Storm Events Research Articles

Dislodgement and mortality challenges when restoring shallow mussel beds (Mytilus edulis) in a Danish estuary

In recent decades, mussel beds in the northern Atlantic and Scandinavia have declined rapidly in extent due to anthropogenic impacts, similar to many other marine habitats. In this study, a large‐scale restoration experiment was conducted to identify major challenges that arise during restoration efforts on shallow subtidal mussel beds. Suspension‐grown mussels (Mytilus edulis) were relayed in two different treatments either directly on bare bottom sandy sediments, or on coir nets (Net), used as a proxy for suitable byssal attachment substrate. The treatments were monitored for 1.5 years and coverage (%), biomass (WW), and population dynamics were quantified. Two main challenges of shallow bed restoration were identified: (1) Lack of suitable attachment substrate resulting in dislodgment of individuals during storm events. The Net treatment had significantly higher coverage and biomass of Mytilus at the end of the monitoring period, clearly demonstrating the importance of suitable substrate at physically exposed locations. (2) High mortality of juvenile mussels. Population dynamics revealed a high mortality of juvenile Mytilus, which resulted in almost complete loss of relayed Mytilus individuals less than 30 mm within the first season. This was most likely due to high meso‐predator densities, as a result of declining top‐predator populations. The high mortality of juvenile Mytilus prevented successful annual recruitment, thereby making the population unsustainable long‐term. Both challenges need to be addressed to create stable beds during restoration. Additionally, the experiment demonstrated the viability of using suspension‐grown Mytilus as a seed‐source when restoring mussel beds.

Iber-PEST: Automatic calibration in fully distributed hydrological models based on the 2D shallow water equations

Calibrating physically based hydrological models manually is time-consuming and challenging. Automatic calibration tools have become prevalent in these models; however, its effective use requires not only a deep knowledge of the calibration procedure itself, but also understanding the structure of the model input and output files. In this study, we introduce Iber-PEST, a novel framework combining the parameter estimation and uncertainty analysis package, PEST, with Iber, a 2D model based on the shallow water equations. We demonstrate its capabilities by successfully calibrating eight storm events in northwestern Spain's basins, achieving promising results (mean NSE equal to 0.84). Furthermore, by applying the iterative ensemble smoother included in PEST, we demonstrate the feasibility to use the calibration data generated by Iber-PEST with alternative PEST algorithms not directly integrated in the Iber-PEST framework. This work successfully addresses the significant implementation barrier associated with the automatic calibration of Iber hydrological models with PEST.

Multi-year time series of daily solute and isotope measurements from three Swiss pre-Alpine catchments

Time series analyses of solute concentrations in streamwater and precipitation are powerful tools for unraveling the interplay of hydrological and biogeochemical processes at the catchment scale. While such datasets are available for many sites around the world, they often lack the necessary temporal resolution or are limited in the number of solutes they encompass. Here we present a multi-year dataset encompassing daily records of major ions and a range of trace metals in both streamwater and precipitation in three catchments in the northern Swiss Pre-Alps. These time series capture the temporal variability observed in solute concentrations in response to storm events, snow melt, and dry summer conditions. This dataset additionally includes stable water isotope data as an extension of a publicly available isotope dataset collected concurrently at the same locations, and together these data can provide insights into a range of ecohydrological processes and enable a suite of analyses into hydrologic and biogeochemical catchment functioning.

