Multiple Storm Research Articles

Testing the hydrological performance of live pole drains (LPD) for mitigation of slope instability

Nature-based solutions (NbS) and soil bioengineering techniques have gained considerable attention due to their relevant hydrological functions and ability to mitigate slope instability. Live pole drains (LPD), a lesser-known NbS, have traditionally been deployed on slopes to drain the excess surface water and regulate the soil's water budget, making it a suitable technique for stormwater management and landslide prevention. However, neither the LPD performance as a plant-based drainage system nor its potential to regulate the soil-water budget through hydrological processes have been thoroughly studied. This paper presents a novel pilot, lab-based approach for testing the hydrological performance of LPD under different soil hydrological conditions. We built three different treatments and investigated their hydrological performance under multiple storm events. We explored how LPD regulate the soil-water budget by partitioning the water inputs (i.e., rainfall precipitation) into water outputs (i.e., surface runoff, subsurface flow, and percolation). The study revealed that LPD can effectively manage stormwater by draining excess runoff and buffering water in the soil, outperforming fallow soil. Subsurface flow and percolation were significantly higher under LPD treatments when compared to fallow ground, suggesting that the presence of an enhanced structure in the soil results in high soil hydrological performance. The presence of a secondary species with the LPD showed a more efficient hydrological performance than an LPD alone, which aligns with the current implementation of NbS fostering biodiversity. Antecedent soil moisture impacted the hydrological performance of LPD by altering the relative infiltration capacity of the soil and by potentially modifying the availability of channels for preferential flow. Our findings provide a sound basis for future research to improve our understanding of the hydrological performance of LPD for slope instability mitigation and encourage their reproduction and upscaling.

Variability Assessment of Global Extreme Coastal Sea Levels Using Altimetry Data

This study assesses the variability of coastal extreme sea levels globally by utilizing nearly three decades of along-track, multi-mission satellite altimetry data. An altimetry-based global coastal database of the non-tidal residual sea level component has been produced. The climate variability of extremes is modeled through a parametric, non-stationary statistical model. This model captures intra-annual, inter-annual and long-term variations in non-tidal residual return levels. Comparisons with tide gauge data demonstrate the ability of altimetry data to capture the variability of coastal extreme sea levels. Our findings reveal a greater complexity in the monthly variability patterns of non-tidal residual extremes in tropical latitudes, often exhibiting multiple storm periods, contrasting with coasts in extratropical latitudes, which are mostly controlled by a winter–summer pattern. This study also highlights the significant influence of established climate circulation patterns on sea level extremes. The positive phase of the Arctic Oscillation pattern leads to increases of over 25% in non-tidal residual return levels in Northwestern Europe with respect to a neutral phase. Furthermore, return levels in the western coast of Central America could be 50% higher during El Niño compared to La Niña. Our results show a robust increasing trend in non-tidal residual return levels along most global coastlines. A comparative analysis shows that variations during the 1995–2020 period were primarily driven by intra-annual variations.

Anthropogenic particle concentrations and fluxes in an urban river are temporally variable and impacted by storm events.

Anthropogenic particles (AP), which include microplastics and other synthetic, semisynthetic, and anthropogenically modified materials, are pollutants of concern in aquatic ecosystems worldwide. Rivers are important conduits and retention sites for AP, and time series data on the movement of these particles in lotic ecosystems are needed to assess the role of rivers in the global AP cycle. Much research assessing AP pollution extrapolates stream loads based on single time point measurements, but lotic ecosystems are highly variable over time (e.g., seasonality and storm events). The accuracy of models describing AP dynamics in rivers is constrained by the limited studies that examine how frequent changes in discharge drive particle retention and transport. This study addressed this knowledge gap by using automated, high-resolution sampling to track AP concentrations and fluxes during multiple storm events in an urban river (Milwaukee River) and comparing these measurements to commonly monitored water quality metrics. AP concentrations and fluxes varied significantly across four storm events, highlighting the temporal variability of AP dynamics. When data from the sampling periods were pooled, there were increases in particle concentration and flux during the early phases of the storms, suggesting that floods may flush AP into the river and/or resuspend particles from the benthic zone. AP flux was closely linked to river discharge, suggesting large loads of AP are delivered downstream during storms. Unexpectedly, AP concentrations were not correlated with other simultaneously measured water quality metrics, including total suspended solids, fecal coliforms, chloride, nitrate, and sulfate, indicating that these metrics cannot be used to estimate AP. These data will contribute to more accurate models of particle dynamics in rivers and global plastic export to oceans. PRACTITIONER POINTS: Anthropogenic particle (AP) concentrations and fluxes in an urban river varied across four storm events. AP concentrations and fluxes were the highest during the early phases of the storms. Storms increased AP transport downstream compared with baseflow. AP concentrations did not correlate with other water quality metrics during storms.

