In the energy transition context, the design of integrated multi-energy systems is key for reaching ambitious sustainability objectives. Due to the intermittent nature of the renewable energy sources, introducing technologies for storing and transforming energy in different carriers (e.g., electricity, gas, heat) is, in fact, a strategic solution for fully exploiting the renewable power generation, increasing the flexibility of the system, and contributing to the decarbonization. Although the need to rely on multi-energy systems is widely shared, identifying their optimal design requires the use of complex modelling tools able to characterize the territory, simulate the system dynamics, and evaluate the solutions with respect to different sustainability objectives. To support the decarbonization decision-making process, in this work we develop a three-step modelling chain for planning optimal multi-energy systems at the local scale. More precisely, we first perform a territory characterization by estimating, through different methodologies, input data of renewable resource availability, territory exploitation potential, and energy demand of electricity and heat. Then, we carry out a multi-energy analysis identifying Pareto optimal system designs with respect to two sustainability objectives, namely the Net Present Cost and the CO2 emissions. Finally, we perform an intersectoral Multi Criteria Analysis-Cost Benefit Analysis (MCA-CBA) for evaluating the solutions obtained in the previous step with respect to a wide range of indicators representing energy, economic, and social acceptance aspects. The CBA approach is adopted for evaluating the financial and economic viability of the investment options, while the assessment of non-monetary impacts is performed through the MCA approach. We apply the modelling chain to the real case study of Sulcis Iglesiente (Sardinia, Italy), a territory characterized by carbon-intensive industries, recently selected for receiving funding from the Just Transition Fund launched by the EU Commission in the context of the Green Deal. Expected results aim to demonstrate the validity of the proposed modelling chain in the identification of the best interventions for supporting the decarbonization and the sustainable development of Sulcis Iglesiente.

Our oceans are critical to the health of our planet and its inhabitants. Increasing pressures on our marine environment are triggering an urgent need for continuous and comprehensive monitoring of the oceans and stressors, including anthropogenic activity. Current ocean observational systems are expensive and have limited temporal and spatial coverage. However, there exists a dense network of Fibre-Optic (FO) telecommunication cables, covering

We employ Magnetospheric Multiscale, Geostationary Operational Environmental and Los Alamos National Laboratory satellites, as well as the ground magnetometer networks over Greenland and North America to study a substorm on 9 August 2016 between 9 and 10 UT.We found that during the substorm two earthward flows, whose dipolarization-injection fronts exceeded 6.5 and 4 Earth’s radii (RE) in YGSM, impinged and rebounded from Earth’s dipolar field lines at L = 6–7 downtail, where L is the McIlwain number. The impingements and rebounds ended with a substorm current system of downward R1 and upward R2 currents which grew to azimuthally cover the whole North American continent. At the fronts, regions of enhanced negative j·E′ were formed and peaked toward the end of the impingements. These regions appeared to be conjugate with eastward moving aurora (along the growth phase arc and together with eastward drifting energetic electrons at geosynchronous equatorial orbit), which manifests ionospheric Ohmic losses.

Phenotype characterization is an exciting new field that links agriculture and data science. Thanks to advances in remote sensing and artificial intelligence, we can now accurately quantify field-scale phenotypic information and integrate this big data into predictive and prescriptive management tools.Our past work has shown that phenotypic information measured from UAS is more precise and reliable than manual field measurements. In addition, we have shown that the vast size of this data can outweigh the complexity of the problem, to the point where even a simpler algorithm can outperform a sophisticated algorithm when big enough training data are available. This is because the big data captures even very infrequent aspects of the problem of interest, which can be modeled with simple logics.The availability of UAS data makes it possible to develop digital twin models to forecast future plant growth and develop in-season management plans. A digital twin model is a virtual representation of real-world entities and processes. These models use early growth patterns of a crop as input to artificial intelligence algorithms so that the algorithms can predict crop performance during the next 10, 20, or 30 days ahead of the last data point collected by the UAS. Crop growth features forecasting results can be useful for the management of irrigation, growth regulators, maturity of the crops, and to obtain early-season yield estimation.

