Simulation experiments comparing nonstationary design-flood adjustments based on observed annual peak flows in the conterminous United States

Hecht, Jory S.; Barth, Nancy A.; Ryberg, Karen R.; Gregory, A. E.

doi:10.1016/j.hydroa.2021.100115

Cited by 4 publications

(3 citation statements)

References 112 publications

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“…As a basin's hydroclimate changes due to both watershed-scale changes as well as global/regional climate change (Sankarasubramanian et al, 2020), this could result in different expression on design-flood moments. It is well known that continued urbanization of the watershed results in a positive trend in AMF (i.e., conditional mean increases) (Hodgkins et al, 2019;Vogel et al, 2011), but changes in regional/global climate, such as weakening of moisture circulation leading to decreased precipitation variability (Pendergrass et al, 2017), could eventually result in decreased AMF variability (i.e., negative trend in standard deviation as observed historically by Hecht et al (2022)) for certain regions, thereby nullifying the trend in mean. Thus, changes in AMF moments occur due to both watershed-scale land-use changes and global/regional climate change.…”

Section: Discussion and Concluding Remarksmentioning

confidence: 99%

“…Several studies have used non-stationary methods with those conditional distributions and reported changes in the risk corresponding to a given flood volume under non-stationarity (Condon et al, 2015;Read & Vogel, 2015;Salas & Obeysekera, 2014). Studies have also reported changes in the conditional flood distribution and associated quantiles over time (Hecht et al, 2022;Serago & Vogel, 2018;Slater et al, 2021). For example, Slater et al (2021) assessed changes in 20-year, 50-year, and 100-year conditional flood quantiles over the observed period across the globe.…”

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confidence: 99%

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Beyond Simple Trend Tests: Detecting Significant Changes in Design‐Flood Quantiles

Awasthi

Archfield

Reich

et al. 2023

Geophysical Research Letters

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Design-floods for various return periods are required for water infrastructure design, and for implementing flood insurance. Hence, it is important to reliably estimate the design-flood-an overestimation will increase the total cost of construction, while an underestimation will result in higher risk than the designed probability of exceedance. Conventionally, T-year flood estimation assumes time-invariant flood distribution parameters (England et al., 2019). However, global climate change and local anthropogenic changes, such as urbanization, no longer allow for this assumption to hold (Salas & Obeysekera, 2014;Vogel et al., 2011). Many studies have now reported monotonic trends in observed flood time-series of annual maximum flows (AMF) across parts of the United

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Section: Discussion and Concluding Remarksmentioning

confidence: 99%

mentioning

confidence: 99%

Beyond Simple Trend Tests: Detecting Significant Changes in Design‐Flood Quantiles

Awasthi

Archfield

Reich

et al. 2023

Geophysical Research Letters

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“…Ultimately, systematic simulation studies are required to make evidence-based statements about the expected power of GAMLSS to detect treatment effects beyond the mean in psychological and educational data. However, based on previously conducted simulation studies in bioinformatics (Ho et al, 2019), genetics (Khondoker et al, 2007; Wahl et al, 2014), health economics (Bohl et al, 2013), and hydrology (Debele et al, 2017; Hecht et al, 2021) suggesting that GAMLSS tends to outperform state-of-the art approaches in the respective fields, we hypothesize that GAMLSS also shows advantages in modeling psychological/educational RCT data. A systematic evaluation of this hypothesis is material for future work.…”

Section: Discussionmentioning

confidence: 99%

Distributional causal effects: Beyond an “averagarian” view of intervention effects.

Wiedermann¹,

Zhang²,

Reinke³

et al. 2022

Psychological Methods

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The usefulness of mean aggregates in the analysis of intervention effectiveness is a matter of considerable debate in the psychological, educational, and social sciences. In addition to studying “average treatment effects,” the evaluation of “distributional treatment effects,” (i.e., effects that go beyond means), has been suggested to obtain a broader picture of how an intervention affects the study outcome. We continue this discussion by considering distributional causal effects. We present formal definitions of causal effects that go beyond means and utilize a distributional regression framework known as generalized additive models for location, scale, and shape (GAMLSS). GAMLSS allows one to characterize an intervention effect in its totality through simultaneously modeling means, variances, skewnesses, kurtoses, as well as ceiling and floor effects of outcome distributions. Based on data from a large-scale randomized controlled trial, we use GAMLSS to evaluate the impact of a teacher classroom management program on student academic performance. Results suggest the teacher classroom management training increased mean academic competence as well as the chance to obtain the maximum score on the academic competence scale. These effects would have been completely overlooked in a traditional evaluation of mean aggregates.

