The intensity and frequency of droughts in Poyang Lake Basin have been increasing due to global warming. To properly manage water resources and mitigate drought disasters, it is important to understand the long-term characteristics of drought and its possible link with large-scale climate indices. Based on the monthly meteorological data of 41 meteorological stations in Poyang Lake Basin from 1958 to 2017, the spatiotemporal variations of drought were investigated using the standardized precipitation evapotranspiration index (SPEI). Ensemble empirical mode decomposition (EEMD) methods and the modified Mann–Kendall (MMK) trend test were used to explore the spatiotemporal characteristics and trends of drought. Furthermore, to reveal possible links between drought variations and large-scale climate indices in Poyang Lake Basin, the relationships between SPEI and large-scale climate indices, such as North Atlantic Oscillation (NAO), El Niño–Southern Oscillation (ENSO), Arctic Oscillation (AO), Indian Ocean Dipole (IOD) and Pacific Decadal Oscillation (PDO) were examined using cross-wavelet transform. The results showed that the SPEI in Poyang Lake Basin exhibited relatively stable quasi-periodic oscillation, with approximate quasi-3-year and quasi-6-year periods at the inter-annual scale and quasi-15-year and quasi-30-year periods at the inter-decadal scale from 1958 to 2017. Moreover, the Poyang Lake Basin experienced an insignificantly wetter trend as a whole at the annual and seasonal scales during the period of 1958–2017, except for spring, which had a drought trend. The special characteristics of the trend variations were markedly different in the basin. The areas in which drought was most likely to occur were mainly located in the Poyang Lake region, northwest and south of the basin, respectively. Furthermore, relationships between the drought and six climate indices showed that the drought exhibited a significant temporal correlation with five climate indices at restricted intervals, except for IOD. The dominant influences of the large-scale climate indices on the drought evolutions shifted in the Poyang Lake Basin during 1958–2017, from the NAO, Niño 3.4, and the Southern Oscillation Index (SOI) before the late 1960s and early 1970s, to the AO and PDO during the 1980s, then to the NAO, AO and SOI after the early 2000s. The NAO, AO and SOI exerted a significant influence on the drought events in the basin. The results of this study will benefit regional water resource management, agriculture production, and ecosystem protection in the Poyang Lake Basin.

Global warming has resulted in unevenly distributed changes in precipitation and evapotranspiration, which has some influence on dry/wet conditions, thus exerting a tremendous impact on national life and the social economy, especially agricultural production. In order to characterize the dry/wet variations in the Poyang Lake basin during 1958–2013, based on the potential evapotranspiration (PET) estimated by the Thornthwaite (TH) and Penman–Monteith (PM) formulas, two types of Standardized Precipitation Evapotranspiration Index (SPEI), namely SPEI_th and SPEI_pm, were calculated in this study. The characteristic of dry/wet variations in the Poyang Lake basin was analyzed and a comparative analysis of two SPEIs was conducted. The results indicate that both SPEI series showed a wet trend in the Poyang Lake basin on an annual scale as well as seasonal scales during 1958–2013, except for spring and autumn. A drying trend was observed in spring, while in autumn, the dry and wet conditions in two SPEIs had opposite trends. However, all trends from two SPEIs were not significant, except for summer SPEI_pm. Meanwhile, significant positive correlations were detected between precipitation and two SPEIs, with the correlation coefficients above 0.95, whereas negative correlations were detected between PET and two SPEIs, with the correlation coefficients ranging from −0.17 to −0.85. This indicates that precipitation was the main climatic factor to determine change in dry/wet conditions in the Poyang Lake basin. Although there were obvious differences between the accumulated values of the Penman–Monteith-based PET (ET_pm) and Thornthwaite-based PET (ET_th), trends in the SPEI_pm series were generally consistent with those in the SPEI_th series, revealing that the method for PET calculation was not critical to the change in dry/wet conditions. Moreover, the results of the conditional probability of SPEI_pm and SPEI_th show that both SPEI_pm and SPEI_th could detect wet or dry events that were identified by SPEI_pm or SPEI_th.

Water resources allocation is a very complex issue involving social, economic, environmental, and political factors, which leads to multi-objective problems. In this paper, a multi-objective water resources allocation model was developed wherein a multi-objective particle swarm optimization (MOPSO) algorithm was introduced to generate a set of Pareto-optimal solutions. At the same time, to facilitate easy implementation for water resources managers, a simple but effective decision-making approach was presented to provide an opportunity for choosing the desired alternative from a set of Pareto-optimal solutions. Finally, the proposed approach was applied to a case study of optimal allocation of water resources for the water-receiving areas of the South-to-North Water Transfer Project in Hebei Province in China, with three objectives of water supply cost, square sum of relative water shortages and the amount of organic pollutants in water. The results show that the proposed approach is able to offer many alternative policies for the water resources managers, giving flexibility to choose the best out of them, and it is a viable alternative to solve multi-objective water resources and hydrology problems.

In this paper, an index-based approach was presented to assess water scarcity risk induced by drought based on relationship between water use and water supply. Firstly, the index system of water scarcity risk was established from the viewpoint of hazard, exposure and vulnerability, which included drought hazard index (frequency, duration, and severity of droughts), exposure index (water use) and vulnerability index (relative water shortage). Then, an integrated risk index, namely water scarcity risk index (WSRI), was proposed using a multiplicative formula of drought hazard index, exposure index and vulnerability index to quantitatively assess drought-induced water scarcity risk. Finally, the WSRI-based approach was applied to a case study of assessing water scarcity risk in the upper and middle reaches of Xiu River, China. The results show that the drought-induced water scarcity risk of upper and middle reaches of Xiu River was at a low level, which was in line with the situation about the level of water resources exploitation and utilization in the study area. This study can serve as a reference for further water resources planning in the Xiu River Basin, and other relevant studies.

Accurate forecasting of water consumption has been one of the most important issues in water supply system. Because of the high generalization performance and the ability to model non-linear relationships, least square support vector machines(LS-SVM) has been successfully employed to solve water consumption forecasting problems over the past few years. However, the practical use of LS-SVM is limited due to its set of parameters to be defined by the user. For this reason, this paper presents a LS-SVM parameter optimization approach based on genetic algorithms and particle swarm optimization (hybrid intelligent algorithm). It makes use of PSO algorithm characteristics such as parallel property and the global convergence performance to avoid the local optimum, and uses the evolution idea of genetic algorithm such as crossover and mutation operations to improve the speed of searching for the global optimization. At the same time, a deterministic searching algorithm is embedded to improve its optimization performance. The application in water consumption forecasting showed, this LS-SVM optimized by hybrid intelligent algorithm achieved better forecasting result.

A data acquisition system that allows to sample ultra-high-frequency (UHF) signals over a long time is presented. The resulting large data rates are tackled with a snapshot-based approach, at which the start time of each snapshot is related to the phase of some reference signal (e.g. the 50 Hz alternating current (AC) signal). A specific application is the detection of signals generated due to partial discharge (PD) effects. The recorded data will be utilized to develop advanced algorithms for detecting and evaluating such kind of signals. First measurement results obtained by the acquisition system are presented, demonstrating the capabilities of the proposed system.