bluuebunny/crossref_metadata_2025 huggingface dataset:
New methods for studying the soil-plant-atmosphere continuum with stable isotope data
Authors: Kirchner James
Published: March 2024

Stable isotopes are essential tools for tracing water and nutrient fluxes in terrestrial ecosystems.  In recent years, studies of the soil-plant-atmosphere continuum have yielded impressive volumes of stable isotope tracer data at previously unattainable precision and spatiotemporal resolution.  These emerging data sets facilitate new methods of analysis that promise new insights into transport, storage and mixing.  For decades, end-member mixing analysis (EMMA) has been the standard workhorse for interpreting tracer data, but new methods can overcome some of its limitations and facilitate new inferences into ecosystem processes.  At the catchment scale, for example, end-member mixing has been widely used to quantify streamflow as a mixture of isotopically distinct sources, but knowing where streamwater comes from is not the same as knowing where precipitation goes, for which one needs end-member splitting (Kirchner and Allen, 2020) instead.  End-member splitting allows summer and winter precipitation to be partitioned between evapotranspiration, summer streamflow, and winter streamflow, without direct measurements of evapotranspiration water fluxes or their isotopic composition.  At the hillslope and plot scale, end-member mixing has been widely used to quantify the relative contributions of isotopically distinct sources to soils and xylem waters.  If any end-members are missing or their tracer distributions overlap, however, conventional mixing models become unusable.  These constraints can be overcome by exploiting the information contained in tracer time-series using ensemble end-member mixing analysis (EEMMA; Kirchner, 2023).  EEMMA can potentially quantify many sources using a single tracer, even if their mean concentrations are indistinguishable. EEMMA can also quantify source contributions when some sources are unknown, and even infer the tracer time series of a missing source.  These new methods will be demonstrated using benchmark data, and proof-of-concept applications will be presented.
The Science Discovery Engine: Connecting Heterogeneous Scientific Data and Information 
Authors: Bugbee Kaylin , Acharya Ashish , Foshee Emily , Ramasubramanian Muthukumaran , Davis Carson , Praveen Bishwas , Nagaraja Kartik , Vishwanathan Shravan , Wingo Stephanie , Wyatt Rachel
Published: March 2024

Transformative science often occurs at the boundaries of different disciplines. Making interdisciplinary science data, software and documentation discoverable and accessible is essential to enabling transformative science. However, connecting this diverse and heterogeneous information is often a challenge due to several factors including the dispersed and sometimes isolated nature of data and the semantic differences between topical areas. NASA’s Science Discovery Engine (SDE) has developed several approaches to tackling these challenges. The SDE is a unified, insightful search experience that enables discovery of NASA’s open science data across five topical areas: astrophysics, biological and physical sciences, Earth science, heliophysics and planetary science. In this presentation, we will discuss our efforts to develop a systematic scientific curation workflow to integrate diverse content into a single search environment. We will also share lessons learned from our work to create a metadata crosswalk across the five disciplines. 
Climate Variations Connected with Earth Orbit Eccentricity 
Authors: Chapanov Yavor
Published: March 2024

The climate variations in the past are strongly connected with the cycles of orbital forcing. The orbital forcing redistributes incoming solar energy on the Earth surface, especially over different latitudes. These cycles affect significantly seasons on millennial time scales. The most important influence on climate is provided by the variations of orbit eccentricity, obliquity and precession of Earth axis of rotation. The so-called Milankovitch cycles of eccentricity and obliquity are connected with the processes of glaciations during the last 3 Ma. Actually, all orbital cycles affect paleoclimate, where the effects of eccentricity dominate. The influence of orbital forcing on paleoclimate variations is investigated by two long time series of eccentricity from Laskar’s solution and sea level variations, reconstructed for the last 65 Ma. Common cycles of eccentricity and sea level in 18 different frequency bands are extracted by the Method of Partial Fourier Approximation. The short-periodical cycles, whose periods are below 400 kyr, have relatively good agreement for the last 3 to 7 Ma. The long-term oscillations of sea level and orbit eccentricity with periodicities between 0.8 Myr and 10.8 Myr have excellent agreement in 4 frequency bands, whose duration is 65 Myr. In other 5 frequency bands a good correlation exists for the last 35 – 40 Ma. The estimated amplitudes of sea level cycles are between 2 and 5 m with accuracy of about 0.4 m. The jumps inside of sea level time series are determined by a high-sensitive Method of Jump Detection, based on numerical integration of the time series. The detected jumps determine various data segments, whose duration is below 2.8 Myr and the rate of their linear trends is between 0.3 cm/kyr and 3 cm/kyr. The remarkable result is that all detected jumps occur during the extrema of eccentricity, while the jumps of sea level during glacial cycles in the last 3 Ma occur only in eccentricity minima. These results can help better understanding of climate response to orbital forcing. 
Exploring the Impact of Various Streamflow Products on Ice-Jam Formation 
Authors: Ghoreishi Mohammad , Gharari Shervan , Elshamy Mohamed , Lindenschmidt Karl-Erich
Published: March 2024

