High-resolution Grid Research Articles

Evaluation of Changes in PM2.5 Exposure Concentration and Health Risk for Urban Resident in Zhengzhou Based on High Spatial Resolution Grids

In recent years, complex air pollution with the characteristic pollutant of PM2.5 has remained serious in China. Long term exposure to PM2.5 might harm residential health and can increase premature death from specific diseases. The annual average concentration of PM2.5 in Zhengzhou was much higher than the national secondary standard, which has an extremely negative impact on the health of residents. Based on the high spatial resolution grids of population density established through web-crawling and outdoor monitoring concentrations and urban residential emissions used to evaluate PM2.5 exposure concentration, the exposure concentration of PM2.5 for urban residents of Zhengzhou was assessed, considering both indoor and outdoor exposures. Relevant health risks were quantified with the integrated exposure-response model. Finally, the contributions of various reducing measures and different standards of air quality to the decreases in PM2.5 exposure concentration were analyzed. The results showed that in 2017 and 2019, the time weighted exposure concentrations of PM2.5 for Zhengzhou's urban residents were 74.06 μg·m-3 and 60.64 μg·m-3, respectively, which was decreased by 18.12%. In addition, the mass fractions of the indoor exposure concentrations in the time weighted exposure concentrations were 83.58% and 83.01%, and its contribution to the drop of the time weighted exposure concentrations was 84.06%. In 2017 and 2019, the numbers of premature deaths attributed to PM2.5 exposures for urban residents of Zhengzhou over the age of 25 were 13285 and 10323, respectively, showing a 22.30% decrease. By using these comprehensive measures, PM2.5 exposure concentration for Zhengzhou's urban residents could be reduced by 86.23% at most, and 8902 premature deaths could be avoided.

ForestClim-Bioclimatic variables for microclimate temperatures of European forests.

Microclimate research gained renewed interest over the last decade and its importance for many ecological processes is increasingly being recognized. Consequently, the call for high-resolution microclimatic temperature grids across broad spatial extents is becoming more pressing to improve ecological models. Here, we provide a new set of open-access bioclimatic variables for microclimate temperatures of European forests at 25 × 25 m2 resolution.

Can urban polycentricity improve air quality? Evidence from Chinese cities

This study utilizes high-resolution population grid data from 2004 to 2018 to assess the extent of urban polycentricity in 284 Chinese prefecture-level cities. A new method for measuring urban polycentricity is devised using the Clauset-Newman-Moore grey modularity maximization algorithm. Additionally, the Spatial Durbin Model is applied to investigate the effect of urban polycentricity on the mean and spatial variation of PM2.5 concentrations. Our statistical results show that the increased distance between urban centers and the increased size disparity between the large and small centers effectively reduces air pollution within the city. On the other hand, in cities with a strong clustering of urban centers and significant size disparity between main and sub-centers, the polycentric urban structure cannot help reduce air pollution but merely shift it to other urban centers within the city. Hence, interpreting the severity of urban air pollution solely on the basis of the citywide average of air pollution may be insufficient. It is necessary to consider the possible relocation of air-polluting activities associated with polycentric urban structures. Policymakers should take into account the potential spatial inequality of air pollution driven by urban polycentricity.

Deconvolution with neural grid compression: A method to accurately and quickly process beamforming results

Beamforming results depend on the spatial resolution of the microphone array used, which may lead to sources close to each other being considered as one. Deconvolution methods that consider all directions simultaneously, such as DAMAS, produce better results in these situations. However, they have a high computational cost, often lack sufficient speed to be used in real-time applications, and have limited accuracy at lower frequencies. This paper introduces a hybrid method to perform deconvolution using a neural network that can improve the speed of deconvolution on high-resolution grids by more than 2 orders of magnitude, while also generating sparser maps without sacrificing accuracy compared to the compressed DAMAS method.

