Formed by Mississippi River sediments, south Louisiana is a flat, low-lying coastal region with high-clay content soils and heavy annual precipitation that is particularly susceptible to damage caused by extreme storms and flooding. In August 2016, a stationary storm system caused over 50 cm of rain to fall across much of southeast Louisiana. Largely rural, the major trends in the hard-hit Amite River Basin have been conversion of agricultural land to forest beginning in the mid-20th century and rapid urbanization and development spurred by economic and population growth in the Baton Rouge area following the oil boom of the 1970s, as well as later waves of migration following Hurricane Katrina and the 2016 floods. This analysis examines the effects of spatially and temporally changing land use on runoff and flooding within the watershed and is part of a larger research project which seeks to also quantify the relative impacts of changes in precipitation and planform geometry. To quantify the effects of land-use change on flooding, runoff curve number (CN) maps were created using NRCS soil type data and USGS land cover data. Areas with a higher CN experience less interception and infiltration of surface water and the flood risk is consequently greater. While CN for the Basin overall dropped from 86 to 79 between 1938 and 2018, CN dropped from 82 to 70 in rural areas due to reforestation and increased from 86 to 90 in the southern portion of the Basin due to urbanization. These data were then input into the HEC-HMS and coupled 1D/2D HEC-RAS components of a numerical model of the Amite River Basin. Flooding behavior under different design storms and land cover conditions was then observed and quantified. In examining the major contributing factors to flooding in south Louisiana, this research project aims to create a more comprehensive understanding of flooding and propose potential mitigation strategies and design interventions for alleviating the worst effects.

The earliest human migration from Sunda (South-East Asian archipelago) to Sahul (Australia and New Guinea) is still heavily debated with proposed timings between c.65-45kaBP depending on the evidence base and interpretation of the data. As part of the EU funded ACROSS project, focused on the mode and route of early migration in to SAHUL, we are undertaking an integrated interpretative study of the evolving submerged landscapes for the Late Pleistocene of the NW Australian Shelf. Oil and gas industry 3D and 2D seismic data, with some core/borehole data, are being used to determine lowstand palaeo-environments and shoreline positions. This information is informing modelling of ocean tide and current patterns that may have been influenced. The seismic is being interpreted supplemented by using time-slices on relative impedance inverted post-stack data. Layer stripping, seismic geomorphology, sequence boundary and depth analysis are being applied to datasets in the Bonaparte Basin, Kimberley Shelf and Arafura regions of Australia’s North-West Shelf area. Interpretation of the seismic data is constrained by dated stratigraphy in shallow cores with lower bounds determined from oil/gas well bores. MIS stages 1-4 are identified, however, the seismic response is a composite of time periods due to varying sedimentation rates, non-depositional hiatuses and minimal vertical seismic travel time covering this interval which limits the analysis to the top 50ms TWT (c. 40-45 m) of events below the seabed. This paper reviews the workflows that have been developed to maximise the fine scale detail that can be recovered for a range of terrestrial and marine environments. Procedures include inverse-Q, impedance inversion, spectral decomposition and time-slicing relative to seabed. High resolution 2D seismic data is also being used to augment and inform the interpretation of the conventional oil/gas 3D seismic data. Data examples will be presented showing the geomorphological characteristics (river channels, avulsions, levees, drainage channels, dunes and near shore carbonate reefs) of the lowstand and transgressive landscapes during this period. The palaeo-reconstructions are now being developed from the interpreted seismic geomorphology for the specific consideration of human seaborne travel.

Understanding the hydrologic response to rainfall events is vital for flood forecasting and design for peak flows. The Time to Peak (Tp) is used to characterize the speed of catchment response, as the time from the start of a rainfall event to the time the peak flow is reached in a stream. Advancing our understanding of a catchment’s temporal response to rainfall is key to our overall understanding of hydrologic processes. In this study, more than 1400 storm hydrographs were isolated and utilized to calculate the Tp value for decades of storms spanning Great Britain. Previous works into understanding Tp have been static, with no variability due to storm magnitude or antecedent conditions, providing a single static value for each catchment. Using this data and machine learning techniques, dynamic Tp values were predicted for each storm within the hundreds of catchments, to allow for fuller understanding of the catchment response. Artificial Neural Networks are utilized in this study to create models which account for antecedent conditions of the catchment, and the storm size, to predict the storm-specific, dynamic Tp value.

