This year marks the centennial of the American Geophysical Union advancing Earth and space science and 89 years of hydrologic science. The last 100 years have seen science and technology dancing a harmonious and progressively accelerated waltz. Hydroelectric power generation has made widespread electrification possible, while the rise of electronics and the advent of computers have enabled hydrologists to exploit increasingly complex models. Scientists and engineers have conquered space, and now satellite-based products and remotely sensed data have become indispensable inputs for hydrometeorological forecasting. Yet several important elements that have accompanied humanity’s history – nature, culture, and people – have been relegated; and it is only very recently, in the face of modern challenges, that they have attracted substantial attention. The advent of robust, cheap, and low-maintenance sensing equipment provides unprecedented opportunities for data collection, especially in a citizen science context. While citizens have been present throughout the history of scientific practice, developments in sensing technology, data processing and visualization, and the communication of ideas and results, are creating a wide range of new opportunities for public participation in scientific research. Integrating societal knowledge with hydrologic science, however, is not only a task for the 21st century. Historically, many civilizations have developed local water harvesting and management practices that cope with water stress by using ancient and nature-based knowledge. Indeed, indigenous peoples developed solutions that were inspired and supported by nature, and use, or mimic, natural processes to contribute to improved water management and to safeguard their water security. Technological development and knowledge integration also have a more fundamental impact on the way in which hydrologic knowledge advances, how it flows between different actors, how it disrupts power relations, and thus how it influences decisions and policy-making. We envisage that, in the next century, hydrologic science will benefit from co-creating knowledge that emerges from citizens, resonates with nature, and integrates ancient wisdom.

The warm rain process is an important part of shallow cumulus convection and can influence both cloud microphysics and cloud radiative effects. Model studies suggest that as shallow cumulus grow in size, the likelihood of warm rain increases due to a decreasing impact of entrainment on cloud updrafts. This implies a reduction in evaporative effects near cloud center that may result in more efficient conversion from cloud water to precipitation as cloud size increases. While these findings have been illustrated with cloud resolving models, the likelihood of precipitation and the sensitivity of precipitation efficiency to cloud size has not yet been tested by global observations. A-Train satellite observations, with sensors sensitive to both cloud and precipitation water, can be used to examine shallow cumulus behavior with cloud size. We combine CloudSat and MODIS observations to create a warm cloud climatology by identifying warm oceanic contiguous cloud objects with top heights below the freezing level from August 2006 - December 2010. The characteristics of each cloud object, including cloud top height, along-track extent (size), vertical reflectivity gradients, integrated cloud and precipitation water, and column water vapor (CWV) environment, are calculated. As a proxy for warm rain efficiency, the ratio of precipitation to cloud water is also analyzed for varying cloud object sizes. For a fixed top height, our results show rain likelihood increases with cloud size. Our initial results support the hypothesis that as shallow cumulus size increases and/or environmental moisture increases, shallow cumulus updrafts are able to support larger droplets that are more likely to fall out as rain. Planned analysis will determine how our proxy for warm rain efficiency changes with cloud size.

Carbon dioxide is a greenhouse gas that has a strong absorption in the 4.2– 4.3 micrometers region of the infrared spectrum. Consequently, non-dispersive infrared (NDIR) spectroscopy using interference optical filters tuned in this spectral band can be utilized to provide reliable, high resolution and fast response measurements of atmospheric CO2 concentrations. As part of eddy covariance systems, open-path gas analyzers based on this principle are widely used in remote locations around the world because of their low-power consumption, fast frequency response, and ease of operation. One of the challenges of the open-path design is that the in-situ optical beam is exposed to the rapid fluctuations in ambient temperature. Besides gas composition and pressure being the two major spectroscopic line broadening mechanisms that affect the absorption of infrared light, air temperature also can influence the broadened half-width and the intensity of the spectral lines. Consequently, the fast temperature fluctuations of the air parcels in the optical path of such a sensor can produce changes in the amount of absorbed light and cause errors in the gas concentration measurement that can propagate into the flux calculations. The temperature dependence of the infrared absorption has not been quantified in the context of the CO2 NDIR gas analyzer methodology. The study will evaluate the temperature effects on absorption spectra of CO2-air-mixtures across the 4.2–4.3 micrometers infrared active region, typically used by NDIR gas analyzers. Infrared spectra will be modeled line-by-line from spectral-line parameters obtained from the high-resolution transmission molecular spectroscopic database (HITRAN). HITRAN-predicted molecular cross sections, the product of component spectral line intensity and spectral line shape at different wavelength, will be used to generate absorption spectra of CO2 air mixtures at ambient pressure using different concentrations and temperatures. The temperature dependence of CO2 absorption will be inferred from the integrated area under the absorptivity curve. Results interpreted in the context of the Beer-Lambert law will further characterize the temperature related spectroscopic effects on CO2 concentration calculations.