Numerical Analysis of Airfoil Flow Control Using a Nanosecond Pulsed Plasma Actuator

The control mechanism of a nanosecond dielectric barrier discharge (NS-DBD) actuator for suppressing laminar separation on airfoils was investigated using two-dimensional scale-adaptive simulations. To model the control effect of the NS-DBD actuator, a surface heating approach based on the analytical solution for transient one-dimensional heat conduction has been used. The focus of the investigation lies in the study of vortex formation and evolution induced by the actuator. A NACA 0015 profile was simulated at 14° with 250,000 chord-based Reynolds number. Regarding energy deposition, a low-energy case and a high-energy case were simulated. Several actuation parameters were varied, including the electrode position and surface temperature. In addition, the influence of constant and temperature-dependent modeling of the kinematic viscosity was investigated. The results for the time-averaged pressure coefficients show excellent agreement with measurement results. High grid resolution in the boundary layer allowed a detailed investigation of the vortex formation process. The main finding is that vortices are generated by baroclinic torque and vortex dilatation and not by a Kelvin–Helmholtz instability. Initially, tiny vortices form at the beginning of the separation region. Subsequently, these vortices are carried downstream and grow in size, thus preventing full separation due to momentum transfer from the external flow.

Assessing uncertainty in hydrological projections arising from local-scale internal variability of climate

Hydrological impact assessments are increasingly performed at fine spatial and temporal resolutions in order to resolve local-scale changes under a future climate. Apart from the uncertainty represented by different climate models, emission scenarios and post-processing methods, the local-scale internal variability of the climate can be a major source of uncertainty for hydrological projections. To assess the latter at the catchment scale, this paper presents a methodology which is particularly suitable for spatially distributed hydrological models. An ensemble of daily precipitation and daily mean temperature realizations on a high-resolution grid is simulated from stochastic weather generators (WGs) trained on historical data and equipped with climate change information obtained from a regional climate model. Based on the resulting simulated daily runoff data, the significance of changes in the runoff regime is assessed using a statistical hypothesis test, and the variability contributed by the two input variables is quantified using the analysis of variance (ANOVA). As a proof of concept, simulations on a 1-km grid over a period of 19 years are carried out for nine catchments in central Norway. Significant changes in runoff regimes are found, indicating that the trends introduced in the WGs are not overwhelmed by the local-scale internal variability. Variability in the runoff simulations varies substantially throughout the year; it is highest in periods with potential snowmelt and lowest during winter. Temperature is the dominant source of variability in the colder months (November–March) due to its influence on rainfall and snowmelt. High variability in May–June is contributed comparably by both temperature and precipitation. In summer and early autumn the runoff variability is precipitation dominated. The results are in line with findings in the literature where the runoff variability is driven by the large-scale internal climate variability. This indicates that ignoring the local-scale internal variability may yield an underestimation of the overall variability in runoff projections and projected changes.

Multi-Branch Deep Neural Network for Bed Topography of Antarctica Super-Resolution: Reasonable Integration of Multiple Remote Sensing Data

Bed topography and roughness play important roles in numerous ice-sheet analyses. Although the coverage of ice-penetrating radar measurements has vastly increased over recent decades, significant data gaps remain in certain areas of subglacial topography and need interpolation. However, the bed topography generated by interpolation such as kriging and mass conservation is generally smooth at small scales, lacking topographic features important for sub-kilometer roughness. DeepBedMap, a deep learning method combined with multiple surface observation inputs, can generate high-resolution (250 m) bed topography with realistic bed roughness but produces some unrealistic artifacts and higher bed elevation values in certain regions, which could bias ice-sheet models. To address these issues, we present MB_DeepBedMap, a multi-branch deep learning method to generate more realistic bed topography. The model improves upon DeepBedMap by separating inputs into two groups using a multi-branch network structure according to their characteristics, rather than fusing all inputs at an early stage, to reduce artifacts in the generated topography caused by earlier fusion of inputs. A direct upsampling branch preserves large-scale subglacial landforms while generating high-resolution bed topography. We use MB_DeepBedMap to generate a high-resolution (250 m) bed elevation grid product of Antarctica, MB_DeepBedMap_DEM, which can be used in high-resolution ice-sheet modeling studies. Moreover, we test the performance of MB_DeepBedMap model in Thwaites Glacier, Gamburtsev Subglacial Mountains, and several other regions, by comparing the qualitative topographic features and quantitative errors of MB_DeepBedMap, BEDMAP2, BedMachine Antarctica, and DeepBedMap. The results show that MB_DeepBedMap can provide more realistic small-scale topographic features and roughness compared to BEDMAP2, BedMachine Antarctica, and DeepBedMap.