Hydroclimatic non-stationarity drives stream hydrochemistry dynamics through controls on catchment connectivity and water ages

We used data from 17 years of routine stream flow sampling to characterise hydrochemical dynamics in the Girnock Burn, an internationally important long-term catchment monitoring site. We combined hydrochemical time series analysis with hydrological modelling to understand short- and long-term dynamics in relation to dominant runoff processes. Isotopic tracer data employed within the model enabled flux tracking of water as it transited the catchment, facilitating water age estimates. The stream drains an upland 31 km2 catchment with complex topography and variable geology, which influences soil distributions; peaty soils dominate valley bottoms which receive drainage from more permeable podzolic soils on steeper slopes. Short-term hydrochemical dynamics largely reflect the relative dominance of hydrological sources: summer low flows are dominated by alkaline groundwater high in weathering products (Ca2+, Mg2+, SiO2) whilst high flows generated by overland flow from peaty soils are more acidic, DOC enriched, and dominated by atmospheric solutes (Na+, Cl-, SO42-). Baseflows are dominated by older groundwater (>∼3years) in contrast to storm runoff dominated by younger (<∼0.5 years), near-surface, waters. Usual seasonal distributions of stream flows, with high flows in winter and summer low flows, result in strong seasonality in most solutes. However, coupled effects of hydroclimatic variability and catchment heterogeneity dictates marked scatter in flow-concentration relationships for most determinands. A long-term decreasing trend was evident for SO42-concentrations, reflecting continuing effects of reductions in atmospheric deposition of pollutants from coal burning. Reductions in large storm event frequency post-2016 and protracted periods of low flows in the droughts of 2018 and 2022 have resulted in the catchment becoming dominated by low flows, derived from older groundwater with more alkaline chemistry for longer periods. Changing climatic conditions and stream flow responses seem likely to continue to cause changes in stream water quality and associated ecosystem services of the Girnock Burn and similar upland catchments.

Stormwater runoff microplastics: Polymer types, particle size, and factors controlling loading rates

Stormwater runoff is a pathway of entry for microplastics (MPs, plastics <5 mm) into aquatic ecosystems. The objectives of this study were to determine MP size, morphology, chemical composition, and loading across urban storm events. Particles were extracted from stormwater samples collected at outfall locations using wet peroxide oxidation and cellulose digestion followed by analysis via attenuated total reflectance (ATR) FTIR. Concentrations observed were 0.99 ± 1.10 MP/L for 500–1000 μm and 0.41 ± 0.30 MP/L for the 1000–5000 μm size ranges. Seventeen different polymer types were observed. MP particle sizes measured using a FTIR-microscope camera indicated non-target size particles based on sieve-size classification, highlighting a potential source of error in studies reporting concentration by size class. A maximum MP load of 38.3 MP/m2 of upstream catchment was calculated. MP loadings had moderate correlations with both rainfall accumulation and intensity (Kendall τ = 0.54 and 0.42, respectively, both p ≤ 0.005). First flush (i.e. rapid wash-off of pollutants from watershed surfaces during rainfall early stages) was not always observed, and antecedent dry days were not correlated with MP abundance, likely due to the short dry periods between sampling events. Overall, the results presented provide data for risk assessment and mitigation strategies.

Inlet-adjacent beach and shoreline variability at decadal scales

Beaches provide storm protection and economic benefit for coastal communities and also host important habitats for keystone species, especially in the state of Florida. However, sea level rise, coastal construction, and intensifying storms are resulting in coastal erosion, which often requires informed mitigation and management strategies. This study aims to explore decadal-scale trends in morphology change and shoreline variability of inlet-adjacent beaches in Jupiter, Florida. Data from three locations over a period of three decades were analyzed to determine volumetric and contour changes above the shoreline (0 m elevation) and across the entire profile. The largest variability in decadal morphology change was measured at beaches closest to Jupiter Inlet. Substantial shoreline and beach changes were also associated with periods of higher storm activity. Variability in storm events, nourishment trends, and longshore sediment transport also influenced decadal morphologic change. To best adapt to coastal change and develop comprehensive beach-inlet management plans, it is imperative to understand past trends of shoreline variability and beach-nearshore erosion and accretion patterns. Results of this study demonstrate an approach to elucidate decadal patterns in morphology changes and drivers of change.

