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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]

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]