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Two-dimensional (2D), depth-averaged shallow water equation (SWE) models are routinely used to simulate flooding in coastal areas – areas that often include vast networks of channels and flood-control topographic features and/or structures, such as barrier islands and levees. Adequately resolving these features within the confines of a 2D model can be computationally expensive, which has led to coupling 2D simulation tools to less expensive one-dimensional (1D) models. Under certain 1D–2D coupling approaches, this introduces internal constraints that must be considered in the generation of the 2D computational mesh used. In this paper, we further develop an existing automatic unstructured mesh generation tool for SWE models, ADMESH+, to sequentially (i) identify 1D constraints from the raw input data used in the mesh generation process, namely the digital elevation model (DEM) and land–water delineation data; (ii) distribute grid points along these internal constraints, according to feature curvature and user-prescribed minimum grid spacing; and (iii) integrate these internal constraints into the 2D mesh size function and mesh generation processes. The developed techniques, which include a novel approach for determining the so-called medial axis of a polygon, are described in detail and demonstrated on three test cases, including two inland watersheds with vast networks of channels and a complex estuarine system on the Texas, USA, coast. [ABSTRACT FROM AUTHOR]

Two-dimensional (2D), depth-averaged shallow water equation (SWE) models are routinely used to simulate flooding in coastal areas — areas that often include vast networks of channels and flood-control topographic features/structures, such as barrier islands and levees. Adequately resolving these features within the confines of a 2D model can be computationally expensive, which has led to coupling 2D simulation tools to less-expensive, one-dimensional (1D) models. Under certain 1D/2D coupling approaches, this introduces internal constraints that must be considered in the generation of the 2D computational mesh used. In this paper, we further develop an existing automatic unstructured mesh generation tool for SWE models, ADMESH+, to sequentially (i) identify 1D constraints from the raw input data used in the mesh generation process, namely, the digital elevation model (DEM) and land/water delineation data, (ii) distribute grid points along these internal constraints according to feature curvature and user-prescribed minimum grid spacing, and (iii) integrate these internal constraints into the 2D mesh-size-function and mesh-generation processes. The developed techniques, which include a novel approach for determining the so-called medial axis of a polygon, are described in detail and demonstrated on three test cases, including two inland watersheds with vast networks of channels and a complex estuarian system on the Texas, US coast. [ABSTRACT FROM AUTHOR]

We describe the instrumentation, calibration, and uncertainty of the network of ground-based, in situ, cavity ring down spectroscopy (CRDS) greenhouse gas (GHG) measurements deployed in the Permian basin. The primary goal of the network is to be used in conjunction with atmospheric transport modeling to determine methane emissions of the Delaware sub-basin of the Permian Basin oil and natural gas extraction area in Texas and New Mexico. Four of the measurements are based on tall communications towers, while one is on a building on a mountain ridge, with the recent addition of a small tower at that site. Although methane (CH4) is the primary specie of interest, carbon dioxide (CO2), hydrogen sulfide (H2S), and the isotopic ratio of methane (δ13CH4) are also reported for a subset of the sites. Measurements were reported following WMO X2004A scale for CH4, and the WMO X2019 scale for CO2. CRDS instruments were calibrated for CH4 and CO2 in laboratory prior deployment. For H2S, data was offset-corrected using the minimum 40-min running mean value of the day, and for δ13CH4, calibrations were based on laboratory data. We describe the characteristics of the data set with a set of illustrative analyses. Methane and carbon dioxide showed strong seasonality, with a well-defined diurnal cycle during the summer, which was opposed to the winter, when a diurnal cycle was absent. CH4 enhancements to the background, during the winter, are up to twice the summer values, which is attributed to the changes in boundary layer depth and wind speed. The largest CH4 enhancements occurred when winds blow from the center of the Delaware sub-basin, where most of the methane emissions come from. The magnitude of enhancements of CO2 did not present seasonality. H2S enhancements indicated a potential source northeast of the tower where the inlet is installed. Isotopic ratios of methane indicated that oil and natural gas extraction is the source of local methane in the region. The hourly-averaged data, starting on 1 March 2020 and described in this paper, are archived at The Pennsylvania State University Data Commons at https://doi.org/10.26208/98y5-t941 (Monteiro et al., 2021). [ABSTRACT FROM AUTHOR]

