Search

Wind power is one of the fastest-growing renewable energy sectors and is considered instrumental in the ongoing decarbonization process. However, wind turbines (WTs) present high operation and maintenance costs caused by inefficiencies and failures, leading to ever-increasing attention to effective Condition Monitoring (CM) strategies. Nowadays, modern WTs are integrated with sensor networks as part of the Supervisory Control and Data Acquisition (SCADA) system for supervision purposes. CM of wind farms through predictive models based on routinely collected SCADA data is envisaged as a viable mean of improving producibility by spotting operational inefficiencies. However, given the large number of variables monitored by SCADA systems, selecting those that contribute the most to the modelling of wind turbine health conditions is an open challenge. In this paper, we propose an unsupervised feature selection algorithm based on a novel multivariate Predictive Power Score (PPS). Unlike other approaches in literature that only consider relationships between pairs of variables, here we propose a Combined PPS (CPPS), where the information content of combinations of variables is considered for the prediction of one or more key parameters. The algorithm has been tested on 9 turbines belonging to the same wind farm located in the Italian territory. The results show that the proposed approach is more flexible and outperforms standard PPS.

Extreme rainfall events have large impacts on society and are likely to increase in intensity under climate change. For design and management decisions, particularly regarding hydraulic works, accurate estimates of precipitation magnitudes are needed at different durations. In this article, an objective approach of the regional frequency analysis (RFA) has been applied to precipitation data for the island of Sicily, Italy. Annual maximum series for rainfall with durations of 1, 3, 6, 12, and 24 h from about 130 rain gauges were used. The RFA has been implemented using principal component analysis (PCA) followed by a clustering analysis, through the k-means algorithm, to identify statistically homogeneous groups of stations for the derivation of regional growth curves. Three regional probability distributions were identified as appropriate from an initial wider selection of distributions and were compared–the three-parameter log-normal distribution (LN3), the generalized extreme value (GEV) distribution, and the two component extreme value (TCEV) distribution. The regional parameters of these distributions were estimated using L-moments and considering a hierarchical approach. Finally, assessment of the accuracy of the growth curves was achieved by means of the relative bias and relative root-mean-square error (RMSE) using a simulation analysis of regional L-moments. Results highlight that for the lower return periods, all distributions showed the same accuracy while for higher return periods the LN3 distribution provided the best result. The study provides an updated resource for the estimation of extreme precipitation quantiles for Sicily through the derivation of growth curves needed to obtain depth–duration–frequency (DDF) curves.

In this paper numerical computations dedicated to the analysis of the maneuvering behavior of a fully appended submarine are presented. The work presented here is part of the CNR-INSEAN activities scheduled in the framework of a joint Italian and Norwegian MoDs project, with the partnership of MARINTEK, under the egida of the European Defence Agency. The aim of the project is to exploit the effect of cruciform and X rudder configuration on the turning qualities of the submarine in two operation conditions, namely open water and snorkeling depth. In this paper, the free running turning circle maneuver in open water condition is analyzed; predictions are achieved by an in-house CFD solver.

In this paper numerical computations dedicated to the analysis of the maneuvering behavior of a fully appended submarine are presented. The work presented here is part of the CNR-INSEAN activities scheduled in the framework of a joint Italian and Norwegian MoDs project, with the partnership of MARINTEK, under the egida of the European Defence Agency. The aim of this numerical work is to analyze the effects of cruciform and X rudder configurations on the maneuvering and turning qualities of the submarine in open water operations. The numerical computations are performed by means of an in-house CFD solver and both captive and free running maneuvers are analyzed.

Le misure di portate defluenti in un corso d’acqua sono solitamente affette da Incertezza dovuta ad una serie di cause concorrenti che influenza fortemente la risposta dei modelli afflussi-deflussi. Nella presente memoria viene analizzata l’influenza dell’incertezza relativa alla scala di deflusso sulla modellazione della risposta idrologica di un bacino idrografico a scala giornaliera. A tal fine è stato utilizzato un modello concettuale, basato sulla teoria dell’IUH (Instantaneous Unit Hydrograph), che tiene conto insieme delle trasformazioni lineari e non lineari che conducono alla formazione del deflusso fluviale a partire da dati giornalieri di pioggia, temperatura dell’aria. La Propagazione dell’incertezza dalla scala di deflusso alla risposta idrologica è stata valutata attraverso l’applicazione della procedura GLUE (Generalised Likelihood Uncertainty Estimation). Con questa Metodologia è stato possibile generare in maniera random una serie di scale di deflusso, a partire da quella storica misurata, e valutarne l’effetto sulla trasformazione afflussi-deflussi. I risultati finali mostrano un aumento di incertezza per la maggior parte delle simulazioni del modello effettuate in corrispondenza delle scale di deflusso generate rispetto a quelle effettuate in corrispondenza della scala delle portate originale. In particolare l’incertezza è più significativa per le simulazioni corrispondenti alle scale di deflusso che sovrastimano quella storica rispetto a quelle che la sottostimano.

