Long-term measurements at the U.S. Department of Energy (DOE) Atmospheric Radiation Measurement (ARM) at the North Slope Alaska (NSA) site in Utqiagvik, Alaska and from the Multidisciplinary drifting Observatory for the study of the Arctic Climate (MOSAiC) expedition are being used to study the climatology of clouds containing supercooled liquid (SCL) in the Western and Central Arctic. Classification of cloud hydrometeors in the liquid, ice or mixed phase of the cloud is determined by using the Cloudnet algorithm developed by the Finish Meteorological Institute. We apply the Cloudnet processing chain to a set of ground-based remote sensing measurements from the NSA site and the ARM mobile facility and the TROPOS shipborne atmosphere observation suite (OCEANET) on board of the RV Polarstern research vessel during MOSAiC. In order to accurately detect cloud droplets and SCL layers within mixed-phase clouds, Cloudnet relies on lidar observations. Lidars however suffer from total signal attenuation at a penetration optical depth of about three. Conversely cloud radars with their capability to penetrate multiple liquid layers can be used to expand the identification of cloud phase to the entire vertical column beyond the lidar signal attenuation height by using information of the cloud radar Doppler spectrum. The Leipzig Institute for Meteorology (LIM) recently developed a deep learning approach for reVealing supercOOled liquiD beyOnd lidar attenuatiOn (VOODOO) which benefits from the morphological features in cloud radar Doppler spectra to extract further information related to the existence of SCL using Cloudnet’s target classification as supervisor. The current contribution presents a SCL climatology obtained using Cloudnet for the NSA site along with case-studies for MOSAiC where VOODOO results are contrasted with the standard Cloudnet outputs. Advantages and limitations of both methods will be presented to the scientific community.

Advancing the understanding of watershed dynamics and underpinning scientific studies on the changes in water cycles, ecological patterns, and climate trends often rely on streamflow data acquired at gaging stations operated by various monitoring agencies. The monitoring methods used at these stations are based on empirical or semi-empirical rating curves obtained with statistical analysis uniformly applied to datasets collected in steady and unsteady flows. These one-to-one ratings are subsequently used for estimating steady and unsteady flows, even though, in the latter case, the relationships between flow variables are different for the rising and falling phases of the flow hydrographs. The non-singled relationships are more prominent in lowland areas subjected to high flows, where the streamflow variables display a hysteretic behavior. Recent advances in measurement technologies (e.g., acoustic-, radar-, and image-based), have dramatically transformed our capabilities to conduct in-situ measurements. This paper demonstrates such capabilities by presenting new information extracted from directly measured data and novel algorithms applied to simultaneous measurements of stages and index velocities acquired with a SonTek Side-Looker (pertaining to the family of Horizontal Acoustic Current Profilers-HADCP) at a river location prone to hysteresis. The presented results demonstrate a) the capability of the conventional monitoring methods (i.e., index-velocity approach supported by HADCPs) to capture the dynamics of the unsteady flows, b) the opportunity offered by HADCP measurements to re-think monitoring methods altogether by using directly measured data and their spatial and temporal gradients in conjunction with canonical flow equations (i.e., St Venant Equations) without using ratings, and c) the opportunity to exploit subtle features of the hysteretic behavior for developing short-term forecasting of flood crest magnitude and its arrival time using only in-situ acquired data, without making recourse to hydrologic/hydraulic modeling. Moving away from the traditional empirically based ratings, would unquestionably contribute to reducing uncertainties related to modeling flow routing, thus improving the quality of conventional forecasts.

Improving the representation of plant hydraulic behavior in vegetation and land-surface models is critical for improving our predictions of the impacts of drought stress on ecosystem carbon and water fluxes. Species-specific hydraulic traits play an important role in determining the response of ecosystem carbon and water fluxes to water stress. Here, we present plans for the development of the Finite-difference Ecosystem-scale Tree Crown Hydrodynamics model version 3 (FETCH3), a tree hydrodynamic model which builds upon its predecessors FETCH and FETCH2. FETCH3 simulates water transport through the soil, roots, and xylem as flow through porous media. The model resolves water potentials along the vertical dimension, and stomatal response is linked to xylem water potential. The tree-level model is scaled to the plot scale based on the species composition and canopy structure of the plot, allowing the model to be validated using both tree-level observations (sap flux) and plot-level observations (eddy covariance). We will collect data from multiple sites that have both sap flux and eddy covariance measurements for analysis. The Predictive Ecosystem Analyzer (PEcAn) will be used for optimization of the hydraulic parameters in FETCH3 for different plant types in multiple sites. We plan to use this new modeling framework to examine the interactions among water stress, species-specific hydraulic strategies, and stomatal regulation across different species and ecosystem types.