Ice jams present a significant flood risk in communities located along northern rivers, especially during the breakup of ice cover, resulting in increased backwater levels and flooding beyond riverbanks. The accurate simulation of ice formation relies on precise streamflow data, a vital input for hydraulic models. This study aims to evaluate how different streamflow products influence ice formation, focusing on simulating ice-jam flooding of the Athabasca River at Fort McMurray, Canada, with the broader goal of assessing the suitability of global datasets for predicting such events at a local scale. In our investigation, we integrate MizuRoute, a river network routing tool, and RIVICE, a one-dimensional, hydrodynamic, and river-ice hydraulic model. By employing various large-scale runoff from different models and datasets, such as MESH, ERA5, and VIC among others, our goal is to comprehensively understand how each product impacts the formation of ice jams and the subsequent flooding events. The incorporation of these runoff products is particularly relevant to investigate utilizing global datasets for predicting ice-jam flooding at a local scale.
Performance Analysis of Internet of Underground Things Network in Smart and Greenhouse Agriculture
Authors: Negm Abdelazim , Esmaiel Hamada , Agamy Adel
Published: March 2024

Yield improvement of the smart and greenhouse agriculture necessities to adopt the recently developed technologies. Therefore, sensors and the Internet of Things (IoT) and/or the Internet of Underground Things (IOUT) should be included. The Internet of Underground Things (IOUT) communication network consists of a large number of sensors or actuators deployed in a geographical area. In IOUT networks, the data is collected by multiple sinks and sent over to the central processing unit. The special needs for this type of network pose great challenges for network architecture and implementation. Accurately modeling the information generated by each sensor device is essential for designing a smart system for precision agriculture. Successful design leads to improving the whole performance of the communication network and increases the productivity of the plants by early detecting hazards and diseases of the plants. This research aims to model and measure the performance of communication networks of underground sensor devices and above-ground networks with different scenarios. The traffic of each device in the network is modeled by truncated power tail distribution to represent the real burst of the collected information traffic. The impact of burst traffic on the performance is investigated analytically and by simulation. The model compares two common types of traffic models using truncated power tail distribution. Analyzing all the collected information could lead to adjusting the environmental conditions (including soil moisture and water feed) to set then to meet the optimal conditions of the plants to improve the smart and greenhouse agriculture productivity to produce more food to help support the related SDGs.Keywords:  Traffic Modeling, Performance Modeling, Communication network, Greenhouse agriculture, Smart/precision agriculture
Nanopore Influence on the Geochemical Properties of Water in Earth's Lithosphere
Authors: Plümper Oliver , Chogani Alireza , King Helen E. , Tutolo Benjamin
Published: March 2024

Water, a principal component of Earth’s fluids, interacts with rocks in ways that profoundly influence lithospheric phenomena. These interactions are fundamental to both geochemical and geodynamic processes, extending their impact to areas of societal importance such as subsurface energy extraction and storage, and the formation of vital metal deposits for green energy technology. Additionally, the interplay between fluids and rocks significantly affects the Earth's carbon cycle, influencing atmospheric CO2 levels, climate, and overall planetary habitability. The traditional perspective suggests that fluids move through the lithosphere without being affected by the unique properties that emerge when matter is confined to the nanoscale. Contradicting this view, our research reveals that rocks involved in a variety of lithospheric processes consistently show nanoporosity, primarily with pores smaller than 100 nanometers. Within these small spaces, water behaves differently than it does in larger environments. Through molecular dynamics simulations, we have quantified water's relative permittivity—a critical factor in its geochemical behavior— when confined in natural nanopores. Our findings demonstrate that, under a wide range of lithospheric conditions, from ambient to extreme temperatures up to 700 °C and pressures up to 5 GPa, water's permittivity within these nanopores significantly deviates from that in its bulk state. Our thermodynamic equilibrium models indicate that this difference markedly reduces mineral solubility and alters ion speciation in natural settings. These pore-size-dependent properties may exert a primary influence on rock reactivity and the evolution of aqueous geochemistry during fluid-rock interactions.
Modeling fluid-induced dyke propagation in elasto-visco-plastic rocks
Authors: Popov Anton A. , Berlie Nicolas , Kaus Boris J.P.
Published: March 2024

