Data of the Earth’s magnetic field strength is diversely applicable from dating archeological artefacts or lava flows to understanding early earth evolution and the mechanisms of the geodynamo. Lava flows are commonly used as a means to obtain records of this paleointensity. Understanding the underlying paleomagnetic and rock magnetic properties and how they vary across a flow is crucial to ensure collection of good quality samples for analysis. In fact, the success of paleointensity as well as paleomagnetic analysis is strongly dependent on the rock-magnetic properties of the samples, and large variations may exist between samples even of the same unit, related mainly to varying cooling rates. The active Tungurahua volcano is one of the most prominent features in the Ecuadorian Eastern Cordillera with numerous basaltic andesite and andesitic lava flows exposed along its flanks. To appraise the relation between volcanic emplacement processes and rock-magnetic properties, we sampled a vertical transect in a ~20 m thick lava flow at Tungurahua volcano. A total of 55 oriented in situ sample from six sites distributed across Ulba Cascada lava flow were collected for this purpose. We will present petrographical analysis of each sample as a function of the depth within the lava flow, with an emphasis on the textural and structural characteristics of magnetic minerals as observed with transmitted, reflected light and scanning electron microscopes. Moreover, a detailed analysis of rock-magnetic properties such as magnetic susceptibility, hysteresis (remanent magnetization, saturation remanent magnetization, coercivity and back-field coercivity), accompanied with detailed stepwise alternating field and thermal demagnetizations will allow us to determine the direction of the magnetic field and assess the variations of magnetic properties with respect to the position within a lava flow. We will also discuss the correlations that may exist between grain size, oxidation state of the magnetic minerals and the emplacement processes of a lava flow, as well as the implications of all these results to paleointensity determinations.

Understanding onset of droughts and its potential linkage to resulting responses like severity (deficit volume) is crucial for providing timely information related to drought sectors including the cultivation planning and monitoring crop productivity. Using high-quality daily observed streamflow records from 82 medium-to-large sized catchments over (tropical) peninsular India, we show that the variability in onset timing drives the severity of hydrological droughts. The strength of onset timing-severity relationships using observed records indicate seasonality of rainfall and catchment characteristics mainly modulate hydrological drought responses in peninsular India, which is not readily apparent from land-surface model simulations. The observed trend for mean onset of drought depicts delayed occurrence for more than half of the catchments. Around one-third of the catchments shows a stronger non-linear significant dependency (>0.7) between severity and onset of drought. The findings of the study highlight the need for accounting feedback between drought onset and severity and their concurrent changes for seasonal-to-sub-seasonal predictability of droughts; and contributes to discussions on building resilience to extreme droughts in a changing climate.

The subdued topography of the Turkana Depression separates the elevated Ethiopian and Kenyan Plateaus in East Africa. Mechanisms to explain its topography are debated because constraints on upper mantle structure and dynamics are lacking. Attempts to understand the role of the mantle below Turkana in the evolution of rifting between the Main Ethiopian and Southern East African rifts and the onset of Ethiopian Flood Basalt volcanism are also hindered by limited data availability. Here, recently deployed seismic networks in Turkana and neighboring Uganda enable us to develop a new absolute P-wavespeed tomographic model (AFRP21) to image mantle structure below the Turkana depression. Additionally, we use P-to-s receiver functions to map the mantle transition zone (MTZ) discontinuity structure. In the shallow mantle, broadly distributed slow wavespeeds reside below the Main Ethiopian rift. To the south, slow wavespeeds occur in a focused zone below the East African rift, but beneath the northern Turkana depression these are cross-cut by a narrow E-W band of fast wavespeeds. At upper MTZ depths slow wavespeeds are broadly continuous below the East African rift but begin to separate into two distinct anomalies at the base of the MTZ. While receiver functions reveal a broadly thinned MTZ below Cenozoic rift-related magmatism in East Africa, the thinnest transition zone exists below the Turkana Depression. Slow wavespeeds and a thinned MTZ below the Turkana Depression indicate hot upwelling material, thus its low-lying nature is not due to the lack of underlying dynamic support. Instead, the depressed topography may be better explained by Mesozoic-Cenozoic E-W rifting associated with the imaged shallow fast wavespeed band. Furthermore, the main eruptive phase of Ethiopian Flood basalt volcanism may be associated with the African plate’s position over the anomalously thinned MTZ in Turkana at ~30Ma.