During each International Ocean Discovery Program (IODP) expedition a vast array of data, typically amounting to hundreds of gigabytes to several terabytes of information, are collected from drill cores. These data include physical, chemical, and magnetic properties and digital images collected continuously or every few centimeters along the cores using automated track systems, as well as a variety of analyses conducted on discrete subsamples taken from the cores. Coring just since the start of Expedition 349 in January 2014 has recovered over 50 km of core, resulting in a very large amount of data, most of which are accessible from the IODP LIMS database. Some of the properties typically measured include P-wave velocity, density, magnetic susceptibility, natural remanent magnetization, natural gamma radiation, and visible spectral reflectance. In addition, the lithology of all cores is described based mainly on visual characteristics of the surface of the split cores, visual examination of smear slides and thin sections, and compositional or mineralogical information derived from geochemical analyses. Our goal in this study is to mine these data for interrelationships that would otherwise be difficult to assess given the way the data are partitioned by specific property within the database. In particular, we extract basic lithologic information from the complex array of descriptive information and then tie that information to all other observations in order to characterize the physical, chemical, and magnetic properties of a myriad of lithologies.

Understanding the consequences of different management practices on vegetation phenology, forage production and quality, plant and microbial species composition, greenhouse gas emissions, and water budgets in tallgrass prairie systems is vital to identify best management practices. As part of the Southern Plains Long-Term Agroecosystem Research (SP-LTAR) grassland study, a long-term integrated Grassland-LivestOck Burning Experiment (iGLOBE) has been established with a cluster of six eddy covariance (EC) systems on differently managed (i.e., different burning and grazing regimes) native tallgrass prairie systems located in different landscape positions. The purpose of this paper is to describe this long-term experiment, report preliminary results on the responses of differently managed tallgrass prairies under variable climates using satellite remote sensing and EC data, and present future research directions. In general, vegetation greened-up and peaked early, and produced greater forage yields in burned years. However, drought impacts were greater in burned sites due to reductions in soil water availability by burning. The impact of grazing on vegetation phenology was confounded by several factors (e.g., cattle size, stocking rate, precipitation). Moreover, prairie systems located in different landscapes responded differently, especially in dry years due to differences in water availability. The strong correspondence between vegetation phenology and eddy fluxes was evidenced by strong linear relationships of greenness index (i.e., enhanced vegetation index) with evapotranspiration and gross primary production. Results indicate that impacts of climate and management practices on vegetation phenology may profoundly impact carbon and water budgets of tallgrass prairie. Interacting effects of multiple management practices and inter-annual climatic variability on the responses of tallgrass prairie highlight the necessity of establishing an innovative and comprehensive long-term experiment to address inconsistent responses of tallgrass prairie to different intensities, frequencies, timing, and duration of management practices, and to identify best management practices.

Measurements of ground tilt are a critical geodetic tool for monitoring active volcanoes because they provide multidimensional data that can resolve complex deformation signals. We are developing a Self-Calibrating Tilt Accelerometer (SCTA) for use in the marine environment and present results from two deployments: on land at the Scripps Institution of Oceanography Cecil and Ida Green Piñon Flat Observatory and on the seafloor at Axial Seamount on the Juan de Fuca Ridge. The SCTA utilizes a Quartz Sensor Solutions triaxial accelerometer on a gimbal system to periodically rotate the horizontal channels into the vertical to calibrate against the local g vector, achieving high precision and stability within 1 microradian. The SCTA tiltmeter has the added benefit of simultaneously measuring ground accelerations and recording seismic signals. We compare the SCTA performance at the center of the summit caldera at Axial Seamount against a co-located Jewell Instruments LILY tiltmeter on the OOI Cabled Array. The tilt measurements in one direction are consistent, but the data suggest that the deployment platform for the SCTA may be settling in the other direction. We are using data from the ensemble of 4 cabled pressure sensors and 5 tilt sensors at Axial, including the SCTA, to study its inflation behavior since its eruption in 2015. We have identified several significant, cm-scale deflation events of durations of tens of days. The tilt and relative elevations of instrument sites are asymmetric about their turning points, suggesting a more complex mechanism than a simple inflation reversal. We are conducting forward modeling of the deformation signals to determine if the geodetic signals are consistent with differential slip rates, normalized to the rate of inflation/deflation, on the caldera’s outwardly dipping ring faults between these periods. Another plausible mechanism that we plan to investigate is the lateral transport of magma from beneath the southern caldera to either the northern caldera or to a secondary reservoir, located 5 km to the east. These deflation events are potentially important for understanding the mechanisms of magma supply, storage, and transport at Axial Seamount, as well as for accurately forecasting future eruptions, which have been shown to be inflation-predictable.

