NASA’s future missions will focus on looking for evidence of life under extreme environments, such as cold arid planets (Mars) and icy moons (Eurpoa). There is particular interest in studying localized geo-chemical systems that host gradients with the potential for harboring evidence of past or present life. Such gradient-driven systems include, for example, layering of minerals of varying oxidation states in the Martian subsurface or in Earth’s seafloor sediments; chemical precipitates separating fluids of different pH or Eh such as in a hydrothermal vent; or radiation-induced oxidant production in planetary ices. A challenge is that these simulated precipitates are redox sensitive and evolve over a relatively short lifetime (1-4 hrs) necessitating viable in-situ, non-destructive analytical techniques. Promising techniques for such studies are electrochemical methods, which are particularly suited for characterizing interfaces and chem-ical gradients. Combined with recent advances in electrochemical instrument technologies, new methods have been developed toward integrating portable, electrochemical systems for ex-situ and/or in-situ chemical characterization. JPL has already made significant progress in developing these new technologies, particularly electro-chemical impedance spectroscopies (EIS) for in-situ characterization of geochemical materials (i.e., soils ice mineralogy, and brines) and even life detection sensoring. Currently, we are exploring further appli-cations of electrical spectroscopy techniques in developing an electrochemistry-based framework using planetary analog systems based on MRO/CRISM and Cassini/INMS observations in attempts to distin-guish abiotic vs biotic environments, and with active and inactive containing organics from electrical properties characterization. We will highlight some of our group’s successes and preliminary results of geochemical experimentation using various electrochemical techniques.

Changes in glacier terminus position have been implicated as one of the primary drivers of the rapid changes in glacier dynamics observed across the globe in the last two decades. Iceberg calving exerts a critical control on the terminus position of the vast majority of marine-terminating glaciers, yet calving is relatively poorly understood due to the inherent difficulties in collecting observations of a stochastic process in a dangerous setting. Time-lapse camera and satellite observations suggest that the style of iceberg calving can vary tremendously in both space and time depending on the physical properties of the terminus, ranging from the detachment of giant tabular icebergs every few decades from Antarctic’s floating ice shelves to the growlers produced nearly daily from serac topples along Alaska’s coast. Here we extract quantitative metrics on the relative importance of calving driven by branching and uncorrelated fractures through application of fragmentation theory to iceberg size distributions extracted from high-resolution digital elevation models for 17 fjords around Greenland. We find that iceberg size distributions typically deviate from the widely-assumed power-law form for icebergs with surface areas >0.05 km^2, with fewer icebergs than predicted by the power-law for larger sizes. Icebergs larger than ~0.1 km^2 primarily calve as the result of full-thickness penetration of uncorrelated fractures (i.e., as tabular icebergs). Although the dataset is temporally sparse for the majority of the study sites, the data suggest that iceberg formation via branching fractures reaches a seasonal peak in summer, when icebergs up to ~0.1 km^2 follow power-law distributions. These data provide a novel means to assess the accuracy of iceberg calving models and potentially to constrain the physical characteristics of termini susceptible to the marine ice cliff instability mechanism.

Nearly one million Rohingya refugees of Myanmar have fled to neighboring Bangladesh for seeking shelter from systematic oppression since 25 August 2017. The speed and scale of the influx has led to unprecedented growth of the Kutupalong refugee camp of eastern coast of Bangladesh within few months. There is a few study focused on environmental degradation of the refugee camp, however, no study has been done so far to estimate the impact of camp expansion on ecosystem services loss. This study, therefore, made an attempt to estimate the changes of Ecosystem Services Value (ESV) in response to camp expansion for the year July 2017 (pre-camp) and July 2018 by GIS technique and corresponding global value coefficient developed by Costanza et.al. (1997). Land cover map of the study area was prepared by using Landsat 8 satellite data. Results show an overall decrease of vegetation of 2486 hectares, of which 20% were used to expand the camp and 80% were deforested. Total ecosystem service values of the study area reduced dramatically, from 51.53×106 US$ to 49.12×106 US$ in the study period. This represents a 4.63% net decline in annual value of ecosystem services in the study area. In terms of 2 km buffer of the camp, the net decline rate is found 32.58%. The significant changes are also recorded in individual ecosystem services function of the area. Hence, the findings of this study may motivate the Bangladesh government to develop better plans to protect the ecologically sensitive forested land and wildlife habitats surrounding the refugee camps and assist in more sustainable resource mobilization for the Rohingya refugees.