Developing a deep understanding of magmatic processes, such as the ascent of magmatic melts through the lithosphere, is a notoriously complex interdisciplinary task that involves contributions from various branches of geosciences. Here, we focus on the implementation of mode-1 plasticity, which is highly relevant for the modeling of dyke propagation through the brittle crust under an excess of fluid (melt) pressure, as part of a nonlinear elasto-visco-plastic rheology that is also appropriate for modeling the ductile-brittle transition in the upper mantle.For this, we adopt a coupled poro-elasto-visco-plastic description of the strain localization process to capture the onset and advance of vertical dykes originating from magma reservoirs located at various depth. We mostly aim to investigate the hydro-mechanical interactions of such a system (e.g. permeability increase and elastic modulii degradation with increasing plastic strain, and influence of fluid pressure on both total stresses and on the yield surface). Thermal effects are essentially ignored, apart from the background geothermal gradient. The reservoir is represented as a cavity subjected to an increased fluid pressure.We describe the brittle deformation using multi-surface plasticity models in the framework of the flow plasticity theory. A number of challenging problems are usually encountered in this case from an algorithmic viewpoint, including a lack of convexity and continuity of both the yield surface and flow potential, spurious elastic domains, singularity points and loss of convergence. We address these issues by using a relatively simple Perzyna-type visco-plastic model that consists of a linear Drucker-Prager shear failure envelop, and a circular tensile cap. Both surfaces are combined with each other in a way that enforces dimensional consistency, convexity, and continuity throughout the entire stress space.Finally, we discuss the algorithmic details of the model which is incorporated in an implicit finite element code and demonstrate the  application results.
Absorbing aerosols can strongly enhance extrem precipitation
Authors: Dagan Guy , Eytan Eshkol
Published: March 2024

Understanding the impact of anthropogenic aerosols on extreme precipitation is of both social and scientific significance. While anthropogenic absorbing aerosols are known to influence Earth's energy balance and atmospheric convection, their role in extreme events remains unclear. This study employs convective-resolving radiative-convective-equilibrium simulations to comprehensively investigate the impact of absorbing aerosols on extreme tropical precipitation. Our findings reveal an underappreciated mechanism whereby absorbing aerosols can, under certain conditions, significantly intensify extreme precipitation despite reducing the mean. Notably, we demonstrate that a mechanism previously observed in much warmer (hothouse) climates—where intense rainfall alternates with multi-day dry spells—can manifest under current realistic conditions due to the influence of absorbing aerosols. This mechanism operates when an aerosol perturbation shifts the lower tropospheric radiative heating rate to positive values, generating a strong inhibition layer. Our work underscores an additional potential effect of absorbing aerosols, with implications for climate change mitigation and disaster risk management.
Evaluation of Soil Mapping Methods for SWAT Hydrological Modeling through Multi-Objective Calibration
Authors: Gimeno Fernando , Zambrano-Bigiarini Mauricio , Galleguillos Mauricio , Marinao Rodrigo
Published: March 2024

Soil data is a crucial component for hydrological models operating at the catchment scale, such as the Soil and Water Assessment Tool (SWAT). Nevertheless, the reliability of these models is heavily contingent upon the quality and spatial resolution of the soil information employed. This study addresses the pressing need for robust soil data in SWAT modeling by evaluating various soil mapping techniques.The first objective was to prove different mapping techniques, such as textural class combination and clustering approach using soil grid data to have different soil maps. The performance of those maps, together with WRB, WSR, Zobler, HWSD v2.0 and DSOLMAPS, was evaluated to improve the accuracy and reliability of the SWAT hydrological model. To achieve this, we conduct a comprehensive investigation involving multi-objective calibration, utilizing both flow data and soil moisture data to calibrate the model. Finally we incorporate a  pedotransfer function to include the landcover effect on Saturated Hydraulic Conductivity to improve the reliability of soil hydrological processes in the SWAT model.The study area, situated within the Cauquenes River Catchment, presents a complex hydrological system characterized by substantial spatial heterogeneity in soil properties. The soil mapping techniques under evaluation encompass traditional soil survey data integrated with remotely sensed soil information and machine learning-based soil mapping methodologies. These methods are compared in their ability to enhance the SWAT model's representation of the catchment's hydrological dynamics.In the case of the kmeans clustering approach the results of soil clusters are equivalent to soil units. A number of clusters from 3 to 100 were evaluated with the lowest DB index. Clusters from 3 to 16 presented an optimal range. The SWAT model calibration was performed under multi-objective evaluation, with kmeans soil cluster and DSoilMaps with better result for daily simulations.The work to correct the application of soil data, including in situ observation, satellite data and machine learning approach, provides a valuable approach to improve the calibration and validation processes of hydrological models in semi-arid regions, important for cacthment management and decision making processes, and to correctly assess the impacts of land use changes, climate variability and extreme events on water resources. 
Volcanic proxies from the northern Pangean margin across the Permian-Triassic boundary: Evidence of intermittent Siberian Traps activity
Authors: Jones Morgan , Rooney Alan , Zuchuat Valentin , Turner Holly , Frieling Joost , Mather Tamsin , Augland Lars , Sleveland Arve , Senger Kim , Betlem Peter , Sartell Anna , Hammer Øyvind , Faleide Jan Inge , Planke Sverre
Published: March 2024

The End Permian Mass Extinction (EPME) occurred at 251.9 Ma and is the largest extinction event in the Phanerozoic. More than 80% of marine species and ~75% of terrestrial species were wiped out in
Analysis of Abrupt Changes in CMIP6 Models Using Edge Detection
Authors: Terpstra Sjoerd , Falkena Swinda K.J. , Bastiaansen Robbin , Bathiany Sebastian , Dijkstra Henk A. , von der Heydt Anna
Published: March 2024

