Special Issue: Water Engineering and Management in a Changing Environment

The paper is the introduction to the special issue Water Engineering and Management in a Changing Environment which presents a set of the most innovative contributions at the EWRA Symposium, held in Catania, Italy on 2011.     

A Neural Networks Approach for Deriving Irrigation Reservoir Operating Rules

A neural networks approach is applied to the derivation of the operating rules of an irrigation supply reservoir. Operating rules are determined as a two step process: first, a dynamic programming technique, which determines the optimal releases byminimizing the sum of squared deficits, assumed as objective function, subject to various constraints is applied. Then, theresulting releases from the reservoir are expressed as a functionof significant variables by neural networks. Neural networks aretrained on a long period, including severe drought events, andthe operation rules so determined are validated on a differentshorter period. The behaviour of different operating rules is assessed by simulating reservoir operation and by computing several performance indices of the reservoir and crop yield through a soil water balance model. Results show that operating rules based on an optimization with constraints resembling real system operation criteria lead to a good performance both in normal and in drought periods, reducing maximum deficits and water spills.     

Correction to: Anomaly Detection Techniques in the Gaia Space Mission Data

Journal of Signal Processing Systems -     

Drought forecasting using the Standardized Precipitation Index

Unlike other natural disasters, drought events evolve slowly in time and their impacts generally span a long period of time. Such features do make possible a more effective drought mitigation of the most adverse effects, provided a timely monitoring of an incoming drought is available. Among the several proposed drought monitoring indices, the Standardized Precipitation Index (SPI) has found widespread application for describing and comparing droughts among different time periods and regions with different climatic conditions. However, limited efforts have been made to analyze the role of the SPI for drought forecasting. The aim of the paper is to provide two methodologies for the seasonal forecasting of SPI, under the hypothesis of uncorrelated and normally distributed monthly precipitation aggregated at various time scales k. In the first methodology, the auto-covariance matrix of SPI values is analytically derived, as a function of the statistics of the underlying monthly precipitation process, in order to compute the transition probabilities from a current drought condition to another in the future. The proposed analytical approach appears particularly valuable from a practical stand point in light of the difficulties of applying a frequency approach due to the limited number of transitions generally observed even on relatively long SPI records. Also, an analysis of the applicability of a Markov chain model has revealed the inadequacy of such an approach, since it leads to significant errors in the transition probability as shown in the paper. In the second methodology, SPI forecasts at a generic time horizon M are analytically determined, in terms of conditional expectation, as a function of past values of monthly precipitation. Forecasting accuracy is estimated through an expression of the Mean Square Error, which allows one to derive confidence intervals of prediction. Validation of the derived expressions is carried out by comparing theoretical forecasts and observed SPI values by means of a moving window technique. Results seem to confirm the reliability of the proposed methodologies, which therefore can find useful application within a drought monitoring system.     

Large Scale Probabilistic Drought Characterization Over Europe

A reliable assessment of drought return periods is essential to help decision makers in setting effective drought preparedness and mitigation measures. However, often an inferential approach is unsuitable to model the marginal or joint probability distributions of drought characteristics, such as drought duration and accumulated deficit, due to the relatively limited number of drought events that can be observed in the historical records of the hydrological variables of interest. As an alternative, the marginal and multivariate probability cdf’s of drought characteristics can be derived as functions of the parameters of the cdf of the underlying variable (e.g. precipitation), whose sample series is usually long enough to obtain trustworthy estimates in a statistical sense. In this study, the latter methodology is applied to investigate space-time variability of drought occurrences over Europe by using the CRU TS3.10.01 precipitation dataset for the period 1901–2009. In particular, a methodology able to take into account autocorrelation in the underlying precipitation series is adopted. First, a spatial analysis of historical droughts at European level is carried out. Then, the joint probability distributions of drought duration and accumulated deficit are derived for each cell, with reference to both historical and design drought events. Finally, the corresponding bivariate drought return periods are computed, as the expected values of the interarrival time between consecutive critical droughts.Results show that several heavy drought episodes have widely affected the continent. Among the most recent events, drought occurred during the period 1985–1995 was the worst in terms of extent of the regions characterized by return periods greater than 250 years. Besides Euro-Mediterranean regions, North Western and Central Eastern regions appear more drought prone than the rest of Europe, in terms of low values of return periods.     

PV electrical plants fire risk assessment and mitigation according to the Italian national fire services guidelines

Photovoltaic (PV) systems design and construction are generally focused on efficiency and reliability, in order to increase the amount of solar energy that can be converted into electrical energy. Therefore, in a PV electrical generation plant, fire risk is not taken into account by technical designers and, furthermore, is not considered by constructors of PV plant installation. This paper shows a procedure to assess and mitigate the fire risk as a result of a PV plant installation located over buildings, according to the Italian National Fire Services Guidelines. Firstly, an introduction to the main faulty modes of PV arrays and modules is reported in order to highlight how the pre‐existing level of fire risk is increased for a building or construction work where a PV electrical generation plant is installed. Hence, the paper explains how the guidelines point out fire risk assessment steps oriented to PV plant installation over buildings. In addition, the guidelines contain some useful fire risk mitigation technical solutions. The guidelines have been developed with safety as their main objective. The PV sector has been presented with certain limitations in roof or façade installations as a result of PV fire ignition characteristics and firefighting suppression techniques. The aim of the guidelines is to provide the solar PV industry with information and technical arrangements to aid the design, construction and installation of solar PV systems meeting the objectives of both solar PV industry and fire safety requirements. Copyright © 2014 John Wiley & Sons, Ltd.