Large Storms Research Articles

Toward the future integration of land-to-ocean observing systems to characterize organic carbon fluxes from storms

Storms can drive massive fluxes of carbon from land to ocean via rivers, contributing a large but relatively uncertain term to coastal carbon budgets. To estimate storm-driven aquatic organic carbon fluxes and develop a framework for future observations, we conducted a case study of extreme precipitation followed by river discharge to the Chesapeake Bay watershed, on the east coast of the United States over two decades from June 2001 to July 2021. Anomalous high precipitation events were defined as the top 5% of five-day accumulated precipitation over the watershed and we identified 80 distinct large storms over the Chesapeake Bay region. The top 1% of five-day accumulated river discharge from the United States Geological Survey (USGS) gage stations provided secondary storm criteria to distinguish extreme precipitation that manifest in very high flow: over the same period, we identified 16 storms that led to very high streamflow and combined streamflow with observations of POC and DOC to estimate daily mass flux from the seven major rivers. An extremely large convective storm and subsequent river discharge in July 2018 was studied to determine the association between precipitation, river discharge, and satellite ocean color in combination with in situ observations. MODIS-Aqua satellite derived dissolved organic carbon (DOC) concentration was calculated for all images in 2004–2021 and the estimated DOC values in 2018 were used to assess the feasibility of the optical satellites to monitor coastal storm fluxes of carbon. Using the USGS Load Estimator stream flow model and historical observations, the 2018 event exported approximately 12.9 Gg C particulate organic carbon (POC) and 12.5 Gg C DOC into the bay for 7.2% and 4.4% of the annual total, respectively. The largest storm over the last 20 years exported up to 16% of the annual total POC and 11% of the annual total of DOC from the major rivers over 10 days. However, monitoring the evolution of riverine carbon in estuarine environments from optical satellites alone remains challenging due to cloud cover, thus additional observing platforms assimilated through models are essential.

Unravelling the excitation mechanism of very long-period (VLP) tremors in the Gulf of Guinea: evidence for vibrations of thin surface crustal plates

SUMMARY The Gulf of Guinea exhibits a continuous emission of narrow-band and long-period signals (16, 26 and 27 s) on teleseismic records, yet the underlying excitation mechanism remains unclear. This study establishes a connection between these tremors and the vibration of thin, decoupled crustal plates at unexplored volcanoes in the gulf. We first formulate the damped plate oscillation equation, by incorporating the vibration of the thin surface crustal plate and magma flow in the subsurface sill. The findings reveal that a fundamental-mode vibration with a period of several dozen seconds can be induced by a crustal plate that is less than 1.0 km thick but extends over tens of kilometres in both length and width, given a subsurface sill depth exceeding 10.0 cm. The thin plate hypothesis also allows for excitation of a few overtone modes, but such waves in higher frequencies diminish over long distances, leaving only the monotonous fundamental-mode vibration at teleseismic stations. The long duration of Guinea tremors at each recurrence is attributed to the presence of low viscosity basaltic magma, which influences the damping factor. Direct wave loads at the shallow gulf serve as the primary vibration source, accounting for seasonal variations and recurring patterns. Sporadic energy bursts may also occur due to large storms. Radiation patterns of Guinea tremors are linked to the geometric structure of the thin plate. Our theoretical estimates of tremor spectra closely align with observed data, confirming the model’s accuracy in capturing reported Guinea tremor characteristics. This study provides valuable insights into the origins of very long-period tremors at continental volcanoes.

Investigating the impact of woodland placement and percentage cover on flood peaks in an upland catchment using spatially distributed TOPMODEL

AbstractWoodlands can reduce downstream flooding, but it is not well known how the extent and distribution of woodland affects reductions in peak flow. We used the spatially distributed TOPMODEL to simulate peak flow during a 1 in 50 year storm event for a range of broadleaf woodland scenarios across a 2.6 km2 catchment in Northern England. Woodland reduced peak flow by 2.6%–15.3% depending on the extent and spatial distribution of woodland cover. Cross slope and riparian woodland resulted in larger reductions in peak flow, 4.9% and 3.3% for a 10‐percentage point increase in woodland cover respectively, compared to a 2.7% reduction for woodland randomly located across the catchment. Our results demonstrate that increased woodland cover can reduce peak flows during a large storm event and suggest that targeted placement of woodland can maximise the effectiveness of natural flood management interventions.

