bluuebunny/crossref_metadata_2025 huggingface dataset:
Improving flood management assessing seasonal flood regulating ecosystem services 
Authors: Galli Nikolas , Lompi Marco , Rulli Maria Cristina , Caporali Enrica
Published: March 2024

Changes in land use can influence flood hazard, as land cover is one of the main factors determining runoff during an extreme rain event. Flood-regulating ecosystem services (FRES) are generally evaluated to assess how much water the environment can hold in a storm event, providing a reduction of runoff and flood hazard in a given river basin. Agriculture areas can be seen as part of the ecosystem that provides flood regulation, as they decrease the runoff with respect to barren areas or urbanized surfaces.  Methodologies developed to evaluate FRES usually assess the reduction of runoff due to land use changes without considering differences in initial soil moisture conditions before the storm event depending on crop rotation or irrigation management during the year.Here we provide a methodology to assess FRES on a seasonal basis, evaluating crops and irrigation management practices that may exacerbate flood hazards in small agricultural river basins. We do so by coupling two hydrological models. Watneeds, an agro-hydrological model which determines the water demand in agriculture, is used to derive daily soil moisture conditions. Mobidic, a fully distributed rainfall-runoff model, sets the soil moisture conditions of Watneeds as initial soil moisture before an extreme event to evaluate the associated flood hazard.We test the methodology on the upper Ombrone Grossetano river basin (Tuscany, central Italy), as more than 60% of its land cover is represented by agricultural areas. Gridded dataset and ground measurements are used to inform and calibrate the models and to perform the analysis. Particularly, the Chirps dataset is bias corrected with ground observation and used to perform the hydrological balance in Watneeds. Rain gauge measurement provided by the Hydrological Regional Service are used in a frequency analysis of extreme rainfall to extract the rainfall quantiles to be modeled in Mobidic.Results show that each crop provides different soil moisture conditions under identical meteorological conditions, impacting the flood hazard accordingly. Indeed, different agricultural scenarios and practices may produce different responses to similar events in different seasons with potential management applications. This underlines how FRES should be evaluated seasonally in agricultural river basins and how crop selection, irrigation scheduling and crop calendarization in small agricultural river basins could be subject to policies that also consider potential impacts on flood risk management.This work is part of the FLORAES project, funded by the Premio Florisa Melone 2023, awarded by the Italian Hydrological Society to stimulate independent research and collaboration among young Italian Hydrologists.
Modeling N2O, NH3 fluxes, and Nmin concentrations in agricultural soils treated with biogas digestate using a modified DNDC model
Authors: Grosz Balázs , Greef Jörg Michael , Tendler Linda , Jarrah Mahboube , Dechow Rene
Published: March 2024

The positive and negative effects of animal manure application to agricultural soils on soil inorganic nitrogen content, crop yield, ammonia (NH3), and nitrous oxide (N2O) emissions are well known and integrated into biogeochemical models. However, it is unclear if the effects of using digestate from biogas plants as fertilizer can be described by biogeochemical process models too. Since in Germany, the number of biogas plants increased drastically in the last two decades, there is a need for an evaluation and calibration of biogeochemical models for the application of digestate on arable land. For this purpose, we used data from a field experiment consisting of a control without fertilization, 3 treatments with mineral fertilizer, and 3 treatments with biogas digestate application (each with 60%, 80%, and 100% of maximum required N) on two cereal/maize crop rotations. Digestate was applied using trailing hoses. Results from experiments are used to calibrate and improve the biogeochemical model DNDCv.Can. Starting from a simplified description of O2 transport, a new sub-module quantifies O2 concentration by coupling decomposition with a 1-dimensional diffusion approach. Since the size of the anaerobic balloon calculated by the model influences many processes occurring in the soil, such as nitrification and denitrification, we hypothesize that a more realistic description of O2 concentration, together with a model calibration addressing the decomposition kinetics of digestate, will lead to a more precise process description and, thus, to a better estimation of N2O and, indirectly, NH3 gas fluxes, and to more reliable estimation of NO3- and NH4+ contents in the topsoil.
Use of Interferometric Synthetic Aperture Radar Techniques as a remote tool for Mineral Extraction Sites Monitoring 
Authors: Falabella Francesco , Pepe Antonio , Oštir Krištof , Calò Fabiana
Published: March 2024