A three-dimensional coupled hydrodynamic–biogeochemical model with multiple nutrient and plankton functional groups was developed and adapted to the Gulf of Mexico to investigate the role of nutrients and the complexity of plankton community in dissolved oxygen (DO) dynamics. A 15-year hindcast was achieved covering the period of 2006–2020. Extensive model validation against in situ data demonstrates that the model was capable of reproducing vertical distributions of DO and spatial distributions of bottom DO concentration, as well as their interannual variations. The study demonstrates that bottom DO dynamics and hypoxia evolution are significantly influenced by both physical processes and local biochemistry, with sedimentary oxygen consumption and vertical diffusion identified as key contributors. Summer hydrodynamics play a critical role in nutrient distribution and limitation: a notable expansion of Si limitation was simulated when coastal currents shifted eastward or northward. This effect, especially pronounced on the western part of the Louisiana–Texas shelf, underscores the importance of nutrient limitation in shaping DO dynamics. The model identifies a bi-peak primary production pattern in spring and early summer, aligned with satellite chlorophyll a variations, attributed to the complexity of the plankton community and interactions among different plankton groups. Our findings emphasize the necessity of integrating sophisticated plankton community dynamics into biogeochemical models to understand primary production variability and its impact on bottom hypoxia. [ABSTRACT FROM AUTHOR]

Drought has long posed an existential threat to society. Engineering and technological advancements have enabled the development of complex, interconnected water supply systems that buffer societies from the impacts of drought, enabling growth and prosperity. However, increasing water demand from population growth and economic development, combined with more extreme and prolonged droughts due to climate change, poses significant challenges for governments in the 21st century. Improved understanding of the cascading multisectoral impacts and adaptive responses resulting from extreme drought can aid in adaptive planning and highlight key processes in modeling drought impacts. The record drought spanning 2008 to 2015 in the Colorado Basin in the state of Texas, United States, serves as an outstanding illustration to assess multisectoral impacts and responses to severe, multi-year drought. The basin faces similar water security challenges to those across the western US, such as groundwater depletion and sustainability, resource competition between agriculture and growing urban populations, limited options for additional reservoir expansion, and the heightened risk of more severe and frequent droughts due to climate change. By analyzing rich, high-quality data sourced from nine different local, state, and federal sources, we demonstrate that characterizing regional multisector dynamics is crucial to predicting and understanding future vulnerability and possible approaches to reduce impacts to human and natural systems in the face of extreme drought conditions. This review reveals that, despite the severe hydrometeorological conditions of the drought, the region's advanced economy and existing water infrastructure effectively mitigated economic and societal impacts. [ABSTRACT FROM AUTHOR]

Given the availability of high-quality and high-spatial-resolution digital elevation maps (DEMs) from the United States Geological Survey's 3D Elevation Program (3DEP), derived mostly from light detection and ranging (lidar) sensors, we examined the effects of these DEMs at various spatial resolutions on the quality of flood inundation map (FIM) extents derived from a terrain index known as Height Above Nearest Drainage (HAND). We found that using these DEMs improved the quality of resulting FIM extents at around 80 % of the catchments analyzed when compared to using DEMs from the National Hydrography Dataset Plus High Resolution (NHDPlusHR) program. Additionally, we varied the spatial resolution of the 3DEP DEMs at 3, 5, 10, 15, and 20 m (meters), and the results showed no significant overall effect on FIM extent quality across resolutions. However, further analysis at coarser resolutions of 60 and 90 m revealed a significant degradation in FIM skill, highlighting the limitations of using extremely coarse-resolution DEMs. Our experiments demonstrated a significant burden in terms of the computational time required to produce HAND and related data at finer resolutions. We fit a multiple linear regression model to help explain catchment-scale variations in the four metrics employed and found that the lack of reservoir flooding or inundation upstream of river retention systems was a significant factor in our analysis. For validation, we used Interagency Flood Risk Management (InFRM) Base Level Engineering (BLE)-produced FIM extents and streamflows at the 100- and 500-year event magnitudes in a sub-region in eastern Texas. [ABSTRACT FROM AUTHOR]