A cost-benefit ‘source-receptor’ framework for implementation of Blue-Green flood risk management

As floods are a major and growing source of risk in urban areas, there is a necessity to improve flood risk management frameworks and civil protection through planning interventions that modify surface flow pathways and introduce storage. Despite the complexity of densely urbanised areas (topography, buildings, green spaces, roads), modern flood models can represent urban features and flow characteristics in order to help researchers, local authorities, and insurance companies to develop and improve efficient flood risk frameworks to achieve resilience in cities. A cost-benefit driven ‘source-receptor’ flood risk framework is developed in this study to identify (1) locations contributing to surface flooding (sources), (2) buildings and locations at high flood risk (receptors), (3) the cost-benefit nexus between the ‘source’ and the ‘receptor’, and finally (4) ways to mitigate flooding at the ‘receptor’ by adding Blue-Green Infrastructure (BGI) in critical locations. The analysis is based on five steps to identify the ‘source’ and the ‘receptor’ in a study area based on the flood exposure of buildings, damages arising from flooding and available green spaces with the best potential to add sustainable and resilient solutions to reduce flooding. The framework was developed using the detailed hydrodynamic model CityCAT in a case study of the city centre of Newcastle upon Tyne, UK. The novelty of this analysis is that firstly, multiple storm magnitudes (i.e. small and large floods) are used combined with a method to locate the areas and the buildings at flood risk and a prioritized set of best places to add interventions upstream and downstream. Secondly, planning decisions are informed by considering the benefit from reduced damages to properties and the cost to construct resilient BGI options rather than a restricted hydraulic analysis considering only flood depths and storages in isolation from real world economics.

A Bayesian Spatial Model to Correct Under-Reporting in Urban Crowdsourcing

Decision-makers often observe the occurrence of events through a reporting process. City governments, for example, rely on resident reports to find and then resolve urban infrastructural problems such as fallen street trees, flooded basements, or rat infestations. Without additional assumptions, there is no way to distinguish events that occur but are not reported from events that truly did not occur--a fundamental problem in settings with positive-unlabeled data. Because disparities in reporting rates correlate with resident demographics, addressing incidents only on the basis of reports leads to systematic neglect in neighborhoods that are less likely to report events. We show how to overcome this challenge by leveraging the fact that events are spatially correlated. Our framework uses a Bayesian spatial latent variable model to infer event occurrence probabilities and applies it to storm-induced flooding reports in New York City, further pooling results across multiple storms. We show that a model accounting for under-reporting and spatial correlation predicts future reports more accurately than other models, and further induces a more equitable set of inspections: its allocations better reflect the population and provide equitable service to non-white, less traditionally educated, and lower-income residents. This finding reflects heterogeneous reporting behavior learned by the model: reporting rates are higher in Census tracts with higher populations, proportions of white residents, and proportions of owner-occupied households. Our work lays the groundwork for more equitable proactive government services, even with disparate reporting behavior.

Identifying and Constraining Marsh-Type Transitions in Response to Increasing Erosion over the Past Century

Marsh environments, characterized by their flora and fauna, change laterally in response to shoreline erosion, water levels and inundation, and anthropogenic activities. The Grand Bay coastal system (USA) has undergone multiple large-scale geomorphic and hydrologic changes resulting in altered sediment supply, depositional patterns, and degraded barrier islands, leaving wetland salt marshes vulnerable to increased wave activity. Two shore-perpendicular transect sites, one along a low-activity shoreline and the other in a high activity area of the same bay-marsh complex, were sampled to investigate how the marshes within 50 m of the modern shoreline have responded to different levels of increased wave activity over the past century. Surface sediments graded finer and more organic with increased distance from the shoreline while cores generally exhibited a coarsening upwards grain-size trend; all cores contained multiple large sedimentological shifts. 210Pb-based mass accumulation rates over the last two decades were greater than the long-term (centurial) average at each site with the fastest accumulation rates of 7.81 ± 1.58 and 7.79 ± 1.63 kg/m2/year at the sites nearest the shoreline. A shoreline change analysis of three time-slices (1848–2017, 1957–2017, 2016–2017) shows increased erosion at both sites since 1848 with modern rates of −0.95 and −0.88 m/year. Downcore sedimentology, mass accumulation rates, and shoreline change rates paired with foraminiferal biofacies and identification of local estuarine indicator species, Paratrochammina simplissima, aided in identifying paleo marsh types, their relative proximity to the shoreline, and sediment provenance. The high-energy marsh site transitioned from middle marsh to low marsh in the 1960s, and the low-energy marsh site transitioned later, at the end of the twentieth and early twenty-first century, due to its more protected location. Marsh type transition corresponds chronologically with the coarsening upwards grain-size trend observed and the degradation of Grand Batture Island; since its submergence, signatures of multiple storm event have been preserved downcore.

