Evaluation and comparison of satellite precipitation estimates with reference to a local area in the Mediterranean Sea

Precipitation is one of the major variables for many applications and disciplines related to water resources and the geophysical Earth system. Satellite retrieval systems, rain-gauge networks, and radar systems are complementary to each other in terms of their coverage and capability of monitoring precipitation. Satellite-rainfall estimate systems produce data with global coverage that can provide information in areas for which data from other sources are unavailable. Without referring to ground measurements, satellite-based estimates can be biased and, although some gauge-adjusted satellite-precipitation products have been already developed, an effective way of integrating multi-sources of precipitation information is still a challenge.In this study, a specific area, the Sicilia Island (Italy), has been selected for the evaluation of satellite-precipitation products based on rain-gauge data. This island is located in the Mediterranean Sea, with a particular climatology and morphology, which can be considered an interesting test site for satellite-precipitation products in the European mid-latitude area. Four satellite products (CMORPH, PERSIANN, PERSIANN-CCS, and TMPA-RT) and two GPCP-adjusted products (TMPA and PERSIANN Adjusted) have been selected. Evaluation and comparison of selected products is performed with reference to data provided by the rain-gauge network of the Island Sicilia and by using statistical and graphical tools. Particular attention is paid to bias issues shown both by only-satellite and adjusted products. In order to investigate the current and potential possibilities of improving estimates by means of adjustment procedures using GPCC ground precipitation, the data have been retrieved separately and compared directly with the reference rain-gauge network data set of the study area.Results show that bias is still considerable for all satellite products, then some considerations about larger area climatology, PMW-retrieval algorithms, and GPCC data are discussed to address this issue, along with the spatial and seasonal characterization of results. © 2013 Elsevier B.V

Evaluation and comparison of satellite precipitation estimates with reference to a local area

Precipitation is one major variable for many applications. Satellite retrieval systems, raingauge network and radar systems are complement to each other in terms of their coverage and capability of monitoring precipitation. Satellite rainfall estimates systems produce data with global coverage that can provide information in areas for which data from other sources are unavailable.Without referring to ground measurement, satellite-based estimates can be bias. Although some gauged adjusted satellite precipitation products are developed, an effective way of integrating multi-sources of precipitation information is still a challenge. In this study we select a specific area in Sicily (Italy) having high density rain gauges to evaluate of satellite precipitation products. Sicily has an area of 26,000 sq.km and the gauge density of the network considered in this study is about 250 sq.km/gauge. It is an island in the Mediterranean sea with a particular climatology and morphology, which is considered as an interesting test site for satellite precipitation products on the European mid-latitude area. Three products (CMORPH, PERSIANN, TRMM_3B42) have been selected for the evaluation. Evaluation and comparisons between selected products is performed with reference to the data provided by the gauge network of Sicily and using statistical and visualization tools. Considerations about differences between the point estimation given by gauges and the gridded surface provided by satellites are discussed as well as the difference between an evaluation based on point estimation and an evaluation based on interpolated data. An analysis of typical interpolation methods used for hydrometerological purposes have been done to choose the most appropriate method considering size of grid satellite data and the density of gauge network. Finally natural neighbor interpolation procedure was adopted to obtain gridded surface data with the same resolution of satellite products. Hypothetical relationship between elevation and results is investigated as well as presence of particular patterns and goodness of extremes detection. Results show that bias is considerable for all satellite products and extremes are rarely well captured. Analysis is described referring to the developing of a local system to get precipitation information for scientific and modeling purpose. It is evaluated the opportunity to elaborate a corrected product applying a bias correction procedure that would improve the quality of final data

A regional GIS-based model for reconstructing natural monthly streamflow series at ungauged sites