In general, calibration of a hydrologic model is essential to better simulate the basin processes and behaviour by fitting the model simulated fluxes to observed fluxes. A major challenge in the calibration process is to choose the appropriate length of the observed data series and spatio-temporal resolution of the model schematization. We present a multi-case calibration approach for determining three pillars of an optimum hydrological model configuration: calibration data length, spin-up period and spatial resolution of the hydrological model. The approach is evaluated for the Moselle River basin using calibration and validation results from the spatially distributed meso-scale Hydrological Model (mHM) for 105 different cases representing the combinations of three calibration data lengths, seven spin-up periods and five spatial model resolutions. A metaheuristic global optimization method, i.e. Dynamically Dimensioned Search (DDS) algorithm, and a well-known hydrological performance metric, i.e. Nash Sutcliffe Efficiency (NSE), are utilized for each of the 105 calibration cases. The results show that a 10-year calibration data length, 2-year spin-up period and a 4 km model resolution are appropriate for the Moselle basin to reduce the computational burden. Analyzing the combined effects further allowed us to understand the interactions of these three usually overlooked pillars in hydrological modeling.

A three-color, extremely high signal to noise, precisely pointed, sub-arcsecond photometer would enable us to distinguish Earth-like exoplanets from other rocky, gassy, or icy worlds - if we had the right three wavelengths, and the ability to block out the primary star’s glare. Color-color discrimination of Earth-like planets has been posited for quite some time. Broadband filters are not seen to be precise diagnostics of planets with life, but rather broad but critical similarity or difference to the one habitable planet we know. Visible wavelengths (Vis) are advantageous due to relatively greater abundance of photons of that wavelength range from planets around Sun-like stars. Near-ultraviolet (UV) wavelengths may be advantageous due to ozone and scattering properties in Earth’s atmosphere that make Earth truly stand out from other known and modeled planets. Near infrared wavelengths (IR) help discriminate potential biological color contributions. We conducted an optimization exercise to arrive at three broadband filters that reliably separate modeled Earth-like, nominally habitable planets from other possible exoplanets. Criteria we use included: - Atmosphere/clouds of habitable worlds - Sea/Land proportions (ocean, granite, basalt, other surface material) - Vegetation (chlorophyll spectra and fictional other photosynthetic materials depending on wavelengths of parent stars etc.) - Other factors that can be spectrally modeled (planet-covering cities, world oceans, etc.) The optimized bands resemble previous work for exoplanets and the solar system, but underscore the advantage of UV wavelengths and indicate their potential utility for exoplanet identification and/or discrimination in concert with other exoplanet observations. An exoplanet survey that could quickly identify such planets could then be followed up by more detailed, longer term study by more intensive campaigns with more capable, but more resource or time constrained.

This study uses cloud and radiative properties collected from in-situ and remote sensing instruments during two coordinated campaigns over the Southern Ocean between Tasmania and Antarctica in January-February 2018 to evaluate the simulations of clouds and precipitation in nudged-meteorology simulations with the CAM6 and AM4 global climate models sampled at the times and locations of the observations. Fifteen SOCRATES research flights sampled cloud water content, cloud droplet number concentration, and particle size distributions in mixed-phase boundary-layer clouds at temperatures down to -25 C. The six-week CAPRICORN2 research cruise encountered all cloud regimes across the region. Data from vertically-pointing 94 GHz radars deployed was compared with radar-simulator output from both models. Satellite data was compared with simulated top-of-atmosphere (TOA) radiative fluxes. Both models simulate observed cloud properties fairly well within the variability of observations. Cloud base and top in both models are generally biased low. CAM6 overestimates cloud occurrence and optical thickness while cloud droplet number concentrations are biased low, leading to excessive TOA reflected shortwave radiation. In general, low clouds in CAM6 precipitate at the same frequency but are more homogeneous compared to observations. Deep clouds are better simulated but produce snow too frequently. AM4 underestimates cloud occurrence but overestimates cloud optical thickness even more than CAM6, causing excessive outgoing longwave radiation fluxes but comparable reflected shortwave radiation. AM4 cloud droplet number concentrations match observations better than CAM6. Precipitating low and deep clouds in AM4 have too little snow. Further investigation of these microphysical biases is needed for both models.

