Assessment of the standard precipitation frequency estimates in the United States

Kim, Jungho; Shu, Evelyn; Lai, Kelvin; Amodeo, Mike F.; Porter, Jeremy R.; Kearns, E.

doi:10.1016/j.ejrh.2022.101276

Cited by 5 publications

(11 citation statements)

References 43 publications

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“…Again the most dramatic growth is seen in SSP5 where the largest growth is expected to occur in areas along the Gulf Coast, Atlantic Coast, and the West US. In regard to the Gulf and Atlantic, this growth intersects directly with the areas most likely to see increased flooding from SLR, intensifying hurricanes, and increases in extreme precipitation (Bates et al, 2021;Kim et al, 2022;Shu et al, 2023). Smaller exposure growth rates are seen in the SSP1 and SSP 2 scenarios due to the more muted growth rates than projected by the innovation/competition driven SSP5 scenario, however, the spatial patterns are relatively consistent.…”

Section: Population Projections and Flood Exposurementioning

confidence: 87%

“…Climate adjusted flood models are early in development within peer-reviewed literature, and are not widely used (Wing et al, 2022). Additionally, most models are unable to estimate risk at a high-resolution (Ward et al, 2013;Alfieri et al, 2017), fail to consider adaptation (Scussolini et al, 2016;Wing et al, 2019), cannot accurately capture extreme precipitation (Kim et al, 2022), and often focus on single flood sources of either pluvial, fluvial, or coastal (Kulp & Strauss, 2019;Marsooli et al, 2019;Wing et al, 2022).…”

Section: Introductionmentioning

confidence: 99%

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Future Population Exposure to Flood Risk: A Decomposition Approach across Shared-Socioeconomic Pathways (SSPs)

Shu,

Hauer,

Porter

2023

Preprint

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Extreme weather events, such as flooding, are expected to become more severe due to climate change. These impacts are connected to impacts on human systems including economic, social, and political crises. Adding to the challenge, populations have concurrently settled in risky areas that were previously thought to have low, or no, exposure. As such, it is important to understand how population exposure to flooding may change over time. Here, we build on existing research to project future populations with their demographic components under all five Shared Socioeconomic Pathways (SSPs) at the block group level across the US. These projections are evaluated with future flood projections to characterize exposure changes over time. Broadly, across the five SSPs, the most increase in exposure will occur in SSP5 (+470,719), and the least will occur in SSP 3 (+57,189). Across all five SSPs, we find that there will be significant increases in expected annual exposure for populations 80 years and older, with an increase in exposure by approximately 216% under SSP5. By decomposing the contributions from flood and population growth, we find that the population growth induced effect contributed to an increase in the population exposure for all of the SSPs except for SSP3. In SSP5, this effect was the largest (+72.7% more exposure), while in SSP3, the population effect decreased the overall exposure by 83.5%.

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Section: Population Projections and Flood Exposurementioning

confidence: 87%

Section: Introductionmentioning

confidence: 99%

Future Population Exposure to Flood Risk: A Decomposition Approach across Shared-Socioeconomic Pathways (SSPs)

Shu,

Hauer,

Porter

2023

Preprint

Self Cite

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“…The "Stage IV" rainfall data set (Nelson et al (2016)) has an hourly time scale and a spatial resolution of approximately 4 km over the continental US, with a record length that exceeds 20 years. Although the Stage IV data have been used for climatological analyses (e.g., Kim et al (2022)), rainfall estimation errors (see Nelson et al (2016)) seriously limit their utility (see Eldardiry et al (2017) and Post and Krajewski (2023) for discussion of problems that arise for rainfall frequency analyses). Reanalysis data sets developed from archived radar fields and algorithms that can be tailored to specific applications provide another path for climatological analyses of rainfall , Nelson et al (2010), Krajewski et al (2013), J.…”

Section: Introductionmentioning

confidence: 99%

“…Although the Stage IV data have been used for climatological analyses (e.g., Kim et al. (2022)), rainfall estimation errors (see Nelson et al. (2016)) seriously limit their utility (see Eldardiry et al.…”

Section: Introductionmentioning

confidence: 99%

Rainfall Frequency Analysis Based on Long‐Term High‐Resolution Radar Rainfall Fields: Spatial Heterogeneities and Temporal Nonstationarities