Damage zones are important to the rupture dynamics, evolution and fluid coupling of earthquakes. However, information about the damage zone at depth is limited. It is unclear if damage zones increase or decrease in intensity with depth. Here we use marine 3-D seismic surveys and modern fault detection methods to address the depth-dependent structure of damage zones. We use two overlapping legacy industry seismic volumes collected offshore of Los Angeles span approximately 20 km of the Palos Verdes strike-slip fault. The data here allows visibility of the damage zone in the sedimentary formations to 2,200 meters depth, which is comparable to the constraints provided by SAFOD and other studies. Using both interpreted mapped primary fault strands and seismic attributes to identify subsidiary faults, we map and quantify spatial variations in damage zone size and intensity. The damage zone consists of subsidiary faults, or linked discontinuities in the seismics selected within assigned ranges of geometries to the primary strands. Damage was identified using a variation of the seismic attribute semblance, or multi-trace similarity. This method allows interrogation of damage zone in response to changes sedimentary lithology and fault geometry. Subsidiary faults delineate the damage zone to approximately 1 km in width and fracture density decays with distance from the primary fault strands for all sedimentary lithologies in the study area. The damage zone narrows with depth, but fracture density increases because the intensity of fracturing more than compensates for the decreased width. In the thickest formation we find that fracture density increases as Z1.8, where Z is depth in meters. These results are then compared to resolution changes with depth. The damage intensity increase and localization potentially provides a strong constraint for efforts to determine an appropriate rheology for producing damage zones and studying their effects.

Location can be described and used to spatially integrate and enable data in a number of different ways. In 2018, the Location Index (LOC-I) project was initiated to bring together a number of government agencies aiming to build a framework to support critical government decision making and to contribute significantly to economic, social and environmental sustainability by linking foundation spatial data with observational data and providing a consistent way for seamless integration of data on people, business, and the environment. This GIS without GIS framework brings together modern technological approaches of Linked Data and Discrete Global Grid Systems (DGGS) as well as important aspects of Social Architecture to ensure relevance, transparency, openness and accessibility of multidisciplinary data for the Australian Government and non-government users. Using this approach, the Loc-I program aims to open a substantial opportunity to all stakeholders by providing a richer set of information to deliver better citizen-centric services, more efficient programs and improved policy advice. Through disseminating new capabilities across Commonwealth agencies, Loc-I objective is to provide users with: stable, persistent and repeatable access to the data increased interoperability with other datasets enhanced information sharing and a greater number of users and re-uses of the data reduced complexity in using of the data standardised governance of data and information and transparency in data management improved efficiency by reduced cost and time in collection, management and delivery governed and managed federated supply chains The Loc-I is looking at extending collaboration by introducing new use cases. A case study was developed to integrate satellite raster data (big data) with vector attribute data (little data). Using Digital Earth Australia Water Observations from Space (WOfS) and Australia Surface hydrology datasets, Geoscience Australia tested how to assign named waterbodies to WOfS giving attribution context to raster information through DGGS.