We utilize a simple, observational-based model to quantitatively estimate the US anthropogenic, background and wildfire contributions to the temporal and spatial distributions of maximum ozone concentrations throughout the southwestern US, including Texas and parts of California. The very different temporal variations of the separate contributions provide the basis for this analysis: over the past four decades the anthropogenic contribution has decreased at an approximately exponential rate by a factor of ~6.3, while the US background concentration rose significantly through the 1980s and 1990s, reached a maximum in the mid-2000s, and has since slowly decreased. We primarily analyze ozone design values (ODVs), an extreme value statistic of relatively rare maximum ozone concentrations upon which the US National Ambient Air Quality Standards (NAAQS) are based; ODV time series provide spatially and temporally resolved records of maximum ozone concentrations throughout the country. Recent contributions of US background ozone to ODVs (primarily due to transported baseline ozone) are 64 to 70 ppb over most of the southwestern US, and wildfires (also generally considered a background contribution) add further enhancements of 2 to 6 ppb in southwestern US urban areas. US anthropogenic emissions from urban and industrial sectors now produce only relatively modest enhancements to ODVs (less than ~6 ppb in 2020) outside of the three largest urban areas considered (Dallas, Houston and Los Angeles), where the 2020 enhancements were in the 17 to 30 ppb range. As a consequence, US background ozone concentrations now dominate over US anthropogenic contributions in the western US, including the Los Angeles urban basin, where the largest US ozone concentrations are observed. This finding has several implications: 1) A pronounced shift in the spatial distribution of maximum US ozone concentrations has occurred; once ubiquitous nearly nationwide, ODVs of 75 ppb or greater have nearly disappeared in the eastern US, but are still frequent in the southwestern US. 2) By 2021, the trend of maximum ODVs in two major eastern urban areas (i.e., New York City and Atlanta) had decreased to the point that they were smaller than those in smaller southwestern US urban areas, and nearly as small as ODVs recorded at isolated rural southwestern US sites. 3) Together, the US background plus wildfire contributions approach or exceed the US NAAQS for ozone of 70 ppb (implemented in 2015) and 75 ppb (implemented in 2008); consequently, in the southwestern US NAAQS achievement has been precluded. 4) Alternate emission control approaches may provide more effective approaches to air quality improvement; since background ozone makes the dominant contribution to even the highest observed concentrations, an international effort to reduce northern midlatitude baseline ozone concentrations could be pursued, or a standard based on the anthropogenic increment above the regionally-varying US background ozone concentration could be considered to provide a regionally uniform challenge of standard achievement. 5) The predominant contribution of US background ozone across the southwestern US presents a profound challenge for air quality modelling, since a manifold of stratospheric and tropospheric processes occurring on small spatial scales, but over hemisphere-wide distances, must be treated in detail to predict present and future background contributions to daily maximum ozone concentrations at local scales. [ABSTRACT FROM AUTHOR]

The Southwestern portions of the United States experience dust events frequently due to the arid and semi-arid environments and close proximity to multiple deserts. On February 26, 2023, a dust event was initiated in New Mexico due to strong winds aloft mixing down to the surface. The dust intensified as it moved eastward into West Texas, and turned into a dust storm (visibility < 1 km) in multiple locations. This study examined the meteorological characteristics of this dust storm using 21 meteorological stations and examines the impacts on PM2.5 and/or PM10 (particulate matter with an aerodynamic diameter < 10 and 2.5 µm) concentrations using 19 air quality stations. The dust event lasted up to 18 hours, and locations experienced dust storm conditions from five minutes up to 65 minutes, with even zero visibility. The highest wind speed and wind gust recorded during the dust were 27.3 m s-1 and 37 m s-1 respectively. This dust had a strong impact on the air quality in the area, as very high PM values were recorded across the region, and nine of the PM stations exceeded the EPA daily threshold. The maximum hourly PM2.5 and PM10 concentrations recorded were 518.4 µg m-3 and 9,983 µg m-3 respectively. For some locations (e.g., Lubbock Texas), these high PM2.5 concentrations were the highest ever recorded, highlighting the significance of this dust storm. [ABSTRACT FROM AUTHOR]

Woody-plant encroachment into grasslands and savannas has been globally widespread during the past century, likely driven by interactions between grazing, fire suppression, rising atmospheric CO2 , and climate change. In the southernmost US Great Plains, Ashe juniper and live oak have increased in abundance. To evaluate potential interactions between this vegetation change and the underlying soil parent material on ecosystem biogeochemistry, we quantified soil organic carbon (SOC), total nitrogen (TN), total phosphorus (TP), and δ13C of SOC in soils obtained from trenches passing through grassland, juniper, and oak patches on soils lying atop the respective Edwards and Buda limestone formations in central Texas. Soils on the Edwards formation are more shallow and have more rock outcropping than those on Buda. The δ13C values of SOC under grasslands was - 19 ‰, whereas those under woody patches were - 21 ‰ to - 24 ‰, indicating that wooded areas were relatively recent components of the landscape. Compared with grasslands, areas now dominated by juniper or oak had elevated SOC, TN, and TP storage in soils lying atop Edwards limestone. In Buda soils, only oak patches had increased SOC, TN, and TP storage compared with grasslands. Woody encroachment effects on soil nutrients were higher in soils on the Edwards formation, perhaps because root and litter inputs were more concentrated in the relatively shallow layer of soil atop the Edwards bedrock. Our findings suggest that geological factors should be considered when predicting nutrient store responses in savannas following vegetation change. Given that woody encroachment is occurring globally, our results have important implications for the management and conservation of these ecosystems. The potential interactive effects between vegetation change and soil parent material on C, N, and P storage warrant attention in future studies aimed at understanding and modeling the global consequences of woody encroachment. [ABSTRACT FROM AUTHOR]

