Data quality is only one of many uncertainties involved in our attempt to understand, model and predict complex nonlinear systems and to identify the properties emerging from them. Perhaps not surprisingly, less than half of published scientific studies can be successfully reproduced, with the earth sciences occupying an intermediate position among the major disciplines. The uncertainty of data quality is only one factor that contributes to the relatively poor reproducibility of research studies and the resulting hypotheses. Potentially more important factors include unsuitably low thresholds for assessing statistical relevance, inappropriate data manipulation, inadequate research design, and outright fraud. Data quality for earth science data can be communicated via measurement errors (e.g., instrument accuracy, technician practices) and addressed by employing measurement quality codes, whereas the other aforementioned factors can be identified or controlled through rigor, methods selection and research competency. A more difficult source of uncertainty includes biases, assumptions, and environmental or social influences that affect the judgment and perceptions of scientists themselves. The difficulty is that many of these factors operate beyond one’s awareness or control, residing within automatic processes of the human brain. From continually seeking, interpreting and projecting patterns (spatial and temporal) to confabulating answers and relying on heuristics, we are largely at the mercy of what the brain has evolved to do, which is not necessarily to accurately perceive the natural world. Peer reviews, precision instruments, mathematical abstractions and digital computers certainly assist us in probing nature; nevertheless, we ultimately perceive the world as we are, rather than as it is. Consequently, a scientifically “objective” view of nature is being questioned in research ranging from quantum mechanics to human consciousness. To what extent are the peculiarities of human brain responsible for the uncertainty and nuances that we believe exist in nature? Are scientists fooled by a brain that constructs, rather than simply observes, the world around us? If so, do we have the tools to deal with this kind of uncertainty?

We propose a new numerical code based on a new multi-species theoretical model to study the mass, momentum and energy exchanges (MMEE) that happen across the magnetospheric boundaries. We use two distinct populations for ions, one cold and one hot (plus one neutralising electron population), to take into account the differences between the properties of the plasmas coming from the magnetosphere and from the solar wind. This approach represents a step forward in the context of the study of coupled large-scale plasma systems being a new and efficient compromise between fluid and kinetic codes in tracing the different plasma contributions during MMEE. Due to the very important role that magnetic reconnection plays in connecting the shocked Solar Wind to the Earth’s magnetosphere, we show and discuss the results we obtained about the simulations of the tearing mode instability occurring across an Earth’s magnetopause that we modelled thanks to our most recents MMS observations [Rezeau 2018]. Rezeau, Belmont, Manuzzo, Aunai, Dargent, 2018, Journal of Geophysical Research: Space Physics, 123, doi: 10.1002/2017JA024526.

Irrigation parameterizations in land surface models have been advanced over the past decade, but the newly available data from the Soil Moisture Active Passive (SMAP) satellite has seldom been used to improve irrigation modeling. Here, we investigate the potential of assimilating SMAP soil moisture (SM) data into the Community Land Model (CLM) to improve irrigation representation. Simulations are conducted at 3 arc-minute resolution over the highly irrigated region in the central US, fully enclosing the upstream areas of the river basins draining over the High Plains Aquifer (i.e., the Missouri and Arkansas), and Colorado River basins. We test the original CLM4.5 irrigation scheme and two new irrigation parameterizations using SMAP data assimilation by: (1) directly integrating raw SMAP data, and (2) integrating SMAP data using 1-D Kalman Filter (KF) smoother. An a priori scaling approach is also used to account for bias correction of the shortly-recorded SMAP data based on the ground observations, enabling us to use SMAP for out-of-sample tests (i.e., assessment of the new parameterizations during a non-SMAP period). The ground-based SM observations from three monitoring networks, namely Soil Climate Analysis Network (SCAN), US Climate Reference Network (USCRN), and SNOwpack TELemetry (SNOTEL) are employed for bias correcting SMAP data and validating SM simulations. Results show that SMAP data assimilation using 1-D KF significantly improves irrigation simulations. Bias correction of SMAP data further improves results from KF assimilation in some regions. However, the improvements are small compared to those achieved from 1-D KF application alone, indicating the robustness of using SMAP data and KF globally even for the regions where ground-based data are not available for bias correction. The data assimilation also improves the accuracy of the temporal dynamics and vertical profile of simulated SM. These results are expected to provide a basis for improved modeling of irrigation water use and land-atmosphere interactions.

