Mars’ controversial hypothesized ocean shorelines have been found to deviate significantly from an expected equipotential surface. While multiple deformation models have been proposed to explain the wide range of elevations, here we show that the historical locations used in the literature and in these models vary widely. We find that the most commonly used version of the Arabia Level does not follow the originally described contact and can deviate laterally by ~500 km in Deuteronilus Mensae. A meta-analysis of different published maps shows that, globally, the minimum lateral offsets between the locations of the putative Arabia and Deuteronilus shorelines vary by an average of 141±142 km and 180±177 km, respectively. This leads to mean elevations of the Arabia Level that vary by up to 2.2 km between different mappings, and topographic ranges within each global mapping ranging from 2.7 to 7.7 km. The younger Deuteronilus Level has less topographic variation as it largely follows a formal contact (the Vastitas Borealis Formation) within the relatively flat northern plains. Given the high variance in position (spatial and topographic) of the maps, the use of such data and conclusions based on them are potentially problematic.
Interpreting seismo-acoustic signals is critical for assessing and characterizing changes in volcanic vents and has implications for interpreting volcanic unrest. This is especially relevant for Stromboli volcano (Italy), an active stratovolcano with a complex plumbing system, continuous activity, and recurring paroxysms. Stromboli is known for its consistent Strombolian style of eruption, multiple active vents on its crater terrace, and for occasional structural modifications including explosive excavation and/or collapsing craters due to near-surface changes to the plumbing system. This study addresses a single localized collapse of the crater terrace, occurring in May of 2019, when one of Stromboli’s vents changed from a pronounced hornito to a pit crater, resulting in a shift in eruption style at this vent from jetting to Strombolian. The days before and after this transition were recorded with eight infrasound sensors and three seismic geophones located on the crater terrace. We investigate the seismo-acoustic timing of these signals as well as the ratio between seismic and acoustic energy to identify changes associated with eruptive signals and associated variations in location of the eruptive sources. This work highlights the effectiveness of seismo-acoustic data analysis, provides insight into Stromboli’s structural modifications, and builds a foundation for focused analysis of seismo-acoustic signals associated with Stromboli and other open-vent volcanic systems.
Marine or coastal wetlands that host a diverse variety of flora and fauna are unique and fragile as they are subjected to changing coastlines and undergo dynamic spatial shifts with respect to tidal movements. In India, the Coastal Regulation Zone (CRZ) notification aims at the conservation of coastal regions, under the Environment (Protection) Act, 1986, and regulates developmental and construction activities within the CRZ regions of marine wetlands, in addition to the coastal belt. Remote sensing techniques can be of great use in understanding if the implementation of the CRZ has helped to regulate the proliferation of settlements in the wetland system. In this study, remote sensing techniques along with machine learning classifiers have been used for detecting and quantifying the recent settlements that have been built in the zones regulated by the CRZ of the Vembanad wetland of Kerala. Three standard change detection pre-processing techniques were used over Linear Imaging Self-Scanning Sensor (LISS) IV imagery which was followed by classification using machine learning algorithms: Support Vector Machine (SVM), random forest, and Artificial Neural Network (ANN) to identify the built-up erected in the CRZ region between 2012 and 2018. Comparing the performance of these classifiers, the random forest model was found to have the highest overall accuracy of 96%. It was found that the total area of new built-up that were constructed between 2012 and 2018 in the CRZ regions of 48 villages, that span across Ernakulam, Kottayam and Alappuzha districts of Kerala is 149 hectares. This usage of change detection techniques aided by machine learning algorithms over high-resolution LISS IV imagery would help to evaluate the effectiveness of the CRZ notification over other marine wetlands in India.
Committees touch nearly every facet in the science, technology, engineering, and mathematics (STEM) research enterprise. However, the role of gatekeeping through committee work has received little attention in Earth and space sciences. We propose a novel concept called, “regenerative gatekeeping” to challenge institutional inertia, cultivate belonging, accessibility, justice, diversity, equity, and inclusion in committee work. Three examples, a hiring committee process, a seminar series innovation, and an awards committee, highlight the need to self-assess policies and practices, ask critical questions and engage in generative conflict. Rethinking committee work can activate distributed mechanisms needed to promote change.