Several hydrologic applications require reliable estimates of monthly runoff in river basins to face the widespread lack of data, both in time and in space. The main aim of this work is to propose a regional model for the estimation of monthly natural runoff series at ungauged sites, analyzing its applicability, reliability and limitations. A GIS (Geographic Information System) based model is here developed and applied to the entire region of Sicily (Italy). The core of this tool is a regional model for the estimation of monthly natural runoff series, based on a simple modelling structure, consisting of a regression based rainfall-runoff model with only four parameters. The monthly runoff is obtained as a function of precipitation and mean temperature at the same month and runoff at the previous month. For a given basin, the four model parameters are assessed by specific regional equations as a function of some easily measurable geomorphic and climate basins’ descriptors. The model is calibrated by a “two-step” procedure applied to a number of gauged basins over the region. The first step is aimed at the identification of a set of parameters optimizing model performances at the level of single basin. Such “optimal” parameters sets, derived for each calibration basin, are successively used inside a regional regression analysis, performed at the second step, by which the regional equations for model parameters assessment are defined and calibrated. All the gauged watersheds across the Sicily have been analyzed, selecting 53 basins for model calibration and using other 6 basins exclusively for validation purposes. Model performances, quantitatively evaluated considering different statistical indexes, demonstrate a relevant model ability in capturing the observed hydrological response at both the monthly level and higher time scales (seasonal and annual). One of the key features related to the proposed methodology is its easy transferability to other arid and semiarid Mediterranean areas; thus, the application here shown may be considered as a benchmark for similar studies. The calibrated model is implemented by a GIS software (i.e. Quantum GIS 2.10), automatizing data retrieving and processing procedures and creating a prompt and reliable tool for filling/reconstructing precipitation, temperature or streamflow time series at any gauged or ungauged Sicilian basin. The proposed GIS plug-in can, in fact, be applied at any point of the hydrographical network of the region, assessing the precipitation, temperature and natural streamflow series (at the monthly or higher time scales) for a desired time-window

USING HIGH RESOLUTION RAINGAUGE DATA FOR STORM TRACKING ANALYSIS IN THE URBAN AREA OF PALERMO, ITALY

This paper presents a comparative analysis between rain-gauge storm tracking techniques in order to achieve a better knowledge of the rainfall dynamics over an urbanized area. The temporal and spatial distribution and kinematics of short term rainfall are recognized as one of the most important reasons in error production in rainfall-runoff on urban catchments. The uncertainty due to rainfall variability can greatly affect urban drainage modeling performance and reliability thus reducing the confidence of operators in their results. Modeling representations of urban catchments and drainage systems are commonly adopted for surface flooding forecasting and management and an adequate knowledge of rainfall spatial and temporal variability should be considered as a fundamental step for a robust interpretation of the physical processes that take part in urban areas during intense rainfall events. The starting basis of such studies is usually given by a network of high resolution raingauges disseminated inside and around the examined urban area. One of the raingauge techniques used is based on simulating the storm motion by visualizing the sequence of the rainfall patterns obtained using rain-gauge data and on spatial correlation. The storm speed and direction are obtained using the rain-gauge method by tracking the advance of the maximum rainfall intensity in time and space. A second method is based on the identification, for each gauge, of the time of occurrence of some significant features such the time of onset of a storm or the time of peak. A third method is based on the classical idea of spacetime autocorrelation function; This function describes the way in which the correspondence between the rainfall patterns at two points in space-time reduces as the distance between two points is increased. The analysis has been carried out on the basis given by high resolution rainfall data collected over Palermo urban area (Italy). The urban area has a surface of around 30 km2 and it is mainly distributed on North West – South East direction. The monitoring network is made of 10 tipping bucket raingauges. Bucket volume is equivalent to 0.1 mm rainfall. Raingauges have been uniformly distributed over the urban areas allocating them mainly over public buildings and school in order to allow for easy access. The network has been put in place in January 2006 and it is still working. Data is monthly collected by the operator that also provide for clock synchronization and ordinary maintenance and cleaning. An accurate analysis of the results of this comparison between the techniques has been carried out and, since the city of Palermo is not covered by any meteorological radar, the analysis of storm dynamics will allow to create a system monitoring hydrometeorological conditions which operates on time basis using the information coming from the raingauge network as forecast triggers

Craftmanship and Digitalization in the Italian Knitwear Industry. A Paradigm Shift for the Narrative of Made in Italy

Knitwear is a consolidated industry in Italy and, at the same time, a typical expression of the Made in Italy paradigm linked to the ideas of craftsmanship. While, on the one hand, knitwear is associated with the idea of craft and manufacturing traditions, on the other hand, it is nowadays produced by numerical control machines (CNC) where the technological contribution and the level of automation are very relevant. The convergence of physical and digital environments, at the heart of the Fashion Industry 4.0 debate, is an established feature of knitwear design practice. In the contemporary industrial scenario, knitted items are produced on digitally programmed machines through sophisticated software, and the manual contribution of the individual operator during the knitting phase is reduced to a minimum. In the light of these premises, this contribution questions the opportunity and value of the integration of digital technologies in the storytelling of traditional manufacturing without losing the power to evoke Made in Italy’s values such as quality, aesthetic refinement, and exclusivity. To analyze these issues, the authors report the case study of SMT – Società Manifattura Tessile, a leading knitting company where the technological presence equals that of traditional manufacturing craftmanship, keeping both elements at balance. The case study suggests the importance of the contemporary knitting craftsman to increasingly develop communication skills to make the relationship between technology andmanufacturing explicit and possibly smoothly blend it with the Made in Italy archetypes