Smart Climate Hydropower Tool is an innovative web-cloud-based service that implements a set of data-driven methods for river discharge. An application for two catchments in South America is discussed (test cases), where management of hydropower plants can benefit from knowledge of incoming discharge forecasts up to 6 months in advance. SCHT has been developed inside H2020 project “CLARA - Climate forecast enabled knowledge service” and exploits several Artificial Intelligence algorithms, evolving by R&D activity to test new available ones. Although tangible results using AI have been published (i.e. Callegari, et al., 2015, De Gregorio et. al 2017) challenges remain for seasonal lead times and rainfall dominated catchments, where forecast of meteorological variables plays a critical role. In this contribution we show results of application of different AI algorithms (from supervised learning regression techniques, to artificial neural networks). Each algorithm is trained over past decades datasets of recorded data, forecast performances are then evaluated using separate test sets with reference to benchmarks (historical average of discharge values and simpler multiparametric regressions). Major operative advantages of AI with respect to mechanistic hydrological models include limited to none a priori knowledge of involved physical phenomena, high level of flexibility when managing heterogeneous sets of variables related to discharge, and quick setup time of the forecast. Major efforts are requested to identity informative input features ranging from earth observation to gauging stations data, to public meteorological forecasts (i.e Copernicus Climate Change Service-C3S). Using AI techniques many combinations of features can be tested together, to predict river discharge to the reservoirs, choosing the best performing one and tailoring the service to the catchment of interest. Once trained, each algorithm just needs to retrieve online data to perform forecasts, with limited maintenance (i.e. annual re-training to consider new available hydrological data). For demonstrational purposes we prototyped a cloud-based service, for immediate visualization, through a common browser, of both past and forecasted data, and get on fly performance metrics calculation of the forecasts.

Soft x-ray and EUV radiation from the Sun is absorbed by and ionizes the atmosphere, creating both the ionosphere and thermosphere. Temporal changes in irradiance energy and spectral distribution can have drastic impacts on the ionosphere, impacting technologies such as satellite drag and radio communication. Because of this, it is necessary to estimate and predict changes in Solar EUV spectral irradiance. Ideally, this would be done by direct measurement but the high cost of solar EUV spectrographs makes this prohibitively expensive. Instead, scientists must use data driven models to predict the solar spectrum for a given irradiance measurement. In this study, we further develop the Synthetic Reference Spectral Irradiance Model (SynRef). The SynRef model, which uses broadband EUV irradiance data from EUVM at Mars, was created to mirror the SORCE XPS model which uses data from the TIMED SEE instrument and the SORCE XPS instrument at Earth. Both models superpose theoretical Active Region and Quiet Sun spectra generated by CHIANTI to match daily measured irradiance data, and output a modeled solar EUV spectrum for that day. By adjusting the weighting of Active Region and Quiet Sun spectra, we update the SynRef model to better agree with the FISM model and with spectral data collected from sounding rocket flights. We also use the broadband EUVM measurements to estimate AR temperature. This will allow us to select from a library of AR reference spectra with different temperatures. We present this updated SynRef model to more accurately characterize the Solar EUV and soft x-ray spectra.