This work addresses the preliminary achievements of the Horizon Europe European project S34I1 (Secure and Sustainable Supply of Raw Materials for EU Industry) (https://s34i.eu) that aims to increase the autonomy of Europe over raw materials resources through research and development of new data-driven methods devoted to the analysis of Earth observation data.
Among the different activities of the project, one of the major contributions is related to the systematic monitoring of mining site instabilities by developing and applying advanced multi- temporal interferometric SAR (InSAR) techniques for the generation of long-term deformation time series. To this purpose, an InSAR multi-grid technique for efficient processing of sequences of differential SAR interferograms (particularly, phase unwrapping operations) has been carried out directly at the native high-resolution spatial grid without any model or assumption on deformation.
Experiments were carried out at the Gummern, Austria, extraction site by processing two sets of ascending and descending SAR datasets collected by the EU Copernicus Sentinel-1A/B sensors from October 2014 to March 2023. The line-of-sight (LOS)-projected ground displacement time series were generated by inverting the unwrapped single-look interferograms using the Small BAseline Subset (SBAS) algorithm. Consequently, non-linear and seasonal patterns that could indicate a sudden failure of the surface can be identified with millimetre precision. A post-operation step was also performed over the group of georeferenced common targets as seen from complementary geometries (i.e., ascending and descending orbits) to compute from LOS-projected ground deformations the long-term 2-D (up-down, east-west) ground displacement time series. To this aim, conventional and new combination methods are applied by assuming the north-south displacements do not significantly contribute to the observed LOS measurements.1This study is funded by the European Union under grant agreement no. 101091616, project S34I – SECURE AND SUSTAINABLE SUPPLY OF RAW MATERIALS FOR EU INDUSTRY, coordinated by Ana C. Teodoro.
Eco-hydrology modelling in arid areas : Study of root density impact on water fluxes in the Sahelian region
Authors: Rapp-Henry Lucie , Cohard Jean-Martial , Tabsoba Mahamadi , Hector Basile , Demarty Jérôme , Condon Laura
Published: March 2024

The Sahelian region experienced intense droughts between 1970 and 1990 and despite a precipitation « recovering », soils remain degraded and a decrease in the soil ability to infiltrate - an essential characteristic for vegetation recover – is observed, together with an increase of desertification and of eroding floods frequency.To tackle with this phenomenon, coordinated agricultural strategies, like the Great Green Wall project, have been encouraged and spread over large areas through NGOs. This consists in applying agro-ecological practices, like micro dams, to harvest water, favor infiltration, and hence vegetation growth. These strategies still require critical assessments and optimisation. To study such agroecological practices we are developing a modelling framework based on ParFlow-CLM to simulate the interactions between surface hydrology and vegetation in the context of crusted Sahelian soils where water transfers are highly dependent on both surface hydrodynamical properties and root distribution below the surface. Indeed, the very thin eolian crust acts as a hydraulic discontinuity that slows down soil evaporation transfers but not transpiration, which benefits from the roots below the crust and from the stems which bridge to the atmosphere.However, since the CLM family models were designed for large scale with a relatively thick mesh at the surface, the root density function proposed as a decreasing exponential function distribute the majority of roots just below the surface. This disposition is irrelevant for finer millimeter underground meshes modelling, particularly in areas with a hot and dry climate such as the Sahelian one, that dries very rapidly the first centimetres of soil.Thus, In the CLM land surface model framework and according to literature, we propose a new root distribution in the soil, using a parameterised function which is zero at the surface and at infinity, and adapt the maximum root density depth and the root concentration around this maximum. We compare the impact of both initial and proposed root functions on a Sahelian case study in Niger where all necessary data are available thanks to the AMMA-CATCH observatory. Studying this function highlighted simple causal relations between root density function parameterisation and evapotranspiration flux.By modifying the root density function, we can find a set of parameters corresponding to a better representation of transpiration, global evapotranspiration and soil moisture, and accordingly, a better representation of the studied ecosystem.Once this representation is relevant, a dynamic LAI based on allocation laws, available in the latest version of CLM, will then complete this modification of the vegetation scheme. We will then introduce the changes on surface due to agricultural practices and study the impact of their sizing.
Pre-event conditions and rainfall–runoff characteristics drive suspended sediment source variability
Authors: Martínez-Carreras Núria , Iffly Jean François , Pfister Laurent
Published: March 2024