Many potential tipping elements have been identified in the climate system over the last decade, although some of them are surrounded by large uncertainties. We perform an updated analysis of abrupt changes in current state-of-the-art climate models to re-evaluate the evidence of these shifts—whether they are tipping points or not. We examine all CMIP6 models (59 in total) under the 1pctCO2 scenario using a Canny edge detection method—adapted for spatiotemporal dimensions—to detect abrupt shifts in climate data. We perform this semi-automatic analysis on 83 two-dimensional variables of the ocean, atmosphere, and land. We aggregate the detected shifts that are connected spatially or temporally. This results in connected regions of abrupt shifts and allows us to map areas that are most at risk of these shifts according to CMIP6 models. We report statistics on number of abrupt changes detected, surface area of abrupt changes, and critical global mean temperature at which these abrupt changes occur. This is done for various climate subsystems and potential tipping elements, such as the Arctic sea ice, Antarctic sea ice and the North Atlantic subpolar gyre. We find evidence for abrupt changes in several systems, but not all models show them equally.
Scientific Content Curation In An Open Science Era 
Authors: Bugbee Kaylin , Smith Deborah , Wingo Stephanie , Foshee Emily
Published: March 2024

Today’s open science environment, in combination with the Big Data era, means more scientific data, software, tools, documentation, publications and other resources are available than ever. The promise of the open science era is that scientists will spend less time reinventing the wheel and more time doing actionable research. Yet navigating this vast and complex information landscape can feel overwhelming to scientists trying to get their bearings. In this presentation, we define and discuss the importance of scientific content curation for enhancing discovery and use of scientific data and information. We also share two examples of scientific content curation in action: the Catalog of Archived Suborbital Earth Science Investigations (CASEI) and the Science Discovery Engine (SDE). 
Rate-dependent friction associated with frictional bridges
Authors: Dyskin Arcady , Pasternak Elena
Published: March 2024

The presence of multiple asperities in the contacting fault surfaces produces sliding resistance associated with the contact between asperities of the opposite sliding surfaces. The pairs of contacting asperities essentially work as frictional bridges: the fault sliding can only proceed when all bridges are broken, that is the corresponding pairs of asperities are either broken or their resistance is overcome by one asperity climbing over its counterpart. Furthermore, each step of sliding produces new pairs of contacting asperities forming new frictional bridges independent of the previous ones. The number of new bridges is usually high. We assume it to be proportional to the sliding velocity. The condition of producing a step of sliding is the failure of all the bridges resisting to the sliding in the area associated with the sliding step. Each bridge is characterised by a different strength (either the actual asperity strength or the force needed to make one asperity to climb over its counterpart). Given the large number of bridges involved in each step, this condition can be expressed in terms of the distribution of the maximum strengths. Using the theory of order statistics, we obtained a logarithmic dependence of the resulting friction coefficient upon the sliding velocity, which is observed in the experiments. Thus, our model suggests a new interpretation of the experimentally observed rate-dependent friction. The model parameters are associated with the parameters of bridge distribution, which are related to the morphology of the sliding surfaces.Acknowledgement. The authors acknowledge support from the Australian Research Council through project DP190103260.
Spatially coherent structure of forecast errors – A complex network approach
Authors: Gupta Shraddha , Banerjee Abhirup , Marwan Norbert , Richardson David , Magnusson Linus , Kurths Jürgen , Pappenberger Florian
Published: March 2024

The quality of weather forecasts has improved considerably in recent decades as models can better represent the complexity of the Earth’s climate system, benefitting from assimilation of comprehensive Earth observation data and increased computational resources. Analysis of errors is an integral part of numerical weather prediction to produce better quality forecasts. The Earth’s climate, being a highly complex interacting system, often gives rise to significant statistical relationships between the states of the climate at distant geographical locations. Likewise, correlated errors in forecasting the state of the system can arise from predictable relationships between forecast errors at various regions resulting from an underlying systematic or random process. Estimation of error correlations is very important for producing quality forecasts and is a key issue for data assimilation. However, the size of the corresponding correlation matrix is larger than what is possible to represent on geographical maps in order to diagnose its full spatial variation.In this work, we propose an approach based on complex network theory to quantitatively study the spatiotemporal coherent structures of medium-range forecast errors of different climate variables. We demonstrate that the spatial variation of the network measures computed from the error correlation matrix can provide insights into the origin of forecast errors in a climate variable by identifying spatially coherent patterns of regions having common sources of error. Notably, the network topology of forecast errors of a climate variable is significantly different from those of random networks corresponding to a deterministic phenomenon which the model fails to simulate adequately. This is especially important to reveal the spatial heterogeneity of the errors – for example, the forecast errors of outgoing long-wave radiation in tropical regions can be correlated across very long distances, indicating an underlying climate mechanism as the source of the error. Additionally, we highlight that these structures of forecast errors may not always be directly derivable from the spatiotemporal co-variability pattern of the corresponding climate variable, contrary to the expectations that the patterns should resemble each other. We further employ other common statistical tools such as, empirical orthogonal functions, to support these findings. Our results underline the potential of complex networks as a very promising diagnostic tool to gain better understanding of the spatial variation, origin, and propagation of forecast errors. 
Refractive Index Matched (RIM) PIV in Free Surface Flows of Particle-Laden Yield Stress Fluids
Authors: Amini Kasra , Chen Yanan , Ancey Christophe , Tammisola Outi , Lundell Fredrik
Published: March 2024