In 2007, the National Academies designated “understanding the structure & composition of the lunar interior” (to provide fundamental information on the evolution of a differentiated planetary body) as the second highest lunar science priority that needed to be addressed. Here we present the current status of the planned response of the Lunar Geophysical Network (LGN) team to the upcoming New Frontiers-5 AO. The Moon represents an end-member in the differentiation of rocky planetary bodies. Its small size (and heat budget) means that the early stages of differentiation have been frozen in time. But despite the success of the Apollo Lunar Surface Experiment Package (ALSEP), significant unresolved questions remain regarding the nature of the lunar interior and tectonic activity. General models of the processes that formed the present-day lunar interior are currently being challenged. While reinterpretation of the Apollo seismic data has led to the identification of a lunar core, it has also produced a thinning of the nearside lunar crust from 60-65 km in 1974 to 30-38 km today. With regard to the deep mantle, Apollo seismic data have been used to infer the presence of garnet below ~500 km, but the same data have also been used to identify Mg-rich olivine. A long-lived global lunar geophysical network (seismometer, heat flow probe, magnetometer, laser retro-reflector) is essential to defining the nature of the lunar interior and exploring the early stages of terrestrial planet evolution, add tremendous value to the GRAIL and SELENE gravity data, and allow other nodes to be added over time (ie, deliver the International Lunar Network). Identification of lateral and vertical heterogeneities, if present within the Moon, will yield important information about the early presence of a global lunar magma ocean (LMO) as well as exploring LMO cumulate overturn. LGN would also provide new constraints on seismicity, including shallow moonquakes (the largest type identified by ALSEP with magnitudes between 5-6) that have been linked to young thrust fault scarps, suggesting current tectonic activity. Advancing our understanding of the Moon’s interior is critical for addressing these and many other important lunar and Solar System science and exploration questions, including protection of astronauts from the strong shallow moonquakes.

Wetlands are the largest environmental sources of methane, and interannual changes in wetland methane fluxes explain most of the variability in the global flux. Despite their importance, global wetland maps, a key component of methane models, are inaccurate for at least three reasons: (1) Their temporal variability is poorly suited for static maps; (2) Optical remote sensing cannot penetrate foliage, making water hard to identify; and (3) satellites cannot resolve their fine-scale features. Furthermore, small, unmapped water bodies may emit methane disproportionately to their size due their shallow depths inhibiting bacterial oxidation from the water column and their large perimeter: volume ratios, which introduce the potential for organic matter input and plant-mediated fluxes from shorelines. However, in boreal regions, there is conflicting evidence on the effects of water body size on methane and carbon dioxide fluxes. Here, we measure methane emissions in lakes and wetlands in an Arctic-Boreal delta and compare to open water and vegetated area with the goal of improving methane emission estimates in this region. We expect small, shallow, and vegetated wetlands to produce more methane than those bordering deeper lakes. To test this hypothesis, we map wetlands in the Peace-Athabasca Delta, a 5,000 km2 inland delta in northern Alberta, Canada containing abundant open and vegetated wetlands. We use airborne remote sensing from three sources: (1) High-resolution (<5 cm pixel) unmanned aerial vehicle (UAV) imagery, (2) Coincident L-band synthetic aperture radar (SAR) from NASA’s UAVSAR airborne imaging system, and (3) 2017 AirSWOT Ka-band interferometric SAR with color-infrared imagery. With a wavelength of 23.8 cm, UAVSAR L-band returns are ideal for mapping vegetated wetlands due to double-bounce backscatter between vegetation and the water surface. Combining two field campaigns of flux chamber gas sampling from over twenty lakes, walked shoreline surveys, and over 70 thousand UAV photos, we present a collection of wetland maps and a methodology for efficiently mapping them from UAV. We then upscale methane and carbon dioxide emissions to the scale of the delta and compare to existing estimates. These results will help improve greenhouse gas emission estimates for boreal zone wetlands.