Dinitrogen (N2) fixers (diazotrophs) fuel primary productivity by providing reactive nitrogen into the ocean ecosystem and promoting CO2 sequestration. N2 fixation has been extensively studied in the low latitudes of the Atlantic and Pacific Oceans. By comparison, the Indian Ocean remains the least explored and most enigmatic ocean basin. This is particularly the case for the Southern Indian Ocean (SIO). Here we explore N2 fixation activity and diazotroph community composition, diversity, and abundance from 20 to 60ºS in the SIO. While this region plays a key biogeochemical role serving as a link between the Atlantic and South Pacific Ocean waters, its N2 fixation potential remains unknown. Our results provide new insights into diazotrophy in a poorly studied region and expand the range of biomes where diazotrophy may be observed.

In the last decades, the Eddy Covariance (EC) technique has become a standard method to measure net ecosystem CO2 exchange (NEE), but it doesn’t let to distinguish between Gross Primary Production (GPP) and Ecosystem Respiration (Reco). Olive (Olea europea L.) is one of the most important agrosystems on the Mediterranean basin, covering 9.5Mha and accounting for 98% of olive groves global surface. In this study we analyze the EC fluxes from an olive orchard of SE Spain with two soil treatments: 1) leaving spontaneous weed cover (WC) growing on the soil, and 2) inhibiting this growth with a glyphosate-based herbicide (WF). These two different treatments provide high differences in NEE, but the contribution of each component (trees, weed and soil) in the NEE require a better understanding. In this study, we analyze Eddy Covariance fluxes from an olive orchard in SE Spain at different altitudes (above and below the olive trees). To study carbon fluxes contribution of weed in the olive orchard 4 EC towers were installed, placing them on two different areas: one area in WC treatment and the other in WF treatment. On each area, a canopy tower and a subcanopy tower were installed. After a data-filtering during the growth season in which only wind directions coming from olive orchard alleys were accepted, preliminary results from the subcanopy towers show that there are prevailing CO2 emission values from the soil in the WF area and CO2 fixation from the weed in the WC area. On the other hand, during senescence period, CO2 emission fluxes were obtained from both subcanopy towers. These results layout the relevant place of subcanopy towers to understand the role in carbon cycle of the different components in an ecosystem.

Louisiana is among the most vulnerable places on Earth to coastal flooding, for many reasons. Tropical-cyclone-induced storm surge, shoreline erosion accelerated by eustatic sea level rise, tidal influences, minimization of river sediment nourishment due to the presence of levees, and land subsidence caused by compaction of marsh lands and underground resource extraction all contribute to the flood hazard. In addition, increasing frequency and intensity of natural hazards under climate change scenarios are expected to exacerbate the coastal flood risk. Many studies focus on flood risk assessment and mitigation strategies both for the present and future, and other research has analyzed future flood risk considering climate change and sea level rise. Yet few studies consider all of these factors in concert. This research represents a comprehensive approach that considers coastal subsidence, eustatic sea level rise, and tropical cyclone storm surge variability under climate change scenarios, to evaluate future flood risk at the individual building level in Grand Isle, Louisiana. Results suggest that on average, the 100-year flood depth will increase by 37 cm at the individual building level in Grand Isle by 2050, with subsidence contributing over 80 percent of this increase. Subsidence is projected to increase structure and content losses by approximately 18 percent above modeled losses at present, while eustatic sea level rise may contribute approximately one percent of additional losses. A 100-year storm surge event amid a “low” scenario of environmental change would increase the structure and content losses at Grand Isle by 68–74 percent of today’s value in ten years, 141–149 percent in 25 years, and 346–359 percent in 50 years. Even more menacingly, “high” scenarios of environmental change are expected to increase the 100-year storm surge losses by approximately 85–91 percent of today’s value in ten years, 199–218 percent in 25 years, and 407–415 percent in 50 years. Outcomes from this study will fill the gap in the current literature by implementing a more realistic risk assessment model and will direct flood risk managers, property owners, and other stakeholders to build a comprehensive framework to minimize future flood risk in one of the most vulnerable sites in the USA to coastal flooding.

