Prediction of the occurrence or non-occurrence of daily rainfall plays a significant role in agricultural planning and water resource management projects. In this study, gamma distribution function (GDF), kernel, and exponential (EXP) distributions were coupled (piecewise) with a generalized Pareto distribution. Thus, the gamma-generalized Pareto (GGP), kernel-generalized Pareto (KGP), and exponential-generalized Pareto (EGP) models were used. The aim of the present study was to introduce new methods to modify the simulated generation of extreme rainfall amounts of rainy seasons based on the preserved spatial correlation. The best approach was identified using the normalized root mean square error (NRMSE) criterion. For this purpose, the 30-yr daily rainfall datasets of 21 synoptic weather stations located in different climates of West Iran were analyzed. The first, second, and third-order Markov chain (MC) models were used to describe rainfall time series frequencies. The best MC model order was detected using the Akaike information criterion and Bayesian information criterion. Based on the best identified MC model order, the best piecewise distribution models, and the Wilks approach, rainfall events were modeled with regard to the spatial correlation among the study stations. The performance of the Wilks approach was verified using the coefficient of determination. The daily rainfall simulation resulted in a good agreement between the observed and the generated rainfall data. Hence, the proposed approach is capable of helping water resource managers in different contexts of agricultural planning.
Water resource management is a complex engineering problem, due to the stochastic nature of inflow, various demands and environmental flow downstream. With the increase in water consumption for domestic use and irrigation, it becomes more challenging. Many more difficulties, such as non-convex, nonlinear, multi-objective, and discontinuous functions, exist in real-life. From the past two decades, heuristic and metaheuristic optimization techniques have played a significant role in managing and providing better performance solutions. The popularity of heuristic and metaheuristic optimization techniques has increased among researchers due to their numerous benefits and possibilities. Researchers are attempting to develop more accurate and efficient models by incorporating novel methods and hybridizing existing ones. This paper's main contribution is to show the state-of-the-art of heuristic and metaheuristic optimization techniques in water resource management. The research provides a comprehensive overview of the various techniques within the context of a thorough evaluation and discussion. As a result, for water resource management problems, this study introduces the most promising evolutionary and swarm intelligence techniques. Hybridization, modifications, and algorithm variants are reported to be the most successful for improving optimization techniques. This survey can be used to aid hydrologists and scientists in deciding the proper optimization techniques.
The current exploitation of freshwater, as well as the significant increase in sewage sludge production from wastewater treatment plants (WWTPs), represent nowadays a critical issue for the implementation of sustainable development consistent with the circular economy concept. There is an urgent need to rethink the concept of WWTPs from the conventional approach consisting in pollutant removal plants to water resource recovery facilities (WRRFs). The aim of this paper is to provide an overview of the demonstration case studies at the Marineo and Corleone WRRFs in Sicily (IT), with the final aim showing the effectiveness of the resources recovery systems, as well as the importance of plant optimization to reduce greenhouse gas (GHG) emissions from WRRFs. This study is part of the H2020 European Project “Achieving wider uptake of water-smart solutions—Wider-Uptake”, which final aim is to demonstrate the water-smart solution feasibility in the wastewater sector. The main project goal is to overcome the existing barriers that hamper the transition to circularity through the implementation of a governance analysis tool. The preliminary actions in the two demonstration cases are first presented, while, subsequently, the water-smart solutions to be implemented are thoroughly described, highlighting their roles in the transition process. The achieved preliminary results underlined the significant potential of WRRF application, a great chance to demonstrate the feasibility of innovative solutions in the wastewater sector to overcome the existing social, administrative and technical barriers.
Treated effluent from a wastewater treatment plant can be further reused as a water resource for a water supply treatment plant. In this case, the treated sewage gathered in the study of the Class V National Water Quality Standard (NWQS) of Malaysia would be treated for use as a water resource for a water treatment plant. In a moving bed biofilm reactor (MBBR) with a 500-L working volume, organic pollutants, undesirable nutrients, and bacteria were removed without disinfectant. At 24-h hydraulic retention time (HRT), the maximum removal efficiency of 5-day biological oxygen demand, ammonia–nitrogen (NH3-N), and total phosphorus were 71%, 48%, and 12%, respectively. The biofilm thickness, which was captured using scanning electron microscopy, increased from 102.6 μm (24-h HRT) to 297.1 μm (2-h HRT). A metagenomic analysis using 16S rRNA showed an abundance of anaerobic bacteria, especially from the Proteobacteria phylum, which made up almost 53% of the total microbes. MBBR operated at 24-h HRT could improve effluent quality, as its characteristics fell into Class IIA of the NWQS of Malaysia, with the exception of the NH3-N content, which indicated that the effluent needed conventional treatment prior to being reused as potable water.
AbstractNatural ecosystems are fundamental to local water cycles and the water ecosystem services that humans enjoy, such as water provision, outdoor recreation, and flood protection. However, integrating ecosystem services into water resources management requires that they be acknowledged, quantified, and communicated to decision-makers. We present an indicator framework that incorporates the supply of, and demand for, water ecosystem services. This provides an initial diagnostic for water resource managers and a mechanism for evaluating tradeoffs through future scenarios. Building on a risk assessment framework, we present a three-tiered indicator for measuring where demand exceeds the supply of services, addressing the scope (spatial extent), frequency, and amplitude for which objectives (service delivery) are not met. The Ecosystem Service Indicator is measured on a 0–100 scale, which encompasses none to total service delivery. We demonstrate the framework and its applicability to a variety of services and data sources (e.g., monitoring stations, statistical yearbooks, modeled datasets) from case studies in China and Southeast Asia. We evaluate the sensitivity of the indicator scores to varying levels data and three methods of calculation using a simulated test dataset. Our indicator framework is conceptually simple, robust, and flexible enough to offer a starting point for decision-makers and to accommodate the evolution and expansion of tools, models and data sources used to measure and evaluate the value of water ecosystem services.