The frequency and intensity of coastal flooding is expected to accelerate in low-elevation coastal areas due to sea level rise. Coastal flooding due to wave overtopping affects coastal communities and infrastructure; however, it can be difficult to monitor in remote and vulnerable areas. Here we use a camera-based system to measure beach and back-beach flooding as part of the after-storm recovery of an eroded beach on the Texas coast. We analyze high-temporal resolution images of the beach using convolutional neural network (CNN)-based semantic segmentation to study the stochastic properties of flooding events. In the first part of this work, we focus on the application of semantic segmentation to identify water and overtopping events. We train and validate a CNN with over 500 manually classified images and introduce a post-processing method to reduce false positives. We find that the accuracy of CNN predictions of water pixels is around 90 % and strongly depends on the number and diversity of images used for training. [ABSTRACT FROM AUTHOR]

Low-intensity but high-frequency coastal flooding, also known as nuisance flooding, can negatively affect low-lying coastal communities with potentially large socioeconomic effects. Partially driven by wave runup, this type of flooding is difficult to predict due to the complexity of the processes involved. Here, we present the results of a probabilistic analysis of flooding events measured on an eroded beach at the Texas coast. A high-resolution time series of the flooded area was obtained from pictures using convolutional neural network (CNN)-based semantic segmentation methods, as described in the first part of this contribution. After defining flooding events using a peak-over-threshold method, we found that their size follows an exponential distribution. Furthermore, consecutive flooding events were uncorrelated at daily timescales but correlated at hourly timescales, as expected from tidal and day–night cycles. Our measurements confirm the broader findings of a recent multi-site investigation of the probabilistic structure of high-water events that used a semi-empirical formulation for wave runup. Indeed, we found a relatively good statistical agreement between our CNN-based empirical flooding data and predictions using total-water-level estimations. As a consequence, our work supports the validity of a relatively simple probabilistic model of high-frequency coastal flooding driven by wave runup that can be used in coastal risk management and landscape evolution models. [ABSTRACT FROM AUTHOR]

As criteria pollutants from anthropogenic emissions have declined in the US in the last 2 decades, biomass burning (BB) emissions are becoming more important for urban air quality. Tracking the transported BB emissions and their impacts is challenging, especially in areas that are also burdened by anthropogenic sources like the Texas Gulf Coast. During the Corpus Christi and San Antonio (CCSA) field campaign in spring 2021, two long-range-transport BB events (BB1 and BB2) were identified. The observed patterns of an absorption Ångström exponent (AAE), a high-resolution time-of-flight aerosol mass spectrometer (HR-ToF-AMS) BB tracer (f60), equivalent black carbon (eBC), acetonitrile and carbon monoxide (CO) during BB1 and BB2 indicated differences in the mixing of transported BB plumes with local anthropogenic sources. The combined information from HYSPLIT backward-trajectory (BT) and satellite observations revealed that BB1 had mixed influence of transported smoke plumes from fires in central Mexico, the Yucatán Peninsula and the central US, whereas BB2 was influenced by fires in the central US. The estimated transport times of smoke from the Mexican fires and the central US fires to our study site were not too different (48–54 and 24–36 h, respectively), and both events appeared to have undergone similar levels of atmospheric processing, as evident in the elemental ratios of bulk organic aerosol (OA). We observed an aging trend for f44 vs. f60 and f44 vs. f43 as a function of time during BB2 but not during BB1. Positive matrix factorization (PMF) analysis of OA showed that BB1 had a mixture of organics from aged BB emissions with an anthropogenic marine signal, while the oxidized organic compounds from aged BB emissions dominated the aerosols during BB2. The size distribution of aerosol composition revealed distinct characteristics between BB1 and BB2, where BB1 was found to be externally mixed, exhibiting a combination of BB and anthropogenic marine aerosols. On the other hand, BB2 exhibited internal mixing dominated by aged BB aerosol. Our analysis from mobile and stationary measurements highlights that both CO and acetonitrile are likely impacted by local sources even during the BB events and specifically that acetonitrile cannot be used as a unique BB tracer for dilute BB plumes in an industrially influenced location. A suitable volatile organic compound (VOC) tracer would need to be emitted in high concentrations during BB, resistant to degradation during transport, unique to BB and able to be measured in the field. This study effectively demonstrates that AAE and aerosol BB tracers served as precise and effective tracers in these complex emission scenarios. Network deployment of multiwavelength photometers holds promise for enhancing our understanding of BB impacts on air quality and supporting informed decision-making for effective mitigation strategies in locations with mixed sources and influence of dilute BB plumes. To demonstrate the relevance of such an aerosol optical network, we provide evidence of the potential regional impacts of these transported BB events on urban O3 levels using measurements from the surface air quality monitoring network in Texas. [ABSTRACT FROM AUTHOR]