Prediction of the SYM‐H Index Using a Bayesian Deep Learning Method With Uncertainty Quantification

AbstractWe propose a novel deep learning framework, named SYMHnet, which employs a graph neural network and a bidirectional long short‐term memory network to cooperatively learn patterns from solar wind and interplanetary magnetic field parameters for short‐term forecasts of the SYM‐H index based on 1‐ and 5‐min resolution data. SYMHnet takes, as input, the time series of the parameters' values provided by NASA's Space Science Data Coordinated Archive and predicts, as output, the SYM‐H index value at time point t + w hours for a given time point t where w is 1 or 2. By incorporating Bayesian inference into the learning framework, SYMHnet can quantify both aleatoric (data) uncertainty and epistemic (model) uncertainty when predicting future SYM‐H indices. Experimental results show that SYMHnet works well at quiet time and storm time, for both 1‐ and 5‐min resolution data. The results also show that SYMHnet generally performs better than related machine learning methods. For example, SYMHnet achieves a forecast skill score (FSS) of 0.343 compared to the FSS of 0.074 of a recent gradient boosting machine (GBM) method when predicting SYM‐H indices (1 hr in advance) in a large storm (SYM‐H = −393 nT) using 5‐min resolution data. When predicting the SYM‐H indices (2 hr in advance) in the large storm, SYMHnet achieves an FSS of 0.553 compared to the FSS of 0.087 of the GBM method. In addition, SYMHnet can provide results for both data and model uncertainty quantification, whereas the related methods cannot.

Quantifying hydrological responses to monsoon‐controlled precipitation across the soil‐groundwater‐stream continuum with long‐term high‐frequency hydrometric monitoring

AbstractStream water, groundwater and soil water in the riparian zone are closely linked. Their responses to rainfall events controlled by monsoon climate are variable and intertwined, which are still not well known. To address this knowledge gap, we established a monitoring transect adjacent to a headwater stream in Huashan Catchment, eastern China, with typical monsoon climate. We monitored precipitation, stream stage, groundwater level and soil moisture content at intervals of maximum 30 min. We then conducted an event‐based analysis of rainfall event characteristics and diverse response metrics, and assessed their correlations and interrelationships through correlation and regression analysis. Our 2‐year monitoring results show that water level responses occurred in most rainfall events. They had smaller threshold of rainfall amount and timing but longer time to peak response. Stream responses exhibited smaller response magnitude and intensities than groundwater responses. Rainfall amount and event duration were the most critical driving factors for groundwater responses. Soil moisture responses varied with depth. Only large storms could propagate into topsoil and generate rapid responses. Middle soil moisture responses had more frequent response occurrence and more variable response magnitude, while deep soil moisture responses had smaller response magnitude, longer time to peak response and larger wetting front movement velocity. Attenuated initial response timing with depth identified preferential flow, reflecting heterogeneity in the soil profile. Monsoon‐controlled heavy rainfall improved hydrologic connectivity in the soil‐groundwater‐stream continuum (SGSC), mediating the influence of heterogeneity on soil moisture responses and potentially contributing more subsurface flow to catchment runoff. Overall, this study aimed to reveal the mechanism of hydrological responses to monsoon‐controlled precipitation across the SGSC.

Mapping Geoelectric Field Hazards in Ireland

AbstractGeoelectric fields are generated at the Earth's surface and can lead to the induction of hazardous geomagnetically induced currents (GIC) in infrastructure like power grids, railways and pipelines during geomagnetic storms. Magnitude and orientation of the geoelectric fields, in relation to the infrastructure, are key features needed to determine the intensity of GIC. Here, we developed the first geoelectric hazard map for the island of Ireland, with the aim of providing detailed information that can help stakeholders mitigate the impact of GICs. The hazard map was developed by modeling and mapping the geoelectric field across Ireland for 28 years (1991–2018) using magnetic field data with magnetotelluric transfer functions. The approach for developing the hazard map calculates the probability of exceeding a hazardous geoelectric field threshold (500 mV/km) during large geomagnetic storms, taking directionality and amplitude into account. We found hazardous geoelectric fields to be mostly localized in areas in the west, south‐west and northern coast. We observed that the geoelectric field have a stronger dominant orientation than the orientation of the geomagnetic field, often constraining the hazardous geoelectric field in particular directions only. We demonstrate a seasonal/diurnal effect is present in the geoelectric field time series. The impact of galvanic distortion was also assessed, and we demonstrate that there is a significant difference in terms of amplitude and direction between both models.