Dynamic topography, defined as the deflection of the Earth’s surface due to the convecting mantle, causes changes in vertical land motions (VLM). We aim to estimate the effect of dynamic topography on VLM along the North American Atlantic Coast, which is a region where land subsidence influences relative sea-level rise and other coastal hazards. The contribution of dynamic topography to VLM continues to be debated since previous studies show a range in the rate change of dynamic topography for this region, which are in part due to uncertainties in Earth’s rheology. In this investigation, we model global mantle convection to better assess the role of dynamic topography on VLM along the North American Atlantic Coast. We are implementing a time-dependent approach using the finite element code ASPECT (v2.2.0). We constrain our initial temperature conditions using the tomography models SAVANI, GYPSUM, S40RTS, and TX2008 and then explore both linear and non-linear rheological models. The two rheological models allow us to assess differences in the rate change of dynamic topography globally with a nonlinear rheology compared to a linear rheology. We expect this work will allow us to better understand the contribution of dynamic topography to VLM along the North American Atlantic Coast.

Watersheds serve as natural spatial boundaries whose characteristics are often indicators of the hydrologic processes within them. Watershed characteristics are frequently used as predictors, parameters, or proxies in models of hydrologic and ecologic dynamics. Developments in DEMs over the past decade have resulted in elevation data spanning the globe that allows watershed delineation at arbitrary locations. In tandem, satellite-based observations and large-scale modeling efforts provide many sources of near-global watershed characteristics, e.g. topography, soil types, vegetation, climate, permafrost extent, and many more. However, with growing data availability comes a growing need for tools that can rapidly query and summarize them. We developed River and Basin Profiler (RaBPro), a Python module providing a pipeline to delineate drainage basins for any point on Earth and calculate watershed statistics for practically any geospatial raster dataset. RaBPro makes use of the MERIT-Hydro or HydroBASINS datasets to define watershed polygons, which can be exported in GeoJSON or ESRI shapefile format for further use in GIS software. RaBPro will also generate streamlines and river elevation profiles. Finally, RaBPro calculates statistics over delineated basins using Google Earth Engine (GEE). By taking advantage of GEE’s vast dataset archive and distributed computing system, RaBPro can quickly compute many statistics over even very large basins efficiently and without the need for storing large geo-rasters locally. Additionally, users may upload their own datasets to GEE and create custom statistic functions.

Domain repositories can be an integral part of extensive community support systems that extend from proposal planning and writing, through project initiation and implementation, data collection, management, and archive, to publication of results and access to data by other community members. These long-term relationships are reflected in multiple contributions (data, software, results, papers, …) by community members and recognizing these contributions should be an important community-building best-practice for these repositories. Identifiers for people and organizations are critical for recognizing community members and, equally important, for making connections between them and all of the various objects in the research ecosystem. This Figure demonstrates connections that can made once identifiers are integrated into the research ecosystem. Most domain repositories provide DOIs for datasets in the repository. The metadata for those DOIs can include identifiers for some authors (ORCIDs) along with names of organizations they are affiliated with (affiliations). In practice, most authors in these metadata records do not have ORCIDs but, if they have an ORCID once, that ORCID can be spread across all of the datasets they have contributed to, increasing connectivity across the repository. Affiliations can also be spread across multiple contributions, with some caveats. If identifiers (i.e. RORs) exist and can be found for the affiliated organizations, they can be inserted into the metadata, again increasing connectivity. Many domain repositories maintain lists of research papers that have used data from the archive. Metadata for these papers also provide a potential source for identifiers and affiliations. These can also be harvested and spread across the repository, again improving connectivity. These ideas and techniques were applied to UNAVCO, a repository for data related to geodesy with a well-developed community with over 5000 archived datasets. The connectivity for the repository is below 10% for dataset contributors and 0% for RORs. Applying these techniques can increase the connectivity to 56% for contributors and 49% for RORs.

