The magnetosphere, ionosphere and thermosphere (MIT) act as a coherently integrated system (geospace), driven in part by solar influences and characterized by variability and complexity. Among the most important and yet uncertain aspects of the geospace system is energy and momentum coupling between regions, which is, in part, accomplished by the transfer of charged particles from the magnetosphere to the ionosphere in a process known as particle precipitation, and in the opposite direction by ion outflow. Both processes are inherently multiscale and manifest the variabilities and complexities of the geospace system. Despite the importance of the transfer of particles, existing models are increasingly ill-equipped to provide the specification necessary for the growing demand for geospace now- and forecasts. Due to recent trends in the availability of data, we now face an exciting opportunity to progress particle transfer in geospace through the intersection of traditional approaches and state-of-the-art data-driven sciences. We reveal novel particle transfer models utilizing machine learning (ML), present results from the models, and provide an evaluation of their capabilities including comparisons with observations and the current ’state-of-the-art’ models (e.g., OVATION Prime for particle precipitation and the Gamera-Ionosphere Polar Wind Model for ion outflow). We detail the data wrangling required to utilize the available geospace observations to make progress on the long-standing challenge of particle transfer and place specific emphasis on the discovery possible when ML models are appropriate and robustly interrogated in the context of physical understanding. Our presentation helps illustrate the trends in the application of data science in space science.

Variability in precipitation frequency, intensity and duration deeply affect vegetation and erosion yields. Under climate change scenarios, alterations in precipitation are expected but it is not well understood how it could affect erosion rates and what is the role of vegetation on landscape geomorphic response. Traditional erosion models normally include basic representations of vegetation that do not account for the dynamic character of biomass variability and feedbacks with hydrological and erosion/deposition processes. Hence, using a new modelling frameworks that account for the effect of varying vegetation cover on erosion, and that includes climate change scenarios is needed. Here we use a new model: COPLAS, a tool that couples a Landform Evolution Model with dynamic vegetation and carbon pools modules to investigate the response of landscapes to climate change. The vegetation module includes a coupled photosynthesis-stomatal conductance representation that responds to climatic data inputs as temperature, CO2 concentration and water availability. We use the model to simulate the erosional and geomorphic responses of dynamic vegetation in Howard Springs (Australia) to predicted changes in daily precipitation under future CO2 concentrations of about 940 ppm. The model was calibrated using Ozflux site historical data. Catchment scale simulations were run for a period of 100 years for three scenarios (bare soil, constant and dynamic vegetation) using a daily time step. We found that, for our study case, bare soil produces on average 139% more erosion than the constant vegetation case and 124% more than the dynamic vegetation case. Moreover, an increase in precipitation of around 23% induces an increase of 25%, 35% and 43% in the erosion rates for dynamic vegetation, constant vegetation and bare soil respectively, while a decrease in 26% reduces it in 36%, 60% and 59%. This could be explained by the nonlinear relation between erosion and vegetation (higher rainfall induces higher erosion potential which can be counteracted by an increase in vegetation cover leading to a decrease in soil erodibilty). This finding highlights the importance of considering the dynamic character of vegetation in order to understand the nonlinear relations between fluvial erosion and vegetation cover.

Iron is the fourth most common element found in the earth crust. Although it may not be as important for soil fertility as, e.g., phosphorus, nitrogen and organic matter, its absence would be detrimental to plant growth. At the same time, iron oxides are highly correlated with phosphorus availability. Iron is thus an indicator for soil fertility and the usability of an area for cultivation of crop. A relatively high spectral resolution is needed for mapping iron oxide contents with spectral reflectance data, and remote sensing is the only suitable tool for surveying large areas at a high temporal and spatial interval. Sentinel-2 MSI (MultiSpectral Instrument) is the Landsat-like spatial resolution (10–60 m) super-spectral instrument of the European Space Agency (ESA), aimed at additional data continuity for global land surface monitoring with Landsat and Satellite Pour l’Observation de la Terre (SPOT) missions. Several studies with simulated and real data have been conducted in the last several years to show the potential of Sentinel-2 MSI, including its use for geological remote sensing, mineral mapping in particular. Sentinel-2 has several bands that cover the 0.9 μm iron absorption feature, while space-borne sensors traditionally used for geologic remote sensing, like ASTER and Landsat, had only one band in this feature. In this paper, we show a comparison of Sentinel-2 and AVIRIS to demonstrate the usability of the VNIR bands for mapping the near-infrared iron absorption feature. Next, we present spectral indices for mapping iron minerals that are important in soil fertility and mineral exploration.

