Nutrient pollution is considered one of America’s most widespread, costly, and challenging environmental problems. Artificial Floating Islands (AFIs), a phytoremediation technology, has been proven as an efficient, environmental-friendly, and cost-effective strategy to address this issue. However, most previous studies of AFIs were done in controlled conditions at mesocosm experiments. In addition, limited information exists on the use of AFIs as a nutrient remediation/prevention strategy in Ohio. This study aims to fill these gaps. We are currently undertaking a combination of mesocosm and natural experiment to assess the nutrient-removal efficiency of AFI systems in the Milliron Research Wetlands (at the Ohio State University Mansfield campus), and establish a performance baseline for two native aquatic plant species, Carex comosa and Eleocharis palustris. In this study, 18 AFIs, 6 planted with Carex comosa, 6 with Eleocharis palustris, and 6 have no plants, were deployed in a section of the Milliron Research Wetlands. Physical and chemical parameters are being monitored bi-weekly. The AFI systems are constructed using PVC pipes to provide buoyance, EVA foam mats as platforms, and nylon nets to cover the system. Each AFI unit has nine luffa sponges, inserted in the foam mat, to hold aquatic plant seedlings, keep the moisture of roots, and enlarge the surface area for bacterial biofilm development. Since nutrient removal from the wetland is affected by numerous natural processes, a mesocosm experiment was set up to assist the quantification of nutrient removal due specifically to the presence of AFIs. The mesocosm experiment mimics the natural experiment at the wetland and contain 12 equal-size tanks containing water pumped directly from the wetland, 3 of which have AFIs with Carex comosa, 3 have Eleocharis palustris, 3 have no plants, and 3 contain just water from the wetland. Physical and chemical measurements (as well as sample collections) are performed weekly in the tanks. Water in the tanks are exchanged bi-weekly. Preliminary results show that the AFI systems quickly developed large root systems and extensive bacterial biofilms. The effects of the associations between plant biomass, biofilm development, and changing chemical and physical conditions will be investigated as the experiment progresses.

Over the last century, runoff from farms and cities, along with land cover and land use changes, have drastically altered the mass balance of nutrients in aquatic systems, affecting both their ecological functioning and the living communities they support. Here we present the results of a multi-year, long-term study designed to assess the control of land-use and hydrology on nutrient fate and transport within a mixed land-use watershed in north-central Ohio. A total of 64 streams (with a mix of urban, cropland, pasture, and forest catchments) have been sampled periodically since the summer of 2008. Hydrological conditions during the study period exhibited marked seasonality, with usually dry winter seasons (average ppt: 23.5±7.4 cm) and wet spring seasons (average ppt: 34.5±8.1 cm). Runoff generation in response to precipitation events is faster in streams draining developed catchments and slowest in forested streams, where runoff is generated only by events > 10 mm/day. Hydrologic connectivity in the watershed appear to be limited, since only about 25% of precipitation inputs were translated into quick flow. There is a significant, positive correlation between runoff and nutrient concentrations (R2 values are: 0.40 for streams draining urban landscapes, 0.34 for forested streams, 0.30 for cropland, and 0.28 for pastureland). We also observed significant inter-annual and seasonal variations on both DIN (p = 0.02) and PO4 concentrations (p < 0.01). Compared to dry years, nutrient fluxes during wetter years are, on average, 16% higher in urban catchments and 47% higher in forested catchments, but 32% lower in pasture-dominated catchments. Baseflow is responsible for only between 20-30% of the annual nutrient export from the watershed.