Flow of lava, avalanches, mudslides, and many geophysical and planetary flow systems are examples of free-surface flows of Yield Stress Fluids (YSFs). This category of fluids is known for its dual behavior below- and above a yielding threshold for the applied shear stress on each fluid element. The material behaves as an amorphous elastic solid below the yielding threshold and fluidizes above it. This will lead to the presence of unyielded plug regions translating and rotating as solid-like segments within the yielded surrounding fluids. The existence of macroscopic particles in the fluid adds to the complexity of the flow setting. Transport of debris in the riverbeds and avalanches, dispersion of the cooled-down agglomerates of lava in the molten medium, and migration of solid material such as icy rocks in high pressure YSF-like, sub-terranean oceans of Europa (Jupiter’s moon) are among numerous natural examples of particle-laden flows of YSFs. To replicate the conditions experimentally, aqueous solutions of Carbopol with yield stress  are used in combination with hydrogel particles. The elastic hydrogel particles have been used in volume fractions φ = 0, 10, 20, and 30 % as mono- and duodispersed suspensions. The excellent refractive index matching of these elastic particles with Carbopol permits accurate recording of the illuminated flow field seeded with tracer particles for PIV measurements, without optical blockage of the macro particles in the optical path. Measurements are performed with channel inclinations ranging from zero to 18°, with controlled deployment of gate opening ranging from 3 cm (i.e. 50 % of the channel width) to a full open dam-break situation. Stream-wise PIV recordings of the transient and semi-steady field are complemented with span-wise recordings targeting statistical results on the particle migration and sedimentation. The results are put in context with the experiments on Newtonian and YSFs in free surface flumes containing rigid particles [1,2], as well as duct flow experiments on Carbopol with the same elastic particles [3].      References [1] Christophe Ancey, Nicolas Andreini, Gaël Epely-Chauvin, The dam-break problem for concentrated suspensions of neutrally buoyant particles, J. Fluid Mech. (2013), vol. 724, pp. 95–122.[2] G Rousseau, C Ancey, An experimental investigation of turbulent free-surface flows over a steep permeable bed, J. Fluid Mech. (2022), vol. 941, A51.[3] Sagar Zade, Tafadzwa John Shamu, Fredrik Lundell, Luca Brandt, Finite-size spherical particles in a square duct flow of an elastoviscoplastic fluid: an experimental study, J. Fluid Mech. (2020), vol. 883, A
A comparison of snow depth scaling patterns from TLS, UAV and Pleiades observations 
Authors: Revuelto Jesús , Mendoza Pablo , Deschamps-Berger Cesar , Alonso-González Esteban , Rojas-Heredia Francisco , López-Moreno Juan Ignacio
Published: March 2024

Understanding the evolution of snowpack in heterogeneous mountain areas is a highly demanding task and requires the application of suitable observation techniques to retrieve snow properties at distinct spatial scales. In turn, once the reliability of these techniques is established, the comprehension of snowpack scaling properties helps to determine which processes are more relevant on the control of snow distribution and its temporal evolution. Previous studies have reported detailed observational datasets and insights on the main drivers of snowpack distribution through variogram analysis up to 500-800 m, identifying scale break lengths and their anisotropies. Here, we examine scale breaks derived from variogram analysis applied to snow depth observations at the Izas Experimental Catchment (located in Central Spanish Pyrenees) and the surrounding area for the period 2019-2023. To this end, we use data retrieved with three observation techniques: Terrestrial Laser Scanning (TLS-LiDAR, 12 acquisitions), Unmanned Aerial Vehicles (UAV-SfM, 20 acquisitions), and satellite stereo images (4 Pléiades acquisitions), covering different domains around the experimental site. First, we analyze the consistency among the observational techniques, and then we explore possible drivers explaining detected scale breaks through variogram analysis up to 4000 m. Overall, similar results were obtained with the three observational techniques, with a very high temporal consistency for the first detected scale break length and little variations with direction. We also found good agreement between the search distance used to compute the topographic position index (TPI), the first scale break length, and the mean distance between peak snow accumulations, which vary between 15 and 25 m, not only for the entire study domain, but also in manually delineated Hydrological Response Units.
Application of artificial neural networks for modeling the climate change impacts on Mediterranean coastal lagoons:  the Venice Lagoon example.
Authors: Bozzeda Fabio , Sigovini Marco , Lionello Piero
Published: March 2024

