Abstract. The role of rainfall space-time structure, as well as its complex interactions with land surface properties, in flood response remains an open research issue. This study contributes to this understanding, specifically in small (< 15 km2) urban watersheds. Using a flood frequency analysis framework that combines stochastic storm transposition-based rainfall scenarios with the physically-based distributed GSSHA model, we examine the role of rainfall spatial and temporal variability in flood frequency across drainage scales in the highly-urbanized Dead Run watershed (14.3 km2) outside of Baltimore, Maryland, USA. The results show the complexities of flood response within several subwatersheds for both short (< 50 years) and long (> 100 years) rainfall return periods. The impact of impervious area on flood response decreases with increasing rainfall return period. For extreme storms, the maximum discharge is closely linked to the spatial structure of rainfall, especially storm core spatial coverage. The spatial heterogeneity of rainfall increases flood peak magnitudes by 50 % on average at the watershed outlet and its subwatersheds for both small and large return periods. The results imply that commonly-made assumption of spatially uniform rainfall in urban flood frequency modeling is problematic even for relatively small basin scales.

The focus of NPCC3 is on high-risk events involving extreme temperatures, extreme precipitation, and drought. Current trends are presented using historical climate records of high temperature, cold snaps, humidity, and extreme precipitation for the New York metropolitan region. The geographical span of the New York metropolitan region considered here includes, in addition to New York City, adjacent sections of New Jersey such as Newark, Jersey City and Elizabeth, as well as other nearby locations in New York such as Yonkers and Long Island. Historical records of droughts in the Delaware watershed region are also examined. Each climate extreme is analyzed for detection of current trends, and future projections are updated for high-temperature extremes as a test of new methods that could be utilized by NPCC4. These represent finer temporal and spatial resolutions that may be of practical use to key stakeholders in New York City for planning purposes and/or emergency responses. They include local projections of extreme heat and demonstrate the role of the heterogeneous landscape of the city in each process (e.g., how the urban heat island (UHI) affects city neighborhoods differently). Each section of the chapter presents definitions, baselines, methods, and projections, along with uncertainties and recommendations for future work. As in NPCC2, NPCC3 makes use of definitions, measurements, baselines, and scenarios to represent how the probabilities of climate events may change in the future. Here, the focus is on extreme events. For most climate hazards, the definitions of extremes are consistent with the NPCC2, specifically for extreme heat, cold spells, and precipitation. NPCC3 confirms the temperature and precipitation projections of NPCC2 as those of record for use in planning. Based on emerging science, NPCC3 introduces a new methodology for analyzing heat and precipitation extremes that could be used for developing future projections of record in NPCC4. In NPCC2, temperature analyses included projections of average temperature changes and changes in heat waves and hot days. NPCC3 explores new methodologies for downscaling heat extremes and introduces new metrics to analyze historical and projected humidity. For precipitation, NPCC2 developed quantitative projections for average rainfall and daily maximum rainfall events, and NPCC3 introduces a methodology for quantifying projections for sub-daily heavy downpour rain events. In addition, NPCC3 examined how current observations of temperature and precipitation changes compare to projected changes from NPCC2 into the 2020s time slice, which encompassed the time period from 2010 to 2039. Figure 2.1 shows the results of this analysis and demonstrates that observations from 2010 to 2017—the period for which both observed data and NPCC2 projections are available to compare—have been largely consistent with projected changes in average conditions for both temperature (Fig. 2.1a) and precipitation (Fig. 2.1b). However, these comparisons should be viewed with caution because of the role that natural variation plays in the short term. As NPCC3 shifts from a focus on average conditions to extremes, the baselines in some cases vary according to the relevance of the period for the extreme event researched and the period for which data are available. To the extent possible, consistency with NPCC2 is maintained. For example, the baseline for heat waves is 1971–2000, which is the same as NPCC2. However, NPCC3 uses summer months for extreme heat events (June, July, and August) for three reference weather stations, while NPCC2 used the whole year with one reference weather station. NPCC3 uses bias-corrected statistical downscaling and develops future projections for extreme heat based on summer seasons only and includes high-resolution dynamical downscaling at 1 km for selected time slices. Summer humidity is included in the projections as a new heat-related variable. The section