Developing an Australian dust early warning system

In Australia, the perception of large continental-scale dust storms has traditionally been restricted to aspects of ‘inconvenience’ or ‘novelty’. However, as the climate changes, there is an increased likelihood of dust storm activity that puts communities at increased risk. The aim of this study was to explore the need and possibility of developing a dust early warning system for Australia. Through a scoping review of internationally used sand and dust early warning systems, we found that an array of systems exist or were theoretically tested in the Northern Hemisphere. The sensor networks, one of which was operational, were of particular interest because Australia already has an operational air quality network that could be expanded to a dust early warning system. The need for a trans-boundary system is critical for Australia and, therefore, requires a combined approach of expanded sensor network with satellite-based systems to increase validation of any future modelling approach. This improved understanding could inform the development of a dust warning system for multi-socio-economic factors that account for cumulative exposure to small, localised and large continental-sized dust events.

Development of a Bayesian network-based early warning system for storm-driven coastal erosion

Coastal hazards such as flooding and erosion can cause large economic and human losses. Under this threat, early warning systems can be very cost-effective solutions for disaster preparation. The goal of this study was to develop, test, and implement an operational coastal erosion early warning system supported by a particular method of machine learning. Thus, the system combines Bayesian Networks, and state-of-the-art numerical models, such as XBeach and SWAN, to predict storm erosion impacts in urbanized areas. This system was developed in two phases. In the development phase, all information required to apply the machine learning method was generated including the definition of hundreds of oceanic synthetic storms, modeling of the erosion caused by these storms, and characterization of the impact levels according to a newly defined eerosion iimpact index. This adimensional index relates the distance from the edge of the dune/beach scarp to buildings and the height of that scarp. Finally, a Bayesian Network that acted as a surrogate of the previously generated information was built. After the training of the network, the conditional probability tables were created. These tables constituted the ground knowledge to make the predictions in the second phase. This methodology was validated (1) by comparing 6-h predictions obtained with the Bayesian Network and with process-based models, the latest considered as the benchmark, and (2) by assessing the predictive skills of the Bayesian Network through the unbiased iterative k-fold cross-validation procedure. Regarding the first comparison, the analysis considered the entire duration of three large storms whose return periods were 10, 16, and 25 years, and it was observed that the Bayesian Network correctly predicted between 64% and 72% of the impacts during the course of the storms, depending on the area analyzed. Importantly, this method was also able to identify when the hazardous conditions disappeared after predicting potential consequences. Regarding the Regarding the second validation approach, second validation approach, the k-fold cross-validation procedure was applied to the peak of a set of varying storms and it demonstrated that the predictive skills were maximized (63%–72%) when including three nodes as input conditions of the Bayesian Network. In the operational phase, the system was integrated into the architecture of a forecast and early warning system that predicts emergencies in coastal and port zones in Portugal, and the alerts are issued to authorities every day. This study demonstrated that the two-phase approach developed here can provide fast and high-accuracy predictions of erosion impacts. Also, this methodology can be easily implemented on other sandy beaches constituting a powerful tool for disaster management.