Mediterranean coastal lagoons play a pivotal role in the environmental, social, and economic facets of the coastal areas.  In general, coastal lagoons serve as biodiversity hotspots, supporting diverse ecosystems, including wetlands, marshes, seagrass beds, and unique fauna. They function as carbon sinks, sequestering substantial amounts of atmospheric carbon dioxide. Coastal lagoons also act as hydrological regulators, serving as natural buffer zones during extreme weather events, regulating hydrological cycles, and minimizing the impacts of flooding.  Mediterranean coastal lagoons hold significant socio-economic value, providing essential fishing grounds and supporting local fishing communities, and as tourist. Finally, coastal lagoons have been integral to Mediterranean cultures for centuries, holding historical and cultural significance. Understanding the impacts of climate change on coastal lagoon is crucial for coastal planning and adaptation strategies. In this study artificial neural networks (ANNs)are applied to estimate the impacts of anthropogenic climate change on water masses characteristics of coastal lagoon. Specifically, ANNs are used to model the associations between climate variables and water mass properties (namely temperature and salinity), which can be used for future projections. The developed ANNs approach can be applied to generic coastal lagoons, if sufficient in situ data of temperature, salinity and sea level are available for developing the model. The driving meteorological variable can be extracted from meteorological reanalysis and model climate projections if their resolution is sufficient to describe the relevant mesoscale features. The method is applied to the Venice Lagoon, the largest Mediterranean lagoon. The Venice lagoon is an ecologically and socio-economically relevant environment with notable susceptibility and a comprehensive description of climate change impacts is essential for its conservation and sustainable management. An advantage of the Venice lagoon is the relative richness of in situ observations, because of extensive past field campaigns. Here we provide and estimate of the expected changes of its temperature and salinity that are produced by low and high climate change scenario, with corresponding uncertainties. The ANN was parameterized using a combination of field data of temperature, salinity, and sea level, along with reanalysis data for ground temperature, wind speed (v and u components), temperature at 2 m, precipitation, evaporation, and humidity. Field data were obtained from a 10-year monitoring campaign, during which 30 stations within the lagoon were sampled. Reanalysis data were downloaded from the Copernicus ERA5 database. The climate scenarios used for projections were obtained from the Med-CORDEX network.
Current and future glacier melt contribution to groundwater dynamics in a high-altitude, Himalaya basin
Authors: Aubry-Wake Caroline , Immerzeel Walter
Published: March 2024

While mountain groundwater in glacierized regions has gained increasing attention, comprehensive insights of glacier melt contributions to groundwater and their resurfacing patterns remain limited. Our study employs a cryosphere-surface hydrology model in combination with numerical groundwater simulations to estimate the water table variations across the high-altitude Langshisha basin in the Langtang Himalaya (4094-6049 m). We evaluate surface water-groundwater interactions amidst current and projected climatic conditions. Utilizing in-situ weather forcings and evaluated with field measurements, our findings indicate that glacier melt contributes up to 70% of groundwater recharge in the Langshisha basin during the 2012-2020 period. This substantial contribution is attributed to the basin's considerable glacier cover (40%) and its high elevation, where cold temperatures in areas above 5300 m limit melt and are underlain by permafrost, restricting recharge. Groundwater simulations based on these recharge rates reveal a high sensitivity to hydraulic conductivity parameters but are constrained by field measurements of creek exfiltration indicating a water table near the surface along the main streams. The combination of groundwater simulations and field measurements suggests that groundwater exfiltration along the proglacial stream is a predominant surface-water-groundwater exchange mechanism within the basin. Considering the important role of glacier melt in groundwater recharge, our study applies future climate scenarios to gauge the impact of warming trends and glacier retreat on surface water-groundwater dynamics within the basin.
Dissolved Nutrients in Natural and Rewetted Peatlands: A Comparative Analysis
Authors: Krishnankutty Nimisha , Iversen Bo , Heckrath Goswin , Hansen Hans Christian , Zak Dominik
Published: March 2024