Vast quantities of marine debris have beached at remote islands in the western Indian Ocean such as Seychelles, but little is known about where this debris comes from. To identify these sources and temporal patterns in accumulation rate, we carried out global Lagrangian particle tracking experiments incorporating surface currents, waves, and variable windage, beaching, and sinking rates, taking into account both terrestrial (coastal populations and rivers) and marine (fisheries and shipping) sources of debris. Our results show that, whilst low-buoyancy terrestrial debris may originate from the western Indian Ocean (principally Tanzania, Comoros, and Seychelles), most terrestrial debris beaching at remote western Indian Ocean islands drifts from the eastern and northern Indian Ocean, primarily Indonesia and, to a lesser extent, India and Sri Lanka. Purse-seine fragments beaching at Seychelles are likely associated with fishing activity in the western Indian Ocean, but longline fragments may also be swept from the southeastern Indian Ocean. The entire of Seychelles is at very high risk from waste discarded from shipping routes transiting the Indian Ocean, and comparison with observations suggests that many bottles washing up on beaches may indeed originate from these routes. Our analyses indicate that marine debris accumulation at Seychelles (and the Outer Islands in particular) is likely to be strongly seasonal, peaking during February-April, and this pattern is driven by local monsoonal winds. This seasonal cycle may be amplified during positive Indian Ocean Dipole phases and El-Ni\~{n}o events. These results underline the vulnerability of small island developing states to marine plastic pollution, and are a crucial first step towards improved management of the issue. The Lagrangian trajectories used in this study are available for download, and our analyses can be rerun under different parameters using the associated scripts.

Local acceleration driven by whistler mode chorus waves is fundamentally important for the acceleration of seed electrons in the outer radiation belt to relativistic energies. This mechanism depends strongly on substorm activity and on the source and seed electron populations injected by substorms into the inner magnetosphere. In this work, a selection of geospace disturbance events, emerging from single and isolated interplanetary drivers, is divided into two groups, one resulting in enhancement and one in depletion of the average relativistic electron Phase Space Density (PSD). Because substorm activity does not always coincide with or depend on magnetic storm occurrence, we have not limited our study to storms, but have included also non-storm events that are able to cause enhancements and depletions of the relativistic electrons in the outer radiation belt. We investigate solar wind and geomagnetic parameters, wave activity and the seed electron PSD in the outer Van Allen radiation belt, looking for the occurrence of characteristic patterns, by performing a Superposed Epoch Analysis (SEA). Our study indicates the importance of substorm-associated enhancements of seed electrons, along with prolonged, intense ULF and VLF wave activity and an earthward displaced plasmapause, as conditions leading to substantial enhancements of relativistic electrons in the outer Van Allen belt. This work has received funding from the European Union’s Horizon 2020 research and innovation programme under grant agreement No 870437 for the SafeSpace project.

The recent severe “Day Zero” drought (2015-2017) over the winter rainfall zone (WRZ) of South Africa has highlighted low-frequency winter climate variability, possible trends and our generally poor understanding of their mechanisms. We investigate the contribution of dynamic conditions and moisture transport to daily station rainfall events in the WRZ and the relationship between the frequency of such states, seasonal to inter-decadal rainfall variability and hemispheric modes of Westerly Wave variability. Dynamic conditions are assessed using reanalysis data over the South-East Atlantic during the austral winter half-year (AMJJASO) from 1979-2017. A self-organising map (SOM) analysis is performed on 500-hPa geopotential height. Nodes indicating strong troughs and ridges in the Westerly Wave are identified using either rainfall or dynamic (divergence and vorticity) criteria. The two approaches produce similar results; most rainfall (around 80%) occurs on days mapped to trough nodes (35% of days). The trough nodes are subjected to a multi-dimensional SOM analysis to identify conditions leading to variations of rainfall within the original SOM nodes. Nodes showing intense (high divergence and vorticity west of South Africa) troughs extending equatorward of the WRZ (14% of all winter days) account for around 60% of trough rainfall. Cut-off lows (COLs) are independently identified as closed, cold-cored lows at the 500-hPa-level and their contribution to precipitation is assessed separately. COLs are detected on approximately 3% of all winter time steps, contributing only about 11% of the total rainfall, although they account for almost all heavy rainfall events not associated with intense troughs. During the Day Zero drought, the frequency of almost all trough nodes decreased, especially in the shoulder seasons, while ridge nodes occurred 1.5-2 times more frequently and persisted for longer, especially in late autumn. Average rainfall per trough node was lower and COL frequency reduced. The only SOM nodes showing significant trend over 1979-2017 are ridge nodes associated with large anticyclonic vorticity anomalies south of the WRZ. Correlation between the Southern Annular Mode and ridge/trough nodes is weak. We conclude that the Day Zero drought resulted from fewer mobile troughs passing the WRZ in the shoulder seasons, possibly linked to a multi-decadal increase in blocking high frequency.