of extreme temperatures closes with a short view of cold spells and winter extremes. The section on urban flooding makes use of shorter, more detailed records of satellite and radar data to demonstrate the spatial distribution of these extreme events at sub-hourly time resolution. For droughts, a much longer precipitation record based on tree rings is used to capture decadal variations in the New York City watershed region, and reconstructions of inflows to reservoirs are used to understand how frequently extreme droughts have occurred in the past. To create the new extreme event projections, bias-corrected statistical downscaling is used (see Section 2.3). In the Appendix, we provide an example of dynamic downscaling, a method that can capture the role of the urban built environment in magnifying heat events and mitigating flooding events. Model outputs from the fifth phase of the Coupled Model Intercomparison Project (CMIP5; Taylor et al., 2012) are used for projections of extreme heat. Methods for calculating future projections are consistent with NPCC2 but are updated to account for climate model biases in simulating the distribution of temperature (National Climate Assessment; Walsh et al., 2014). Results are provided in 30-year intervals centered on the 2020s, 2050s, and 2080s as defined by NPCC2. The ensemble of CMIP5 results includes two representative concentration pathways (RCPs) (see Box 2.1). Summer (defined as the months of June, July, and August) temperatures are expected to increase in New York City throughout the 21st century (Horton et al., 2015), leading to more frequent and intense extreme heat events known as heat waves. Here, we follow the definition of heat waves according to the National Weather Service (NWS), that is, an interval of 3 (or more) consecutive days with temperatures of at least 90 °F (32.22 °C). Heat waves affect a wide range of human activities. These effects include increasing energy demand (Schaeffer et al., 2012; Sailor, 2001; Santamouris, 2014) and mortality (Knowlton et al., 2007; Luber and McGeehin, 2008; Anderson and Bell, 2010; Rosenthal et al., 2014). Moreover, higher temperatures associated with urbanization, a phenomenon called the Urban Heat Island (UHI) (Oke, 1982), exacerbate the impacts of extreme heat events (Li and Bou-Zeid, 2013; Ramamurthy and Bou-Zeid, 2016; Ramamurthy et al., 2017; Ortiz et al., 2018). New York City, being the most populated urban area in the United States with over 8 million people (U.S. Census Bureau, 2018), has a large human and economic incentive to understand and mitigate the negative impacts of these events now and in the future. Extreme heat projections have primarily been developed on global (Meehl and Tebaldi, 2004) or continental scales (Gao et al., 2012), with less work focusing on local urban projections that require accounting for finer-scale processes and feedbacks that may affect the occurrence and characteristics of high-temperature events. An example of these processes is the soil moisture-heat wave feedback, wherein dry soil conditions may amplify heat waves by reducing available moisture for evaporative cooling (Seneviratne et al., 2006; Lorenz et al., 2010; Fischer et al., 2007). Cities may amplify these feedbacks by reducing exposed soil area, greatly reducing the capacity for water retention near the land surface (Li and Bou-Zeid, 2013; Ramamurthy and Bou-Zeid, 2016; Ramamurthy et al., 2017). Other relevant city-scale processes include waste heat from buildings and transportation (Taha, 1997; Ichinose et al., 1999; Offerle et al., 2005), lower surface reflectivity of built surfaces (Taha et al., 1988; Morini et al., 2016; Ramamurthy et al., 2015) and increased heat storage in buildings and built structures (Oke et al., 1981; Arnfield and Grimmond, 1998). Humidity content of the atmosphere can play an adverse role in how humans react to high heat conditions (Davis et al., 2016; Hass et al., 2016). As air becomes more saturated with water vapor, the human body becomes less able to shed excess heat through evaporative cooling of perspiration. This can lead to exacerbation of high-temperature impacts such as fatigue and heat exhaustion. This section presents extreme heat and specific humidity projections for New York City using new methods, accounting where possible for urban effects via statistical processing of global climate model (GCM) simulation data. This statistical processing, or downscaling, is necessary because global models have, in general, very coarse spatial resolution (>100 km2) and are thus not able to resolve coastlines, topography, and land cover. The downscaling technique used by NPCC3 is histogram matching. It aims to adjust the model representations of observed climate by correcting their mean and variance to match a representative set of observations in the target domain (see Appendix 2.B). This differs from the bias adjustment procedure of NPCC2 that combined GCM results with station records to downscale the projections to the New York metropolitan region using the "delta method" (Horton et al., 2015), where mean monthly projected changes are applied to daily observations. In NPCC3, as in NPCC2, the climate projections are based on multiple climate models, driven by two RCPs—RCP4.5 (referred to as medium emissions) and RCP8.5 (referred to as high emissions) (see Box 2.1). The aim of this approach is to capture the uncertainties emerging from the range of model results as well as those related to the impacts of future industrial activity, energy use, and technology on greenhouse gases (GHGs), aerosol emissions, and land use change. For consistency, NPCC3 uses the same baseline period as the NPCC2 (1971–2000). Definitions and methods are detailed in Table 2.1. Climate change refers to a significant change in the state of the climate that can be identified from changes in the average state or the variability of weather and that persists for an extended time period, typically decades to centuries or longer. Climate change can refer to the effects of (1) persistent anthropogenic or human-caused changes in the composition of the atmosphere and/or land use, or (2) natural processes such as volcanic eruptions and Earth's orbital variations (IPCC, 2013). A GCM is a mathematical representation of the behavior of the Earth's climate system over time that can be used to estimate its sensitivity to atmospheric concentrations of greenhouse gases (GHGs), aerosols, and land use change. Each model simulates physical exchanges among the ocean, atmosphere, land, and ice. RCPs are sets of trajectories of concentrations of GHGs, aerosols, and land-use changes developed for climate models as a basis for long-term and near-term climate-modeling experiments (Moss et al., 2010). RCPs describe different climate futures based on different amounts of climate forcings. These data are used as inputs to GCMs to project the effects of these drivers on future climate. The NPCC uses sets of GCM simulations driven by two RCPs, known as 4.5 and 8.5. The set of GCM simulations driven by RCP 4.5 is defined here as a medium-emissions scenario, and that by RCP 8.5 as a high-emissions scenario. On the basis of the selection of the RCPs and GCM simulations, local climate change information is developed for key climate variables—temperature, precipitation, and associated extreme events. These results and projections reflect a range of potential outcomes for the New York metropolitan region. A climate hazard is a weather or climate state such as a heat wave, flood, high wind, heavy rain, ice, snow, or drought that can cause harm and damage to people, property, infrastructure, land, and ecosystems. Climate hazards can be expressed in quantified measures, such as flood height in feet, wind speed in miles per hour, and inches of rain, ice, or snowfall that are reached or exceeded in a given period of time. Uncertainty denotes a state of incomplete knowledge that results from lack of information, natural variability in the measured phenomenon, instrumental and modeling errors, and/or from disagreement about what is known or knowable (IPCC, 2013). Historical trends of daily maximum summer temperature in New York were analyzed using Central Park weather station, John F. Kennedy (JFK), and LaGuardia Airports during June, July, and August (Fig. 2.2). Central Park has the longest historical record, dating back to 1900, where the average annual daily maximum summer temperature has been rising at an average of 0.2 °F per decade from 1900 to 2013. JFK and LaGuardia weather stations go back to 1970, where average annual daily maximum summer temperatures have been increasing at a rate of 0.5 °F per decade and 0.7 °F per decade, respectively. The distance between these weather stations provide insights into processes that affect temperatures near the surface, such as sea breezes1 and the UHI. Sea breeze effects appear in stations located close to Long Island's southern shore (e.g., JFK), with lower daily maximum temperatures compared to their in-land counterparts (Fig. 2.2). Sea breeze impacts on temperatures show that geospatial heterogeneity of the urban landscape plays a role in near-surface temperatures, and therefore impact occurrences of extreme heat. The weather station located at JFK, which experiences afternoon sea breezes, has a mean summer maximum temperature of 80.6 °F, whereas the other stations have a mean value of 82.7 °F, which is 2.1 °F higher. This is consistent with climatological studies (e.g., Gedzelman et al., 2003) of the UHI in the region, which have found that afternoon summer sea breezes may shift the center of the urban heat island west and north, toward New Jersey and The Bronx. Heat wave characteristics considered here are their frequency (events/year), mean event duration (number of days/event), and intensity (average maximum temperature/heat wave). NPCC2 had previously analyzed frequency and mean event duration; heat wave intensity is a new metric in NPCC3. While a new methodology is tested here that is different from NPCC2, NPCC3 confirms the use of NPCC2 projections as the projections of record for New York City to plan for extreme heat. The new methodologies presented in NPCC3 could be used in developing new projections of record in NPCC4. Using a composite observed temperature record derived by averaging the daily maximum temperature over the three New York City stations, results from 26 GCMs were bias corrected in order to project distributions of heat waves for the NPCC3 time slices following the methods of Piani et al. (2010) and Hawkins et al. (2013). (See Appendix 2.C for detailed methods.) The