Il verde pensile è un sistema tecnologico che prevede l’utilizzo della vegetazione come strato superiore della copertura di un edificio, impiegato sia per le sue funzioni ambientali che per le sue caratteristiche estetiche (Cascone, 2019). Infatti, la realizzazione di un tetto verde, oltre al suo intrinseco valore ecologico, consente numerosi vantaggi economici ed ambientali rispetto alle coperture tradizionali (Berardi & Ghaffarianhoseini, 2014). Nel tempo, l’interesse nei confronti dei tetti verdi è sempre più cresciuto, sia per i benefici conseguibili che rispondono a molti degli obiettivi delle diverse politiche nazionali, europee e internazionali per la mitigazione e l’adattamento al cambiamento climatico e lo sviluppo sostenibile (es., 2030 Agenda for Sustainable Development, EU Cohesion Policy 2021-2027, Next Generation EU, Programma Nazionale Ricerca 2021-2027, Piano Nazionale di Ripresa e Resilienza), che per la migliorata conoscenza delle tecnologie di cui si dispone, anche grazie alla pubblicazione di norme di settore come la norma UNI 11235:2015, e la diminuzione delle spese d’installazione legate anche a una serie di politiche incentivanti e di agevolazioni fiscali. Tra i benefici legati all’utilizzo delle coperture a verde uno dei più importanti è sicuramente quello legato al miglioramento del microclima dei centri urbani grazie alla presenza della vegetazione che: 1) ha effetti positivi sulla riduzione delle “isole di calore” urbano e sull’aumento dei livelli di ombreggiamento e di isolamento acustico; 2) permette di abbattere considerevolmente le polveri inquinanti e di sequestrare la CO2 atmosferica; 3) consente di abbattere il carico inquinante delle precipitazioni attraverso i processi di biofiltrazione su matrici porose pedologiche e attraverso l’attività dei microrganismi presenti nel suolo; 4) permette un effetto di laminazione delle acque meteoriche mediante processi d’immagazzinamento temporaneo e rilascio controllato (De Nardo et al., 2003; Liu & Minor, 2005; Fioretti et al., 2010). Con riferimento a quest’ultimo punto, l’installazione di un tetto verde, e in particolare di un sistema multistrato caratterizzato da una maggiore capacità d’immagazzinamento rispetto ai tetti verdi convenzionali, permette una migliore gestione delle acque meteoriche e una riduzione del carico sui sistemi fognari. In particolare, nel caso di più installazioni distribuite all’interno di una data area, è possibile ottenere una significativa riduzione della frequenza e/o della gravità degli allagamenti urbani, soprattutto in occasione di precipitazioni intense. I tetti verdi multistrato potrebbero inoltre consentire il riutilizzo delle acque piovane per l’irrigazione e come acque grigie per l’uso domestico, riducendo i consumi di acqua potabile. Tali soluzioni rientrano tra le Nature Based Solution spesso adottate in ambito urbano anche per l’efficientamento energetico degli edifici, poiché conferiscono un maggiore isolamento termico alle coperture consentendo una riduzione dei consumi energetici per il condizionamento dei locali interni. Lo scopo di questo studio è quello di descrivere un tetto verde multistrato installato sul tetto dell’Edificio 8 del Dipartimento di Ingegneria dell’Università di Palermo. L’installazione, avvenuta nell’ambito del progetto pilota Polder Roof Field Lab a metà del 2020, ha consentito un intensivo monitoraggio durante tutto il 2021, di cui si riportano nel presente lavoro i risultati principali.

