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Extreme rainfall events have been more frequent in recent decades, potentially as a climate change effect. This has been leading to a higher risk of the failure of existing hydraulic infrastructures, and to a higher awareness regarding the unreliability of design rainfall calculated with reference to historical data recorded in the last century. With this in mind, the present study questions the stationary assumption of the rainfall Depth–Duration–Frequency curves commonly used in Sicily, the biggest island of the Mediterranean Sea. Quantiles derived from the most up-to-date regional method, regarding Sicily, based on observations in the period 1928–2010, have been compared with those extracted from a high-resolution dataset related to the period 2002–2022, provided by the SIAS agency. The results showed a remarkable underestimation of the rainfall quantiles calculated with the regional approach, especially at the shortest durations and low return periods. This means that new hydraulic works should be designed with reference to longer return periods than in the recent past, and those that currently exist may experience a higher risk of failure. Future investigation of this aspect is crucial for enhancing the effectiveness of water management and detecting hydrological risks under a changing climate.

L’esperienza del Progetto Mentore (PM) nasce nel 2013 dalla percezione, da parte di diversi docenti dell’Ateneo di Palermo, che l’Accademia italiana stesse abbandonando a sé stessa la funzione didattica, confidando di fatto nelle capacità di ognuno di organizzarsi, aggiornarsi e valutarsi (Felisatti, Scialdone, Cannarozzo, & Pennisi, 2019; Caradonna, Morale, Pace, Scargiali, Scialdone, & Auteri, 2020). Tale impressione era tra le altre cose basata sul fatto che nessuno dei sistemi di valutazione dei docenti universitari (il processo di abilitazione scientifica nazionale, ma anche la stessa valutazione della qualità dei corsi di studio) mettesse enfasi sulla necessità da parte del docente di essere anche un efficace insegnante, oltre che un bravo ricercatore. Da qui l’idea di organizzarsi autonomamente, con l’obiettivo iniziale di creare gruppi di colleghi che lavorassero insieme al miglioramento della qualità della didattica, su base volontaria.

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.

Assessing potential deviations of the fundamental river basins’ hydrological processes and streamflow characteristics from the “natural trajectory” represents a high-priority objective to understand the biological impact of altered flow regime on river ecosystems. Existing approaches are mainly based on the analysis of daily-based indicators of hydrologic alteration, which requires wide database, including “pre-impact” and “post-impact” daily flow data frequently unavailable. The hydrological modeling is commonly used to face data missing problems or reconstruct natural conditions, even if models, especially at the daily scales, are often complex and computationally intensive. The use of simpler and more parsimonious models results, sometimes, essential for practical applications, also in consideration of the typical scarce availability of some data. This paper proposes an alternative approach for the evaluation of rivers flow regime alterations, based on different monthly hydrological indicators that are first computed and then combined to provide a global index of alteration. The procedure, conceptually derived from the Range of Variability Approach (RVA), is applied and tested on two Sicilian river basins (Italy) subject to anthropogenic influence. Streamflow regime for both the basins results differently disturbed by upstream human pressures. An alteration index is computed using available observations as “post-impact” monthly flow time-series, while time-series relative to “pre-impact” conditions have been reconstructed by the Tri.Mo.Ti.S. model, an innovative monthly and high-performing regional regressive hydrological model. The methodology, easily transferable to other regions, has revealed particularly efficacious in identifying and quantifying the existing human pressures and can be considered as a suitable tool for water resource management and policy planning activities.

During the last six years, at the University of Palermo, a group of academics has been involved in a project called “MENTORE” (“Modifying and ENhancing Teaching through peer Observation and Reflections with Experts”). The objectives of the project are: to help teachers improve their teaching, through the help of two mentors; to experiment new approaches in pilot courses to extend, if useful, to other ones; and to change the traditional model of academic teaching based on one single teacher with the class to go towards a model where there is a group of teachers working together in search of improvements. All the participants of the MENTORE project attend to lectures/workshops on teaching and learning and participate to meetings where they share experiences and ideas. In the program, each participant has two mentors that help him/her to improve the quality of his/her teaching. The two mentors are other academics that participate to the program. Indeed, each participant is both mentor and mentee during the same year. Mentors follow some of the lectures of the mentee, meet the students in the final part of the course in order to collect their opinion and, finally, meet the mentee in order to discuss the actions to carry out for improving the quality of his/her teaching. This chapter describes the aforementioned peer observation practice adopted at the University of Palermo and focuses on the role of mentors describing their activities during the peer observation process

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.