mean and standard deviation of a given variable were used to adjust the model distribution against the target observed distribution. For each GCM, the closest land grid point was selected, as was done in NPCC2, and the distribution of maximum daily temperature at this point was bias corrected against the city's composite maximum temperatures. This method is referred to as a "single-point" bias correction. The previous NPCC2 approach may have resulted in a bias toward slightly cooler projected extreme temperatures compared to those projected using the NPCC3 bias-correction methods, particularly toward the warmer periods in the 2080s time slice. These changes may be due to the correction to the variance that, at least partially, addresses the fact that GCM grid boxes near coasts may include water. NPCC3 analysis of the bias-corrected single-point projections shows overall increase across all heat wave metrics throughout the 21st century (Fig. 2.3). To highlight the sensitivity to emission scenarios, we present the response to medium-emission and high-emission scenarios separately. In Table 2.2, the projections are based on the distribution of multimodel results showing the 10th, 25th, 75th, and 90th percentile outcomes across both RCP scenarios, as was done in NPCC2. Mean daily maximum temperature (Fig. 2.3a) shows a nearly linear trend in the high-emissions scenario (RCP8.5), whereas the rate of change in the medium-emissions scenario (RCP4.5) slows after 2040. The number of heat waves per year (Fig. 2.3b) shows far less deviation between the two emissions scenarios. Both scenarios increase at a pace of about one additional yearly event every 20 years until 2060, where growth slows down considerably. This may be due to consecutive events coalescing into very long heat waves, which becomes more likely as heat waves increase in length and frequency. It is also an artifact of the definition of heat wave used, which establishes an unchanging temperature threshold through the entire century. As mean temperatures increase, meeting the 90 °F on consecutive days becomes more likely. Uncertainty in projections as described by confidence intervals increase over time, with a spread of 1 event in the first half of the century that grows to a spread of about two events by end of century. Mean event duration projections (Fig. 2.3c) are similar across the scenarios in the first half of the century, growing by around 2 days per 20-year period. However, the high-emissions scenario projections show accelerated growth in the latter half of the century, as well as more spread in the model ensemble, with an uncertainty band spanning about 10 days, compared to about 2 days in the first half. This accelerated increase in event duration may explain the stabilization of event frequency projections in Figure 2.3(b), as events may aggregate into longer heat waves. Mean intensity, defined as the mean of event maximum temperatures, shows large interannual variation (Fig. 2.3d), with projected values that increase from about 93 °F early in the century, to 95–98 °F by the end of the century. Confidence interval bands increase slightly throughout by end of century, reaching an ensemble spread of about 1 °F. Additional key metrics of extreme heat explored are number of days above 90 and 100 °F in the summer season (Table 2.2). Projected days above 90 °F are expected to become more likely as summer temperatures increase. By the 2080s, projections show 24 (10th percentile) to 75 (90th percentile) days above 90 °F compared to the 1971–2000 baseline (10 days).2 The humidity content of the atmosphere can play an adverse role in how humans react to high heat conditions (Davis et al., 2016; Hass et al., 2016). We present projections of daily mean specific humidity based on a 26 multimodel ensemble, across medium- and high-emissions scenarios, as in Section 2.3.2. For each model, the land grid point closest to New York City is used. Due to a lack of specific humidity records from all weather stations, GCM humidity was bias corrected based on LaGuardia Airport only. In addition, the 1971–2000 baseline for specific humidity is based only on the LaGuardia Airport weather station. Humidity is a new metric being considered by the NPCC3. Results show an increase between the 2020s and 2080s time slices of around 9% at each period's 10th percentile, while changes in the 90th percentile represent a 16% increase (Table 2.3). The uncertainty in these projections as characterized by the model ensemble 95% confidence bands (Fig. 2.4) is relatively large. Increases in specific humidity combined with increasing temperatures might lead to higher heat index (see Box 2.2), which has major consequences for human health and is a driver of peak energy demand for space cooling, as air conditioning systems remove sensible (temperature-related) and latent (moisture-related) heat from buildings. To assess combined air moisture and temperature impacts, concurrent hourly values must be used, rather than daily outputs from the model ensemble. Specific humidity: A measure of the amount of water in the atmosphere; the mass fraction of water vapor per unit mass of moist air. Absolute humidity: Mass of air per unit volume of moist air. Relative humidity: The ratio of water vapor pressure to the saturation vapor pressure. It