Sedimentary bed configurations that are stable under weak fluid-driven transport conditions can be divided into two groups: (1) meso-scale features that influence flow and sediment transport through roughness and drag partitioning effects (“mesoforms”), and (2) grain-scale features that can effectively be ignored at the macroscopic scale (“microforms”). In practice, these groups delineate ripples and dunes from quasi-planar bed configurations. They are thought to be separated by a transition in processes governing the relief of the bed; however, the physical mechanisms responsible for this transition are poorly understood. Previous studies suggest that planar topography is unstable when interactions between moving particles lead to stabilized bed disturbances that initiate morphodynamic pattern coarsening. This study presents a kinetic interpretation of this hypothesis in terms of parameters describing particle motion. We find that the microform/mesoform transition corresponds to a critical transition in particle behavior associated with increasing importance of particle collisions. This transition also corresponds to the point where continuum-based morphodynamic models are permissible at the most unstable wavelength predicted from linear stability theory, providing a link between descriptive and mathematical theories of bedform initiation.
The NEXUS area covers approximately 30% of the Brazilian territory. In order to assist preservation and sustainable development policies in that region, this study proposes to replicate the work done by Yeh et al in Africa , in which a convolutional neural network estimates indicators through satellite images, each covering a region of approximately 45 km². This work compares the size and distribution of Brazil’s census tracts with those in Africa to define if the scale of images can be maintained and to define the clusters that will be used. To avoid biasing the model, special care must be taken in selecting clusters, such as keeping a balance between urban and rural sectors and, most importantly, making sure that there is little to no overlap of clusters. To do so, two approaches were proposed. The first one samples tracts in each municipality as centroids for clusters, the second merges neighboring urban tracts into a single group and fits clusters to these groups.
Because cross-polarized radar returns are highly associated with volume scatter, radar polarimetry returns tend to show strong evidence of wildfire scars and recovery in forest and chaparral. We focus on the polarimetry images from UAVSAR line SanAnd_08525, which covers a roughly 20 km wide swath over the Transverse Range including parts of the Santa Monica, San Gabriel and San Bernardino Mountains. We select images from four acquisition dates from October 2009 to September 2020, very roughly four years apart. These are compared to fire perimeters from the national GeoMAC and NIFC databases for years 2003-2020, which shows the areas affected by the major fires (west to east) Springs2013, Woolsey2018, Topanga2005, LaTuna2017, Station2009, BlueCut2016, Pilot2016, Slide2007, Butler2007 and many smaller fires. UAVSAR polarimetry images are shown to be helpful in identifying types and boundaries of fire, fifty-meter scale details of vegetation loss, and variability of vegetation recovery in post-fire years.
This study introduces the results from fitting a Bayesian hierarchical spatiotemporal model to COVID-19 cases and deaths at the county-level in the United States for the year 2020. Two models were created, one for cases and one for deaths, utilizing a scaled Besag, York, Mollié model with Type I spatial-temporal interaction. Each model accounts for 16 social vulnerability variables and 7 environmental measurements as fixed effects. The spatial structure of COVID-19 infections is heavily focused in the southern U.S. and the states of Indiana, Iowa, and New Mexico. The spatial structure of COVID-19 deaths covers less of the same area but also encompasses a cluster in the Northeast. The spatiotemporal trend of the pandemic in the U.S. illustrates a shift out of many of the major metropolitan areas into the U.S. Southeast and Southwest during the summer months and into the upper Midwest beginning in autumn. Analysis of the major social vulnerability predictors of COVID-19 infection and death found that counties with higher percentages of those not having a high school diploma and having minority status to be significant. Age 65 and over was a significant factor in deaths but not in cases. Among the environmental variables, above ground level (AGL) temperature had the strongest effect on relative risk to both cases and deaths. Hot and cold spots of COVID-19 cases and deaths derived from the convolutional spatial effect show that areas with a high probability of above average relative risk have significantly higher SVI composite scores. Hot and cold spot analysis utilizing the spatiotemporal interaction term exemplifies a more complex relationship between social vulnerability, environmental measurements, and cases/deaths.