During the last 6 years at the University of Palermo, a group of academics has been involved in a project called “MENTORE” (“Modifying and ENhancing Teaching through peer Observation and Reflections with Experts.”) The objectives of the project are to help teachers improve their teaching, through the help of two mentors; to experiment new approaches in pilot courses to extend, if useful, to other ones; and to change the traditional model of academic teaching based on one single teacher with the class to go toward a model where there is a group of teachers working together in search of improvements. All the participants of the MENTORE project attend to lectures/workshops on teaching and learning and participate in meetings where they share experiences and ideas. In the program, each participant has two mentors who help him or her to improve the quality of his or her teaching. The two mentors are other academics who participate in the program. Indeed, each participant is both mentor and mentee during the same year. Mentors follow some of the lectures of the mentee, meet the students in the final part of the course in order to collect their opinion, and, finally, meet the mentee in order to discuss the actions to carry out for improving the quality of his or her teaching. This chapter describes the aforementioned peer observation practice adopted at the University of Palermo and focuses on the role of mentors describing their activities during the peer observation process.

Sustainable use of water resources requires a modern approach for complex water systems management based on system’s modeling, as unsung operation alternatives may only be devised and tested through a model. The now available IT computing tools enable setting the traditional modelling methodologies for planning and operation of such systems in a new framework. The present work deals with the development of decision support models for optimizing the operation of complex water supply systems with multiple uses and focuses on the validation of the optimization model of the regional water supply systems managed by an Italian water utility. The software used is Aquator, a state-of-the-art commercial tool for generic water resources system simulation and operational optimization. The paper shows and comments the results of the validation of the model and uses them to draw some general principles for the validation of such kind of models: in fact, operational optimization models are conceived for the enhancement of systems management and performance. Consequently, while on the one side model output is expected to reflect the actual state of the system, on the other it can point out significant management enhancements.

Simulation models supported by state-of-the-art software packages are nowadays available to explore operation rules of regional water supply systems or to select structural alternatives for improving long-term service performances.Given the, sometimes high, complexity of these systems, model calibration can become a lengthy procedure and many runs are necessary before obtaining convincing results. However, even after calibration, depending on system's complexity and the number of time steps investigated, a single run can take up to several minutes, even on state-of-the-art computers, so that simulation time can become a true bottleneck if such models are to be coupled with metaheuristic optimization techniques, such as genetic optimization.This paper investigates the possibility to reduce computational time through skeletonization of the models. The regional water supply system of Apulia, Southern Italy, was adopted as a case study and the software package AQUATOR was employed to model the system.

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.

In questo lavoro viene esaminata la possibilità di ridurre i tempi di calcolo, mediante la "scheletrizzazione" dei modelli, adottando come caso di studio un sistema di approvvigionamento idrico regionale reale, modellato in ambiente AQUATOR. La scheletrizzazione è stata in origine introdotta nel campo dell'ingegneria idraulica al fine di includere nei modelli idraulici delle reti solo le parti dalla rete reale che hanno un significativo impatto sul funzionamento del sistema, riducendo l'onere computazionale ma permettendo comunque di ottenere risultati accurati ed affidabili (Walski et al., 2007; Eggener & Polkowski, 1976; Shamir & Hamberg, 1988a,b). Il principio di base della scheletrizzazione è quello di tenere conto degli effetti delle parti di sistema non modellate, tramite le caratteristiche (capacità idraulica, domande, perdite idriche, costi unitari, ecc.) degli elementi inclusi nel modello.

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.

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 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.

[1] 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 hydroclimatic 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 in plant water stress and favor vegetation with shallower root systems.

The LAI is a key parameter in hydrological processes, especially in the physically based distribution models. It is a critical ecosystem attribute since physiological processes such as photosynthesis, transpiration and evaporation depend on it. The diffusion of water vapor, momentum, heat and light through the canopy is regulated by the distribution and density of the leaves, branches, twigs and stems. The LAI influences the sensible heat flux H in the surface energy balance single source models through the calculation of the roughness length and of the displacement height. The aerodynamic resistance between the soil and within-canopy source height is a function of the LAI through the roughness length. This research carried out a sensitivity analysis of some of the most important parameters of surface energy balance models to the LAI time variation, in order to take into account the effects of the LAI variation with the phenological period. Finally empirical retrieved relationships between field spectroradiometric data and the field LAI measured via a light-sensitive instrument are presented for a cereal field.