Inland waters are recognized as a significant source of CO2 to the atmosphere; however, the global magnitude of this flux remains uncertain. In particular, CO2 concentrations and fluxes in stream systems are extremely variable at scales of 10’s to 100’s of meters, complicating monitoring and prediction efforts. Thus, models of pCO2 that capture these scales of spatial variability are necessary for the accurate prediction and monitoring of stream CO2 fluxes. Despite a strong conceptual framework for the hydrologic processes that control stream CO2, predictive models to date have been empirical, based on Strahler stream order and regressions between observed pCO2 and landscape variables. We hypothesize that models incorporating well-described hydrologic processes may lead to new insights into the magnitude of various CO2 sources and improve predictions. Here, we develop and apply a process-based stream network model of CO2 based on NHDplus flowlines and driven by groundwater inputs, hyporheic exchange, water-column metabolism, advective transport, and atmospheric exchange. Model output is compared with 151 measurements of pCO2 (424 - 9718 ppm) collected in August, 2019 across the upper East River watershed in Gothic, CO, a mountainous, high-elevation headwaters system within the Colorado River basin. We find that modeled pCO2 captures observed spatial patterns and predicts measured values with a RMSE of ~250 ppm and R2 of 0.47 (p<10-15). Additionally, our process-based model performs significantly better than a multiple linear regression model between observations and a geomorphic variables (r2=0.35, p<10-7). Estimates from an optimized stream network model give additional insight into CO2 sources, suggesting that groundwater accounts for 70-80% of evasion fluxes, hyporheic processes for 20-30%, and water-column metabolism for ~1% across the East River watershed. The ability of our model to predict pCO2 at the spatial scales of variability may provide an important next step in estimating global CO2 fluxes, and future research will test the predictive power of process-based models at regional and global scales.

The isotopic composition of water vapor (e.g. its Deuterium content) evolves along the water cycle as phase changes are associated with isotopic fractionation. In the tropics, it is especially sensitive to convective processes. Consequently the isotopic composition of precipitation recorded in paleoclimate archives has significantly contributed to the reconstruction of past hydrological changes. It has also been suggested that observed isotopic composition of water vapor could help better understand convective processes and evaluate their representation in climate models. Yet, water isotopes remain rarely used beyond the isotopic community to answer today’s pressing climate questions. A prerequisite to better assess the strengths and weaknesses of the isotopic tool is to better understand what controls spatio-temporal variations in water vapor isotopic composition through the tropical atmosphere. A first step towards this better understanding is to understand what controls the isotopic composition of the sub-cloud layer water vapor over the ocean. Isotopic measurements show that the water vapor is the most enriched in trade-wind regions, and becomes more depleted as precipitation increases. To understand this pattern, we use global simulations with the isotope-enabled general circulation model LMDZ, large-eddy simulation in radiative-convective equilibrium and with large-scale ascent or descent, with the isotope-enabled model SAM and simple analytical models. We show that increased precipitation rate is associated with increased isotopic depletion if it is associated with stronger large-scale ascent, but with decreased isotopic depletion if it is associated with warmer surface temperature. As large-scale ascent increases, the isotopic vertical gradient in the lower troposphere is steeper, which makes downdrafts and updrafts more efficient in depleting the sub-cloud layer water vapor. The steeper gradient is caused mainly by the larger quantity of snow falling down to the melting level, forming rain whose evaporation depletes the water vapor.

China offers the Global Energy Interconnections as a planetary project to deliver sustainable development and climate action through large-scale energy transition. What does this mean for Southeast Asian countries’ regional quest for greater energy connectivity? Although China-Southeast Asia interconnections promise a durable regional energy connectivity, bringing distant renewable energy to demand centers using GEI technologies, these future energy systems are not free of challenges: technical and political. A closer look at this future interconnection further reveals other chokepoints: politically powerful domestic energy regimes; levels of trust between China and these countries; aging and absent transmission infrastructures; heterogeneous national energy markets and regulations; and differing receptions towards China’s externalization of its surplus industrial capacity.

The usage of the term Knowledge Graph (KG) has gained significant popularity since 2012, when Google introduced its own knowledge graph, and how they used it to enhance their searches and question answering systems. While various definitions and interpretations for knowledge graphs have been presented, what remains consistent is that knowledge graphs are commonly used with reasonsers to make inferences about data, based on assertions and axioms written by human experts. But knowledge graphs, which store complex, multi-dimensional data contain hidden patterns and trends that cannot be explored simply using reasoners. In such a case it becomes necessary to extract parts of the knowledge graph (focusing on the instances related to one property at a time) and analyze them individually in order to conduct a focused but tractable exploration of the domain. In this presentation, we present one way to gain insights from knowledge graphs, using network science. To achieve this goal, we have formalised the partitioning of knowledge graphs to unipartite knowledge networks, and present various ways to explore and analyse such knowledge networks to form scientific hypotheses, gain scientific insights and make discoveries.