This study introduces a water-driven crop model aiming to quantitatively link olive yield to climate and soil moisture dynamics using an ecohydrological approach. A mathematical model describing soil moisture, evapotranspiration and assimilation dynamics of olive orchards is developed here. The model is able to explicitly reproduce two different hydroclimatic phases in Mediterranean areas: the well-watered conditions in which evapotranspiration and assimilation assume their maximum values and the real conditions where the limitations induced by soil moisture availability are taken into account. Annual olive yield is obtained by integrating the carbon assimilation during the growing season, including the effects of vegetation water stress on biomass allocation. This numerical model has been tested on an olive orchard located in Sicily (Italy) obtaining a satisfactory reproduction of historical olive yield data. This model is useful for simulating the influence of soil moisture dynamic on biomass growth, fruit productivity, also in a context of climatic change.

A companion paper (Arena et al, 2019) has introduced the architecture of the DSS and has described its governing equations. In a real-time, dynamic decision-making context, it is a tool to support decisions at the current time step concerning water allocations to municipal demand centres and irrigation districts as well as additional intakes from costly water sources in a water resources system featuring reservoirs with over-year behaviour. The DSS is designed as a linearized MIP (mixed integer programming) optimization model and as such, it includes an objective function and constraints on 1) mass balances at system’s nodes, 2) systems’ topology, 3) component’s capacity, 4) spills, as well as non-empty conditions on reservoir storage at the end of the Forecasting Horizon (FH). It is a multi-scenario optimization tool because future, uncertain inflows are modelled, until the end of the current water year, as three different inflow scenarios: low flows, normal flows and high flows. The optimization model is solved for the three different scenarios and a unique solution that can be turned into one actual, implementable, decision at the present time step is obtained by imposing non-anticipatory (or congruity) constraints according to the principle of scenario aggregation (Rockfellar and Wets, 1987). The objective function is the weighted sum of the scarcity costs at all demand centres and of cost of water supply from additional sources along the multi-year FH, discounted to their present value, being the weights the occurrence probability of each of the three inflow scenarios. Given the over-year nature of the systems of interest for this study, the time unit is one month. This paper first discusses estimation of scenario probabilities and of scenario inflows, then describes the application of the DSS to a real-world, two-reservoir system in Southern Italy. Its performances, in terms of scarcity costs and costs of additional, costly, water resources, are simulated over a forty-year historical period, on a monthly basis. Sensitivity of the DSS to different demand levels is explored considering different drift scenarios. Furthermore, in order to contrast the performances of the multi-scenario DSS presented here (DSS-SC), we introduce a single-scenario DSS, identical to the multi-scenario one, except that decisions are made based on exogenous inflow forecast vectors. We look at two different types of forecast vectors that are meant to provide a lower and upper bound of DSS performances: the first type is a vector containing only the long-term means of monthly inflows and gives rise to DSS-WF (where WF stands for “worst forecast”). The second vector type contains instead real (i.e. actually occurred) inflows in the first six months of the FH and long-term means of monthly inflows for the remaining FH – 6 months. It gives rise to a DSS-BF (BF stands for “best forecasts”). Results show that DSS-SC compares quite favourably with DSS-BF: differences in total costs range from 37% for drift=0.75 to 27% for drift = 0.5. DSS-BF clearly outperforms DSS-WF with improvements ranging from 17% (drift = 0.75) to 62% (drift = 0.90). In one demand condition, described by a drift of 0.9, DSS-SC even outperforms DSS-BF. Investigation of this behaviour led to recognize that, at least for this drift, DSS-SC would manage the system so to keep the largest reservoir of the system full enough to allow issuing less restrictive irrigation supply reductions than its DSS-BF counterpart, and would therefore reduce the associated scarcity costs. This thought-provoking situation, if on the one hand confirms that in these long-memory systems “abrupt” failures can be the consequence of long-term policies and decision styles, on the other hand stimulates the reflection that DSS performances can indeed depend on a number of different factors that need to be investigated in deeper detail. From this standpoint, a single historic time series is probably not enough to explore the different possible behaviours of the DSS. For this reason, a stochastic validation of DSS-SC, by simulating its behaviour through synthetic time series, is in order and is the next research objective.

