Hot extremes may adversely impact human health and agricultural production. Owing to anthropogenic and climate changes, the close and dynamic interaction between drought and hot extremes in most areas of China need to be revisited from the perspective of nonstationarity. This study therefore proposes a time-varying Copula-based model to describe the nonstationary dependence structure of extreme temperature (ET) and antecedent soil moisture condition to quantify the dynamic risk of hot extremes conditioned on dry/wet condition. This study proposed a new approach to identify the soil moisture driving law over extreme temperature from the point view of tail monotonicity and nonstationary risk assessment. Owing to the LTI-RTD (left tail increasing and right tail decreasing) tail monotonicity for dependence structure of these two extremes derived from most areas, the driving laws of soil moisture over ET follows DDL1-WDL1 laws (DDL1: drier antecedent soil moisture condition would trigger a higher risk of ET; WDL1: wetter antecedent soil moisture condition would alleviate the occurrence risk of ET). Because of the spatiotemporal divergence of sensitivity index derived from tail monotonicity (SITM), we can conclude that the spatial and temporal heterogeneity of response degree of ET over the variations of antecedent dry/wet conditions is evident. Incorporation of nonstationarity and tail monotonicity helps identify the changes of driving mechanism (laws) between soil moisture and hot extremes. From the comparison of different kinds of nonstationary behaviours over the spatial distribution of conditional probability of ET (CP1), the dependence nonstationarity can impose greater variations on the spatial distribution of conditional risk of ET given antecedent dry condition (CP1).

The flow regime is of vital importance for the sustainable development of both human society and aquatic biota. Alterations in natural streamflow will modify the stability and biophysical distribution of river conditions, causing a series of adverse ecological and economic consequences. Climate change has been proven to pose potential threats to ecosystems; however, few studies have been conducted to quantify the variations between the flow regime of a future period and pristine natural flow specifically. This study investigates the future impacts induced by the changing climate in the Jinsha River Basin, which is known as the “Asian Water Tower” due to its rich hydroelectric energy resources. The SWAT model is used and calibrated to predict future streamflow. Seven GCMs from NASA NEX-GDDP with one ensemble average under two RCPs (RCP4.5 and RCP8.5) are used for both the NFP (2040s) and the FFP (2080s). The Indicators of Hydrologic Alteration (IHA) software and the river regime index (RRI) are used to assess the potential flow alterations of the Jinsha River. The results show that Pr, Tmax and Tmin all denote increasing trends, with the temperature trends being more obvious. For interannual alterations in flow regimes, most IHA values show moderate and high changes in all predicted conditions. In regard to the intra-annual changes, the results of the RRI show that river flow tends to be more concentrated in wet seasons than in cold seasons and denote evident seasonality and transience with advanced overall peaks of the river system. These findings together indicate that the flow patterns may have noticeable changes corresponding to the natural river regime.