High-resolution seafloor mapping provides insights into the dynamics of past ice-sheets/ice-shelves on high-latitude continental margins. Geological/geophysical studies in the Arctic Ocean suggest widespread Pleistocene ice grounding on the Chukchi–East Siberian continental margin. However, flow directions, timing, and behavior of these ice masses are not yet clear due to insufficient data. We present a combined seismostratigraphic and morphobathymetric analysis of the Chukchi Rise off the northwestern Chukchi margin using the densely acquired sub-bottom profiler (SBP) and multibeam echosounder (MBES) data. Comparison with deeper airgun seismic records shows that the SBP data cover most of the glaciogenic stratigraphy possibly spanning ca. 0.5–1 Ma. Based on the stratigraphic distribution and geometry of acoustically transparent glaciogenic diamictons, the lateral and vertical extent of southern-sourced grounded ice became smaller over time. The older deposits are abundant as debris lobes on the slope contributing to a large trough mouth fan, whereas younger till wedges are found at shallower depths. MBES data show two sets of mega-scale lineations indicating at least two fast ice-streaming events of different ages. Contour-parallel recessional morainic ridges mark a stepwise retreat of the grounded ice margin, likely controlled by rising sea levels during deglaciation(s). The different inferred directions of ice advances and retreats reflect complex geomorphic settings on the borderland. The overall picture shows that the Chukchi Rise was an area of intense interaction(s) of different ice-sheets/ice-shelves. In addition to glaciogenic deposits, we identify a number of related or preceding seabed features including mounds, gullies/channels, and sediment waves.
Grassland ecosystems cover one-fourth of the global land area and harbor over 30% of the global carbon stored in soils. However, grasslands are subjected to extensive and intensive land degradation, which threatens biodiversity, the well-being and food-security of millions of people, and poses challenges for climate change mitigation. The question is where grasslands have degraded and where long-term greening is taking place. Time series of satellite data can be used for trend analyses, but when testing for statistical significance, it is important to account for temporal and spatial autocorrelation. Here we present our new statistical method to analyze long-term trends in grasslands based on physically-based Cumulative Endmember Fractions (annual sums of monthly ground cover fractions). Our trend analysis incorporates two steps: first we apply an autoregressive time series to each pixel to obtain a slope estimate while accounting for temporal autocorrelation. Second, we apply a general least-square regression to the slope estimates, in which we incorporate spatial covariance structure, as well as explanatory variables. We tested our approach mapping long-term trends in grasslands in Central Asia using MODSI 2001 2019 time series, which we regressed against meteorological measurements. Our results showed long term changes of both, positive (i.e., revegetation; e.g., east part of Central Asia) and negative trajectories (i.e., desiccation; e.g., north-west part of the Central Asia). Importantly, our method is scalable and transferable to other time series of satellite data and regions, and can be implemented in any computational environment, assuring accessibility and reproducibility.
The Track Zero Charitable Trust (http://trackzero.nz) was founded in early 2018 with the goal of inspiring action on climate change through engagement with the arts. The main motivation is that artistic expression connects on an emotional level in a way that direct communication of science does not. Also, around 80 percent of New Zealanders engage in some way with the arts, a much larger audience than those who take an active interest in science. This presentation discusses the first major activity of TrackZero, a series of public meetings around New Zealand where scientists partnered with local arts practitioners to interact with audiences on the science, on people’s feelings and concerns, and on ways forward to action on adapting to or mitigating climate change. Different communities had different concerns and the range of conversations was rich and varied. While no formal evaluation has been carried out, we provide some general reflections on impact and engagement, and ideas for future activities.
Deciduous larch is a weak competitor when growing in mixed stands with evergreen taxa but is dominant in many boreal forest areas of Eastern Siberia. However, it is hypothesized that certain factors such as a shallow active layer thickness and high fire frequency favor larch dominance. Our aim is to understand how thermohydrological interactions between vegetation, permafrost, and atmosphere stabilize the larch forests and the underlying permafrost in Eastern Siberia. A tailored version of a one-dimensional land surface model (CryoGrid) is adapted for the application in vegetated areas and used to reproduce the energy transfer and thermal regime of permafrost ground in typical boreal larch stands. In order to simulate the responds of Arctic trees to local climate and permafrost conditions we have implemented a multilayer canopy parameterization originally developed for the Community Land Model (CLM-ml_v0). The coupled model is capable of calculating the full energy balance above, within and below the canopy including the radiation budget, the turbulent fluxes and the heat budget of the permafrost ground under several forcing scenarios. We will present first results of simulations performed for different study sites in larch-dominated forests of Eastern Siberia and Mongolia under current and future climate conditions. Model performance is thoroughly evaluated based on comprehensive in-situ soil temperature and radiation measurements at our study sites.