Generation Z (Gen-Z) is composed of those born in the late 1990’s or after. The first cohorts of Gen-Z students gradually started to enter colleges after 2013. Gen-Z students are very different than millennials in almost all aspects. They are more likely to reject the traditional and conventional teaching methods that lack active learning components. A shift towards modern and interactive student-centered methods is being implemented by a new generation of instructors to better serve Gen-Z needs. However, this transition from passive lecture-based instruction to active learning methods is hindered by a gap that we call “the missing step”. This missing step in the transition phase refers to training and preparing future faculty to adopt research-based instructional strategies. My fellow aspiring professors and I were mostly educated via old-school methods. Without proper training on STEM teaching, we were likely to approach our teaching by modeling what we saw. If granted the opportunity to teach engineering in a faculty role, we would have come into it lacking teaching theory and instructional strategies rooted in engineering education. If a new instructor is not familiar with subjects such as “active learning”, “backward design”, “evidence-based instructional strategies”, “flipped classroom”, and “Bloom’s revised taxonomy”, or is not acquainted with importance of modern classroom assessment techniques, rubrics, and motivation strategies, the result will be an unfillable gap between the instructor and the Gen-Z students. I was in the first cohort of Ph.D. students who completed the College of Engineering’s graduate teaching fellowship program at the University of Nebraska-Lincoln. This one-year, well-structured, peer-observation program gave me invaluable knowledge about teaching following the research, helped me in designing a course in Canvas system, taught me how to write my teaching philosophy properly, and prepared me to achieve the CIRTL associate status. Starting such a program in every school for interested graduate students can make a significant difference. I will present my perspectives on the program and its impact.

Climate variability and change in the Southern Hemisphere (SH) is influenced by the southern annual mode (SAM) and is closely related to changes in the kinematic properties of the SH surface zonal winds. The SAM and SH surface zonal winds have strong effects on the atmospheric and oceanic circulation system. In this study we investigate the variability and trend in the SAM and position and strength of the surface zonal wind stress (TAUX), using two ensembles of simulations covering the historical record from the Energy Exascale Earth System Model (E3SM-HIST and AMIP) for 1979-2014. In addition, performance of two CO2 forcing simulations from the E3SM (E3SM-1pctCO2 and 4xCO2) is assessed to examine the sensitivity of the variability and changes in the SAM and SH surface TAUX to climate forcing. In general, all E3SM simulations tend to capture the dominant feature of the SAM pattern reasonably well. The annual SAM index in the E3SM-HIST simulation shows a significant increasing trend. These features are similar to the trends in the strength (along with poleward shift in the position) of the annual surface TAUX. For the climatological surface TAUX position and strength, the two CO2 forcing simulations show slightly poleward movement and stronger intensity, while the E3SM-HIST is equatorward and weaker than observations. In the relationship between the SAM and surface TAUX, we show that the SAM index exhibits a positive (negative) relationship with the strength (position) of the surface TAUX in the variability for all seasons and annual mean.

Upscaled transport models are needed to address regional-scale groundwater quality degradation in major aquifers worldwide, and these models must accurately characterize anomalous (non-Fickian) transport (e.g., early solute arrival due to preferential flow, and late time tailing). Although it is well known that seasonal pumping and recharge can cause vertical hydraulic gradient values to exceed horizontal gradients by 10-100 times, the relationship between large shifts in vertical to horizontal gradient ratio (VHGR) and plume migration is less understood. We simulate flow and transport of a conservative solute in a heterogeneous alluvial aquifer under varying VHGR representative of typical seasonal hydraulic gradient fluctuations and find that VHGR strongly impacts anomalous transport, plume migration, and mass transfer rates between hydrofacies. At low VHGR (e.g., ambient hydraulic gradient conditions corresponding to a more horizontal flow direction), low-K facies are diffusion-dominated, resulting in low mass transfer rates out of low-K material, increased spatial spreading along the mean flow direction, and tortuous particle trajectories. In contrast, at high VHGR (e.g., representative of pumping and recharge, and corresponding to an increasingly vertical mean flow direction), results in increasingly Fickian transport, explained by a shift from diffusion- to advection-dominated mass transfer in low-K facies, and illustrated by relatively non-tortuous, vertically oriented particle trajectories. We conclude that seasonal fluctuations in VHGR in a typical alluvial aquifer system may create oscillating transport behavior, with implications for contaminant transport and cleanup. The strong dependence of anomalous transport on VHGR implies that approaches to upscale anomalous transport may benefit from incorporating information about the VHGR, and highlights specific hydrogeologic forcings that cause upscaled transport methods to fail under transient boundary conditions.