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.

The fragility of human life has been tragically demonstrated through history by numerous natural hazards (e.g. earthquakes, tsunami, drought, floods, volcanic eruptions and landslides) leading in some cases to large-scale disasters. Given that the natural processes that underpin such hazards will continue into the foreseeable future, there is widespread international agreement about the need to focus on reducing risk and increasing resilience. In line with such objectives, Indigenous knowledge (IK) in Disaster Risk Reduction (DRR) is increasingly being recognised as an alternative domain of understanding and knowing, that is highly relevant to present-day societal challenges such as natural hazards and disasters. In Aotearoa-New Zealand, rapid social, political and environmental changes have led a growing chorus of Māori leaders expressing the need to have their perspectives and histories surrounding diverse natural hazards and risk heard. Some of the contributions that Māori knowledge can make to the management and mitigation of natural hazards in Aotearoa-New Zealand have already been identified, but much remains to be done before plural knowledge learning and development can take place. In this presentation we will explore some of the benefits of epistemological and empirical differences in knowledge about natural hazards, and consider how scientific and Māori complexes of knowing and being can inform one another to contribute to effective DRR and Recovery.

Long period magnetotelluric (MT) data is being collected as a component of the USArray covering the continent of North America at a quasi-regular grid. Data from 250 MT stations were acquired covering the north-western part of the United States of America (USA). Dimensionality analysis was applied using Swift skew, Bahr skew and ellipticity. Results show a 1-D/2-D character for the short periods (< 1 sec) for the shallower structures, while the longer periods (> 1 sec) shows a 3-D character for the deeper structures. Two dimensional Occam inversion was applied for a set of E-W trending profiles to map the regional electrical resistivity distribution to a depth up to crust/mantle transition zone. 2-D resistivity structures were obtained by inverting both the transverse electric (TM) and transverse magnetic (TM) modes. Results show a resistive upper crust with a decreasing resistivity for lower crust. To the west, the subudcting Gorda plate was mapped as a relatively higher resistivity body nearly equal to the Wyoming basin to the east. Thick resistive lithosphere was mapped under Wyoming to the east. The results of this research shows the distribution of the electrical resistivity with adequate details for the shallow depth, while 3D inversion is necessary for the larger depths.

Land use sector is important in stabilizing global mean temperature rise to 2 °C or less, because land use changes are likely to affect the climate system. Changes in the extent and magnitude of local-to-regional climate by anthropogenic modifications of land use are still largely debated. In this study, we simulate and analyze the climate response to different ranges of idealized extreme land cover changes with two regional climate models (COSMO-CLM v.4.8 and WRF v.3.9.1 in EURO-CORDEX (European branch of the international Coordinated Regional climate Downscaling Experiment-CORDEX initiative) domain. Different experiments are envisioned in this study, including a control run and simulations based on idealized extensive deforestation (replacement of today forestland to bare land or herbaceous vegetation) or afforestation (conversion of today cropland to evergreen needle-leaf forest or deciduous broad-leaf forest). The simulations also include more realistic land cover changes across different land cover classes. The investigated parameters will be the changes of temperature, precipitation, and frequency of temperature extremes at both the entire EURO-CORDEX domain (regional scale) and the changed grids (local scale). Results will also be compared to observation data gathered from satellite retrievals. In the grid cell affected by land cover change, we expect to find temperature changes that are more significant than in non-affected areas. A latitudinal pattern and seasonal variability should also emerge. Of particular interest will be the understanding of the spatial patterns of the climate response to the individual types of land cover changes, their sensitivity to space and location, and the analysis of possible correlations with key vegetation and climate parameters. As biophysical effects from land cover changes shape European climate in different ways, further developments and better understanding of land-climate interactions can ultimately assist decision makers to modulate land management strategies at different scales in light of climate change mitigation and adaptation.