Seismic interferometry is a powerful tool to monitor the seismic velocity change associated with volcanic eruptions. For the monitoring, changes in seismic velocity with environmental origins (such as precipitation) are problematic. In order to model the environmental effects, we propose a new technique based on a state-space model. An extended Kalman filter estimates seismic velocity changes as state variables, with a first-order approximation of the stretching method. We apply this technique to three-component seismic records in order to detect the seismic velocity change associated with the Shinmoe-dake eruptions in 2011 and 2018. First, ambient noise cross-correlations were calculated from May 2010 to April 2018. We also modeled seismic velocity changes resulting from precipitation and the 2016 Kumamoto earthquake, with exponential type responses. Most of the results show no significant changes associated with the eruptions, although gradual inflation of the magma reservoir preceded the 2011 eruption by one year. The observed low sensitivity to static stress changes suggests that the fraction of geofluid and crack density at about 1 km depth is small, and the crack shapes could be circular. Only one station pair west of the crater shows the significant drop associated with the eruption in 2011. The gradual drop of seismic velocity up to 0.05% preceded the eruption by one month. When the gradual drop began, volcanic tremors were activated at about 2 km depth. These observations suggest that the drop could be caused by damage accumulation due to vertical magma migration beneath the summit.

Landfalling atmospheric rivers (ARs) frequently trigger heavy and sometimes prolonged precipitation, especially in regions with favored orographic enhancement. The presence and strength of ARs are often described using the integrated water vapor (IWV) and the integrated vapor transport (IVT). However, the associated precipitation is not directly correlated with these two variables. Instead, the intensity of precipitation is mainly determined by the net convergence of moisture flux and the initial degree of saturation of the air column. In this study, a simple algorithm is proposed for estimating the heavy precipitation attributable to the IVT convergence. Bearing a strong resemblance to the Kuo-Anthes parameterization scheme for cumulus convection, the proposed algorithm calculates the large-scale primary condensation rate (PCR) as a proportion of the IVT convergence, with a reduction to account for the general moistening in the atmosphere. The amount of reduction is determined by the column relative humidity (CRH), which is defined as the ratio of IWV to its saturation counterpart. Our analysis indicates that the diagnosable PCR compares well to the forecast precipitation rate given by a numerical weather prediction model. It is also shown that the PCR in an air column with CRH < 0.50 is negligibly small. The usefulness of CRH and PCR as two complements to standard AR analysis is illustrated in three case studies. The potential application of PCR to storm classification is also explored.

The Gotvand dam was built on the most important Iranian river to support a number of populated cities with freshwater, provide irrigation water for million hectares of fertile farmlands, and meet water demand for the country’s hub industrial zones. This dam is known as one of the worst engineering failures in Iran’s history because its impoundment submerged the enormous salty unit of Gachsaran evaporite formation (GEF) outcropped in the reservoir, leading to reservoir water salinization in deep layers up to several times greater than that of in the high-seas. Given the failed practical application of direct intervention strategies to control the salinity crisis, we suggested a low-cost salinity management strategy based on the reservoir operation to mitigate the dam outlet salinity and preserve the downstream environment from the salinity hazards. The three-dimensional MIKE3 model, was run to calculate the GEF dissolution rate, accumulated salt in the reservoir, and the dam outlet salinity. Then, we ran the model considering different outlet salinity levels to explore the best reservoir operation strategy to prohibit the accumulated salt in the reservoir and keep the safe salinity for downstream irrigation-use. Simulation results suggested that the GEF dissolution rate varied from 0.5 to 7 cm/hr, mainly due to incremental submergence of the GEF during multi-stage impoundment of the reservoir. Considering the final dissolution rate of 0.5 cm/hr and inlet salinity from the upstreams, salt accumulation inside the reservoir can be gradually prevented by setting the outlet salinity to its maximum historical downstream level, i.e., 1400 µmhos/cm.

At the international level, the term “water security” (WS) has gained increasing attention in recent decades. At the operational level, WS is assessed using tools that define the concept using a variety of dimensions and sub-dimensions, with qualitative and quantitative indicators and parameters. The breadth of tools and concepts is an obstacle to the operationalization of the concept of water security (WS). Clearly we need a range of diverse data to evaluate water security (WS). However, there are several barriers to designing an optimal Data Gathering Strategy (DGS). Such a strategy must strike a balance between a wide range of competing and overlapping data requirements and characteristics including: resources, information, and impact. The conceptual aim of the framework can be summarised as shifting the focus of the DGS from a “data to information approach” to a “data to action approach”. The specific aims of this paper are to: identify the key issues that should be addressed in designing a Data Gathering Strategy for Water Security (DGSxWS); communicate the key issues with a clear conceptual framework; and suggest approaches and activities that could help water practitioners in dealing with the issues identified.

