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.

There is an increasing trend in the use of multi-objective evolutionary algorithms (MOEAs) to solve multi-objective optimization problems of the allocation of water resources. However, typically the outcome is a set of Pareto optimal solutions which make up a trade-off surface between the objective functions. For decision makers to choose a satisfactory alternative from a set of Pareto-optimal solutions, this paper suggests a new method based on least squares support vector machine (LSSVM) and k-means clustering for ranking the optimal solutions for the multi-objective allocation of water resources. First, the k-means clustering method was adopted to reduce the large set of solutions to a few representative solutions. Then, to capture and represent the decision maker's preferences as well as to select the most desirable alternative, the LSSVM method was applied to obtain the utility value for each representative solution. According to the magnitude of the utility values, the final priority orders of the representative solutions were determined. Finally, this methodology was applied to rank the Pareto optimal solution set obtained from the multi-objective optimization problems of water resources allocation for the water-receiving areas of the South-to-North Water Transfer Project in Hebei Province, China. Moreover, the comparisons of the proposed method with the information entropy method and the artificial neural network (ANN) model were given. The results of the comparison indicate that the proposed method has the ability to rank the non-dominated solutions of the multi-objective operation optimization model and that it can be employed for decision-making on water allocation and management in a river basin.