Measuring and modeling soil moisture and runoff at solar farms using a disconnected impervious surface approach

AbstractGround‐mounted photovoltaic sites are often treated as impervious surfaces in stormwater permits. This ignores the pervious soils beneath and between solar arrays and leads to an overestimation of runoff. Our objective was to improve solar farm stormwater hydrology models by explicitly considering the disconnected impervious nature of solar design and site characteristics. Experimental sites established on utility scale solar farms in Colorado, Georgia, Minnesota, New York, and Oregon had perennial vegetative plantings with mean precipitation ranging from 40.6 to 124.5 cm, and soil texture ranging from loamy sand to clay. Soil moisture measurements were collected beneath arrays, under drip edges, and in the vegetated area between arrays at each site. Hydrus‐3D models for soil moisture and stormwater hydrology were developed that accounted for precipitation falling on solar panels, drip edge redistribution of rainfall, infiltration, and runoff in the pervious areas between solar arrays and beneath panels. Drip edge runoff averaged 3‐ to 10‐times incident precipitation at the New York and Minnesota sites, respectively. Root mean square error values between measured sub‐hourly soil moisture and predicted moisture for large measured single storm events averaged 0.029 across all five sites. Predicted runoff depths were strongly affected by precipitation depth, soil texture, soil profile depth, and soil bulk density. Runoff depths across the five experimental sites averaged 13%, 25%, and 45% of the 2‐, 10‐, and 100‐year design storm depths, clearly showing that these solar farms do not behave like impervious surfaces, but rather as disconnected impervious surfaces with substantial infiltration of runoff in the vegetated areas between and beneath solar arrays.

Sediment resuspension and transport due to synoptic winter winds in the Bohai Sea

The Bohai Sea is the receiving basin of the Yellow River (Huanghe), one of the largest contributors of global terrestrial materials to the sea. An area of fine-grained sediment deposition extends northeast of the Yellow River and its formation mechanism is still unclear. A calibrated, high-resolution coupled model of the Bohai and Yellow Seas was used to investigate sediment transport pathways in the region under the influence of strong and varying winds in winter. Numerical results highlight the importance of storm events in modifying the regional sediment resuspension and transport. During winter, the prevailing winds are typically from the northeast and northwest, interspersed by relaxation periods with weak winds. Winds influenced the currents and sediment movement in the Bohai Sea through several processes: local wind waves and resuspension, coastal-trapped waves in the Bohai Sea, and remotely forced coastal-trapped waves in the Yellow Sea. During northwesterly winds, sediment off the Yellow River Delta was mainly transported eastward along the south coast of Bohai Sea and escaped the Bohai Sea through the southern Bohai Strait. When northeasterly winds prevailed, sediment transport split into three branches: westward into the Bohai Bay, northeastward to the central Bohai Sea, and eastward along the south coast of Bohai Sea. Modeling results driven by synoptic winds indicate a more complex sediment transport pattern than previously understood with monthly average wind forcing. These findings challenge the traditional view of predominantly eastward sediment transport during winter and help to explain the observed grain size distribution in the Bohai Sea, in particular the accumulation of fine-grained sediments northeast of the Yellow River.

Повторяемость штормового волнения в море Лаптевых, Восточно-Сибирском и Чукотском морях

The analysis of number of storms in the Laptev Sea, East Siberian and Chukchi Seas according to modeling data from 1979 to 2021, taking into account the ice situation, is presented. The number of storm events is calculated based on the Peak Over Threshold method. The significant wave height of 2-5 m was taken as the threshold values. The number of storms with a wave height of more than 2 m in the Laptev Sea averages about 20 times a year, more than 3 m – about 8 times. In the East Siberian Sea, cases with a wave height of more than 2 m are observed on average about 23 times a year. In 1990, a local maximum of 28 storms with a height of more than 3 m was observed. Cases with a wave height of more than 2 m in the Chukchi Sea are observed on average about 39 times a year, more than 3 m – about 24 times. The trends in number of storms for all the seas under consideration are positive and significant.

Longitudinal stream synoptic (LSS) monitoring to evaluate water quality in restored streams.