Astronomical variations in tidal magnitude can strongly modulate the severity of coastal flooding on daily, monthly, and interannual timescales. Here, we present a new quasi-nonstationary skew surge joint probability method (qn-SSJPM) that estimates interannual fluctuations in flood hazard caused by the 18.6 and quasi 4.4-year modulations of tides. We demonstrate that qn-SSJPM-derived storm tide frequency estimates are more precise and stable compared with the standard practice of fitting an extreme value distribution to measured storm tides, which is often biased by the largest few events within the observational period. Applying the qn-SSJPM in the Gulf of Maine, we find significant tidal forcing of winter storm season flood hazard by the 18.6-year nodal cycle, whereas 4.4-year modulations and a secular trend in tides are small compared to interannual variation and long-term trends in sea-level. The nodal cycle forces decadal oscillations in the 1% annual chance storm tide at an average rate of ±13.5 mm/y in Eastport, ME; ±4.0 mm/y in Portland, ME; and ±5.9 mm/y in Boston, MA. Currently (in 2020), nodal forcing is counteracting the sea-level rise-induced increase in flood hazard; however, in 2025, the nodal cycle will reach a minimum and then begin to accelerate flood hazard increase as it moves toward its maximum phase over the subsequent decade. Along the world’s meso-to-macrotidal coastlines, it is therefore critical to consider both sea-level rise and tidal non-stationarity in planning for the transition to chronic flooding that will be driven by sea-level rise in many regions over the next century.

Global groundwater volumes in the upper 2 km of the Earth’s continental crust – critical for water security – are well estimated. Beyond these depths, a vast body of largely saline and non-potable groundwater exists down to at least 10 km —a volume that has not yet been quantified reliably at the global scale. Here, we estimate the amount of groundwater present in the upper 10 km of the Earth’s continental crust by examining the distribution of sedimentary and cratonic rocks with depth and applying porosity-depth relationships. We demonstrate that groundwater in the 2-10 km zone (what we call ‘deep groundwater’) has a volume comparable to that of groundwater in the upper 2 km of the Earth’s crust. These new estimates make groundwater the largest continental reservoir of water, ahead of ice sheets, provide a basis to quantify geochemical cycles, and constrain the potential for large-scale isolation of waste fluids.

Lead (Pb) is a neurotoxicant that particularly harms young children. Urban environments are often plagued with elevated Pb in soils and dusts, posing a health exposure risk from inhalation and ingestion of these contaminated media. Thus, a better understanding of where to prioritize risk screening and intervention is paramount from a public health perspective. We have synthesized a large national dataset of Pb concentrations in household dusts from across the United States (U.S.), part of a community science initiative called “DustSafe.” Using these results, we have developed a straightforward logistic regression model that correctly predicts whether Pb is elevated (> 80 ppm) or low (< 80 ppm) in household dusts 75% of the time. Additionally, our model estimated 18% false negatives for elevated Pb, displaying that there was a low probability of elevated Pb in homes being misclassified. Our model uses only variables of approximate housing age and whether there is peeling paint in the interior of the home, illustrating how a simple and successful Pb predictive model can be generated if researchers ask the right screening questions. Scanning electron microscopy supports a common presence of Pb paint in several dust samples with elevated bulk Pb concentrations, which explains the predictive power of housing age and peeling paint in the model. This model was also implemented into an interactive mobile app that aims to increase community-wide participation with Pb household screening. The app will hopefully provide greater awareness of Pb risks and a highly efficient way to begin mitigation.

The stated mission of the International Arctic Research Center (IARC) at the University of Alaska Fairbanks (UAF) is ”to foster Arctic research to help the nation and the world understand, prepare for, and adapt to the pan-Arctic impacts of climate change.” IARC’s work and collaborations are everywhere in the Arctic and beyond. One of IARC’s core values is actively cultivating an environment of diversity and inclusion where all individuals and groups feel welcomed and are heard. Through the work of the IARC Diversity and Inclusion Working Group, we affirm that our different experiences, expertise, and ways of knowing are our strength, fostering diverse thoughts and ideas. Collaborative science and administrative support by people from diverse backgrounds and knowledge systems helps IARC build a professional culture of inclusion, listening, caring and respect. We face challenges in DEI work and are actively addressing them. For example, in addressing the challenge of time constraints by the all-volunteer membership, a DEI coordinator position to spearhead our efforts is being created and funding sought. We highlight efforts from the IARC Diversity and Inclusivity Working Group to cultivate a culture of inclusion, equity and access: 1) collaboratively developing an institutional diversity and inclusivity statement and land acknowledgement statement, 2) creating safe spaces for conversation and action through open biweekly working group meetings and discussions, 3) elevating awareness through relevant workshops, trainings and discussions, 4) collectively incorporating diversity and inclusion values into the IARC strategic plan, 5) making presentations and documents accessible, and 6) actively collaborating on inclusion efforts within UAF, Alaska and beyond. We welcome new members with their ideas and perspectives to the working group. At IARC, we recognize that inclusion is an ongoing process that has to be woven into all that we do in research, education and outreach.

