A regional probabilistic model for the estimation of medium-high return period flood quantiles is presented. The model is based on the use of theoretically derived probability distributions of annual maximum flood peaks (DDF). The general model is called TCIF (Two-Component IF model) and encompasses two different threshold mechanisms associated with ordinary and extraordinary events, respectively. Based on at-site calibration of this model for 33 gauged sites in Southern Italy, a regional analysis is performed obtaining satisfactory results for the estimation of flood quantiles for return periods of technical interest, thus suggesting the use of the proposed methodology for the application to ungauged basins. The model is validated by using a jack-knife cross-validation technique taking all river basins into consideration.

Understanding the spatial variability of key parameters of flood probability distributions represents a strategy to provide insights on hydrologic similarity and building probabilistic models able to reduce the uncertainty in flood prediction in ungauged basins. In this work, we exploited the theoretically derived distribution of floods model TCIF (Two Component Iacobellis and Fiorentino model; Gioia et al., 2008), based on two different threshold mechanisms associated to ordinary and extraordinary events. The model is based on the hypotheses that ordinary floods are generally due to rainfall events exceeding a constant infiltration rate in a small source area, while the so-called outlier events responsible for the high skewness of flood distributions are triggered when severe rainfalls exceed a storage threshold over a large portion of the basin. Within this scheme, a sensitivity analysis was performed with respect to climatic and geomorphologic parameters in order to analyze the effects on the skewness coefficient and provide insights in catchment classification and process conceptualization. The analysis was conducted to investigate the influence on flood distribution of physical factors such as rainfall intensity, basin area, and particular focus on soil behavior.

The objective of this paper is to report on the activities carried out during the first year of the Italian project "Use of COSMO-SkyMed data for LANDcover classification and surface parameters retrieval over agricultural sites" (COSMOLAND), funded by the Italian Space Agency. The project intends to contribute to the COSMO-SkyMed mission objectives in the agriculture and hydrology application domains

The analysis of runoff thresholds and, more in general, the identification of main mechanisms of runoff generation controlling the flood frequency distribution is investigated, by means of theoretically derived flood frequency distributions, in the framework of regional analysis. Two nested theoretically-derived distributions are fitted to annual maximum flood series recorded in several basins of Southern Italy. Results are exploited in order to investigate heterogeneities and homogeneities and to obtain useful information for improving the available methods for regional analysis of flood frequency.

The paper suggests a methodology, based on performance metrics, to select the optimal set of input and parameters to be used for the simulation of river flow discharges with a semi-distributed hydrologic model. The model is applied at daily scale in a semi-arid basin of Southern Italy (Carapelle river, basin area: 506 km2) for which rainfall and discharge series for the period 2006–2009 are available. A classification of inputs and parameters was made in two subsets: the former – spatially distributed – to be selected among different options, the latter – lumped – to be calibrated. Different data sources of (or methodologies to obtain) spatially distributed data have been explored for the first subset. In particular, the FAO Penman–Monteith, Hargreaves and Thornthwaite equations were tested for the evaluation of reference evapotranspiration that, in semi-arid areas, represents a key role in hydrological modeling. The availability of LAI maps from different remote sensing sources was exploited in order to enhance the characterization of the vegetation state and consequently of the spatio-temporal variation in actual evapotranspiration. Different type of pedotransfer functions were used to derive the soil hydraulic parameters of the area. For each configuration of the first subset of data, a manual calibration of the second subset of parameters was carried out. Both the manual calibration of the lumped parameters and the selection of the optimal distributed dataset were based on the calculation and the comparison of different performance metrics measuring the distance between observed and simulated discharge data series. Results not only show the best options for estimating reference evapotranspiration, crop coefficients, LAI values and hydraulic properties of soil, but also provide significant insights regarding the use of different performance metrics including traditional indexes such as RMSE, NSE, index of agreement, with the more recent Benchmark Efficiency (Schaefli and Gupta, 2007) and Kling–Gupta Efficiency (Gupta et al., 2009).