TSI-SD: A time-sequence-involved space discretization neural network for passive scalar advection in a two-dimensional unsteady flow

Numerical simulation of fluid is a great challenge as it contains extremely complicated variations with a high Reynolds number. Usually, very high-resolution grids are required to capture the very fine changes during the physical process of the fluid to achieve accurate simulation, which will result in a vast number of computations. This issue will continue to be a bottleneck problem until a deep-learning solution is proposed to utilize large-scale grids with adaptively adjusted coefficients during the spatial discretization procedure—instead of traditional methods that adopt small grids with fixed coefficients—so that the computation cost is dramatically reduced and accuracy is preserved. This breakthrough will represent a significant improvement in the numerical simulation of fluid. However, previously proposed deep-learning-based methods always predict the coefficients considering only the spatial correlation among grids, which provides relatively limited context and thus cannot sufficiently describe patterns along the temporal dimension, implying that the spatiotemporal correlation of coefficients is not well learned. We propose the time-sequence-involved space discretization neural network (TSI-SD) to extract grid correlations from spatial and temporal views together to address this problem. This novel deep neural network is transformed from a classic CONV-LSTM backbone with careful modification by adding temporal information into two-dimensional spatial grids along the x-axis and y-axis separately at the first step and then fusing them through a post-fusion neural network. After that, we combine the TSI-SD with the finite volume format as an advection solver for passive scalar advection in a two-dimensional unsteady flow. Compared with previous methods that only consider spatial context, our method can achieve higher simulation accuracy, while computation is also decreased as we find that after adding temporal data, one of the input features, the concentration field, is redundant and should no longer be adopted during the spatial discretization procedure, which results in a sharp decrease of parameter scale and achieves high efficiency. Comprehensive experiments, including a comparison with SOTA methods and sufficient ablation studies, were carried out to verify the accurate and efficient performance and highlight the advantages of the proposed method.

Using the two-way nesting technique AGRIF with MARS3D V11.2 to improve hydrodynamics and estimate environmental indicators

Abstract. In the ocean, mesoscale or submesoscale structures and coastal processes are associated with fine scales. The simulation of such features thus requires the hydrodynamic equations to be solved at high-resolution (from a few hundred meters down to a few tens of meters). Therefore, local mesh refinement is a primary issue for regional and coastal modeling. The AGRIF (adaptive grid refinement in Fortran) library is committed to tackling such a challenge for structured grids. It has been implemented in MARS3D (Model for Application at Regional Scale), a semi-implicit, free-surface numerical model developed by Ifremer (the French Research Institute for Exploitation of the Sea) for coastal environmental research and studies. As its time scheme uses an alternating-direction implicit (ADI) algorithm, the two-way nesting implementation differs from the one in explicit models. The present paper describes the specifics of the AGRIF introduction and how the nesting preserves some essential properties (mass, momentum and tracer conservations) along with the induced constraints (bathymetric coherence between grids and increase in computation cost). The use and the performance of this new tool are detailed over two configurations that illustrate the wide range of scales and resolutions typically targeted by coastal applications. The first one is based on multiple high-resolution (500 m) grids that pave the coastal ocean over thousands of kilometers, allowing a continuum between the regional and coastal scales. The second application is more local and has a finer resolution (50 m). It targets a recurrent question for semi-enclosed bays, i.e., the renewal time indicator. Throughout these configurations, the paper intends to compare the two-way nesting method with the traditional one-way approach. It highlights how the MARS3D-AGRIF tool proves to be an efficient way to both improve the physical hydrodynamics and unravel ecological challenges.

