major flood events that occurred in the Liguria region in autumn 2014; results are presented, comparing streamflow forecasts obtained using rainfall predictions from different configurations of the system in order to evaluate the benefit of using some modules rather than others.

The paper is organized as follows: in Sect. 2 the area of study and the data used are introduced. In Sect. 3 the models, the methods and the type of analysis performed are explained. The results of the work are presented in Sect. 4, while the conclusions are drawn and final considerations are made in Sect. 5.

The area of study is the Liguria region, which is located in north-western Italy
(Fig. 1a) and is mainly mountainous; it faces the Ligurian Sea, and most of its
urban areas were developed along the coast. The presence of a large number of
catchments, characterized by a small drainage area (often less than 100 km

Area of interest.

The main input for the hydrological forecasting chain is the observed
rainfall. This observed rainfall field comes from the Doppler polarimetric
C-band radar, located on Mount Settepani (Fig. 1a) at an altitude of 1386 m,
that works operationally with a 10 min scansion time and 1 km

The analysis has been performed according to a distributed approach all over
the Liguria region domain, considering all the points with a drainage area
larger than 15 km

The NWP model used in this work is MOLOCH (details in Malguzzi et al., 2006;
Buzzi et al., 2014; Davolio et al., 2017b), which integrates the
non-hydrostatic, fully compressible equations for the atmosphere on a
latitude–longitude rotated Arakawa C grid, with a resolution of 0.02

The assimilation method, whose implementation in MOLOCH is shown in Fig. 2, is based on nudging. During the assimilation window, model-specific humidity profiles at each grid point are progressively modified depending on the comparison between observed and forecasted rainfall. To attain this aim, hourly precipitation estimates provided by the Settepani radar are used as observations. The assimilation scheme is explained in detail in Davolio et al. (2017a). The set-up conceived for the present application takes into account the time requirements for an operational implementation. Considering the timing for global data availability and radar estimates of delivery and processing, the first assimilation windows covers the first 6 h of the forecast. At the end of this period, the model state is stored and a free forecast is run to cover the following hours. In this way, once a new hourly rainfall observation is available, an additional 1 h assimilation is performed, restarting the model from the stored condition. This procedure can proceed for several hours, at least until the following global analysis is available, and allows for updating and improving forecasts, as a consequence of a longer assimilation period. In the present application, an appropriate time window for frequent assimilation was chosen for each event in order to minimize the amount of computational resources.

MOLOCH forecasts and data assimilation implementation. The first assimilation (MOLOCH CNTR; on the top) is performed during the first 6 h of forecast, while after that a 1 h assimilation is conducted, restarting the model from the stored conditions of the previous run (dots).

The nowcasting technique used in this paper is a radar-based probabilistic
technique, PhaSt (Metta et al., 2009). PhaSt is a “phase stochastic”
spectral-based nowcasting procedure that, starting from two following
rainfall fields observed by radar, realizes a nonlinear–empirical
transformation and stochastically evolves the fields within the spectral
space. The use of the spectral space allows for preserving the spatial
correlation within the rainfall fields. The evolution of Fourier phases
through the stochastic process generates many realizations to be used as
members of an ensemble of precipitation nowcasts. All the ensemble members
are characterized by the same amplitude distribution and very similar power
spectra. However, the phase evolution (i.e. the positioning of rainfall
structures) evolves differently in the altered realizations, providing an
estimate of the probability of occurrence of precipitation at a given point
in space and a given instant in time. The main equations of the algorithm
are reported below (Eq. 1):

To allow for the presence of time correlations in the angular frequencies, a
Langevin-type model is used: the temporal evolution of the Fourier
phase

To improve the nowcasting algorithm, its original formulation (Metta et al.,
2009) is modified in a constraint regarding the spectral amplitude of the
rainfall field. This spectral amplitude was previously kept constant along
the forecast horizon, which means fixing, in the real space, the total volume
of the forecast rainfall fields equal to that of the last observed radar
image. This hypothesis of constant volume is relaxed. Therefore, the volume
is modified according to the information provided by the NWP model
corrected through the data assimilation technique. The volume trend is
calculated for the hours of the forecast, using the information about the total
volume of precipitation on the radar domain (Fig. 3), according to the
following formula (Eq. 2):

Volume trend for rainfall field modification (example for 10 November 2014 at 12:00 UTC). The total volume on the domain considered is summed for each time step of MOLOCH forecast with data assimilation (DA). The trend volume is applied to the first rainfall forecast by the nowcasting technique.

