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To accurately describe the energetics of transition metal systems, density functional approximations (DFAs) must provide a balanced description of s- and d- electrons. One measure of this is the sd transfer error, which has previously been defined as $E\left({3\mathrm{d}}^{n-1}{4\mathrm{s}}^1\right)-E\left({3\mathrm{d}}^{n-2}{4\mathrm{s}}^2\right)$. Theoretical concerns have been raised about this definition due to its evaluation of excited-state energies using ground-state DFAs. A more serious concern appears to be strong correlation in the 4s²configuration. Here, we define a ground-state measure of the sd energy imbalance, based on the errors of s- and d-electron second ionization energies of the 3d atoms, that effectively circumvents the aforementioned problems. We find an improved performance as we move from the local spin density approximation (LSDA) to the Perdew-Burke-Ernzerhof (PBE) generalized gradient approximation (GGA) to the regularized and restored Strongly Constrained and Appropriately Normed (r²SCAN) meta-GGA for first-row transition metal atoms. However, we find large (∼2 eV) ground-state sd energy imbalances when applying a Perdew–Zunger 1981 self-interaction correction. This is attributed to an “energy penalty” associated with the noded 3d orbitals. A local scaling of the self-interaction correction to LSDA results in a balance of s- and d-errors. 

Abstract Federal and local agencies have identified a need to create building databases to help ensure that critical infrastructure and residential buildings are accounted for in disaster preparedness and to aid the decision-making processes in subsequent recovery efforts. To respond effectively, we need to understand the built environment—where people live, work, and the critical infrastructure they rely on. Yet, a major discrepancy exists in the way data about buildings are collected across the United SStates There is no harmonization in what data are recorded by city, county, or state governments, let alone at the national scale. We demonstrate how existing open-source datasets can be spatially integrated and subsequently used as training for machine learning (ML) models to predict building occupancy type, a major component needed for disaster preparedness and decision -making. Multiple ML algorithms are compared. We address strategies to handle significant class imbalance and introduce Bayesian neural networks to handle prediction uncertainty. The 100-year flood in North Carolina is provided as a practical application in disaster preparedness. 

Abstract Effective flood prediction supports developing proactive risk management strategies, but its application in ungauged basins faces tremendous challenges due to limited/no streamflow record. This study investigates the potential for integrating streamflow derived from synthetic aperture radar (SAR) data and U.S. National Water Model (NWM) reanalysis estimates to develop improved predictions of above-normal flow (ANF) over the coterminous US. Leveraging the SAR data from the Global Flood Detection System to estimate the antecedent conditions using principal component regression, we apply the spatial-temporal hierarchical model (STHM) using NWM outputs for improving ANF prediction. Our evaluation shows promising results with the integrated model, STHM-SAR, significantly improving NWE, especially in 60% of the sites in the coastal region. Spatial and temporal validations underscore the model’s robustness, with SAR data contributing to explained variance by 24% on average. This approach not only improves NWM prediction, but also uniquely combines existing remote sensing data with national-scale predictions, showcasing its potential to improve hydrological modeling, particularly in regions with limited stream gauges. 

This study highlights the application of self-regulated learning and cognitive strategy use research in the Connecting Students with Autism to Geographic Information Science & Technology (CSA-GIST) study, an NSF-funded project. This study investigated the program’s impact on autistic high school students’ self-regulated learning (SRL) and cognitive strategy use during STEM instruction. Though the findings were not significant regarding the impact of the program on students SRL and cognitive strategy use at the midpoint of the program, increased interest in math, science, and technology were reported for each cohort during year one. These findings provide implications for adaptations to this workforce development program to increase students’ SRL and cognitive strategy use for GIST and STEM careers among autistic students. Implications for research and practice are discussed. 

Abstract Floods cause hundreds of fatalities and billions of dollars of economic loss each year in the United States. To mitigate these damages, accurate flood prediction is needed for issuing early warnings to the public. This situation is exacerbated in larger model domains flood prediction, particularly in ungauged basins. To improve flood prediction for both gauged and ungauged basins, we propose a spatio‐temporal hierarchical model (STHM) using above‐normal flow estimation with a 10‐day window of modeled National Water Model (NWM) streamflow and a variety of catchment characteristics as input. The STHM is calibrated (1993–2008) and validated (2009–2018) in controlled, natural, and coastal basins over three broad groups, and shows significant improvement for the first two basin types. A seasonal analysis shows the most influential predictors beyond NWM streamflow reanalysis are the previous 3‐day average streamflow and the aridity index for controlled and natural basins, respectively. To evaluate the STHM in improving above‐normal streamflow in ungauged basins, 20‐fold cross‐validation is performed by leaving 5% of sites. Results show that the STHM increases predictive skill in over 50% of sites' by 0.1 Nash‐Sutcliffe efficiency (NSE) and improves over 65% of sites' streamflow prediction to an NSE > 0.67, which demonstrates that the STHM is one of the first of its kind and could be employed for flood prediction in both gauged and ungauged basins. 

Abstract Infrasound may be used to detect the approach of hazardous volcanic mudflows, known as lahars, tens of minutes before their flow fronts arrive. We have analyzed signals from more than 20 secondary lahars caused by precipitation events at Fuego Volcano during Guatemala’s rainy season in May through October of 2022. We are able to quantify the capabilities of infrasound monitoring through comparison with seismic data, time lapse camera imagery, and high-resolution video of a well-recorded event on August 17. We determine that infrasound sensors, deployed adjacent to the lahar path and in small-aperture (10 s of meters) arrays, are particularly sensitive to remote detection of lahars, including small-sized events, at distances of at least 5 km. At Fuego Volcano these detections could be used to provide timely alerts of up to 30 min before lahars arrive at a downstream monitoring site, such as in the frequently impacted Ceniza drainage. We propose that continuous infrasound monitoring, from locations adjacent to a drainage, may complement seismic monitoring and serve as a valuable tool to help identify approaching hazards. On the other hand, infrasound arrays located a kilometer or more from the lahar path can be effectively used to track a lahar’s progression. 

Abstract Using mass–radius composition models, small planets (R≲ 2R_⊕) are typically classified into three types: iron-rich, nominally Earth-like, and those with solid/liquid water and/or atmosphere. These classes are generally expected to be variations within a compositional continuum. Recently, however, Luque & Pallé observed that potentially Earth-like planets around M dwarfs are separated from a lower-density population by a density gap. Meanwhile, the results of Adibekyan et al. hint that iron-rich planets around FGK stars are also a distinct population. It therefore remains unclear whether small planets represent a continuum or multiple distinct populations. Differentiating the nature of these populations will help constrain potential formation mechanisms. We present theRhoPopsoftware for identifying small-planet populations.RhoPopemploys mixture models in a hierarchical framework and a nested sampler for parameter and evidence estimates. UsingRhoPop, we confirm the two populations of Luque & Pallé with >4σsignificance. The intrinsic scatter in the Earth-like subpopulation is roughly half that expected based on stellar abundance variations in local FGK stars, perhaps implying M dwarfs have a smaller spread in the major rock-building elements (Fe, Mg, Si) than FGK stars. We applyRhoPopto the Adibekyan et al. sample and find no evidence of more than one population. We estimate the sample size required to resolve a population of planets with Mercury-like compositions from those with Earth-like compositions for various mass–radius precisions. Only 16 planets are needed when ${\sigma }_{M_p}=5\%$and ${\sigma }_{R_p}=1\%$. At ${\sigma }_{M_p}=10\%$and ${\sigma }_{R_p}=2.5\%$, however, over 154 planets are needed, an order of magnitude increase.