Connecting Hydrometeorological Processes to Low‐Probability Floods in the Mountainous Colorado Front Range

Yu, Guo; Wright, Daniel B.; Holman, K. D.

doi:10.1029/2021wr029768

Cited by 10 publications

(4 citation statements)

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“…Land surface models (LSMs) and land-atmosphere reanalyzes could be used to extend this or other analyses into a longer-term and geographically broader investigation of mixture flood distributions. Even more physically rooted FFA approaches such as Yu et al (2019Yu et al ( , 2020Yu et al ( , 2021, which resolve probable combinations of different hydrometeorological drivers within physically-based numerical model simulations, can also provide insights and test hypotheses about the connections between mixed flood regimes, flood frequency, and how these are changing in a warming climate.…”

Section: Discussionmentioning

confidence: 99%

“…Even more physically rooted FFA approaches such as Yu et al. (2019, 2020, 2021), which resolve probable combinations of different hydrometeorological drivers within physically‐based numerical model simulations, can also provide insights and test hypotheses about the connections between mixed flood regimes, flood frequency, and how these are changing in a warming climate.…”

Section: Discussionmentioning

confidence: 99%

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Diverse Physical Processes Drive Upper‐Tail Flood Quantiles in the US Mountain West

Wright

Davenport

2022

Geophysical Research Letters

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Riverine flooding can be triggered by a variety of distinct hydrometeorological drivers. In the eastern U.S., most floods are due to extreme tropical cyclone rainfall or springtime extratropical systems (Smith et al., 2011;Villarini & Smith, 2010). Looking further west, extratropical systems combined with summertime mesoscale convective systems make up the flood climatology in the midwestern U.S. (Villarini et al., 2011), while floods in the mountainous western United States can be caused by extreme rainfall (often from atmospheric rivers [ARs]; e.g., Barth et al., 2017), snowmelt, or their combination (Berghuijs et al., 2016;Davenport et al., 2020). Such "mixtures" of flood types associated with different physical drivers are also well-documented in Europe (Berghuijs et al., 2019;Blöschl et al., 2017Blöschl et al., , 2019 and likely exist in most terrestrial regions around the globe. Watersheds with these mixtures generally do not experience each type with equal frequency or severity (Smith et al., 2018).Flood mixtures have two important implications. The first is related to estimation of extreme streamflow quantiles such as the 100-year average recurrence interval (ARI; corresponding to a 1% annual exceedance probability [AEP]). These and other quantiles from extreme streamflow distributions-derived via methods broadly referred to as flood frequency analysis (FFA)-are central to infrastructure design, dam safety analysis, and floodplain mapping (e.g., NRC, 1988NRC, , 1994 USBR, 2006 USBR, , 2011. For mathematical convenience, FFA practices typically treat a sample of streamflow observations at a given site as independent and identically distributed (iid; e.g., Sivapalan & Samuel, 2009) regardless of whether the sample stems from one or multiple causes. The difficulty of mixed sample FFA was recognized decades ago in FFA guidelines ("Bulletin 17B"; ICWD, 1982), and a variety of "mixture distribution FFA" techniques have since appeared (e.g.,

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Section: Discussionmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

Diverse Physical Processes Drive Upper‐Tail Flood Quantiles in the US Mountain West

Wright

Davenport

2022

Geophysical Research Letters

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“…Numerous studies have shown that record-breaking short-duration rainfall is an important factor causing the increasingly serious urban flood, while the lack of high temporal resolution rainfall records restricts the practices of hydrological engineering and urban flood analysis [8][9][10]. Zhu et al [11] and Yu et al [12] emphasized that hydrologic model-based flood analysis should carefully consider rainfall temporal resolution in the changing complex environment; they found that the simulated peak discharges can be significantly impacted by rainfall with different temporal resolution (e.g., 1-h and 24-h) at the same magnitude. However, most regions lack long-term and hightemporal resolution (sub-daily) rainfall records, especially for developing countries and newly built cities [13].…”