A model for quantifying the effectiveness of leaky barriers as a flood mitigation intervention in an agricultural landscape

AbstractLeaky barriers have become an important mitigation option within natural flood management to reduce downstream flood risk. Modelling is a key tool to aid in the design of leaky barrier installations for flood mitigation, but there is limited evidence about the accuracy of model representations of the system. Here, the hydrological model SWAT was combined with a water routing model that simulates multiple leaky barriers as permeable sluice gates. Storage behind individual barriers was quantified using barrier dimensions and LIDAR topography. The model was applied to a series of 27 leaky barriers installed as part of a long‐term manipulation experiment into a 11‐km2 intensive lowland agricultural catchment in Leicestershire, England. Evaluation of the model against flow data collected before and after leaky barrier installation and time‐lapse photography taken across storm events at individual barriers demonstrated robust model performance (Nash‐Sutcliffe efficiency and R2 across 19 validation events were 0.84 ± 0.14 and 0.91 ± 0.08, respectively). Empirical and modelling data were then combined to demonstrate that the 17,700 m3 of water storage provided by the 27 leaky barriers reduced peak flows at the catchment outlet by 22 ± 6% and delayed the peak in flow by up to 5 h for 11 storm events recorded after all barriers had been installed. The volume of storage utilised prior to the flood event was a key factor influencing the reduction in peak flow, and a sensitivity analysis indicated that barriers should be permeable to accelerate drain‐down of the barrier and help to mitigate risk from multiple storm events occurring in sequence.

Sediment-Nitrogen (N) connectivity: suspended sediments in streams as N exporters and reactors for denitrification and assimilatory N uptake during storms

Nitrogen (N) pollution in riverine ecosystems has substantial environmental, economic, and policy consequences. Various riverine N removal processes include permanent dissimilatory sinks such as denitrification (Uden) and temporary assimilatory sink such as microbial N uptake (Uassim). Both processes have been extensively evaluated in benthic sediments but only sparsely in the water column, particularly for storm flows producing high suspended sediment (SS) concentrations. Stormflows also increase the sediment bound N (Sed-N) export, and in turn, the overall N exports from watersheds. The balance between N removal by Uden and Uassim vs. Sed-N export has not been studied and is a key knowledge gap. We assessed the magnitude of Uden and Uassim against stormflow Sed-N exports for multiple storm events of varying magnitude and across two drainage areas (750 ha and 15,330 ha) in a mixed landuse mid-Atlantic US watershed. We asked: How do the Uden and Uassim sinks compare with Sed-N exports and how do these N fluxes vary across the drainage areas for sampled storms on the rising and falling limbs of the discharge hydrograph? Mean Uden and Uassim as % of the Sed-N exports ranged between 0.1–40% and 0.6–22%, respectively. Storm event Uassim fluxes were generally slightly lower than the corresponding Uden fluxes. Similarly, comparable but slightly higher Uden fluxes were observed for the second order vs. the fourth order stream, while Uassim fluxes were slightly higher in the fourth-order stream. Both of these N sinks were higher on the falling vs. rising limbs of the hydrograph. This suggests that while the N sinks are not trivial, sediment bound N exports during large stormflows will likely overshadow any gains in N removal by SS associated denitrification. Understanding these N source-sink dynamics for storm events is critical for accurate watershed nutrient modeling and for better pollution mitigation strategies for downstream aquatic ecosystems. These results are especially important within the context of climate change as extreme hydrological events including storms are becoming more and more frequent.