The Natural Resources Conservation Service (NRCS, formerly the Soil Conservation Service, SCS) unit hydrograph (UH) is one of the most commonly used synthetic UH methods for hydrologic modeling and engineering design all over the world. However, previous studies have shown that the application of the NRCS UH method for some ungauged watersheds in the state of Indiana produced unrealistic flood predictions for both the peak discharge and the time to peak. The objective of this work is to customize the NRCS UH by analyzing the role of its two key parameters, namely, the peak rate factor (PRF) and the lag time, in creating the runoff hydrograph. Based on 120 rainfall-runoff events collected from 30 small watersheds in Indiana over the past two decades, the observed UHs are derived and the corresponding PRF and lag time are extracted. The observed UHs in Indiana show that the mean value of PRF is 371, which is lower than the standard PRF of 484, and the NRCS lag time equation tends to underestimate the “true” lag time. Moreover, a multiple linear regression method, especially the stepwise selection technique, is employed to relate the NRCS UH parameters to the most appropriate geomorphic attributes extracted from the study watersheds. Both the statewide and regional regression models show that the main channel slope is a major factor in determining the PRF and lag time. A customized Indiana unit hydrograph (INUH) is derived with updated parameters and the Gamma function. Validation results show that the INUH provides more reliable and accurate predictions in terms of the peak discharge and the time to peak than the original NRCS UH for the watersheds in Indiana.

The hydrology community is engaged in an intense debate regarding the merits of machine learning (ML) models over traditional models. These traditional models include both conceptual and process-based hydrological models (PBHMs). Many in the hydrologic community remain skeptical about the use of ML models, because they consider these models “black-box” constructs that do not allow for a direct mapping between model internals and hydrologic states. In addition, they argue that it is unclear how to encode a priori hydrological expertise into ML models. Yet at the same time, ML models now routinely outperform traditional hydrological models for tasks such as streamflow simulation and short-range forecasting. Not only that, they are demonstrably better at generalizing runoff behavior across sites and therefore better at making predictions in ungauged basins, a long-standing problem in hydrology. In recent model experiments, we have shown that ML turbulent heat flux parameterizations embedded in a PBHM outperform the process-based parameterization in that PBHM. In this case, the PBHM enforced energy and mass constraints, while the ML parameterization calculated the heat fluxes. While this approach provides an interesting proof-of-concept and perhaps acts as a bridge between traditional models and ML models, we argue that it is time to take a bigger leap than incrementally improving the existing generation of models. We need to construct a new generation of hydrologic and land surface models (LSMs) that takes advantage of ML technologies in which we directly encode the physical concepts and constraints that we know are important, while being able to flexibly ingest a wide variety of data sources directly. To be employed as LSMs in coupled earth system models, they will need to conserve mass and energy. These new models will take time to develop, but the time to start is now, since the basic building blocks exist and we know how to get started. If nothing else, it will advance the debate and undoubtedly lead to better understanding within the hydrology and land surface communities regarding the merits and demerits of the competing approaches. In this presentation, we will discuss some of these early studies, illustrate how ML models can offer hydrologic insight, and argue the case for the development of ML-based LSMs.

Evaluation of spatially distributed crop coefficient (Kc) for estimating evapotranspiration (ETc) based on remotely sensed imagery has become an essential topic in managing the demand for agricultural water. Currently, satellite (MODIS, Landsat, etc.) imageries are not insufficient to detect variability within the small agricultural field due to its lack of desired spatial and temporal resolutions. Unmanned Aerial Vehicle (UAV) equipped with various sensors like Multispectral (MS), Thermal, and Hyperspectral cameras is becoming an emerging technology to overcome these limitations over small agricultural fields. A field experiment is carried out in the Agricultural and Food Engineering (AGFE) Department, IIT Kharagpur, to estimate Kc over the small Agri. Field using UAV-based MS cameras during Kharif (monsoon) 2019-2020 season. Lysimeters are used for estimating daily ETc for conventionally irrigated paddy crops. Reference evapotranspiration (ET0) is also calculated using the weather data of the study area. High-resolution multispectral imageries are acquired using a quad-copter UAV. The imageries are pre-processed using Pix4Dmapper software, and various vegetation indices (such as NDVI, TNDVI, NDRE, RVI, GNDVI, and LCI) are evaluated. The vegetation indices (VIs) are correlated with ground truth Kc values and spatially distributed Kc maps for the whole study area are generated based upon the excellent correlation between the VIs and ground Kc. The spatial Kc maps clearly show the variation in Kc within the plots and will be helpful for the calculation of Kc for any field without a lysimeter experiment. Generated Kc maps describe the crop water demand by visual color variations within the field. This approach may be helpful in understanding the variability in crop water requirements within the field Keywords: UAV, Crop Coefficient (Kc), Crop Evapotranspiration (ETc), Vegetation Indices, Remote Sensing.