In this way, the information about the potential growth and decay of the rainfall structures is provided as additional information to the nowcasting rainfall field. The idea is to make the forecast able to include, in a simple way, the tendency of decreasing or increasing precipitation volume, leaving the task of describing the spatial and temporal distribution of the rainfall field to the original scheme.

The nowcasting models have shown a predictability limit after several hours of forecasting. This limit is also valid for the nowcasting model PhaSt according to previous studies (Metta et al., 2009). This behaviour is mainly due to the fact that in the nowcasting model the evolution of the precipitation systems is not included: the physics that represents growth and decay of the precipitation systems becomes progressively more important with an increasing lead time (Germann et al., 2006). These physical processes are represented in the NWP model so that it is worth connecting the nowcasting and the meteorological model in a resulting rainfall forecast that is possibly more accurate. The blending technique tries to overcome this limit, connecting these two components. Blending has been previously analysed in some studies with the purpose of improving the rainfall forecast (Golding, 1998; Kober et al.,2012; Atencia et al., 2010; Kilambi and Zawadzky, 2005; Nerini et al., 2018). In this application, a blending function is written in order to balance the forecast reliability of the two models.

Different blending functions analysed: in panel

Many of the previous methodologies to estimate the blending function started from the statistical indices computed on the forecast rainfall field for the two models. These indices allow the calculation of the weight for the two different forecast rainfall fields. This strategy cannot be applied in this case due to the scarcity of the rainfall events considered: having only three case studies, it is not possible to have representative scores for the two methodologies. In this work, while being aware of the blending functions already presented in the literature and according to the arguments presented before concerning the capacity of the model to represent the physical processes after the first hours of forecasting, a first guess of the blending functions is selected, as shown in Fig. 4.

It is worth noting that the weight of the NWP model forecast rainfall is
calculated as the complementary function (Eq. 3)

At first glance it could seem that all blending functions give too much
weight in the first time steps to the nowcasting model. However, it must be
kept in mind that the nowcasting rainfall field is obtained by exploiting the
information from the NWP model corrected with data assimilation to modify
the radar rainfall volume (as described in Sect. 3.3). Moreover we expect
that forecasts derived by extrapolation from observation can capture the
spatio-temporal pattern better than NWP during the first 2 h of
the forecast (Metta et al., 2009; Collier, 1981; Seed, 2003; Xu and
Chandrasekar, 2005; Berenguer et al., 2011). In this way more weight is
given to the information of the nowcasting model regarding the positioning
of the rainfall structures, and a first correction of the forecast is done
through the modification of the volume. Then, for longer forecast times, less
weight is given to the nowcasting rainfall field in favour of the NWP model
forecast. The latter gains progressively more importance up to almost 5 h ahead of the event. After this, the rainfall field is entirely provided by the
meteorological model. Hence, the blended rainfall field at a certain
forecast time

Since the forecast of the NWP model is deterministic (rain

Continuum is a continuously distributed hydrological model that relies on a morphological approach that identifies the drainage network components from a digital elevation model (DEM; Giannoni et al., 2000, 2005). The DEM resolution generally coincides with the model spatial resolution. Flow in the soil is divided into a sub-surface flow component that is based on a Horton schematization (Gabellani et al., 2008) and that follows the drainage network directions and a deep flow component that moves based on the hydraulic head obtained by the water-table modelling. The energy balance is solved explicitly at the cell scale by using the force–restore equation. A complete description of the model is reported in Silvestro et al. (2013).