The effects of climate on eroding landscapes and the delivery of sediment from these remain poorly understood. The sampling and dating of river terraces provide one way to address this question, because these embed information about the interconnected dynamics of mountain valley widening, river incision, sediment delivery, and their sensitivity to external forcing. We developed a new approach to OSL dose rate correction of gravels to derive the most detailed chronology of river terrace stratigraphy in NW Africa to date. We sampled river terraces 10-20 m above the modern river plain for dating in a 1200 km2 river catchment, the River M’Goun, eroding the High Atlas Mountains hinterland of the continental river Draa. We applied OSL and IRSL dating to determine the age of 23 samples, using Bayesian methods to derive a robust chronology. We show terrace strath planation starts at ~180 ka in the MIS 6 glacial maximum, followed by aggradation from 140 – 57 ka in MIS 5 to MIS 4 which deposited the up to 10 m stratigraphy of fluvial conglomerate (imbricated rounded cobbles). Incision and abandonment of river terraces occur in MIS 3 to 2 during the transition to the last glacial maximum. Our results compared with an Atlantic record of aridity in the Northern Sahara over the last 120 ka show that aggradation and valley widening occur in response to periods of northward penetration of the African summer monsoon into the High Atlas. We note that these signals persist across the different tectonic zones, from the fold and thrust belts into the sedimentary basins. More widely, our data demonstrate how changes in monsoon patterns can cause changes in the erosion of mountains and transport of sediment in arid continental interiors and these form new inputs for numerical landscape evolution models.

The zone of interaction between the Cocos (CO), Caribbean (CA) and North America (NA) plates in Guatemala is defined by the sub-parallel Motagua and Polochic strike-slip faults, a series of north-south-trending extensional grabens immediately south of the Motagua Fault, the Middle America trench, and faults within the Middle America volcanic arc. Historical earthquakes associated with these faults include the destructive 1976 Mw 7.5 earthquake along the Motagua fault and the 2012 Mw 7.5 Champerico thrust earthquake. The latest published GPS-based present-day kinematic model of the region shows that about two-thirds of the strain accumulation from the NA/CA relative motion concentrates on the Motagua fault and one third across the Polochic fault, suggesting that slip varies with time as a result of mechanical interactions within the Motagua-Polochic fault system. As part of the efforts to quantify the present-day kinematics and slip behavior of these faults, we use interferometric synthetic aperture radar (InSAR) to measure the strain rates across faults in Guatemala and to constrain slip partitioning among them. We processed L-band ALOS-1 images spanning from 2006 to 2011, and C-band Sentinel-1 images spanning from 2015 to 2019, from ascending and descending tracks covering the Polochic and Motagua faults, the Ipala and Guatemala City grabens, and part of the volcanic arc to the south. We are using the New Small temporal and spatial baselines (NSBAS) workflow to compute the interferograms, make tropospheric and ionospheric corrections, and perform time-series analysis. We present the first InSAR-based maps of interseismic velocity for this region, which will contribute to the refinement of interseismic locking estimates across the Motagua-Polochic fault system, the subduction zone, and other nearby faults.

Planning for hydropower, water resources management, and climate change adaptation requires statistically unbiased hydrologic predictions. However, all hydrologic models contain systematic errors, e.g., incorrect mathematical representations of physical processes and effects of uncertainties in data sources. Statistical post-processing, or bias correction, is often used to reduce the effects of these systematic errors in model outputs. A large number of techniques for performing bias correction has been developed, primarily in the context of correcting statistical properties of independent locations. However, when bias correcting streamflow predictions within the same stream network, this assumption of spatial independence breaks down. Independently bias correcting locations from the headwaters to the mouth of a river system destroys the spatial consistency of the streamflow across a river network. We describe work toward maintaining spatial consistency in streamflow bias correction using a number of locations in the western United States. We simulate the hydrology of the Columbia River in the Pacific Northwestern United States, a river system that spans a number of hydroclimatic and flow regimes that contains a large number of flow gages. We develop a mapping from the modeled output at the gages with flow observations, which we use as the basis for training a machine learning (ML) model to perform the site-specific bias correction. We then apply the ML model to local streamflow contributions for each river segment, including river segments without flow observations. Finally, we combine the local bias corrections across the stream network, to create accumulated bias-corrected streamflow time series that are spatially-consistent across the stream network. We compare our method against several commonly used bias correction techniques to evaluate both model performance and spatial consistency.

