The compound extremes of hydrometeorological variables might not only affect as an extreme from a single variable, but might also result due to their interaction in time and space. This occurrence is called compound unusual events. A statistical approach based on the data depth function is being used to identify these occurrences in the multivariate dataset. To illustrate the methodology, the daily historical series of precipitation and mean temperature series from several selected observation stations in Germany will be used. The zero values of precipitation and the anomalies of mean temperature are used in this study. This study will help in answering the occurrence of unusual events for both variables on temporal and spatial scales.

Year-round demand for luxury food crops, such as berries, has rapidly increased for high income populations in the last twenty years. For example, the United States per capita import of berries has increased tenfold in twenty years, with at least 98% of its imported strawberries, raspberries, and blackberries being sourced from Mexico. In the Mexican state of Michoacan, agricultural areas have transformed to satisfy the global demand, altering local water and land resource patterns. While previous studies have examined the water resource sustainability of export agriculture, more research is needed to understand how export-oriented agriculture competes with or complements common pool resource systems previously established in production regions. In this study, we combine a national dataset on communal-tenured land (ejidal land) with geospatial agricultural data to quantify the water and land use resource changes of Michoacan – the booming heart of Mexican berries. This study captures the berry boom that occurred in the state between 2010 and 2020 using machine learning algorithms on satellite images, land cover change mapping, and biophysical modelling. It asks to what extent the communal land, with its associated natural resources, has been incorporated into the agro-export crop industry. This study adds to the existing commons literature by assessing local land use and resource use change within an increasingly globalized world.

High-resolution observations made by the Hubble Space Telescope have developed the modern picture of the Jovian main oval emission as the signature of field-aligned corotation-enforcement currents which keep magnetospheric plasma rotating at the same rate as Jupiter's magnetic field. This model explains the slowly changing behavior and bright emissions of the main oval and allows the magnetosphere to be studied by remote observations, as the auroral oval directly reflects processes in the magnetosphere and properties of the plasma therein. Recent results from the Juno mission have called these results into question, as the strong, field-aligned currents required by the corotation-enforcement current system have not been measured. Where is the corotation-enforcement current-- which must exist to move magnetospheric plasma-- a dominant driver of the main oval emission? Where do other processes drive the main oval aurora instead? We present one of the widest surveys of Jupiter's main oval auroral features to date by combining over 180 hours of Hubble Space Telescope observations to address these questions. This comprehensive survey is the first to measure the corotation rate, an important tool for distinguishing auroral drivers, of all individual auroral features. This analysis is made possible due to the development of automated pipelines to reduce observations, produce keograms, identify discrete auroral emission features, and measure the corotation rates of these features, among other properties. We present the measured properties of emission features as a function of location along the main oval, in terms of longitude, local time, auroral local time, and magnetic local time. These results serve as the foundation for comparison to in-situ measurements from both the Galileo and Juno missions, which will ultimately help reveal which magnetospheric conditions are likely responsible for driving corotating emissions and which are responsible for sub-corotating emissions, such as the dawn storms.

Stratospheric aerosol injection (SAI) can provide global cooling by adding aerosols to the lower stratosphere, and thus is considered as a possible supplement to emission reduction. Previous studies have shown that injecting aerosols at different latitude(s) and season(s) can lead to differences in regional surface climate, and there are at least three independent degrees of freedom (DOF) that can be used to simultaneously manage three different climate goals. To understand the fundamental limits of how well SAI might compensate for anthropogenic climate change, we need to know the possible surface climate responses to SAI by evaluating the SAI design space. This research work quantifies the number of meaningfully-independent DOFs of the SAI design space. This number of meaningfully-independent DOF depends on both the climate metrics that we care about and the amount of cooling. From the available simulation data of different SAI strategies, we observe that between surface air temperature and precipitation, surface air temperature dominates the change of surface climate. The number of injection choices that produce detectably different surface temperature is more than the number of injection choices that produce detectably different precipitation. At low levels of cooling, only a small set of injection choices yield detectably different surface climate responses. As more cooling is needed, more injection choices produce detectably different surface climate. For a cooling level of 1-2C, we find that there are likely between 6 and 12 DOFs. This reveals new opportunities for exploring alternate SAI designs with different distributions of climate impacts and evaluating the underlying trade-offs between different climate goals.