Observations reveal end of summer Arctic sea ice extent is declining at an accelerating rate. Model projections underestimate this decline and continue to have a broad spread in forecasted September sea ice extent. This suggests some important summer processes, such as melt pond formation and evolution, may not be properly represented in current models. Melt ponds form on the sea ice surface as snow melts, and pools in low lying areas on the sea ice surface. The evolution of the ponds depends on snow depth, ice thickness, and surface conditions. Melt water may spread across a level surface, or be confined to depressions between sea ice ridges. Ponds decrease the albedo of the surface and enhance the positive ice albedo feedback, accelerating further melt. Until recently, Arctic-wide observations of individual melt ponds were not available. ICESat-2, a photon counting laser altimeter launched in 2018, provides high resolution detail of sea ice and snow topography due to its unique combination of a small footprint (~12 m) and high-resolution along-track sampling (0.7 m). The green laser (532 nm) is able to penetrate water, enabling melt pond depth measurements. We have developed methods to track the melt pond surface and bathymetry in ICESat-2 data to determine melt pond depth. We also track melt pond evolution through application of a sea ice classification algorithm to 10 m resolution Sentinel-2 imagery. The combination of these two datasets allows for an evolving, three-dimensional view of the melting sea ice surface. We focus on the evolution of summer melt on multiyear ice in the Central Arctic north of Greenland and Canada in 2020. Our findings are put in context of existing literature on melt pond depth, volume, and evolution. We also discuss our results in relation to the melt pond fraction north of the Fram Strait, where we expect different ice conditions in the vicinity of the 2020 MOSAiC field studies. Observational data products comprising melt pond fraction and pond depth are being developed for public distribution. These products may be of interest to those studying under-ice light and biology, as well as modelers who are interested in understanding the evolution of melt pond parameters for model initialization and validation.

Understanding the dynamical behavior of plasma and energetic particles in Earth’s inner magnetosphere requires carefully designed and calibrated instrumentation. The Van Allen Probes Mission included two instruments capable of measuring the proton distribution function in-situ. The Energetic Particle Composition and Thermal Plasma Suite (ECT) – Helium Oxygen, Proton, and Electron (HOPE) spectrometer (Spence et al., 2013; Funsten et al., 2013) used a top-hat detector designed to measure protons from the SC potential through 50 KeV in logarithmic energy steps. The Radiation Belt Storm Probes Ion Composition Detector (RBSPICE) instrument (Mitchell, 2013) used a time of flight and SSD detector design to measure protons from approximately 7 KeV through 650 KeV in logarithmic energy steps. Using the overlap of energy channels between the two instruments, the two instrument teams have worked diligently during the final Phase F of the mission to calibrate the observations so that a continuous distribution function can be resolved on nearly a spin-by-spin basis. During the life of these two instruments calibration changes have been required both on-board the spacecraft as well as within the final production datasets. Manweiler (2018) provided an early report on the intercalibration factors between HOPE and RBSPICE with a nominal factor of two difference between the proton data sets in the energy range between 7 and 50 KeV. With the final production of each of these data sets occurring in Fall 2021, both teams have been worked together to provide for an understanding of the required intercalibration factors to be used so that a full distribution function is available on a spin-by-spin basis. In this poster we report on the final efforts to provide this calibrated set of data products between the two instruments. Details of the intercalibration calculations are presented as well as year by year L by MLT maps of the factors required to match both datasets. Finally, we report on a supplementary data set that is to be made available which contains the spin-by-spin factors required to match the ECT/HOPE and RBSPICE/TOFxPH proton datasets. Funsten, H.O., et al. Space Sci Rev 179, 2013 Manweiler, J. W., et al., 2018 GEM Summer Workshop. Mitchell, D.G., et al., Space Sci. Rev., 179, 2013 Spence, H.E., et al. Space Sci Rev 179, 2013