Given the availability of high quality and high spatial resolution digital elevation models (DEMs) from the United States Geological Survey's 3-Dimensional Elevation Program (3DEP) derived from mostly Light Detection and Ranging sensors, we examined the effects of these DEMs at various spatial resolutions on the quality of flood inundation map (FIM) extents derived from a terrain index known as Height Above Nearest Drainage (HAND). We found that using these DEMs improved the quality of resulting FIMs at around 80 % of the catchments analyzed when compared to using DEMs from the National Hydrography Dataset Plus High Resolution program. Additionally, we varied the spatial resolution of the 3DEP DEMs from 3, 5, 10, 15, and 20 meters and the results showed no significant overall effect on FIM extent quality across resolutions. However, our experiments demonstrated a significant burden on the computational time to produce HAND. We fit a multiple linear regression model to help explain catchment scale variation in the four metrics employed and found that the lack of reservoir flooding, or inundation upstream of river retention systems, was a significant factor in our analysis. For validation, we used Interagency Flood Risk Management Base Level Engineering produced FIM extents and streamflows at the 100 and 500 year event magnitudes in a sub-region in Eastern Texas. [ABSTRACT FROM AUTHOR]

Studies focusing on virga are rare, even though it is a commonly occurring phenomenon. In this study, we investigated aerosol backscatter profiles from a ceilometer located on The University of Texas at El Paso (UTEP) campus from 2015–2021 to identify virga events. Ceilometer data effectively captured virga events from regular precipitation based on the backscattering intensities. To characterize the virga phenomena, a systematic method was developed using ceilometer profiles, soundings, surface rain gauges, and radar data from the nearest National Weather Service (NWS) site. A total of 50 virga events were identified during the study period. These events appeared only during a specific time of the year, revealing a seasonal occurrence pattern. We identified and classified these virga events and investigated their impact on the surface measurements recorded by the on-campus Continuous Ambient Air Monitoring Station (CAMS). Virga events were classified as columnar and non-columnar events based on their aerosol profiles. We observed that during some of the columnar virga events, surface particulate matter (PM) levels displayed a sudden upward trend indicating aerosol loading in the surface layer after precipitation evaporation. In total, 20 of the virga events showed a columnar structure out of the 50 identified in this study. More detailed analysis of selected events shows that virga affects regional air quality. A significant result of this study is that analysis of sudden changes in local air quality needs to consider the possible effects of virga on the surface layer. [ABSTRACT FROM AUTHOR]

Hurricane Harvey deposited over 90×109 m 3 of rainwater over central Texas, USA, during late August/early September 2017. During four cruises (June, August, September and November 2017) we observed changes in hydrography and nutrient and oxygen concentrations in Texas coastal waters. Despite intense terrestrial runoff, nutrient supply to the coastal ocean was transient, with little phytoplankton growth observed and no hypoxia. Observations suggest this was probably related to the retention of nutrients in the coastal bays and rapid uptake by phytoplankton of nutrients washed out of the bays, as well as dilution by the sheer volume of rainwater and the lack of significant carbon reserves in the sediments, despite the imposition of a strong pycnocline. By the November cruise conditions had apparently returned to normal, and no long-term effects were observed. [ABSTRACT FROM AUTHOR]

The Tropospheric Monitoring Instrument (TROPOMI) on the Sentinel-5 Precursor (S5P) satellite is a valuable source of information to monitor the NOx emissions that adversely affect air quality. We conduct a series of experiments using a 4×4 km 2 Comprehensive Air Quality Model with Extensions (CAMx) simulation during April–September 2019 in eastern Texas to evaluate the multiple challenges that arise from reconciling the NOx emissions in model simulations with TROPOMI. We find an increase in NO2 (+17 % in urban areas) when transitioning from the TROPOMI NO2 version 1.3 algorithm to the version 2.3.1 algorithm in eastern Texas, with the greatest difference (+25 %) in the city centers and smaller differences (+5 %) in less polluted areas. We find that lightning NOx emissions in the model simulation contribute up to 24 % of the column NO2 in the areas over the Gulf of Mexico and 8% in Texas urban areas. NOx emissions inventories, when using locally resolved inputs, agree with NOx emissions derived from TROPOMI NO2 version 2.3.1 to within 20 % in most circumstances, with a small NOx underestimate in Dallas–Fort Worth (-13 %) and Houston (-20 %). In the vicinity of large power plant plumes (e.g., Martin Lake and Limestone) we find larger disagreements, i.e., the satellite NO2 is consistently smaller by 40 %–60 % than the modeled NO2 , which incorporates measured stack emissions. We find that TROPOMI is having difficulty distinguishing NO2 attributed to power plants from the background NO2 concentrations in Texas – an area with atmospheric conditions that cause short NO2 lifetimes. Second, the NOx/NO2 ratio in the model may be underestimated due to the 4 km grid cell size. To understand ozone formation regimes in the area, we combine NO2 column information with formaldehyde (HCHO) column information. We find modest low biases in the model relative to TROPOMI HCHO, with -9 % underestimate in eastern Texas and -21 % in areas of central Texas with lower biogenic volatile organic compound (VOC) emissions. Ozone formation regimes at the time of the early afternoon overpass are NOx limited almost everywhere in the domain, except along the Houston Ship Channel, near the Dallas/Fort Worth International airport, and in the presence of undiluted power plant plumes. There are likely NOx -saturated ozone formation conditions in the early morning hours that TROPOMI cannot observe and would be well-suited for analysis with NO2 and HCHO from the upcoming TEMPO (Tropospheric Emissions: Monitoring Pollution) mission. This study highlights that TROPOMI measurements offer a valuable means to validate emissions inventories and ozone formation regimes, with important limitations. [ABSTRACT FROM AUTHOR]

