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Under global warming, a novel category of extreme events has become increasingly apparent, where flood and hot extremes occur in rapid succession, causing significant damages to infrastructure and ecosystems. However, these bivariate compound flood‐hot extreme (CFH) hazards have not been comprehensively examined at the global scale, and their evolution under climate warming remains unstudied. Here, we present the first global picture of projected changes in CFH hazards by using a cascade modeling chain of CMIP6 models, satellite and reanalysis data sets, bias correction, and hydrological models. We find an increasing percentage of floods will be accompanied by hot extremes under climate change; the joint return periods of CFHs are projected to decrease globally, particularly in the tropics. These decreasing joint return periods are largely driven by changes in hot extremes and indicate a likely increase of CFH hazards, and ultimately highlight the urgent need to conduct adaptation planning for future risks.

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Operating rules have been used widely to decide reservoir operations because of their capacity for coping with uncertain inflow. However, stationary operating rules lack adaptability; thus, under changing environmental conditions, they cause inefficient reservoir operation. This paper derives adaptive operating rules based on time‐varying parameters generated using the ensemble Kalman filter (EnKF). A deterministic optimization model is established to obtain optimal water releases, which are further taken as observations of the reservoir simulation model. The EnKF is formulated to update the operating rules sequentially, providing a series of time‐varying parameters. To identify the index that dominates the variations of the operating rules, three hydrologic factors are selected: the reservoir inflow, ratio of future inflow to current available water, and available water. Finally, adaptive operating rules are derived by fitting the time‐varying parameters with the identified dominant hydrologic factor. China's Three Gorges Reservoir was selected as a case study. Results show that (1) the EnKF has the capability of capturing the variations of the operating rules, (2) reservoir inflow is the factor that dominates the variations of the operating rules, and (3) the derived adaptive operating rules are effective in improving hydropower benefits compared with stationary operating rules. The insightful findings of this study could be used to help adapt reservoir operations to mitigate the effects of changing environmental conditions.

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A neural network model is constructed based on Van Allen Probes observations to predict the dynamic plasmapause location. The model parameterized by AE or Kp without inclusion of other parameters shows good accuracy to predict the plasmapause location. Our neural network model is capable of predicting the global plasmapause location with low RMSE.