UNAVCO and IRIS, major repositories for global geodetic and seismic data, are in the process of joining their operations to form a unified facility for supporting the broad spectrum of geophysical observations and science required to help understand and predict the behavior of Earth Systems. This process would be complicated in a static data management environment, but both repositories are also migrating archives and services to the cloud as part of the merger. To simplify and unify archive data management, the organizations are collaborating to create common data and metadata models for observations from a wide variety of instruments and disciplines. For data, the initial focus has been on the xArray data model, already used in the geodetic and magnetotelluric communities, which can be implemented with several disc- and cloud-native approaches (HDF5, netCDF4, and Zarr). For metadata, the SensorML Standard developed by the Open Geospatial Consortium is being explored because 1) SensorML accommodates the large parameter space associated with instrument metadata required to use and trust complex observations and 2) the ability to extend the standard when required. The merger of two large repositories combined with migration to the cloud requires careful identification and on-going testing of a wide variety of assumptions about data management systems. This presentation will focus on lessons learned so far.

Changes to the eastern North Pacific tropical intraseasonal oscillation (ISO) at the end of the 21st Century and implications for tropical cyclone (TC) genesis are examined in the Shared Socioeconomic Pathways (SSP585) scenario of the Coupled Model Intercomparison Project phase 6 (CMIP6) data set. Multimodel mean composite low-level wind and precipitation anomalies associated with the leading intraseasonal mode indicate that precipitation amplitude increases while wind amplitude weakens under global warming, consistent with previous studies for the Indo-Pacific warm pool. The eastern North Pacific intraseasonal precipitation/wind pattern also tends to shift southwestward in a warmer climate, associated with weaker positive precipitation anomalies near the coast of Mexico and Central America during the enhanced convection/westerly wind phase. Implications for the modulation of TC genesis by the leading intraseasonal mode are then explored using an empirical genesis potential index (GPI). In the historical simulation, GPI shows positive anomalies in the eastern North Pacific in the convectively enhanced phase of the ISO. The ISO’s modulation of GPI weakens near the coast of Mexico and Central America with warming, associated with a southward shift of GPI anomalies. Further examination of the contribution from individual environmental variables that enter the GPI shows that relative humidity and vorticity changes during ISO events weaken positive GPI anomalies near the Mexican coast with warming and make genesis more favorable to the southwest. The impact of vertical shear anomaly changes is also to favor genesis away from the coast. These results suggest a weaker modulation of TCs near the Mexican Coast by the ISO in a warmer climate.

Due to the coupled nature of the earth system, precipitation forecast errors at S2S lead times are caused by a combination of errors/biases from the atmosphere, ocean, ice and land across a range of spatial and temporal scales. We show that UFS precipitation errors over the U.S. at Weeks 3-4 can be directly related to biases in simulating tropical dynamics. In particular, the inability of the UFS to realistically simulate the Madden-Julian oscillation (MJO) leads to biases in the teleconnection to North America that produces these errors. When the tropics are nudged to produce an accurate representation of the MJO and other tropical disturbances, U.S. West Coast precipitation biases are substantially reduced. A clustering analysis is used to show that the greatest forecast improvements with nudging occur during warm ENSO events when MJO convection is in the Indian Ocean and about to move into the Maritime Continent. Physical mechanisms that explain the improvement in tropical-extratropical teleconnections during certain MJO and ENSO states will be discussed. We will also present future plans to combine state-of-the-art developments in machine learning with process-based diagnostics of the tropical moisture and moist static energy (MSE) budgets to understand and correct precipitation biases in coupled UFS hindcasts. In particular, we will discuss how model biases and errors in tropical variability (e.g. MJO) and associated teleconnections to midlatitudes lead to errors in U.S. precipitation on S2S timescales, and present methods to reduce these errors via post-processing on a forecast-by-forecast basis.

