Roberto Ranzi

MAP D-PHASE: Real-Time Demonstration of Weather Forecast Quality in the Alpine Region

Demonstration of probabilistic hydrological and atmospheric simulation of flood events in the Alpine region (D-PHASE) is made by the Forecast Demonstration Project in connection with the Mesoscale Alpine Programme (MAP). Its focus lies in the end-to-end flood forecasting in a mountainous region such as the Alps and surrounding lower ranges. Its scope ranges from radar observations and atmospheric and hydrological modeling to the decision making by the civil protection agents. More than 30 atmospheric high-resolution deterministic and probabilistic models coupled to some seven hydrological models in various combinations provided real-time online information. This information was available for many different catchments across the Alps over a demonstration period of 6 months in summer/ fall 2007. The Web-based exchange platform additionally contained nowcasting information from various operational services and feedback channels for the forecasters and end users. D-PHASE applications include objective model verification and intercomparison, the assessment of (subjective) end user feedback, and evaluation of the overall gain from the coupling of the various components in the end-to-end forecasting system.

Use of multispectral ASTER images for mapping debris-covered glaciers within the GLIMS project

The problem of mapping debris-covered glaciers using images derived from satellite-borne optical scanners is addressed in this paper. Results using also Terra-ASTER images on the Belvedere and Miage glaciers, both located in the Italian Alps, are presented. Field measurements and energy balance modeling indicate that for debris superimposed on ice, surface temperatures of some degrees colder than for pure, debris are to be expected in the morning. This fact is also confirmed by processing satellite images taking into account the thermal band. The results of black glacier detection can be useful for the project GLIMS (Global Land Ice Monitoring from Space) when debris-covered glaciers are to be mapped

Comparing the opportunities of Landsat-TM and Aster data for monitoring a debris covered glacier in the Italian Alps within the GLIMS project

The difficulties in monitoring partly debris covered glaciers by the application of remote sensing techniques is a well known problem. The use of visible (VIS) and near infrared (NIR) bands does not always provide sufficient information to detect the glaciers margins with remote sensing data. In this study the value of the thermal band for glacier detection is investigated. Particular attention is hereby drawn to the different opportunities of glacier monitoring using Landsat-TM and Terra-Aster data. Accordance and discrepancies of the respective data depending on the spatial resolution are shown and discussed. The presented case study was investigated at the debris covered Belvedere glacier, one of the test sites of the GLIMS project.

Monitoring of melting refreezing cycles of snow with microwave radiometers: the Microwave Alpine Snow Melting Experiment (MASMEx 2002-2003)

A study of the melting cycle of snow was carried out by using ground-based microwave radiometers, which operated continuously 24 h/day from late March to mid-May in 2002 and from mid-February to early May in 2003. The experiment took place on the eastern Italian Alps and included micrometeorological and conventional snow measurements as well. The measurements confirmed the high sensitivity of microwave emission at 19 and 37 GHz to the melting-refreezing cycles of snow. Moreover, micrometeorological data made it possible to simulate snow density, temperature, and liquid water content through a hydrological snowpack model and provided additional insight into these processes. Simulations obtained with a two-layer electromagnetic model based on the strong fluctuation theory and driven by the output of the hydrological snowpack model were consistent with experimental data and allowed interpretation of both variation in microwave emission during the melting and refreezing phases and in discerning the contributions of the upper and lower layers of snow as well as of the underlying ground surface.

On the derivation of the areal reduction factor of storms

Abstract A stochastic derivation of the areal reduction factor (ARF) of the storm intensity is presented: it is based on the analysis of the crossing properties of the rainfall process aggregated in space and time. As a working hypothesis, the number of crossings of high rainfall intensity levels is assumed to be Poisson-distributed and a hyperbolic tail of the probability of exceedances of rainfall intensity has been adopted. These hypotheses are supported by the analysis of radar maps during an intense storm event which occurred in Northern Italy. The reduction factor derived from this analysis shows a power-law decay with respect to the area of integration and the duration of the storm. The areal reduction results as a function of the storm duration and of its frequency. A weak, but significant decrease of the areal reduction factor with respect to the return period is shown by the functions derived, and this result is consistent with that of some recent studies on this topic. The results derived, although preliminary, may find useful applications for the definition of the design storm in urban catchments of a size greater than some square kilometres and with duration of some hours.