Validation of cloud hydrometeors simulation from the global models is important issue as it pertains to the accuracy of climate predictions. In this study, the cloud hydrometeor data from Korean Integrated Model (KIM) is validated using different Reanalysis (ERAI, ERA5, and MERRA) and Satellite Observations (Cloudsat). In ERA5 products, cloud snow water and rain water are also available. Satellite observations are gridded to compare with model simulations. Cloud liquid water (Qc), Cloud snow water (Qs), Cloud ice water (Qi), Cloud rain water (Qr), Vapour mixing ratio (Qv) for January (dry) and July (wet seasons) of 2017 are considered for validation. BIAS and RMSE are calculated for comparison. To understand the vertical distribution of hydrometeors, contour frequency altitude diagrams (CFADs) are plotted. Early validation of KIM hydrometeors shows the reasonable estimate of different hydrometeors with KIM model showing more Qc at surface, more Qv at upper levels. The vertical structure of Qi has showed significant bias at upper levels with model showing large ice values at higher levels. ERAI and ERA5 products has showed distinct pattern of Qi due to different configurations. More Qs at upper levels is also evident in model simulations. Combined distribution (Qc+Qi) of KIM at lower (upper) levels is more comparable with ERA5 (MERRA) products. Further, Qr distribution shows underestimation at the equator and over estimation at the latitude belts. To examine the contribution of different physics modules related to the bias, the hydrometeors from cumulus, microphysics and shallow convection are also analyzed separately. Accuracy of KIM simulated cloud hydrometeors against different products and possible causes for biases will be discussed in the conference.

New geophysical data from Antarctica’s Ross Embayment reveal the structure and subglacial geology of extended continental crust beneath the Ross Ice Shelf. We use airborne magnetic data from the ROSETTA-Ice Project to locate the contact between magnetic basement and overlying sediments. We delineate a broad, segmented basement high with thin (0-500m) non-magnetic sedimentary cover which trends northward into the Ross Sea’s Central High. Before subsiding in the Oligocene, this feature likely facilitated early glaciation in the region and subsequently acted as a pinning point and ice flow divide. Flanking the high are wide sedimentary basins, up to 3700m deep, which parallel the Ross Sea basins and likely formed during Cretaceous-Neogene intracontinental extension. NW-SE basins beneath the Siple Coast grounding zone, by contrast, are narrow, deep, and elongate. They suggest tectonic divergence upon active faults that may localize geothermal heat and/or groundwater flow, both important components of the subglacial system.

Creeping faults are typically not associated with large earthquakes. However, new K/Ar dating and biomarker maturity data on the San Andreas Fault Observatory at Depth (SAFOD) present evidence that large paleoearthquakes have occurred in the creeping section of the San Andreas Fault, California. K/Ar ages of bulk samples with evidence of coseismic heating range from 3.3 to 15.8 Ma, and argon diffusion experiments suggest that these ages are only partially reset and the actual event ages may be even younger. Thus, questions remain as to how we can refine such dates to reveal the precise age and location of these earthquakes. To refine the ages and more accurately assess seismic hazard, we date size separates of eight samples from different sections of the SAFOD core. Following Stokes’ Law, we split each sample into five size fractions using hydrodynamic settling: <0.2, 0.2-0.5, 0.5-0.8, 0.8-1.4, and 1.4-2 micrometers. The finest size fractions contain the most authigenic illite, which form during fault slip. We determined chemical composition and separated illite polytypes using x-ray diffraction, and also measured K/Ar ages on each sample. Preliminary results from two scaly black fault rock samples, previously shown to have hosted earthquakes, (3,193.69 m and 3,193.96 m along the core) support that the finest size fractions contain the greatest ratio of authigenic illite. With a York regression between age and detrital illite abundance, we place the authigenic illite ages at 1.08 ± 2.40 Ma and 0.88 ± 5.08 Ma for these two samples, and observe that the detrital illite matches the late Cretaceous age for the country rock. This new age estimate for the authigenic illite means that large earthquakes must have propagated into the creeping section within the last million years. Not only is it significantly younger than the bulk sample age, it is recent enough that translation of faulted material from the locked southern San Andreas fault into the creeping section cannot explain the record. Moving forward, we will expand our procedure to include isotope dilution for measuring K concentration and analyze the other samples previously measured for biomarker maturity and bulk K/Ar age. Resulting insights into the fault rock composition and the timing of past earthquakes will be crucial in assessing the region’s seismic hazard.