Section: Introductionmentioning

confidence: 99%

Urban Flood Analysis in Ungauged Drainage Basin Using Short-Term and High-Resolution Remotely Sensed Rainfall Records

et al. 2021

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Analyzing flooding in urban areas is a great challenge due to the lack of long-term rainfall records. This study hereby seeks to propose a modeling framework for urban flood analysis in ungauged drainage basins. A platform called “RainyDay” combined with a nine-year record of hourly, 0.1° remotely sensed rainfall data are used to generate extreme rainfall events. These events are used as inputs to a hydrological model. The comprehensive characteristics of urban flooding are reflected through the projection pursuit method. We simulate runoff for different return periods for a typical urban drainage basin. The combination of RainyDay and short-record remotely sensed rainfall can reproduce recent observed rainfall frequencies, which are relatively close to the design rainfall calculated by the intensity-duration-frequency formula. More specifically, the design rainfall is closer at high (higher than 20-yr) return period or long duration (longer than 6 h). Contrasting with the flood-simulated results under different return periods, RainyDay-based estimates may underestimate the flood characteristics under low return period or short duration scenarios, but they can reflect the characteristics with increasing duration or return period. The proposed modeling framework provides an alternative way to estimate the ensemble spread of rainfall and flood estimates rather than a single estimate value.

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“…The fundamental aim of process-based FFA is to reconstruct the complex joint relationships among different flood drivers (e.g., rainfall, snowpack, soil moisture, and, in this case, fire impacts on runoff production) via Monte Carlo simulation to produce large simulated flood samples, from which a flood probability distribution can be derived. We have previously developed and applied process-based FFA approaches to understand the impacts of rainfall spatiotemporal structures (Wright et al, 2014;Zhu et al, 2018), different runoff generation processes (Yu et al, 2021), and nonstationary flood seasonality (Yu et al, 2019(Yu et al, , 2020 on derived flood frequencies for different watersheds across the United States. These previous studies established the core of the fire continuum FFA framework that is used herein.…”

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confidence: 99%

Process‐Based Quantification of the Role of Wildfire in Shaping Flood Frequency

Yu,

Liu,

McGuire

et al. 2023

Water Resources Research

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Moderate to high severity wildfire can abruptly alter watershed properties and enhance extreme hydrologic responses such as debris flows and floods. The compounding effects of wildfire on flood hazard, represented here via flood frequency analysis (FFA; e.g., 100‐year flood) are of growing importance. Standard statistical FFA approaches are ill‐suited to examining this issue because wildfire‐affected flood peak observations are limited in number and violate the assumption of independent and identically distributed events. Here, we developed a process‐based FFA framework that integrates a stochastic rainfall generator, wildfire simulation, inverse modeling, and a physics‐based hydrological model to directly simulate the impacts of wildfire on FFA. We applied this framework in the upper Arroyo Seco watershed in Southern California, which experienced Moderate to high burn during the 2009 Station Fire. An FFA analysis, performed with simulated peak flows from the first year since fire demonstrates the 100‐year flood can be three times larger than simulations that only consider peak flows in non‐fire‐affected years. On the other hand, coupling process‐based FFA with stochastically simulated wildfire events and watershed's time‐varying hydrologic recovery yields “fire continuum FFA”, a concept introduced here for the first time. Fire continuum FFA accounts for multiple wildfires within very long synthetic time series. Variability in upper tail flood peaks is substantially higher in fire continuum results as compared with pre‐wildfire FFA. This result highlights the importance of wildfire inter‐arrival time and post‐wildfire recovery processes, both of which are expected to change as a result of climatic change and evolving fire management strategies.

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Connecting Hydrometeorological Processes to Low‐Probability Floods in the Mountainous Colorado Front Range

Cited by 10 publications

References 58 publications

Diverse Physical Processes Drive Upper‐Tail Flood Quantiles in the US Mountain West

Diverse Physical Processes Drive Upper‐Tail Flood Quantiles in the US Mountain West

Urban Flood Analysis in Ungauged Drainage Basin Using Short-Term and High-Resolution Remotely Sensed Rainfall Records

Process‐Based Quantification of the Role of Wildfire in Shaping Flood Frequency

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