Low-lying coastal and estuarine areas are among the most populated regions globally, have high economic significance, and are increasingly threatened by climate change, sea level rise, nuisance flooding, and extreme storms. Nature-based coastal protections are sustainable and sea-level resilient alternatives compared to traditional solutions such as dikes and seawalls. Submerged aquatic vegetation (SAV) or seagrasses can provide coastal flood and erosion protection by attenuating storm wave and current energy and stabilizing seabed sediments. However, more research is needed to understand the interactions between flow, SAVs, and sediments. These dynamic interactions affect flow at different scales and seagrass productivity. In this study, we present field measurements of current and wave evolution over a seagrass meadow in South Bay, Virginia. The high vertical resolution measurements show how currents change from above-canopy to in-canopy waters. Wave measurements indicate the dissipation and frequency modulation over the canopy. The results are compared with hydrodynamic simulations using a two-way coupled flow-vegetation interaction model that simulates nonlinear current and wave evolution as well as dynamics of highly flexible vegetation.

Long term climate change on early Mars is characterized by a shift in the spatial distribution of rivers and lakes. Geological datasets suggest earlier paleo-rivers prefer higher surface elevations compared to rivers that formed later (Kite, 2019). On the other hand, modeling work also suggest a transition of surface lapse rate that comes with atmospheric escape throughout the Martian history (Wordsworth, 2016). The surface lapse rate follows the atmospheric lapse rate, which is close to dry adiabatic, when the CO2 atmosphere is thick, but decouples when the atmosphere is thin. Figuring out the surface temperature distribution on early Mars is critical, because it tells us where the water sources from ice/snowmelt would have been during warming episodes. We use the MarsWRF GCM to explore the transition of river-forming climates. We assume the atmosphere is CO2-only, but allow additional greenhouse warming by a gray gas scheme. To simplify the relation between elevation and surface temperature, we set 0 obliquity and include simulations with both idealized topography and real topography. The range of surface pressure is between 0.01 bar and 2 bar. We use a surface energy budget framework to analyze outputs (Fig. 2). Under the framework, variations in surface emission LWs correspond to surface temperature variations. We find greenhouse heating LWa is the only term that scales with surface temperature under high PCO2, in contrast to predictions from the previous literature that sensible heat SH was the cause of the regime transition (Wordsworth, 2016). This conclusion does not change with switching to realistic topography or switching CO2 radiation to a gray gas scheme. Under the low Ps but high-optical-depth κ gray gas case, the surface lapse rate still follows the atmosphere, so the regime transition can be attributed to the evolution of greenhouse gases other than CO2. In future, we will add a surface liquid water potential algorithm to link the surface energy balance to paleo-river observations. Assuming surface liquid water is formed during transient ice-melting period, surface liquid water potential can be calculated from the Ts and Ps distributions. The output will be compared with different historical epochs to find the best-fit scenario with both CO2 and non-CO2 greenhouse forcing.