In connection to drought, the southwestern United States is experiencing an increase in blowing dust, an increase in incidence of valley fever, an increase in visibility-related traffic accidents, reduction in solar panel efficiency, and earlier mountain snowmelt. The response of the land surface to the wind is greatly dependent on the proportion of land cover, soil and sediment characteristics, weather conditions, and spatial distribution of vegetation, all factors affecting aeolian processes. Combined use of optical and radar satellite imagery products can provide invaluable benefits in characterizing surface properties of desert playas (ephemeral lakes)- the preferred landform for wind erosion. As a home for high temporal coverage of Landsat optical images and a cloud computing platform, Google Earth Engine (GEE) provides a multi-petabyte catalog of satellite imagery, powerful algorithms, and application programming interface capabilities. Processing and analyzing moderate to high spatial resolution images from Landsat and Sentinel-2 in GEE are crucial resources for extracting contributions of different endmembers to pixel mixtures. Radar images, with the capability to penetrate through clouds, darkness, and rain, have a power to detect the extent and levels of changes in land cover and soil moisture. In this study, we identified the fractional abundance of soil, vegetation, and water endmembers of each pixel of images using the spectral unmixing algorithm in GEE in Lordsburg Playa (New Mexico, USA), which is prone to aeolian erosion. Here, we used Landsat-8 and Landsat-5 at 30 meters spatial resolution and Sentinel-2 Multispectral instrument at 10-20 meters resolution. By employing Interferometric Synthetic Aperture Radar (InSAR) techniques, we explored the changes in the water level of the playa and the possible hot spots of erosion-inducing sediment loading to the playa. In this data recipe, we analyzed a pair of Sentinel-1 images that bracket a monsoon day with high rainfall event and a pair of images representing dry day and monsoon day. The results from this approach clearly show locations prone to the change in phase and displacement due to water and thus sediment loading susceptible to wind.

Pine Island Glacier Ice Shelf (PIGIS) is melting rapidly from beneath due to the circulation of relatively warm water under the ice shelf, driven primarily by buoyancy of the meltwater plume. Basal melt rates predicted by ocean models with thermodynamically active ice shelves depend on the representation of environmental characteristics including geometry (grounding line location, ice draft and seabed bathymetry) and ocean hydrographic conditions, and subgrid-scale parameterizations. We developed a relatively high resolution (lateral grid spacing of 0.5 km, 24 terrain following levels) model for the PIGIS vicinity based on the Regional Ocean Modeling System (ROMS). Initial stratification was specified with idealized profiles based on observed hydrographic data seaward of the ice front. Predicted basal melt rate distributions were compared with satellite-derived estimates and stratification beneath PIGIS was compared with Autosub profiles. As in previous studies, we found that the melt rate was strongly dependent on the (specified) depth of the thermocline separating cold surface waters from deep, relatively warm waters, and on the presence of a submarine ridge under the ice shelf that impedes circulation of warm deep water into the back portion of the cavity. Melt rates were sensitive to the model’s subgrid-scale parameterizations. The quadratic drag coefficient, which parameterizes roughness of the ice shelf base, had a substantial effect on the melt rate through its role in the three-equation formulation for ice-ocean buoyancy exchange. Turbulent tracer diffusion, which was parameterized by a constant value or various mixed layer models, played an important role in determining stratification in the cavity. Numerical diffusion became significant in some cases. We conclude that flow of warm water into the inner portion of the PIGIS cavity near the deep grounding line is sensitive to poorly constrained mixing parameterizations, both at the ice base and as a mechanism for allowing inflowing ocean heat to cross the sub-ice-shelf sill. Improved understanding of mixing processes is required as the community moves towards fully coupled ocean/ice-sheet models with evolving ice thickness and grounding lines.

