An operational flash-flood forecasting chain applied to the test cases of the EU project HYDROPTIMET

International audience The application of a flash-flood prediction chain, developed by CIMA, to some testcases for the Tanaro river basin in the framework of the EU project HYDROPTIMET is presented here. The components of the CIMA chain are: forecast rainfall depths, a stochastic downscaling procedure and a hydrological model. Different meteorological Limited Area Models (LAMs) provide the rainfall input to the hydrological component. The flash-flood prediction chain is run both in a deterministic and in a probabilistic configuration. The sensitivity of forecasting chain performances to different LAMs providing rainfall forecasts is discussed. The results of the application show how the probabilistic forecasting system can give, especially in the case of convective events, a valuable contribution in addressing the uncertainty at different spatio-temporal scales involved in the flash flood forecasting problem in small and medium basins with complex orography

General calibration methodology for a combined Horton-SCS infiltration scheme in flash flood modeling

Abstract. Flood forecasting undergoes a constant evolution, becoming more and more demanding about the models used for hydrologic simulations. The advantages of developing distributed or semi-distributed models have currently been made clear. Now the importance of using continuous distributed modeling emerges. A proper schematization of the infiltration process is vital to these types of models. Many popular infiltration schemes, reliable and easy to implement, are too simplistic for the development of continuous hydrologic models. On the other hand, the unavailability of detailed and descriptive information on soil properties often limits the implementation of complete infiltration schemes. In this work, a combination between the Soil Conservation Service Curve Number method (SCS-CN) and a method derived from Horton equation is proposed in order to overcome the inherent limits of the two schemes. The SCS-CN method is easily applicable on large areas, but has structural limitations. The Horton-like methods present parameters that, though measurable to a point, are difficult to achieve a reliable estimate at catchment scale. The objective of this work is to overcome these limits by proposing a calibration procedure which maintains the large applicability of the SCS-CN method as well as the continuous description of the infiltration process given by the Horton's equation suitably modified. The estimation of the parameters of the modified Horton method is carried out using a formal analogy with the SCS-CN method under specific conditions. Some applications, at catchment scale within a distributed model, are presented

Applicability of a forecasting chain in a different morphological environment in Italy

International audienceThe operational meteo-hydrological forecasting chain of the Liguria Region (NW Italy) is applied to a different morphoclimatic environment, such as the Emilia Romagna Region (N Italy). Modification to the chain, both in models and in procedures, are introduced to overcome problems related to medium dimension catchments (A?1000km2), characterized by complex altimetry profiles and antropical interventions along the river. The main feature of the original operational procedure, that is the probabilistic approach, is maintained. Hydraulic hazard reduction through artificial reservoirs management is exploited with reference to a specific event occurred on the Reno basin (Emilia Romagna Region)

A hydrological analysis of the 4 November 2011 event in Genoa

On the 4 November 2011 a flash flood event hit the area of Genoa with dramatic consequences. Such an event represents, from the meteorological and hydrological perspective, a paradigm of flash floods in the Mediterranean environment. <br><br> The hydro-meteorological probabilistic forecasting system for small and medium size catchments in use at the Civil Protection Centre of Liguria region exhibited excellent performances for the event, by predicting, 24–48 h in advance, the potential level of risk associated with the forecast. It greatly helped the decision makers in issuing a timely and correct alert. <br><br> In this work we present the operational outputs of the system provided during the Liguria events and the post event hydrological modelling analysis that has been carried out accounting also for the crowd sourcing information and data. We discuss the benefit of the implemented probabilistic systems for decision-making under uncertainty, highlighting how, in this case, the multi-catchment approach used for predicting floods in small basins has been crucial

A particle filter scheme for multivariate data assimilation into a point-scale snowpack model in an Alpine environment