The frontier of wildfire-related risk assessment is moving into data science territory, and with good reason. Computational statistics, built on a foundation of high resolution remote sensing data, ground data, and theory, forms the basis of powerful risk assessment tools. The need for data based risk assessment has increased in past years, in view of longer wildfire seasons in the U.S., associated with more frequent droughts, more human ignitions and accumulating fuel loads. We present an application of machine learning (ML), which makes it possible to analyze complex data without a priori definition of interactions—this is a major advantage because these interactions are not known beforehand. Specifically, we build a stochastic gradient boosting machine (GBM) toolkit to assess the change in river flow after wildfire in the contiguous United States (CONUS) over a 5-year period. The GBM accounts for nonlinear relationships and interactions between wildland fire characteristics, watershed geometry, climate variability, topography and land cover. Building the GBM is a sequential process where a loss function is minimized at each fold, along a gradient defined by pseudo-residuals. This process allows the program to progressively learn more about how the variables in the large dataset interact to result in the response (i.e., river flow). Our results show that wildfires increase annual river flow in the CONUS when more than 20% of a gaged basin is burned. Data science tools like the GBM presented here, are essential in generating practical knowledge on how wildfire impacts on ecohydrology can ultimately affect hydrological services, socio-hydrosystems and water security in fire-affected regions.

In long-established cities such as Detroit, MI, stormwater runoff from impervious surfaces (roads, roofs, etc.) is combined with sanitary waste and piped beneath the city to a wastewater treatment plant. These engineered methods of water management, termed gray infrastructure, currently dominate management practices but have drawbacks due to issues of cost, capacity, reliability, and maintenance requirements. In addition, combined sewer systems in cities like Detroit can become overwhelmed during extreme storm events, requiring the diversion of untreated sewage into local bodies of water. In a world of growing urbanization and intensifying storm events, the development of innovative approaches to stormwater management is expected to play a large role in mitigating negative environmental impacts from polluted urban runoff and flooding. The eco-friendly counterpart of gray infrastructure, known as green infrastructure (green roofs, rain gardens, bioswales, etc.), is an integrated approach to stormwater management that utilizes decentralized infiltration systems to mimic the natural processes of water filtration and deceleration. Correspondingly, our research focuses on a bioswale that was retrofitted into the center island of a previously designed parking lot in the post-industrial urban ecosystem of Detroit. The main goals of our work are to: 1) quantify potential pollutant removal, 2) quantify runoff infiltration capacity, and 3) provide qualitative insight into best management practices for future bioswale retrofits. We collected soil samples from 2 depths (30 and 45 cm) at 9 points from the bioswale for characterization (pH, organic matter, electric conductivity, and texture). We also collected and analyzed undisturbed soil cores with and without added metal contaminants common to Detroit and with a surface addition of biochar to test infiltration and determine the potential of increased contaminant sorption. The results will allow us to advance with an improved design and methodology and convey a strong argument for the expansion of bioswales and other forms of green infrastructure across the greater Detroit area, potentially leading to a more robust system of controlling stormwater that brings aesthetics, health, and a wide array of ecosystem services to a redeveloping concrete jungle.

The ocean floor broadband seismology has been established based on several practical observations by using broadband ocean bottom seismometer (BBOBS) and its new generation system (BBOBS-NX) in Japan since 1999. The data obtained by our BBOBS and BBOBS-NX is adequate for broadband seismic analyses, especially the BBOBS-NX enables the quality of the horizontal data comparable to land sites in longer periods (10 s –). And, the BBOBS-NX with tilt measurement function, BBOBST-NX, is in practical evaluation for the mobile tilt observation that may realize a dense seafloor geodetic monitoring with low cost. The weak point of the BBOBS-NX system lies in the intrinsic limitation of the submersible in its operation. If this system can be operated alone like as the BBOBS, it should be a true breakthrough of ocean bottom seismology. We call this new autonomous BBOBS-NX as the NX-2G in short. Several problems to realize the NX-2G have been almost cleared through test observations since 2012. The function of the NX-2G system is based on 3 stage operations as shown in the image. The glass float is added to obtain enough buoyancy to extract the sensor unit from the seafloor and also to suppress the oscillating tilt of the system in descending, not to exceed the tilt allowance of the broadband seismic sensor. In Oct. 2016, the first in-situ test of the NX-2G system was performed. The landing of the NX-2G looked well and the maximum tilt in descending was about ±2.5°, that ensured the effective suppression for the oscillating tilt by the glass float. As the final step test of the NX-2G, the one-year-long observation has been started in April 2017 with the BBOBS deployed nearby, to obtain simultaneous data for the noise level evaluation. The free-fall deployment and the transition from the landing stage to the observation stage were completed, those were monitored through the acoustic communication from the ship. This NX-2G was recovered in Oct. 2018 with the ROV, KAIKO Mk-IV, to watch the transition from the observation stage to the recovery stage at the seafloor. All function of the NX-2G at the seafloor was perfect with immediate extraction of the sensor unit. Noise level comparison with the BBOBS shows about 10 dB improvement that is not enough as expected, which may lie in a small tension of the cable between the sensor unit and the recording unit. And, scenes of the landing and the first transition were selfied by the Deep-Sea CAM.