Information on atmospheric particles' concentration and sizes is important for environmental and human health reasons. Air quality monitoring stations (AQMSs) for measuring particulate matter (PM) concentrations are found across the United States, but only three AQMSs measure PM 2.5 concentrations (mass of particles with an aerodynamic diameter of < 2.5 µ m) in the Southern High Plains of West Texas (area ≥ 1.8 × 10 5 km 2). This area is prone to many dust events (∼ 21 yr -1), yet no information is available on other PM sizes, total particle number concentration, or size distribution during these events. The Aerosol Research Observation Station (AEROS) was designed to continuously measure these particles' mass concentrations (PM 1 , PM 2.5 , PM 4 , and PM 10) and number concentrations (0.25–35.15 µ m) using three optical particle sensors (Grimm 11-D, OPS, and DustTrak) to better understand the impact of dust events on local air quality. The AEROS aerosol measurement unit features a temperature-controlled shed with a dedicated inlet and custom-built dryer for each of the three aerosol instruments used. This article provides a description of AEROS as well as an intercomparison of the different instruments using laboratory and atmospheric particles. Instruments used in AEROS measured a similar number concentration with an average difference of 2 ± 3 cm -1 (OPS and Grimm 11-D using similar particle size ranges) and a similar mass concentration, with an average difference of 8 ± 3.6 µ g m -3 for different PM sizes between the three instruments. Grimm 11-D and OPS had a similar number concentration and size distribution, using a similar particle size range and similar PM 10 concentrations (mass of particles with an aerodynamic diameter of < 10 µ m). Overall, Grimm 11-D and DustTrak had good agreement in mass concentration, and comparison using laboratory particles was better than that with atmospheric particles. Overall, DustTrak measured lower mass concentrations compared to Grimm 11-D for larger particle sizes and higher mass concentrations for lower PM sizes. Measurement with AEROS can distinguish between various pollution events (natural vs. anthropogenic) based on their mass concentration and size distribution, which will help to improve knowledge of the air quality in this region. [ABSTRACT FROM AUTHOR]

This study presents a novel ensemble regression model for forecasts of the hypoxic area (HA) in the Louisiana–Texas (LaTex) shelf. The ensemble model combines a zero-inflated Poisson generalized linear model (GLM) and a quasi-Poisson generalized additive model (GAM) and considers predictors with hydrodynamic and biochemical features. Both models were trained and calibrated using the daily hindcast (2007–2020) by a three-dimensional coupled hydrodynamic–biogeochemical model embedded in the Regional Ocean Modeling System (ROMS). Compared to the ROMS hindcasts, the ensemble model yields a low root-mean-square error (RMSE) (3256 km 2), a high R2 (0.7721), and low mean absolute percentage biases for overall (29 %) and peak HA prediction (25 %). When compared to the shelf-wide cruise observations from 2012 to 2020, our ensemble model provides a more accurate summer HA forecast than any existing forecast models with a high R2 (0.9200); a low RMSE (2005 km 2); a low scatter index (15 %); and low mean absolute percentage biases for overall (18 %), fair-weather summer (15 %), and windy-summer (18 %) predictions. To test its robustness, the model is further applied to a global forecast model and produces HA prediction from 2012–2020 with the adjusted predictors from the HYbrid Coordinate Ocean Model (HYCOM). In addition, model sensitivity tests suggest an aggressive riverine nutrient reduction strategy (92 %) is needed to achieve the HA reduction goal of 5000 km 2. [ABSTRACT FROM AUTHOR]