Availability of fresh water in cold climate regions

Challenges related to the availability of fresh water in cold climate regions are discussed with emphasis on possible degradation of the quality of drinking water. The permafrost in this region has been consistent in making sure there is little drainage in the ground. This is important, as fresh water is mainly supplied through melting water from snow and ice in the many winter months. Climate change and warmer weather plays a role in heating up the permafrost and increasing the rate of the melting snow. This has an impact on both the amount of water and potentially the drinking quality. In certain cold region areas, fresh water sources are located not far from the shore. With increased shorline erosion due to warming climate, the fresh water supply is in danger of being contaminated by salt water during large storm events. Furthermore, human activities such as road transport and sewage contamination do also have a significant impact on the water’s quality. A bowtie diagram will be used to illustrate the different risk factors and suggestions on how to maintain the quality of fresh water supplies in cold climate areas. The primary focus areas will be Isdammen, near Longyearbyen on Svalbard and Utqiagvik (formerly Barrow), Alaska.

Separating impacts of storm characteristics and mitigation activities on suspended sediment: Implications for sampling to evaluate mitigation success

Mitigation activities are often implemented to reduce anthropogenic sediment loads to sensitive ecosystems, including coral reefs, but effectiveness is rarely documented due to a lack of pre-mitigation data and to the difficulty of separating the impacts of mitigation from those of storm size and intra- and inter-storm variability. Here we present a method using stormwise metrics to quantify the effectiveness of a sediment mitigation project in American Samoa, where an aggregate quarry covering <1 % of a small watershed (drainage area 1.8 km2) generated approximately half of the annual suspended sediment load to the coast. Rainfall, runoff, turbidity and suspended sediment concentrations (SSC, mg L–1), yield (SSY, tons km−2 per unit time) and loads (SSL, tons per unit time) were monitored periodically over a five-year period upstream and downstream of the quarry. Mitigation included revegetation and placement of gravel on roads and processing platforms followed by installation of retention ponds. Storms were identified using automated storm separation, and a simple regression model using maximum area-normalized storm discharge (Qmax) estimated SSC, SSY and SSL for each mitigation stage (post-gravel, post-ponds) for a reference storm sequence. Regression parameters, and SSL and SSC for the reference storm sequence were calculated for a range of storm separation parameters. Monte Carlo sampling of the regression errors was used to calculate a mean and standard deviation of SSL and its change following mitigation. SSC was nearly 100x higher during large storms than during small storms, demonstrating the need to account for storm size to detect impacts of mitigation. After mitigation, SSL downstream of the quarry decreased by 63–82 % for all storms and by 88–92 % for small storms, and SSY downstream of the quarry was similar to SSY from the forested watershed upstream. Mitigation of sediment loads can target a small fraction of a watershed area that accounts for the majority of the human impact. Combining stormwise metrics with simple regression modeling and uncertainty analysis is a simple and novel way to detect and quantify the effectiveness of mitigation activities on sediment loads.

An eight-year climatology of the martian northern polar vortex

The martian polar atmosphere is characterised by the presence of polar vortices, regions of cold and isolated air over the winter poles common to planetary atmospheres. The polar vortices have important meteorological effects via their interactions with atmospheric tracers and the broader circulation. In turn, they have been shown to be affected by large atmospheric dust loadings such as from dust storms. We make use of an extensive reanalysis dataset of Mars’ weather and climate (Open access to Mars Assimilated Remote Soundings; OpenMARS) to investigate the seasonal and interannual behaviour of Mars’ northern polar vortex over eight recent martian years, Mars Years (MY) 28–35 (2006–2021). We find that the northern polar vortex shows a high degree of interannual repeatability in its structure and evolution, with the key exception of during the presence of large (regional and global scale) dust storms. Such storms cause significant perturbations to the northern polar vortex, compressing it towards the pole and reducing its radius. However, not all dust storms have equal impacts. We find that the seasonal timing of large dust storms appears to be the key factor determining their impacts on the northern polar vortex. Storms occurring closer to southern summer solstice have greater impacts than those occurring closer to equinox. We propose that this seasonal dependence is due to the structure of the background meridional circulation, which is at its greatest strength and latitudinal extension around southern summer solstice. The enhancement of this existing circulation by dust-induced heating allows greater impacts if the circulation is already stronger and more latitudinally extended, and impacts are likewise lesser if the storm occurs during a period with a weaker and less latitudinally extended circulation.

