Rainfall downscaling in a space-time multifractal framework

Publication History: Issue published online: 9 JUL 2010 Article first published online: 9 JUL 2010 Manuscript Accepted: 17 FEB 2000 Manuscript Received: 18 AUG 1999A space-time multifractal analysis on radar rainfall sequences selected from the Global Atmospheric Research Program Atlantic Tropical Experiment database is presented. It is shown that space-time rainfall can be considered with a good approximation to be a self-similar multifractal process, so that a multifractal analysis can be carried out assuming Taylor's hypothesis to hold for rainfall over a wide range of spatial and temporal scales. The advection velocity needed to rescale the time dimension is estimated using different tracking techniques. On each selected rainfall sequence, a very good scaling is observed for spatial scales ranging from 4 to 256 km and for timescales from 15 min to 16 hours. A recently developed scale-covariant multifractal model is then reformulated for numerical simulation of space-time rainfall fields. The two parameters of the log-Poisson distribution used as cascade generator within the model are systematically estimated from each selected rainfall sequence, and the dependence of one of these parameters on the large-scale rain rate is highlighted. The model is then applied to disaggregate large-scale rainfall, and some comparisons between synthetically downscaled and observed rainfall are discussed.1779–1794Pubblicat

Prototipo di bollettino mensile di bilancio idrologico in atmosfera nella regione mediterranea: simulazione numerica con un modello meteorologico nei due trimestri prototipali

La presente relazione riassume l'attività svolta nell'ambito della prima fase della convenzione CRS4-DSTNPC \prototipo del bollettino mensile di bilancio idrologico in atmosfera". In particolare è descritta l'analisi delle metodologie utilizzabili rispetto agli obbiettivi del progetto, i criteri addottati per la selezione dei due periodi prototipali e le integrazioni numeriche ottenute mediante il Modello di circolazione atmosferica ad Area Limitata (LAM) nei due periodi trimestrali scelti. Vengono discussi, inoltre, il bilancio idrologico atmosferico ottenuto utilizzando le analisi meteorologiche e le medie mensili di precipitazione sulla Sardegna relativamente al periodo dal 1986 al 1988 e presentata una descrizione del codice LAM (BOLAM) utilizzato per eseguire i run previsti dalla convenzione

Prototipo di bollettino mensile di bilancio idrologico in atmosfera nella regione mediterranea: analisi degli output del LAM e sviluppo dei codici per il calcolo dei bilanci idrologici atmosferici nei due periodi prototipali

La presente relazione riassume l'attività svolta nell'ambito della seconda fase della convenzione CRS4-DSTNPC “prototipo del bollettino mensile di bilancio idrologico in atmosfera". In particolare, vengono analizzati gli output ottenuti con il BOLAM durante la prima fase del progetto dando particolare enfasi agli aspetti legati al bilancio idrologico. Si discutono poi le procedure per il calcolo del bilancio idrologico in atmosfera utilizzando i campi di umidità specifica, precipitazione, evapotraspirazione e vento prodotti dal BOLAM e le metodologie da utilizzare per stimare nel modo migliore tale bilancio. Infine vengono illustrati i risultati ottenuti relativamente ai due periodi prototipali oggetto del presente studio

Misura di variabili meteoclimatiche su scala di grigliato modellistico-numerico mediante radar CASSINI. Definizione di una procedura di assimilazione della velocità verticale nei modelli meteorologici idrostatici e sviluppo della diagnostica della instabilità simmetrica su grigliato numerico