The Space Physics Data Facility (SPDF https://spdf.gsfc.nasa.gov) and Solar Data Analysis Center (SDAC https://umbra.nascom.nasa.gov/), as the NASA Heliophysics active final archives, will be preserving and distributing the data from Parker Solar Probe. Working in cooperation with current operating missions and the heliophysics community, SPDF ingests, preserves and serves a wide range of past and current public science-quality data from the ionosphere into the furthest reach of deep-space exploration. SPDF has been working with the Parker Solar Probe mission in preparation for archiving and serving its in-situ data starting 2019 Nov 12, and also has arrangements to serve in-situ data from Solar Orbiter when those data become public. SPDF will facilitate scientific analysis of multi-instrument and multi-mission datasets to enhance the science return of Parker Solar Probe mission. SPDF develops and maintains the Common Data Format (CDF) and the associated ISTP/SPDF metadata guidelines. SPDF services include CDAWeb, which supports both survey and burst mode data with graphics, listings and data superset/subset functions. All public data held by SPDF are also available for direct file download by HTTPS or FTPS links from the SPDF home page (https://spdf.gsfc.nasa.gov). SPDF is currently receiving and serving from missions including Helios, MMS, Van Allen Probes, THEMIS/ARTEMIS, GOLD, ACE, Cluster, Geotail, Polar, Wind and many others, and >120 Ground-Based investigations. SPDF recently added support for ARASE/ERG and MAVEN as supplementary access at the requests of those missions. SPDF also operates the multi-mission orbit displays and query services of SSCWeb and the Java-based 4D Orbit Viewer, as well as the Heliophysics Data Portal (HDP) discipline-wide data inventory and access service, and the OMNIweb near-Earth solar wind plasma and magnetic field database.

Water scarcity, population growth and climate change dilemmas imperatively require adaption strategies for a more efficient and sustainable use of water resources. Agricultural systems are part of a wider network, where all social, economic and, ecologic parameters must be taken into consideration to assess the performance and resilience of said network. The importance of accounting the complexity of human decisions and their impact on the water cycle has been increasingly studied, nevertheless the integration and analysis of different decision making theories into hydrological models still remains a major challenge and uncertainty source. Therefore, the ongoing project is aimed to improve the understanding of social dynamics in agrohydrological networks by assessing different irrigation practices including rainfed agriculture and deficit irrigation within a hydro-economic network. We developed an agent-based model (ABM) of farmer decision making on crop water productivity and groundwater levels using two existing optimization models: (i) the Assessment, Prognosis, Planning and Management Tool (APPM) (Schmitz, et al. 2010) that integrates the complex interactions of the strongly nonlinear meteorological, hydrological and agricultural phenomena, considering the socio-economic aspects and (ii) the Deficit Irrigation Toolbox (DIT) (Schütze and Mialyk 2019) to maximize crop-water productivity by analyzing the crop yield response to climate change, soil variability, water management practices. The developed ABM was assessed with the different theories on human decision-making based on the Modelling Human Behavior (MoHuB) framework (Schlüter, et al. 2017). As a result of this study, a sensitivity analysis of how different behavioral theories affect the dynamics of social-ecological systems which enables the evaluation of the robustness of policy implementation to different assumptions of human behavior where cooperation is a mechanism to improve resilience. This research was funded by the Technische Universität Dresden, by means of the Excellence Initiative by the German Federal and State Governments.