Material samples are vital across multiple scientific disciplines with samples collected for one project often proving valuable for additional studies. The Internet of Samples (iSamples) project aims to integrate large, diverse, cross-discipline sample repositories and enable access and discovery of material samples as FAIR data (Findable, Accessible, Interoperable, and Reusable). Here we report our recent progress in controlled vocabulary development and mapping. In addition to a core metadata schema to integrate SESAR, GEOME, Open Context, and Smithsonian natural history collections, three small but important controlled vocabularies (CVs) describing specimen type, material type, and sampled feature were created. The new CVs provide consistent semantics for high-level integration of existing vocabularies used in the source collections. Two methods were used to map source record properties to terms in the new CVs: Keyword-based heuristic rules were manually created where existing terminologies were similar to the new CVs, such as in records from SESAR, GEOME, and Open Context and some aspects of Smithsonian Darwin Core records. For example specimen type =liquid>aqueous in SESAR records mapped to specimen type = liquid or gas sample and material type = liquid water. A machine learning approach was applied to Smithsonian Darwin Core records to infer sampled feature terms from record text describing habitat, locality, higher geography, and higher classification fields. Applying fastText with a 600-billion-token corpus in the general domain, we provided the machine a level of “understanding” of English words. With 200 and 995-record training sets, 87%, 94% precision and 85%, 92% recall were obtained respectively, yielding performance sufficient for production use. Applying these approaches, more than 3x106 records of the four large collections have been mapped successfully to a common core data model facilitating cross-domain discovery and retrieval of the sample records.

Two sites in adjacent catchments located in eastern NSW provide hydrological data over 200 years since European settlement: (a) height of the Hawkesbury River at Windsor, within the Sydney Basin (HR); (b) level of the ephemeral Lake George, sited 100 km inland (LG). HR has experienced 43 moderate to major floods since 1799 with the timing of floods grouping into approximate 40-year segments of greater or lesser flood frequency. LG has a reconstructed history of annual levels (Short et al, 2020) which shows obvious spacings with range 50 to 80 years. Three features are clear. The close correlation in time between HR floods, and the deep LG records, in separate hydrological catchments, suggests that these were not random occurrences. The sunspot record shows clear correlation in timing of occurrence (but not of amplitudes) of anomalously weak sunspot maxima with high rainfall/flood-prone segments. High sunspot maxima are associated with dryer 40 yr segments. Both datasets yield meaningful spectra via Lomb-Scargle spectral analysis (the data lengths being too short for reliable Fourier spectra). These power spectra show maxima at periods 82-88 yr (HR) and 80 yr (LG). Subsidiary peaks at periods 50, 30, 20and 11 yr appear on both. These peaks align with the first three of the six named periodicities in solar activity; the Schwabe(11yr); Hale (22yr) and the Gleissberg (87 yr) periodicities. These three periodicities are present in the sunspot record from1609 CE, and in the cosmogenic (Be and C ) record of the past 50+ kyr where high sunspot activity correlates with low cosmic radiation and high total solar insolation (TSI). In particular HR for 1810-20 CE and LG for 1819-20 CE coincide with the last portion of the sunspot Dalton Minimum, the last of the “Little Ice Ages” experienced worldwide. The timing of HR floods correlates closely with La Nina events, and a subset correlates with solar cycle terminators described by Leamon et al (2021). We believe it is significant that a subset of terminators associated with dryer segments also approximate a pattern consistent with the 87 yr Gleissberg cycle. We conclude that just as solar cycle terminators appear to have predictive value for La Nina events, recognition of the Gleissberg cycle may have predictive value for ~80 yr cycles of flood-prone and drought-prone times.