Gaussian copula modeling of extreme cold and weak-wind events over Europe conditioned on winter weather regimes

A transition to renewable energy is needed to mitigate climate change. In Europe, this transition has been led by wind energy, which is one of the fastest growing energy sources. However, energy demand and production are sensitive to meteorological conditions and atmospheric variability at multiple time scales. To accomplish the required balance between these two variables, critical conditions of high demand and low wind energy supply must be considered in the design of energy systems. We describe a methodology for modeling joint distributions of meteorological variables without making any assumptions about their marginal distributions. In this context, Gaussian copulas are used to model the correlated nature of cold and weak-wind events. The marginal distributions are modeled with logistic regressions defining two sets of binary variables as predictors: four large-scale weather regimes (WRs) and the months of the extended winter season. By applying this framework to ERA5 data, we can compute the joint probabilities of co-occurrence of cold and weak-wind events on a high-resolution grid .Our results show that (a) WRs must be considered when modeling cold and weak-wind events, (b) it is essential to account for the correlations between these events when modeling their joint distribution, (c) we need to analyze each month separately, and (d) the highest estimated number of days with compound events are associated with the negative phase of the North Atlantic Oscillation (3 days on average over Finland, Ireland, and Lithuania in January, and France and Luxembourg in February) and the Scandinavian blocking pattern (3 days on average over Ireland in January and Denmark in February). This information could be relevant for application in sub-seasonal to seasonal forecasts of such events.

Parallel fully implicit chemical potential-based modeling of unconventional shale gas reservoirs

The design of novel mathematical models and state-of-the-art simulators for unconventional shale gas reservoirs plays an increasingly important role in the current energy mix of the world’s growing energy demand. In this paper, we present a robust and scalable framework, which is based on the chemical potential-based modeling and the fully implicit method (FIM), to model and simulate this highly nonlinear flow transport problem on a large scale. In the proposed approach, a thermodynamically consistent mathematical model of gas flow in shale formations, which employs the gas density and the chemical potential gradient, is developed to satisfy the second law of thermodynamics and meanwhile guarantee the energy dissipation property. And then the family of fully implicit finite element algorithms is utilized to accurately capture the complicated flow physics behind the transport process in shale media on high resolution grids. In particular, our approach further enhances the numerical formulation by proposing the family of Newton–Krylov methods for efficiently computing, and the parallel implementation of the simulator is achieved by using the domain decomposition technique. Numerical experiments are presented to demonstrate the robustness and parallel scalability of the solution strategies for several interesting shale gas flow problems in two or three dimensions. With the proposed parallel method, large-scale reservoir simulation can be obtained on the Shaheen-II supercomputer with up to 40960 processors, which enables to achieve a good strong scalability by saving more than 90 percent of computing time, when the problem size is enlarged to hundreds of millions of degrees of freedom.

Statistical power of spatial earthquake forecast tests

SUMMARYThe Collaboratory for the Study of Earthquake Predictability (CSEP) is an international effort to evaluate earthquake forecast models prospectively. In CSEP, one way to express earthquake forecasts is through a grid-based format: the expected number of earthquake occurrences within 0.1° × 0.1° spatial cells. The spatial distribution of seismicity is thereby evaluated using the Spatial test (S-test). The high-resolution grid combined with sparse and inhomogeneous earthquake distributions leads to a huge number of cells causing disparity in the number of cells, and the number of earthquakes to evaluate the forecasts, thereby affecting the statistical power of the S-test. In order to explore this issue, we conducted a global earthquake forecast experiment, in which we computed the power of the S-test to reject a spatially non-informative uniform forecast model. The S-test loses its power to reject the non-informative model when the spatial resolution is so high that every earthquake of the observed catalog tends to get a separate cell. Upon analysing the statistical power of the S-test, we found, as expected, that the statistical power of the S-test depends upon the number of earthquakes available for testing, e.g. with the conventional high-resolution grid for the global region, we would need more than 32 000 earthquakes in the observed catalog for powerful testing, which would require approximately 300 yr to record M ≥ 5.95. The other factor affecting the power is more interesting and new; it is related to the spatial grid representation of the forecast model. Aggregating forecasts on multi-resolution grids can significantly increase the statistical power of the S-test. Using the recently introduced Quadtree to generate data-based multi-resolution grids, we show that the S-test reaches its maximum power in this case already for as few as eight earthquakes in the test period. Thus, we recommend for future CSEP experiments the use of Quadtree-based multi-resolution grids, where available data determine the resolution.