Impervious surface cover increases peak flows and degrades stream health, contributing to a variety of hydrologic, water quality, and ecological symptoms, collectively known as the urban stream syndrome. Strategies to combat the urban stream syndrome often employ engineering approaches to enhance stream-floodplain reconnection, dissipate erosive forces from urban runoff, and enhance contaminant retention, but it is not always clear how effective such practices are or how to monitor for their effectiveness. In this study, we explore applications of longitudinal stream synoptic (LSS) monitoring (an approach where multiple samples are collected along stream flowpaths across both space and time) to narrow this knowledge gap. Specifically, we investigate (1) whether LSS monitoring can be used to detect changes in water chemistry along longitudinal flowpaths in response to stream-floodplain reconnection and (2) what is the scale over which restoration efforts improve stream quality. We present results for four different classes of water quality constituents (carbon, nutrients, salt ions, and metals) across five watersheds with varying degrees of stream-floodplain reconnection. Our work suggests that LSS monitoring can be used to evaluate stream restoration strategies when implemented at meter to kilometer scales. As streams flow through restoration features, concentrations of nutrients, salts, and metals significantly decline (p < 0.05) or remain unchanged. This same pattern is not evident in unrestored streams, where salt ion concentrations (e.g., Na+, Ca2+, K+) significantly increase with increasing impervious cover. When used in concert with statistical approaches like principal component analysis, we find that LSS monitoring reveals changes in entire chemical mixtures (e.g., salts, metals, and nutrients), not just individual water quality constituents. These chemical mixtures are locally responsive to restoration projects, but can be obscured at the watershed scale and overwhelmed during storm events.

Assessment of rainfall-derived inflow and infiltration in sewer systems with machine learning approaches.

Rainfall-derived inflow/infiltration (RDII) modelling during heavy rainfall events is essential for sewer flow management. In this study, two machine learning algorithms, random forest (RF) and long short-term memory (LSTM), were developed for sewer flow prediction and RDII estimation based on field monitoring data. The study implemented feature engineering for extracting physically significant features in sewer flow modelling and investigated the importance of the relevant features. The results from two case studies indicated the superior capability of machine learning models in RDII estimation in the combined and separated sewer systems, and LSTM model outperformed the two models. Compared to traditional methods, machine learning models were capable of simulating the temporal variation in RDII processes and improved prediction accuracy for peak flows and RDII volumes in storm events.

New data-based analysis tool for functioning of natural flood management measures reveals multi-site time-variable effectiveness

Temporary storage areas (TSAs) are a type of Natural Flood Management measure or nature-based solution that can provide additional storage during flood events by intercepting and attenuating surface runoff. Pressures on land use and an increase in climate change induced storms means there is a need to create additional storage within multifunctional rural landscapes. Implementation of small-scale TSAs is slowly gaining momentum, but practitioners still require further evidence on their functioning during different storm events. Here we present the TSA Drainage Rate Analysis tool (TSA-DRA tool), a novel data-based mechanistic method that only requires rainfall and TSA water level data to describe individual TSA drainage rates. We developed and then used the TSA-DRA tool to perform a multi-site assessment of different TSAs, allowing comparisons of TSA functioning across different types or time-variable factors. TSA design and outlet were found to be the dominant controls on drainage rates when the feature is full. Meanwhile, time-variable differences in functioning were more evident at lower water levels, when soil infiltration was the main TSA outflow. Results from a modelling experiment using observed time-variable TSA drainage rates suggested that these can impact the TSAs flood mitigation effectiveness. Specifically, for a particular event, soil conditions of a TSA in NE Scotland were more effective during spring than winter in attenuating surface runoff. Understanding spatial and temporal differences in TSA drainage rates will help optimise existing and future TSA designs, ensuring small-scale headwater TSAs are successfully integrated within rural catchments to mitigate an increasing exposure to hydrological extremes.