In 2019–20 Australia was devastated by the worst wildfires observed in decades. NASA’s ECOsystem Space-borne Thermal Radiometer Experiment on Space Station (ECOSTRESS) mission, launched in 2018, captured many dynamics of the fires at high resolution, including ecosystem stress prior to the fires. We aimed to determine the predictive capacity of ECOSTRESS observations for fire occurrence and intensity in Southeast Australia. We found that ECOSTRESS data (evaporative stress index and water use efficiency) were highly predictive of fire dynamics (25-65% occurrence prediction accuracy for ESI; and, 40-95% occurrence prediction for WUE > 1 gCkg-1H2O alone, depending on their levels) with the ESI coefficient averaging approximately three times stronger than general topographic variables or meteorological variables. Our results, based on a logistic regression model, had an overall predictive accuracy of 83%, suggesting high potential of using ECOSTRESS data to project and examine fires in Australia and other similar regions of the world.
Permafrost is permanently frozen ground that covers over 10% of the Earth’s surface. Many northern regions have extensive infrastructure built on this hard, frozen ground. When permafrost thaws, the ground becomes a softer mix of soil and water, which can cause degradation and damage to critical infrastructure. Permafrost thaw has substantial economic, strategic, and environmental implications. While thawing permafrost due to climate change will affect energy infrastructure in many countries, this work focuses on Russia’s current and planned arctic energy infrastructure. To quantify Russian energy infrastructure locations on permafrost, geospatial data was collected and mapped. Specifically, our analysis focuses on Russian gas and oil terminals and power plants. First, we determined the types of permafrost extents (e.g., continuous, discontinuous, sporadic, and isolated) on which each energy facility lies. Next, to evaluate the infrastructure hazard potential of permafrost thaw, we leveraged an existing analysis by [Karjalainen et al., 2019] in which data on ground conditions were weighted and aggregated to generate low, medium, or high hazard classifications under various greenhouse gas trajectories. For the time frame 2041-2060 and assuming greenhouse gas trajectories consistent with RCP 4.5, most facilities were found to be located in moderate and high hazard zones. A similar analysis was conducted for the years 2060-2081 under various climate conditions. Next, we generated supplemental analysis to define similar hazard classifications under current climatic conditions. The future climate scenario findings are compared with current conditions to identify potential variations in hazard zones, which could heighten infrastructure destabilization. Findings are applied to a targeted case study of the Yamal Peninsula to assess implications for Russia in the areas of energy capacity, foreign investment and supply chains, and future infrastructure construction projects. Citation: Karjalainen, O., Aalto, J., Luoto, M. et al. Circumpolar permafrost maps and geohazard indices for near-future infrastructure risk assessments. Sci Data 6, 190037 (2019). https://doi.org/10.1038/sdata.2019.37
Moderate to large earthquakes can increase the amount of water feeding stream flows, raise groundwater levels, and thus grant plant roots more access to water in water-limited environments. We examine tree growth and photosynthetic responses to the Maule Mw 8.8 Earthquake in small headwater catchments of Chile’s Mediterranean Coastal Range. We combine high-resolution wood anatomic (lumen area) and biogeochemical ( of wood cellulose) proxies of daily to weekly tree growth on cores sampled from trees on floodplains and close to ridge lines. We find that, immediately after the earthquake, at least two out of six tree cores show changes in these proxies: lumen area increased and decreased in the valley trees, whereas the sign of change was reversed in trees on the hillslope. Our results indicate a control of soil water on this response, largely consistent with models that predict how enhanced post-seismic vertical soil permeability causes groundwater levels to rise on the valley floor, but fall along the ridges. Statistical analysis with boosted regression trees indicates that streamflow discharge gained predictive importance for photosynthetic activity on the ridges but lost importance on the valley floor after the earthquake. We infer that earthquakes may stimulate ecohydrological conditions favoring tree growth over days to weeks by triggering stomatal opening. The weak and short-lived signals that we identified, however, show that such responses are only valid under water-limited instead of energy-limited tree growth. Hence, dendrochronological studies targeted at annual resolution may overlook some earthquake effects on tree vitality.