The Kepler mission revealed that sub-Neptunes are about as common as stars, which defied our pre-existing notion of planet demographics. The prevailing view for sub-Neptunes was that they are mostly core by mass and atmosphere by volume (Lopez & Fortney, 2014). However, current formation models do not consider dissolution at the atmosphere-core interface. The temperature and pressure at the magma-atmosphere interface can rise to >3000 K and ~5-30 GPa (Lee et al., 2014; Piso et al., 2015), high enough for dissolution of hydrogen gas into the magma (Chachan & Stevenson, 2018). The dissolution of atmosphere into the magma may explain the drop-off in exoplanet abundance at 3 times Earth radius (Kite et al., 2019), but the puff-up of the magma due to gas dissolution has not previously been included. We propose a simple model to calculate sub-Neptune mass-radius relation, including, for the first time, the puff-up effect. Key assumptions include: (1) nonlinear solubility of gas in magma is constrained by limited laboratory data (Hirschmann et al, 2012); (2) the Fe/core mass fraction is Earth-like, and He/gas mass fraction is Solar-like; (3) ideal mixing between the dissolved gas and magma; (4) the dissolved gas is well mixed within the magma-layer. The EoS used are an Mg2SiO4 for the magma (Stewart et al., 2020); the H/He EoS (Chabrier et al., 2019); and a simple model for Fe (Seager et al., 2007). The model is integrated from the radiative-convective boundary and iterated until atmosphere-magma solubility equilibrium. We have varied the core mass, atmospheric mass and equilibrium temperature in the atmosphere. Our preliminary results are shown in Figures. The critical point for the puff-up of the core due to the dissolved gas corresponds to ~1% solubility at the magma-atmosphere boundary (Fig. 1). The puff-up effect can be important up to 0.3 Earth radius (Fig. 2), much larger than the radius error bars for a single planet in the CKS survey with Gaia DR2 data (Fulton & Petigura, 2018). In future, we will add additional constraints on gas/core mass fraction (Lee, 2019), forward-model the relationship between mass and photospheric radius, and generate predictions for exoplanet masses and radii that can be used to help interpret data from ESA’s PLATO and NASA’s TESS.

Tropical islands are highly dependent on rainfall to provide resources for drinking and agriculture. Hence, understanding changes to precipitation under a changing climate is critical for societal planning. Decadal variability in the climate system causes the strength of SST gradients to vary across the tropical Pacific that can cause precipitation patterns to transition from one decade to the next, even in the presence of longer term climate trends. To study 21st Century changes to tropical rainfall patterns in the presence of decadal variability, we use the CESM1 Large Ensemble (Kay et al. 2015) forced under RCP8.5. Each ensemble member uses different initial conditions that can be used to examine climate projections on short term (e.g. weeks, years) through long term (e.g. century) time scales. Since climate models contain climate variability such as El Niño Southern Oscillation (ENSO) and the Pacific Decadal Oscillation (PDO), each ensemble member can have diverse projection outcomes in a given decade. While all ensemble members eventually show an El Niño-like warming pattern by 2100 relative to 1985-2005, before the mid 21st Century, preferential SST warming and precipitation intensity in the tropics in any given 20-year period can be weighted toward the west, central, or east Pacific. Further, while Niño3.4 SST generally goes up relative to 1985-2005, the tropical Pacific east-west temperature gradient change does not show as consistent an upward trend. Implications of SST and precipitation change patterns for Guam, Samoa, Hawaii, and Puerto Rico are examined. Spearman’s correlation is used to examine the relationship between station island precipitation and the east-west Pacific SSTs gradient change. A strong negative correlation relative to gradient change is found for Guam, in contrast to Samoa having a high positive correlation. This study highlights the importance of decadal climate variability for understanding changes in water resources in island nations in a changing climate. This study was conducted as part of the Earth System Modeling and Education Institute summer REU program at Colorado State University.

Continuous observations from geostationary satellites have been utilized to understand land surface seasonal dynamics and fill data gaps caused by clouds. However, the limited spatial resolution of geostationary satellite products constrained the processes of detecting the terrestrial changes in landscape scale, particularly over heterogeneous areas. Moreover, the variation of sun-target-sensor geometry in geostationary satellites results in diurnal changes in surface reflectance products. To overcome the limitations of geostationary satellite products over heterogeneous areas, we conducted the series of processes: 1) characterizing the effect of the solar and viewing geometry in surface reflectance using the bidirectional reflectance distribution function (BRDF), 2) harmonizing the satellite products from different platforms into a seamless product, and 3) fusing the different satellite products to enhance the spatial resolution of geostationary satellite products. Finally, we adopted spatial and temporal gap-filling methods to achieve daily gap-filled surface reflectance products. For robust application, the results from the integrated process were evaluated in both space and time using hyperspectral maps derived from unmanned aerial vehicle (UAV) and in situ tower-based continuous spectral measurements. We expect the geostationary satellite products with high spatial resolution to uncover the cloud-bound areas towards sensing the changes of the Earth in space and time.