Geoscience employers have increasingly called for the future workforce (students) to demonstrate competence in non-technical skills, including teamwork. This descriptive qualitative study contributes to ongoing efforts to identify the specific practices, skills, habits, and knowledge that make up these desired teamwork competencies in the geosciences. We collected data from three online focus group discussions centered around teamwork. Focus group participants were hydrogeology and environmental geology employers and team managers from government, private industry, and non-profit organizations in the United States. Using the Marks et al. (2001) teamwork taxonomy model as our conceptual framework, we generated three categories of teamwork skills specific to environmental geoscience teams. First, our data indicate that these employers value team transition skills related to specifying goals, interpreting team tasks, identifying resources, and planning. The second category of desired teamwork competencies included action skills such as metacognition, coordination and mentoring. These skills directly impact successful task completion. The third category captured interpersonal skills such as emotional intelligence, proactive communication, and organization. A fourth category of desired teamwork competencies emerged from data analysis and include ethical skills related to trust, integrity and humility. This study provides a detailed description of teamwork competencies desired by environmental geoscience employers and suggests implications for how to prepare students for this workforce.

Marathwada division of Maharashtra is one of the semi-arid regions of India where the agricultural conditions are highly vulnerable to the scanty rain and severe droughts. Over this area, significantly depleting groundwater level and low amount of monsoon rains demands for a good irrigation system for maximum crop cultivation and day-to-day activities. Therefore, this study estimates the spatio-temporal water and vegetation stress to make out the sensitive regions of Marathwada where irrigation network is necessary. The area-specific water requirement is based on the water demand for crops from that particular area. The rate of Evapotranspiration (ET) with potential ET (PET) directly represents the field-specific crop water demands, hence; to understand their relevance we have analyzed the monthly ET and Normalized Difference Vegetation Index (NDVI) by the data acquired from Moderate Resolution Imaging Spectrometer (MODIS) for the period of 2001 to 2017. Water deficiency of particular region is highly dependent on the rainfall vagaries of that area. Therefore, the monthly rainfall product from Tropical Rainfall Measuring Mission (TRMM) was also studied. Station based groundwater data, Soil profile, Land use land cover etc. were the other datasets considered to prioritize the areas for building the irrigation facilities. Study area as a whole, a significant correlation was observed between ET and NDVI. Among the eight districts of Marathwada, Latur, Parbhani Aurangabad and Hingoli were the regions where higher water stress was noticed. On the temporal scale (from last 17 years) maximum water scarcity was noticed over the year 2002-2003 and 2014 -2015. Ensemble bi-decadal analysis of five different drought indices with the aid of IMD observations shows water stress as a proxy variable for drought monitoring. Hence the proven correlation between ET, NDVI and water stress from this study, gives the new interdisciplinary approach to deal with the drought hazards and to priorities the drought-affected area which demands more concern.

Distributed sensor networks empower real-time in-situ computing and seismic imaging without transmitting the raw data to the remote data center. This attribute is valuable for planet exploration that has stringent bandwidth. The previously distributed ambient noise imaging algorithm computes results using data from near neighbors, while the information from distant neighbors is not utilized, hence the image quality is compromised. To overcome this problem, this work proposes an innovative common-receiver-based decentralized ambient noise imaging algorithm. In this algorithm, the basic imaging algorithm is still Eikonal tomography, but the distant neighbors are also used to computing the seismic image so that the quality of the output image can be preserved. An in-situ computing and clustering algorithm is created to optimize the data transition and computation while meeting the bandwidth constrains. The experiments were performed on both synthetic data from Enceladus and real data from the USArray archives. The new algorithm generates higher-resolution images under the same bandwidth constraints, comparing to previous algorithms, and the quality of the output image is satisfactorily preserved. The communication cost reduction over the raw data collection is in several orders of magnitude (e.g., 1: 1600). It meets the desired bandwidth constraint in planetary exploration applications.

