Historical Observations Research Articles

Why do seismic hazard models worldwide appear to overpredict historical intensity observations?

Probabilistic seismic hazard assessments (PSHAs) provide the scientific basis for building codes to reduce damage from earthquakes. Despite their substantial impact, little is known about how well PSHA predicts actual shaking. Recent PSHA for California, Japan, Italy, Nepal, and France appear to consistently overpredict historically observed earthquake shaking intensities. Numerical simulations show that observed shaking is equally likely to be above or below predictions. This result from independently developed models and datasets in different countries and tectonic settings indicates possible systematic bias in the hazard models, the observations, or both. Analysis of possible causes shows that much of the discrepancy is due to a subtle and rarely considered issue: the conversion equations used in comparing the models-which forecast shaking as peak ground acceleration or velocity-and observations-parameterizations of qualitative shaking reports. Historical shaking reports fill a crucial data gap, but more research is warranted on how qualitative observations relate to instrumental shaking measures for earthquakes.

Online detection of the incidence via transfer learning

AbstractThe counting process has abundant applications in reality, and Poisson process monitoring actually has received extensive attention and research. However, conventional methods experience poor performance when shifts appears early and only small number of historical observations in Phase I can be used for estimation. To overcome it, we creatively propose a new online monitoring algorithm under the transfer learning framework, which utilizes the information from observations of additional data sources so that the target process can be described better. By making the utmost of the somewhat correlated data from other domains, which is measured by a bivariate Gamma distributed statistic presented by us, the explicit properties (e.g., posterior probability mass function, posterior expectation, and posterior variance) are also strictly proved. Furthermore, based on the above theoretical results, we design two computationally efficient control schemes in Phase II, that is a control chart based on the cumulative distribution function for large shifts and an exponentially weighted moving average control chart for small shifts. For a better understanding of the more practical applications and transferability matter, we provide some optimal values for parameter setting. Extensive numerical simulations and a case of skin cancer incidence in America verify the superiorities of our approach.

Climate change impairs the effects of vegetation improvement on soil erosion control in the Qinghai-Tibetan Plateau

Water erosion is a wide ecosystem degradation threat for the Qinghai-Tibetan Plateau (QTP) region and surrounding areas. Climate wetting and vegetation greening in the QTP could have complex impacts on water erosion. However, there is limited information about the erosion dynamics under climate change and the effects of vegetation improvement on erosion control in this alpine region. By calibrating the Revised Universal Soil Loss Equation (RUSLE) model, we estimated water erosion in the QTP and simulated future changes using different Shared Socioeconomic Pathway-Representative Concentration Pathway (SSP) scenarios. We showed intensified erosion and that the benefits of vegetation improvement to erosion control was impaired under climate change. Under the SSP585 scenario, annual water erosion in the QTP was predicted to increase by 0.35 t∙ha−1∙yr−1 and be dominated by an accelerative rise of rainfall erosivity (R factor of RUSLE). Compared with historical observations, the C factor of RUSLE under the SSP585 scenario was believed to reduce by 4.35 % on average during the 2081–2100 period, but predicted soil loss will increase by more than 90 %. Although more considerable vegetation improvement could occur under high climate forcing, there may also be more intensified rainfall erosivity and water erosion across the QTP than those under SSP126 and SSP245 scenarios. Our results suggest that mitigating climate change rather than vegetation improvement is essential to address climate-dominated ecosystem degradation threats such as water erosion in the QTP.

Dialogues of styles

Every major style goes through a series of metamorphoses after its inception. Styles echo each other, learn from each other, argue and penetrate each other. The selection of materials in this section contains the results of historical observations and research, showing these processes on specific examples. How was the great style of classicism reflected in the works of its fiery overthrowers? How was the Enlightenment style transformed on the Far Eastern land? How did Russian architects influence the new architecture of China? How is the cultural heritage of Soviet and German cities reinterpreted? All these materials are united by the theme of the dialogue of styles and the metamorphoses that arise in this process.

