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Abrupt transitions between droughts and floods present greater challenges to water resource management than independent drought or flood events. It is therefore of significant importance to further include drought-flood transitions in the risk analysis of water-related hazards under a changing climate. This study more generally evaluates the risks of combinations of dry and wet conditions between adjacent seasons. First, dry and wet conditions are monitored by the standardized precipitation index (SPI). Then, a copula-based framework is proposed for the deviation of joint return periods of dryness-wetness combinations at different severity levels. In addition, SPI series trend detection is conducted using the Mann-Kendall test to analyse the temporal-spatial changes in dry and wet conditions. Wavelet analysis is applied to investigating correlations of dry and wet conditions with climate variability signals, which may provide predictive signals for dryness-wetness combinations. The results of a case study in the Pearl River basin (PRB), China over the period of 1960-2015 indicate that (a) the flood season (from July to October) tends towards dryness and there are wetting trends in the late autumn and winter; (b) as the joint return period is considered the proxy for the risk of dryness-wetness combination, shorter joint return periods remind a higher risk of suffering from abrupt dryness-wetness transitions in the spring-summer and summer-autumn, as well as the more frequent occurrence of continued dryness/wetness in the autumn-winter and winter-spring; (c) the western and eastern PRB are separately characterized by intensified and reduced risks of the most frequent combinations under a changing climate; and (d) El Niño-Southern Oscillation events, the Pacific Decadal Oscillation and sunspot activities have a close association with dry and wet conditions in the PRB. The study provides a supplement for the current risk map and may benefit the early warning and mitigation of water-related hazards.

This study presents finite control set model predictive control (FCS-MPC) methods to eliminate leakage current for a three-level T-type transformerless photovoltaic (PV) inverter without any modification on topology or any hardware changes. The proposed FCS-MPC methods are capable of eliminating the leakage current in the transformerless PV system by applying the defined candidate voltage vector (VV) combinations with only six medium and one zero VVs (6MV1Z) or three large and three small VVs, which generate constant common-mode voltage to perform the optimisation in every control period. With fewer VVs used for the optimisation, the computational burden can be significantly reduced. Furthermore, comparative analysis is performed to show that among these proposed methods, the 6MV1Z method can achieve satisfactory performances in both grid current tracking and neutral point potentials balance control even with less number of candidate VVs, which exhibits the FCS-MPC as an alternative control strategy to be used in the grid-connected transformerless PV system. Finally, experiments are performed to validate the analysis and the effectiveness of the proposed methods.

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