The North American monsoon (NAM) is an important source of precipitation across the southwestern United States (US). The approximate northern boundary of this feature crosses the Navajo Nation, in the Four Corners region, where NAM rains have long been important to the livelihoods of Native Americans. Relatively little is known about the characteristics and hydrological significance of the NAM in this region. Here we report a new 4-year record of stable H and O isotope ratios in monsoon-season rainfall and water resources across the Navajo Nation. Monthly precipitation samples collected at 39 sites document a characteristic pattern of 2H- and 18O-enrichment associated with monsoonal precipitation. These changes are weakly correlated with local precipitation intensity, however, and the correlation that does exist is dominated by sub-cloud evaporation effects. In contrast to precipitation amount, monsoon-season isotopic values exhibited limited spatial variability across the region, and after correction for sub-cloud evaporation Navajo Nation values were similar to those from a site in southern Arizona. Airmass back-trajectory analysis suggests that the uniformly high NAM isotope values across the region may reflect 1) a region-wide shift from mid-latitude to low-latitude moisture sources at the onset of the peak monsoon, and 2) substantial land-surface recycling of NAM moisture in upwind regions. Comparison of precipitation isotope data with surface and groundwater values implies that, despite its hydroclimatic significance, monsoon rainfall contributes little to rand subsurface water resources. This highlights the monsoon’s importance for warm-season land-surface ecology and hydrology critical to residents of the Four Corners region.

Forest soil is the largest carbon pool in terrestrial ecosystem, and the soil-to-atmosphere CO2 flux (soil respiration, Rs) is the main link between soil and atmosphere. However, due to the lack of integration of field observations, substantial uncertainties exist in quantifying large-scale soil carbon effluxes, which limit our understanding of the fate of forest soil in a warming world. Here, China’s forest ecosystems were divided into six forest types in six regions, and an integrated soil respiration database (N=634) was compiled to evaluate soil carbon effluxes by random sampling with replacement. Average annual Rs was 783 g C m-2 yr-1 across China, ranking from the highest to the lowest as follows: East, Southwest, South, Northwest, Northeast and North. Total soil carbon emissions were 1472.6 Tg C yr-1 in China’s forest ecosystems, and about 69% from three southern regions (i.e., Southwest, Southern China and Eastern China) and 31% from three northern regions (i.e., Northwest, Northern China and Northeast). Evergreen needleleaf forest (529.09 Tg C yr-1, 52%) and evergreen broadleaf forest (343.01 Tg C yr-1, 34%) were the main source of soil carbon emissions in three southern regions, while deciduous broadleaf forest (334.36 Tg C yr-1, 74%) was the main emissions in three northern regions. Our results provide a better understanding of the distribution and magnitude of soil carbon effluxes in China’s forest ecosystems.

The Snow Ensemble Uncertainty Project (SEUP) is an effort to establish a baseline characterization of snow water equivalent (SWE) uncertainty across North America with the goal of informing global snow observational needs. An ensemble-based modeling approach, encompassing a suite of current operational models, is used to assess the uncertainty in SWE and total snow storage (SWS) estimation during the 2009-2017 period. The highest modeled SWE uncertainty is observed in mountainous regions, likely due to the relatively deep snow, forcing uncertainties, and variability between the different models in resolving the snow processes over complex terrain. This highlights a need for high-resolution observations in mountains to capture the high spatial SWE variability. The greatest SWS is found in Tundra regions where, even though the spatiotemporal variability in modeled SWE is low, there is considerable uncertainty in the SWS estimates due to the large areal extent over which those estimates are spread. This highlights the need for high accuracy in snow estimations across the Tundra. In mid-latitude boreal forests, large uncertainties in both SWE and SWS indicate that vegetation-snow impacts are a critical area where focused improvements to modeled snow estimation efforts need to be made. Finally, the SEUP results indicate that SWE uncertainty is driving runoff uncertainty and measurements may be beneficial in reducing uncertainty in SWE and runoff, during the melt season at high latitudes (e.g., Tundra and Taiga regions) and in the Western mountain regions, whereas observations at (or near) peak SWE accumulation are more helpful over the mid-latitudes.

Hydropower is a low-carbon emission renewable energy source that provides competitive and flexible electricity generation and is essential to the evolving power grid in the context of decarbonization. Assessing hydropower availability in a changing climate is technically challenging because there is a lack of consensus in the modeling representation of key dynamics across scales and processes. The SECURE Water Act requires a periodic assessment of the impact of climate change on the United States federal hydropower. The uncertainties associated with the structure of the tools in the previous assessment was limited to an ensemble of climate models. We leverage the second assessment to evaluate the compounded impact of climate and reservoir-hydropower models’ structural uncertainties on monthly hydropower projections. While the second assessment relies on a mostly-statistical regression-based hydropower model, we introduce a mostly-conceptual reservoir operations-hydropower model. Using two different types of hydropower model allows us to provide the first hydropower assessment with uncertainty partitioning associated with both climate and hydropower models. We also update the second assessment, performed initially at an annual time scale, to a seasonal time scale. Results suggest that at least 50% of the uncertainties, both at annual and seasonal scales, are attributed to the climate models. The annual predictions are consistent between hydropower models which marginally contribute to the variability in annual projections. However, up to 50% of seasonal variability can be attributed to the choice of the hydropower model in regions over the western US where the reservoir storage is substantial. The analysis identifies regions where multi-model assessments are needed and presents a novel approach to partition uncertainties in hydropower projections. Another outcome includes an updated evaluation of CMIP5-based federal hydropower projection, at the monthly scale and with a larger ensemble, which can provide a baseline for understanding the upcoming 3rd assessment based on CMIP6 projections.