Regional frequency analysis of extreme precipitation for Sicily (Italy)

The analysis of extreme precipitation has always been included among most relevant hydrological applications because of the several important activities linked to the availability of tools for the estimation of extreme rainfall quantiles. These activities include the design of hydraulic civil structures and the evaluation and management of hydraulic and hydrological risk. In this study a frequency analysis of annual maxima precipitation measurements has been carried out for the area of Sicily (Italy). A typical hierarchical regional approach has been adopted for the parameter estimation procedure based on the L-moments method. The identification of homogeneous regions within the procedure has been pursued with a data driven procedure constituted by a principal component analysis of an ensemble of selected auxiliary variables, and a K-means cluster analysis algorithm. Auxiliary variables comprise meteo-climatic information and a representation of the average seasonal distribution of intense events. Results have been evaluated by means of a Monte Carlo experiment based on the comparison between at-site and regional fitted frequency distributions. Moreover, results have been compared with previous analyses performed for the same area. The study provides an updated tool for the modelling of extreme precipitation for the area of Sicily (Italy), with different features respect to previous tools both in terms of definition of homogeneous zones and in terms of parameters of the frequency distribution. Meteo-climatic information and the seasonality of extreme events retrieved from the dataset has been proficuously exploited in the analysis

L’INFLUENZA DELLA MORFOLOGIA SULLA DISTRIBUZIONE DELLE PIOGGE INTENSE

Le precipitazioni intense costituiscono uno dei principali pericoli naturali perché sono all’origine di processi, come innesco di frane o piene improvvise, che possono rappresentare una grave minaccia per la vita umana. Il problema di determinare la variazione spaziale delle precipitazioni intense e in particolare, di indagare sulle relazioni che intercorrono tra queste e la morfologia del territorio, è molto importante soprattutto per gli studi connessi alla realizzazione di efficienti sistemi di allerta e di allarme. Tuttavia la variabilità delle piogge intense con la morfologia è scarsamente studiata in idrologia. In questo lavoro si intende affrontare l’argomento a scala regionale, assumendo che le precipitazioni intense siano rappresentate dalle curve di probabilità pluviometrica che forniscono il quantile Tennale di assegnata durata come prodotto di un coefficiente di crescita in frequenza per una relazione di potenza che serve a riscalare le medie orarie nelle durate maggiori tramite due parametri, a e n. In tal modo lo studio può essere ricondotto all’analisi della variazione di questi due parametri in funzione di alcuni caratteri morfologici e fisiografici. Lo studio è stato condotto sui dati provenienti da 276 stazioni pluviografiche funzionanti sul territorio siciliano. E’ stata utilizzata sia la regressione stepwise sia la Geographically Weighted Regression (GWR) pervenendo a risultati interessanti

Machine-learning blends of geomorphic descriptors: value and limitations for flood hazard assessment across large floodplains

Recent literature shows several examples of simplified approaches that perform flood hazard (FH) assessment and mapping across large geographical areas on the basis of fast-computing geomorphic descriptors. These approaches may consider a single index (univariate) or use a set of indices simultaneously (multivariate). What is the potential and accuracy of multivariate approaches relative to univariate ones? Can we effectively use these methods for extrapolation purposes, i.e., FH assessment outside the region used for setting up the model? Our study addresses these open problems by considering two separate issues: (1) mapping flood-prone areas and (2) predicting the expected water depth for a given inundation scenario. We blend seven geomorphic descriptors through decision tree models trained on target FH maps, referring to a large study area (∼ 105 km2). We discuss the potential of multivariate approaches relative to the performance of a selected univariate model and on the basis of multiple extrapolation experiments, where models are tested outside their training region. Our results show that multivariate approaches may (a) significantly enhance flood-prone area delineation (accuracy: 92%) relative to univariate ones (accuracy: 84%), (b) provide accurate predictions of expected inundation depths (determination coefficient ∼0.7), and (c) produce encouraging results in extrapolation