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Solar Irradiance depicts the light energy produced by the Sun that hits the Earth. This energy is important for renewable energy generation and is intrinsically fluctuating. Forecasting solar irradiance is crucial for efficient solar energy generation and management. Work in the literature focused on the short-term prediction of solar irradiance, using meteorological data to forecast the irradiance for the next hours, days, or weeks. Facing climate change and the continuous increase in greenhouse gas emissions, particularly from the use of fossil fuels, the reliance on renewable energy sources, such as solar energy, is expanding. Consequently, governments and practitioners are calling for efficient long-term energy generation plans, which could enable 100% renewable-based electricity systems to match energy demand. In this paper, we aim to perform the long-term prediction of daily solar irradiance, by leveraging the downscaled climate simulations of Global Circulation Models (GCMs). We propose a novel Bayesian deep learning framework, named DeepSI (denoting Deep Solar Irradiance), that employs bidirectional long short-term memory autoencoders, prefixed to a transformer, with an uncertainty quantification component based on the Monte Carlo dropout sampling technique. We use DeepSI to predict daily solar irradiance for three different locations within the United States. These locations include the Solar Star power station in California, Medford in New Jersey, and Farmers Branch in Texas. Experimental results showcase the suitability of DeepSI for predicting daily solar irradiance from the simulated climate data, its superiority over related machine learning methods, and its ability to reproduce the daily variability. We further use DeepSI with future climate simulations to produce long-term projections of daily solar irradiance, up to year 2099. [ABSTRACT FROM AUTHOR]

Community resilience refers to the degree to which a community can survive and recover following a disaster. While resilience itself is well understood, decisions that would enhance resilience are interdependent and involve various stakeholders. There are indices for evaluating community resilience, but these have the shortcoming that they compare between political entities, such as counties. Therefore, one cannot ascertain that a county is truly resilient. In addition, natural disasters depend on the landscape and thus have no relation to the political boundaries. Our metric aims to capture the information into a Community Intrinsic Resilience Index (CIRI), which embodies the resilience level of four critical sectors: transportation, energy, health and socio-economic. As a case study, we computed CIRI for the counties within New Jersey. Results showed that within NJ, CIRI ranged from 63 to 80%. A post-disaster CIRI, following a scenario of flooding, revealed that two coastal counties would have low CIRI values due to the reduction in the road area and/or the GDP (local economy shut down) to below minimum values. We believe that our platform would further advance the efforts to fill the gap between resilience research and applications and would help decision and policy makers to integrate resilience within the planning and design phases of disaster management. [ABSTRACT FROM AUTHOR]

The increasing oil demand and busy waterways highlight the importance of oil spill preparedness and responses. Dispersants attract attention as an effective response tool to manage the impacts of major spill incidents. Despite in-depth laboratory evaluations on the effectiveness of chemical dispersants and their impacts on the transportation and fate of spilled oils, how dispersant works at sea remains a question and calls for the tests with greater realism to validate laboratory results, bring in energy impacts, and evaluate dispersant application equipment. Mesoscale studies and field trials have thus been widely conducted to assist better spill response operations. Such research attempts, however, lack a systematic summary. This study tried to fill the knowledge gaps by introducing the mesoscale facilities developed to advance the understanding of dispersant effectiveness on various sea conditions. An up-to-date overview of mesoscale studies and field trial assessments of dispersant effectiveness has also been conducted. We ended this review by highlighting the importance of public perception and future research needs to promote the approval and application of dispersants in spill incidents. [ABSTRACT FROM AUTHOR]

Oil dispersion under a deep-water plunging breaker of height 0.15 m was studied by coupling the Lagrangian particle tracking code (NEMO3D) with the population balance model (VDROP). The wave hydrodynamics obtained in Part I (Cui et al. in Environ Fluid Mech 2020 (in press)) was used as input. It was observed that droplet inertia and major forces on droplets significantly impacted the transport of oil droplets under wave conditions, and neglecting it caused less entrainment into the water column and horizontal spread of oil plume. For droplets less than 400 microns, the droplet size distribution (DSD) tended to follow a power-law distribution with an exponent close to − 2.3, which was consistent with earlier experimental observations by Delvigne and Sweeney (Oil Chem Pollut 4(4):281–310, 1988). The distribution of large size droplets evolved with time and showed agreement with a power-law distribution having an exponent of − 9.7 about 20 s after the passage of the wave train. Reducing the interfacial tension enhanced droplets breakup and increased the exponent of power-law distribution to − 6.1 for droplets smaller than 400 microns. It was also found that neglecting the vertical gradient of eddy diffusivity led to the accumulation of oil droplets in low eddy diffusivity regions at the bottom part of the wave breaker. The investigation herein could be used to obtain design values for breakers that could be used in oil spill models to predict the oil droplet size distribution. [ABSTRACT FROM AUTHOR]