This work presents system concepts, integration efforts and results of the incorporation of recent advances in geospatial technologies, including augmented reality, virtual reality and unmanned aerial systems (UAS), into teaching and learning in the geosciences. Descriptions include the exploration of multiple technological alternatives and introduce system design and integration to enhance and innovate instructional materials in classrooms. The 3D Immersion and Geovisualization (3DIG) system, implemented at the Center for Geospatial Research at the University of Georgia incorporates augmented/customized commercial-off-the-shelf solutions for data acquisition, visualization and human-machine interaction. Through the immersive capabilities of 3DIG, students can be involved in a full data acquisition-processing-analysis workflow. Data streams are used for system integration, with emphasis to model generation/manipulation and remote sensing applications, including multispectral data acquisition/analyses, structure-from-motion based point-cloud/model generation, DEM and texture extraction, and orthomosaics. Resulting products are used with virtual and mixed reality holographic devices, a Geographic Information System (GIS) and with game engines (Unreal Engine and Unity) to create realistic multi-scale multi-theme 3D reconstructions of planet Earth, landscapes and/or objects. Among other system components, an augmented reality digital sandbox equipped with two depth cameras supports experiential learning and experimentation involving scaled down replicas of landscapes or user-defined topographies. The system allows for fast representation of landscape changes (near-real time response), which simulates fluid flow over modified terrain, as well as quantitative analyses, modeling and what-if scenarios through the integration with a GIS. The 3DIG system has been incorporated into classwork and results have been evaluated. This work introduces the interconnected and complementary technologies of 3DIG; presents lessons learned during system design; introduces system implementation and evolution (including the recent integration of new components); describes system use for hands-on and immersive experiential learning; and discusses system evaluation.
Challenges in remote sensing, including remote sensing of vegetation, include the spectral characterization of objects over space and time. One key aspect for this characterization involves the geometry of data acquisition and positional relationships between light source, the target and the remote sensor. Several configurations of goniometers have been used to acquire spectral data as a function of this geometry and this strategy has been particularly efficient when applied to the study of short canopies (e.g., grasses). Tall canopies present logistical challenges when conducting these analyses, which can be resolved by replacing physical structures (rails) with flying systems capable to conform to different canopy geometries and data acquisition requirements. This work (the Droniometer Experiment) investigates anisotropies of a forest using radiometrically calibrated images from a multispectral camera (MicaSense RedEdge) mounted on a rotary-wing unmanned aerial system programmed to follow a planned flight that simulates data acquisition by a goniometer assembled over tall canopy. The experiment used multiple planned flights, conducted to represent changes in illumination, considering sun azimuth and elevation (multiple flights per day and over the course of months). Multi-angle data acquisition was addressed by controlling aircraft position and camera pitch at regular intervals. This work presents the integration of the droniometer system, including platform and camera requirements and control, data acquisition and processing, and analyses of results for target/vegetation characterization and to support information extraction and multi-angle remote sensing. A radiative transfer model, the Soil Canopy Observation, Photochemistry and Energy fluxes (SCOPE) was used for comparative analysis and to further describe anisotropies in spectral responses of tall canopies.
The Open Global Glacier Model (http://oggm.org) is an open source modelling framework, helping various research groups to simulate and understand mountain glacier change at the regional or global scale. OGGM is modular, which means that we encourage users to develop their own physical parameterizations while staying compatible with the OGGM workflow. To achieve this goal, we need OGGM to be easy to understand, install, apply, and extend. In this presentation, I will talk about how we make use of the wealth of open-source tools available in the python and Jupyter ecosystems to provide an online documentation platform for OGGM. Our documentation combines text with interactive examples that run in your web browser, avoiding the typical installation and data download burden for newcomers. With selected examples from the collaborative educational content platform OGGM-Edu (http://edu.oggm.org), I will show how anyone can apply these ideas and tools to their own documentation or outreach project. Finally, I will talk about some of the challenges faced by the OGGM project in its pursuit of becoming a community model. With the increasing pressure on geoscientists who have to learn new complex technologies while navigating the “publish or perish” career model, the growing demand for better open science practices can be a blessing as well as a curse for many early career scientists.