measures how saturated with water vapor the atmosphere is. As air becomes more saturated with water vapor, it becomes more difficult for the human body to shed excess heat through evaporative cooling of perspiration. Heat index: A measure of the combined effects of temperature and relative humidity. It is defined by the National Weather Service. See Appendix 2.C of this chapter for an expanded discussion of how climate change is projected to impact the heat index. NPCC3 recommends further testing of this methodology for the development of new projections of record in NPCC4. Other definitions vary, including the use of standard deviations (Vavrus et al., 2006). Cold spell changes have been reported on regional scales (e.g., Europe, de Vries et al., 2012; China, Zhang et al., 2017; Northeast United States, Thibeault and Seth, 2014) and for global scales (Vavrus et al., 2006; Konrad, 1996) using GCM ensembles and long-term climate records. In most cases, cold days have shown decreases, and notably in Northern latitudes, it has been found that accelerated decreases of cold spells outpace increases in summer maxima (Thibeault and Seth, 2014). We used data from Central Park to establish a benchmark for cold spells. The 10th percentile threshold for cold days at this station was computed from the entire 1900–2017 record, with a value of 24.08 °F. In general, cold days per year decreased by 1.46 days every decade between 1900 and 2017, while days below freezing temperatures decreased at a rate of 1.85 days per decade (Fig. 2.5). This results in recent years having, on average, about 22 fewer days below freezing and 17 fewer cold days than in 1900. The rate of change of these trends is slightly lower than those reported for the entire Northeast by Thibeault and Seth (2014). For the case of New York City, the attribution of these rapid decreases of cold spells may be a combined effect of global warming and urbanization. Urbanization leads to the UHI effect, which tends to have a larger effect in the winter. The impact of global warming on climate implies an overall decrease in the number of cold extremes, while the number of warm extremes increases (Horton et al., 2015). However, recent persistent winter events of record cold weather in the Northeast United States and in other Northern Hemisphere regions raise concern of a possible connection to climate change. Both the science community (Screen et al., 2015) as well as the public (Lyons et al., 2018) have been engaged in research and discussion about cold air outbreaks associated with the Polar Vortex. An aspect of these discussions is the connection between the gradual disappearance of Arctic sea ice due to the polar amplification of global warming, the increase in atmospheric "blocking" events, and the slowing down and deepening of the wavy circulation in the midlatitudes (Screen and Simmonds, 2010; Overland et al., 2015). With the increase in amplitude and slowdown of atmospheric waves, cold air can flow down from the Arctic deep into the midlatitudes, and vice versa, warm air flows north. This creates protracted deviations from normal conditions in either place. In early January of 2014, a large cold air mass moved from Canada into the northern Great Plains states and made its way slowly to the Northeast. The unusual cold weather in the eastern half of the United States did not abate until April. At the same time, other areas in the Northern Hemisphere experienced record warm winter weather. Shorter events similar to this have happened since, as was the case during winter months in 2017–2018 and 2019. These events were connected to stratospheric warming, where the low-pressure vortex that is usually centered on the North Pole moves equatorward. This change in circulation is communicated down to the troposphere and results in anomalous weather situations during the winter season (Kretschmer et al., 2018; Screen et al., 2018). There has been much debate whether such events are linked to the gradual melting of sea ice in the Arctic, and it appears that the answer is that there is a link (Overland et al., 2015; Screen et al., 2018). This was shown in climate models (Zhang et al., 2018) and is consistent with the observation that polar vortex events are on the rise (Kretschmer et al., 2018). There is, however, no evidence that cold air outbreaks in the United States have increased as a result of this or other phenomena (Screen et al., 2015). The increase in polar vortex events was found to influence surface weather in Siberia, where a significant cooling of the average winter weather has been detected, in contrast with the observed warming elsewhere around the globe (Kretschmer et al., 2018; Zhang et al., 2018). New methodologies for projections of heat wave characteristics for the New York metropolitan region were tested in NPCC3 using bias-corrected climate model projections. For the early part of the century (2020s), these results are consistent with those of NPCC2. In the later part of the century (2050s and 2080s), the NPCC3 results display the potential for more intense heat events with longer durations. Results show large changes across all heat wave metrics throughout the 21st century. The high-emissions scenario (RCP8.5) projects, in many cases, several times larger effects than the medium-emissions