The accuracy of hydrological predictions in snow-dominated regions deeply depends on the quality of the snowpack simulations, with dynamics that strongly affect the local hydrological regime, especially during the melting period. With the aim of reducing the modelling uncertainty, data assimilation techniques are increasingly being implemented for operational purposes. This study aims to investigate the performance of a multivariate sequential importance resampling – particle filter scheme, designed to jointly assimilate several ground-based snow observations. The system, which relies on a multilayer energy-balance snow model, has been tested at three Alpine sites: Col de Porte (France), Torgnon (Italy), and Weissfluhjoch (Switzerland). The implementation of a multivariate data assimilation scheme faces several challenging issues, which are here addressed and extensively discussed: (1) the effectiveness of the perturbation of the meteorological forcing data in preventing the sample impoverishment; (2) the impact of the parameter perturbation on the filter updating of the snowpack state; the system sensitivity to (3) the frequency of the assimilated observations, and (4) the ensemble size.The perturbation of the meteorological forcing data generally turns out to be insufficient for preventing the sample impoverishment of the particle sample, which is highly limited when jointly perturbating key model parameters. However, the parameter perturbation sharpens the system sensitivity to the frequency of the assimilated observations, which can be successfully relaxed by introducing indirectly estimated information on snow-mass-related variables. The ensemble size is found not to greatly impact the filter performance in this point-scale application.</p

Towards a definition of a real-time forecasting network for rainfall induced shallow landslides

PREVIEW is an European Commission FP6 Integrated Project with the aim of developing, at an European level, innovative geo-information services for atmospheric, geophysical and man-made risks. Within this framework, the Landslides Platform Service 2 (forecasting of shallow rapid slope movements) has developed an integrated procedure for the forecasting and warning of distributed shallow landsliding to be used for civil protection purposes. The Service consists of an automated end-to-end forecasting chain which uses data from a probabilistic downscaled short-term rainfall forecast, soil saturation estimates and meteorological radar outputs. The above data are entered into a hydro-geological model that makes use of an infinite slope approach to calculate the distributed Factor of Safety over the entire basin. All outputs, and much of the input data, are shown on a WebGIS system so that end-users can interactively access and download data. A distinctive feature of the service is the use of an innovative soil depth model for predicting the distributed thickness of the regolith cover within the basin, which is one of the most important parameters controlling shallow landslide triggering. The service was developed in a pilot test site in NE Italy, the Armea basin. Validation makes use of two rainfall events: one that occurred in 2000 and a smaller, more recent event (2006) that caused fewer landslides. Rainfall data have been used to compute a distributed factor-of-safety map that has been overlaid onto the landslide inventory. Instead of a traditional validation approach based on the number count of correctly identified landslides, we carried out an alternative procedure based on the landslides area that gave outcomes which, for this preliminary stage of the research, can be considered promising

A Complete Meteo/Hydro/Hydraulic Chain Application to Support Early Warning and Monitoring Systems: The Apollo Medicane Use Case

Because of the ongoing changing climate, extreme rainfall events’ frequency at the global scale is expected to increase, thus resulting in high social and economic impacts. A Meteo/Hydro/Hydraulic forecasting chain combining heterogeneous observational data sources is a crucial component for an Early Warning System and is a fundamental asset for Civil Protection Authorities to correctly predict these events, their effects, and put in place anticipatory actions. During the last week of October 2021 an intense Mediterranean hurricane (Apollo) affected many Mediterranean countries (Tunisia, Algeria, Malta, and Italy) with a death toll of seven people. The CIMA Meteo/Hydro/Hydraulic forecasting chain, including the WRF model, the hydrological model Continuum, the automatic system for water detection (AUTOWADE), and the hydraulic model TELEMAC-2D, was operated in real-time to predict the Apollo weather evolution as well as its hydrological and hydraulic impacts, in support of the early warning activities of the Italian Civil Protection Department. The WRF model assimilating radar data and in situ weather stations showed very good predictive capability for rainfall timing and location over eastern Sicily, thus supporting accurate river flow peak forecasting with the hydrological model Continuum. Based on WRF predictions, the daily automatic system for water detection (AUTOWADE) run using Sentinel 1 data was anticipated with respect to the scheduled timing to quickly produce a flood monitoring map. Ad hoc tasking of the COSMO-SkyMed satellite constellation was also performed to overcome the S1 data latency in eastern Sicily. The resulting automated operational mapping of floods and inland waters was integrated with the subsequent execution of the hydraulic model TELEMAC-2D to have a complete representation of the flooded area with water depth and water velocity