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Comparison of on-site versus NOAA’s extreme precipitation intensity-duration-frequency estimates for six forest headwater catchments across the continental United States

Mukherjee

Amatya

Jalowska

et al. 2023

Stoch Environ Res Risk Assess

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Urgency of Precipitation Intensity-Duration-Frequency (IDF) estimation using the most recent data has grown significantly due to recent intense precipitation and cloud burst circumstances impacting infrastructure caused by climate change. Given the continually available digitized up-to-date, long-term, and fine resolution precipitation dataset from the United States Department of Agriculture Forest Service’s (USDAFS) Experimental Forests and Ranges (EF) rain gauge stations, it is both important and relevant to develop precipitation IDF from onsite dataset (Onsite-IDF) that incorporates the most recent time period, aiding in the design, and planning of forest road-stream crossing structures (RSCS) in headwaters to maintain resilient forest ecosystems. Here we developed Onsite-IDFs for hourly and sub-hourly duration, and 25-yr, 50-yr, and 100-yr design return intervals (RIs) from annual maxima series (AMS) of precipitation intensities (PIs) modeled by applying Generalized Extreme Value (GEV) analysis and L-moment based parameter estimation methodology at six USDAFS EFs and compared them with precipitation IDFs obtained from the National Oceanic and Atmospheric Administration Atlas 14 (NOAA-Atlas14). A regional frequency analysis (RFA) was performed for EFs where data from multiple precipitation gauges are available. NOAA’s station-based precipitation IDFs were estimated for comparison using RFA (NOAA-RFA) at one of the EFs where NOAA-Atlas14 precipitation IDFs are unavailable. Onsite-IDFs were then evaluated against the PIs from NOAA-Atlas14 and NOAA-RFA by comparing their relative differences and storm frequencies. Results show considerable relative differences between the Onsite- and NOAA-Atlas14 (or NOAA-RFA) IDFs at these EFs, some of which are strongly dependent on the storm durations and elevation of precipitation gauges, particularly in steep, forested sites of H. J. Andrews (HJA) and Coweeta Hydrological Laboratory (CHL) EFs. At the higher elevation gauge of HJA EF, NOAA-RFA based precipitation IDFs underestimate PI of 25-yr, 50-yr, and 100-yr RIs by considerable amounts for 12-h and 24-h duration storm events relative to the Onsite-IDFs. At the low-gradient Santee (SAN) EF, the PIs of 3- to 24-h storm events with 100-yr frequency (or RI) from NOAA-Atlas14 gauges are found to be equivalent to PIs of more frequent storm events (25–50-yr RI) as estimated from the onsite dataset. Our results recommend use of the Onsite-IDF estimates for the estimation of design storm peak discharge rates at the higher elevation catchments of HJA, CHL, and SAN EF locations, particularly for longer duration events, where NOAA-based precipitation IDFs underestimate the PIs relative to the Onsite-IDFs. This underscores the importance of long-term high resolution EF data for new applications including ecological restorations and indicates that planning and design teams should use as much local data as possible or account for potential PI inconsistencies or underestimations if local data are unavailable.

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Simulation experiments comparing nonstationary design-flood adjustments based on observed annual peak flows in the conterminous United States

Cited by 4 publications

References 112 publications

Beyond Simple Trend Tests: Detecting Significant Changes in Design‐Flood Quantiles

Beyond Simple Trend Tests: Detecting Significant Changes in Design‐Flood Quantiles

Distributional causal effects: Beyond an “averagarian” view of intervention effects.

Comparison of on-site versus NOAA’s extreme precipitation intensity-duration-frequency estimates for six forest headwater catchments across the continental United States

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