Extratropical cyclones are weather phenomena with significant transfer of energy between the surface (over the ocean or on land) and the atmosphere. Recurrently, reanalysis data are used to understand the behavior of cyclonic tracks and to study extreme events, with constant updates and validations with the observational base in the Northern Hemisphere. However, studies using cyclone tracking in the Southwestern Atlantic, has proven more difficult. This disagreement seems to be in function of the structure and intensity of the forcing factors that influence both cyclogenesis and the displacement to the South Atlantic, when compared to the Northern Hemisphere. In this work, synoptic pressure charts at sea level, manually made and processed by the Brazilian Navy every 12 hours between the years 2010 and 2020, as a product resulting from a consensus among Navy meteorologists, were used to study the cyclonic pathways in the Southwestern Atlantic (METAREA V). Data obtained for all cyclones identified in the charts, based on their position and displacement, formed a database with 10737 cyclones, containing speed, dimensions, and pressure gradient. The cyclones identified have a higher radius frequency between 200/400 km and a faster-moving center shift. In addition, about 60% of cyclones associated with cold fronts have a life cycle ranging from 3 to 4 days. There is also a expressive cyclogenesis between latitudes 23ºS and 43ºS where, in austral autumn winter, increases its frequency over the ocean and close to the southern Brazilian coast. During spring, the greater cyclogenesis frequency occurs over the continent, close to Chaco area in Argentina and Uruguay. The impacts of these statistical figures on the south and southeastern Brazilian coast, mainly the continental insertion point of the cold fronts and cyclonic displacement that influence rough seas and storm surges, are discussed in this work. Keywords: EXTRATROPICAL CYCLONES, CYCLONE TRACK, SYNOPTIC CHARTS, SOUTHWESTERN ATLANTIC

In support of the American College & University Presidents’ Climate Leadership Commitments, the University of Maryland College Park (UMD) has established a goal to become climate neutral by 2050. While much progress has been made to lower the University’s carbon footprint across multiple emissions sectors, tree conservation or restoration has traditionally been excluded due to concerns about the reliability and consistency of the science. For the past several years, faculty and students in UMD’s Department of Geographical Sciences have been working with state governments across the region to inform climate action planning with advanced forest carbon science. However, with student support and leadership, we identified an opportunity to retool this same science to help UMD “walk the walk” and advance our own forest climate goals in parallel with Maryland and other U.S. Climate Alliance states. By partnering with the Office of Sustainability and other land management entities, we have been able to directly inform the campus climate action plan with robust forest carbon estimates as well as influence and support the carbon budgeting process of all universities that have pledged support for the “Carbon Commitment.” Unlike state governments, the university’s approach to sustainability broadly follows that of a corporation, requiring enhanced collaboration to ensure the science is provided in user-relevant formats while remaining consistent with science approaches utilized by state partners. Our experience during the first year of this project underscores the value of building out scientific approaches that meet specific stakeholder needs while remaining poised to adapt these tools in support of new partnerships and collaborations.