This project aims to quantify the resiliency of prairie ecosystems in the U.S. Pacific Northwest (PNW) to climate change. Prairies in this region sustain over one million beef cows, and cow­-calf production costs are expected to increase to offset warming-induced plant productivity loss. We investigated the above- and belowground effects of experimental warming in prairie ecosystems by assessing biogeochemical controls on and patterns of asymbiotic nitrogen fixation (ANF), plant species diversity, and legume cover to address a major challenge for sustainable agriculture in the region. We hypothesize that the effect of warming on prairie functional diversity increases soil asymbiotic nitrogen inputs by decreasing legume cover and soil nitrogen availability. We quantified the effects of decadal warming stress (+2.5ºC) on soil biogeochemical properties and plant species and functional diversity during fall and spring seasons in three sites along a 520­km latitudinal gradient—from central Washington to southern Oregon—representing a drought severity gradient. At each site, we collected composite soil samples from five co-­located prairie plots under control (ambient) and warming conditions. We incubated these soils using 15N-labeled dinitrogen (15N2), and quantified total soil carbon­, total and available nitrogen, and available phosphorus and iron pools to better understand the underlying mechanisms governing warming-­induced changes in ANF. We used a point intercept technique to survey plot-level plant community composition and calculate Shannon’s diversity index and percent cover of legumes (members of Fabaceae according to the Integrated Taxonomic Information System). Warming significantly decreased plant species diversity which also decreased along the drought severity gradient. Legume cover significantly increased from 3.1% in the north to 9.2% in the south. ANF response to warming varied by season and site, where rates increased with the drought severity gradient in the fall but decreased during the spring. Total soil inorganic nitrogen availability was the strongest predictor of ANF response to warming in the spring but not in the fall. Our study highlights the importance of using soil-plant-atmosphere interactions to assess prairie ecosystem resilience to climate change in the PNW.

Migration is a complex and interdisciplinary problem involving multiple factors such as social interactions, resource scarcity, and geographical features. These factors must be incorporated in migration models, but how? We feel that the issue how different factors should be incorporated is not carefully addressed in existing models. Configuring factors in ways that are theoretically unsound can lead to false migration patterns and undermine the usefulness of models; indeed, factor configurations may be more critical than the factors themselves or other inputs. Therefore, we ask: i) How important is factor configuration to output results comparing with other inputs?; ii) How do different factor configurations produce different migration patterns?; and iii) How can multimodality of certain output distributions be controlled in a management perspective? To address the questions, we develop a “toy” migration agent-based model (ABM) and explore three possible configurations between two factors: i) two factors are perfectly substitutable (ADD), ii) both factors are indispensable (AND), and iii) either is enough (OR). ABM results are analyzed by global sensitivity analysis (GSA) and Monte-Carlo Filtering (MCF). The relative importance of factor configurations quantified by GSA emphasizes why we need to consider how the factors are incorporated. Depending on factor configurations, we also observe unimodal or multimodal output distributions. MCF is then applied to the ABM-GSA results to address how policymakers should control certain inputs to sustain systems with desirable outputs. Altogether, we have integrated ABM, GSA, and MCF to disentangle complexity of migration models and better understand underlying mechanisms and patterns of migration.

Low-current tributary-estuaries and embayments along the margin of the Hudson River are uniquely at risk for harmful algal blooms of cyanobacteria (cyanoHABs) due to rising temperatures as a result of climate change. An increased prevalence of cyanoHABs in near-shore, low-current sections of the Hudson River could be extremely harmful to nearby communities, aquatic organisms and wildlife. To address this increased risk, it is imperative to understand the current in-stream and upstream abiotic environmental controls (nutrients, water temperature, etc.) on the current background levels of cyanobacteria within the Hudson River. It is also important to understand how these controls and cyanobacterial populations vary spatially with relation to the higher risk, lower-flow sections along the margins of the Hudson River. Locations of tributary-estuaries of special concern within the Hudson Valley include Esopus Creek in Saugerties, Rondout Creek in Kingston, and Wappingers Creek in Wappingers, NY. Other locations of concern are embayments along the Hudson River such as Long Dock Park in Beacon, Port Ewen in Kingston and Norrie Point in Staatsburg, NY. Given the lower-flow nature of these sites, elevated surface water temperatures are likely a result of settled, striated layers from decreased current. These locations are also susceptible to growth of the invasive species Trapa natans or commonly known as the European water chestnut. High concentrations of nutrients like nitrogen and phosphorous within the water chestnut bloom and the captured sunlight from metabolic processes like photosynthesis can create an ideal microhabitat for harmful algae like cyanobacteria. The background levels of cyanobacteria in outflows of tributaries, and their lower-flow estuary extensions were observed alongside the water quality within the water chestnut blooms of these sites at varying depths. By studying the weekly changes in background abundance of cyanobacteria and their drivers occurring at contrasting locations along the Hudson River, it was found that the strongest controls included turbidity, temperature and levels of phosphorous. In locations of low turbidity and high surface water temperatures, the background levels of cyanobacteria were higher in these lower-flow areas than in areas with increased turbidity. Cyanobacteria was found in greater number within water chestnut blooms than in whole water samples outside the area of the bloom. High surface temperature and riverbed temperature also related to higher levels of cyanobacteria. Given the concluded information, it is apparent that invasive water chestnuts within lower-flow extensions of the Hudson River hold a greater threat than originally understood; creating an ideal habitat for potential cyanoHABs in the wake of climate change.