Deformation bands are the main structural element of fault damage zones within sandstone reservoirs. The prediction of band occurrence and their petrophysical impacts is based largely on the understanding that the yield and deformation mechanism of sandstones is primarily controlled by porosity and mean grain size. Whilst this is supported by field observations within aeolian successions, where bands are predictably favoured within coarse-grained, high-porosity sandstones, the prediction of deformation bands within texturally complex mixed aeolian-fluvial reservoirs on the basis of porosity and grain size alone, may be unreliable. The effect of grain sorting on the mechanical behaviour of sandstones is not well understood, although it is generally regarded that deformation band formation is inhibited in texturally immature sandstones with a poor level of sorting. We examine the effect of sorting on both the inelastic yield of sandstones, the dominant deformation mechanism by which yield occurs, and the textural and microstructural changes with deformation, using a series of triaxial experiments on unconsolidated quartz sands. Hydrostatic experiments were conducted on over-consolidated samples of very well- to moderately-sorted sands with a range of mean grain sizes from 128-700µm. We report accurate prediction of P* using porosity x grain radius, with P* reduced with decreased sorting. Constant displacement rate triaxial experiments are performed at up to 10% axial strain to explore yield behaviour in both the brittle dilatant regime and shear-enhanced compactive regime. Experiments were repeated with systematically varied grain sorting whilst mean grain size and porosity was maintained. The textural and petrophysical changes are observed and quantified using pore volumometry, back scattered electron microscopy, digital image analysis and point counting. Results show that in well-sorted sands, localised cataclasis and deformation band formation is the dominant deformation mechanism. In poorly-sorted sands deformation occurs through a combination of grain boundary sliding and randomly distributed pockets of cataclasis. Using grain size analysis we identify greater levels of cataclasis and production of fines in well-sorted sands, resulting in permeability reduction up to one order of magnitude more than that of poorly-sorted sands deformed at the same conditions. We hypothesise that band formation within poorly sorted sandstones may be promoted by the formation and propagation of bands in adjacent well sorted sandstones where band formation is favoured. These results give insight into the deformation, textural changes, and permeability impact of both unconsolidated and consolidated siliciclastic reservoirs.

Organisms leave traces of DNA as they move through their environments. The extraction of these DNA traces is known as environmental DNA (eDNA). eDNA provides scientists and researchers a non-invasive, rapid, cost-effective and sensitive way to detect and quantify species. Traditional eDNA sampling consists of manually filtering water, which is labor and cost-intensive for remote locations. Furthermore, commercialized solutions are expensive and require a field operator. This eDNA sampler project aims to provide an affordable, open-sourced, remotely deployable, fully automated, and customizable alternative. The PolyWAG (Water Acquired Genomics) system can run up to 24 inline filter units with support for different conditions including pressure, time and volume limit. The pumps deliver maximum 400mL/min with solenoid valves separating each inline filter to minimize cross-contamination. At the end of each sample, the desired stabilizing solution can be injected to fully submerge the filter for preservation. An optional river depth sensor can provide a proxy for flow to correct eDNA concentrations to allow for improved quantification of organisms. Data acquired during operation including water depth, pressure, temperature, and flow rate will be stored on microSD card in CSV format, which allows easier data export and analysis. A web application provides an intuitive UI for in-field programming, real-time sensor updates, scheduling tasks, and manual operations. We present data from multiple tests showing the length of the preservation period and the contamination level between samples. The PolyWAG system is estimated to be $3000 each, with add-on river depth sensor and 10ah 12V battery.

It is increasingly recognized that light-absorbing impurities deposited on a surface can reduce its albedo and lead to increased absorption of solar radiation. Natural dust can travel substantial distances in the Earth’s atmosphere from its original source. It affects all climatic zones from the tropics to the poles and it may have a regional or global impact on air quality and human health. In the Arctic, a rapid increase in temperature compared to the global change, known as Arctic Amplification, is closely linked to snow albedo feedback. Furthermore, recent studies detail an extreme climate change scenario in the history of our planet that lead to catastrophic cascading events and global mass extinction triggered by atmospheric soot injections. Therefore, knowledge of optical properties of dust particles is important for improved climate models and dust effect studies. Here we report detailed results of multi-angular polarized measurements of light scattered by volcanic sand particles obtained with the FIGIFIGO goniospectrometer (Peltoniemi et al. 2014). The design concept of this custom made instrument has a well designed user friendly interface, a high level of automation, and an excellent adaptability to a wide range of weather conditions during field measurements. The foreoptics is connected to an ASD FieldSpec Pro FR 350-2500 nm spectroradiometer by an optical fiber. A calcite Glan-Thompson prism is used as a polarizer, covering the full spectral range with better than 1% accuracy. The samples studied in this work were collected from the Mýrdalssandur area in Iceland (in March 2016) and from the Villarica area in Chile (in July 2019). Following established FGI practices in laboratory conditions samples are further divided into the following categories: (1) natural volcanic sand, (2) sieved volcanic sand (dust) where the size of the particles is less than 250 μm, including dry and wet sample condition, and (3) a fine-grained powder of milled volcanic sand measurable also as aerosol. The potential use of the results from our measurements are diverse, including their use as a ground truth reference for Earth Observation and remote sensing studies, estimating climate change over time, as well as measuring other ecological effects caused by changes in atmospheric composition or land cover.