International frameworks for climate mitigation that build from national actions have been developed under the United National Framework Convention on Climate Change and advanced most recently through the Paris Climate Agreement. In parallel, sub-national actors have set greenhouse gas (GHG) reduction goals and developed corresponding climate mitigation plans. Within the U.S., multi-state coalitions have formed to facilitate coordination of related science and policy. Here, utilizing the forum of the NASA Carbon Monitoring System’s Multi-State Working Group (MSWG), we collected and reviewed climate mitigation plans for 11 states in the Regional Greenhouse Gas Initiative (RGGI) region of the Eastern U.S. For each state we reviewed the 1) policy framework for climate mitigation, 2) GHG reduction goals, 3) inclusion of forest carbon in the state’s climate action plan, 4) existing science used to estimate forest carbon, and 5) stated needs for carbon monitoring science. Across the region, we found important differences across all categories. While all states have GHG reduction goals and framework documents, nearly three-quarters of all states do not account for forest carbon when planning GHG reductions; those that do account for forest carbon use a variety of scientific methods with various levels of planning detail and guidance. We suggest that a common, efficient, standardized forest carbon monitoring system would provide important benefits to states and the geographic region as a whole. In addition, such a system would allow for more effective transparency and progress tracking to support state, national, and international efforts to increase ambition and implementation of climate goals.

The 2018 eruption of Kīlauea volcano produced the largest and most destructive lava flows in the lower East Rift Zone (LERZ) in the past 200 years. Average effusion rates exceeded 100 m3 s-1 (DRE) for more than two months as lava covered > 30 km2 of land area. The largest and longest-lived lava flow was produced by fissure 8 and had flow advance rates exceeding 100 m hr-1 and a run-out length of 13 km. While residents were able to safely evacuate from this rapidly advancing flow, hundreds of structures were destroyed. We integrate observed eruption parameters from the fissure 8 flow with numerical models for lava flows to investigate how eruption rate, topography, and rheology affect the initial path, advance rate, and extent of a lava flow. Many numerical models have been created to represent the advance and/or extent of lava flows. We apply both 1D and 2D, rules-based and physics-based models to explore the advantages and limitations of these model types. First, we validate the models for fissure 8 flow parameters using existing datasets from field observations and sample analysis. Second, we vary the eruption rate and lava rheology to test the influence of these parameters on the advance rate and flow extent. This analysis demonstrates the level of confidence that can be associated with modeling results when estimating difficult-to-constrain parameters during an eruption. Third, using input digital elevation models (DEM) of different resolutions, we examine the sensitivity of model accuracy to DEM resolution, with a specific focus on the influence on flow advance of smaller-scale topographic features that may not be resolved in coarse-resolution DEMs. Through better understanding of how different parameters control flow emplacement, and how to best apply the models describing that emplacement, we aim to improve the ability to estimate advance rate and flow path during (and prior to) the initial stages of flow emplacement and provide more detailed hazard assessments for future eruptions.

If the university can be thought of as an incubator for ideas and thought leadership, then each department is a learning ecosystem unto itself. The IDEEAS (Inclusion, Diversity, and Equity in Earth and Atmospheric Sciences) Working Group formed organically in Cornell’s Earth and Atmospheric Sciences department as a grassroots group with a desire to improve the department ecosystem. Self-selected from the full cross-section of the department, our members comprise students, staff, researchers, faculty, and emeriti. IDEEAS is a non-hierarchical group within the very hierarchical setting of academia, and our work provides a model for disrupting traditional power structures while leveraging their influence to reimagine how an academic unit could and should function. IDEEAS is not a committee; we are a collective. We believe that, irrespective of rank or role, every member of the department community has the capacity to practice leadership. As such, we lead by action. Each IDEEAS project or initiative is organized around an action team, who collectively carry out a community-informed vision of the culture we would like to co-create with the rest of the department. Our commitment to collective leadership empowers constituencies (e.g., students, non-academic staff, post-docs) who have traditionally lacked a pathway to provide input or participate in department-level decision making. IDEEAS is developing formal channels of communication between the group and department leadership in an effort to develop a sustainable ecosystem that will outlive its founders. IDEEAS events combine community building and intentional learning opportunities to promote critical reflection and foster connections. Events included a well-attended kickoff party with facilitated conversation that drew 56 attendees (~40% of the department), and community conversations about implicit bias and structural racism. IDEEAS organizers have been critically responsive during ongoing COVID19 isolation, providing numerous opportunities for social connection and using the disruption as a catalyst to cultivate connection and build community resilience that will outlast the pandemic. We invite discussion and collaboration with those engaged in similar justice, equity, diversity, and inclusion work in the geosciences.