A proposed null hypothesis for fluvial terrace formation is that internally generated or autogenic processes, such as lateral migration and river-bend cutoff, produce variabilities in channel incision that lead to the abandonment of floodplain segments as terraces. Alternatively, fluvial terraces have the potential to record past environmental changes from external forcings that include temporal changes in sea level and hydroclimate. Terraces in the Trinity River valley have been previously characterized as Deweyville groups and interpreted to record episodic cut and fill during late Pleistocene sea level variations. Our study uses high-resolution topography of a bare-earth digital elevation model derived from airborne lidar surveys along ∼ 88 linear kilometers of the modern river valley. We measure both differences in terrace elevations and widths of paleo-channels preserved on these terraces in order to have two independent constraints on terrace formation mechanisms. For 52 distinct terraces, we quantify whether terrace elevations fit distinct planes – expected for allogenic terrace formation tied to punctuated sea level and/or hydroclimate change – by comparing variability in a grouped set of Deweyville terrace elevations against variability associated with randomly selected terrace sets. Results show Deweyville groups record an initial valley floor abandoning driven by allogenic forcing, which transitions into autogenic forcing for the formation of younger terraces. For these different terrace sets, the slope amongst different terraces stays constant. For 79 paleo-channel segments preserved on these terraces, we connected observed changes in paleo-channel widths to estimates for river paleo-hydrology over time. Our measurements suggest the discharge of the Trinity River increased systematically by a factor of ∼ 2 during the late Pleistocene. Despite this evidence of increased discharge, the similar down-valley slopes between terrace sets indicate that there were likely no increases in sediment-to-water discharge ratios that could be linked to allogenic terrace formation. This is consistent with our elevation clustering analysis that suggests younger terraces are indistinguishable in their elevation variance from autogenic terrace formation mechanisms, even if the changing paleo-channel dimensions might, viewed in isolation, provide a mechanism for allogenic terrace formation. Methods introduced here combine river-reach-scale observations of terrace sets and paleo-hydrology with local observations of terraces and paleo-channels to show how interpretations of allogenic versus autogenic terrace formation can be evaluated within a single river system. [ABSTRACT FROM AUTHOR]

Dust events are an important and complex constituent of the atmospheric system that can impact Earth's climate, the environment, and human health. The frequency of dust events in the Southern High Plains of West Texas has increased over the past 2 decades, yet their impact on air quality in this region is still unclear. This is due to the fact that there is only one air quality monitoring station that measures only PM 2.5 concentrations (particulate matter with an aerodynamic diameter < 2.5 µ m), and there is no information on other PM sizes or the particle size distribution. The Aerosol Research Observation Station (AEROS) unit provides insight into the local variation in particle concentration during different dust events and allows for a better understanding of the impact of dust events on air quality. As this area is prone to dust events, we were wondering if dust events generated by different meteorological causes (synoptic vs. convective) would present similar particle concentrations or particle size distributions. Thus, in this project, three different dust events were measured by AEROS and compared. Each dust event originated from a different direction and lasted a different duration. One of the dust events was synoptic (10 April 2019) and two were convective (5 and 21 June 2019). Measurements of particle mass and number concentration, size distribution, and meteorological conditions for each dust event were compared. The synoptic dust event (on 10 April) was longer (12 h) and had stronger wind speed conditions (up to 22.1 m s -1), whereas the two respective convective dust events on 5 and 21 June lasted only 20 and 30 min and had lower wind speeds (up to 16.5 and 13.4 m s -1). Observation of PM based on daily and hourly values showed an impact on air quality, yet measurements based on daily and hourly values underestimate the impact of the convective dust events. Observations based on a shorter timescale (10 min) reveal the true impact of the two convective dust events. A comparison of the particle size distribution showed that all three dust events presented an increase in particles in the 0.3–10 µ m size range. Comparisons of the particle concentration for particles > 5 and > 10 µ m show very high values during the dust events. Some particle sizes even increase in concentration by ∼ 2 orders of magnitude compared with the time before the dust event. This leads us to speculate that the impact of convective dust events on air quality in this region is underestimated with the current (hourly basis) method. [ABSTRACT FROM AUTHOR]