scenario. The uncertainty of the projections increases through time. The new NPCC3 methods include humidity, which is projected to increase by more than 30% from baseline values. These increases in atmospheric humidity with extreme temperatures are likely to have large societal implications reflected in public health and energy demands. NPCC3 confirms the NPCC2 projections for heat waves, hot days, and cold days as those of record for New York City in planning for the impacts of climate change and recommends the incorporation of the new methodologies into revised projections of record in NPCC4. Future work in projecting extreme heat and humidity for the NPCC should be directed to incorporating the spatial distribution of these extreme heat events to account for coastal influence and UHI effects (e.g., sea breeze effects). This may require using regional climate models (RCMs) to dynamically downscale projections to finer spatial scales within the New York metropolitan region. Carrying this out for an ensemble of GCMs and RCMs will require large computational efforts. New methods may be needed to account for uncertainties in dynamic downscaling. See Appendix 2.C for an example of the possible approach, utilizing one GCM and one RCM for two time slices, as a potential guide for new research directions in NPCC4. NPCC2 projected quantitative changes in daily extreme rainfall amounts for 1 inch, 2 inches, and 4 inches (Table 2.4). NPCC2 also included a qualitative projection in relation to extreme rainfall, stating that heavy downpours in the New York metropolitan region are very likely to increase by the 2080s (Horton et al., 2015). NPCC3 does not provide new projections for heavy rainfall and confirms the NPCC2 projections as those of record for city planning and adaptation. It provides new analyses of the dynamics of heavy rainfall events in the New York metropolitan region recommended for use in developing new projections of record in NPCC4. NPCC3 focuses on observed annual rainfall (see Section 2.2) and observed heavy rainfall days in recent years compared to the NPCC2 2020s time slice projections. NPCC3 also analyzes the types of storm systems associated with heavy rainfall events, and the regional drivers of historical flash flooding events. This section also conducts a trend analysis of sub-daily heavy precipitation events at the 1-, 3-, 6-, and 24-h duration. Finally, this section explores ways to illustrate the spatial variation of urban flooding events. It is recommended that this work serve as a foundation for new projections of record for heavy rainfall that are to be developed in NPCC4. NPCC3 analyses of heavy downpours build on NPCC2 projections for daily extreme rainfall by more closely examining the past and present rainfall across New York City and across timescales. Additionally, NPCC3 includes observations of urban flooding (definition in Table 2.5), in New York City and surrounding areas. NPCC3 refocuses discussion from daily extreme rainfall to sub-daily "heavy downpours," defined as rarely occurring rainfall at less than daily timescales that can produce urban flooding. NPCC3 lays the groundwork for a new set of future projections in NPCC4 using these metrics. Extreme rainfall is defined as a rainfall amount that is a rare event, that is, one that approaches the end of the probability distribution of all events. In NPCC2, daily extreme rainfall in the current climate was represented by the number of occurrences of rainfall above 1 inch, 2 inches, or 4 inches per day at the Central Park weather station in New York City. Extreme rainfall measured at Central Park has significant year-to-year variation such that no statistically significant trends in extreme rainfall can be identified (Horton et al., 2015). (A statistically significant trend indicates that this trend in extreme rainfall would be unlikely to occur by chance). NPCC2 did note that the heaviest 1% of daily rainfalls have increased by approximately 70% between 1958 and 2011 in the Northeast (Horton et al., 2015). NPCC2 used the observed measurements as a baseline (Horton et al., 2015) for projections of extreme rainfall (Table 2.6; Horton et al., 2015). This section focuses on extreme rainfall by describing the approaches to heavy downpours in NPCC2 and NPCC3, studying regional drivers of daily and sub-daily heavy rainfall, providing a revised historical analysis of heavy rainfall across New York City, and summarizing new research projecting future changes in heavy downpours in the region. NPCC2 results included projections for extreme rainfall in the 2020

Urban soil is a sink for anthropogenic lead (Pb) and the latter is a persistent threat to human health, especially to children and the gardening population. In the past decade, several organizations have tested soil samples for Pb in New York City. Here we summarize the available soil Pb data for New York City and create a spatial distribution map. The highest Pb levels were present in the oldest parts of the city, and mostly industrial and high traffic areas. There is overlap between high Pb areas with areas of high population density and high poverty rates. The analyses help delineate parts of the city that are most affected, possible sources of Pb, and where to prioritize resources for mitigation and remediation.