Cloud Modelling of Property-Level Flood Exposure in Megacities

Surface water flood risk is projected to increase worldwide due to the growth of cities as well as the frequency of extreme rainfall events. Flood risk modelling at high resolution in megacities is now feasible due to the advent of high spatial resolution terrain data, fast and accurate hydrodynamic models, and the power of cloud computing platforms. Analysing the flood exposure of urban features in these cities during multiple storm events is essential to understanding flood risk for insurance and planning and ultimately for designing resilient solutions. This study focuses on London, UK, a sprawling megacity that has experienced damaging floods in the last few years. The analysis highlights the key role of accurate digital terrain models (DTMs) in hydrodynamic models. Flood exposure at individual building level is evaluated using the outputs from the CityCAT model driven by a range of design storms of different magnitudes, including validation with observations of a real storm event that hit London on the 12 July 2021. Overall, a novel demonstration is presented of how cloud-based flood modelling can be used to inform exposure insurance and flood resilience in cities of any size worldwide, and a specification is presented of what datasets are needed to achieve this aim.

Predicting Postfire Sediment Yields of Small Steep Catchments Using Airborne Lidar Differencing

AbstractPredicting sediment yield from recently burned areas remains a challenge but is important for hazard and resource management as wildfire impacts increase. Here we use lidar‐based monitoring of two fires in southern California, USA to study the movement of sediment during pre‐rainfall periods and postfire periods of flooding and debris flows over multiple storm events. Using a data‐driven approach, we examine the relative importance of terrain, vegetation, burn severity, and rainfall amounts through time on sediment yield. We show that incipient fire‐activated dry sediment loading and pre‐fire colluvium were rapidly flushed out by debris flows and floods but continued erosion occurred later in the season from soil erosion and, in ∼9% of catchments, from shallow landslides. Based on these observations, we develop random forest regression models to predict dry ravel and incipient runoff‐driven sediment yield applicable to small steep headwater catchments in southern California.

Predicting Compound Coastal Flooding in Embayment-Backed Urban Catchments: Seawall and Storm Drain Implications

Urban coastal flooding is a global humanitarian and socioeconomic hazard. Rising sea levels will increase the likelihood of hydrologic events interacting with high marine water levels. These compound events may, in turn, nonlinearly interact with urban infrastructure, potentially resulting in more extreme coastal flooding events. Here, an integrated Delft3D-FM based numerical modeling framework is used to concomitantly resolve multi-source flood processes (i.e., high marine water levels, precipitation) and infrastructure (e.g., seawalls, storm drains). Hydrodynamic model results are validated with embayment pressure sensor data and photographic observations from historical events. The impact of tide and precipitation phasing are examined. Multiple storm drain characterizations are presented and evaluated. Results show seawall and storm drain infrastructure is fundamental to accurately resolving spatial and temporal flood dynamics. Importantly, coastal management strategies such as raising seawall elevations to mitigate tidal flooding may exacerbate precipitation-based flooding in low-lying urban regions.

Exploring the relationship between urban flood risk and resilience at a high-resolution grid cell scale

The assessment of flood risk and resilience has become increasingly important in recent years for effective urban flood management. While flood resilience and risk are two distinct concepts with unique assessment metrics, there is lack of quantitative analysis and understanding of the relationship between them. This study aims to investigate this relationship at the grid cell level in urban areas. To assess flood resilience for high-resolution grid cells, this study proposes a performance-based flood resilience metric, which is calculated using the system performance curve based on flood duration and magnitude. Flood risk is calculated as the product of maximum flood depth and probability, considering multiple storm events. The case study of Waterloo in London, UK is analyzed using a two-dimensional cellular automata-based model CADDIES, which consists of 2.7 million grid cells (5 m × 5 m). The results indicate that over 2 % of grid cells have risk values exceeding 1. Furthermore, there is a 5 % difference in resilience values below 0.8 between the 200-year and 2000-year design rainfall events, specifically 4 % for the former and 9 % for the latter. Additionally, the results reveal a complex relationship between flood risk and resilience, though decreasing flood resilience generally leads to increasing flood risk. However, this relationship varies depending on the land cover type, with building, green land, and water body cells showing higher resilience for the same level of flood risk compared to other land uses such as roads and railways. Classifying urban areas into four categories, including high risk vs. low resilience, high risk vs. high resilience, low risk vs. low resilience, and low risk vs. high resilience, is crucial in identifying flood hotspots for intervention development. In conclusion, this study provides an in-depth understanding of the relationship between risk and resilience in urban flooding, which could help improve urban flood management. The proposed performance-based flood resilience metric and the findings from the case study of Waterloo in London could be valuable for decision-makers in developing effective flood management strategies in urban areas.

