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The efficient operation of a multipurpose reservoir requires information on high and low flows. However, analyses of inflows for high flows and for low flows are typically done independently. In this paper, we considered the joint dependence of the low flow on the preceding high flow volume and duration for the wet season in the Three Gorges region of the Yangtze River Basin in China. High flow volume and duration were found to have a strong association with the annual minimum 7‐day flow in Cuntan, Wanxian, and Yichang stations. Furthermore, we identified the Arctic Oscillation, Pacific Decadal Oscillation, and snow cover in the Tibetan Plateau to have statistically significant teleconnections with the annual minimum 7‐day flow. Bayesian models that consider a different level of pooling of the site by site regressions were then developed for the annual minimum 7‐day flow conditional on the climate indices and high flow volume (or duration). The full pooling model performed best, suggesting that a homogeneous regional response is best identified given the global climate predictors. Statistics such as the deviance information criterion and reduction of error, coefficient of efficiency, and coverage rate under cross validation indicate the good performance of the model. Snow cover in the western Tibetan Plateau and high flow volume were identified as the most influential factors of the annual minimum 7‐day flow through their impact on water storage in the basin. Recent simulations since June 2003, when the Three Gorges Dam operation started, were used to analyse the effect of dam operation on the annual minimum 7‐day flow. A comparison of observations and predictions during the post‐dam period demonstrated that the dam operation effectively modifies the annual minimum 7‐day flow period to have higher flows.

This New York City Panel on Climate Change (NPCC3) chapter builds on the projections developed by the second New York City Panel on Climate Change (NPCC2) (Horton et al., 2015). It confirms NPCC2 projections as those of record for the City of New York, presents new methodology related to climate extremes, and describes new methods for developing the next generation of climate projections for the New York metropolitan region. These may be used by the City of New York as it continues to develop flexible adaptation pathways to cope with climate change. The main topics of the climate science chapter are: (1) Comparison of observed temperature and precipitation trends to NPCC2 2015 projections. (2) New methodology for analysis of historical and future projections of heatwaves, humidity, and cold snaps. (3) Improved characterization of observed heavy downpours. (4) Characterization of observed drought using paleoclimate data. (5) Suggested methods for next generation climate risk information.

Multivariate simulations of a set of random variables are often needed for risk analysis. Given a historical data set, the goal is to develop simulations that reproduce the dependence structure in that data set so that the risk of potentially correlated factors can be evaluated. A nonparametric, copula-based simulation approach is developed and exemplified. It can be applied to multiple variables or to spatial fields with arbitrary dependence structures and marginal densities. The nonparametric simulator uses logspline density estimation in the univariate setting, together with a sampling strategy to reproduce dependence across variables or spatial instances, through a nonparametric numerical approximation of the underlying copula function. The multivariate data vectors are assumed to be independent and identically distributed. A synthetic example is provided to illustrate the method, followed by an application to the risk of livestock losses in Mongolia.