The hydrological model is implemented at a spatial resolution of 0.005

It is worth noting for the scope of the present study that the model is
distributed so that the streamflow is available in each point of the modelled
drainage network; this characteristic is exploited for the verification
(presented in the Sect. 4). Due to the limited horizontal resolution of
the model in this application, the grid points, and, as a consequence, the
basins with drainage area lower than 15 km

Within the hydrological nowcasting chain, all the elements presented in the
previous sections are connected together. The main input is the observed
radar rainfall, which is used up to the “now” as is; then it is used to
evolve the last radar image within the nowcasting phase, and it is used in
the data assimilation process of the NWP model. Then, for the rainfall
forecasting time window, three configurations are considered and compared.
These configurations differ for the 6 h forecast rainfall field that is
provided as input to the same hydrological model
(Fig. 5), which is computed as follows:

Nowcasting is done with PhaSt without volume variation (as described in Sect. 3.2) and no blending with the NWP model (Fig. 5a). This is hereinafter referred to as NOWC.

Nowcasting is done with PhaSt with volume variation according to the NWP model with data assimilation (as described in Sect. 3.3); there is no blending with the NWP model (Fig. 5b). This is hereinafter referred to as NOWC VOL.

Blending occurs between rainfall fields from nowcasting with volume variation (as in step 2) and from the NWP model (deterministic forecast), using a blending function varying in time, as presented in Sect. 3.4 (Fig. 5c). This is hereinafter referred to as NOWC BLEND; specifically, it uses the observed rainfall fields to build the rainfall scenarios in the recent past and the nowcasting model with volume correction for the first 2 h of forecast. Then, from the second to the sixth hour, the rainfall field is a linear combination between nowcasting and NWP model outputs according to the blending function.

The hydrological nowcasting chain in its three configurations: the
first one, which is used only the nowcasted rainfall field without volume
modification

For all the configurations, the hydrological model takes, as input, the rainfall scenarios and produces the forecast in terms of streamflow. The output of the chain is an ensemble of possible discharge scenarios (20 ensembles) for the following 12 h.

The comparison against observations is done in terms of the final output of the chain, namely the discharge forecast. This comparison is performed, taking, as a reference, the discharge forecast obtained using the radar rainfall estimates as input to the distributed hydrological model (hereinafter referred to as the “reference hydrograph”). This approach does not consider errors in the hydrological model, since it is aimed at evaluating possible improvements in the rainfall forecast (Borga, 2002; Vieux and Bedient, 2004; Berenguer et al., 2005).

Before comparing the three configurations described in Sect. 3.6, a preliminary analysis is performed to evaluate different blending functions (as described in Sect. 3.4). This allows us to identify the best function for every event and an overall (on average) best function. Then the comparison is made between the three configurations in order to investigate the importance of each element used to build the rainfall scenarios in the streamflow prediction.

A first analysis evaluates the performances of the hydrological forecast in three control sections, with one for each considered event. In this analysis, three scores are used: the Nash–Sutcliffe (NS) coefficient (Nash and Sutcliffe, 1970), the variance of the discharge (Var) and the continuous rank probability score (CRPS).

The Nash–Sutcliffe (see Eq. 5) coefficient is chosen, since it is one of
the widely used measures to evaluate model performance, especially for
streamflow reproduction:

To relate this index to the spread of the ensemble of the discharge forecast,
the variance is calculated for the analysis on the punctual section (Eq. 6):

The reduced continuous rank probability score (RCRPS; Eq. 7; Trinh et al.,
2013) is computed as the CRPS (Brown, 1974; Matheson and Winkler, 1976;
Unger, 1985; Stanki et al., 1989; Hersbach, 2000), reduced with the
standard deviation of the observed discharge over the analysed time period
(hereafter

The NS coefficient and Var(

River network grid points divided in three classes of drained area
for the Continuum computational domain: small catchments of 15–50 km

The distributed analysis is carried out by exploiting the distributed maps of
discharge produced by Continuum. This allows us to use several sampling points
for the comparison even if only three events are analysed. In this case
distinction is done by grouping all the relevant points according to their
upstream drained area into three classes (see Fig. 6):

points with an upstream drainage area in the range of 15 to 50 km

points with an upstream drainage area in the range of 50 to 150 km

points with an upstream drainage area in the range of 150 to 500 km

The distributed analysis, executed on the entire domain on which the hydrological model is run, provides more general information about the performance of the hydrological nowcasting chain. The score used for the distributed analysis is the RCRPS, calculated for each point of the domain interested by the event (a filter on the pixel in which the forecast discharge does not exceed a threshold is performed). Also the RCRPS is expressed as a function of lead time and for the three classes of drained areas.