RegiSTORM: channel registration for multi-color stochastic optical reconstruction microscopy

BackgroundStochastic optical reconstruction microscopy (STORM), a super-resolution microscopy technique based on single-molecule localizations, has become popular to characterize sub-diffraction limit targets. However, due to lengthy image acquisition, STORM recordings are prone to sample drift. Existing cross-correlation or fiducial marker-based algorithms allow correcting the drift within each channel, but misalignment between channels remains due to interchannel drift accumulating during sequential channel acquisition. This is a major drawback in multi-color STORM, a technique of utmost importance for the characterization of various biological interactions.ResultsWe developed RegiSTORM, a software for reducing channel misalignment by accurately registering STORM channels utilizing fiducial markers in the sample. RegiSTORM identifies fiducials from the STORM localization data based on their non-blinking nature and uses them as landmarks for channel registration. We first demonstrated accurate registration on recordings of fiducials only, as evidenced by significantly reduced target registration error with all the tested channel combinations. Next, we validated the performance in a more practically relevant setup on cells multi-stained for tubulin. Finally, we showed that RegiSTORM successfully registers two-color STORM recordings of cargo-loaded lipid nanoparticles without fiducials, demonstrating the broader applicability of this software.ConclusionsThe developed RegiSTORM software was demonstrated to be able to accurately register multiple STORM channels and is freely available as open-source (MIT license) at https://github.com/oystein676/RegiSTORM.git and https://doi.org/10.5281/zenodo.5509861 (archived), and runs as a standalone executable (Windows) or via Python (Mac OS, Linux).

Direct runoff hydrograph model’s collation for a Pakistan’s region

Rainfall-Runoff modeling is among the classical applications of hydrology. This paper examines the results of 3 hydrologic approaches, particularly Clark Model, Nash Model, and Geomorphic Instantaneous Unit Hydrograph Model. Assumptions are forwarded for the long run use of the Rawalpindi Division, Pakistan’s Small Dam Organization. The catchment of Shahpur Dam was an area under consideration for the study. The Digital elevation model (DEM) was implicated to measure the Nash and Clark model’s geomorphic parameters. Using ArcGIS, catchment satellite imagery was processed to estimate geomorphological parameters. The models have been applied to multiple storm cases. Geographic Instantaneous Unit Hydrograph (GIUH) model gave direct surface runoff hydrograph, whereas, on measured precipitation excess rainfall hyetograph was obtained. Four types of statistical parameters, namely efficiency of the model (EFF), percentage defect in time to attain peak (PETP), percent defect in attained peak Q (PEP), percentage defect in runoff rate (PEV) are used to check model’s efficiency. The comparison is done between the findings of Clark and Nash GIUH models and the original Clark and Nash models. It was observed that GIUH models are equally good even when optimization is done for Clark and Nash model’s parameters. Since the results obtained from these models are more credible, so, these models can be used in ungauged catchments to estimate the hydrographs.

Tropical cyclone impacts on seagrass-associated fishes in a temperate-subtropical estuary.

Major storms can alter coastal ecosystems in several direct and indirect ways including habitat destruction, stormwater-related water quality degradation, and organism mortality. From 2010–2020, ten tropical cyclones impacted coastal North Carolina, providing an opportunity to explore ecosystem responses across multiple storms. Using monthly trawl and contemporaneous seagrass surveys conducted in Back Sound, NC, we evaluated how cyclones may affect the nursery role of shallow-water biogenic habitats by examining seagrass-associated fish responses within a temperate-subtropical estuary. We employed a general before-after-control-impact approach using trawls conducted prior (before) and subsequent (after) to storm arrival and years either without (control) or with (impact) storms. We examined whether effects were apparent over short (within ~three weeks of impact) and seasonal (May-October) timescales, as well as if the magnitude of storm-related shifts varied as a function of storm intensity. Our findings suggest that the ability of these shallow-water habitats to support juvenile fishes was not dramatically altered by hurricanes. The resilience exhibited by fishes was likely underpinned by the relative persistence of the seagrass habitat, which appeared principally undamaged by storms based upon review of available–albeit limited seagrass surveys. Increasing cyclone intensity, however, was correlated with greater declines in catch and may potentially underlie the emigration and return rate of fish after cyclones. Whether estuarine fishes will continue to be resilient to acute storm impacts despite chronic environmental degradation and predicted increases major tropical cyclone frequency and intensity remains a pressing question.