Submerged aquatic vegetation in estuaries and coastal areas can alter the hydrodynamics of coastal waves by attenuating the energy of waves generated by storm surges and cyclones. Generally, wave attenuation by seagrass meadows is studied by considering a constant vegetation drag coefficient across the meadow which is an oversimplification. This study provides a better understanding of how submerged vegetation alters surface wave amplitude and velocity by developing a coupled flow-vegetation interaction model, which consists of a nonhydrostatic wave model and a numerical model for vegetation blade dynamics. The model captures wave attenuation rate and quantifies the effect of vegetation flexibility on wave attenuation. The vegetation model divides up each blade into an arbitrary number of segments that allows us to simulate strong deflection of blades under combined wave and current conditions. The two models are dynamically coupled which means that at each time step, the hydrodynamic model solves the free surface elevation and depth-varying velocity which is then used as input for the vegetation model. Then, the vegetation model calculates the effective drag coefficient of each segment of a stem in the canopy that is dependent on the forces applied over the blade and orientation of the blade. The computed vertically variable vegetation drag coefficient of each stem is then used in the hydrodynamic model for the next time step. Model results suggest that considering a rigid vegetation with simplified drag coefficient would result in larger vegetation-induced damping compared to flexible vegetation condition. The model results also confirm that there is a strong dependency between the vegetation-induced wave dissipation and vegetation parameters (e.g., canopy length and vegetation blade height).

Machine learning (ML) models that classify a sample as non-indicative or indicative of life can play an important role in planning life-detection missions. They are based on clearly defined and consistent algorithms, regardless of sample type or origin, and make their predictions from weighted combinations of multiple features rather than from any singular feature. These weighted combinations can reveal the most informative measurements within the operational constraints of a life-detection mission. The Ladder of Life Detection (Neveu 2018) identifies the need for an understanding of how combinations of multiple biosignatures affect overall confidence. The present work provides a starting point to answer this need, and future work will expand the data types to obtain even more predictive combinations of features. Elemental composition and isotope fractionation were chosen as the data types, as they are available for both biogenic and abiogenic systems and not unique to Earth biochemistry. Measurements of these data types across a wide range of unambiguously non-indicative or indicative samples were gathered from published literature. The varied sample measurements were then integrated into twenty-one representative samples. The ML models only made binary classifications of non-indicative or indicative of life. Nonetheless, the indicative samples broadly fell into three categories: mixed, non-alive, and alive. Four classification algorithms were trained and tested with Monte Carlo simulations using a 70:30 train to validation ratio. Between the models, around 75% of the test samples were correctly classified, with variations in sensitivity and specificity of the models. For elemental abundances predictive of a non-indicative of life sample: all models found Ti and Si as strong and Fe, Al, Mn, and Mg as medium. For predicting an indicative of life sample, all models found C, N, and Carbon-13 as strong and K, H, P, and Ca as medium. A weighted combination of multiple biosignatures is shown to be a more effective approach to classifying sample-data than relying on any individual biosignature or on an unweighted group of biosignatures. Different models also made different chronic misclassifications, suggesting that combining the outputs of multiple models may be more effective than relying on the output of a singular model. Which type of model to use may depend on the application, e.g., higher sensitivity models might be preferred in first-pass situations where false-negatives are more costly than false-positives. Lastly, the weighted combination of measurements in a model suggests how to combine biosignatures to affect the overall confidence of the classification. These results provide evidence of elemental biosignatures beyond the CHNOPS of Earth-based life and serve as a proof of concept for algorithmic biosignature classification.