Landsat-based monitoring of seasonal and near real-time evapotranspiration (ET) in California vineyards is currently challenged by its low temporal revisit period and missing data under cloudy conditions. Gap-filling approaches, such as data fusion with high-temporal resolution images (e.g., MODIS) and interpolation of actual to potential ET ratio (ET/ETo) between image acquisition dates are now commonly used to overcome this challenge. However, these methods may not fully capture non-linear changes in crop condition due to scheduled irrigation, and other management decisions affecting ET during days when satellite images are unavailable and can lead to biased ET estimates. In this study, we combined Landsat-8 and Sentinel-2 data to develop a Shuttleworth-Wallace (SW) based near real-time ET modeling framework for mapping daily ET across three California Vineyard sites. In addition, we utilized daily Leaf area index (LAI) products derived from the Harmonized Landsat and Sentinel-2 (HLS) surface reflectance and MODIS LAI data products to constrain key resistance parameters in the SW model and tested the model across nine flux towers covering three vineyard sites in California. Results suggest that compared to the linear interpolation-based ET/ETo approach, this framework can help reduce biases and root mean squared error of estimated daily ET by over 10%. Results point to a potential utility of the combined Landsat-8 and Sentinel-2 based approach to monitor near real-time ET and complement ongoing thermal remote sensing-based ET modeling approaches to better characterize near real-time crop water status in California vineyards.

Efficient use of available water resources is key to sustainable viticulture management in California (CA) and other regions with limited water availability in the western US and abroad. This requires remote and frequent field-scale information on vineyard water status. Though the Sentinel-2 sensors offer good spatial (10-60m) and temporal (~5 days) coverages, their utility in monitoring vineyard evapotranspiration (ET) has not been considered viable primarily due to the lack of a thermal band. However, recently, a new spectral-based Shuttleworth Wallace (SW) ET model, which uses a contextual framework to determine dry and wet extremes from the Sentinel-2 (SW-S2) surface reflectance data, has shown promise when tested over a single GRAPEX (Grape Remote-sensing Atmospheric Profile and Evapotranspiration eXperiment) site in CA. However, current knowledge on its applicability across a climate gradient in CA with different topography, soils, trellis design and vine variety is lacking. Moreover, how the selection of modeling domain and meteorological forcing data influence model output is limited. Consequently, this presentation expands the evaluation of the SW-S2 model across multiple domains and meteorological inputs covering all three GRAPEX vineyard sites spanning a north to south climate gradient over three recent growing seasons (2018-2020). In comparison with flux tower observations, the size of the modeling domain and the source and quality of meteorological forcing data on the performance of the SW-S2 model as well as application to the three different vineyard study sites will be presented. Future research on merging output from more-frequent spectral and less-frequent thermal-based ET models to reduce latency in ET monitoring of California vineyards will also be discussed.

Estimating the fraction of photosynthetically active radiation absorbed by vegetation (i.e., fPAR) is crucial for quantifying the carbon uptake activity of terrestrial ecosystems. Satellite-derived vegetation index (i.e., NDVI) is the powerful indicator of fPAR, enabling us to capture spatiotemporal variations in ecosystem carbon uptake activity. Since 2015, Sentinel-2 having about 3-day of revisit interval and 10 m of spatial resolution has been operated. This study evaluates the relationship between ground observed fPAR and Sentinel-2 derived NDVI in four temperate forests in Niigata prefecture, central Japan. Briefly, fPAR for April to July (from 0.44 to 0.96) varied depending on the seasons and the forest ages and types (i.e., two young mixed forests, a mature mixed forest, and a mature needleleaf forest). In the two young mixed forests and the mature needleleaf forest, NDVI was positively correlated with fPAR (r = 0.81 to 0.99), where a 0.1 increase in NDVI implied a 0.2 increase in fPAR. However, the correlation between NDVI and fPAR in the mature mixed forest was weak (r = 0.38 to 0.53). Thus, this study confirmed that the effectiveness of Sentinel-2 derived NDVI tracking spatiotemporal variations in fPAR and carbon uptake activity likely varied depending on types and ages of forests.

The immunity to slope failure is the concept that once slope failure occurs, slope failure does not recur until the soil layer recovers. It is adequate to examine whether two slope failures occurred on the same slope or not in order to verify whether the immunity to slope failures is maintained during a certain period. Two heavy rainfall the 1945 Makurazaki typhoon and the 2018 heavy rainfall occurred in the southern part of Hiroshima Prefecture, southwestern Japan in the almost same region. The aim of this study is to reveal the distribution of slope failures induced by the Makurazaki typhoon and to verify whether the immunity to slope failures is maintained during the Makurazaki typhoon and the 2018 heavy rainfall. Based on the interpretation of aerial photograph, it is revealed that the number of slope failures induced by the Makurazaki typhoon was 3,787. It is considered that the occurrence of slope failures and the amount of precipitation may be related. Areas of high slope failure density overlap in both the Makurazaki typhoon and the 2018 heavy rainfall. Thus, the immunity does not exist, or the effective period is shorter than 73 years on a mountain or watershed scale. However, 113 slope failures were caused with the 2018 heavy rainfall on the same slope where slope failure occurred with the Makurazaki typhoon, and the slope failure which recurred was only 1.4% of the whole slope failures. In addition, the length of the slope failure caused with the 2018 heavy rainfall on the same slope where slope failure occurred with the Makurazaki typhoon was shorter than that of the Makurazaki typhoon.These results suggest that the surface soil on the slope where slope failure occurred with the Makurazaki typhoon did not recover sufficiently during the period of 73 years up to the 2018 heavy rainfall, and that the immunity to the slope failure was maintained.