A New Index to Describe the Response of Geomagnetic Disturbance to the Energy Injection from the Solar Wind

In this paper, we establish a new non-dimensional global geomagnetic disturbance index JpG by applying the spectral whitening method to the horizontal components of geomagnetic fields observed at eight ground-based stations distributed at low and middle latitudes during years 1998 to 2014. This index can describe the development of geomagnetic storms and its relationship with the Dst index has been verified, which gives a correlation coefficient (CC) of about 0.72. We also check the response of JpG to the arrival of upstream solar wind energy based on a proxy that the ring current injection term Q. The variation of JpG in course of geomagnetic storms is similar to the variation of Q, and the recorded minimum values of Q (Qmin) and JpG (JpGmin) for 30 great storms yields a relatively better CC of about 0.82. These results illustrate that JpG can effectively depict the storm evolution and is well related to the associated Q in amplitude, which provides an alternative means of geomagnetic storm forecasting. In addition, we note that the time difference between Qmin and JpGmin, as well as the time difference when JpG recovers from JpGmin to half and/or one-third of its value, are shorter than those of the corresponding Dst index. And especially, for multiple storms that occurred continuously on a short time scale, the recovery of the Dst index to a quiet period level can be affected by the following solar wind energy input, while the JpG index does not and exhibits independently.

Testing the Feature Alignment Technique (FAT) in an Ensemble-Based Data Assimilation and Forecast System with Multiple-Storm Scenarios

Abstract Storm displacement errors can arise from a number of potential sources of error within a data assimilation (DA) and forecast system. Conversely, storm displacement errors can cause issues for storm-scale, ensemble-based systems using an ensemble Kalman filter (EnKF), such as NSSL’s Warn-on-Forecast System (WoFS). A previous study developed a fully grid-based feature alignment technique (FAT) to mitigate these phase errors and their impacts. However, that study developed and tested the FAT for single-storm cases. This study advances that work by implementing an object-based merging and matching technique into the FAT and tests the updated FAT in more complex scenarios of multiple storms. Ensemble-based experiments are conducted with and without the FAT for each of the scenarios. The experiments’ analyses and forecasts of storm-related fields are then evaluated using subjective and objective methods. Results from these idealized multiple-storm experiments continue to reveal the potential benefits of correcting storm displacement errors. For example, running the FAT even once can mitigate the “spinup” period experienced by the no-FAT experiments. The new results also show that running the FAT prior to every DA cycling step generally leads to more skillful forecasts at the smaller scales, especially in earlier-initialized forecasts. However, repeatedly running the FAT prior to every DA step can eventually lead to deterioration in analyses and forecasts. Potential solutions to this problem include using longer cycling intervals and running the FAT prior to DA less often. Additional ways to improve the FAT along with other results are presented and discussed. Significance Statement The purpose of this work is to explore the impact of correcting storm displacements on analyses and forecasts of storms using an ensemble-based data assimilation and forecast system in an idealized framework. Storm displacement errors are a common problem in current operational and experimental storm-scale forecast systems, so understanding their impact on these systems and providing a method to help mitigate them is important. Results from this study indicate that correcting storm displacement errors with the feature alignment technique can greatly improve analyses and forecasts in multiple-storm scenarios. Future work will focus on exploring the impact of correcting storm displacement errors in a real-data, storm-scale data assimilation and forecast system.

Mitigation of Deicing Salt Loading to Water Resources by Transpiration from Green Infrastructure Vegetation

Green infrastructure (GI) protects aquatic ecosystems from stormwater runoff caused by urban development. Bioretention (BR) is a typical GI system wherein stormwater runoff is routed to a soil basin planted with vegetation and has been shown to reduce deicing salt loads in surface runoff, but the removal mechanism of salt is poorly understood. This study explores the potential of different vegetation types to reduce deicing salt released from a BR by transpiration. Six engineered soil media columns were built in a laboratory greenhouse to simulate a 1012 m2 BR basin along Lorton Road, Fairfax County, VA, USA. The effect of vegetation type (Blue Wild Indigo and Broadleaf Cattail) and influent salt concentration on flow volume and salt mass reduction were quantified for multiple storm events. For all storm events, chloride inflow concentrations, and vegetation types, Cl− load reduction ranged from 26.1% to 33.5%, Na+ load reduction ranged from 38.2% to 47.4%, and volume reductions ranged from 11.4% to 41.9%. Different inflow salt concentrations yielded different removal rates of deicing salt, and for a given column, salt removal decreased over sequential storm events. For each influent salt concentration, columns planted with Broadleaf Cattail (BC) performed better for volume and salt mass reductions than columns planted with Blue Wild Indigo (BWI), which in turn performed better than the controls.