The savanna - forest transition in the tropics has a large and complex variation in vegetation structure both vertically and horizontally. 3D-imaging technologies provide detailed high-resolution measurements of the vegetation structure. However, the use of these observations globally faces practical challenges due to spatio-temporal gaps and operational restrictions, mainly in tropical regions. NASA’s Global Ecosystem Dynamics Investigation (GEDI) is the first quasi-global LiDAR (light detection and ranging) observations of 3D vegetation structure at a footprint resolution of 25 m. Here we use GEDI data (GEDI02_Bv001) to analyze vegetation structure in the savanna - tropical forest transition of northern South America, using canopy height, canopy cover, total Plant Area Index (PAI), maximum Plant Area volume Density (PAVD), and vertical profile of PAI and PAVD as vegetation structure descriptors. Despite contrasts between savanna (open-canopy) and forest (closed-canopy), our results show a gradual variation along the transition in canopy height, canopy cover, total PAI, and maximum PAVD. Our results support that the savanna- forest transition in tropical regions can be described as a grassland - forest continuum. Results also indicate that GEDI data allow a better characterization of vegetation lower than 5 m in height, mainly in savanna, an improvement from other global databases (e.g. MODIS). Further, our study illustrates the potential of GEDI data to advance in the characterization of large-scale patterns of vegetation structure in tropics, key for supporting biogeography, and macroecology studies relevant in the phase of current ecosystem changes.

A catchment in southern England, UK, included a substantial area of bare ground within the surrounding heathland and woodland. Runoff from this area has, in the past, contributed large volumes of sediment to a large lake; although this input is now significantly reduced as a result of previous and on-going management works that are reported on in this paper. Historic realignment and re-sectioning of the main watercourse, has also resulted in the overdeepening, vertical and lateral erosion of the stream channel resulting in downstream transport of sediment to the lake. In addition to sediment erosion, the associated limited connectivity with the floodplain and focus of sediment transport in the fluvial channel has been a key factor in the shallowing and deterioration in the condition of the lake. Over the last 15 years a wide range of investigative, monitoring and management work has been undertaken within the catchment by a partnership between UK Government organisations, a local authority and a charity, with continuous involment by the author throughout this period. This work has evaluated the causes and effects associated with this erosion and transportation, tested and defined viable practical solutions (the delivery of natural sediment and flood management solutions and habitat restoration) and delivered a series of sustainable management interventions to reduce erosion, promote sediment deposition and to reconnect the stage zero and larger fluvial pathways to the floodplain – supporting the restoration of the lake. These works have resulted in the reduction in erosion at source and increased deposition through the catchment system, ultimately contributing to the improvement in condition of the lake and associated wetland habitats. Works in the headwaters of the catchment focused on defining the existing distribution, status and significance of areas of sediment generation, transport and deposition to the stream and lake, facilitating sustainable sediment management within this area. Works in the lower reaches focused on slowing flow velocities and diverting higher velocity sediment rich flows into new channels to reconnect with the floodplain and promote deposition. Management measures included the use of small diversion channels through woodland with the creation of glades to increase understory recovery and sediment deposition; use of geotextile cells filled with sand, gravel or stone to increase the flow path, reduce velocity and promote out of channel flooding and deposition of sediment; use of scrub and woody material to form leaky dams and increase channel roughness promoting out of bank flooding and deposition; use of online ponds, backwaters and embayments; blanking off channels to promote overland flow through woodland to reduce flow depth and velocity and promote deposition; use of leaky dams to promote higher flows transporting sediment into new sinuous channels and allowing out of bank flooding to promote sediment deposition.

