HydroShare is a domain specific data and model repository operated by the Consortium of Universities for the Advancement of Hydrologic Science Inc. (CUAHSI) to advance hydrologic science by enabling individual researchers to more easily share products resulting from their research. The community platform supports, not just the scientific publication summarizing a study, but also the data, models and workflow scripts used to create the scientific publication and reproduce the results therein. HydroShare accepts data from anybody, and supports Findable, Accessible, Interoperable and Reusable (FAIR) principles. HydroShare is comprised of two sets of functionality: (1) a repository for users to share and publish data and models, collectively referred to as resources, in a variety of formats, and (2) tools (web apps) that can act on content in HydroShare and support web based access to compute capability. Together these serve as a platform for collaboration and computation that integrates data storage, organization, discovery, and analysis through web applications (web apps) and that allows researchers to employ services beyond the desktop to make data storage and manipulation more reliable and scalable, while improving their ability to collaborate and reproduce results. This presentation will describe the capabilities developed for HydroShare to support the full research data management life cycle. Data can be entered into HydroShare as soon as it is collected, and initially shared only with the team directly working on the data. As analysis proceeds, tools, scripts and models that act on the data to produce research results may be stored in HydroShare resources alongside the data. At the time of publication these resources may be permanently published and receive digital object identifiers and cited in research papers. Resources may themselves include citations to the research papers, thereby linking the publications to the supporting data, scripts and models. HydroShare design choices and capabilities for establishing relationships and versioning, based on simplicity, and ease of use, and some of the challenges encountered, will be discussed.

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.

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.

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.

Coastal wetlands provide a wealth of ecosystem services, including improved water quality, protection from storm surges, and wildlife habitat. Louisiana’s wetlands, however, are threatened by development, pollution, and relative sea level rise (RSLR)—the combination of sea level rise and subsidence rates. Despite widespread wetland loss, areas such as the Wax Lake and Atchafalaya river deltas are in fact growing due to their sediment loads, resulting in a complex of both degradation and aggradation along the Louisiana coast. In order to understand and model how coastal wetlands are responding to RSLR, there is a need for improved vegetation mapping, biomass estimation, and landscape-scale study of accretionary processes. AVIRIS-NG offers high spatial and spectral resolution data that can be integrated with external datasets—including from in situ measurements, monitoring stations, and other remote sensing platforms—to study these distributions and processes. Spectra derived from AVIRIS-NG imagery were used to parameterize Multiple Endmember Spectral Mixture Analysis (MESMA) for mapping vegetation functional types in addition to partial least squares regression (PLSR) models for plant aboveground biomass (AGB). The historical Landsat record complemented this analysis by deriving maps of change in wetland health and sediment availability through time. Each of these remotely sensed parameters were investigated to determine their combined relationship to Louisiana’s coastal accretion rates. In quantifying landscape-scale processes that impact wetland accretion, this research aids the assessment of coastal resiliency in the face of sea level rise. Further, the investigated imaging spectroscopy methods pertaining to vegetation mapping, biomass estimation, and accretionary modeling will inform future studies under the global Surface Biology and Geology mission.

Wireless sensor networks are increasingly important in monitoring water quality changes. High frequency monitoring can be used to gather water quality information, identify existing problems and improve water quality management activities. However, missing data are unavoidable because of network communication issues, sensor maintenance or failure. Data interpolation is a process for constructing missing values based on known data points. Though traditional methods like polynomial or linear interpolation are widely used in sensor data pre-processing, there are still many limitations. Firstly, current interpolation methods give poor estimations when a continuous number of data within a period of time are missing. Secondly, many interpolation methods reconstruct missing data based on other parameters available at the same time step. When all the data are missing, these methods cannot be used. In our work, we are developing a sequence-to-sequence interpolation model (SIM) for recovering missing data sequences in wireless sensor networks. SIM uses the state-of-the-art sequence-to-sequence architecture. It consists of two parts: an encoder that reads from the source water quality time series data and a decoder that generates the missing data sequences. In our design, Bi-directional Long Short Term Memory Network is used as the encoder and decoder due to its capability in using both past and future information for a given time. The attention mechanism is applied to make the SIM focus on different parts of the input time series when interpolating missing values at different time steps. We evaluated the SIM by using time series data from Queensland government’s water quality monitoring network. Compared to Seasonal-ARIMA, the SIM reduced 23.2% MAE and 40.3% RMSE when recovering missing data in 2 adjacent time steps. The reason for the superior performance is that SIM interpolates missing data based on both the inner relationships between water quality parameters and the accumulated information through time.

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.