A first analysis is performed to evaluate the performance of the complete chain implemented using four different blending functions to linearly combine nowcasting and NWP rainfall fields. This analysis is done by evaluating the discharge forecast using the RCRPS score; the distributed approach is applied, and hence the predictions in all the points of the domain are considered. The results are then analysed for the three classes of drainage area presented in Sect. 3.7.

RCRPS for all the events: analysis of the discharge output with
the four different blending functions. The red column represents the results
obtained by applying the step function, and the other three columns refer to the
other three smoothed blending functions (see Sect. 3.4). Inside each box,
the circle indicates the mean value, while the line indicates the median.
The results are presented in the form of a box plot for the three classes of
drainage area (15–20 km

Figure 7 presents a summary of the results concerning the entire sample of
data for the three events; in fact, for each grid point, for each time of
the forecast and for each lead time, it is possible to estimate a value of the RCRPS.
The box plot represents the RCRPS's variability (

Example of transition from a rainfall field forecast
through nowcasting

In general, the best score is obtained with the step function. However, this is an average behaviour for the three events; each event has its own best blending function, as shown in Sect. 4.2 and 4.3.

The results regarding the discharge forecasts for the main basins struck by the analysed events are examined starting from a first qualitative visualization of the comparison between the hydrographs for each configuration at every time step (every 20 min). Two examples are shown in Fig. 9. The first one is the forecast at 20:10 UTC on 9 October 2014 at the Bisagno creek, which was mainly responsible for the flood of the municipality of Genoa during this event. The second one is the forecast at 19:40 UTC on 11 November 2014 for the Entella basin, mainly interesting during this event for its tributaries Graveglia and Lavagna. The figures show the envelopes of the discharge forecasts for one time step during the considered time window of the event. The black thick line is the reference hydrograph, the black stars are the observed discharge, and the dashed lines represent the mean of the discharge ensemble forecast for each lead time. The first hydrograph envelope (light blue) refers to the configuration NOWC obtained with 6 h of nowcasting without volume modification. For the second hydrograph (orange), 6 h of rainfall is predicted by the configuration NOWC VOL. For the last hydrograph (red) the input rainfall field results from the blending (configuration NOWC BLEND), using the mean best function for the three events identified in Sect. 4.1, which is the blending step function.

Example of hydrographs for 9 October and 11 November events, for Bisagno and Entella outlet sections, respectively. In both figures, discharges are obtained using, as input, the 6 h rainfall field from NOWC (light blue), NOWC VOL (orange) and NOWC BLEND (pink).

Results clearly indicate that the spread of the discharge forecast ensemble is markedly smaller when input rainfall is provided by blending, instead of nowcasting alone (with or without volume trend modification). This is possibly ascribable to the fact that, while nowcasting provides a probabilistic forecast (20 ensembles) of different rainfall scenarios, blending connects a deterministic forecast from the NWP model with the nowcasting ensemble. Since each member of this ensemble is blended with the same NWP model forecast, the spread of the final ensemble is smaller.

Analysis at basin scale for the three events, namely 9 October 2014
at Bisagno creek

Figure 10 summarizes the results for some representative basins for each event, using the statistical scores described in Sect. 3.7. The three configurations of the hydrological chain presented in Sect. 3.7 are compared; for the blending configuration, the best blending function (for the blending step, see Sect. 4.1) and the local best blending function, which is different for each event, are applied.

The event of 9 October is sadly known for the flood that affected the
municipality of Genoa during the evening. The main creek interested by the
flood was the Bisagno creek, and the results at the Passerella Firpo section (

Although the event on 11 November affected the whole region of Liguria, the
main effects on the ground were caused by the Entella river, which flooded
the urban area of Chiavari, and by its tributaries Lavagna and Graveglia.
The scores for the Graveglia basin at the Caminata section (

For the event of 15 November, the performance of the system is evaluated for
Polcevera at the Rivarolo section (140 km

While in the previous section the scores were computed for single punctual sections of the basins, here the distributed analysis is carried out, aimed at giving a more general picture of the performance of the hydrological nowcasting chain fed with different rainfall inputs. In fact, with the distributed analysis, it is possible to compute the score, in this case the RCRPS, over all the points of the domain, increasing the data sample used for the analysis.