Episodes of slow uplift and subsidence of the ground, called bradyseism, characterize the recent dynamics of the Campi Flegrei caldera (Italy). In the last decades two major bradyseismic crises occurred, in 1969/1972 and in 1982/1984, with a ground uplift of 1.70 m and 1.85 m, respectively. Thousands of earthquakes, with a maximum magnitude of 4.2, caused the partial evacuation of the town of Pozzuoli in October 1983. This was followed by about 20 years of overall subsidence, about 1 m in total, until 2005. After 2005 the Campi Flegrei caldera has been rising again, with a slower rate, and a total maximum vertical displacement in the central area of ca. 70 cm. The two signals of ground deformation and background seismicity have been found to share similar accelerating trends. The failure forecast method can provide a first assessment of failure time on present‐day unrest signals at Campi Flegrei caldera based on the monitoring data collected in [2011, 2020] and under the assumption to extrapolate such a trend into the future. In this study, we apply a probabilistic approach that enhances the well‐established method by incorporating stochastic perturbations in the linearized equations. The stochastic formulation enables the processing of decade‐long time windows of data, including the effects of variable dynamics that characterize the unrest. We provide temporal forecasts with uncertainty quantification, potentially indicative of eruption dates. The basis of the failure forecast method is a fundamental law for failing materials: ẇ-α ẅ = A, where ẇ is the rate of the precursor signal, and α, A are model parameters that we fit on the data. The solution when α >1 is a power law of exponent 1/(1 − α) diverging at time Tf , called failure time. In our case study, Tf is the time when the accelerating signals collected at Campi Flegrei would diverge if we extrapolate their trend. The interpretation of Tf as the onset of a volcanic eruption is speculative. It is important to note that future variations of monitoring data could either slow down the increase so far observed, or suddenly further increase it leading to shorter failure times than those here reported. Data from observations at all locations in the region were also aggregated to reinforce the computations of Tf reducing the impact of observation errors.

Shallow glacial aquifers systems are the primary source of drinking water for millions of residents in the upper Midwest and Great Lakes regions of the United States. Studies show that a significant number of municipal and private groundwater wells in these regions are impacted by high nitrate concentrations, which can have negative health impacts for humans. Reducing nitrate contamination through good land management practices will reduce the need for costly nitrate treatment systems and help mitigate other ecological concerns related to nutrient pollution of groundwater. This study presents a Python-based modelling tool that uses a local groundwater flow model and historical land use data (USDA CropScape) to estimate nitrate concentrations at a high-capacity pumping well. Nitrate concentrations predicted by this model are within 5% of median annual values observed at a study site in Waupaca, WI. The model is user-friendly and can easily be adapted to other locations, where it has the potential to help local and state agencies, landowners, and growers make cost-effective decisions about land-use and agricultural practices.

The in-situ magnetospheric exploration of the four large planets of our solar system had started with Pioneer 10’s flyby of Jupiter in Dec. 1973. The second collection of field, particle and radio data of the gas giant was carried out by Pioneer 11 in Dec. 1974, before this spacecraft made its closest approach to Saturn in Sep. 1979. Around the same period, Voyager 1 (2) flew by Jupiter in Mar. (Jul.) 1979 then Saturn in Nov. (Aug.) 1980 (1981). As of today, only Voyager 2 visited the magnetospheres of Uranus (Jan. 1986) and Neptune (Aug. 1989). Galileo had remained the only spacecraft to orbit an outer planet for several years (1995 - 2003) until the arrival of Juno at Jupiter in 2016. Between 2004 and 2017, the Cassini mission had provided a wealth of in-situ data pertinent to the study of magnetospheric particles at Saturn. In this paper, we present our current understanding of the processes that shape the spatial distributions of energetic electrons trapped in the magnetospheres of Jupiter (L < 6), Saturn (L < 15) and Uranus (L < 15) obtained by combining multi-instrument analyses of data from past missions (Pioneer, Voyager, Galileo, Cassini) and computational models of charged particle fluxes. To determine what controls the energy and spatial distributions throughout the different magnetospheres, we compute the time evolution of particle distributions with the help of a diffusion theory particle transport code that solves the governing 3-D Fokker-Planck equation. Particle, field and wave datasets are either used to provide model constraints, assist in modeling physical processes, or validate our simulation results. We first emphasize our latest results regarding the relative (or coupled) role of mechanisms at Saturn, including the radial transport and interactions of electrons with Saturn’s dust/neutral/plasma environments and waves, as well as particle sources from high-latitudes, interchange injections, and outer magnetospheric region. The lessons learned from our modeling of electron distributions at Saturn are used to identify the processes that may be missing in our modeling of Jupiter’s energetic electron environment or those in need to be implemented using new modeling concepts. Our first physics-based modeling of electron populations at Uranus is also assessed with our data-model comparison approach.