Low bottom water dissolved oxygen conditions (hypoxia) occur almost every summer in the northern Gulf of Mexico due to a combination of nutrient loadings and water column stratification. Several statistical and mechanistic models have been used to forecast the midsummer hypoxic area, based on spring nitrogen loading from major rivers. However, sub-seasonal forecasts are needed to fully characterize the dynamics of hypoxia over the summer season, which is important for informing fisheries and ecosystem management. Here, we present an approach to forecasting hypoxic conditions at a daily resolution through Bayesian mechanistic modeling that allows for rigorous uncertainty quantification. Within this framework, we develop and test different representations and projections of hydrometeorological model inputs. We find that May precipitation over the Mississippi River basin is a key predictor of summer discharge and loading that substantially improves forecast performance. Accounting for spring wind conditions also improves forecast performance, though to a lesser extent. The proposed approach generates forecasts for two different sections of the Louisiana–Texas shelf (east and west), and it explains about 50 % of the variability in the total hypoxic area when tested against historical observations (1985–2016). Results also show how forecast uncertainties build over the summer season, with longer lead times from the nominal forecast release date of 1 June, due to increasing stochasticity in riverine and meteorological inputs. Consequently, the portion of overall forecast variance associated with uncertainties in data inputs increases from 26 % to 41 % from June–July to August–September, respectively. Overall, the study demonstrates a unique approach to assessing and reducing uncertainties in temporally resolved hypoxia forecasting. [ABSTRACT FROM AUTHOR]

Regional hurricane risk is often assessed assuming a static housing inventory, yet a region's housing inventory changes continually. Failing to include changes in the built environment in hurricane risk modeling can substantially underestimate expected losses. This study uses publicly available data and a long short-term memory (LSTM) neural network model to forecast the annual number of housing units for each of 1000 individual counties in the southeastern United States over the next 20 years. When evaluated using testing data, the estimated number of housing units was almost always (97.3 % of the time), no more than 1 percentage point different than the observed number, predictive errors that are acceptable for most practical purposes. Comparisons suggest the LSTM outperforms the autoregressive integrated moving average (ARIMA) and simpler linear trend models. The housing unit projections can help facilitate a quantification of changes in future expected losses and other impacts caused by hurricanes. For example, this study finds that if a hurricane with characteristics similar to Hurricane Harvey were to impact southeastern Texas in 20 years, the residential property and flood losses would be nearly USD 4 billion (38 %) greater due to the expected increase of 1.3 million new housing units (41 %) in the region. [ABSTRACT FROM AUTHOR]

We describe the development of a non-hydrostatic version of the regional climate model RegCM4, called RegCM4-NH, for use at convection-permitting resolutions. The non-hydrostatic dynamical core of the Mesoscale Model MM5 is introduced in the RegCM4, with some modifications to increase stability and applicability of the model to long-term climate simulations. Newly available explicit microphysics schemes are also described, and three case studies of intense convection events are carried out in order to illustrate the performance of the model. They are all run at a convection-permitting grid spacing of 3 km over domains in northern California, Texas and the Lake Victoria region, without the use of parameterized cumulus convection. A substantial improvement is found in several aspects of the simulations compared to corresponding coarser-resolution (12 km) runs completed with the hydrostatic version of the model employing parameterized convection. RegCM4-NH is currently being used in different projects for regional climate simulations at convection-permitting resolutions and is intended to be a resource for users of the RegCM modeling system. [ABSTRACT FROM AUTHOR]

Coastal cliff erosion is alongshore-variable and episodic, with retreat rates that depend upon sediment as either tools of abrasion or protective cover. However, the feedbacks between coastal cliff planform morphology, retreat rate, and sediment cover are poorly quantified. This study investigates Sargent Beach, Texas, USA, at the annual to interannual scale to explore (1) the relationship between temporal and spatial variability in cliff retreat rate, roughness, and sinuosity and (2) the response of retreat rate and roughness to changes in sand and shell hash cover of the underlying mud substrate as well as the impact of major storms using field measurements of sediment cover, erosion, and aerial images to measure shore platform morphology and retreat. A storm event in 2009 increased the planform roughness and sinuosity of the coastal cliff at Sargent Beach. Following the storm, aerial-image-derived shorelines with annual resolution show a decrease in average alongshore erosion rates from 12 to 4 myr-1 , coincident with a decrease in shoreline roughness and sinuosity (smoothing). Like the previous storm, a storm event in 2017 increased the planform roughness and sinuosity of the cliff. Over shorter timescales, monthly retreat of the sea cliff occurred only when the platform was sparsely covered with sediment cover on the shore platform, indicating that the tools and cover effects can significantly affect short-term erosion rates. The timescale to return to a smooth shoreline following a storm or roughening event, given a steady-state erosion rate, is approximately 24 years, with the long-term rate suggesting a maximum of ∼107 years until Sargent Beach breaches, compromising the Gulf Intracoastal Waterway (GIWW) under current conditions and assuming no future storms or intervention. The observed retreat rate varies, both spatially and temporally, with cliff face morphology, demonstrating the importance of multi-scale measurements and analysis for interpretation of coastal processes and patterns of cliff retreat. [ABSTRACT FROM AUTHOR]