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Soil and underground conditions prior to the initiation of rainfall events control the hydrological processes that occur in slopes, affecting the water exchange through their boundaries. The present study aims at identifying suitable variables to be monitored to predict the response of sloping soil to precipitation. The case of a pyroclastic coarse-grained soil mantle overlaying a karstic bedrock in the southern Apennines (Italy) is described. Field monitoring of stream level recordings, meteorological variables, and soil water content and suction has been carried out for a few years. To enrich the field dataset, a synthetic series of 1000 years has been generated with a physically based model coupled to a stochastic rainfall model. Machine learning techniques have been used to unwrap the non-linear cause–effect relationships linking the variables. The k -means clustering technique has been used for the identification of seasonally recurrent slope conditions in terms of soil moisture and groundwater level, and the random forest technique has been used to assess how the conditions at the onset of rainfall controlled the attitude of the soil mantle to retain much of the infiltrating rainwater. The results show that the response in terms of the fraction of rainwater remaining stored in the soil mantle at the end of rainfall events is controlled by soil moisture and groundwater level prior to the rainfall initiation, giving evidence of the activation of effective drainage processes. [ABSTRACT FROM AUTHOR]

The assessment of the response of slopes to precipitation is important for various applications, from water supply management to hazard assessment due to extreme rainfall events. It is well known that the underground conditions prior to the initiation of rainfall events control the hydrological processes that occur in slopes, affecting the water exchange through their boundaries. The present study aims at identifying suitable variables to be monitored and modelled to predict the response of the slope to precipitations. A case study consisting of a loose pyroclastic coarse grained soil cover overlaying a karstic bedrock located in the southern Apennines (Italy) is described, where field monitoring has been carried out, comprising stream level recordings, meteorological recordings, and soil water content among others. Nevertheless, to enhance the field dataset, the slope hydraulic behaviour of the case study has been simulated with a physically based model linked to a synthetic rainfall time series, getting a consistent hourly timeseries dataset of 1000 years, containing information on rainfall, aquifer water level and soil volumetric water content at different depths. Machine Learning techniques have been used to unwrap the relationships amongst the studied variables, which relations are commonly non-linear. The Random Forest technique has been used to assess the way the slope response could be addressed and the importance of each variable on the slope response and the k-means clustering technique has been used to explore the geometrical disposition of data, and so the identification of seasonally recurrent different scenarios linked to the slope response. It has been shown that the slope response in terms of the rainwater being stored in the soil cover is naturally highly dependent on the rainfall amount, but water drainage and storage processes can be identified by normalizing the change in water storage with the rainfall depth. Indeed, with the methodology presented here, different hydrometeorological scenarios controlling major hydrological processes have been identified not only from the meteorological and seasonal behaviour but also from the underground conditions prior to the rainfall initiation, weighting the role, on one hand, of the field capacity value on the ease of the water to flow in and out of the soil cover and, on the other hand, of the ground water level, the increase of which gives evidence of the activation of slope drainage even during relatively intense rainfall events. [ABSTRACT FROM AUTHOR]

Robustness of water distribution networks is related to their connectivity and topological structure, which also affect their reliability. Flow entropy, based on Shannon's informational entropy, has been proposed as a measure of network redundancy and adopted as a proxy of reliability in optimal network design procedures. In this paper, the scaling properties of flow entropy of water distribution networks with their size and other topological metrics are studied. To such aim, flow entropy, maximum flow entropy, link density and average path length have been evaluated for a set of 22 networks, both real and synthetic, with different size and topology. The obtained results led to identify suitable scaling laws of flow entropy and maximum flow entropy with water distribution network size, in the form of power-laws. The obtained relationships allow comparing the flow entropy of water distribution networks with different size, and provide an easy tool to define the maximum achievable entropy of a specific water distribution network. An example of application of the obtained relationships to the design of a water distribution network is provided, showing how, with a constrained multi-objective optimization procedure, a tradeoff between network cost and robustness is easily identified. [ABSTRACT FROM AUTHOR]

Water Network Partitioning (WNP) improves water network management, simplifying the computation of water budgets and, consequently, allowing the identification and reduction of water losses. It is achieved by inserting flow meters and gate valves into a network which has been previously clustered into subsystems. Generally, the procedures are subdivided into two main steps: the clustering and partitioning phases. At first, network nodes are assigned to each cluster and then the appropriate pipes are selected, in which flow meters or gate valves are to be inserted. In this paper, an improved multilevel-recursive bisection algorithm was used to achieve network clustering. To better allocate the hydraulic devices, the partitioning phase was carried out through the minimization of a novel, multi-objective function, taking simultaneous account of energy and economic aspects. The aim is to define a solution that occupies a minimum possible number of flow meters, simplifying the water budget computation, preserving the hydraulic performances, and minimizing the capital and the operational costs. The procedure was tested on an extensive and real Mexican network, providing different optimal solutions and a smart Decision Support System (DSS) (based on visual diagrams and innovative energy, robustness, and balancing metrics). [ABSTRACT FROM AUTHOR]