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A common feature within coastal cities is small, urbanized watersheds where the time of concentration is short, leading to vulnerability to flash flooding during coastal storms that can also cause storm surge. While many recent studies have provided evidence of dependency in these two flood drivers for many coastal areas worldwide, few studies have investigated their co-occurrence locally in detail or the storm types that are involved. Here we present a bivariate statistical analysis framework with historical rainfall and storm surge and tropical cyclone (TC) and extratropical cyclone (ETC) track data, using New York City (NYC) as a mid-latitude demonstration site where these storm types play different roles. In contrast to prior studies that focused on daily or longer durations of rain, we apply hourly data and study simultaneous drivers and lags between them. We quantify characteristics of compound flood drivers, including their dependency, magnitude, lag time, and joint return periods (JRPs), separately for TCs, ETCs, non-cyclone-associated events, and merged data from all events. We find TCs have markedly different driver characteristics from other storm types and dominate the joint probabilities of the most extreme rain surge compound events, even though they occur much less frequently. ETCs are the predominant source of more frequent moderate compound events. The hourly data also reveal subtle but important spatial differences in lag times between the joint flood drivers. For Manhattan and southern shores of NYC during top-ranked TC rain events, rain intensity has a strong negative correlation with lag time to peak surge, promoting pluvial–coastal compound flooding. However, for the Bronx River in northern NYC, fluvial–coastal compounding is favored due to a 2–6 h lag from the time of peak rain to peak surge. 

Abstract Climate‐induced shifts in mosquito phenology and population structure have important implications for the health of humans and wildlife. The timing and intensity of mosquito interactions with infected and susceptible hosts are a primary determinant of vector‐borne disease dynamics. Like most ectotherms, rates of mosquito development and corresponding phenological patterns are expected to change under shifting climates. However, developing accurate forecasts of mosquito phenology under climate change that can be used to inform management programs remains challenging despite an abundance of available data. As climate change will have variable effects on mosquito demography and phenology across species it is vital that we identify associated traits that may explain the observed variation. Here, we review a suite of modeling approaches that could be applied to generate forecasts of mosquito activity under climate change and evaluate the strengths and weaknesses of the different approaches. We describe four primary life history and physiological traits that can be used to constrain models and demonstrate how this prior information can be harnessed to develop a more general understanding of how mosquito activity will shift under changing climates. Combining a trait‐based approach with appropriate modeling techniques can allow for the development of actionable, flexible, and multi‐scale forecasts of mosquito population dynamics and phenology for diverse stakeholders.