Understanding interactions among greenhouse gas (GHG) emissions, air pollution, race, and poverty is critical to developing strategies to slow climate warming, and is socially important as large GHG-emitting facilities often occur in poor and historically-marginalized communities. We examined such patterns in the American South, where a multi-centennial history of race and poverty coincides with a petroleum and petrochemical industry that is >100 yr old using open-access data to quantify emissions on a 0.1o x 0.1o scale annually from 1970 to the mid-2010s. 26-55% of Louisiana’s emissions of several dominant GHGs and air pollutants are concentrated along the Mississippi River Industrial Corridor, which is < 5% of the state’s area. Despite some statewide emission reductions, fluxes in this corridor, and several parishes with large Black populations, have reduced more slowly or increased, raising environmental justice concerns. Methods herein provide a blueprint for future studies, particularly in marginalized communities, where limited scientific resources have hindered efforts to understand how climate change, air pollution and equity interact.
A new method is presented to efficiently estimate daily groundwater level time series at unmonitored sites by linking groundwater dynamics to local hydrogeological system controls. The presented approach is based on the concept of comparative regional analysis, an approach widely used in surface water hydrology, but uncommon in hydrogeology. The method uses regression analysis to estimate cumulative frequency distributions of groundwater levels (groundwater head duration curves (HDC)) at unmonitored locations using physiographic and climatic site descriptors. The HDC is then used to construct a groundwater hydrograph using time series from distance-weighted neighboring monitored (donor) locations. For estimating times series at unmonitored sites, in essence, spatio-temporal interpolation, stepwise multiple linear regression, extreme gradient boosting, and nearest neighbors are compared. The methods were applied to ten-year daily groundwater level time series at 157 sites in alluvial unconfined aquifers in Southern Germany. Models of HDCs were physically plausible and showed that physiographic and climatic controls on groundwater level fluctuations are nonlinear and dynamic, varying in significance from “wet” to “dry” aquifer conditions. Extreme gradient boosting yielded a significantly higher predictive skill than nearest neighbor and multiple linear regression. However, donor site selection is of key importance. The study presents a novel approach for regionalization and infilling of groundwater level time series that also aids conceptual understanding of controls on groundwater dynamics, both central tasks for water resources managers.
Natural and anthropogenic disturbances act as important drivers of tree mortality, shaping the structure, composition and biomass distribution of forests. Disturbance regimes may emerge from different characteristics of disturbance events over time and space. We design a model- based experiment to investigate the links between disturbance regimes at the landscape scale and spatial features of biomass patterns. The effects on biomass of a wide range of disturbance regimes are simulated by varying three different parameters, i.e. μ (probability scale), α (clustering degree), and β (intensity slope) that shape the extent, frequency, and intensity of disturbance events, respectively. A simple dynamic carbon cycle model is used to simulate 200 years of plant biomass dynamics in response to circa +2000 different disturbance regimes, depending on the different combinations of μ, α, and β. Each parameter combination yields a spatially explicit estimate of plant biomass for which sixteen synthesis statistics are estimated on the spatial distributions of biomass, including information-based and texture features. Based on a multi-output regression approach we link these synthesis statistics with additional gross primary production (GPP) constraints to retrieve the three disturbance parameters. In doing so we evaluate the confidence in inferring disturbance regimes from spatial distributions of biomass. Our results show that all three parameters can be confidently retrieved. The Nash-Sutcliffe efficiency for the prediction of the μ, α, and β is 97.3%, 96.6%, and 97.9%, respectively. A feature importance analysis reveals that the distribution statistics dominate the prediction of μ and β, while features quantifying texture have a stronger connection with α. Overall, this study clarifies the association between biomass patterns emerging from different underlying disturbance regimes, while overcoming the previously found equifinality between mortality rates and total biomass. Given the links between decadal vegetation dynamics and the uncertainties in the role of terrestrial ecosystems in the global biogeochemical cycles, a better understanding and the quantification of disturbance regimes would improve our current understanding of controls and feedback at the biosphere-atmosphere interface in the current Earth system models.
Discerning the relationship between urban structure and function is crucial for sustainable city planning and requires examination of how components in urban systems are organized in three-dimensional space. The Structure of Urban Landscape (STURLA) classification accounts for the compositional complexity of urban landcover structures including the built and natural environment. Building on previous research, we develop a STURLA classification for Philadelphia, PA and study the relationship between urban 1 structure and land surface temperature. Finally, we evaluate the results in Philadelphia as compared to previous case studies in Berlin, Germany and New York City, USA. In Philadelphia, STURLA classes hosted ST that were unique and significantly different as compared to all other classes. We find a similar distribution of STURLA class composition across the three cities, though NYC and Berlin showed strong correlation with each other but not with Philadelphia. Our research highlights the use of STURLA classification to capture a physical property of the urban landscape.