Lo studio presentato in queste note si inquadra nell'ambito del progetto finanziato dall'ASI per la messa a punto e la sperimentazione di un radar (CASSINI) per la stima da satellite di parametri meteorologici, ed ha come obiettivo, a lunga scadenza, l'approfondimento delle procedure per valutare l'impatto potenziale, sul forecast dei fenomeni di precipitazione intensa, dovuto alla assimilazione di misure telerilevate nella condizione iniziale di un modello meteorologico ad area limitata. Tenendo presente quest'obiettivo generale, è stato effettuato lo studio di fattibilità di un processo di assimilazione della velocità verticale nei modelli meteorologici idrostatici, e realizzato un codice numerico per la diagnostica della instabilità condizionale simmetrica umida che si pensa sia uno degli elementi che concorrono agli eventi di precipitazione intensa a bande. Dopo aver applicato tale strumento alle analisi meteorologiche dei mesi di Gennaio e Febbraio dell'anno 1991, si è studiato mediante un modello di circolazione atmosferica ad area limitata (BOLAM) un caso di precipitazione intensa localizzato nella zona del canale di Sardegna alla risoluzione spaziale di ∼ 30 km

Infectious diseases and autoimmunity

Introduction: Autoimmunity occurs when the immune system recognizes and attacks host tissue. In addition to genetic factors, environmental triggers (in particular viruses, bacteria and other infectious pathogens) are thought to play a major role in the development of autoimmune diseases. Methodology: We searched PubMed, Cochrane, and Scopus without time limits for relevant articles. Results: In this review, we (i) describe the ways in which an infectious agent can initiate or exacerbate autoimmunity; (ii) discuss the evidence linking certain infectious agents to autoimmune diseases in humans; and (iii) describe the animal models used to study the link between infection and autoimmunity. Conclusions: Besides genetic predisposition to autoimmunity, viral and bacterial infections are known to be involved in the initiation and promotion of autoimmune diseases. These studies suggest that pathogens can trigger autoimmunity through molecular mimicry and their adjuvant effects during initiation of disease, and can promote autoimmune responses through bystander activation or epitope spreading via inflammation and/or superantigens.</br

Retention performance of green roofs in representative climates worldwide

The ongoing process of global urbanization contributes to an increase in stormwater runoff from impervious surfaces, threatening also water quality. Green roofs have been proved to be innovative stormwater management measures to partially restore natural states, enhancing interception, infiltration and evapotranspiration fluxes. The amount of water that is retained within green roofs depends not only on their depth, but also on the climate, which drives the stochastic soil moisture dynamic. In this context, a simple tool for assessing performance of green roofs worldwide in terms of retained water is still missing and highly desirable for practical assessments. The aim of this work is to explore retention performance of green roofs as a function of their depth and in different climate regimes. Two soil depths are investigated, one representing the intensive configuration and another representing the extensive one. The role of the climate in driving water retention has been represented by rainfall and potential evapotranspiration dynamics. A simple conceptual weather generator has been implemented and used for stochastic simulation of daily rainfall and potential evapotranspiration. Stochastic forcing is used as an input of a simple conceptual hydrological model for estimating long-term water partitioning between rainfall, runoff and actual evapotranspiration. Coupling the stochastic weather generator with the conceptual hydrological model, we assessed the amount of rainfall diverted into evapotranspiration for different combinations of annual rainfall and potential evapotranspiration in five representative climatic regimes. Results quantified the capabilities of green roofs in retaining rainfall and consequently in reducing discharges into sewer systems at an annual time scale. The role of substrate depth has been recognized to be crucial in determining green roofs retention performance, which in general increase from extensive to intensive settings. Looking at the role of climatic conditions, namely annual rainfall, potential evapotranspiration and their seasonality cycles, we found that they drive green roofs retention performance, which are the maxima when rainfall and temperature are in phase. Finally, we provide design charts for a first approximation of possible hydrological benefits deriving from the implementation of intensive or extensive green roofs in different world areas. As an example, 25 big cities have been indicated as benchmark case studies

How much green roofs and rainwater harvesting systems can contribute to urban flood mitigation?