The dispersion (transport and breakup) of oil droplets under breaking waves is essential for evaluating the impact of oil spills on the environment, and for designing countermeasures. In this part of the work (Part I), the hydrodynamics of a wave breaker in a 1.0 m deep tank obtained experimentally by Rapp and Melville (Philos Trans R Soc Lond A Math Phys Eng Sci 331(1622):735–800, 1990) using the dispersive focusing method was investigated numerically using Reynold-averaged Navier–Stokes (RANS) within the CFD code ANSYS Fluent. The renormalization group (RNG) k–ε turbulence closure model was adopted to simulate wave turbulence, and the transient water–air interface was captured using volume of fluid (VOF) method. The simulated surface excursion and velocity fields matched closely the experimental observations during wave breaking. The energy dissipation of the wave crest showed good agreement with recent laboratory work and numerical simulations through evaluating the breaking parameter. The RANS approach was able to reproduce the turbulent kinetic energy field engendered by the breakers and simulated the residual turbulence within about two wave periods after the passage of the wave train. The VOF scheme Compressive provided a better agreement with the observation than the Geo-reconstruct scheme. The approach herein suggests that RANS method coupled with VOF in ANSYS Fluent is capable of capturing the major hydrodynamic forces and turbulence, and thus could be used to predict environmental processes within the breaking waves such as oil droplet formation and transport. [ABSTRACT FROM AUTHOR]

Competing time scales involved in rapid rising micro-droplets in comparison to substantially slower biodegradation processes at oil-water interfaces highlights a perplexing question: how do biotic processes occur and alter the fates of oil micro-droplets (<500 μm) in the 400 m thick Deepwater Horizon deep-sea plume? For instance, a 200 μm droplet traverses the plume in ~48 h, while known biodegradation processes require weeks to complete. Using a microfluidic platform allowing microcosm observations of a droplet passing through a bacterial suspension at ecologically relevant length and time scales, we discover that within minutes bacteria attach onto an oil droplet and extrude polymeric streamers that rapidly bundle into an elongated aggregate, drastically increasing drag that consequently slows droplet rising velocity. Results provide a key mechanism bridging competing scales and establish a potential pathway to biodegradation and sedimentations as well as substantially alter physical transport of droplets during a deep-sea oil spill with dispersant. [ABSTRACT FROM AUTHOR]

A conservative solute tracer experiment was conducted in Indian Creek, a small urban stream in Philadelphia, Pennsylvania to investigate the role of subsurface properties on the exchange between streamwater and the hyporheic zone (subsurface surrounding the stream). Sodium Bromide (NaBr) was used as a conservative tracer, and it was monitored in the surface water at two stations and in the upper bed sediments (shallow hyporheic zone extending from 7.5 to 10 cm below the streambed). The hydraulic conductivity ( K) of the upper bed sediments and the lower bed sediments (10–12.5 cm below the streambed) was measured in situ. High tracer concentrations were observed in the upper layer at locations where the hydraulic conductivity of the upper layer was larger than that of the lower layer. Low concentrations in the upper layer were observed in the converse case. A statistically significant relationship between the mass retained in the upper layer and the difference of K values between layers was observed. [ABSTRACT FROM AUTHOR]

Scaling analyses on geophysical measurements of electrical conductivity, gamma radiation, and magnetic fields, at the Oak Ridge Reservation were conducted. The electrical conductivity and magnetic data exhibited multifractality in the north-south and east-west directions. The radiation data were observed to be non-scaling; a variogram with a sill was found to be more appropriate. The scaling of the EC and magnetic was generally within a range smaller than the maximum distance selected, as periodicity dominated at the larger distances. The electrical conductivity had anisotropy in the scaling of their variograms. But the magnetic data appear to have an isotropic scaling. The underlying statistics of the fields were near Gaussian for the electrical conductivity, but essentially Gaussian for the magnetic data. In environmental hydrogeology, knowledge of the spatial distribution of the intrinsic permeability, K, is very helpful in understanding the transport and spreading of contaminant plumes. Our previous studies have shown that the subsurface permeability, K, is multifractal. Detailed measurement of K is costly. Hence, large data sets of value collected both on a fine scale and over large distances are rare. In this study, we hypothesize that geophysical data could be used indirectly as a surrogate measurement for K, for obtaining statistical information on scale limited K data, and perhaps, directly at sites where K and electrical conductivity are correlated. [ABSTRACT FROM AUTHOR]

An amendment to this paper has been published and can be accessed via a link at the top of the paper. [ABSTRACT FROM AUTHOR]

Tides have been recognized as a major driving forcing affecting coastal aquifer system, and deterministic modeling has been very effective in elucidating mechanisms caused by tides. However, such modeling does not lend itself to capture embedded information in the signal, and rather focuses on the primary processes. Here, using yearlong data sets measured at beaches in Alaska Prince William Sound, we performed spectral and correlation analyses to identify temporal behavior of pore-water pressure, temperature and salinity. We found that the response of the beach system was characterized by fluctuations of embedded diurnal, semidiurnal, terdiurnal and quarterdiurnal tidal components. Hydrodynamic dispersion of salinity and temperature, and the thermal conductivity greatly affected pore water signals. Spectral analyses revealed a faster dissipation of the semi-diurnal component with respect to the diurnal components. Correlation functions showed that salinity had a relatively short memory of the tidal signal when inland freshwater recharge was large. In contrast, the signature of the tidal signal on pore-water temperature persisted for longer times, up to a week. We also found that heterogeneity greatly affected beach response. The response varied from a simple linear mapping in the frequency domain to complete modulation and masking of the input frequencies. [ABSTRACT FROM AUTHOR]