41 thousand years ago, the Laschamps geomagnetic excursion caused Earth’s geomagnetic field to drastically diminish to ~4% of modern values and modified the geomagnetic dipole axis. While the impact of this geomagnetic event on environmental factors and human lifestyle has been contemplated to be linked with modifications in the geospace environment, no concerted investigation has been conducted to study this until recently. We present an initial investigation of the global space environment and related plasma environments during the several phases of the Lachamps event using an advanced multi-model approach. We use recent paleomagnetic field models of this event to study the paleomagnetosphere with help of the global magnetohydrodynamic model BATS-R-US. Here we go beyond a simple dipole approximation but consider a realistic geomagnetic field configuration. The field is used within BATS-R-US to generate the magnetosphere during discrete epochs spanning the peak of the event. Since solar conditions have remained fairly constant over the last ~100k years, modern estimates of the solar wind were used to drive the model. Finally, plasma pressure and currents generated by BATS-R-US at their inner boundary are used to compute auroral fluxes using a stand-alone version of the MAGNIT model, an adiabatic kinetic model of the aurora. Our results show that changes in the geomagnetic field, both in strength and the dipole tilt angle, have profound effects on the space environment and the ensuing auroral pattern. Magnetopause distances during the deepest phase of the excursion match previous predictions by studies like Cooper et al. (2021), while high-resolution mapping of magnetic fields allow close examination of magnetospheric structure for non-dipolar configurations. Temporal progression of the event also exhibits rapid locomotion of the auroral region over ~250 years along with the movement of the geomagnetic poles. Our estimates suggest that the aurora extended further down, with the center of the oval located at near-equatorial latitudes during the peak of the event. While the study does not find evidence of any link between geomagnetic variability and habitability conditions, geographic locations of the auroral oval coincide with early human activity in the Iberian peninsula and South China Sea.
Flood depth grids from U.S. Federal Emergency Management Agency (FEMA) provide model-output estimates of the depth of water that can, on average, be expected to occur at various return periods for localized areas. However, use of these depth grids can be limited by spurious data and an insufficient number of return periods for certain planning applications. This research proposes a new method for estimating flood depth grids to address these shortcomings. The Gumbel distribution is used to characterize the flood depth-return period relationship for grid cells for which the data are plausible. Then the Gumbel parameters of slope (α) and intercept (u) are used to project flood elevations for extreme return periods for which an entire area can be assumed to be submerged. Spatial interpolation methods are then used to impute the flood elevations for spurious or missing grid cells. Then, the flood depth is recomputed from the flood elevations, once they are re-calculated at the shorter return periods. Validation of this technique for a Metairie, Louisiana, U.S.A. study area suggests that the cokriging spatial interpolation technique provides the most suitable estimates of flood depth, provided that the FEMA-generated model output is assumed to provide the “correct” results. These methods may assist engineers, developers, planners, and others in mitigating the world’s most widespread and expensive natural hazard.
In 2015, at the United Nations Conference of the Parties in Paris, France, countries agreed to limit the global mean surface temperature (GMST) increase to 2°C above preindustrial levels, and to pursue efforts to limit it to 1.5°C. However, risks from sea level rise are not well encapsulated by temperature targets. Near term emissions will dictate long term sea level rise responses, but the tendency for policy and negotiations to concentrate on the year 2100 can limit our understanding of intergenerational justice concerns arising from this commitment. Here we present an analysis of the long term spatial variability of sea level rise, and an interdisciplinary review of associated justice considerations from across a wide range of literatures. We center the positioning of the Alliance of Small Island States (AOSIS) to show that AOSIS nations are disproportionately impacted by sea level rise, and that ice sheet instabilities, which could dominate the long term trend in sea level, are associated with feedbacks which can potentially exacerbate climate justice implications.
Human populations and infrastructure in high mountain regions are exposed to a wide range of natural hazards, the frequency, magnitude, and location of which are extremely sensitive to climate change. In cases where several hazards can occur simultaneously or where the occurrence of one event will change the disposition of another, assessments need to account for complex process chains. While process chains are widely recognized as a major threat, no systematic analysis has been undertaken. We therefore assemble a broad set of process chain events from across the globe to establish new understanding on the factors that directly trigger or alter the disposition for subsequent events in the chain. Based on this new understanding, we derive a novel classification scheme and parameters to aid natural hazard assessment. Most process chains in high mountains are commonly associated with glacier retreat or permafrost degradation. Regional differences exist in the nature and rate of sequencing---some process chains are almost instantaneous, while other linkages are delayed. Process chains involving rapid sequences are difficult to predict or mitigate, and impacts are often devastating. We demonstrate that process chains are initialized most frequently as threshold failures, being the result of gradual landscape weakening and not due to the occurrence of a distinct trigger. The co-occurrence of fluvial processes or activation of sediment deposition areas increases the reach of process chains. Climate change is therefore expected to increase the reach of events in the future, as glacial environments transform into sediment-rich paraglacial and fluvial landscapes.