Arctic plants are small in stature and spectrally diverse, which presents challenges to current NASA missions to visualize effects of disturbance or directional vegetation change via mapping. Remotely sensed data having fine spatial (ca. 10 cm pixels) and spectral grain (eg. “hyperspectral”) will therefore help resolve patches of many arctic plant groups, such as dwarf shrubs, bryophytes and lichens and separate them from litter, wood or rock/soil. To address these challenges, in summer 2018 we sampled vegetation at 15 different sites around Fairbanks, Alaska using ground-based and airborne hyperspectral sensors under eight different AVIRIS ng flight lines next gen flight lines (circa 2017-2018). At each AVIRIS flight line, we estimated percent cover of plant functional types in eleven 1m2 quadrats every 10 m along a 100m transect. We then flew our UAV and imaging spectrometer (Headwall Micro A-series VNIR, 400-1000 nm, 330 bands, 10 cm pixels). Spectral signatures of any surfaces were sampled using a field spectroradiometer (PSR+ Spectral Evolution, 400-2500 nm, 1nm bands). We collected 600+ georeferenced scans from 70+ species/plant functional types at 25+ different sites around Alaska. Spectral profiles showed many different plant species have similar to indistinguishable signatures (eg. Paper birch and Alder) while many plant functional types that have been grouped together (eg. Moss) were very spectrally heterogenous. UAV-based hyperspectral imagery (ca. 4-10 cm pixels) resolved pure pixels of many artic plants. Our approach resolves fine grained ecological features, such as networks of circular patches (mostly lichens, brophytes and mineral soil) over very large areas (ca. 10,000 m2), created by small cryoturbation features (frost boils). We explore spectral unmixing and other statistical approaches to compare mapping results using our spectral library with AVIRIS ng (4 m pixels) and our UAV-based VNIR hyperspectral imagery.

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.

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.

Chapter 25 of the Fourth National Climate Assessment (NCA4) is an assessment of climate change and variability, climate-related risks, impacts and adaptation in the U.S. Southwest. The chapter builds on assessments of climate change in the Southwest from the three previous U.S. National Climate Assessments. Each assessment has consistently identified drought, water resource reliability, and loss of ecosystem integrity as climate change challenges for the Southwest region. Chapter 25 further examines interconnections among water, ecosystems, coastal and marine systems, food, and human health and adds new key messages concerning energy and Indigenous peoples. The Southwest chapter is one of 29 chapters in Volume II of the Fourth National Climate Assessment - Impacts, Risks, and Adaptation in the United States. The National Climate Assessment fulfills the mandate of the Global Change Research Act (GCRA) of 1990 to provide the nation with a timely assessment and analysis of scientific findings of the effects of global change on multiple economic and natural resource sectors in the United States, and an analysis of observed and projected trends in global change. Chapter 25, Southwest, was written by a team of scientists and practitioners with expertise spanning areas specified in the GCRA, after extensive stakeholder engagement that involved the collection of input on key climate-related challenges, impacts, and opportunities in the Southwest region. The chapter went through multiple rounds of public and governmental review, during 2017 and 2018. This poster will focus on the findings from Chapter 25.

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.

Magnetic flux ropes (MFRs) are believed to be one kind of fundamental structures in solar eruptions, of which the formation is being intensely studied. Here we report a rapid buildup process of an MFR during a confined X2.2 class flare that occurred on 2017 September 6 in NOAA AR 12673, three hours after which the MFR erupted as a major coronal mass ejection (CME) accompanied by an X9.3 class flare. For the X2.2 class flare, we do not find separation of the two flare ribbons and clear CME signature in the coronagraph images, suggesting it to be confined. For the X9.3 class flare, apparent separation of the two ribbons and a CME show it to be an eruptive one. We perform a time sequence of nonlinear force-free fields (NLFFFs) extrapolations covering the two flares and find that: although the flux-weighted mean twist number of the MFR was almost unchanged prior to the eruptive flare, the axial flux and magnetic helicity of the MFR were dramatically enhanced after the confined flare, as much as 86% for the former and 260% for the latter. The above three parameters were all significantly reduced after the eruptive flare. It clearly evidences the buildup and release of the MFR during the confined and eruptive flare, respectively. The buildup of the MFR may be achieved by reconnection during the confined flare. We also calculate the pre-flare distributions of the decay index above the main polarity inversion line (PIL) and do not find any significant difference. It indicates that the enhancement of the MFR flux may play a role in facilitating the subsequent successful eruption.

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.

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 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.

Das2 describes a collection of cooperating programs originally created to support daily review and analysis activities of the Cassini Radio and Plasma Wave Science (RPWS) investigation. The system proved useful and is now relied on for rapid access to working data sets from many planetary missions including Mars Express and Juno, as well as ground-based radio astronomy results from the Nançay Decameter Array and Long Wave Array 1. As more teams have begun using das2 tools, the need for updates to what was largely an in-house protocol have become apparent. Though Autoplot continues to be the most flexible and commonly used das2 client program, interfacing with other tools would benefit the research community. We will provide an overview of recent updates to the das2 interface definition supporting redundant servers and federated data source catalogs. We will describe a cross site deployment between Southwest Research Institute and the University of Iowa to aid collaboration among ASPERA-3 and MARSIS investigators. We will give an update on the status of new client libraries for NumPy and IDL, as well as das2 stream adapters for HAPI and VOTables. Finally, we will describe how working data sets can be published in-place by registering das2 data sources with a VESPA EPN-TAP service.

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.