Canary Islands constitute an active volcanic archipelago. From the time immediately before the Castilian conquest of the islands, 17 volcanic eruptions have occurred: 2 prehispanic and 15 historical, some of them with multiple eruptive vents, affecting the islands of Lanzarote, Tenerife, La Palma and El Hierro. In order to carry out the inventory of geosites for the Canary Islands at a regional scale, it has been applied a methodology consisting of two sequential phases: the first one address the selection of geosites that will be part of the inventory, and the second one deals with the characterization and assessment of the selected geosites. In this methodology, geosites are selected within geological frameworks previously established given their regional significance for Canary Islands. With this aim, 12 geological frameworks representative of the geodiversity of Canary Islands have been identified, which include the essential elements, processes and morphologies of the Canarian geology, covering all stages of construction of the islands as well their geological evolution, including processes, morphologies, fossils and deposits associated both to the volcanism and the external geological agents. The selection of geosites is then constrained and facilitated by its representativeness within each geological framework. One of these 12 geological frameworks corresponds to “Historical and prehistorical volcanism”. The scarce number of volcanic eruptions, their low frequency and their general characteristics –fissure mafic eruptions, low VEI and strombolian eruptive styles- determine an apparent geological homogeneity. Nevertheless, the high variety of processes, morphologies and deposits associated to this framework, their good conservation status, as well as the information from the historical chronicles, have permitted to identify 19 geosites of high scientific value. They highlight the existence of eruptive styles ranging from hawaian to vulcanian, with short phases of water-magma contact, quiet emissions of water or in geysers, phreatomagmatic explosions, etc. These geosites constitute a unique and representative record of the volcanology, geomorphology, tectonics and petrology characterizing the most recent mafic volcanism of the Canarian archipelago.

We conducted sandbox analogue experiments for subduction of trench-fill sediments beneath accretionary wedge and backstop in order to explain how protoliths of high-pressure/low-temperature (HP-LT) metamorphic rocks are transported to high pressure environment. At accretionary-type subduction zones, it is commonly difficult that coarse-grained sandy trench-fill deposits subduct deeper than high pressure environment (>10 km in depth), because they are accreted at the shallower part of the wedge (<5 km) in association with stepping down of decollement due to progressive dewatering under the accretionary wedge. However, ancient exhumed accretionary complexes sometimes accompany with low-grade accretionary rocks from trench-fill turbidites and HP-LT metamorphic rocks including psammitic and even conglomeratic schists, whose provenance and depositional ages are similar to each other. Therefore, we need a model to explain growth of accretionary wedge and subduction of coarse-grained trench-fill sediments beneath the wedge at the same time. In this study, we attempt to identify an importance of seafloor roughness for transportation of trench-fill sediments to deep during subduction. For this purpose, we performed sandbox analogue experiments by using an unfixed rigid backstop on a subduction channel with the cases of smooth surface (Exp. A) and rough surface representing a seamount or ridge on subducting lower plate (Exp. B). The results of Exp. A showed progressive thickening of the accretionary wedge pushed the backstop down, meaning stepping down of the decollement and narrowing the subduction channel. On the other hand, Exp. B showed a subducting seamount lifted up the backstop, stepped up the decollement, and then widened the subduction channel. Subduction of a rigid material like seamounts is a possible mechanism to open subduction channels for transportation of terrigenous sediments from the trench to high-pressure condition. Significant sediment supply to the trench and rough surface of subducting oceanic plate are required to enable subduction of protolith of HP-LT metamorphic rocks and accretion of trench-fill sediments at the shallow part.