On the 22nd of May 2021, although no alarming precursory unrest had been reported, Nyiragongo volcano erupted and lava flows threatened about 1 million of inhabitants living in the cities of Goma (Democratic Republic of Congo) and Giseny (Rwanda). After January 1977 and January 2002, it was the beginning of the third historically known flank eruption of Nyiragongo volcano and the first ever to be recorded by dense measurements both on the ground and from space. In the following days, seismic and geodetic data as well as fracture mapping revealed the gradual southward propagation of a shallow dike from the Nyiragongo edifice underlying below Goma airport on May 23-24, then Goma and Gisenyi city centers on May 25-26 and finally below the northern part of Lake Kivu on May 27. Southward migration of the associated seismic swarm slowed down between May 27 and June 02. Micro seismicity became more diffuse, progressively activating transverse tectonic structures previously identified in the whole Lake Kivu basin. Here we exploit ground based and remote sensing data as well as inversion and physics-based models to fully characterize the dike sized, the dynamics of dike propagation and its arrest against a structural lineament known as the Nyabihu Fault. This work highlights the shallow origin of the dike, the segmented dike propagation controlled by the interaction with pre-existing fracture networks and the incremental crater collapse associated with drainage which led to the disappearance of the world’s largest long-living lava lake on top of Nyiragongo.

During an explosive eruption, large volumes of ash and gases are ejected into the atmosphere, forming a volcanic plume which is transported by the wind. The dispersion of volcanic ash in atmosphere represents a threat for aviation safety, whereas the tephra fallout, together with gas emission, may strongly affect population health and damage to environment and infrastructure as well. Volcanic monitoring from space offers now a powerful tool to quantify hazards on both population and air traffic and gain insight into processes and mechanism of violent explosive eruptions. Here we propose a machine learning (ML) algorithm that exploits the Thermal Infrared (TIR) bands of the images acquired by the sensor Spinning Enhanced Visible and InfraRed Imager (SEVIRI), on board Meteosat Second Generation (MSG) geostationary satellite, to identify the components of ash and SO2 gas in a volcanic plume. The detection and assessment of volcanic ash clouds has been performed applying the brightness temperature difference (BTD) approach, between bands at 10.8 μm and 12.0 μm, which highlights the presence of thin volcanic ash, while the algorithm for the SO2 retrieval is based on the contributions given by the bands at 10.8 μm and 8.7 μm. Combining the latter two bands with the 10.8 μm band in the RGB channels, it is possible to create an Ash RGB image, used both day and night for the detection and monitoring of volcanic ash and sulphur dioxide gas. The advantage of the machine learning algorithm is to detect and extract automatically these features from an Ash RGB image. As test cases, we considered the sequence of explosive eruptions occurred at Etna volcano (Italy) in early 2021, which produced very long and high plume columns. Thanks to the high temporal resolution of SEVIRI (one image every 15 minutes), it was possible to visualize and to follow the plumes, from their formation to their complete dispersion in the atmosphere. The comparison of our ML algorithm with the consolidated procedure based on a RGB channels combination in the visible (VIS) spectral range showed a good agreement.

Crystalline basement aquifers are characterized by complex flow pathways controlled by varying overburden stratigraphy and thickness as well as fracture network and connectivity within the crystalline rocks. Understanding the hydraulic connection within the fracture network and the overburden regolith is critical to predicting recharge/discharge and contaminant transport pathways. In this study, we combined geophysical imaging with multiple hydraulic testing to quantify hydraulic connectivity within the crystalline basement aquifers at the Ibadan Hydrogeophysical Research Site (IHRS) in Ibadan, Nigeria. The 50 m × 50 m field experimental site is first of its kind established in 2019 to investigate hydrological dynamics within these complex crystalline basement aquifers in sub–Saharan Africa. We acquired multiple parallel 2D electrical resistivity profiles which were also jointly inverted to obtain multiple 2D and 3D electrical resistivity tomograms of the subsurface. The resistivity tomograms were later constrained with lithological profiles from 4 test wells installed down to depths of 30 m at the site to create a conceptual model elucidating potential flow pathways. We also performed a series of 12 hours pumping tests and a NaCl tracer test to estimate flow and transport parameters including hydraulic conductivity, aquifer storage, yield, and groundwater travel time and to assess connection between the four test wells. The resistivity tomograms show 3 major resistivity zones interpreted as a clay-rich topsoil, a saturated weathered overburden, and a fractured basement rock. The delineated fractured bedrock shows an undulating topography with several primary fracture successions at 9, 14, 16 and 22 m. Hydraulic conductivities from pumping tests range from 2.6 x 10-7 to 1.2 x 10-5 m/s for the fractures and 1.7 x 10-10 to 6.4 x 10-6 m/s for the matrix while specific storage range from 3.5 x 10-8 to 1.8 x 10-3. Preferential flow is also observed with stronger connection between wells A and C. Results of this study provide a basis for detailed numerical study which will be focused on predicting recharge and solute transport under different flow and climate regime. This work will provide a scalable framework for a sustainable management of groundwater resources within the crystalline basements of Nigeria.