A modelling approach for evaluating the wind resource and power generation using a high-resolution grid at selected regions in the northeast of Thailand

A modelling approach for evaluating the wind resource and power generation using a high-resolution grid at selected regions in the northeast of Thailand

Modelling of Deep Learning-Based Downscaling for Wave Forecasting in Coastal Area

Wave prediction in a coastal area, especially with complex geometry, requires a numerical simulation with a high-resolution grid to capture wave propagation accurately. The resolution of the grid from global wave forecasting systems is usually too coarse to capture wave propagation in the coastal area. This problem is usually resolved by performing dynamic downscaling that simulates the global wave condition into a smaller domain with a high-resolution grid, which requires a high computational cost. This paper proposes a deep learning-based downscaling method for predicting a significant wave height in the coastal area from global wave forecasting data. We obtain high-resolution wave data by performing a continuous wave simulation using the SWAN model via nested simulations. The dataset is then used as the training data for the deep learning model. Here, we use the Long Short-Term Memory (LSTM) and Bidirectional LSTM (BiLSTM) as the deep learning models. We choose two study areas, an open sea with a swell-dominated area and a rather close sea with a wind-wave-dominated area. We validate the results of the downscaling with a wave observation, which shows good results.

A computational modeling framework for pre-clinical evaluation of cardiac mapping systems.

There are a variety of difficulties in evaluating clinical cardiac mapping systems, most notably the inability to record the transmembrane potential throughout the entire heart during patient procedures which prevents the comparison to a relevant "gold standard". Cardiac mapping systems are comprised of hardware and software elements including sophisticated mathematical algorithms, both of which continue to undergo rapid innovation. The purpose of this study is to develop a computational modeling framework to evaluate the performance of cardiac mapping systems. The framework enables rigorous evaluation of a mapping system's ability to localize and characterize (i.e., focal or reentrant) arrhythmogenic sources in the heart. The main component of our tool is a library of computer simulations of various dynamic patterns throughout the entire heart in which the type and location of the arrhythmogenic sources are known. Our framework allows for performance evaluation for various electrode configurations, heart geometries, arrhythmias, and electrogram noise levels and involves blind comparison of mapping systems against a "silver standard" comprised of computer simulations in which the precise transmembrane potential patterns throughout the heart are known. A feasibility study was performed using simulations of patterns in the human left atria and three hypothetical virtual catheter electrode arrays. Activation times (AcT) and patterns (AcP) were computed for three virtual electrode arrays: two basket arrays with good and poor contact and one high-resolution grid with uniform spacing. The average root mean squared difference of AcTs of electrograms and those of the nearest endocardial action potential was less than 1ms and therefore appears to be a poor performance metric. In an effort to standardize performance evaluation of mapping systems a novel performance metric is introduced based on the number of AcPs identified correctly and those considered spurious as well as misclassifications of arrhythmia type; spatial and temporal localization accuracy of correctly identified patterns was also quantified. This approach provides a rigorous quantitative analysis of cardiac mapping system performance. Proof of concept of this computational evaluation framework suggests that it could help safeguard that mapping systems perform as expected as well as provide estimates of system accuracy.