Storm-driven hydrological, seasonal, and land use/land cover impact on dissolved organic matter dynamics in a mid-Atlantic, USA coastal plain river system characterized by 21 T FT-ICR mass spectrometry

Introduction: Dissolved organic matter (DOM) as primary and secondary energy sources can be transported via rivers to estuaries and impact coastal biogeochemical cycles. Storm-induced high discharge events can alter the dominant river flow paths and enhance leaching of shallow organic-rich soil layers, leading to elevated terrestrial DOM export. Land use/land cover (LULC) and associated anthropogenic interventions (including artificial reservoirs and agricultural irrigation) can control sources and transformation processes of exported DOM along with hydrologic factors. The relative significance of LULC, hydrological factors, and temperature variations with seasons will differ depending on geographical locations and complicate their incorporation in biogeochemical models of DOM dynamics. This study investigates the role of LULC, seasonality, and storm events on DOM concentrations and molecular composition in the Murderkill River system.Method: Surface water samples were collected seasonally and before/after storm events from 6 sites representing forested, agricultural, and developed LULC units. The DOM was characterized via parallel factor analysis of excitation-emission matrix data and electrospray ionization 21 T Fourier-transform ion cyclotron resonance mass spectrometry to determine potential DOM sources and enable the development of a conceptual model for DOM dynamics in rivers impacted by anthropogenic reservoirs.Result and Discussion: Our results suggest that storm-induced shallow and overland flow paths can increase surface-vegetation/plant-litter derived DOM based on atomic ratios associated with specific biogenic precursors (i.e., lignin, tannins, and/or oxygenated aromatic DOM), particularly in winter when autochthonous production was suppressed due to reduced temperatures. We further demonstrate that the damming effects of artificial reservoirs enhance the role of seasonal patterns of autochthonous production, disrupting storm-shunt process and stimulating significantly more bio-produced DOM export during spring and summer (i.e., tryptophan/tyrosine-like. N- and S- containing, phytoplankton-derived compounds). Collectively, these results demonstrate how artificial reservoirs alter the characteristics of DOM exported from rivers with implications for understanding carbon export and fate at river-estuary interfaces.

Use of Artificial Intelligence Modelling for the Dynamic Simulation of Urban Catchment Runoff

The complex topography and inherent nonlinearity affiliated with influential hydrological processes of urban catchments, coupled with limited availability of measured data, limits the prediction accuracy of conventional models. Artificial Neural Network models (ANNs) have displayed commendable progress in recognising and simulating highly complex, non-linear associations allied with input-output variables, with limited comprehension of the underlying physical processes. Therefore, this paper investigates the effectiveness and accuracy of ANN models, in estimating the urban catchment runoff, employing minimal and commonly available hydrological data variables – rainfall and upstream catchment flow data, employing two powerful supervised-learning-algorithms, Bayesian-Regularization (BR) and Levenberg-Marquardt (LM). Gardiners Creek catchment, encompassed in Melbourne, Australia, with more than thirty years of quality-checked rainfall and streamflow data was chosen as the study location. Two significant storm events that transpired within the last fifteen years - the 4th of February 2011 and the 6th of November 2018, were nominated for calibration and validation of the ANN model. The study results advocate that the use of the LM-ANN model stipulates accurate estimates of the historical storm events, with a stronger correlation and lower generalisation error, in contrast to the BR-ANN model, while the integration of upstream catchment flow alongside rainfall, vindicate for their collective impact upon the dynamics of the flow being spawned at the downstream catchment locations, significantly enhancing the model performance and providing a more cost-effective and near-realistic modelling approach that can be considered for application in studies of urban catchment responses, with limited data availability.