New York City (NYC) is an ultra-urban region, with over 50% impervious cover and buried stream channels. Traditional flood studies rely on the presence of stream gages to detect flood stage and discharge, but these methods cannot be used in ultra-urban areas. Here we create a high-resolution radar rainfall dataset for NYC and utilize citizen and expert reports of flooding throughout the city to study flash flooding in NYC. Results indicate that interactions between the urban area and land–sea boundary have an important impact on the spatial variability of both heavy rainfall and flooding, sometimes in contrast to results obtained for other cities. Top days of daily and hourly rainfall exhibit a rainfall maximum over the city center and an extended region of higher rainfall downwind of the city. The mechanism for flooding appears to vary across the city, with high groundwater tables influencing more coastal areas and high rain rates or large rain volumes influencing more inland areas. There is also a strong relationship between sewer type and flood frequency, with fewer floods observed in combined sewer areas. Flooding is driven by maximum one-hour to one-day rainfall, which is often substantially less rain than observed for the city-wide daily maximum.

The structure and evolution of flash flood producing storms over a small urban watershed in the mid Atlantic United States with a prototypical flash flood response is examined. Lagrangian storm properties are investigated through analyses of the 32 storms that produced the largest peak discharges in Moores Run between January 2000 and May 2014. The Thunderstorm Identification, Tracking, Analysis, and Nowcasting (TITAN) algorithm is used to track storm characteristics over their life cycle with a focus on storm size, movement, intensity, and location. First, the 13 June 2003 and 1 June 2006 storms, which produced the two largest peak discharges for the study period, are analyzed. Heavy rainfall for the 13 June 2003 and 1 June 2006 storms were caused by a collapsing thunderstorm cell and a slow-moving, low-echo centroid storm. Analyses of the 32 storms show that collapsing storm cells play an important role in peak rainfall rate production and flash flooding. Storm motion is predominantly southwest-to northeast, and approximately half of the storms exhibited some linear organization. Mean storm total rainfall for the 32 storms displayed an asymmetric distribution around Moores Run, with sharply decreasing gradients southwest of the watershed (upwind and into the city) and increased rainfall to the northeast (downwind and away from the city). Results indicate urban modification of rainfall in flash flood producing storms. There was no evidence that the storms split around Baltimore. Flood-producing rainfall was highly concentrated in time; on average, approximately 21% of the storm total rainfall fell within 15 min.

5 Flash flooding is a high impact weather event that requires clear communication regarding severity and potential hazards among forecasters, researchers, emergency managers, and the general public. Current standards used to communicate these characteristics include return periods and the United States (U.S.) National Weather Service (NWS) 4-tiered river flooding severity scale. Return periods are largely misunderstood, and the NWS scale is limited to flooding on gauged streams and rivers, often leaving out heavily populated urban corridors. To address these shortcomings, a student-led group of interdisciplinary researchers came together in a collaborative effort to develop an impact-based Flash Flood Severity Index (FFSI). The index was proposed as a damage-based, post-event assessment tool, and preliminary work toward the creation of this index has been completed and presented here. Numerous case studies were analyzed to develop the preliminary outline for the FFSI, and three examples of such cases are included in this paper. The scale includes five impact-based categories ranging from Category 1 very minor flooding to Category 5 catastrophic flooding. Along with the numerous case studies used to develop the initial outline of the scale, empirical data in the form of semi-structured interviews were conducted with multiple NWS forecasters across the country and their responses were analyzed to gain more perspective on the complicated nature of flash flood definitions and which tools were found to be most useful. The feedback from these interviews suggests the potential for acceptance of such an index if it can account for specific challenges. Published by Elsevier B.V.