The paper presents a set of experiments on the Rolling Horizon Technique (RHT), applied to the management of a multi-reservoir, multipurpose water resources system with over-year behaviour. In the RHT, decisions on releases from reservoirs are taken for a number of time steps ahead (the Forecasting Horizon – FH) through an optimization model, based on the present water availability in reservoirs and on some forecasts of future inflows over the FH. Only the decision concerning the first time step (the current month) is then implemented, as new information on reservoir levels and forecasts becomes available so that the process can start again with updated information. The paper investigates how the quality of forecasts and the length of the FH impact on the effectiveness of the decisions taken. The evaluation exercise is carried out in two different demand scenarios considering some deficit-related indices, such as TSSD, the Sum of Squared Standardized Deficits, over an Operation Horizon (OH) of forty years. The scenarios are designed to evaluate the role of different demand levels (corresponding to higher/lower stress) on system’s behaviour. Results show that in systems with over-year behaviour, effective forecasts (here simulated through the best possible: actually occurred inflows from the current month up to the end of water year) seem to add little value to system’s operation compared to the most naive forecast (average monthly values), especially in contexts characterized by high pressure on resources. Where there is less pressure on resources, good-quality forecasts are more effective to improve operation. A FH of 24 months seems to enhance significantly system’s performances compared to a FH of 12 months, while FHs longer than two years produce no improvements in system’s performances. The paper also tries to define the limits of a finite-horizon operation technique such as the RHT by benchmarking it with an idealistic release schedule obtained by a model that performs allocation with Perfect Foresight over the whole Operation Horizon.

The paper introduces and applies a methodology to screen investments aimed at reducing water supply risks due to hydrologic failures in headwork systems for municipal use, based on the principles of cost-benefit analysis. As risk includes both the probability of a failure and its effect, the methodology combines a simulation module of the system, fed by a stochastic hydrologic input to reproduce the probability distribution of the failures, with a metric for supply failure damage provided by the price – demand relationship for municipal water. Benefits are assessed as the averted damage compared to a base case without investments. This approach is then combined with the classic discounted cashflow approach of cost– benefit analysis to allow for the dynamics of both water supply and demand due to trends in population growth, individual consumption and, above all, planned reduction of losses in water distribution networks. The methodology is applied to screen a number of different supply-side projects for the headwork system supplying Apulia, in southern Italy featuring both regulated surface and groundwater resources and providing drinking water to over 4,000,000 persons. The procedure allows both ranking of single projects by their economical performances and the economic evaluation of combinations of different projects. The study also aims to assess the impact of the selected time scale, of cross-correlation among production sites, and of the specification of the demand function on projects' economic indicators. Results show that each modelling assumption has a considerable impact on the value of the economic indicators in absolute terms, but ranking of the different projects seems to be less sensitive to such modelling aspects.

The main challenge associated to regional water supply systems planning lies in understanding the best infrastructure alternatives to improve service given a certain configuration of the resources system. While cost-effectiveness assessments are still widespread in this type of analyses, they do not account for the fact that, especially in mature systems, service improvements tend to follow the law of marginal diminishing returns, so that a cost – benefit assessment should be preferable. Both costs and benefits can be organized into a cost-benefit analysis (CBA) framework. In a complex system, a model reproducing its topology and characteristics better describes the impacts of the alternatives. In this work some alternatives to improve service in the regional water supply system of Apulia, Southern Italy, are illustrated and compared through CBA. The system has been modelled through AQUATOR, a state-of-the-art simulation software package.

The paper presents a hydro-economic model of the Apulian water resources system, in southern Italy, i.e. a mathematical programming model of the allocations explicitly accounting for the different values of water and for hydrological variability at the appropriate time scale. Traditionally a water-scarce region, Apulia is now supplied by extra-regional regulated surface resources as well as by a coastal aquifer; altogether, they constitute a multipurpose system with conflicting uses: municipal, irrigation and, to a lesser extent, industrial supply. The system is at present affected by high streamflow variability and by a high level of water losses along the urban distribution networks. In the model, the variability of surface water resources is accounted for by a 30-yrs. time series of seasonal streamflow into the reservoirs. The model provides the means to assess the effectiveness of different supply and demand side measures to improve the regional water balance and reduce drought impact