Abstract Gas relative permeability, krg, is a key parameter to determine gas production in unconventional reservoirs. Several theoretical approaches were proposed to study gas relative permeability in tight and ultra-tight porous rocks. Some models are based on a “bundle of capillary tubes” concept. Some others were developed based upon a combination of universal scaling laws from percolation theory and the effective-medium approximation (EMA). Although applications from the EMA have been successfully used to estimate single-phase permeability in permeable media (Ghanbarian et al., 2017; Ghanbarian and Javadpour, 2017), non-universal scaling from the EMA has never been invoked to model gas relative permeability in tight and/or ultra-tight porous rocks. In this study, it was assumed that pore-throat sizes follow the log-normal distribution. It was further assumed that gas transport in shales is mainly controlled by molecular and hydraulic flow, two mechanisms contributing in parallel. Using the EMA, effective pore-throat radii, effective conductances, and gas relative permeabilities were determined at various gas saturations. Comparison with three-dimensional pore-network simulations showed that the proposed krg model estimated gas relative permeability accurately. We also compared our model with experimental data reported in Yassin et al. (2016) including three Montney tight gas siltstone samples from the Western Canadian Sedimentary Basin. Results showed that our model estimated krg reasonably well, although it slightly overestimated krg. This might be because the fitted log-normal probability density function underestimated the probability of small pore-throat sizes. References Ghanbarian, B., & Javadpour, F. (2017). Upscaling pore pressure‐dependent gas permeability in shales. Journal of Geophysical Research: Solid Earth, 122(4), 2541-2552. Ghanbarian, B., Torres-Verdin, C., Lake, L. W., & Marder, M. P. (2017). Upscaling gas permeability in tight-gas sandstones. AGU Fall Meeting Abstracts. New Orleans LA. Yassin, M. R., Dehghanpour, H., Wood, J., & Lan, Q. (2016). A theory for relative permeability of unconventional rocks with dual-wettability pore network. SPE Journal, 21(06), 1970-1980.

Tracing the field in the past at the centennial and millennial timescale (i.e., the archeomagnetic timescale) is important in improving geomagnetic field models and also for field forecast analyses. The distribution of archeomagnetic data across the globe is very inhomogeneous with almost all data coming from the northern hemisphere, particularly from Europe and Asia. Yet, the southern hemisphere is the one presenting the highest variability for the past millennia and, it is also the region comprising the South Atlantic Anomaly (SAA). The SAA is characterized by the lowest total field intensity and it is presently located in Southern Brazil. Some studies suggest that SAA is a persistent field feature in South Atlantic for periods longer than historical times, but the beginning of the influence of strong non-dipole fields at the centennial-scale evolution in South Atlantic region, could give important information about the recurrence of SAA at longer timescales. Here, we report six new high-quality archeointensity results for the Pelotas city region, in South Brazil with ages ranging from 1790 to 1943 CE. Archeointensity measurements were performed with the double heating technique, including partial termoremanent magnetization (pTRM) checks and pTRM tail-checks. Measurements were corrected from anisotropy of TRM and cooling-rate. Archeointensity results vary from 36.4±0.6 μT to 27.8±2.0 μT. The new data complement previous results obtained in South and Southeast Brazil, Argentina and Chile and provide additional evidence for the rapid decay of the field in the region. In addition, it demonstrates the gradual increase in the contribution of non-dipolar components in the geomagnetic field of South America since 1800 CE, therefore tracking the arrival of the South Atlantic Anomaly to these times.