Improving Earth System Model Selection Methodologies for Projecting Hydroclimatic Change: Case Study in the Pacific Northwest

AbstractThe rapid expansion of Earth system model (ESM) data available from the Coupled Model Intercomparison Project Phase 6 (CMIP6) necessitates new methods to evaluate the performance and suitability of ESMs used for hydroclimate applications as these extremely large data volumes complicate stakeholder efforts to use new ESM outputs in updated climate vulnerability and impact assessments. We develop an analysis framework to inform ESM sub‐selection based on process‐oriented considerations and demonstrate its performance for a regional application in the US Pacific Northwest. First, a suite of global and regional metrics is calculated, using multiple historical observation datasets to assess ESM performance. These metrics are then used to rank CMIP6 models, and a culled ensemble of models is selected using a trend‐related diagnostics approach. This culling strategy does not dramatically change climate scenario trend projections in this region, despite retaining only 20% of the CMIP6 ESMs in the final model ensemble. The reliability of the culled trend projection envelope and model response similarity is also assessed using a perfect model framework. The absolute difference in temperature trend projections is reduced relative to the full ensemble compared to the model for each SSP scenario, while precipitation trend errors are largely unaffected. In addition, we find that the spread of the culled ensemble temperature and precipitation trends includes the trend of the “truth” model ∼83%‐92% of the time. This analysis demonstrates a reliable method to reduce ESM ensemble size that can ease use of ESMs for creating and understanding climate vulnerability and impact assessments.

Cyber-physical event reasoning for distributed energy resources

The widespread adoption of internet-connected and remotely controllable solar plants and energy storages renders coordinated cyber–physical attacks against distributed energy resources (DERs) an emerging risk for power systems. Effective incident response can be facilitated by online DER monitoring providing real-time information on event root causes and physical impacts. Such online event identification is challenged by the lack of historical attack observations, and emergence of new attack strategies. The Cyber-Physical Event Reasoning System CyPhERS provides real-time information on both known and unknown attack types in form of informative and interpretable event signatures, without need to be trained on historical attack samples. To date, CyPhERS has only been demonstrated on a laboratory water distribution testbed of limited complexity, considering human evaluation of event signatures. This work methodologically adapts CyPhERS to specificities of DER operation such as weather and consumer-induced volatility, and introduces an automated signature evaluation system. The feasibility of applying CyPhERS for automated DER monitoring is investigated on a dataset recorded from a real photovoltaic-battery system targeted by several cyber and cyber–physical attack types. The results demonstrate that the proposed methodological adaptations and signature evaluation system enable the application of CyPhERS for automated online identification of different attack types targeting DERs, while greatly reducing the false positive rate.

Assessing inconsistencies in historical land-use reconstructions for Africa at 1800

Reconstructing historical land-use and land-cover change (LULCC) inevitably involves interpolation across regions for which there is limited data to support reconstructions. Here, we evaluate the extent to which the global land-use model HYDE v3.2.1 reflects historical land use in sub-Saharan Africa (including Madagascar) when compared to archaeological and historical reconstructions. Both the HYDE models and Widgren’s archaeological and historical reconstructions estimate that agriculture was widely scattered across sub-Saharan Africa in 1800 CE. However, in steep terrain, at slopes greater than 10°, the HYDE model estimated 0% cropland allocation while archaeological and historical observations suggest intensive, mixed and extensive farming was common in these areas. In addition, while HYDE allocates farmland primarily into high-quality soils, archaeological reconstructions suggest that intensive farming occurred across a wide range of soil qualities. Gaps in our knowledge of cropland distribution have significant impacts on models of biodiversity change since understanding biodiversity in the Anthropocene is reliant on our understanding of past land-use changes. While HYDE’s mismatches are known, the research presented here provides an important resource in identifying where these allocation rules fail. These mismatches in global land-use models such as HYDE might also be replicated for other regions of the world, such as South America. These mismatches also need to be accounted for when generating model projections that use historical land-use models to impute present and future trends in land-use, climate and biodiversity change. Localised archaeological and historical data can therefore be used to support historical global land-use reconstructions for Africa and other regions across the world.

Evidence of a narwhal (Monodon monoceros) summer ground in Nares Strait

Our limited knowledge of the marine mammal fauna in northernmost Greenland and Canada, specifically north of 80°N, relies largely on opportunistic observations collected during expeditions with different objectives. The narwhal (Monodon monoceros) migrates long distances in response to ice formation and decay and is notoriously skittish, avoiding areas with ice breakers. Scattered observations from the past 20 years, assessed together with historical observations after 1881, suggest that there is a population of narwhals that uses Hall Basin and its adjacent fjord systems—for example, Nares Strait—as a summer ground. Dating the tusks and bones that have been found shows that narwhals were present in this area as far back as nearly 7000 years ago. The wintering locations of these narwhals remain unknown, highlighting the need to investigate whether they are vulnerable to hunting activities in north-west Greenland. By gaining a better understanding of the narwhals’ winter behaviour and potential hunting risks, we can develop more informed conservation and management strategies for this population.