Streambed inhabitants mix and mobilize sediments by actions such as burrowing, feeding, and excretion, a process referred to as sediment reworking. This sediment-organism interaction could modify the hydro-physical properties of streambeds and significantly influence the hyporheic flow regime in streams as highlighted by our recent research. In this work, we further advance the understanding of the sediment reworking process by investigating the influence of the organism size on the modification of streambed properties and hyporheic exchange. Laboratory experiments were conducted in long recirculating flumes to simulate streamflow environment and Lumbriculus variegatus of two different sizes (large worms were double the thickness of small worms) were used as model organisms. The organisms of both sizes were allowed to rework the sediment beds in their respective flumes for 10 days after which dye tracer tests were performed to characterize hyporheic exchange flows. Visual observations reveal that the burrow openings at the bed surface were readily visible in the flume reworked by larger organisms compared to the flume reworked by organisms of smaller size. The former also exhibits higher hyporheic flux and shorter residence times compared to the latter which could be attributed to the rapid exchange of solutes across the streambeds due to the presence of a dense network of voluminous burrows. The exchange depths in both the flumes were similar and a potential reason for this observation could be the reworking of sediment beds up to similar depths in the flumes. We suggest that further exploration must be done at both small and large scales to comprehend the role of sediment-organism interaction in modulating hyporheic exchange flows as it will have direct implications on critical stream ecosystem services such as natural processing of nutrients and contaminants.

The Nankai Trough is a locus of slow slips, low frequency, and large-magnitude classical earthquakes. It is assumed that high pore pressure contributes substantially to earthquake dynamics. Hence, a full understanding of the hydraulic regime of the Nankai accretionary prism is needed to understand this diversity of behavior.We contribute to understanding the full hydraulic regime within the Nankai accretionary prism by innovatively integrating the drilling and logging data of the NanTroSEIZE project. We focus on the toe of the Nankai accretionary prism by studying data from hole C0024A drilled during IODP expedition 358. This drilled hole intersected the Nankai décollement at 813 mbsf, about 3 km from the trench. Pore pressure was first estimated using Eaton’s method on both drilling and sonic velocity data. Both results show that pore pressure follows hydrostatics until the top of the hemipelagites, with local pore pressure rising up to 38% above hydrostatic especially crossing the décollement. Downhole Annular Pressure was also monitored during drilling, and a careful reanalysis of its variation shows the occurrence and the locus of fluid flow between the formation and the borehole. Primarily, there are two identified fluid flow anomalies intervals: (1) at the shallow depths <100 mbsf with loose coarse sediments, which could be related to erosional unloading, landslide, slope instability. (2) Below the décollement (<813 mbsf) at the two asymmetric damage zones. The damage zones at the footwalls of the major faults are predominantly permeable with significant porosity and permeability values with orders of magnitude between ∼10−16 to 10−17 m2 as quantitatively estimated using the Hvorslev equation for a fully penetrating well in a confined aquifer. Our results show that the formation fluids are getting significantly over-pressurized only a few hundred meters from the toe of the décollement. The décollement is already impermeable across the fault, and the fluid flow is channelized along the damage zones. The impermeable décollement acts as a hydraulic barrier inhibiting fluid flow upward, keeping high pore pressure at the footwall and increasing the structural weakness of the lithologies. It’s therefore probable that high pressure is also expected further down in the locus of tremors and slow slip events.