The onset of plant flowering is one of many factors affected by climate change. This has important consequences for ecosystems, where plants and pollinators are at risk of a temporal mismatch. This also has implications for crops. The timing of their highest-sensitivity window is affected, despite some control exerted by agricultural management. Current efforts to monitor the onset of flowering rest on numerous but sparse ground observations, or on proxies like temperature models or vegetation indices. In crop studies, a time-invariant harvesting calendar is most widely used to infer the flowering period for crops. We use spectral data from satellite imagery, combined with convolutional neural networks, to create a large-scale, high-resolution proxy for the timing of flowering. We exploit a transfer learning method to overcome the sparsity of ground truth data on plant phenology. We first train a temporal convolutional neural network to predict a temperature-based proxy for flowering (the First Bloom Index from the National Phenology Network), using as input the 8-day composite from MODIS at a 500m resolution. This model learns relevant temporal patterns on both short and long time scales within a year, thanks to a dilated convolution structure. We then fine-tune the parameters from the last fully connected layer using ground observations from the National Phenology Network as training data. The root mean squared error of our model is about 8.6 days, though this average hides much spatial heterogeneity: in many places, the model performs well-enough to capture year-to-year variations, with a prediction within a couple days of the target, while it performs extremely poorly in a few places. Our model outperforms the temperature-based proxy since the latter has a root mean squared error of about 9.3 days on the same testing data. More importantly, our model extrapolates much better over space: it has a root mean squared error of about 12 days when predicting outside of our study region, compared to a 35-day error for the temperature-based model. This is a key feature, since our goal is to fill in the spatial gaps left by the sparse ground truth observations. These large-scale predictions of annual flowering times could offer insights into plant-pollinator desynchronization and crop calendar variation under climate change, and could be used for further applications like studying the health impacts of shifts in spring flowering times.

The rapid changes of magnetic fields associated with nighttime magnetic perturbations with amplitudes |ΔB| of hundreds of nT and 5-10 min periods can induce bursts of geomagnetically-induced currents that can harm technological systems. Recent studies of these events in eastern Arctic Canada, based on data from four ground magnetometer arrays and augmented by observations from auroral imagers and high-altitude spacecraft in the nightside magnetosphere, showed them to be highly localized, with largest |dB/dt| values within a ~275 km half-maximum radius that was associated with a region of shear between upward and downward field-aligned currents, and usually but not always associated with substorms. In this study we look in more detail at the field-aligned currents associated with these events using AMPERE data, and compare the context and characteristics of events not associated with substorms (occurring from 60 min to over two days after the most recent substorm onset) to those occurring within 30 min of onset. Preliminary results of this comparison, based on events with |dB/dt|≥ 6 nT/s observed during 2015 and 2017 at Repulse Bay (75.2° CGMLAT), showed that the SYM/H distributions for both categories of events were similar, with 85% between -40 and 10 nT, and the SME values during non-substorm events coincided with the lower half of the range of SME values for events during substorms (200 – 700 nT). Dipolarizations of ≥ 20 nT amplitude at GOES 13 occurred within 45 minutes prior to 73% of the substorm events but only 29% of the non-substorm events. These observations suggest that predictions of GICs cannot focus solely on the occurrence of intense substorms.

We discuss an efficient implementation of the analog ensemble algorithm, the distilled analog ensemble, which is achieved by distilling the post-processing transformation generated by the analog ensemble into a deep neural network. While the analog ensemble has been shown to be able to improve deterministic forecasts and create calibrated probabilistic predictions in many contexts, a common issue with operationalizing a large, global analog ensemble-based system is the amount of data (a corpus of historical forecasts) and post-processing latency required to process that data in the time-critical path of producing a forecast. Deep neural networks are high capacity function approximators, and we demonstrate that we are able to train a network that memorizes the post-processing behavior of the analog ensemble on a particular corpus of forecasts. This technique breaks the scale factor between the size of the historical forecast corpus (larger is better for forecast skill improvements) and the calculation required to post-process the current forecast in real-time operations. We show that the distilled analog ensemble is able to improve European Centre for Medium-Range Weather Forecasts (ECMWF) high-resolution deterministic forecasts of winds in the lower stratosphere using as ground-truth either the ECMWF analysis or observations from Loon high altitude balloons. In this case, rather than requiring terabytes of historical forecast data to apply the conventional analog ensemble, we can perform the post-processing that improves forecast quality on the fly doing computationally efficient forward passes through a pre-trained network that has a data size of only 100’s of kilobytes.