We report on the annual variation of quiet-time suprathermal ion composition and spectral properties for C-Fe using Advanced Composition Explorer (ACE)/Ultra-Low Energy Ion Spectrometer (ULEIS) over the energy range 0.3 MeV/nuc to 1.28 MeV/nuc from 1998 through 2020. This extends the work of Desai et al. (2006) and Dayeh et al. (2009, 2017) to cover Solar Cycle 23’s rising phase through Solar Cycle 24’s declining phase. With 5 additional years of data, we show that the number of quiet-time hours strongly anti-correlates with the Sunspot Number (SSN) at better than the -0.9 level. We also show (1) a clear ordering of the cross correlation between abundance (normalized to O) and SSN as a function of solar wind M/Q; (2) the slope of X/O’s abundance as a function of Fe/C decreases with increasing M/Q; and (3) discuss the trend of annual spectral indicies with respect to Oxygen’s spectral index as a function of solar cycle and M/Q. The contrast between our abundance and spectral index results suggests that the source from which suprathermal ions are drawn or accelerated varies with solar activity and is tied to each element’s chemistry, but he acceleration mechanism that governs the spectral shape does not.

With the urgent necessity of geo-energy resources to achieve carbon neutrality, fluid injection and production in the fractured media will significantly increase. Applications such as enhanced geothermal systems, geologic carbon storage, and subsurface energy storage involve pressure, temperature, and stress changes that affect fracture stability and may induce microseismicity. To eventually have the ability to control induced seismicity, it is first necessary to understand its triggering mechanisms. To this end, we perform coupled thermo-hydro-mechanical (THM) simulations of cold water injection and production into a rock containing two fracture sets perpendicular between them. The permeability of fractures being four orders of magnitude higher than the one of the rock matrix leads to preferential pressure and cooling advancement, which induce stress changes that affect fracture stability. We find that the fracture set that is oriented favorably to undergo shear slip in the considered stress regime becomes critically stressed, inducing microseismicity. In contrast, the fracture set that is not favorably oriented for shear remains stable. These results contrast with those obtained for an equivalent porous media that does not explicitly include fractures in the model, which fails to reproduce the direction-dependent stability of fractures present in the subsurface. We contend that fractures should be directly embedded in the numerical models when inhomogeneities are of the spatial scale of the reservoir to enable reproducing the THM coupled processes that may lead to induced microseismicity.

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.

This study’s objective was to evaluate the potential geospatial relationship between agricultural pesticide use and cancer incidences by using the concept of consolidated environmental burden indices. We conducted two sets of experiments, using pesticide data collected from the U.S. Geological Survey Pesticide National Synthesis Project database and using cancer data collected from the National Cancer Institute State Cancer Profiles and Cancer Data Registry of Idaho. In the first experiment, we analyzed the associations between several potentially carcinogenic pesticides and metals to the pediatric cancer incidence among the counties of Idaho. Principal component analysis was adopted to consolidate the environmental variables. This study identified that the principal components (PCs) were significantly associated with pediatric cancer at P-value < 0.01 and a t-statistic > 3. The PC’s improved model performance substantially, from NSE = 0.22 for the control case to NSE = 0.52 for the treatment case. In the second experiment, we evaluated the associations of pesticides to two cancer metrics (pediatric cancer incidence and total cancer incidence) across each of the 11 contiguous states in the Western U.S. at the state and county spatial scales. A multi-level model was developed using fumigant mass, fumigant mass tertiles as well as county and state boundaries, which strongly predicted total cancer incidence (R-squared = 0.95, NSE = 0.91, and SSR = 8.22). Further, this study also identified significant associations between total fumigant mass, total pesticide mass, medium and high fumigant class (tertiles > 0.33), and high pesticide class (tertile > 0.66), relative to total cancer incidence (P-value < 0.01) in the U.S. west. Fumigant application rate was shown to be significantly related to the incidence of total cancers and pediatric cancer. Moreover, this relationship was maintained regardless of the spatial resolution used in the analysis (county or state). Both studies reinforce inferences from previous studies that pesticides are related to an elevated risk for pediatric cancer. Similarly, among the pesticides, fumigants such as metam were shown to be important relative to the cancer incidences for both experiments, highlighting the significance of further analysis.