The “thermal inertia” method to retrieve surface soil water content maps on bare or sparsely-vegetated soils is analysed. The study area is a small experimental watershed, where optical and thermal images (in day and night time) and in situ data were simultaneously acquired. The sensitivity of thermal inertia to the phase difference between incoming radiation and soil temperature is demonstrated. Thus, to obtain an accurate value of the phase difference, the temporal distance between thermographs using a three-temperature approach is evaluated. We highlight when a cosine correction of the temperature needs to be applied, depending on whether the thermal inertia formulation includes two generic acquisition times, or not. Finally, the deviation in soil water content retrieval is quantifies for given values of each parameter by performing a sensitivity analysis on the basic parameters of the thermal inertia method that are usually affected by calibration errors. La méthode de « l’inertie thermique » pour la cartographie de la teneur en eau de la surface des sols nus ou à végétation clairsemée est analysée. La zone d’étude est un petit bassin versant expérimental, où des images optiques et thermiques (de jour et de nuit) et des données in situ ont été acquises simultanément. On a mis en évidence la sensibilité de l’inertie thermique à la différence de phase entre le rayonnement incident et la température du sol. Ainsi, pour obtenir une valeur précise de la différence de phase, nous avons évalué la distance temporelle entre thermographes en utilisant une approche à trois températures. Cet article montre la nécessité éventuelle d’une correction cosinusoïdale de la température, selon que la formulation de l’inertie thermique comprend ou non deux temps d’acquisition génériques. Enfin, l’article quantifie la variabilité de la teneur en eau du sol pour des valeurs données de chaque paramètre en effectuant une analyse de sensibilité sur les paramètres de base de la méthode de l’inertie thermique qui sont généralement affectés par des erreurs d’étalonnage.

The paper presents a Mixed Integer Non Linear Programming (MINLP) model of the water resources system supplying Genoa, in northern Italy. The system presently features five reservoirs, three main river intakes, and two well fields. The hydrological regime is typically Mediterranean; water availability is however relatively abundant, so that drought issues are limited, especially now that water demand from the supply sources has decreased due to reduced population, deindustrialization and to improvement in the operation and maintenance of the water distribution network. In this context, it is worthwhile considering the possibility to relax an over-conservative management of resources, justified by the experience of previous drought events, and to explore the viability of exploiting resources from reservoirs for hydropower production. The MINLP model expresses cost minimization over a 40 year time period on a monthly basis, subject to physical constraints. Costs include scarcity costs (the economic value of possible water deficits) and extraction costs from wells, minus hydropower production. The model has been written in GAMS and solved through the SBB (Simple Branch and Bound) solver. Results show that the system is able to meet demand over the 40 year hydrologic scenario with negligible water deficits and that hydropower production may be enhanced compared to present by increasing releases from reservoirs, which ultimately implies accepting keeping reservoirs emptier than presently done.

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.

La diffusione crescente di laghetti collinari sul nostro territorio ha suggerito la possibilità di avviare un’attività di ricerca volta ad identificare la portata del fenomeno, la sua evoluzione nel tempo e l’incidenza che esso può avere sul bilancio idrologico e sulla stima della risorsa idrica disponibile. Con questo primo lavoro si è iniziato ad indagare i limiti e le potenzialità delle tecniche di elaborazione di dati remoti nell’identificazione degli invasi, di vario tipo, e nella stima della risorsa idrica da essi intercettata. La metodologia, basata sulla classificazione d’immagini multispettrali, è stata esaminata per diverse risoluzioni spaziali ed ha permesso di conseguire una stima plausibile di alcune grandezze significative, mentre si è rivelata meno efficace per altre.

Flow duration curves are simple and powerful tools to deal with many hydrological and environmental problems related to water quality assessment, water-use assessment and water allocation. Unfortunately the scarcity of streamflow data enables the use of these instruments only for gauged basins. A regional model is developed here for estimating flow duration curves at ungauged basins in Sicily, Italy. Due to the complex ephemeral behaviour of the examined region, this study distinguishes dry periods, when flows are zero, from wet periods using a three parameters power law to describe the frequency distribution of flows. A large dataset of streamflows has been analysed and the parameters of flow duration curves have been derived for about fifty basins. Regional regression equations have been developed to derive flow duration curves starting from morphological basin characteristics.

This paper performs annual runoff frequency analysis in Sicily, Italy using the index runoff method. Homogeneous regions, not necessarily defined by geographical boundaries but sharing common features from a morphologic and climatic standpoint, have been identified. For each region a single probability distribution function can be fitted to the available annual runoff data scaled by the index runoff. Starting from an initial dataset of 105 hydrometric stations, 57 stations have been selected using heuristic criteria associated with the Hosking and Wallis’s discordancy measure. The Mantel tests have been then applied to find out the physical and morphological parameters best correlated with the runoff using the distances matrix approach. These parameters have been successively used in the cluster analysis to delineate homogeneous regions and the Hosking and Wallis’s heterogeneity test has been applied to the clusters in order to verify their homogeneity. Further analysis has been carried out to test the goodness-of-fit of four different candidate distributions of the scaled annual runoff using the chi-square test. For all the identified homogeneous regions the three-parameters lognormal distribution has been proved the most suitable distribution for the dimensionless annual runoff. In order to predict the runoff quantiles at ungauged sites, the procedure finally envisages the estimation of mean annual runoff (index runoff) by multiple regression analysis, providing expressions that relate mean annual runoff to some climatic and morphologic features of the basins.

