Greater flood risks in response to slowdown of tropical cyclones over the coast of China.

The total amount of rainfall associated with tropical cyclones (TCs) over a given region is proportional to rainfall intensity and the inverse of TC translation speed. Although the contributions of increase in rainfall intensity to larger total rainfall amounts have been extensively examined, observational evidence on impacts of the recently reported but still debated long-term slowdown of TCs on local total rainfall amounts is limited. Here, we find that both observations and the multimodel ensemble of Global Climate Model simulations show a significant slowdown of TCs (11% in observations and 10% in simulations, respectively) from 1961 to 2017 over the coast of China. Our analyses of long-term observations find a significant increase in the 90th percentile of TC-induced local rainfall totals and significant inverse relationships between TC translation speeds and local rainfall totals over the study period. The study also shows that TCs with lower translation speed and higher rainfall totals occurred more frequently after 1990 in the Pearl River Delta in southern China. Our probability analysis indicates that slow-moving TCs are more likely to generate heavy rainfall of higher total amounts than fast-moving TCs. Our findings suggest that slowdown of TCs tends to elevate local rainfall totals and thus impose greater flood risks at the regional scale.

Attribution of streamflow changes across the globe based on the Budyko framework.

Differentiating and clarifying the driving factors behind streamflow changes are critical for highlighting hydrological responses to changing environments. However, due to the limited number of hydrological stations, the dominant factor controlling global observed streamflow change remains unclear and intensely debated. Here, we revisit this scientific issue by using the most comprehensive dataset to attribute the observed global streamflow changes during 1960-2014. The results suggest that other factors than precipitation (P) and potential evaporation (E0) are the most important contributors to global observed streamflow changes, which dominate streamflow change for 48.9-50.9% of the stations. In contrast, the dominant factor translated into P in 72.3-72.9% of stations when using reconstructed streamflow datasets, in agreement with most previous global assessments. These differences indicate that streamflow attributions using reconstructed streamflow might overestimate the effects of P while underestimating the roles of other factors, such as the vegetation and human impact. At the global scale, the other factors affected by many catchment characteristics and their impacts on streamflow change have remarkable regional differences. This study highlights the necessity to apply the observed data in streamflow attribution to avoid biased conclusions regarding the dominant factor of streamflow changes.