Characterization of the turbulent field behavior of an elevated jet-in crossflow investigated using direct numerical simulation

Direct numerical simulation (DNS) of a turbulent flow in an elevated jet in crossflow has been carried out at a Reynolds number (Re) of 3500 based on the outer width of the stack and crossflow velocity. The jet to crossflow velocity ratio is kept constant at VR=2. Second order spatial and temporal discretization have been used to solve the Navier–Stokes equations employing a high-resolution grid to determine the essential characteristics of the coherent and the incoherent flow fields accurately. The large-scale coherent and small-scale incoherent flow fields are extracted using a reference signal-based phase-averaged method that uses a reference signal against which the phases are identified in each cycle to minimize the phase jitter. The rate of decay of circulation of the stack-wake vortices is observed to be more than that of the jet-wake which has a lower circulation value. The contribution of the incoherent fluctuations to the total turbulent kinetic energy is more compared to its coherent counterpart in the wake as well as jet region, except for the near-wake of the stack. The normal and shear stresses of both coherent and incoherent fields reveal a relatively higher contribution in the near-wake of both stack-wake and jet-wake, while the far-wake shows the opposite trend. The anisotropy factor of both the large-scale coherent and the small-scale fluctuating fields provides the evidence of total anisotropy of the stack-wake. The characterization of both types of fluctuating fields is also carried out using the anisotropy-invariant-map which displays the one-component limit for the coherent fluctuating field in most of the regions, but the anisotropy state of the incoherent fluctuating field is found to switch among various limits, namely, the axisymmetric contraction, the axisymmetric expansion, and isotropic limits.

Improving spatial and temporal variation of ammonia emissions for the Netherlands using livestock housing information and a Sentinel-2-derived crop map

Ammonia emissions to the atmosphere have a range of negative impacts on environmental quality, human health, and biodiversity. Despite the considerable efforts in quantifying spatially explicit ammonia emissions, there are significant uncertainties in ammonia emission estimates at regional scales. We aimed to improve the modeling of atmospheric ammonia emission variability in space and time across the Netherlands by updating an agricultural ammonia emission model with a newly derived high-resolution crop map and a livestock housing location database of the Netherlands. To generate a crop map of 12 agricultural land cover classes, we applied random forest classification to the multi-temporal multispectral observations of surface reflectance and vegetation indices derived from Sentinel-2. The crop statistics were used to calculate ammonia emission distribution based on nitrogen demand (manure and mineral fertilizer needed) of different crop types using the INTEGRATOR model. Next, the crop map was utilized to spatially allocate the ammonia emissions to a high-resolution grid across the Netherlands. In addition, ammonia emissions from livestock housing systems were introduced as point sources using location data from the Geographic Information Agricultural Business system. The temporal emission variability was updated using a recently developed TIMELINES module. After the spatial and temporal distribution of ammonia emission was obtained with the crop map and housing information, it was imported into the chemistry transport model LOTOS-EUROS to model ammonia surface concentration for validation with in situ measurements. The performed crop classification has an average accuracy score of 0.73. The derived crop map was compared with Dutch national statistics, and the results showed that the absolute median of the relative difference between Sentinel-2 derived crop areas and national statistical information is around 5%. The newly modeled ammonia monthly surface concentrations compared better with in situ measurements in terms of the magnitude and temporal variability than those derived from the original emission distribution, indicating that the temporal distribution of ammonia emissions was improved. The comparison of modeled and measured annual averaged surface concentrations illustrated that the spatial distribution of ammonia emission was also improved. All model performance indicators significantly improved, and the performance of the updated model was more stable and robust. The improvement was more evident at the stations where livestock housing is the main emission source. This study illustrates that apart from a satellite-derived crop map, information on the locations of animal housing systems also plays an essential role in better estimates of the spatial and temporal distribution of ammonia emissions. It can be worthwhile to extrapolate the method to other regions in Europe and elsewhere.

An integrated global model of local urban development and population change

High resolution models are essential to assess the localised impacts of global environmental change. To enable the estimation of the impacts of location-specific change, this paper presents a new modelling approach that disaggregates scenario-based national-level urban population estimates derived from the often-applied Shared Socioeconomic Pathways (SSPs) to high-resolution spatial grid (30 arc sec) representations of urban area and local population. By combining high-resolution spatial data, a sophisticated statistics-based calibration approach and a computationally efficient modelling framework, we can coherently describe local changes in urban area and population across the globe. Both developments are modelled simultaneously using the same scenario assumptions, but divergent patterns are shown to arise when, for example, population decline coincides with decreasing densities that yield an increase in urban area. The model can be applied in integrated global assessment studies focused on trends in local exposure to natural hazards or environmental impacts of urban change. The improved resolution of this new model is crucial to assess the impacts of future population growth in developing regions where most of the planet's biodiversity concentrates near large populations that live in clusters of smaller urban areas previously not captured by coarser resolution models.