Ten years of monitoring areal snowpack in the Southern Alps using NOAA-AVHRR imagery, ground measurements and hydrological data

Monitoring snow cover in alpine areas is important for the estimation of the water storage during the snowmelt season, especially in view of irrigation, hydropower production and water supply. Cost-efliciency and fine temporal resolution of images from the satellite-borne NOAA-AVHRR sensor indicate this source of information as a suitable candidate for monitoring snow cover extent. This information can also be used for validation of distributed snowmelt models. As a result of a long-term study, ten years of snow covered area depletion curves have been estimated using remote sensing in seven watersheds of size larger than 400 km 2 in the Southern Alps. Coupling of satellite imagery with detailed topographic data and some ground measurements of snowpack depth and density provides regional estimates of snow water equivalent in northern Italy, upstream of Lakes Maggiore, Como, Iseo, Idro, and Garda. The basin water equivalent estimates are compared with the values obtained from the hydrological water balance equation used in two of the selected watersheds and computed for different snowmelt seasons.

Flooding Hazard Mapping in Floodplain Areas Affected by Piping Breaches in the Po River, Italy

AbstractIn recent years, flood-related risk has been increasing worldwide, being inundations among the natural disasters which induce the maximum damage in terms of economic losses. In the research reported in this paper, a methodology to map the flooding residual hazard due to levee failure events induced by piping in embankments protecting flood-prone areas is proposed. Ensemble simulations are used to account for uncertainties in location, geometry, and time-evolution of the levee breaches. Probabilistic flooding-hazard maps are generated combining the results of 192 inundation scenarios, simulated by using one-dimensional (1D) and two-dimensional (2D) hydrodynamic models. The methodology is applied considering 96 different locations and sizes of breaches occurred along a 23-km reach protected by the right levee of the Po River, the right levee of the Taro River, and the left levee of the Parma River, which delimit a 100-km2 study area. The influence of obstacles to the flood propagation and consequent...

A conceptual model of people's vulnerability to floods

Hydraulic risk maps provide the baseline for land use and emergency planning. Accordingly, they should convey clear information on the potential physical implications of the different hazards to the stakeholders. This paper presents a vulnerability criterion focused on human stability in a flow specifically devised for rapidly evolving floods where life, before than economic values, might be threatened. The human body is conceptualized as a set of cylinders and its stability to slipping and toppling is assessed by forces and moments equilibrium. Moreover, a depth threshold to consider drowning is assumed. In order to widen its scope of application, the model takes the destabilizing effect of local slope (so far disregarded in the literature) and fluid density into account. The resulting vulnerability classification could be naturally subdivided in three levels (low, medium, and high) that are limited by two stability curves for children and adults, respectively. In comparison with the most advanced literature conceptual approaches, the proposed model is weakly parameterized and the computed thresholds fit better the available experimental data sets. A code that implements the proposed algorithm is provided.

Statistical characterization of spatial patterns of rainfall cells in extratropical cyclones

The assumption of a particular type of distribution of rainfall cells in space is needed for the formulation of several space-time rainfall models. In this study, weather radar-derived rain rate maps are employed to evaluate different types of spatial organization of rainfall cells in storms through the use of distance functions and second-moment measures. In particular the spatial point patterns of the local maxima of rainfall intensity are compared to a completely spatially random (CSR) point process by applying an objective distance measure. For all the analyzed radar maps the CSR assumption is rejected, indicating that at the resolution of the observation considered, rainfall cells are clustered. Therefore a theoretical framework for evaluating and fitting alternative models to the CSR is needed. This paper shows how the “reduced second-moment measure” of the point pattern can be employed to estimate the parameters of a Neyman-Scott model and to evaluate the degree of adequacy to the experimental data. Some limitations of this theoretical framework, and also its effectiveness, in comparison to the use of scaling functions, are discussed.

Study of the snow melt–freeze cycle using multi-sensor data and snow modelling

The melt cycle of snow is investigated by combining ground-based microwave radiometric measurements with conventional and meteorological data and by using a hydrological snow model. Measurements at 2000 m a.s.l in the basin of the Cor- devole river in the eastern Italian Alps confirm the high sensitivity of microwave emission at 19 and 37 GHz to the snow melt^freeze cycle, while the brightness at 6.8 GHz is mostly related to underlying soil. Simulations of snowpack changes performed by means of hydrological and electromagnetic models, driven with meteorological and snow data, provide additional insight into these processes and contribute to the interpretation of the experimental data.