The initial list of Essential Water Variables (EWVs) evolved from wide meta-surveys of water data needs for research and applications that were carried out in 2010 to support GEO Societal Benefit Areas (SBAs). These EWVs were formalized in the Group on Earth Observations System of Systems (GEOSS) Water Strategy Report (WSR) “From Observations to Decisions”, released in 2014. Subsequently, discussions with additional user communities have augmented the list, for example with Surface Water Extent. Besides “primary” EWVs that identify key water variables, including precipitation, soil moisture, and water quality, a set of “supplementary” EWVs is also needed to complete the information that the formal list of primary EWVs should provide, such as Digital Elevation Models. It is clear that all available observing systems, employing both remote sensing and in situ observing instruments and networks are required to address the range of space/time resolutions, accuracies, and data latencies that the end-user applications require. In fact, there are still gaps in our ability to deliver all variables as required. In some cases this is a technical challenge, such as remote sensing capabilities for some water quality variables, while in many other cases it is a matter of administrative and resource challenges. This paper summarizes EWVs as currently defined and required by key end-user research and applications sectors. As a follow up to the WSR, we highlight the relevance of EWVs to the indicator monitoring objectives of the UN Sustainable Development Goals (SDGs), various international Conventions and Frameworks, and the GEO Global Water Sustainability (GEOGloWS) priority thematic communities.
Improving photosynthesis has been considered critical to increasing crop yield to meet food demands from a growing population. To achieve this goal, high-throughput phenotyping techniques are highly needed to explore both natural and genetic variation in photosynthetic performance among crop cultivars. Due to the non-invasive nature of hyperspectral imaging, there is an increasing use of hyperspectral imaging for phenotyping of photosynthesis or photosynthetic physiology. The use of hyperspectral sensors has resulted in the accumulation of large amounts of data, shifting the research efforts into efficiently mining spectral information for high-throughput phenotyping. In this presentation, we will introduce data pipelines developed to leverage proximal sensing platforms and data sources including both reflectance spectra and solar-induced fluorescence (SIF) for quantifying photosynthetic performance at the canopy level. Photosynthetic performance was represented by the maximum carboxylation rate (Vcmax) and the maximum electron transport rate (Jmax). The experiments were conducted using eleven tobacco cultivars grown in field conditions during 2017 and 2018 at Energy Farm at University of Illinois. Time-synchronized hyperspectral images from 400 to 900 nm and irradiance measurements of sunlight under clear-sky conditions were collected for capturing reflectance spectra and SIF (and SIF related parameters). Within 30 minutes of spectral measurements, ground-truth Vcmax and Jmax were obtained from portable leaf gas exchange system. Our results suggested both reflectance spectra and SIF can provide accurate estimations of Vcmax and Jmax. The presented data pipelines have potential to relieve bottleneck in phenotyping of photosynthesis for breeding cultivars of enhanced photosynthesis.
Wildfire is common across the pan-Arctic tundra. Tundra fires exert significant impacts on terrestrial carbon balance and ecosystem functioning. Interactions between fire and climate change can enhance their impacts on the Arctic. However, the driving mechanisms of tundra fire occurrences remain poorly understood. This study focuses on identifying key environmental factors controlling fire occurrence in Arctic tundra of Alaska. Our random forest models, considering ignition source, fuel, fire weather, and topography, have shown a strong predictive capability with an overall accuracy above 91%. We found cloud-to-ground (CG) lightning probability by far the dominant driver controlling tundra fire occurrence. Warmer and drier near-surface weather was required to support burning, while fuel composition and topography have modest impacts on fire occurrence. Our results highlight the critical role of CG lightning in driving tundra fires and that incorporating lightning modeling is essential for fire monitoring, forecasting, and management in the Arctic.
The role of greenhouse gases (GHGs) in global climate change is now well recognised and there is a clear need to measure emissions and verify the efficacy of mitigation measures. To this end, reliable estimates are needed of the GHG balance at national scale and over long time periods, but these estimates are difficult to make accurately. Because measurement techniques are generally restricted to relatively small spatial and temporal scales, there is a fundamental problem in translating these into long-term estimates on a regional scale. The key challenge lies in spatial and temporal upscaling of short-term, point observations to estimate large-scale annual totals, and quantifying the uncertainty associated with this upscaling. Here, we review some approaches to this problem, and synthesise the work in the recent UK Greenhouse Gas Emissions and Feedbacks Programme, which was designed to identify and address these challenges. Approaches to the scaling problem included: instrumentation developments which mean that near-continuous data sets can be produced with larger spatial coverage; geostatistical methods which address the problem of extrapolating to larger domains, using spatial information in the data; more rigorous statistical methods which characterise the uncertainty in extrapolating to longer time scales; analytical approaches to estimating model aggregation error; enhanced estimates of C flux measurement error; and novel uses of remote sensing data to calibrate process models for generating probabilistic regional C flux estimates.