The Hargreaves equation (HE) is the most reliable and diffused temperature-based method for the estimate of the reference crop evapotranspiration ${\mathrm{ET}}_o$ in areas with limited meteorological data. Several studies proposed its further enhancement by adopting correction equations for recalibrating its parameters, specifically the empirical Hargreaves coefficient, $C=0.0023$ . In this paper, a local recalibration for southern Italy is shown, taking as a reference the Food and Agriculture Organization of the United Nations (FAO) Penman-Monteith (PM) equation applied over 137 (34 coastal and 103 inland) meteorological stations, allowing the estimation of 357,920 daily ${\mathrm{ET}}_o$ values in the period 1994–2011. Through a regression analysis, the adjusted Hargreaves coefficients (AHC), minimizing the errors for each individual station between the HE and the FAO Penman-Monteith equation, and providing better performances than the original HE applied at the monthly scale, were correlated to the available daily meteorological data; reliable regionalization equations of the AHCs were found determined by either the average daily temperature range $\overline{\mathrm{\Delta }T}$ or the ratio between the average daily temperature $\overline{T}$ and $\overline{\mathrm{\Delta }T}$ , with similar coefficients to analogous relationships proposed in the literature. Specifically, the $\overline{\mathrm{\Delta }T}$ dependent model proved to be very reliable over the whole study area for all climate types, from the shore up to 1,928 m. The improvement in the performances of the regional model with respect to the original HE was on average 22%, and it also proved able to simulate the yearly cumulative ${\mathrm{ET}}_o$ evolution in several meteorological stations. The strength of the model was positively tested through a jackknife resampling procedure and a sensitivity analysis, taking into account possible trends for the average daily temperature range.

Understanding the expansion and contraction dynamics of flowing drainage networks is important for many research fields like ecology, hydrology, and biogeochemistry. This study analyzes for the first time the network shrinking and dry down in two seasonally dry hot‐summer Mediterranean catchments (overall area 1.15 km²) using a comprehensive approach based on monitoring and modeling of the flowing network. A field campaign consisting of 19 subweekly visual surveys was carried out in the early summer of 2019. These observations were used to calibrate and validate an integrated model aimed to estimate the time evolution of the total flowing drainage network length based on meteorological drivers and define the position of the stretches with flowing water based on topographic and geological information. We used a statistical model to describe the observed variations in the total flowing length based on the accumulated difference between antecedent precipitation and evapotranspiration. The study emphasizes the relevant role of evapotranspiration in the seasonal network contraction. Then, we modeled spatial patterns of the flowing channels using an empirical approach based on topographic data, achieving satisfactory performances. Nevertheless, the performance further increased when site‐specific geological information was integrated into the model, leading to accuracies up to 92% for cell‐by‐cell comparisons. The proposed methodology, which combines meteorological, topographic, and geological information in a sequential manner, was able to accurately represent the space‐time dynamics of the flowing drainage network in the study area, proving to be an effective and flexible tool for investigating network dynamics in temporary streams.

In spite of the prevalence of temporary rivers over a wide range of climatic conditions, they represent a relatively understudied fraction of the global river network. Here, we exploit a well‐established hydrological model and a derived distribution approach to develop a coupled probabilistic description for the dynamics of the catchment discharge and the corresponding active network length. Analytical expressions for the flow duration curve (FDC) and the stream length duration curve (SLDC) were derived and used to provide a consistent classification of streamflow and active length regimes in temporary rivers. Two distinct streamflow regimes (persistent and erratic) and three different types of active length regimes (ephemeral, perennial, and ephemeral de facto) were identified depending on the value of two dimensionless parameters. These key parameters, which are related to the underlying streamflow fluctuations and the sensitivity of active length to changes in the catchment discharge (here quantified by the scaling exponent b), originate seven different behavioral classes characterized by contrasting shapes of the underlying SLDCs and FDCs. The analytical model was tested using data gathered in three study catchments located in Italy and USA, with satisfactory model performances in most cases. Our analytical and empirical results show the existence of a structural relationship between streamflow and active length regimes, which is chiefly modulated by the scaling exponent b. The proposed framework represents a promising tool for the coupled analysis of discharge and river network length dynamics in temporary streams.

Proper water resources management involves the analysis and resolution of various optimization problems according to climate change effects on the availability and distribution of the resources themselves. Specifically, these conditions require the identification of new resource allocation optimization solutions capable of taking into account the water resource losses due to climate change scenarios. As is well known, Southern Italy is a region that is potentially very sensitive to climate change. In this paper, a 1717 km² area, corresponding to the province of Crotone, was analyzed as a study case. This area is characterized by a sufficient availability of resources as a whole as compared to the needs of the users, but has an unbalanced distribution of water through its various systems. After identifying water resource allocations in detail for this area, an optimization solution accounting for the expected reduced availability of water resources in the context of climate change was created and was compared with the optimization solution for current water availability.

This work compares forecasting strategies for improving the prediction of extreme precipitation events at different timescales. Our study focuses on forecasting extreme precipitation in Calabria, southern Italy. This region with complex and abruptly varying topography poses additional challenges in forecasting precipitation. Here, we use gridded observational precipitation data from the E‐OBS dataset, reanalysis data from ERA5, and forecasts from ECMWF reforecasts. We show that different forecasting horizons require different forecasting strategies. For short to medium‐range lead times, post‐processing the forecasted extreme precipitation probabilities provides the most informative outputs for end users. For extended‐range forecasts though, it is beneficial to use the large‐scale weather variability, as depicted based on nine Mediterranean patterns in combination with moisture‐related information, to infer reliable information about extreme precipitation. We present benefits and limitations of the methods based on long‐term statistical analysis using a range of indicators, such as the Brier skill score and its decomposition, and the cost–loss model.

Wildfire occurrence and severity in the Mediterranean region during the summer season is increasing, being favoured by climate change-induced conditions (i.e., drought, heatwaves). Moreover, additional natural sources frequently impact this region, particularly Saharan dust intrusions. This study focuses on the combined effect of wildfires and Saharan dust on the air quality of the central Mediterranean Basin (CMB) during 2017, an exceptional year for forested burned areas in southern Italy. The annual behaviors of PM_2.5, PM₁₀, CO, benzene, and benzo(a)pirene measurements that were recorded at a rural regional-background station located in southern Italy, highlighted a concentration increase during summer. Both Saharan dust and wildfire events were identified while using Navy Aerosol Analysis and Prediction System (NAAPS) model maps, together with high-resolution Weather Research and Forecast—Hybrid Single-Particle Lagrangian Integrated Trajectory (WRF-HYSPLIT) back-trajectories. Additionally, Visible Infrared Imaging Radiometer Suite (VIIRS) satellite detections were considered to establish the enrichment of air masses by wildfire emissions. Finally, the occurrence of these natural sources, and their influence on particulate matter, were examined. In this case study, both PM_2.5 and PM₁₀ exceedances occurred predominantly in conjunction with wildfire events, while Saharan dust events mainly increased PM₁₀ concentration when overlapping with wildfire effects.