The Seismometer to Investigate Ice and Ocean Structure (SIIOS) project is exploring the science capabilities of seismometers in ocean world analog environments. Ocean worlds, such as Europa, Enceladus and Titan, have thick global icy shells overlying liquid oceans. The icy shells may be seismically active due to tidal stresses. SIIOS tests several seismometers in a small-aperture array in a mock-lander configuration to quantify the ability to detect, locate, and identify seismic sources, as well as constrain local ice structure. The SIIOS experiment was deployed on two terrestrial analogs for ocean worlds. We first deployed on Gulkana Glacier in Alaska in September 2017, and then deployed in Northwestern Greenland, over a subglacial lake from May 2018-August 2018. Both areas serve as analog locations for Europa due to the layering of ice, water and rock. Gulkana was a relatively noisy site due to surface runoff and drainage, higher topographic variation (inducing rockfalls), and proximity to active plate boundaries. Greenland was a quieter site, in part due to its geologic setting high on the ice sheet, as well as from the installation process. During the Greenland deployment, we covered instruments with a large aluminum box that was buried, thus reducing noise from atmospheric and thermal effects. At both analog sites, the instruments passively recorded seismicity and seismic background noise. The passive data was used to create power spectral density (PSDs) and then probability density functions (PDFs), of the background noise. The PDFs of Gulkana showed higher noise levels compared to those of Greenland. Using the passive data, we detected and identified events originating from ice quakes, and in the case of Gulkana; rockfalls and drainage from a nearby moulin. A frequency-dependent polarization analysis was also conducted to indicate the dominant directionality of the background signals through time. The results indicate how background or ambient signals could be used on ocean worlds to characterize the local seismicity.

The ocean covers 70% of the surface of the planet, yet absorbs a remarkable 93% of the additional heat trapped by anthropogenic greenhouse gases due to its large size, the omnipresent stirring by wind, and the “high heat capacity” of water; yet the molecular basis for the high specific heat of water (CP) is not well known. It has long been established that specific heats are closely tied to molecular weight. Ironically, water has no fixed molecular weight: it exists as a bi-molecular fluid consisting of the singlet H2O form and an ensemble of hydrogen bonded forms in a temperature dependent equilibrium. We show that the mean molecular weight of water over the range 0-40°C is 82-79. The warming of water induces the breaking of hydrogen bonds (8.364 kJ/mol), increasing the population of the singlet H2O form at the expense of the hydrogen bonded forms. Although warming of sea water by 10°C yields only a 2% increase in free H2O, this accounts for some 36% of the energy consumed. Consequently, the high heat capacity of water, and water in sea water, is attributable (64%) to the large molecular weight of the hydrogen bonded forms, dominantly as the tetrahedral pentamer (H2O)5, and also (36%) to the energy required to break hydrogen bonds. The CP for pure water decreases with increased temperature due to the decrease in the ensemble molecular weight with warming, while the CP for sea water increases with increasing temperature due the work required to increase the translational energy of the large hydrated cations, which are dominantly Na(H2O)6 with a molecular weight of 131. Thus, it is the multiple forms of water and their hydrogen bonding that accounts for the high specific heat of both pure water and sea water.