Water sources for street trees in mesic urban environments

Street trees support climate resiliency through a variety of pathways, such as offsetting urban heat and attenuating storm water runoff. While urban trees in arid and semiarid ecosystems have been shown to take up water from irrigation, it is unknown where street trees in mesic cities obtain their water. In this study, we use natural abundance stable isotopes to estimate the proportional sources of water taken up by Acer platanoides street trees in Boston, Massachusetts, United States, including precipitation, irrigation, groundwater, and wastewater. We use Bayesian multisource mixing models to estimate water sources by comparing the natural abundance isotopic ratios of hydrogen and oxygen across potential water sources with water extracted from tree stem samples. We find that during the summer of 2021, characterized by anomalously high rainfall, street trees predominantly utilized water from precipitation. Precipitation accounted for 72.3 % of water extracted from trees sampled in August and 65.6 % from trees sampled in September. Of the precipitation taken up by street trees, most water was traced back to large storm events in July, with July rainfall alone accounting for up to 84.0 % of water found within street trees. We find strong relationships between canopy cover fractions and the proportion of precipitation lost to evapotranspiration across the study domain, supporting the conclusion that tree planting initiatives result in storm water mitigation benefits due to utilization of water from precipitation by urban vegetation. However, while the mature trees studied here currently support their water demand from precipitation, the dependency of street trees on precipitation in mesic cities may lead to increased water stress in a changing climate characterized by a higher frequency and severity of drought.

Modeling Morphodynamic Impacts and Optimization of Marine Hydrokinetic Arrays in Shallow Offshore Environments

Marine hydrokinetic (MHK) devices hold the promise of expanding renewable energy production by tapping into the power of waves and currents for electricity generation. However, these devices remain in the developmental stage, necessitating research to understand their environmental impacts, lower operational costs, and prevent equipment failures. In this study, we investigate various MHK array configurations to gain insights into their effects on wave patterns, water flow, and sediment conditions, considering both short-term and long-term morphodynamic changes under average and extreme conditions in shallow offshore environments. Our objectives encompass understanding the influence of mean and extreme environmental conditions on MHK devices, evaluating their impact on the bathymetry of the ocean floor, and exploring the role of different array configurations in morphodynamic evolution. Our findings, based on modeling these devices as static lumps, reveal that sediment erosion downstream of MHKs increases by 50% after one year of average conditions. When accounting for the absorption of 30% of the energy by MHK devices, downstream sediment deposition surges by almost 125%. Moreover, alterations in MHK arrays, such as spacing, size, and number, result in noticeable changes in sedimentation magnitudes during storm conditions. While long-term mean wave conditions have minimal effects on sedimentation, extreme wave conditions, akin to large storm events, bring about significant alterations in ocean floor bathymetry, potentially leading to costly maintenance of the MHK arrays. Our research provides a valuable framework for site analysis, enabling the estimation of maintenance needs and the optimization of array configurations to minimize sedimentation-related issues.

Component Assessment of the Electric Transmission Grid to Hurricanes

AbstractThe increased frequency and intensity of extreme weather events from climate change necessitates understanding impacts on critical infrastructure, particularly electrical transmission grids. One of the foundational concepts of a grid's resilience is its robustness to extreme weather events, such as hurricanes. Resilience of the electric grid to high wind speeds is predicated upon the location and physical characteristics of the system components. Previous modeling assessments of electric grid failure were done at the systems level with assumptions on location and type of specific components. To facilitate more explicit adaptation metrics, accurate component‐level information is needed. In this study, we build and utilize a data set of location, physical characteristics, and age of transmission structures for nine counties in the Florida Panhandle. These component characteristics were then simulated for failure under a variety of scenarios using fragility curves. Eight hurricanes were modeled using Hazus from the Federal Emergency Management Administration and the resulting impact to the network was assessed. The network was generated using the transmission lines and towers, showing increasing impacts to network efficiency with larger storms. Although modern transmission structures are built under the more stringent extreme wind loading construction standards, the prevalence of older, wooden transmission structures throughout the region poses a substantial risk to reliable electricity transmission during tropical cyclone events from the Gulf of Mexico.