Soil respiration represents one of the dominant fluxes of CO2 from terrestrial ecosystems to the atmosphere, therefore, it is important to understand how it is controlled across a wide variety of sites. As part of developing a soil respiration data product based on freely available National Ecological Observatory Network (NEON) data, soil respiration rates were calculated at 30 sites throughout the USA (from Puerto Rico to North Dakota and Virginia to California) to investigate controls on soil respiration at a continental scale. The sites spanned a wide range of ecosystems, including deserts, grasslands, and forests, as well as managed and wildland sites. Soil respiration was calculated in 30-minute intervals using the gradient method based on soil CO2 concentrations measured at three different depths in conjunction with estimates of soil CO2 diffusivity based on soil physical properties, soil moisture and temperature profiles, and barometric pressure. Inevitably with such a large number of data inputs, the temporal coverage of good quality (i.e., unflagged) soil respiration values was relatively low (8%) because one or more of the input data were flagged, but this was significantly higher for some sites and soil plots (maximum: 58%). Ongoing efforts to increase the quality of input data are expected to substantially improve temporal coverage. Despite these gaps, over 54,000 unflagged half-hourly soil respiration data points were generated for the period of Apr-Jun 2019 (the temporal range and the number of sites will be increased further over coming months). Across all sites and times, soil temperature and soil moisture explained only a moderate amount of variation in soil respiration (20%). However, including site in the model increased the proportions of variation explained to 85%, indicating the importance of site-specific properties, such as vegetation and microbial community composition and the accessibility of soil carbon, in controlling soil respiration rates. Within each site, soil temperature was typically positively correlated with respiration and in the cases where it was not, this was often due to large fluxes of CO2 leaving the soil during snowmelt. The relationship between soil moisture and respiration within each site was more variable, with both positive and negative relationships observed.

Ground-based microwave radiometry is a common tool to estimate profiles of the atmosphere. With a high temporal resolution radiometers have became an alternative to atmospheric sounding like radiosondes. However remote sensing radiometry requires the use of inversion algorithms, where methods like linear-, quadratic-regression or Artificial Neural Network are commonly used. The present study implements a Bayesian inversion technique as alternative to the state-of-the-art retrieval algorithms provided by the radiometer’s manufacturer firmware. The Bayesian inversion provides advantages over other established methods, namely: the use of a-priori suited for the specific climatology under observation, the estimation of the most likely profile along with its uncertainty obtained from the posteriori distribution, and the feasibility to add synergistic observations from other instruments to increase retrieval capabilities. To estimate the uncertainties resulting from the Bayesian and firmware retrieval algorithms, synthetic radiometer data have been created by means of radiative transfer simulations using radiosonde profiles as descriptor of atmospheric states. These synthetic data mimics the instrument’s firmware binary files letting the radiometer to perform retrievals as real measurements. By analyzing the differences from retrieval results relative to the known true profile we assess uncertainty metrics to characterize the algorithms. It has been found that Bayesian inversion reproduces more accurately the profile vertical structure as compared to the firmware, specially for humidity profiles. Absolute errors have been strongly reduced mainly at the lower atmosphere. The study concludes that Bayesian inversion for ground-based atmospheric profiling produces results resembling observations by radiosondes when a suitable a-priori distribution is used.