Excess nutrient loading to downstream waters has been a persistent environmental concern, especially in agricultural settings. Drainage water management (DWM) is a best management practice intended to reduce nitrogen export from fertilized lands by increasing groundwater levels, slowing the loss of nutrient-rich water and increasing its time in contact with the soil, thus creating greater opportunity for denitrification. This BMP has shown to be effective at reducing dissolved nitrate (TDN) export, but a question remains about potential unintended pollution swapping. The concern is that denitrification could result in nitrous oxide (N2O) emissions and that higher soil moisture could also create suitable conditions for methanogenesis and methane (CH4) emissions. Here we report on two years of monthly static soil gas chamber fluxes and hydrologic nutrient fluxes during a full corn/soybean rotation cycle on the Eastern Shore of Maryland. For N2O, there were significant interactions between season, crop type, and treatment, such as higher fluxes during the fertilization period in the corn year in the DWM treatment, which was consistent with our concern about pollution swapping. However, this brief additional pulse of N2O did not result in a statistically significant increase at an annual scale, nor was there an increase in annual CH4 emissions. At the same time, annual TDN load was significantly lower in the DWM ditches compared to the control. With no significant treatment effect on soil gas fluxes and a significant treatment effect on TDN export, we conclude that pollution swapping of nitrate reduction for greenhouse gases did not occur significantly in this application of DWM to a corn/soybean system. We did, however, find evidence of pollution swapping of phosphorus and nitrogen, as total phosphorus load was higher in the DWM. With more water in the field, the reduced conditions appear to cause a release of soil bound phosphorus. While greenhouse gas production may not be as much of a concern, increased phosphorus export represents a form of pollution swapping that must be accounted for in determining the value of this BMP.

Recent advancement of computational linguistics, machine learning, including a variety of toolboxes for Natural Language Processing (NLP), help facilitate analysis of vast electronic corpuses for a multitude of objectives. Research papers published as electronic text files in different journals offer windows into trending topics and developments, and NLP allows us to extract information and insight about these trends. This project applies Latent Dirichlet Allocation (LDA) Topic Modeling for bibliometric analyses of all abstracts in selected high-impact (Impact Factor > 0.9) journals in hydrology. Topic modeling uses statistical algorithms to extract semantic information from a collection of texts and has become an emerging quantitative method to assess substantial textual data. The resulting generated topics are interpretable based on our prior knowledge of hydrology and related sub-disciplines. Comparative topic trend, term, and document level cluster analyses based on different time periods was performed. These analyses revealed topics such as climate change research gaining popularity in Hydrology over the last decade. An inter-topic correlation analysis also revealed the nature of information exchange and absorption between various communities within the hydrology domain. The primary objective of this work is to allow researchers to explore new branches and connections in the Hydrology literature, and to facilitate comprehensive and inclusive literature reviews. We aim to use these results combined with probability distribution between topics, journals and authors to create an ontology that is useful for scientists and environmental consultants for exploring relevant literature based on topics and topic relationships.

Improved monitoring of inundation area variations in lakes and reservoirs is crucial for assessing surface water resources in a growing population and a changing climate. Although long-record optical satellites, such as Landsat missions, provide sub-monthly observations at fairly fine spatial resolution, cloud contamination often poses a major challenge for producing temporally continuous time series. We here proposed a novel method to improve the temporal frequency of usable Landsat observations for mapping lakes and reservoirs, by effectively recovering inundation areas from contaminated images. This method automated three primary steps on the cloud-based platform Google Earth Engine. It first leveraged multiple spectral indices to optimize water mapping from archival Landsat images acquired since 1992. Errors induced by minor contaminations were next corrected by the topology of isobaths extracted from nearly cloud-free images. The isobaths were then used to recover water areas under major contaminations through an efficient vector-based interpolation. We validated this method on 428 lakes/reservoirs worldwide that range from ~2 km2 to ~82,000 km2 with time-variable levels measured by satellite altimeters. The recovered water areas show a relative root-mean-squared error of 2.2%, and the errors for over 95% of the lakes/reservoirs below 6.0%. The produced area time series, combining those from cloud-free images and recovered from contaminated images, exhibit strong correlations with altimetry levels (Spearman’s rho mostly ~0.8 or larger) and extended the hypsometric (area-level) ranges revealed by cloud-free images alone. The combined time series also improved the monthly coverage by an average of 43%, resulting in a bi-monthly water area record during the satellite altimetry era thus far (1992–2018). Given such performance and a generic nature of this method, we foresee its potential applications to assisting water area recovery for other optical and SAR sensors (e.g., Sentinel-2 and SWOT), and to estimating lake/reservoir storage variations in conjunction with altimetry sensors.