With population surge, industrialization and urbanization; use of surfactants has increased manifold. After their use in households and industries, surfactants either end up in sewerage systems or are directly discharged into surface waters. They can hence be found dispersed in water phase or adsorbed onto aquatic sediments and sewage sludge. Due to limited metabolic pathways, most of the common surfactants are not degradable in anaerobic conditions that generally prevail in sewage ingressed water bodies so sludge accumulated in these water bodies after the anaerobic digestion process is rich in surfactants (Ying, 2006, Environ. Int.). Sediment is a complex mixture of organic (bacteria, proteins, humic and fulvic acids, humin, etc.) and inorganic (silica, minerals, metal oxides and hydroxides, aluminosilicates) components. Absorptivity of surfactant depends on the sorbent composition and type of surfactant (Ishiguro & Koopal, 2016, Adv. Colloid Interface Sci.). A crucial question related to surfactant absorption is its reversibility- as to whether the surfactant can desorb. The knowledge of this can be beneficial in understanding the environmental fate of surfactants. The presence of surfactants in water bodies can form foams. Generally, foaming in surface water is a result of a mixture of surfactants from various sources (Schilling & Zessner, 2011, Water Res.). However, contribution of sediment as a source of surfactant in a foaming water body has not been studied adequately. Also, existence of surfactants in water, beyond certain concentrations not only induces unpleasant taste and odor, but also causes undesirable changes in the ecosystem. Thus, it becomes imperative to study the ratio of the surfactant associated with water, to the extent of surfactant associated the sorbent such as sediment/sludge to understand the environmental risk associated with surfactant. This study aims to understand a foaming urban lake which foams only after heavy rainfalls. This study tests the hypothesis that surfactants accumulate in the lake sediment in significant proportions and desorbs upon dilution occurring due to addition of rainwater into the lake. This when churned by heavy runoff causes large quantities of stable foam. This study aims at analyzing the role of lake sediment in foaming of a lake.

The proposed Interstellar Probe mission (ISP) will have unprecedented capabilities to detect and characterize interstellar dust flowing into the heliosphere from the surrounding interstellar medium (ISM). Knowledge of that dust has the potential to substantially advance our understanding of interstellar dust in general and the local ISM in particular. However, the flux of dust that can be observed varies due to the evolving heliosphere and the interaction of the charged grains with the heliospheric magnetic field. We will present the results of calculations of the dust density relative to that in the pristine ISM as a function of time and position for a possible trajectory of ISP. We will show how different polarities of the solar wind magnetic field affect the observed flux and how these depend on distance from the Sun.

Researchers and end users using climate data face a challenge when they analyze the data they need. Data volumes are increasing very rapidly, and the ability to download all needed data is often no longer possible. Most of the climate analysis tools for research and application needs must use very large datasets, often distributed among several data centres and into a large quantity of files. This is especially true when they are stored in a federated architecture like the ESGF. One of these tools is icclim (https://github.com/cerfacs-globc/icclim ), a flexible python software package to calculate climate indices and indicators. This tool adhere as much as possible to metadata conventions such as CF, implementing also provenance information. It also aims at providing increasing support for all FAIR aspects. It is designed with performance and optimisation in mind, because the goal is to provide on-demand calculations for users. It provides the implementation of most of the international standard climate indices such as ECAD, ETCCDI, ET-SCI, including the correct methodology for calculating percentile indices using the bootstrapping method. It has been validated against R.Climdex as well (https://cran.r-project.org/web/packages/climdex.pcic/index.html ). The new 5.x version of icclim is now based on functions from the xclim python library, which was inspired by earlier versions of icclim, but using xarray and dask for data access and processing. icclim is also a candidate as the software to calculate climate indices for the C3S toolbox (https://cds.climate.copernicus.eu/cdsapp#!/toolbox ). An example of a complex analysis tool used in climate research and adaptation studies is a tool to follow storm tracks. In the context of climate change, it is important to know if storm tracks will change in the future, in both their frequency and intensity. Storms can cause significant societal impacts, hence it is important to assess future patterns. These tools are integrated in the IS-ENES C4I 2.0 platform (https://climate4impact.eu/ ), using a Jupyter notebook collection in a SWIRRL environment (Software for Interactive Reproducible Research Labs https://gitlab.com/KNMI-OSS/swirrl ). Having access to this type of complex analysis tool is very useful, and integrating them with front-ends like C4I enable the use of those tools by a larger number of researchers and end users. This project (IS-ENES3) has received funding from the European Union’s Horizon 2020 research and innovation programme under grant agreement N°824084.