Intensive agriculture and artificial drainage have transformed natural peatlands into significant sources of greenhouse gas emissions as well as phosphorus and nitrogen pollution, the latter leading to an increased risk of eutrophication of adjacent water bodies. To address this issue and comply with the EU Water Framework Directive, restoring peatlands is a key strategy involving widespread rewetting to re-establish their roles as nutrient and carbon sinks. To achieve restoration objectives, it is essential to analyse and understand the temporal and spatial variability in porewater composition within natural and rewetted peatlands, with a specific focus on the influence of peatland type (bog or fen) and peat degradation status. Hence, an extensive international field survey of soil water and bulk soil was conducted from 1997 to 2017 on 60 natural and rewetted peatlands (both bogs and fens) in Germany, Poland, Estonia, Sweden, and the United Kingdom. The anoxic porewater samples were collected from water-saturated soil layers between 0 and 0.6 m depth. To quantify the concentrations of various chemical compounds in porewater at each location, at least three samples were taken using dialysis samplers within a spatial range of 5 to 10 meters. Selected sites were monitored for seasonal changes over a post-wetting period of 10 to 20 years. The results show significant differences in peat characteristics of upper soil layers from rewetted peatlands and natural peatlands, with the lowest values for nutrient contents, particularly in bogs. Notably, rewetted peatlands did not consistently display higher pore water concentrations of dissolved compounds compared to natural peatlands. However, in heavily drained and rewetted fens, anoxic pore waters exhibited concentrations of soluble reactive phosphorus (SRP), ammonium, and dissolved organic carbon one to two orders of magnitude higher than those in their natural counterparts. For example, SRP concentration in highly degraded peatlands ranged from 0.54 mg/L to 18.9 mg/L compared to 0.01 mg/L to and 3.6 mg/L in the natural peatlands. Weakly drained peatlands had, in some cases, slightly higher concentrations of dissolved substances compared to natural peatlands, but the differences were not statistically significant. Therefore, the research highlights the importance of porewater composition concerning the type of peatland, its degradation status, and spatial-temporal fluctuations in restored peatlands.
Impact of a new land surface package in Canadian  numerical weather prediction system on the medium range weather forecast in the lower and upper atmosphere
Authors: Alavi Nasim , Belair Stephane , Carrera Marco , Abrahamowicz Maria , Bilodeau Bernard , Simjanovski Dragan , Charpentier Dorothee , Badawy Bakr , Leroyer Sylvie
Published: March 2024

A new land surface package developed at Environment and Climate Change Canada (ECCC) has been evaluated in the context of the medium-range global deterministic numerical weather prediction (NWP) system. The evaluation is performed by comparison of NWP forecasts against near-surface andatmospheric analyses. The new land surface package includes i) new databases to specify soils and vegetation characteristics, ii) improved initialization of land surface variables by the assimilation of space-based remote sensing observations, and iii) a more sophisticated land surface scheme.Evaluation for the screen-level air temperature and humidity indicates that the new land surface package resulted in smaller STDEs and larger temporal correlation between forecasts and analyses comparing to the current operational configuration. The improvement is greater for humidity than for air temperature.Upper-air evaluation indicates that the impact of the new land surface package on the Planetary boundary layer (PBL) is substantial but more mixed, with large spatial variability in terms of its effect.This study also investigated the physical and statistical links between near-surface and upper-air forecast errors at the medium range.
The effects of including Antarctic subglacial meltwater flux to the ocean in the Energy Exascale Earth System Model
Authors: Branecky Begeman Carolyn , Vaňková Irena , Asay-Davis Xylar , Comeau Darin , Hager Alex , Hoffman Matthew , Maltrud Matthew , Shafer Courtney , Wolfe Jonathan
Published: March 2024

Subglacial runoff beneath ice shelves is a source of freshwater, and therefore buoyancy, at the grounding line. Being released at depth, it accelerates an ascending plume along the ice-shelf base, enhancing entrainment of ambient waters, and increasing melt rates. By now it is understood that subglacial runoff is a key control on melt rate variability at the majority of Greenland's glaciers. However, its importance in present-day and future Antarctic melt rates is less clear.To address this point, we use the Energy Exascale Earth System Model (E3SM) and investigate the effects of Antarctic freshwater volume flux addition in both idealized setups and realistic, global, sea-ice ocean coupled configurations. For realistic Antarctic configurations, we use the subglacial hydrology model from the MALI ice-sheet model with both distributed and channelized drainage run at 4-20 km resolution to calculate steady state subglacial discharge across the grounding line under historical ice-sheet conditions.  This meltwater discharge is implemented as a freshwater flux in MPAS-Ocean, the ocean component of E3SM.
Expected Performance of the GeoCarb Integrated Instrument from Thermal Vacuum Measurements During a Limited Performance Test
Authors: Crowell Sean , Moore III Berrien , Burgh Eric , Adamkovicz Mate , Miller Timothy , Somkuti Peter , Webb Alex , Spiers Gary , Mentzell Eric , O'Dell Chris , McGarragh Greg
Published: March 2024