Historical volcanic eruptions in Canary Islands have traditionally been considered as quiet eruptions, mostly effusive and with scant explosions (VEI 1-2), attending to the general characteristics of this volcanism. Nevertheless, the geological record of these eruptions shows abundant signs of moderately explosive activity with PDC desposits and bombs and blocks of considerable size outcropping in the islands. Scientific studies of Canarian historical volcanism have been focused on the analysis of the volcanic processes and the assessment of related hazards, paying special attention to the monitoring of volcanic activity in the last few years. However, there is hardly any data of their effects on the population, like number of victims, affected people, displaced, emigrated, damage to the crops, lands, animals, etc. In general, there is also no accurate information on the actions of authorities or on the crises management. The knowledge of the impacts caused by volcanic eruptions provides very significant information to be prepared for future eruptions, and can be found in documentary sources, both handwritten and printed and graphic, in official or private documentation. The analysis of these historical chronicles has shed light on these aspects not previously considered neither by the authorities nor the scientist or general public. The results of this research examining the historical chronicles shows that, contrary to what is generally believed, historical eruptions in Canary Islands did cause several impacts both on the population (dead, injured or missing, displaced and evacuated), and on the economy (damage to crops, livestock, buildings, roads and various infrastructures). The historical documents also highlight that there was indeed crisis management, both from the political and scientific authorities in most of the eruptions. From all these we can conclude that no social interpretation has been made about the legacy of historical eruptions in Canary Islands nor about the impact they had. We should implement this knowledge in the prevention and emergency plans made by the authorities for a better management of future eruptions, considering the population increase in the Canary Islands from ca. 360000 inhabitants in 1900 up to ca. 2.13 millions in 2018, plus some millions of tourists (15.5 in 2018).

The African Flood and Drought Monitor (AFDM) was developed by Princeton University in collaboration with UNESCO-IHP, regional hydroclimate centers in Africa and ICIWaRM. Based on advanced land surface modeling driven by satellite data, the system provides 25-km resolution near-real-time evaluations of the terrestrial water cycle, plus forecasting by merging seasonal climate forecasts with hydrological modeling. Originally targeted toward hydrometeorological services, drought managers and researchers, the AFDM has found many applications in health and epidemiology, e.g.: Malaria: In northern Zambia, populations of the two malaria-carrying mosquito species had strong, time-lagged correlations to rainfall events. Relations to temperature were more complex. The AFDM helped show the effectiveness of targeted indoor residual spraying (Hast et al., 2019a, b). Trypanosomiasis: In the Maasai Steppe of Tanzania, negative relationships were found between abundance of two tsetse fly species and AFDM temperature. Trypanosome prevalence in the species increased with rising maximum temperatures from 26-31° C, and then declined (Nnko et al., 2017). Cholera: In Cameroon, associations were found between AFDM average daily maximum temperature and risk of cholera transmission that varied across four climate subzones (Ngwa et al., 2016). Related studies using AFDM data with implications for human health and well-being in Africa, include: Stunted growth: In four African countries, a rainfall deficit of 1 standard deviation from the mean was associated with an increase of stunting in children age 0-60 months of 2.2 to 3.2% (Hill et al., 2019). Human migration: Rainfall shortages and excess temperature, and less so soil moisture, are strong drivers of out-migration from South Africa, especially for black and low-income migrants (Mastrorillo et al., 2015). Herbivore parasites: In Botswana, temperature had a small negative effect on abundance of nematode parasites of herbivores (Walker et al., 2016), and modeled helminth larval development affecting goats was primarily driven by daily rainfall (Walker et al., 2018). Overall, these examples provide evidence that the provision of climate and hydrological information in consistent and accessible ways can help enable health-related research and applications.