Smith,

Baeck,

Miller

et al. 2024

Water Resources Research

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Rainfall frequency analysis methods are developed and implemented based on high‐resolution radar rainfall data sets, with the Baltimore metropolitan area serving as the principal study region. Analyses focus on spatial heterogeneities and time trends in sub‐daily rainfall extremes. The 22‐year radar rainfall data set for the Baltimore study region combines reflectivity‐based rainfall fields during the period from 2000 to 2011 and polarimetric rainfall fields for the period from 2012 to 2021. Rainfall frequency analyses are based on non‐stationary formulations of peaks‐over‐threshold and annual peak methods. Increasing trends in short‐duration rainfall extremes are inferred from both peaks‐over‐threshold and annual peak analyses for the period from 2000 to 2021. There are pronounced spatial gradients in short‐duration rainfall extremes over the study region, with peak values of rainfall between Baltimore City and Chesapeake Bay. Spatial gradients in 100‐year, 1 hr rainfall over 20 km length scale are comparable to time trends over 20 years. Rainfall analyses address the broad challenge of assessing changing properties of short‐duration rainfall in urban regions. Analyses of high‐resolution rainfall fields show that sub‐daily rainfall extremes are only weakly related to daily extremes, pointing to difficulties in inferring climatological properties of sub‐daily rainfall from daily rainfall analyses. Changing measurement properties are a key challenge for application of radar rainfall data sets to detection of time trends. Mean field bias correction of radar rainfall fields using rain gauge observations is an important tool for improving radar rainfall fields and provides a useful tool for addressing problems associated with changing radar measurement properties.

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“…Consequently, the Atlas 14 precipitation records are losing their representativeness of current conditions almost exclusively due to the non-stationarity of extreme precipitation 8 . Indeed, the non-stationarity of meteorological phenomena over time has been accelerated by global warming, and extreme weather events’ severity and frequency have increased faster than expected 9 , 10 .…”

Section: Introductionmentioning

confidence: 99%

Exposure of the US population to extreme precipitation risk has increased due to climate change

Kim,

Porter,

Kearns

2023

Sci Rep

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The magnitude and frequency of extreme precipitation events in the early twenty-first century have already proven to be increasing at a rate more quickly than previously anticipated. Currently, the biggest consequence of the change in extreme precipitation is the lack of a climate-adjusted national standard taking into account these recent increases that could be used to prevent life and property loss from catastrophic precipitation-driven floods. Here, we address how severe the change in extreme precipitation compares against the current national standard for precipitation climatology (NOAA Atlas 14) and how much of the population is affected by the underestimation of this risk in the contiguous United States (CONUS). As a result, extreme precipitation in the early twenty-first century has outpaced our current national standard in half of CONUS, and the heavy precipitation events experienced recently are quickly becoming a “new normal”, which will increase in severity and frequency in a continually changing climate. Over three-quarters of the U.S. population will likely experience this new normal occurrence of extreme precipitation. As much as one-third of the population is expected to experience the current definition of a 1-in-100-year storm as often as three times in their lifetime. Additionally, the current precipitation standards for designing transportation infrastructure and urban stormwater drainage systems that are built upon Atlas 14 may be insufficient to protect the public's safety and personal/community property from severe flooding. Areas where flood risk is mitigated by operating hydraulic and adaptation structures urgently need to assess the impact of the increased-hourly extreme precipitation and reevaluate their applicable operation rules. Understanding and predicting patterns and the likelihood of short-duration heavy precipitation would be beneficial in preparing for severe precipitation-driven disasters, such as flash floods and landslides, which would happen more frequently in a changing climate. Following the results of this analysis, accelerating the development and dissemination of the next generation of the national standard that has been climatically adjusted to adapt to the new normal is strongly recommended.

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Assessment of the standard precipitation frequency estimates in the United States

Cited by 5 publications

References 43 publications

Future Population Exposure to Flood Risk: A Decomposition Approach across Shared-Socioeconomic Pathways (SSPs)

Future Population Exposure to Flood Risk: A Decomposition Approach across Shared-Socioeconomic Pathways (SSPs)

Rainfall Frequency Analysis Based on Long‐Term High‐Resolution Radar Rainfall Fields: Spatial Heterogeneities and Temporal Nonstationarities

Exposure of the US population to extreme precipitation risk has increased due to climate change

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