A 4000-year paleoenvironmental reconstruction and extreme event record from Laguna Nuxco, Guerrero, Mexico

Reconstructing paleoenvironments on Mexico's Pacific coast is crucial to determine past climate conditions and natural hazard periodicities, vital for predicting future activities. Two sediment cores extracted from Laguna Nuxco, a coastal lagoon in Guerrero, were analyzed to provide a 4000-year paleoenvironmental record. Sediments dominated by shelly clay with low water and organic content, high carbonate content, low Ti/Ca ratio and the presence of Anomalocardia subrugosa suggest that a sheltered bay existed from ~4000–~1300 cal yr BP. During this stage, intermittent beach ridge gaps facilitated the deposition of storm-induced shell deposits, likely from tropical cyclones. Frequent thick shell hash deposition toward the top of the lithologic unit indicates heightened storminess likely from an increase in ENSO activity. An intense tropical cyclone, or perhaps multiple storms during an active period deposited a thick shell hash layer at ~1500 cal yr BP, which helped strengthen the beach ridge system and lead to the modern lagoon phase (~1300 cal yr BP to present), as suggested by the presence of Mytella sp. A more isolated lagoon with significant terrestrial sediment input was reflected by the predominance of lagoonal clay with decreased shell and carbonate contents, increased water and organic contents, and increased Ti/Ca ratio. A quiet period with lower tropical cyclone activity occurred from 1300 to 800 cal yr BP and from 300 cal yr BP to the present, as inferred from no or relatively few shell hash deposits, respectively. A period of hyperactivity occurred from 800 to 300 cal yr BP, as evidenced by seven shell hash layers. During the modern lagoon phase, shell hash deposition was not caused by marine inundation processes, but rather ‘blowout’ events driven by intense precipitation during heightened ENSO activity. Findings shed additional light on Late Holocene environmental changes in a dynamic coastal zone related to regional controlling factors such as tropical cyclone activity, ENSO, and beach ridge development. • A 4000-yr reconstruction at Laguna Nuxco uncovers a transition from bay to lagoon. • Thick shell hash units indicate marine deposition via tropical cyclones. • Thin shell hash layers indicate lagoon “blowouts” from heavy precipitation events. • Activity regimes are largely tied to periods of heightened ENSO. • Notable activity periods occurred from 1500 to 1300 and 800–300 cal yr BP.

Acropora cervicornis and Acropora palmata cultured on a low maintenance line nursery design in The Bahamas.

Acroporid corals are one of the most important corals in the Caribbean because of their role in building coral reefs. Unfortunately, Acropora corals have suffered a severe decline in the last 50 years thus prompting the development of many restoration practices, such as coral nurseries, to increase the abundance of these species. However, many coral nursery designs require constant visits and maintenance limiting restoration to more convenient sites. Additionally, most studies lack the details required for practitioners to make informed decisions about replicating nursery designs. Two line nurseries were monitored for three years in The Bahamas to assess the survival of corals, Acropora cervicornis and Acropora palmata, as well as evaluate the durability and cost effectiveness of the nursery design. Survivorship ranged from 70 to 97% with one location experiencing significantly higher survivorship. The initial year build-out cost was high for a nursery, $22.97 per coral, but each nursery was comprised of specific materials that could withstand high storm conditions. Some unique aspects of the design included the use of longline clips and large-diameter monofilament lines which allowed for easier adjustments and more vigorous cleaning. The design proved to be very durable with materials showing a life expectancy of five years or more. Additionally, the design was able to withstand multiple hurricanes and winter storm conditions with little to no damage. Only two maintenance visits a year were required reducing costs after construction. After three years, this nursery design showed promising durability of materials and survivorship of both Acropora cervicornis and Acropora palmata despite being serviced just twice a year.