Deterioration of water distribution networks causes a progressive increase of main failures with both a growth of maintenance cost and a general reduction of service performance indicators. For this reason, maintenance activities represent one of the most important items for a water utility budget and therefore, any decision on possible alternative actions, such as pipes repair, rehabilitation or renewal, must be based on economical and technical issues that must be taken into account in both short and long-term planning. Cost of such actions are really considerable and thus any strategy must be planned on the basis of a careful analysis of failures data. This paper presents a comparison between the statistical distributions of water main breakage data recorded within some districts of the distribution network of an Italian municipality recently subjected to a complete pipes replacement action within a large area. The analysis has been performed considering the breakage patterns over time in relation to several factors such as pipe length, material, age, water pressure, operational conditions, spatial location, type of failure, etc.

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.

The feared global climate change could have important effects on various environmental variables including rainfall in many countries around the world. Changes in precipitation regime directly affect water resources management, agriculture, hydrology and ecosystems. For this reason it is important to investigate the changes in the spatial and temporal rainfall pattern in order to improve water management strategies. In this study a non-parametric statistical method (Mann–Kendall rank correlation method) is employed in order to verify the existence of trend in annual, seasonal and monthly rainfall and the distribution of the rainfall during the year. This test is applied to about 250 rain gauge stations in Sicily (Italy) after a series of procedures finalized to the estimation of missing records and to the verification of data consistency. In order to understand the regional pattern of precipitation in Sicily, the detected trends are spatially interpolated using spatial analysis techniques in a GIS environment. The results show the existence of a generalized negative trend for the entire region.

This paper compares two generators of yearly water availabilities from sources located at multiple sites with regard to their ability to reproduce the characteristics of historical critical periods and to provide reliable results in terms of the return period of critical sequences of different length. The two models are a novel multi-site Markov mixture model explicitly accounting for drought occurrences and a multivariate ARMA. In the case of the multisite Markov mixture model parameter estimation is limited to a search in the parameter space guided by the value of parameter λ to show the sensitivity of the model to this parameter. Application to two of the longest time series of streamflows available in Sicily (Italy) shows that the models can provide quite different results in terms of estimated return periods of historic droughts, although they seem to perform more uniformly when it comes to simulate drought-related statistics such as drought length, severity and intensity. The role of parameter selection for the multisite Markov mixture model and of the marginal probability of generated flows in providing results consistent with the characteristics of the observed series is discussed. Both models are applied to the system of sources supplying the city of Palermo (Sicily) and its environs showing the applicability of the newly developed multisite Markov mixture model to medium-to-large scale water resources systems.

The aim of this study is to investigate and quantify changes in the rainfall regime of the metropolitan area of Palermo characterised by increasingly strong urbanisation. The rainfall data, considered in this study, were collected on a yearly basis from eight rain gauges within and outside the metropolitan area of Palermo, Sicily, Italy. A preliminary analysis made on the annual total rainfall depths showed a global reduction of total annual rainfall, with two different trends: more regular for the series observed in the rain gauges within the urbanised area and more variable for the series observed in the rain gauges outside the area. A further analysis has been performed using the series of maximum intensity for fixed duration (1, 3, 6, 12, 24 hrs) and annual daily maxima. The analysis of the trend in the extreme rainfall series has been performed by estimating the maximum rainfall depth corresponding to a fixed return period using the EV1 distribution with parameters estimated using L-moments. The analysis of all series indicates a global reduction of rainfall intensities, both for internal and external series, in disagreement with the results obtained by other authors.

A conceptual rainfall-runoff model for urban catchment, which incorporates, semi-distributed modelling concepts has been developed. In the proposed model, the catchment is divided in external sub-catchments connected to the drainage network. Each external sub-catchment is modelled as two separate conceptual linear elements, a reservoir and a channel, one for the pervious part, the other for the impervious part of the drainage area. The drainage network is schematised as a cascade of non-linear cells and the flood routing is simplified in the form of kinematic wave and represented as a flux transfer between adjacent cells. Tests carried out using data from the Parco d'Orleans experimental catchment located in the University Campus of Palermo, allowed to focus relative results due to changes the spatial representation of the input rainfall and to changes in the spatial discretisation of the sub-catchments. The results of the simulations have pointed out how both the variation in the spatial representation of the rainfall and the variation in the spatial discretisation of the catchment has a definite influence on the outlet hydrographs, especially for high intensity events.