Aircraft observations of aerosol and BC during the East Asian dust storm event: Vertical profiles, size distribution and mixing state

The vertical structure of meteorological elements, black carbon (BC), and aerosols were in situ characterized by the King Air 350 aircraft measurements over the northeast of China during an East Asian dust storm event from April 10 to 13, 2023. The main causes of this dust storm event were the strong winds generated by the Mongolian cyclone, the cold front on the ground provided dynamic conditions, and the unstable atmospheric state provided thermal conditions and less precipitation in the dust source areas led to exposed the ground surface. The number concentrations of BC and aerosol had significant vertical distribution. The number concentration of aerosol in the dust storm (DS) layer increased rapidly with height, while the number and mass concentrations of BC were higher and varied slightly with height. The BC core mass median diameters (MMD) first increased with height, reaching a maximum value of 0.24 μm at 3000 m; and it rapidly decreased to 0.18 μm at 3200 m and then varied slightly with height above 3200 m. BC's bulk relative coating thickness (coated diameter divided by uncoated core diameter Dp/Dc) first decreased with height, reaching a minimum value of 1.16 at 3000 m, then increased with height, reaching a maximum of 1.38 (4250 m). A wind shear layer (7.9 m s−1 at 1850 m) with a sudden drop in temperature (the cooling rate can reach 7.4 °C·(100 m)−1) at 1900 m hinders the transportation of dust particles to the ground, allowing the dust particles to be transported at long-term distances. There were two stratifications in the dust storm layer: DS1 (1700–3000 m, the vertical wind speed was downward, the horizontal wind speed was small) and DS2 (3000–4500 m, the vertical wind speed was upward, the horizontal wind speed was large). The distinct atmospheric flow field conditions between DS1 and DS2 led to different BC and aerosol pollution characteristics. In DS2, the number concentrations of BC and aerosol were higher, the aging ratio of BC particles was higher, and the size of the core particle of BC was smaller. The concentrations of BC, aerosol, Dp/Dc, and MMD in DS2 were 1.1, 59.0, 1.02, and 0.84 times higher than those in DS1, respectively. Dust particles transported over long distances were primarily small. The number concentrations of aerosols at 0.1–0.12 μm and 0.1–0.24 μm were higher in DS1 and DS2, respectively. Still, the number concentration of aerosol at 0.12–0.28 μm in DS1 was lower than in the planetary boundary layer (PBL) and residual layer (RL).

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.

Microfacies and palaeoenvironments of late Cisuralian and Guadalupian (Early to Middle Permian) alatoconchid-bearing limestone in Loei fold belt, Indochina Terrane

This study presents the first record of Early Permian alatoconchid bivalves from Thailand, the Erawan section, in the north of Loei fold belt with prolific fusulines in association with gastropods, and brachiopods. Fusuline taxa are dominated by Pseudofusulina sp. with Darvasites sp., Staffella cf. labanalensis, Staffella sp., Nankinella sp., Neofusulinella sp., Schubertella sp., Pamirina darvasica, Pseudoendothyra sp. and others, indicating an Artinskian age. Fusuline wackestone with common, well-preserved smaller foraminifers, Pseudovermiporella sp. and ostracods indicates a restricted lagoon environment with low to moderate water circulation. These fossils, along with well-preserved, articulated alatoconchids in life-position, suggest the autochthonous nature of the deposits in an intertidal environment.In the central part of the fold belt, the Pak Chong section exhibits intervals of alatoconchid biostromes and coquinites. Carbonate build-ups of the alatoconchid biostromes with gregarious bivalves, in life-position embedded in a micritic wackestone matrix, suggest their preferred life habit and environment. The bivalve coquinites containing packed bivalve shells and fragments, however, indicate a high-energy event. This rock fabric along with common fusuline storm sheets suggests occasional storm events in a restricted, with moderate water circulation to open lagoon environment. Fusulines are predominated by staffellids and others including Staffella sphaerica, Staffella sp., Sphaerulina croatica, Pisolina subsphaerica, Nankinella sp., Neoschwagerina simplex, Presumatrina sp., Afghanella sp. and others, indicating a Wordian (Middle Permian) age.In the southern portion of the fold belt along the Thai-Cambodian border, the Khao Taa Ngog section contains limestone with alatoconchid shells with massive rugose corals which formed local carbonate build-ups in high-energy open platform environments. Poorly-sorted bioclasts with common fusuline tests, shell fragments and coated grains suggest an open marine, lagoon, back reef environment. The occurrence of a fusuline assemblage including Neoschwagerina sp., Yabeina sp., Lepidolina sp., Codonofusiella sp., Verbeekina verbeeki and Chusenella sp. indicates the Capitanian (late Middle Permian) age.