For this event the best blending function (Sect. 3.4) is f3. Figure 11 shows the general behaviour during the entire event: the use of the information retrieved by the NWP model in the rain forecast worsens the hydrological forecast. This can be due to the peculiar type of event characterized by stationary and persistent heavy precipitation on the same portion of territory that was not forecasted precisely by the NWP model but reproduced well by the nowcasting model. However, even if the information regarding the location of the rainfall, coming from the meteorological model, is misleading for the hydrological forecast, the information about the total volume on the domain adds value to the nowcasting rainfall field.

9 October 2014 event: RCRPS for three distinct classes of area. Each column refers to a different configuration of the forecasting system: NOWC (light blue), NOWC VOL (orange), NOWC BLEND using the step blending function (red) and NOWC BLEND using the best local blending function (blue), which in this case is the function f3.

For this event the best blending function is f2. As already pointed out in the previous basin-scale analysis, the distributed analysis (Fig. 12) confirms, even more clearly, that for this event the system using the blending performs markedly better. Especially for the bigger basins, due to their response time, the effects of a proper rainfall forecast provided with blending are beneficial for longer lead times, probably due to the slow response of the basins.

11 November 2014 event: RCRPS for three distinct classes of area. Each column refers to a different configuration of the forecasting system: NOWC (light blue), NOWC VOL (orange), NOWC BLEND using the step blending function (red) and NOWC BLEND using the best local blending function (yellow), which in this case is the function f2.

For this event the best blending function coincides with the best on average for all the three events, which is the step function. In this case (Fig. 13) the differences among the configurations can be noticed mostly in the first two classes of the area, where the use of the blending technique improves the rainfall forecast: the column related to the NOWC BLEND configuration is always lower and presents less spread with respect to NOWC and NOWC VOL configurations. In the last class of the area (larger basin) the behaviour is different, especially at the lead time corresponding to the transition in the blending between the rainfall field from nowcasting and from the NWP model. This transition is confirmed to be the most critical phase for the blending, as it can produce unrealistic rainfall fields.

15 November 2014 event: RCRPS for three distinct classes of area. Each column refers to a different configuration of the forecasting system: NOWC (light blue), NOWC VOL (orange), NOWC BLEND using the step blending function (red) and NOWC BLEND using the best local blending function, which in this case coincides with the step function (red).

A hydrological nowcasting chain is a useful instrument for flood and flash flood warnings. The use of an accurate QPF for input to the hydrological model is essential in extending the lead time of the hydrological forecast. The aim of this study is to improve the single elements in order to provide, in real time, an accurate forecast of the rainfall field which is able to improve the performance of the hydrological prediction at a temporal scale of up to 6–8 h.

The elements involved in the chain are the convection-permitting NWP model MOLOCH, which is able to provide high-resolution rainfall forecasts and to assimilate rainfall estimates with rapid updates, the probabilistic nowcasting model PhaSt, which extrapolates the rainfall evolution from observations, and the hydrological distributed model Continuum, which predicts the ground effects of the precipitation in terms of streamflow over all the basins of the studied region. The way in which these elements are combined with each other and integrated with observations from radar and available rain gauges represents the major novelty of the present study with respect to similar existing nowcasting procedures. Instead of just combining the spatial distribution of two rainfall fields, obtained independently of a nowcasting algorithm and an NWP model, first the nowcasting fields are modified according to information provided by the NWP. This can be considered to be a first blending step, during which the predicted rainfall fields produced by PhaSt are modified along the forecast horizon according to the predicted variation in rainfall volume from the NWP model corrected with assimilation. Specifically the rainfall volume trend by the NWP model is used to overcome the current limitation of the PhaSt algorithm, which is based on the hypothesis that the most recent observed rainfall volume is kept constant along the prediction. This first blending is aimed at better taking into account the processes of growth and decay of the precipitation structures as described by the NWP model that progressively gain importance at increasing lead times and cannot be realistically reproduced by nowcasting alone. Then a standard blending technique is applied to linearly combine the rainfall fields from nowcasting and the NWP model, based on a blending function that gives different weight to the QPF, depending on the lead time. The probabilistic QPF obtained (20 rainfall ensembles) is the input of the distributed hydrological model Continuum, which produces an ensemble of discharge forecasts in a frequently updated flood forecasting system (every 20 min).