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.

Xavier University of Louisiana, in collaboration with the Institute of Earth Science Research and Education (IESRE) run the ECoSTEM project. This project aims to improve the quality of undergraduate STEM education by introducing a computational, community-oriented component into STEM programs at Xavier University. Specifically, undergraduate students develop microcontroller-based systems for collecting airborne particulate data. They work with public school teachers (and their students) and Louisiana Department of Environmental Quality (LDEQ) officers to deploy the sensors at locations around New Orleans and to analyze the resulting data. Airborne particulates are often the pollutant of greatest concern in minority and other underserved communities. This project provide undergraduates at Xavier and the local teachers (and their students) with learning experiences that are relevant to them and their communities. ECoSTEM uses a tiered mentoring system in which the project team members mentor undergraduates, who in turn work with public school teachers and mentor their students. Research has shown positive effects from student involvement in such real-world, personally relevant STEM projects, including increases in student interest in STEM-related careers after graduation. As a result, the project has the potential to broaden student interest in STEM and increase pursuit of STEM-related majors.

Airborne LiDAR has become an essential data source for large-scale, high-resolution modeling of forest biomass and carbon stocks, enabling predictions with much higher resolution and accuracy than can be achieved using optical imagery alone. Ground noise filtering – that is, excluding returns from LiDAR point clouds based on simple height thresholds – is a common practice meant to improve the ‘signal’ content of LiDAR returns by preventing ground returns from masking useful information about tree size and condition contained within canopy returns. Although this procedure originated in LiDAR-based estimation of mean tree and canopy height, ground noise filtering has remained prevalent in LiDAR pre-processing, even as modelers have shifted focus to forest aboveground biomass (AGB) and related characteristics for which ground returns may actually contain useful information about stand density and openness. In particular, ground returns may be helpful for making accurate biomass predictions in heterogeneous landscapes that include a patchy mosaic of vegetation heights and land cover types. We applied several ground noise filtering thresholds while mapping two regions within New York State, one a forest-dominated area and the other a mixed-use landscape. We observed that removing ground noise via any height threshold systematically biases many of the LiDAR-derived variables used in AGB modeling. By fitting random forest models to each of these predictor sets, we found that that ground noise filtering yields models of forest AGB with lower accuracy than models trained using predictors derived from unfiltered point clouds. The relative inferiority of AGB models based on filtered LiDAR returns was much greater for the mixed land-cover study area than for the contiguously forested study area. Our results suggest that ground filtering should be avoided when mapping biomass, particularly when mapping heterogeneous and highly patchy landscapes, as ground returns are more likely to represent useful ‘signal’ than extraneous ‘noise’ in these cases.

Lahars, or volcanic mudflows, are one of the most devastating natural, volcanic hazards. We present an analysis of several rain-triggered lahar events at Volcán Fuego in Guatemala with an infrasound array and time-lapse camera network deployed along the Las Lajas river channel. While infrasound detects the passage of low frequency sound waves associated with these mudflows, the time-lapse cameras can be used to verify lahar passages and channel responses as these flows move downstream. Twenty-three infrasound microphones and five time-lapse cameras were deployed during the 2021 rainy season (May-October) in the Las Lajas drainage on the southeasterly side of Volcán de Fuego. With the data collected over this field campaign, we hope to quantify flow parameters such as volumes, velocities, and the frequency of these rain-triggered mudflows, as well as characterize flow behaviors in our infrasound signals by correlating data to the time-lapse imagery. This study allows us to identify the occurrence of several lahars, quantify important characteristics, and use a multi-faceted approach to verify and delineate important flow behaviors related to these volcanic hazards.