Modeling range dynamics through time to inform conservation planning: Canada lynx in the contiguous United States

Assessment of historical species range limits is rarely conducted. Yet, understanding where species were distributed in the past is fundamentally important for making informed policy decisions, including assessing species status and recovery in present day and in the context of future environmental change. Here, we provide a case study using a threatened species, Canada lynx, which is the subject of recent conservation policy debate and species recovery planning. We use species distribution models (SDM), combined with historic observations of lynx, to estimate historic southern range limits for the species at year 1900. We then forward project our SDM to estimate lynx vulnerability and likely spatial pattern of range retraction in the future. Our analysis reveals that substantial suitable lynx habitat existed in the past beyond their current range in the contiguous USA, and many extra-limital observations fell within this habitat suggesting relatively broad historic occupancy. This finding implies substantial lynx range contraction over the 20th century and is contrary to recent species status assessment. Moreover, some areas of the contiguous USA that may remain resilient to climate change for lynx in the future, fall outside current species range limits but potentially within historically occupied areas. Our results indicate that future recovery efforts for lynx should be informed by this new evidence regarding species range dynamics through time, including investigating suitability of sites outside current range limits. More broadly, our findings highlight the need for conservation planning and species recovery efforts to consider historical range limits whenever possible.

FlightBERT++: A Non-autoregressive Multi-Horizon Flight Trajectory Prediction Framework

Flight Trajectory Prediction (FTP) is an essential task in Air Traffic Control (ATC), which can assist air traffic controllers in managing airspace more safely and efficiently. Existing approaches generally perform multi-horizon FTP tasks in an autoregressive manner, thereby suffering from error accumulation and low-efficiency problems. In this paper, a novel framework, called FlightBERT++, is proposed to i) forecast multi-horizon flight trajectories directly in a non-autoregressive way, and ii) improve the limitation of the binary encoding (BE) representation in the FlightBERT. Specifically, the FlightBERT++ is implemented by a generalized encoder-decoder architecture, in which the encoder learns the temporal-spatial patterns from historical observations and the decoder predicts the flight status for the future horizons. Compared with conventional architecture, an innovative horizon-aware contexts generator is dedicatedly designed to consider the prior horizon information, which further enables non-autoregressive multi-horizon prediction. Moreover, a differential prompted decoder is proposed to enhance the capability of the differential predictions by leveraging the stationarity of the differential sequence. The experimental results on a real-world dataset demonstrated that the FlightBERT++ outperformed the competitive baselines in both FTP performance and computational efficiency.

Towards Reliable Learning in the Wild: Generalization and Adaptation

The real-world deployment of machine learning algorithms often poses challenges due to shifts in data distributions and tasks. These shifts can lead to a degradation in model performance, as the model may not have encountered such changes during training. Additionally, they can make it difficult for the model to generalize to new scenarios and can result in poor performance in real-world applications. In this talk, I will present our research on building machine learning models that are highly generalizable and easily adaptable to different shifts. Specifically, I will first discuss our approach to improving out-of-distribution robustness and mitigating spurious correlations by training environment-invariant models through selective augmentation and post-hoc rectification. Second, I will present our techniques for continuous and rapid adaptation of models to new tasks and environments. This includes methods to facilitate compositional generalization and adaptation by extracting relationships from historical observations and to enhance reliable adaptation even in the face of imperfect observations. Additionally, I will showcase our successful practices for addressing shifts in real-world applications, such as in the healthcare, e-commerce, and transportation industries. The talk will also touch upon the remaining challenges and outline future research directions in this area.

Interreligious Interaction in the Ilorin Metropolis

Abstract This study explores interreligious interactions in Ilorin, Nigeria, with a focus on Sobi Hill. Employing a phenomenological approach, it examines how different religious groups interpret and engage with this symbol of unity. Through historical analysis, site visits, observations, questionnaires, and interviews, the research identifies three key factors that contributes to peaceful coexistence: influential figures, evolving generational attitudes, and Sobi Hill’s religious significance. The study concludes that Ilorin’s harmonious coexistence is epitomized by Sobi Hill, where residents from various faiths gather for joint celebrations that emphasize the city’s commitment to unity.