The meteo-hydrological chain is tested for three main floods occurred during the autumn of 2014 that affected different areas of the Liguria region: the 9 October event with the flood of the Bisagno creek that hit the municipality of Genoa, the 11 November event, involving the Graveglia catchment, and the 15 November event, in which the flood of Polcevera occurred. Even if the number of analysed events is restricted, the resulting distributed maps produced by Continuum allow us to verify the performance of the chain on a large data sample.

A first analysis compared the results of the application of various blending functions to combine the forecast fields, highlighting the presence of a best function on average for the three events and a best function for each single event. This evaluation, however, suffers from the limited statistics due to the low number of analysed events. In order to identify the best blending function with more confidence, a larger sample of events is needed. In any case, using the best function for any single event, a comparison between three configurations of the hydrological nowcasting chains is performed. In the first two configurations, rainfall input is provided by nowcasting, with or without volume modification, while in the third one the blended rainfall fields are used.

Statistical scores show that in various cases the use of the rainfall fields resulting from the blending process leads to an improvement in the performances of the whole chain with respect to the use of nowcasting alone. In other cases, the benefit gained using the complete configuration with the blending is not so evident, and the performances results are similar to the use of the nowcasting for the whole lead time. However, a worsening of the performance is rarely observed and occurs in the time window corresponding to the transition between nowcasting and the NWP model forecast rainfall field. Hence, there is added value in the use of the blending between nowcasting and the NWP model, as it produces better or equal scores with respect to the use of the nowcasting alone.

A limitation of the presented study is related to the reduced number of considered events, which is mainly due to computational costs. A direct consequence is that it is not possible to provide a complete and exhaustive evaluation of the system performances, especially in terms of false alarms (Antonetti et al., 2019). In any case, this study can be considered to be a first step in the implementation and evaluation of a novel nowcasting procedure, which provides positive and encouraging results but still requires a much more robust validation, already planned for the future. However, it is worth noticing that the distributed analysis tries to partially overcome this issue because it considers a large number of grid points, increasing the sample size for the analysis, although in many cases the sample elements are evidently not independent, so it should be desirable in any case to increase the number of events for further analysis.

Future works and improvements to the chain presented in this work will be always explored in terms of improvement of the elements that compose it. Other techniques of data assimilation with increasing degrees of complexity can be used to assimilate not only the observed precipitation field but also other variables in the NWP model. Following recent works (Atencia et al., 2010), further investigations can be carried out using another type of blending, called spatial blending, which introduces spatial dependence of weights as a distance function to rainfall structures.

MOLOCH forecast rainfall data are available through a THREDDS Data Server upon request to s.davolio@isac.cnr.it, while the radar rainfall data used can be requested from the Regional Environmental Protection Agency of Liguria, Italy (ARPAL;

MLP, FS and SD designed the study. MLP and FS planned and coordinated the collection of data and performed the research activity related to the nowcasting, the blending technique and the hydrological application including the analysis of the results. SD carried out radar data assimilation into the NWP and meteorological simulations. All authors contributed to the analysis and to writing and revising the paper.

The authors declare that they have no conflict of interest.

This article is part of the special issue “Hydrological cycle in the Mediterranean (ACP/AMT/GMD/HESS/NHESS/OS inter-journal SI)”. It is not associated with a conference.

This work is a contribution to the HyMeX international programme, and it is supported by the Italian Civil Protection Department. We acknowledge the Regional Environmental Protection Agency of Liguria, Italy (ARPAL), for fruitful discussions and for providing the data.

This research has been developed within the 2017–2019 framework agreement stipulated between the CIMA Research Foundation and the Civil Protection Department.

This paper was edited by Uwe Ehret and reviewed by Massimiliano Zappa and one anonymous referee.