We inspected a distribution of events in spatial and temporal scales for two earthquake sequences of South Korea: the obtained spatiotemporal evolutions suggested a fault geometry in meter-scale without geological information and an expansion of hypocentral area without surface ruptures for the 2013 Boryeong offshore earthquake sequence and the 2016 Gyeongju earthquakes, respectively. The 2013 Boryeong offshore earthquake sequence was the consecutive occurrence of earthquakes (ML 1.3-3.8) over a two-month period in the Yellow Sea, west of the Korean Peninsula. The relocated 149 events formed a clear lineament in map view, which proceeded mainly toward NE without an obvious mainshock. The sequence, however, had a backward migration toward SW at first and produced the largest event that occurred after a pause of the sequence. We defined four event groups from the sequence using dendrogram for waveform similarity. The four event groups formed four distinct segments in spatial scale and showed its own features in temporal scale. The length of the second segment, 250 m, was comparable to the rupture length of the largest event (ML 3.8). The 2016 Gyeongju earthquake (ML 5.8), the largest instrumentally recorded event in South Korea, occurred at the southeastern part of the Korean Peninsula. We determined the expansion of the hypocentral zone from relocated 536 hypocenters during the first three months of the Gyeongju earthquake sequence. The foreshock zone covered depths of 13-15.3 km, with a length of 2 km in map view. The mainshock occurred 1 km south of the foreshock. The aftershocks, occurring within 12 hr, then widened the hypocentral area in the strike direction by ~1 km toward NNE and expanded it toward the surface. In this sequence, we observed that the strike of fault plane inferred from the hypocenter distribution was shifted slightly after the mainshock. The hypocentral area was finally stretched in the strike direction over ~1 km toward SSW by the largest aftershock (ML 4.5) that occurred 7 days after the mainshock and following events.

Water managers face the daunting task of managing freshwater resources in the face of industrialization and population growth. As surface water resources become fully allocated, increased groundwater use can fill the void, particularly during periods of drought. Improper groundwater management can result in reduced water quality, land subsidence, increased pumping costs, and in some cases, the complete exhaustion of an aquifer and the loss of groundwater as a buffer during times of drought. Assessing the long-term impact of various groundwater management decisions can be difficult and costly, and therefore many decisions are made without sufficient analysis. Advancements in the acquisition and dissemination of Earth observations, coupled with advances in cloud computing, web apps, online mapping, and visualization provide a unique opportunity to deliver tools and actionable information to groundwater managers to assist them in addressing global and regional challenges and opportunities. We have developed a web-based tool that ingests in situ groundwater level measurements for specific aquifers and generates time series plots, maps, and raster animations showing groundwater depletion over time and short-term projections into the future. This process involves both temporal and spatial interpolation algorithms. In some aquifers, the observation wells are sparse and/or the historical observations have large gaps, leading to greater uncertainty in the interpolation and the resulting groundwater depletion estimates. To address this, we utilize Earth observations (GRACE, SMAP, etc.) and a co-kriging algorithm to enhance the interpolation process. The utility of the Earth observations in improving the estimates is evaluated using a jackknifing process. We present case studies for application of the system in the states of Utah and Texas.

Larsen A and B ice shelves were affected by surface melt which preconditioned them for rapid disintegration due to hydrofracture and densification. Recently, warm and dry foehn winds have been discovered to melt the vulnerable Larsen C Ice Shelf (LCIS) surface via sensible heat transfer during polar night. The climatological extent and intensity of polar night surface melt and their effects on the ice surface energy budget are unknown. Here we quantify the spatial pattern and temporal variability of foehn winds and associated melt events during polar night to understand the ice shelf surface mass balance and indirect implications for ice shelf vulnerability. Our Foehn Detection Algorithm (FonDA) uses events identified from in situ Automated Weather Stations (AWS) to calibrate foehn detection from reanalysis data covering all of Antarctica and Greenland. We present a climatology of foehn-driven surface melt days, melt water equivalent, fraction of melt that occurs during polar night, and the surface energy budget. Preliminary results show that foehns perturb sensible heat fluxes by up to 300 Wm-2 and surface air temperatures by up to 13 °C in the absence of shortwave radiation.