Increased urbanization combined with the intensification of short rainfall events has worsened the urban flood issue. Among the different blue-green solutions to mitigate pluvial floods, green roofs (GR) and rainwater harvesting (RWH) have been investigated as sustainable systems to reduce runoff from rooftops. Their flood mitigation capacity, however, has been estimated mostly at building-scale. Following the need to estimate discharge reduction at large scale over entire cities, we simulated the installation of (extensive, intensive and multilayer blue) GRs on flat roofs and RWH systems for sloped ones. Performances of such systems were investigated in selected cities, representing different climate regimes. Although at building-scale GRs showed higher retention capacity, the cost-efficiency analysis highlights that at large-scale RWH tanks ensure higher retention with lower costs, due to rooftop distribution. The coupled system of multilayer blue-GRs and RWH tanks guarantees a discharge reduction of 5% even during extreme events

On the Role of Serial Correlation and Field Significance in Detecting Changes in Extreme Precipitation Frequency

Statistical trend analyses of observed precipitation (P) time series are key to validate theoretical arguments and climate projections suggesting that extreme P will increase in a warmer climate. Recent work warned about possible misinterpretation of trend tests if the presence of serial correlation and field significance are not considered. Here, we investigate these two aspects focusing on extreme P frequencies derived from 100-year daily records of 1,087 worldwide gauges of the Global Historical Climatology Network. For this aim, we perform Monte Carlo experiments based on count time series generated with the Poisson integer autoregressive model and characterized by different sample size, level of autocorrelation, and trend magnitude. The main results are as follows. (a) Empirical autocorrelations are consistent with those of uncorrelated and stationary or nonstationary count time series, while empirical trends cannot be explained as the exclusive effect of autocorrelation; incorporating the impact of serial correlation in trend tests on extreme P frequency has then limited impacts on tests' performance. (b) Accounting for field significance improves interpretation of test results by limiting type-I errors, but it also decreases test power; results of local tests could complement field significance outcomes and help identify weak trend signals where several trends of coherent sign are detected. (c) Based on these findings, evident patterns of statistically significant increasing (decreasing) trends emerge in central and eastern North America, northern Eurasia, and central Australia (southwestern America, southern Europe, and southern Australia). The methodological insights of this work support trend analyses of any hydroclimatic variable

A meteo-hydrological forecasting chain: performance of the downscaling and rainfall-runoff steps in a small catchment

Forecasting ground effects of severe meteorological events with an adequate lead time is fundamental for civil protection scopes and is therefore an important challenge for the scientific community. The paper focuses on the performance of some steps of a meteo-hydrological forecasting chain that can be applied in small watersheds to assess hydrological risk deriving by an intense storm predicted at the large meteorological scale. The proposed procedure integrates large-scale rainfall fields, as those produced by numerical weather prediction (NWP) models, with statistical rainfall downscaling and hydrological modelling. More in details, assuming a large scale rain rate as the input of the process, the forecasting chain produces an ensemble of hydrographs that are post-processed in order to give a probabilistic representation of mean streamflow maxima for different time windows. The outcome of this procedure can be thus applied to assess the risk that some critical streamflow thresholds may be exceeded. The procedure has been tested on more than one thousand recorded events in the Araxisi catchment in Sardinia, Italy. Results and performances are presented and discussed

Distributed hydrologic modeling of a sparsely monitored basin in Sardinia, Italy, through hydrometeorological downscaling

The water resources and hydrologic extremes in Mediterranean basins are heavily influenced by climate variability. Modeling these watersheds is difficult due to the complex nature of the hydrologic response as well as the sparseness of hydrometeorological observations. In this work, we present a strategy to calibrate a distributed hydrologic model, known as TIN-based Real-time Integrated Basin Simulator (tRIBS), in the Rio Mannu basin (RMB), a medium-sized watershed (472.5 km2) located in an agricultural area in Sardinia, Italy. In the RMB, precipitation, streamflow and meteorological data were collected within different historical periods and at diverse temporal resolutions. We designed two statistical tools for downscaling precipitation and potential evapotranspiration data to create the hourly, high-resolution forcing for the hydrologic model from daily records. Despite the presence of several sources of uncertainty in the observations and model parameterization, the use of the disaggregated forcing led to good calibration and validation performances for the tRIBS model, when daily discharge observations were available. The methodology proposed here can be also used to disaggregate outputs of climate models and conduct high-resolution hydrologic simulations with the goal of quantifying the impacts of climate change on water resources and the frequency of hydrologic extremes within medium-sized basins