The use of multispectral satellite imagery for water monitoring is a fast and cost-effective method that can benefit from the growing availability of medium–high-resolution and free remote sensing data. Since the 1970s, multispectral satellite imagery has been exploited by adopting different techniques and spectral indices. The high number of available sensors and their differences in spectral and spatial characteristics led to a proliferation of outcomes that depicts a nice picture of the potential and limitations of each. This paper provides a review of satellite remote sensing applications for water extent delineation and flood monitoring, highlighting trends in research studies that adopted freely available optical imagery. The performances of the most common spectral indices for water segmentation are qualitatively analyzed and assessed according to different land cover types to provide guidance for targeted applications in specific contexts. The comparison is carried out by collecting evidence obtained from several applications identifying the overall accuracy (OA) obtained with each specific configuration. In addition, common issues faced when dealing with optical imagery are discussed, together with opportunities offered by new-generation passive satellites.

The characterization of stormwater runoff on urbanized surfaces by means of comparison between experimental data and simulations is a strict requirement for a sustainable management of urban sewer systems. A monitoring campaign was carried out within a residential area in Puglia (Southern Italy) in order to collect and evaluate quantity and quality data. A strong correlation was observed between COD (Chemical Oxygen Demand) and TSS (Total Suspended Solid) concentrations, whose values exceed water quality standards. TSS was used for calibration of Storm Water Management Model (SWMM) which was then validated with reference to the pollutograph’s shape and the peak-time. The first flush phenomenon occurrence was also investigated by looking at the distribution of pollutant mass vs. volume in stormwater discharges, using the so-called “M(V) curves”. Results show that on average the first 30% of that washed off carries 60% of TSS and provides important information for the design of efficient systems for first flush treatment.

In general, different mechanisms may be identified as responsible of runoff generation during ordinary events or extraordinary events at the basin scale. In a simplified scheme these mechanisms may be represented by different runoff thresholds. In this context, the derived flood frequency model, based on the effect of partial contributing areas on peak flow, proposed by Iacobellis and Fiorentino (2000), was generalized by providing a new formulation of the derived distribution where two runoff components are explicitly considered. The model was tested on a group of basins in Southern Italy characterized by annual maximum flood distributions highly skewed. The application of the proposed model provided good results in terms of descriptive ability. Model parameters were also found to be well correlated with geomorphological basin descriptors. Two different threshold mechanisms, associated respectively to ordinary and extraordinary events, were identified. In fact, we found that ordinary floods are mostly due to rainfall events exceeding a threshold infiltration rate in a small source area, while the so-called outlier events, responsible of the high skewness of flood distributions, are triggered when severe rainfalls exceed a threshold storage in a large portion of the basin.

Protection of surface water quality plays a crucial role for sustainable urban watershed management since the wash-off from impervious contaminated surfaces generates transport phenomena from a range of pollutants (like nutrients, such as total nitrogen (N_tot) and total phosphorus (P_tot)). This leads to the consequent reduction of water quality, and to phenomena, such as eutrophication and the presence of algae blooms. For this reason, a comprehensive understanding of nutrient build-up and wash-off is essential for efficient stormwater treatment design. However, data scarcity could represent one of the main limitations in this context. This manuscript presents a methodological framework able to tackle such limitations by an in-depth investigation of the main factors that influence the build-up and wash-off from impervious surfaces, including rainfall, watershed, and drainage-network characteristics. The outcomes highlight the key role played by the antecedent dry period, among the rainfall characteristics, and the width of the overland flow path, among the catchment/drainage characteristics. It is also confirmed as appropriate to use suspended solids as a surrogate for the investigation of the behavior of other pollutant species. Additionally, the capability of this approach in assessing modeling performance was successfully tested. The results of the present study are expected to contribute valuable knowledge for defining effective management strategies to minimize stream pollution and protect the health of aquatic ecosystems in urban watersheds characterized by data scarcity.

In the design of a water supply network, the use of traditional formulas of the peak factor may lead to over-dimensioning the network pipelines, especially in small towns. This discrepancy is probably due to changes in human habits as a consequence of a general improvement of living conditions. Starting from these considerations, and given the availability of a wide random sample data, an analysis of the water demand for several towns in Puglia was carried out, leading to the definition of a relationship between the above mentioned peak factor and the number of inhabitants, based on a stochastic approach. An interesting outcome of this study is that the design of water supply network is possible without considering the use of monthly and weekly peak factors, since the current water demands appear not specifically sensitive to these variations; moreover, the magnitude of the peak factor, as shown by measured data, is considerably lower compared to literature values, especially for small towns.