Understanding the simple mechanism of variable solar irradiance ocean warming/cooling allows for effective attribution, detection, and prediction of ocean-related extreme weather events based on solar activity level and duration. Modern and historical solar and ocean data were used to find the ocean warms from rising and high solar activity over any duration, and vice-versa, such as cooling from low activity during the Dalton Minimum. Equatorial ocean heat content and sea surface temperature are sensitive to daily total solar irradiance (TSI) variation and level, and to the upwelled heat accumulation from prior days sub-surface absorbed solar energy penetration. Equatorial evaporation from the absorbed solar energy performs as the pump of Earth’s hydrological system, sourcing atmospheric rivers and associated extreme weather events, follows a solar cycle pattern. Decadal scale ocean warming and post-solar cycle maximum El Nino events were empirically found to occur after solar activity rises above a decadal average of 120 sfu F10.7cm, equivalent to 94 v2 SIDC sunspot number and 1361.25 W/m2 LASP SORCE TSI. HadSST3 was found to linearly vary with the annual change in TSI, nominally at 0.5°C/W/year, varying with rate of TSI change. An empirical F10.7-TSI-SST model was derived combining a F10.7cm-SORCE TSI correlation model and the HadSST3-TSI sensitivity factor, predicated on the SWPC Solar Cycle 24 panel 2016 F10.7cm flux forecast. The author used this model in December of 2015 to uniquely and successfully predict the 2016 HadSST3 temperature fall to within 0.03°C. Cross correlation analysis indicate solar minimum La Nina events result from insufficient TSI over time, producing less equatorial evaporation, less cloud cover and precipitation, causing drought in the US. The Solar Modern Maximum that peaked in late 2003 warmed the ocean creating 20th century climate change via higher than average solar activity, higher than the 1361.25 W/m2 decadal TSI warming threshold. The 2003 European heat wave was forced by cumulative high solar activity warming of the AMO, and from high TSI during the heat wave. The 2013/14 Cold Wave is partly attributable to periods of lower than solar minimum level irradiance from October 2013 through February 2014. Future extremes depend on the duration of future solar cycle activity extremes.

End Users of Climate data have nowadays to struggle with accessing the data they need for their research because of the rapid increase in data volumes. The whole climate data archive is expected to reach a volume of 30 Pb in 2018 and up to 2000 Pb in 2022 (estimated). On-demand data processing solutions as close as possible to the data storage are emerging, thanks to newly developed standards, provenance and infrastructures. In Europe several initiatives are taking place to support scientific on-demand data analytics at the European scale. They offer the huge potential of interoperability, as for example the DARE e-science platform (http://project-dare.eu), designed for efficient and traceable development of complex experiments and domain-specific services on the Cloud. Also, the IS-ENES (https://is.enes.org) consortium has developed a platform to ease access to climate data for the climate impact community (C4I: https://climate4impact.eu). The platform is based on existing standards (ISO and OGC), such as WPS (Web Processing Service). DARE will integrate services from the EUDAT CDI, enabling generic access and cross-domain interoperability, as well as providing compliance and integration with the future EOSC platform. The DARE platform will use containerization technologies, so that it can be easily deployed on heterogeneous architectures. A scientific pilot has been designed within the DARE project for the ENES community (climate domain). The objectives are to enable delegation of on-demand computational-intensive calculations to the DARE platform, from the IS-ENES C4I interface, seamlessly. The DARE architecture and the solutions being implemented will be presented, along with the generic and agile approach taken to implement the pilot.

Soil contamination is mainly attributed to certain factors such as industrialization and increasing population with negative impact on natural resources such as petroleum. The petroleum industry affects the environment through oil spills with negative effect on human health and the surrounding ecosystem due to presence of Polycyclic Aromatic Hydrocarbons (PAHs) that can be carcinogenic to humans. The aim of this research is to compare the effectiveness of Chrypsopogon zizanioides also known as vetiver grass under the influence of biosurfactants and N.P.K. fertilizer in degrading and immobilizing persistent oil pollutants particularly the 16 Polycyclic Aromatic Hydrocarbons (PAHs) classified by United States Environmental Protection Agency (US EPA) as priority pollutants. The experiment was conducted in a glasshouse by growing the plant C. zizanioides in a freshly spiked crude oil contaminated soil and a weathered soil added with biosurfactant (ramphnolipids) produced by Pseudomonas aeruginosa. Similarly, all contaminated samples were amended with N.P.K fertilizer to promote the growth of C. zizanioides and microbial activities. Likewise, the assessment of the (bio) distribution of the petroleum hydrocarbons particularly the PAHs was carried out via Gas Chromatography Mass Spectrometry (GC MS). The result of this research has already indicated an improvement in plant growth and biomass in samples amended with N.P.K. fertilizer. It is also highly anticipated that the findings of this research will help in dissipating persistent contaminants such as PAHs in the crude oil contaminated soils under the influence of C. zizanioides and ramphnolipids and N.P.K. fertilizer as compared to the control samples.