Lateral flow in bedrock dominates baseflow and flood flow in a catchment underlain with permeable sedimentary rock in the high‐relief Chichibu Mountains

AbstractDetermining the mechanisms of runoff generation is essential to land and water resource conservation, but lateral flow processes in high‐relief mountain areas remain unclear due to the difficulty to observe and measure these processes. This study demonstrates that lateral flow in bedrock dominates both high and low flows in a catchment underlain with permeable sedimentary bedrock in the high‐relief Chichibu Mountains. Through measurement of precipitation, discharge, soil, and stream temperatures and tracers (SiO2 and δ18O) in the first‐order stream Bakemonozawa (0.51 km2) and comparison of its water balance with the nested Kawamata catchment (93.58 km2) i, we showed that 831–1012 mm/year or 47%–53% of annual precipitation leaked from Bakemonozawa. The streamflow was discharged from an average depth of 2.3 m or deeper, which were deeper than the measured deepest soil near the gauge. Furthermore, stream temperature decreased significantly during a large summer storm, demonstrating that the relative contribution of the deep bedrock flowpath increased. Large isotopic variation in precipitation was dampened in the stream, suggesting that most baseflow and some higher flows arose from groundwater storage with a constant isotopic ratio due to dispersion and mixing. The discharge–SiO2 relationship suggested at least two groundwater sources, namely, shallow and deep bedrock groundwater, with different runoff responses. Discharge increased dramatically when the catchment became wet, coinciding with the deep bedrock groundwater contribution. Further research is needed to clarify the hydrological characteristics of bedrock.

A Probabilistic Prediction of Rogue Waves from a WAVEWATCH III Model for the Northeast Pacific

Abstract Rogue waves are stochastic, individual ocean surface waves that are disproportionately large compared to the background sea state. They present considerable risk to mariners and offshore structures especially when encountered in large seas. Current rogue wave forecasts are based on nonlinear processes quantified by the Benjamin Feir index (BFI). However, there is increasing evidence that the BFI has limited predictive power in the real ocean and that rogue waves are largely generated by bandwidth-controlled linear superposition. Recent studies have shown that the bandwidth parameter crest–trough correlation r shows the highest univariate correlation with rogue wave probability. We corroborate this result and demonstrate that r has the highest predictive power for rogue wave probability from the analysis of open ocean and coastal buoys in the northeast Pacific. This work further demonstrates that crest–trough correlation can be forecast by a regional WAVEWATCH III wave model with moderate accuracy. This result leads to the proposal of a novel empirical rogue wave risk assessment probability forecast based on r. Semilogarithmic fits between r and rogue wave probability were applied to generate the rogue wave probability forecast. A sample rogue wave probability forecast is presented for a large storm 21–22 October 2021. Significance Statement Rogue waves pose a considerable threat to ships and offshore structures. The rare and unexpected nature of rogue wave makes predicting them an ongoing and challenging goal. Recent work based on an extensive dataset of waves has suggested that the wave parameter called the crest–trough correlation shows the highest correlation with rogue wave probability. Our work demonstrates that crest–trough correlation can be reasonably well forecast by standard wave models. This suggests that current operational wave models can support rogue wave prediction models based on crest–trough correlation for improved rogue wave risk evaluation.

Using high-frequency monitoring data to quantify city-wide suspended-sediment load and evaluate TMDL goals

Excess sediment is a common reason water bodies in the USA become listed as impaired resulting in total maximum daily loads (TMDL) that require municipalities to invest millions of dollars annually on management practices aimed at reducing suspended-sediment loads (SSLs), yet monitoring data are rarely used to quantify SSLs and track TMDL progress. A monitoring network was created to quantify the SSL from the City of Roanoke, Virginia, USA (CoR), to the Roanoke River and Tinker Creek and help guide TMDL assessment and implementation. Suspended-sediment concentrations were estimated between 2020 and 2022 from high-frequency turbidity data using surrogate linear-regression models. Sixty-one percent of the total three-year SSL resulted from five large storm events. The average suspended-sediment yield from the CoR (58.1 metric tons/km2/year) was similar to other urban watersheds in the Eastern United States; however, the yield was nearly five times larger than the TMDL allocation (12.2 metric tons/km2/year). The TMDL allocated load was modeled based on a predominantly forested reference watershed and may not be a practical target for highly impervious watersheds within the CoR. The TMDL model used daily input data which likely does not capture the full range of SSLs during storm events, particularly from flashy urban streams. The average SSL following the five large storm events doubled that of the CoR’s annual allocated load from the TMDL. The results of this study highlight the importance of using high-frequency monitoring data to accurately estimate SSLs and evaluate TMDLs in urban areas.