The Wayanad Schist Belt (WSB) located along the southern margin of the Western Dharwar Craton (WDC) is a small discontinuous schistose belt occur as pockets and patches within basement gneisses. WSB is oriented parallel to the E-W trending Moyar Shear Zone (MSZ) separating the WDC and Southern Granulite Terrane (SGT). The major rock types include metapelitic schists, metamafic schists, banded magnetite quartzite, pyroxene granulite, amphibolite, metapyroxenite, mylonite, hornblende-biotite schist/gneiss, micaceous quartzite etc. The fabrics in schistose rocks are defined mostly by the orientation of shape-preferred aggregates of biotite-chlorite-muscovite-sillimanite (in metapelites) and tremolite-talc-chlorite-actinolite (in metamafites). Whereas the gneissic fabric is defined by the quartz-plagioclase rich leucocratic layers and biotite-garnet-amphibole-pyroxene rich melanocratic layers. The WSB has undergone at least three phases of deformation. The most prominent fabric in WSB is the E-W to ESE-WNW trending sub-vertical foliations developed during the D3 deformation. Poles to the steep-dipping S3 foliations are WNW-ESE trending and the F3 folds are steeply plunging in nature. At the low strain domains of D3 deformation the early foliations are preserved. The N-S to NNW-SSE trending sub-vertical S2 fabric developed axial planar to moderately tight to open (F2) folds during D2 deformation. The early S1 foliations are rarely preserved in the interfolial domains of S2 foliation. In the regional scale, the poles to S1 fabrics lie on a well-defined girdle with NE-SW trending upright asymmetric folds. The U-Th-total Pb dating of texturally controlled monazites from WSB has yielded prominent age populations at around 700-850 Ma and 500-600 Ma with minor age peaks at 2.2-2.3 Ga, 2.4-2.6 Ga. The structural and chronological data suggest that, the deformation events in WSB are similar to that reported from the WDC. However, the D3 deformation is more prominent and all the earlier fabrics are truncated or brought into parallelism with the S3 fabric during D3 deformation. The 700-850 Ma and 500-600 Ma monazite growths post-tectonic with respect to the D3 deformation indicates that the Neoproterozoic accretionary events affected the whole SGT must have recrystallized the monazites present in the MSZ.

The Surface Biology and Geology (SBG) mission is one of the core missions of NASA’s Earth System Observatory (ESO). SBG will acquire high resolution solar-reflected spectroscopy and thermal infrared observations at a data rate of ~10 TB/day and generate products at ~75 TB/day. As the per-day volume is greater than NASA’s total extant airborne hyperspectral data collection, collecting, processing/re- processing, disseminating, and exploiting the SBG data presents new challenges. To address these challenges, we are developing a prototype science pipeline and a full-volume global hyperspectral synthetic data set to help prepare for SBG’s flight. Our science pipeline is based on the science processing operations technology developed for the Kepler and TESS planet-hunting missions. The pipeline infrastructure, Ziggy, provides a scalable architecture for robust, repeatable, and replicable science and application products that can be run on a range of systems from a laptop to the cloud or an on-site supercomputer. Our effort began by ingesting data and applying workflows from the EO- 1/Hyperion 17-year mission archive that provides globally sampled visible through shortwave infrared spectra that are representative of SBG data types and volumes. We have fully implemented the first stage of processing, from the raw data (Level 0) to top-of-the-atmosphere radiance (Level 1R). We plan to begin reprocessing the entire 55 TB Hyperion data set by the end of 2021. Work to implement an atmospheric correction module to convert the L1R data to surface reflectance (Level 2) is also underway. Additionally, an effort to develop a hybrid High Performance Computing (HPC)/cloud processing framework has been started to help optimize the cost, processing throughput and overall system resiliency for SBG’s science data system (SDS). Separately, we have developed a method for generating full-volume synthetic data sets for SBG based on MODIS data and have made the first version of this data set available to the community on the data portal of NASA’s Advanced Supercomputing Division at NASA Ames Research Center. The synthetic data will make it possible to test parts of the pipeline infrastructure and other software to be applied for product generation.