Dispersive staggered grid finite difference modelling of Rayleigh waves

Non-destructive surface wave-based ground exploration techniques such as multichannel analysis of surface wave (MASW) require a rigorous numerical model to predict the soil properties accurately. The staggered grid finite difference (SGFD) method is widely used to simulate elastic wave propagation due to its stability and ease of implementation. However, in the presence of low-velocity layers, the SGFD method requires computationally expensive high spatial grid resolution to accurately model the surface waves. On the other hand, velocity dependent grid approaches become unstable for the highly irregular soil profile. In order to overcome these challenges, a vertically non-uniform dispersive staggered grid scheme is proposed that significantly reduces the computing time without sacrificing accuracy. The proposed non-uniform discretization strategy is based on the Rayleigh wave dispersion relation, where the penetration depth of each frequency is assumed to be roughly one and half of the wavelength. Based on the above assumption, an exponential relationship between grid size and grid number is developed. The proposed method calculates continuously increasing grid length while ensuring grid points at the exact layer interface. Five different soil profiles: a homogenous elastic half-space, an irregularly dispersive crustal level profile, a low velocity layer at a deeper depth model, a real-world model, and a lateral heterogeneous model are used to demonstrate the efficacy of the proposed approach. For all the cases, the results of the proposed dispersive grid match those of the uniform grid with a correlation coefficient value (R) > 0.99. Compared to the uniform and velocity dependent grid approaches, the proposed dispersive grid scheme requires ½ to 1/4th of elements and CPU time. Furthermore, unlike the velocity dependent grid scheme, the proposed method does not become unstable for a highly irregular profile. Results indicate that the proposed method is superior to the traditional uniform and velocity dependent grid approaches for modelling elastic waves in layered media.

Simulation of high-frequency dissolved oxygen dynamics in a shallow estuary, the Corsica River, Chesapeake Bay

Understanding shallow water biogeochemical dynamics is a challenge in coastal regions, due to the presence of highly variable land-water interface fluxes, tight coupling with sediment processes, tidal dynamics, and diurnal variability in biogeochemical processes. While the deployment of continuous monitoring devices has improved our understanding of high-frequency (12 - 24 hours) variability and spatial heterogeneity in shallow regions, mechanistic modeling of these dynamics has lagged behind conceptual and empirical models. The inherent complexity of shallow water systems is represented in the Corsica River estuary, a small basin within the Chesapeake Bay ecosystem, where abundant monitoring data have been collected from long-term monitoring stations, continuous monitoring sensors, synoptic sensor surveys, and measurements of sediment-water fluxes. A state-of-the-art modeling system, the Semi-implicit Cross-scale Hydroscience Integrated System Model (SCHISM), was applied to the Corsica domain with a high-resolution grid and nutrient loads from the most recent version of the Chesapeake Bay watershed model. The Corsica SCHISM model reproduced observed high-frequency variability in dissolved oxygen, as well as seasonal variability in chlorophyll-a and sediment-water fluxes. Time-series signal analyses using Empirical Model Decomposition and spectral analysis revealed that the diurnal and M2 tide frequencies are the dominant high-frequency modes and physical transport contributes a larger share to dissolved oxygen budgets than biogeochemical processes on an hourly time scale. Heterogeneity and patchiness in dissolved oxygen resulting from phytoplankton distributions and geometry-driven eddies amplify the physical transport effect, and on longer time scales oxygen is controlled more by photosynthesis and respiration. Our simulation demonstrates that interactions among physical and biological dynamics generate complex high-frequency variability in water quality and non-linear reposes to nutrient loading and environmental forcing in shallow water systems.