Resurrecting historical observations to characterize species-specific nesting traits of bumblebees

Resurrecting historical observations to characterize species-specific nesting traits of bumblebees

A GCN-based adaptive generative adversarial network model for short-term wind speed scenario prediction

Wind prediction is of great significance for wind energy utilization due to the stochastic nature of wind. To effectively facilitate various downstream decision-making tasks such as wind turbine control, predictive wind Scenario Generation (SG), which is capable of providing a set of deterministic instantiated wind prediction results, plays a critical role. In this paper, a novel Graph neural networks-based Adaptive Predictive Generative Adversarial Network (GAPGAN) model is proposed for accurate prediction of short-term future scenarios of a wind field. In GAPGAN, the original multivariate time series data are first reconstructed into the form of a graph, and spatiotemporal features are then extracted using Graph Convolutional Networks (GCNs). Next, a predictive generative adversarial network (PGAN) framework is proposed, which could generate different outputs corresponding to given historical observations as conditions. Finally, an adaptive PGAN training mechanism is introduced to stabilize the training process, and the best SG model is selected based on the proposed comprehensive evaluation system. Based on wind speed data collected from 11 wind turbines, computational experiments validate that the GAPGAN outperforms five benchmarking models in terms of point prediction accuracy, shape similarity, uncertainty prediction quality, and prediction scenario diversity.

Propensity of Predator Mimicry in Steller’s Jays

Avian vocal mimicry has been described in a variety of contexts, suggesting its function is multifaceted within and across species; however, basic empirical data describing mimetic signal prevalence and context are lacking for numerous species. We examined the occurrence and context of mimicked Red-shouldered Hawk (Buteo lineatus) calls over a 12-month period in a population of 49 individually color-marked Steller’s Jays (Cyanocitta stelleri). We documented mimicry of Red-shouldered Hawk calls in 14 of 49 (28.6%) jays during this 12-month period. We also reviewed the occurrence of the behavior in historic observation data. Hawk mimicry occurred more often during the early breeding season when jays were within home territories, their mates were present, and aggression was absent. Younger, larger, and bolder jays were most likely to perform imitations. These results suggest jays individually vary in mimetic propensity, and individuals’ proclivity for mimicry may be influenced by social and ecological contexts, physical characteristics, and personality traits.

The etiologies of Kawasaki disease.

Kawasaki disease (KD) is a systemic vasculitis that affects young children and can result in coronary artery aneurysms. The etiology is currently unknown, but new clues from the epidemiology of KD in Japan, the country of highest incidence, are beginning to shed light on what may trigger this acute inflammatory condition. Additional clues from the global changes in KD incidence during the COVID-19 pandemic, coupled with a new birth cohort study from Japan, point to the potential role of person-to-person transmission of an infectious agent. However, the rising incidence of KD in Japan, with coherent waves across the entire country, points to an increasing intensity of exposure that cannot be explained by person-to-person spread. This Review discusses new and historical observations that guide us toward a better understanding of KD etiology and explores hypotheses and interpretations that can provide direction for future investigations. Once the etiology of KD is determined, accurate diagnostic tests will become available, and new, less expensive, and more effective targeted therapies will likely be possible. Clearly, solving the mystery of the etiologies of KD remains a priority for pediatric research.

Toward the future integration of land-to-ocean observing systems to characterize organic carbon fluxes from storms

Storms can drive massive fluxes of carbon from land to ocean via rivers, contributing a large but relatively uncertain term to coastal carbon budgets. To estimate storm-driven aquatic organic carbon fluxes and develop a framework for future observations, we conducted a case study of extreme precipitation followed by river discharge to the Chesapeake Bay watershed, on the east coast of the United States over two decades from June 2001 to July 2021. Anomalous high precipitation events were defined as the top 5% of five-day accumulated precipitation over the watershed and we identified 80 distinct large storms over the Chesapeake Bay region. The top 1% of five-day accumulated river discharge from the United States Geological Survey (USGS) gage stations provided secondary storm criteria to distinguish extreme precipitation that manifest in very high flow: over the same period, we identified 16 storms that led to very high streamflow and combined streamflow with observations of POC and DOC to estimate daily mass flux from the seven major rivers. An extremely large convective storm and subsequent river discharge in July 2018 was studied to determine the association between precipitation, river discharge, and satellite ocean color in combination with in situ observations. MODIS-Aqua satellite derived dissolved organic carbon (DOC) concentration was calculated for all images in 2004–2021 and the estimated DOC values in 2018 were used to assess the feasibility of the optical satellites to monitor coastal storm fluxes of carbon. Using the USGS Load Estimator stream flow model and historical observations, the 2018 event exported approximately 12.9 Gg C particulate organic carbon (POC) and 12.5 Gg C DOC into the bay for 7.2% and 4.4% of the annual total, respectively. The largest storm over the last 20 years exported up to 16% of the annual total POC and 11% of the annual total of DOC from the major rivers over 10 days. However, monitoring the evolution of riverine carbon in estuarine environments from optical satellites alone remains challenging due to cloud cover, thus additional observing platforms assimilated through models are essential.