The present study proposes some performance indicators for analysing urban water distribution system characterised by intermittent supply service. Specific indicators have been proposed for investigating the reduction of quality of water service under resources scarcity conditions. A modelling procedure has been implemented in order to allow for analysing complex network schemes in which users self adapt to scarcity conditions. The procedure has been applied to analyse the performance of an existing supply system in Palermo (Italy). The network and users’ behaviour in intermittent distribution conditions have been monitored in order to calibrate the model and verify the reliability of the proposed methodology. The approach allowed for evaluating the impact of water scarcity events on population thus suggesting actions for improving resources distribution equity.

The Generalised Likelihood Uncertainty Estimation (GLUE) approach is presented here as a tool for estimating the predictive uncertainty of a rainfall–runoff model. The GLUE methodology allows to recognise the possible equifinality of different parameter sets and assesses the likelihood of a parameters set being acceptable simulator when model predictions are compared to observed field data. The results of the GLUE methodology depend greatly on the choice of the likelihood measure and on the choice of the threshold which determines if a parameters set is behavioural or not. Moreover the sampling size has a strong influence on the uncertainty assessment of the response of a rainfall–runoff model. This is one of the most controversial and criticized aspect of the GLUE methodology, because it seems that this procedure does not learn from observations. Following these premises, this paper investigated first on the effect of different likelihood measures on the uncertainty analysis in the rainfall–runoff modelling of a mediterranean catchment. Performance of individual parameter sets has been assessed using three likelihood measures with a shaping factor. The acceptability threshold influence on the uncertainty analysis has been also evaluated. Finally it can be demonstrated how, using the GLUE, the predictive uncertainty grows with the streamflow variance while remains almost the same with the sampling size. In order to overcome these inconsistencies, a new simple likelihood measure, which explicitly takes into account the sample variance and extension, is here proposed.

Plants in Mediterranean ecosystems have developed different strategies to cope with transient soil-moisture dynamics induced by the markedly out of phase seasonal behavior of rainfall and temperature. Deep-rooted plants use the soil moisture stored in the wet winter (extensive users), while shallower rooted plants exploit both the wet season storage and the more sporadic growing season rainfall (intensive users). Using stochastic models of soil moisture dynamics, we present an analytical and numerical description of the probabilistic structure of the soil-moisture storage at the beginning of the growing season in relation to the dynamics of the wet season, and then study its evolution during the subsequent growing season. Special attention is devoted to plant water stress as a function of the rooting depth and the soil moisture storage at the beginning of the growing season. The existence of an optimal rooting depth for Mediterranean climates and its dependence on future hydro-climatic scenarios are discussed with reference to a test case in Sicily, Italy. Our analyses suggest that the forecasted decrease in rainfall for the Mediterranean regions might lead to a considerable increase of plant water stress and favor vegetation with shallower root systems.

La memoria valuta l’applicabilità dei modelli markoviani a stato nascosto (Hidden state Markov Models) per la simulazione delle portate giornaliere di un corso d’acqua. In questi modelli la variabile osservata è l’emissione di un processo markoviano con un numero discreto di stati, caratterizzato da una matrice di probabilità di transizione, che viene “ricoperto” da una certa funzione densità di probabilità. Il problema della selezione del modello consiste in questo caso nella scelta del numero di stati e del tipo di distribuzione di probabilità di appartenenza ad uno stato. Identificato il modello, la sua calibrazione consiste nella stima delle probabilità di transizione e dei parametri della funzione densità di probabilità e viene effettuata massimizzando la log-verosimiglianza del processo. La procedura è applicata a sette anni di portate giornaliere del fiume Sosio a S.Carlo (90 km2) nel versante meridionale della Sicilia. I risultati sono incoraggianti, per quanto riguarda la distribuzione marginale delle portate e la loro struttura seriale. I confronti tra valori osservati e simulati di alcuni statistici relativi ai picchi ed alle recessioni mettono in evidenza la necessità di incrementare la struttura interna agli stati, anche se con piccoli aggiustamenti la capacità del modello di riprodurre gli statistici osservati migliora considerevolmente.