Nell’ambito della gestione e pianificazione delle risorse idriche di un territorio i modelli idrologici di trasformazione afflussi-deflussi sono attualmente riconosciuti come validi strumenti scientifici di tipo quantitativo e qualitativo. Operando alla scala di bacino, l’enorme variabilità spaziale e temporale dei processi idrologici, dovuta all’eterogeneità delle proprietà e dei parametri che descrivono l’ambiente entro cui i processi stessi hanno luogo, indirizza sempre più la comunità scientifica verso l’impiego di modelli di tipo distribuito. L’applicabilità di tali modelli presuppone però la disponibilità di dati di input aggiornati e significativamente rappresentativi dell’area di studio. In tale contesto, si inserisce il presente studio avente l’obiettivo di investigare l’influenza della stima di alcuni parametri di input nella modellistica idrologica distribuita. In particolare, sono stati considerati due aspetti nell’interazione suolo-vegetazione-clima: l’evapotraspirazione potenziale stimata con note formule di letteratura e l’indice di area fogliare (LAI) derivato da immagini satellitari. L’analisi, riferita ad un bacino della Puglia settentrionale a clima tipicamente Mediterraneo, è stata supportata dall’impiego di diversi indici di fitting, confrontando i deflussi simulati da un modello idrologico distribuito con quelli registrati. I risultati evidenziano che un ruolo chiave nelle simulazioni idrologiche spetta alla scelta del metodo di stima dell’evapotraspirazione potenziale. La tecnica di derivazione del LAI, inoltre, risulta più significativa della risoluzione spaziale del dato telerilevato.

The accuracy of rainfall predictions provided by climate models is crucial for the assessment of climate change impacts on hydrological processes. In fact, the presence of bias in downscaled precipitation may produce large bias in the assessment of soil moisture dynamics, river flows and groundwater recharge. In this study, a comparison between statistical properties of rainfall observations and model control simulations from a Regional Climate Model (RCM) was performed through a robust and meaningful representation of the precipitation process. The output of the adopted RCM was analysed and re-scaled exploiting the structure of a stochastic model of the point rainfall process. In particular, the stochastic model is able to adequately reproduce the rainfall intermittency at the synoptic scale, which is one of the crucial aspects for the Mediterranean environments. Possible alteration in the local rainfall regime was investigated by means of the historical daily time-series from a dense rain-gauge network, which were also used for the analysis of the RCM bias in terms of dry and wet periods and storm intensity. The result is a stochastic scheme for bias-correction at the RCM-cell scale, which produces a realistic representation of the daily rainfall intermittency and precipitation depths, though a residual bias in the storm intensity of longer storm events persists.

In this paper, we discuss validation of hydrological models, namely the process of evaluating performance of a simulation and/or prediction model. We briefly review the validation procedures that are frequently used in hydrology making a distinction between scientific validation and performance validation. Finally, we propose guidelines for carrying out model validation with the aim of providing agreed methodologies to efficiently assess model peculiarities and limitations, and to quantify simulation performance.

Le valutazioni di bilancio idrico per il territorio di competenza dell'Autorità di Bacino della Puglia sono supportate da un'ampia attività scientifica, di cui un filone è quello riguardante la stima delle disponibilità di risorsa idrica, con particolare riferimento alla valutazione delle curve probabilistiche di durata delle portate in sezioni dei corsi d'acqua pugliesi strumentate e non, basata sull'utilizzo del modello EtaBeta proposto da Iacobellis (2008). I parametri del modello ben si prestano ad analisi di tipo regionale; in particolare questo lavoro presenta i risultati della ricerca di relazioni a carattere regionale tra i parametri della distribuzione di probabilità adottata per rappresentare la curva di durata e grandezze geomorfoclimatiche rilevabili per i bacini idrografici. Tanto al fine della definizione di un metodo per la generazione sintetica di curve di durata per la previsione in bacini non strumentati. Lo studio è stato condotto applicando il modello EtaBeta ai bacini del territorio di competenza dell'Autorità di Bacino della Puglia, aventi comportamento idrologico tipico degli ambienti semi-aridi, ovvero a forte variabilità stagionale della portata.

LAI is defined as one sided green leaf area per unit ground area in broadleaf canopies and is an important input parameter to monitor crop growth conditions and to improve the performance of crop yield models. Because direct measurements of LAI are usually time-consuming and require continuous updates, remote sensing is an alternative to estimate this attribute over large areas as watershed scale. The primary objective of this work was to derive a reliable LAI estimation model from VHR satellite data to be compared with moderate resolution satellite products in order to improve LAI estimation performance for next validation activities. Due to lack of contemporaneous satellite and on-site sensor data acquisitions and intrinsic complexity of physical models, in our study case the semi-empirical approach with the CLAIR model was applied. It is based on an inverse exponential relationship between LAI and the WDVI (Weighted Difference Vegetation Index) related to different land covers. LAI values were generated from multispectral GeoEye-1 sensor data covering a time space of 5 years (2009-2013) to study crop phenological stages on the study area of the Carapelle watershed located in the North of Puglia region (Southern Italy). Data were preliminarily pre-processed (geometric and radiometric correction), classified (ISODATA method) and texture based analyzed in order to extract the vegetated areas (mainly cereal crops). Finally, the resulted maps were compared with moderate resolution satellite data by reaching a possible correspondence.