Depositional Architecture of a Neoproterozoic Clastic Shelf: Clue from the Owk Shale Formation, Kurnool Group, India

Abstract Process-based facies and facies association analysis from the Neoproterozoic Owk Shale Formation, Kurnool Group allowed identification of seven facies types, grouped under four facies associations viz. (i) Greenish grey shale without sand interbed, (ii) Grey shale with thin fine sandstone/siltstone interbeds (iii) Grey shale interbedded with HCS-bearing sheet sandstone (iv) Coarsegrained sandstone interbedded with reddish brown shale. A shallow-marine setting ranging from outer shoreface to distal shelf set-up is interpreted. From facies succession analysis, a steady progradation is documented in course of Owk shale depositional history whereby distal shelf shale is successively overlain by the mid, inner shelf shale and finally by outer shoreface succession. Stacked decimeter-thick progradational depositional cycles (demarcated by mid to inner shelf transitions) constitute the progradational depositional motif. The Owk shelf was storm influenced, though from restriction of storm features within the inner shelf deposit, it is argued that no large-scale coastal set-up occurred at the Owk coastline in course of storm events. Instead, a low to moderate offshore-ward pressure gradient is inferred with coriolis effect that possibly hindered active sediment transport in the distal part of the Owk shelf.

Generative Ensemble Deep Learning Severe Weather Prediction from a Deterministic Convection-Allowing Model

Abstract An ensemble postprocessing method is developed for the probabilistic prediction of severe weather (tornadoes, hail, and wind gusts) over the conterminous United States (CONUS). The method combines conditional generative adversarial networks (CGANs), a type of deep generative model, with a convolutional neural network (CNN) to postprocess convection-allowing model (CAM) forecasts. The CGANs are designed to create synthetic ensemble members from deterministic CAM forecasts, and their outputs are processed by the CNN to estimate the probability of severe weather. The method is tested using High-Resolution Rapid Refresh (HRRR) 1–24-h forecasts as inputs and Storm Prediction Center (SPC) severe weather reports as targets. The method produced skillful predictions with up to 20% Brier skill score (BSS) increases compared to other neural-network-based reference methods using a testing dataset of HRRR forecasts in 2021. For the evaluation of uncertainty quantification, the method is overconfident but produces meaningful ensemble spreads that can distinguish good and bad forecasts. The quality of CGAN outputs is also evaluated. Results show that the CGAN outputs behave similarly to a numerical ensemble; they preserved the intervariable correlations and the contribution of influential predictors as in the original HRRR forecasts. This work provides a novel approach to postprocess CAM output using neural networks that can be applied to severe weather prediction. Significance Statement We use a new machine learning (ML) technique to generate probabilistic forecasts of convective weather hazards, such as tornadoes and hailstorms, with the output from high-resolution numerical weather model forecasts. The new ML system generates an ensemble of synthetic forecast fields from a single forecast, which are then used to train ML models for convective hazard prediction. Using this ML-generated ensemble for training leads to improvements of 10%–20% in severe weather forecast skills compared to using other ML algorithms that use only output from the single forecast. This work is unique in that it explores the use of ML methods for producing synthetic forecasts of convective storm events and using these to train ML systems for high-impact convective weather prediction.

MHD Waves in Solar Wind Plasma during Geomagnetic Storm Events in February–March 2023

MHD Waves in Solar Wind Plasma during Geomagnetic Storm Events in February–March 2023