Past studies reported a drastic growth in the wildland-urban interfaces (WUI), the locations where man-made structures meet or overlap wildland vegetation. There is a perception that damages due to wildfires are mainly located at the WUI. However, there is no clear evidence that wildfire intensity and frequency are highest in these regions. In this work, we have reported the actual occurrences of wildfires with respect to WUI and how much of the WUI are on complex topography in California (CA), the state with the highest burned area and risk of wildfires. We calculated the overlap of the burned area from previous wildfire events in California in the last ten years with the WUI perimeters. Two currently existing WUI definitions are used for this purpose. Furthermore, we also calculated the number of fire ignition points that lie within the WUI perimeters. We found that a very small percentage of wildfire ignitions actually occurred in the WUI areas. Moreover, the overlap between the wildfire burned area and WUI areas was also found to be small. To find out if the wildfires burned in the vicinity of WUI areas, we created buffers around both the WUI areas and the wildfire perimeters separately and computed the impact of buffer distance on the overlap. This behavior has been connected to the importance of firebrand ignition from spot fires in the WUI. Moreover, a majority of WUI areas in CA was found to be situated on complex topography. Therefore, we conclude that in CA, wildfires are not limited to WUI regions only, but their main fire fronts burn farther away from the WUI and are mostly located on complex topography, where controlling large wildfires is more difficult and fire behavior is more complex. Results from this study will give direction for remapping the existing WUI definitions, will be helpful for wildfire management and will benefit policymakers and land managers at the state and local level to focus on the factors that determine the high-risk prone areas for future wildfires.

An assessment was carried out of the relationship between the Corona Virus (covid-19) and the spread across Iraq of various heavy metal contaminations. At the onset, all the confirmed, recovered and death cases of covid-19 virus in Iraq up to the date of May, 2nd, 2020 were collected and compared with the top three infected countries in the world (USA, Spain and Italy). On the other hand, numerous heavy metal contaminations in different Iraqi cities have been summarized and associated with allowable upper and lower worldwide standard limits. In addition, the study introduced a hierarchical predictive approach for the relationship between confirmed and death cases of covid-19 viruses with heavy metal contamination in various Iraqi cities. It was concluded that all the studied Iraqi cities have heavy metal contamination for different chemical elements exceeding the allowable standard limits. In addition, it was shown that the extreme contents of Copper (Cu), Nickel (Ni), Lead (Pb), and Zinc (Zn) are concentrated in Al-Qadisiyah, Al-Sulaimaniyah, Erbil and Baghdad cities with limits of 160 µg / g, 240.9 µg / g, 378 µg / g and 1080 µg / g respectively. Based on the hierarchical prediction approach, a linear positive relationship between both confirmed and death covid-19 cases with different heavy metal contamination was obtained with a maximum coefficient of determination (R2) of 0.97.

Maximizing crop yield while conserving resources is a pressing challenge in modern agriculture. Maize (Zea mays L. ), a staple crop worldwide, relies heavily on photosynthesis, making radiation interception a pivotal factor in crop growth and yield. This study presents a novel approach to improve maize crop productivity by harnessing the principles of phyllotaxy and optimizing planting patterns to efficiently intercept solar radiation. Through the enhanced 3D maize model generation algorithm, the simulation incorporates critical factors such as curved surface leaf area, leaf arrangement, plant spacing, and solar angles, allowing us to quantify the radiation intercepted by the maize canopy. We investigate the most efficient phyllotaxy for individual plants and evaluate a range of planting patterns and their impact on radiation interception at various growth stages. Simulation results reveal that optimizing planting patterns based on phyllotactic principles can substantially enhance radiation capture compared to traditional planting methods. In conclusion, our research signifies a significant step towards harnessing the power of plant arrangement and optimizing planting strategies, including the use of an enhanced 3D maize model, to maximize radiation interception in maize crops. The optimal phyllotaxis and planting patterns establish a distinct phenological target for breeders. These findings hold promise for the development of more resilient and productive agricultural systems in an era of growing global food demand and resource constraints.