Cloud tomography (CT) is a promising approach in passive remote sensing using space-based imaging sensors like MISR and MODIS. In contrast with current cloud property retrievals in the VNIR-SWIR, which are grounded in 1D radiative transfer (RT), CT embraces the 3D nature of convective clouds. Forster et al. (2020) defined the “veiled core” (VC) of such clouds as the optically deep region where detailed 3D structure of the cloud has little impact on the multi-angle/multi-spectral images as long as average VC extinction and any significant cloud-scale gradient are preserved. Quantitatively, the difference between radiance fields escaping clouds remains commensurate with sensor noise when said clouds differ only in the small-scale distribution of extinction inside their VC. An important corollary for the large and ill-posed CT inverse problem is that the only unknowns of interest for the whole VC are its mean and any cloud-scale vertical trend in the extinction coefficient. Another ramification for CT algorithms under development is that the forward 3D RT model driving the inversion may be vastly simplified in the VC to gain efficiency. We explore that possibility here, assuming radiative diffusion as the simplified RT for the VC. We also describe the relevant RT physics that unfold in the VC and in the outer shell (OS) where detailed spatial structure does matter for image formation. This includes control by the VC of the cloud-scale contrast between brightnesses of illuminated and shaded boundaries, as well as the gradual blurring of spatial structure via directional diffusion with increasing optical distance into the OS. “Transport” space is the merger of 3D (or 1D) physical space and 2D direction space. Cloud image formation involves radiative diffusion processes (i.e., random walks) in both of these spaces, depending on what transport regime prevails. Fortunately for the future of computed CT and of passive cloud remote sensing in general, there is a clear spatial separation: asymptotic limit of radiative diffusion in the VC, standard RT in the OS. A hybrid forward model for CT will make use of this fact. Reference: Forster, L., Davis, A. B., Diner, D. J., & Mayer, B. (2021). Toward Cloud Tomography from Space Using MISR and MODIS: Locating the “Veiled Core” in Opaque Convective Clouds, Journal of the Atmospheric Sciences, 78(1), 155-166.

The Sandur greenstone belt (SGB) is a distinctive greenstone belt as it is perched within the Closepet granitoid rocks (CG). The emplacement of the CG is attributed to intrusion in a crustal scale transcurrent shear zone towards the end of Archaean (Moyen et al., 2003). The Chitradurga shear zone that forms the eastern margin of the Chitradurga greenstone belt, located west of the Closepet granite, is considered as the boundary between WDC and EDC. As per the division of the Dharwar craton, the domination of volcano-sedimentary sequences in SGB with abundant BIF and considerable greywacke-argillite lithologies, attest their similarity to the greenstone belts of WDC. Closer to the SGB, the rocks of the CG are known to host excellent mafic microgranular enclaves (MME) which might indicate interaction between the granitic magma with the older greenstone belt lithologies during intrusion. It is interesting to note that there is a progressive increase in crustal thickness from north towards south in the Dharwar craton and the SGB is found associated with the CG in the shallow zones of the north (Moyen et al., 2003). The mafic volcanic rocks are predominantly basaltic in composition and are composed of amphibole, pyroxene, plagioclase and quartz with titanite and magnetite as accessory minerals. The rocks are classified as tholeiitic basalts that were metamorphosed to amphibolite grade. Preliminary geochemical studies on these rocks show significant differences in their trace element distribution. The chondrite-normalized REE patterns show moderate to high contents of REE and have unfractionated pattern. The basalts show a flat to slightly LREE enriched pattern. Some samples show slight negative Eu anomaly and some do not show any significant anomaly. Some associated rocks also have complementary enrichment-depletion of certain elements. All of these point to multiple petrogenetic processes involved in the generation of these magmatic precursor rocks.