Reach-scale morphological channel classifications are underpinned by the theory that each channel type is related to an assemblage of reach- and catchment-scale hydrologic, topographic, and sediment supply drivers. However, the relative importance of each driver on reach morphology is unclear, as is the possibility that different driver assemblages yield the same reach morphology. Reach-scale classifications have never needed to be predicated on hydrology, yet hydrology controls discharge and thus sediment transport capacity. The scientific question is: do two or more regions with quantifiable differences in hydrologic setting end up with different reach-scale channel types, or do channel types transcend hydrologic setting because hydrologic setting is not a dominant control at the reach scale? This study answered this question by isolating hydrologic metrics as potential dominant controls of channel type. Three steps were applied in a large test basin with diverse hydrologic settings (Sacramento River, California) to: (1) create a reach-scale channel classification based on local site surveys, (2) categorize sites by flood magnitude, dimensionless flood magnitude, and annual hydrologic regime type, and (3) statistically analyze two hydrogeomorphic linkages. Statistical tests assessed the spatial distribution of channel types and the dependence of channel type morphological attributes by hydrologic setting. Results yielded ten channel types. Nearly all types existed across all hydrologic settings, which is perhaps a surprising development for hydrogeomorphology. Downstream hydraulic geometry relationships were statistically significant. In addition, cobble-dominated uniform streams showed a consistent inverse relationship between slope and dimensionless flood magnitude, an indication of dynamic equilibrium between transport capacity and sediment supply. However, most morphological attributes showed no sorting by hydrologic setting. This study suggests that median hydraulic geometry relations persist across basins and within channel types, but hydrologic influence on geomorphic variability is likely due to local influences rather than catchment-scale drivers.
Meteorological and geophysical hazards will concur and interact with coronavirus disease (COVID-19) impacts in many regions on Earth. These interactions will challenge the resilience of societies and systems. A comparison of plausible COVID-19 epidemic trajectories with multi-hazard time-series curves enables delineation of multi-hazard scenarios for selected countries (United States, China, Australia, Bangladesh) and regions (Texas). In multi-hazard crises, governments and other responding agents may be required to make complex, highly compromised, hierarchical decisions aimed to balance COVID-19 risks and protocols with disaster response and recovery operations. Contemporary socioeconomic changes (e.g. reducing risk mitigation measures, lowering restrictions on human activity to stimulate economic recovery) may alter COVID-19 epidemiological dynamics and increase future risks relating to natural hazards and COVID-19 interactions. For example, the aggregation of evacuees into communal environments and increased demand on medical, economic, and infrastructural capacity associated with natural hazard impacts may increase COVID-19 exposure risks and vulnerabilities. COVID-19 epidemiologic conditions at the time of a natural hazard event might also influence the characteristics of emergency and humanitarian responses (e.g. evacuation and sheltering procedures, resource availability, implementation modalities, and assistance types). A simple epidemic phenomenological model with a concurrent disaster event predicts a greater infection rate following events during the pre-infection rate peak period compared with post-peak events, highlighting the need for enacting COVID-19 counter measures in advance of seasonal increases in natural hazards. Inclusion of natural hazard inputs into COVID-19 epidemiological models could enhance the evidence base for informing contemporary policy across diverse multi-hazard scenarios, defining and addressing gaps in disaster preparedness strategies and resourcing, and implementing a future-planning systems approach into contemporary COVID-19 mitigation strategies. Our recommendations may assist governments and their advisors to develop risk reduction strategies for natural and cascading hazards during the COVID-19 pandemic.