The objective of this study is to examine the structure and evolution of storms that produce flash floods in small urban watersheds. The study site is Harry's Brook, a 1.1km(2) urban watershed in Princeton, New Jersey. A catalog of 15 storms is developed for Harry's Brook based on paired observations of streamflow and rainfall. Lagrangian analyses of storm properties are based on storm tracking procedures utilizing 3-D radar reflectivity observations from the KDIX (Fort Dix, New Jersey) Weather Surveillance Radar, 1988 Doppler. Analyses focus on the storm elements that were responsible for the peak rainfall rates over the watershed. The 22 July 2006 storm, which produced the record flood peak in the catalog (a unit discharge of 26.8m(3)s(-1)km(-2)) was characterized by thunderstorm cells that produced more than 50 cloud-to-ground lightning strikes and collapsed over Harry's Brook. The 3 June 2006 storm, which produced the third largest flood peak (a unit discharge of 11.1m(3)s(-1)km(-2)), was a low-echo centroid storm with no lightning. We use cloud-to-ground flash rate, echo top height, maximum reflectivity, and height of maximum reflectivity as key variables for characterizing convective intensity. Storm motion is examined through a time series of storm speed and direction. The 22 July 2006 and 3 June 2006 storms provide end-members of storm properties, centering on convective intensity, which are associated with flash flooding in small urban watersheds. Extreme 1-15min rainfall rates are produced by warm season convective systems at both ends of the convective intensity spectrum.

The authors identify the flashiest watersheds in the contiguous United States based on frequency of discharge peaks exceeding 1 m(3) s(-1) km(-2). The entire digitized record of USGS instantaneous discharge data is used for all stream gauging stations with over 10 years of data. Using the 1 m(3) s(-1) km(-2) threshold, the flashiest basins in the contiguous United States are located in urban areas along a swath of states from the south-central United States to the mid-Atlantic and in mountainous areas of the West Coast, especially the Pacific Northwest. The authors focus on small watersheds to identify the flashiest cities and states across the country and find Tulsa, Oklahoma; Baltimore, Maryland; and St. Louis, Missouri, to be the flashiest cities in the contiguous United States. Thunderstorms are major agents for peak-over-threshold flood events east of the Rocky Mountains, and tropical cyclones play a secondary role, especially in the Southeast. West Coast flood events are associated with winter storms. Flooding west of and within the Rockies is linked to steeply sloped terrain and compact watersheds. East of the Rockies, urban areas dominate flashy watersheds. The authors find that watersheds northeast (downwind) of city centers are flashier than other urban watersheds, consistent with the downwind maximum in rainfall found in many urban regions. They examine anomalous flood response in the Illinois-Missouri region; St. Louis is among the flashiest cities in the United States, while Chicago is among the least flashy. Their flashiness map is compared with other measures of flooding, including flood damage and National Weather Service flash flood reports.

On July 17–18, 1996, two mesoscale convective systems (MCSs) passed through northeastern Illinois, causing a record 440-mm total storm rainfall within a 24-h period at Aurora, Illinois, with values exceeding 200 mm throughout a broad area of the region. The storm caused flooding with a return period larger than 100 years at different USGS regional stream gauging locations. The Davenport, Iowa, Weather Surveillance Radar–1988 Doppler (WSR-88D) radar coverage allows high-quality characterization of the storm at fine spatial and temporal scales. Of particular interest is the inter- and intravariability in watershed response to the two pulses of intense rainfall. Spatial distribution of rainfall and the degree of urbanization of the individual basins are the dominant factors determining the magnitude of runoff response. These properties are highly dependent on the extent and history of urbanization. Examination of the annual maximum instantaneous peak discharge and the peaks-over-threshold (POT) time series at three stream gauging stations in Illinois (Blackberry Creek, DuPage River, and Sawmill Creek) over the past 50 years points to the large effect of urbanization on the flood peak distribution in the greater Chicago metropolitan area.