Since 2013 Susquehanna Shale Hills Critical Zone Observatory (SSHCZO) has been monitoring using a Ground Hydrological Observation System (GroundHOG) design consisting of various sensor types in the Shavers Creek watershed in central Pennsylvania. The GroundHOG design was established to study interactions between hydrological systems (surface and groundwater), soils, and ecosystems along catenas. We currently have three GroundHOG sites with differing land uses and geology: one located in a pristine shale watershed, another in a pristine sandstone watershed, and the third in an agricultural setting with mixed lithology. Each catena has three pits set up to compare hill slope position and one additional pit to compare north versus south aspect. Each pit is equipped with both automated and manual sensors that measure soil moisture and soil gas at varying depths. The GroundHOG deployment is accompanied by precipitation gauges, surface water monitoring gauges, and groundwater monitoring wells at all sites. In 2019 we built on the shale site to include weekly electrical resistivity measurements and seismometry near the GroundHOG. Specifically, we designed and installed an innovative chronoamperometric system that can respond to real-time redox reactions that change in response to changes in soil moisture, temperature, and saturation. These experimental sensors are co-located with the GroundHOG sites where soil gas measurements take place and can be coupled to understand the connection of hydrological processes to microbial communities. This presentation will emphasize the design of the GroundHOG and how the new measurements are made and compared in the shale GroundHOG site.

Solar, auroral, and radiation belt electrons enter the atmosphere at polar regions leading to ionization and affecting its chemistry. For example particle-produced OH and NO molecules affect the ozone content in the middle atmosphere. Climate models usually parametrize this ionization and the related changes in chemistry based on satellite particle measurements. Precise measurements of the particle and energy influx into the upper atmosphere are difficult because they vary substantially in location and time. Widely used particle data are derived from the POES and GOES satellite measurements which provide electron and proton spectra. We present electron energy and flux measurements from the Special Sensor Ultraviolet Spectrographic Imagers (SSUSI) satellite instruments. This formation of satellites observes the auroral zone in the UV from which electron energies and fluxes are inferred. We use these observed electron energies and fluxes to calculate ionization rates and electron densities in the mesosphere and lower thermosphere (≈ 40–200 km). We also present an initial comparison of these rates to other models and compare the electron densities to those measured by the EISCAT radar. Together with photochemical models, trace gas concentrations, for example of NO, can be calculated from these electron densities. These concentrations then provide an independent source for comparing and validating satellite trace gas measurements.

Recent studies have demonstrated a direct relation between climate characteristics and vegetation in catchments. For example, plants appear to develop a root system that allows both optimal growth and resistance against region-specific droughts (Gao et al., 2014; Ho et al., 2005). As climatic conditions also affect the way catchments store and release water (i.e., the transit times), we expect a direct relation between vegetation and transit times. To test this hypothesis, we have established a dataset of water balance and stable water isotope data across more than 50 catchments in various climate zones, which will be further expanded over the course of the project. This dataset allows for determining root zone storage capacities and transit time metrics such as the young water fraction (Kirchner, 2016) across catchment scales and climate zones. We will present how transit time metrics vary as a function of root zone storage capacities and how this can be related to catchment and vegetation characteristics and climatic conditions. The results will help understand how changing vegetation cover due to climate and land use change might affect catchment water storage and release in future. We see a vast potential of isotope studies across diverse catchments. We are thus calling for a community effort to provide streamflow isotope data from previous work in a unified framework as a basis for further global analyses using stable water isotopes. Gao, H.; Hrachowitz, M.; Schymanski, S. J.; Fenicia, F.; Sriwongsitanon, N.; Savenije, H. H. G., Climate controls how ecosystems size the root zone storage capacity at catchment scale. Geophysical Research Letters 2014, 41, 7916–7923, doi:10.1002/2014GL061668. Ho, M. D.; Rosas, J. C.; Brown, K. M.; Lynch, J. P., Root architectural tradeoffs for water and phosphorus acquisition. Functional Plant Biology 2005, 32, (8), 737-748. Kirchner, J. W., Aggregation in environmental systems – Part 1: Seasonal tracer cycles quantify young water fractions, but not mean transit times, in spatially heterogeneous catchments. Hydrology and Earth System Sciences 2016, 20, (1), 279-297.