The environmental pollution, property losses and casualties caused by wildfires in California are getting worse by the year. To minimize the interference of wildfires on economic and social development , and formulate targeted mitigation strategies, it is imperative to understand the scale and extent of previous wildfire occurrences. In this study, we studied the trend of wildfires in different time scales (monthly, seasonal, and yearly), as well as the distribution of wildfires caused by natural and anthropogenic factors across different spatial scales (administrative units, land use) in California from 2000 to 2019. Furthermore, a regression analysis of environmental and human-related variables with the occurrence and frequency of wildfires was carried out, to compare the importance of variables on the risk of wildfire occurrence. The results show that the frequency distribution of the burned area conforms to the characteristics of the Pareto distribution in the twenty years of this research. The spatial distribution of wildfires was closely related to factors such as the causes, population density, and land-use types. In terms of the variables related to the risk of wildfire occurrence, distance to human constructions, the elevation of the terrain, grass cover, and the vapor pressure deficit are crucial. This study reveals the relationship between environmental and social background conditions and the spatial-temporal distribution of wildfires, which can provide a reference for wildfire management, the formulation of future targeted wildfire emergency plans, and the planning of future land use in California.
The Hudson Bay Lowlands (HBL) is a vast continuous peatland in Northern Canada. The landscape is a mosaic of mostly bogs and fens, with more limited swamp, marsh, forest and open water. Owing to rapid rates of isostatic uplift, younger peats are found closer to the coasts of Hudson and James Bays, with fen-type peatlands somewhat more prevalent on these younger surfaces. More than 30 Pg of carbon have accumulated in the HBL over the Holocene. The rates of Holocene carbon accumulation vary considerably both spatially and temporally, with some sites showing more rapid rates of carbon accumulation in the first 2-3 millennia following peatland initiation. We evaluate here the hypothesis that vegetation changes over the course of the Holocene, including fen-to-bog transitions, partially explain the variability in carbon accumulation. We find that in some cases, more rapid rates of C accumulation in the middle Holocene (5000-8000 yrs before present) are associated with early successional minerotrophic fens with higher carbon densities. Fen-to-bog transitions are recorded in many peat cores collected from present day bogs; however, these transitions are time transgressive, and can depend on the time since initiation, suggesting that climate changes may play a secondary role, relative to hydrological changes and local ecological processes. Fens are highly prevalent in the HBL landscape (covering about 38% of land cover). Cores taken from present day fens and analyzed for carbon accumulation and vegetation change indicate that many fen sites have remained fens since peat initiation. Variability in rates of Holocene carbon accumulation within fen records which have not been subject to any major vegetation change may more closely reflect climate drivers.
Atmospheric ice-nucleating particles (INPs) from mineral dust and non-proteinaceous biological sources can influence cloud formation, precipitation, and Earth’s radiation budget due to their efficient freezing abilities. The ambient aerosol particles from these sources are abundant with ambient concentrations exceeding a few µg m^-3 for each type. Thus, the characterization of INPs and aerosol particles from these sources is important. We typically characterize their specific surface area (SSA), which is the primary variable to estimate their ice-nucleation active surface site density, using a sorbate gas, such as nitrogen. However, it is still uncertain how these particles interact with water vapor under subzero temperatures. To fill this gap, we used the 3Flex instrument (Micromeritics Instrument Corp.) with multiple sorbates to comprehensively characterize the nanoscale surface structure, pore size distribution, and accessibility to water molecules of a commercially available model proxy of mineral dust (illite NX) and cellulose materials. To date, we have completed more than 60 physisorption 3Flex experiments with various sorbates, such as CO2, H2O, Kr, and N2, for each sorbent. In particular, we examined SSA by water vapor sorption at temperatures relevant to atmospheric heterogeneous freezing (~ 0 to -20 °C). We will present our results as physisorption isotherms. In addition, our preliminary results of temperature-dependent SSA observed for micro- and nano-crystalline cellulose materials as well as illite NX will be discussed. Our preliminary result suggests that the SSA of illite NX is less temperature-dependent compared to the cellulose materials, which may be potentially swelling while interacting with water. Therefore, illite NX may be suitable for an INP test proxy.
Latin America has emerged as an epicenter of the COVID-19 pandemic. Brazil, Peru, and Ecuador report some of the highest COVID-19 rates. These countries also face dual threats from development challenges and El Niño, which impact local disease ecologies. A country like Peru, e.g., which is highly sensitive to El Niño, already copes with an ecosyndemic burden, i.e., co-occurring multiple infectious diseases, which heighten during climate extreme events. In this commentary, we highlight the importance of El Niño as a major factor that not only may aggravate COVID-19 incidence, but also the broader health problem of ecosyndemics in Latin America.