Influence of Weather and Climate on Multidecadal Trends in Atlantic Hurricane Genesis and Tracks

Abstract This study investigates the relative roles of sea surface temperature–forced climate changes and weather variability in driving the observed eastward shift of Atlantic hurricane tracks over the period from 1970 to 2021. A 10-member initial condition ensemble with a ∼25-km horizontal resolution tropical cyclone permitting atmospheric model (GFDL AM2.5-C360) with identical sea surface temperature and radiative forcing time series was analyzed in conjunction with historical hurricane track observations. While a frequency increase was recovered by all the simulations, the observed multidecadal eastward shift in tracks was not robust across the ensemble members, indicating that it included a substantial contribution from weather-scale variability. A statistical model was developed to simulate expected storm tracks based on genesis location and steering flow, and it was used to conduct experiments testing the roles of changing genesis location and changing steering flow in producing the multidecadal weather-driven shifts in storm tracks. These experiments indicated that shifts in genesis location were a substantially larger driver of these multidecadal track changes than changes in steering flow. The substantial impact of weather on tracks indicates that there may be limited predictability for multidecadal track changes like those observed, although basinwide frequency has greater potential for prediction. Additionally, understanding changes in genesis location appears essential to understanding changes in track location. Significance Statement From the 1970s to the present, there has been an increase in the frequency of North Atlantic hurricanes, but they have also shifted in location to the east, away from land. We explore whether this shift in hurricanes’ locations was caused by climatic factors or randomness to understand if and how these trends will persist. We also consider whether the shift was due to a change in where hurricanes started or how they moved over their lifespan. Analyzing data from observed and simulated hurricanes, we find that the shift was made more likely by climate factors, but ultimately occurred due to random variability in the hurricanes’ starting locations. These results suggest a higher uncertainty in the future location and impact of hurricanes and highlight the importance of studying why hurricanes originate where they do.

Substantial Differences in Crop Yield Sensitivities Between Models Call for Functionality‐Based Model Evaluation

AbstractCrop models are often used to project future crop yield under climate and global change and typically show a broad range of outcomes. To understand differences in modeled responses, we analyzed modeled crop yield response types using impact response surfaces along four drivers of crop yield: carbon dioxide (C), temperature (T), water (W), and nitrogen (N). Crop yield response types help to understand differences in simulated responses per driver and their combinations rather than aggregated changes in yields as the result of simultaneous changes in various drivers. We find that models' sensitivities to the individual drivers are substantially different and often more different across models than across regions. There is some agreement across models with respect to the spatial patterns of response types but strong differences in the distribution of response types across models and their configurations suggests that models need to undergo further scrutiny. We suggest establishing standards in model evaluation based on emergent functionality not only against historical yield observations but also against dedicated experiments across different drivers to analyze emergent functional patterns of crop models.

High resolution climate change observations and projections for the evaluation of heat-related extremes

The Climate Hazards Center Coupled Model Intercomparison Project Phase 6 climate projection dataset (CHC-CMIP6) was developed to support the analysis of climate-related hazards, including extreme humid heat and drought conditions, over the recent past and in the near-future. Global daily high resolution (0.05°) grids of the Climate Hazards InfraRed Temperature with Stations temperature product, the Climate Hazards InfraRed Precipitation with Stations precipitation product, and ERA5-derived relative humidity form the basis of the 1983–2016 historical record, from which daily Vapor Pressure Deficits (VPD) and maximum Wet Bulb Globe Temperatures (WBGTmax) were derived. Large CMIP6 ensembles from the Shared Socioeconomic Pathway 2-4.5 and SSP 5-8.5 scenarios were then used to develop high resolution daily 2030 and 2050 ‘delta’ fields. These deltas were used to perturb the historical observations, thereby generating 0.05° 2030 and 2050 projections of daily precipitation, temperature, relative humidity, and derived VPD and WBGTmax. Finally, monthly counts of frequency of extremes for each variable were derived for each time period.