Data repositories and research networks worldwide are publishing a diverse array of long-term and experimental data for meaningful reuse, repurpose, and integration. However, in synthesis research the largest time investment is still in discovering, cleaning and combining primary datasets until all are completely understood and converted to a usable format. To accelerate this process, we have developed an approach to define flexible domain specific data models and convert primary data to these models using a light-weight and distributed workflow framework. The approach is based on extensive experience in synthesis research workflows, takes into account the distributed nature of original data curation, satisfies the requirement for regular additions to the original data, and is not determined by a single synthesis research question. Furthermore, all data describing the sampling context are preserved and the harmonization may be performed by data scientists that are not specialists in each specific research domain. Our harmonization process is 3-phased. First, a Design Phase captures essential attributes, considers already existing standardization efforts, and external vocabularies that disambiguate meaning. Second, an Implementation Phase publishes the data model and best practice guides for reference, followed by conversion of relevant repository contents by data managers, and creation of software for data discovery and exploration. Third, a Maintenance Phase implements programmatic workflows that run automatically when parent data are revisioned using event notification services.In this presentation we demonstrate the harmonization process for ecological community survey data and highlight the unique challenges and lessons learned. Additionally, we demonstrate the maintenance workflow and data exploration and aggregation tools that plug in to this data model

The north Ecuador subduction zone exhibits segmentation and clustering of seismicity through megathrust, interseismic, and aftershock seismicity. In 1906, a Mw 8.8 megathrust event ruptured a 500 km segment, portions of which were re-ruptured in ’42 (Mw 7.8), ’58 (Mw 7.7), ’79 (Mw 8.2) and 2016 (Mw 7.8 Pedernales event). Segmentation between the ruptures is caused in part by subducting topography and upper plate structure. Upper plate structure in north Ecuador includes major faults, sedimentary basins and accreted terranes. An international aftershock deployment and the Ecuador permanent network (RENSIG) recorded aftershocks of the 2016 Pedernales event. We performed finite difference tomography in a joint inversion for 3D velocity and earthquake location, using body wave arrivals of aftershocks. The Manabi, Manta-Jama and Borbon sedimentary basins are observed as high Vp/Vs features with the Manabi basin seen as a low Vp and Vs feature. High Vp and Vs are associated with accreted forearc terranes. Relocation of aftershocks in the 3D velocity results in previously described “bands” of seismicity collapsing to smaller clusters ranging from ~8-40 km across. South of the rupture area, a cluster near Manabi collapsed landward, and a cluster appeared west of the trench. Three clusters between the trench and directly south of the rupture contain lower plate and plate interface events. The cluster within the rupture area between the patches of greater slip became more focused, and a cluster became defined on the north side of the northern patch of slip. Two clusters outline subducting Atacames seamounts, with events in the lower plate and interface beneath and in front of the seamounts. North of the rupture, the clusters offshore and onshore near Galera contain mostly interface with some upper plate events. The onshore cluster focused around major faults in a transition from north/south to northeast/southwest structures along the coastal range. Events in the cluster near Atacames relocated mainly in the upper plate, and events in the cluster near Esmeraldas remained in the upper plate. Interseismic events cluster in the same locations as aftershock events. Existing features including upper plate structure and subducting features control and focus both postseismic and interseismic deformation across megathrust cycles.

This paper presents microphysical inference retrievals obtained from spectral polarimetry during the Relampago (Remote sensing of Electrification, Lightning, And Mesoscale/Microscale Processes with Adaptive Ground Observations) campaign. Spectral processing has been an essential part of weather radar moments estimation for a long period of time. Various processing can be performed in the spectral domain including precipitation detection in presence of strong clutter and noise, clutter & interference mitigation by algorithms such as GMAP, object-oriented filters and many more. However spectral applications to polarimetry have been rare. The C band CSU-CHIVO radar that was deployed in Cordoba region in Argentina between June 2018 and April 2019 during the Relampago campaign, recorded some of the tallest storms in the world characterized by strong wind shear, updrifts, turbulence and occurrence of severe hail and rain. The polarimetric spectrum in precipitation with rain and hail mixtures were characterized. This Spectral polarimetry revealed different spectral characteristics including multi-modal spectrum, spectral broadening, slopes in spectral differential reflectivity and lowering of coherency spectrum. These results characterized occurrence of mixed hydrometeor types in a radar resolution volume such as presence of rain and hail mixture, large drops formation and size sorting. Spectral displays are inherently noisy, and the paper also presented methodology to obtain clean quality spectrum implementing spectral quality index, that is used to process the observations and the results are presented.