After selection as the second Earth Venture Mission in 2016, the Geostationary Carbon Observatory (GeoCarb), led by the University of Oklahoma PI Dr. Berrien Moore III, was developed until its cancellation during Phase C in November 2022. The GeoCarb PI was directed by NASA to complete as much of the instrument as remaining funding permitted.  The GeoCarb team successfully completed alignment and focus of the spectrograph before verification through a sequence of thermal vacuum campaigns, during which other characteristics of the instrument were determined (e.g., SNR, spectral range, stray light). These measurements suggested that the GeoCarb integrated instrument would have sufficient performance to deliver on the promise of the originally proposed greenhouse gas observing mission.  As a result, the project continued with integration of the optical subassemblies and electronics into a fully integrated instrument as of August 2023.  The instrument underwent a final thermal vacuum campaign during the fall of 2023 and was shipped to NASA for storage in November 2023.  Since that time, the GeoCarb science team has been analyzing the test data to determine the capabilities of the integrated instrument with positive results.  All indications are that the GeoCarb instrument will meet its key performance requirements. In this presentation, we will discuss the spectral, radiometric, and polarimetric performance of the GeoCarb instrument from the measurement campaigns, including the spectral and spatial image quality, polarization response, and SNR.  We will address the implications for the scientific capabilities of the fully characterized and calibrated observatory should NASA restart the program.
Tropical forests: a source of CO!
Authors: van Asperen Hella , Warneke Thorsten , Carioca de Araújo Alessandro , Forsberg Bruce , José Filgueiras Ferreira Sávio , Röckmann Thomas , van der Veen Carina , Bulthuis Sipko , Komiya Shujiro , P Jones Sam , Botía Santiago , Ramos de Oliveira Leonardo , de Lima Xavier Thiago , da Mata Jailson , de Oliveira Sá Marta , Ricardo Teixeira Paulo , Andrews de França e Silva Julie , Notholt Justus , Trumbore Susan
Published: March 2024

CO is an indirect greenhouse gas because it reacts with OH, therefore increasing the lifetime of methane: its possible indirect radiative forcing has been estimated as larger than that of N2O. Previous studies have indicated that temperate and boreal forests act as a net sink for CO, but the role of tropical rain forest ecosystems has not been investigated. We present the first CO flux measurements from tropical forest and forest soils, and can show that tropical rain forests are a net source of CO to the atmosphere.During two intensive field campaigns at tropical rain forest fieldsite ZF2 (Manaus, Brazil), soil CO fluxes were determined by use of flux chambers. In addition, nighttime vertical CO concentration profiles were measured and different micro-meteorological techniques were applied to estimate ecosystem CO fluxes. Furthermore, we performed nocturnal CO concentration measurements in a seasonally inundated valley, which was hypothesized as a potential hotspot for ecosystem CO emissions.Soil CO fluxes ranged from -0.19 (net soil uptake) to 3.36 (net soil emission) nmol m-2 s-1, averaging ∼1 nmol CO m-2 s-1. Fluxes varied with season and topographic location, with highest fluxes measured in the dry season in a seasonally inundated valley. Nocturnal canopy air profiles show consistent decreases in CO mixing ratios with height, which requires positive surface fluxes between 0.3 and 2.0 nmol CO m-2 s-1. Similar fluxes are derived using a canopy layer budget method, which considered the nocturnal increase in CO over time (1.1 to 2.3 nmol CO m-2 s-1). Using wet season concentration profiles of CO, the estimated valley ecosystem CO production exceeded the measured soil valley CO fluxes, indicating a potential contribution of the valley stream to overall CO emissions.Based on our field observations, we expect that tropical rain forest ecosystems are a net source of CO. Extrapolating our first observation-based tropical rain forest soil emission estimate of ∼1 nmol m-2 s-1, a global tropical rain forest soil emission of ∼16.0 Tg CO yr-1 is suggested. Total ecosystem CO emissions might surpass this estimate, considering that valley streams and inundated areas could serve as local CO emission hotspots. To further improve tropical forest ecosystem CO emission estimates, more in-situ tropical forest soil and ecosystem CO flux measurements are essential.
Deep learning prediction of measured earthquake waveforms from synthetic data
Authors: Bauer Alexander , Walda Jan , Hammer Conny
Published: March 2024

Seismic waveforms of teleseismic earthquakes are highly complex since they are a superposition of numerous phases that correspond to different wave types and propagation paths. In addition, measured waveforms contain noise contributions from the surroundings of the measuring station. The regional distribution of seismological stations is often relatively sparse, in particular in regions with low seismic hazard such as Northern Germany. However, a detailed knowledge of the seismic wavefield generated by large earthquakes can be crucial for highly precise measurements or experiments that are carried out for instance in the field of particle physics, where seismic wavefields are considered noise. While synthetic waveforms for cataloged earthquakes can be computed for any point on the Earth’s surface, they are based on a highly simplified Earth model. As a first step towards the prediction of a dense seismic wavefield in a region with sparsely distributed stations, we propose to train a convolutional neural network (CNN) to predict measured waveforms of large earthquakes from their synthetic counterparts. For that purpose, we compute synthetic waveforms for numerous large earthquakes of the past years with the IRIS synthetics engine (Syngine) and use the corresponding actual measurements from stations in Northern Germany as labels. Subsequently, we test the performance of the trained neural network for events not part of the training data. The promising results suggest that the neural network is able to largely translate the synthetic waveforms to the more complex measured ones, indicating a means to overcome the lack of complexity of the Earth model underlying the synthetic waveform computation and paving the way for a large-scale prediction of the seismic wavefield generated by earthquakes.

 

 

 
 