Contrasting Storm‐Time Radiation Belt Events With and Without Dropouts—The Importance of CME Shocks

AbstractThis study compares three different geomagnetic storms (designated as storms 1, 2, and 3) observed by NASA's Van Allen Probes within the spacecraft's first 50 days in orbit. These storms were Coronal Mass Ejection (CME)‐driven with minimum DST around −138, −100, and −106 nT, respectively. Storms 1 and 2 occurred following the arrival of fast‐forward shocks, which compressed the magnetopause inward to about 6.5 and 7.5 RE, respectively, as a result of the increase in solar wind dynamic pressure and density. The inward magnetopause motion helped contribute to a rapid depletion of MeV electrons across the entire outer belt. For the third storm, however, there was little or no dropout of MeV electrons in the heart of the outer belt during the storm main phase. This third storm was generated by a CME without an associated shock, and the magnetopause actually moved outward at the start of the storm, suppressing loss of electrons through the outer boundary. The study reveals that under certain solar wind driving conditions radiation belt electron dropouts may not occur, even during large geomagnetic storms (Dst_min &lt; −100 nT).

Sampling frequency, load estimation and the disproportionate effect of storms on solute mass flux in rivers

Riverine discharge (Q) and dissolved concentrations (C) dictate solute mass export from watersheds. Commonly Q is tracked at a much higher frequency than C for most major solutes, leading to the necessity of load estimation algorithms which are often based on sparse data. The result is that the disproportionate effects of short-duration events (e.g., storms) on solute mass fluxes are poorly known. Here we use novel lab-in-the-field instrumentation to compare high temporal-resolution (∼30 min to 7 h) datasets of major ion chemistry collected over a year of continuous monitoring in three watersheds ranging over four orders of magnitude in drainage area. In these diverse settings, we quantify the errors associated with common load estimation algorithms and reduced sampling frequencies. When sample frequencies are coarsened, the mass flux of solutes which are diluted by storm events (i.e., Ca2+, Mg2+, Na+, Cl− and SO42−) are systematically overestimated, while nutrients which become mobilized by these events (K+ and NO3−) are underestimated. This is most pronounced in the largest river, and strongly tied to the increasing likelihood that storm events are missed as sampling frequencies decrease. Coarsening our high-resolution data to monthly sampling frequency yields an average overestimate of 8 % for Na+ and an average underestimate of 32.5 % for K+ across the three watersheds, illustrating clear implications for estuary and coastal water eutrophication, chemical weathering budgets, and agricultural land management practices. SynopsisA new ‘lab-in-the-field’ technology produces continuous high-frequency records of the full suite of major ions in rivers. These data highlight the disproportionate effect of large storms on catchment solute exports and the error associated with temporally coarse monitoring.

Modeling bioinfiltration surface dynamics through a hybrid geomorphic-infiltration model

Abstract Bioinfiltration systems are an increasingly prevalent mechanism for urban stormwater mitigation. One major challenge for the sustainability of bioinfiltration systems is erosion and channelization due to high bed shear stresses developed during large storm events. Sedimentation within these systems could also impact their performance as fine sediment may clog pathways necessary for infiltration. Understanding the geomorphology, shear stress, and sediment flux in the system can help predict maintenance needs associated with erosion and deposition. The current study introduces a framework for addressing this problem by combining a sediment transport model, FaSTMECH, with the Green-Ampt infiltration model. A comparison of observed and predicted ponding depths shows very good agreement (median Nash–Sutcliffe efficiency coefficient = 0.93) and demonstrates the ability of this novel framework in predicting the hydraulics and morphology within a bioswale bioinfiltration system. The framework introduced in this study opens the door to understanding sediment transport dynamics within a bioswale, which has the potential to advance planning and design to minimize impacts due to excessive erosion or deposition within bioswale bioinfiltration systems.