Search for: All records

Urban expansion and the increasing frequency and intensity of extreme precipitation events bring new challenges to stormwater collection systems. One underrecognized issue is the occurrence of transient flow conditions that lead to adverse multiphase flow interactions (AMFI): essentially, the formation, collapse, and uncontrolled release of air pockets within stormwater system flows. While the fundamental physics of AMFI have been evaluated in laboratory experiments and idealized modeling studies, much less is known about their development in real or simulated stormwater networks, and about the roles played by rainfall and network properties. A necessary precursor to AMFI is the development of pressurized flow conditions within a network. The goal of this study is to understand how spatiotemporal rainfall variability affects the occurrence of pressurized conditions in a stormwater drainage network in the Richmond district of San Francisco, California. High-resolution bias-corrected radar rainfall fields for 24 recent storms were used as the independent variable of EPA-SWMM simulations. Model analyses indicate that the incidence of pressurized flow increases with storm intensity and is more sensitive to rainfall temporal variability than spatial variability. This research provides a reference for analyzing AMFI precursors in other networks and may have important implications for the improvement of stormwater infrastructures. 

Abstract Biological clocks enable organisms to anticipate cyclical environmental changes. Some habitats, such as those at high latitudes or deep sea, experience seasonally diminished or absent diel cues upon which species entrain their circadian rhythms. Fishes of the order Perciformes have rapidly diversified and adapted to these arrhythmic ecosystems, raising the possibility that evolutionary modifications to their circadian biology contributes to their success as one of the most species-rich orders of vertebrates. Here, we used a comparative genomic approach to investigate patterns of biological clock gene loss and circadian rhythms across 33 perciform and six outgroup species. We found both widespread and lineage-specific loss and relaxed selection in core clock genes, particularly in the convergently evolving polar and deep-sea Notothenioidei and Cottioidei suborders. This trend of circadian gene loss was significantly correlated with latitude, with higher-latitude species showing greater loss. Whether these losses and relaxed selection lead to changes in circadian rhythms is unknown for most perciforms. To address this, we performed metabolic phenotyping on three notothenioid species and found no circadian metabolic oscillations during the late austral fall, including in the sub-AntarcticEleginops maclovinus, sister to the Antarctic adaptive radiation. We propose that diminished reliance on endogenous biological clocks may be an adaptive feature that facilitates the survival and diversification of perciform fishes in polar and arrhythmic environments. 

The EPA’s StormWater Management Model (SWMM) has been applied across the globe for citywide stormwater modeling due to its robustness and versatility. Recent research indicated that SWMM, with proper setup, can be applied in the description of more dynamic flow conditions, such as rapid inflow conditions. However, stormwater systems often have geometric discontinuities that can pose challenges to SWMM model accuracy, and this issue is poorly explored in the current literature. The present work evaluates the performance of SWMM 5 in the context of a real-world stormwater tunnel with a geometric discontinuity. Various combinations of spatiotemporal discretization are systematically evaluated along with four pressurization algorithms, and results are benchmarked with another hydraulic model using tunnel inflow simulations. Results indicated that the pressurization algorithm has an important effect on SWMM’s accuracy in conditions of sudden diameter changes. From the tested pressurization algorithms, the original Preissmann slot algorithm was the option that yielded more representative results for a wider range of spatiotemporal discretizations. Regarding spatiotemporal discretization options, intermediate discretization, and time steps that lead to Courant numbers equal to one performed best. Interestingly, the traditional SWMM’s link-node approach also presented numerical instabilities despite having low continuity errors. Results indicated that although SWMM can be effective in simulating rapid inflow conditions in tunnels, situations with drastic geometric changes need to be carefully evaluated so that modeling results are representative. 

Abstract Existing stochastic rainfall generators (SRGs) are typically limited to relatively small domains due to spatial stationarity assumptions, hindering their usefulness for flood studies in large basins. This study proposes StormLab, an SRG that simulates precipitation events at 6‐hr and 0.03° resolution in the Mississippi River Basin (MRB). The model focuses on winter and spring storms caused by water vapor transport from the Gulf of Mexico—the key flood‐generating storm type in the basin. The model generates anisotropic spatiotemporal noise fields that replicate local precipitation structures from observed data. The noise is transformed into precipitation through parametric distributions conditioned on large‐scale atmospheric fields from a climate model, reflecting spatial and temporal nonstationarity. StormLab can produce multiple realizations that reflect the uncertainty in fine‐scale precipitation arising from a specific large‐scale atmospheric environment. Model parameters were fitted monthly from December–May, based on storms identified from 1979 to 2021 ERA5 reanalysis data and Analysis of Record for Calibration (AORC) precipitation. StormLab then generated 1,000 synthetic years of precipitation events based on 10 CESM2 ensemble simulations. Empirical return levels of simulated annual maxima agree well with AORC data and show an overall increase in 1‐ to 500‐year events in the future period (2022–2050). To our knowledge, this is the first SRG simulating nonstationary, anisotropic high‐resolution precipitation over continental‐scale river basins, demonstrating the value of conditioning such stochastic models on large‐scale atmospheric variables. StormLab provides a wide range of extreme precipitation scenarios for design floods in the MRB and can be further extended to other large river basins. 

Abstract. Conventional rainfall frequency analysis faces several limitations. These include difficulty incorporating relevant atmospheric variables beyond precipitation and limited ability to depict the frequency of rainfall over large areas that is relevant for flooding. This study proposes a storm-based model of extreme precipitation frequency based on the atmospheric water balance equation. We developed a storm tracking and regional characterization (STARCH) method to identify precipitation systems in space and time from hourly ERA5 precipitation fields over the contiguous United States from 1951 to 2020. Extreme “storm catalogs” were created by selecting annual maximum storms with specific areas and durations over a chosen region. The annual maximum storm precipitation was then modeled via multivariate distributions of atmospheric water balance components using vine copula models. We applied this approach to estimate precipitation average recurrence intervals for storm areas from 5000 to 100 000 km2 and durations from 2 to 72 h in the Mississippi Basin and its five major subbasins. The estimated precipitation distributions show a good fit to the reference data from the original storm catalogs and are close to the estimates from conventional univariate GEV distributions. Our approach explicitly represents the contributions of water balance components in extreme precipitation. Of these, water vapor flux convergence is the main contributor, while precipitable water and a mass residual term can also be important, particularly for short durations and small storm footprints. We also found that ERA5 shows relatively good water balance closure for extreme storms, with a mass residual on average 10 % of precipitation. The approach can incorporate nonstationarities in water balance components and their dependence structures and can benefit from further advancements in reanalysis products and storm tracking techniques.