Abstract
Abstract. The weather forecasts for precipitation have considerably improved in recent years thanks to the increase of computational power. This allows for the use of both a higher spatial resolution and the parameterization schemes specifically developed for representing sub-grid scale physical processes at high resolution. However, precipitation estimation is still affected by errors that can impact the response of hydrological models. To the aim of improving the hydrological forecast and the characterization of related uncertainties, a regional-scale meteorological–hydrological ensemble is presented. The uncertainties in the precipitation forecast and how they propagate in the hydrological model are also investigated. A meteorological–hydrological offline coupled ensemble is built to forecast events in a complex-orography terrain where catchments of different sizes are present. The Best Discharge-based Drainage (BDD; both deterministic and probabilistic) index, is defined with the aim of forecasting hydrological-stress conditions and related uncertainty. In this context, the meteorological–hydrological ensemble forecast is implemented and tested for a severe hydrological event which occurred over Central Italy on 15 November 2017, when a flood hit the Abruzzo region with precipitation reaching 200 mm (24 h)−1 and producing damages with a high impact on social and economic activities. The newly developed meteorological–hydrological ensemble is compared with a high-resolution deterministic forecast and with the observations (rain gauges and radar data) over the same area. The receiver operating characteristic (ROC) statistical indicator shows how skilful the ensemble precipitation forecast is with respect to both rain-gauge- and radar-retrieved precipitation. Moreover, both the deterministic and probabilistic configurations of the BDD index are compared with the alert map issued by Civil Protection Department for the event showing a very good agreement. Finally, the meteorological–hydrological ensemble allows for an estimation of both the predictability of the event a few days in advance and the uncertainty of the flood. Although the modelling framework is implemented on the basins of the Abruzzo region, it is portable and applicable to other areas.
Highlights
Floods and extreme rainfall are among the major natural hazards in Europe with over 1000 fatalities and an estimated cost of about EUR 52.000 billion in damages, between 1998 and 2009 alone (European Environment Agency, 2010)
On 15 November 2017 a severe hydrological event hit the Abruzzo region, causing damages with a high social and economic impact on human activities. This event is used to investigate the reliability of a meteorological–hydrological ensemble chain
The meteorological ensemble correctly reproduces the signal of the event by catching the area of the maximum precipitation a few days before the event
Summary
Floods and extreme rainfall are among the major natural hazards in Europe with over 1000 fatalities and an estimated cost of about EUR 52.000 billion in damages, between 1998 and 2009 alone (European Environment Agency, 2010). The scientific community paid an increasing amount of attention to study the EPS coupled to hydrological models, with the aim of improving early-warning systems on different spatial scales ranging from global to regional (Addor et al, 2011; Alfieri et al, 2012; McCollor and Stull, 2008; Davolio et al, 2008; Calvetti and Pereira Filho, 2014; Hally et al, 2015; Saleh et al, 2016) In this context, the possibility of quantifying and estimating forecast uncertainties allows the end users of hydrological models to manage the risk and to decide the actions to be taken with the aim of reducing the possible damages (Hamill et al, 2005; Schaake et al, 2007; Alfieri et al, 2012). The novelty of this work consists of applying a coupled probabilistic approach to both the weather and the hydrological ensemble forecasts; for a small catchment in complex orography, this improvement has been recognized to be extremely beneficial for flash flood and landslide prediction (Alfieri et al, 2012). A deeper analysis of the impact of a larger ensemble will be the topic of a paper, following the work of Buizza and Palmer (1998), though Jaun et al (2008) showed that using 10 members only can be sufficient for having benefits from an ensemble approach for flood forecasting
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