Most of the total sediment load transported in river systems is carried in suspension. Therefore, if we are to reduce soil erosion and sediment export, it is essential to determine suspended sediment sources and the drivers of its mobilisation into the river network. In this study, we combined the monitoring of suspended sediment fluxes and the sediment fingerprinting approach to test if pre-event conditions and rainfall-runoff characteristics drive suspended sediment source variability in catchments under a semi-oceanic climate. The sedimentological response to storm runoff events was studied in seven nested sub-catchments of the Attert River basin (0.4 - 245 km2), which have contrasting geological bedrock (sandstone, marls and shale) and land uses (forest, cropland and grassland). We collected stream water samples during storm runoff events (~30 events per catchment) using automatic water samplers to measure suspended sediment fluxes. In parallel, time-integrated suspended sediment and sediment sources samples (i.e., from different land use types) were collected and analysed in the laboratory (geochemistry, colour and organic properties) to determine the sediment origin using the sediment fingerprinting approach. Each sampled event was parameterized to describe rainfall, runoff, sediment transport and the relative contribution of each land use type to the sampled suspended sediment. Next, we assessed the relationships between variables. We found higher significant correlations between suspended sediment loads and runoff parameters (i.e., peak discharge and event runoff) than between suspended sediment loads and rainfall parameters (i.e., event precipitation, antecedent rainfall, and maximum rainfall intensity). Peak discharge for single events was found to be the best predictor of sediment loads in the studied catchments. We show that most events exhibit clockwise hysteretic loops between discharge and suspended sediment concentration in all studied catchments. We attribute this finding to the erosion or remobilization of sediment previously deposited on the channel bed or an adjacent area. During most of these events with clockwise hysteretic loops, sediment source apportionment presented a consistent pattern.
A top-down modeling approach to assess regional scale groundwater vulnerability: a case study for Berlin-Brandenburg
Authors: Somogyvári Márk , Brill Fabio , Tsypin Michael , Krueger Tobias
Published: March 2024

Regional scale groundwater vulnerability models are urgently needed to cope with the challenges presented by climate change. Traditional modeling approaches in hydrology and hydrogeology however often require detailed process understanding, and geological information to reliably simulate the hydrological system. In this study we present an alternative, top-down model development framework, starting from the big picture of the hydrology of the region, then focusing on the smaller details and complexities in a gradual way.Groundwater vulnerability of the Brandenburg region is assessed by investigating the response of the groundwater table to different weather patterns. In order to achieve this a regional dataset for Brandenburg is prepared, using monthly groundwater and surface water data from the time period 1990-2022. This data is then reflected to weather timeseries taken from the Central European Refined analysis dataset, a gridded climate reanalysis dataset for the same time period. The datasets are aggregated on a subcatchment scale, which allows closing the water balance for the individual hydrological response units. Both water balance, linear regression and non-linear regression models are used with automatic calibration, because of the large number of modelled subcatchments.Due to the big-data nature of the modeling approach, the interpretation of the results is also done in an automatized way. We delineate regions of different vulnerability characteristics by unsupervised methods based on their response dynamics. We also try to identify major turning points in the climatic water balance timeseries. The presented framework produces models that can be used towards deriving actionable insights for groundwater management.
Assessing avalanche activity in seismic data with modern machine learning methods.
Authors: Simeon Andri , Pérez-Guillén Cristina , Volpi Michele , Seupel Christine , van Herwijnen Alec
Published: March 2024

Monitoring snow avalanche activity is essential for operational avalanche forecasting and the successful implementation of mitigation measures to ensure safety in mountain regions. To facilitate and automate the monitoring process, avalanche detection systems equipped with seismic sensors provide a cost-effective solution. Still, automatically differentiating avalanche signals from other sources in seismic data remains rather challenging. This is mainly due to the complexity of the seismic signals generated by avalanches, the relatively rare occurrence of avalanches and the presence of multiple sources in the continuous recordings.To discriminate avalanches from other sources in the continuous seismic recordings, we test three random forest classifiers using two feature sets extracted with two autoencoders and a set of 57 statistical features. We extract these features from 10s windows of the seismograms recorded with an array of five seismometers installed in Davos, Switzerland. The statistical feature set includes waveform, spectral and spectrogram attributes. The first autoencoder is composed of convolutional layers and a long short-term memory unit. This neuronal network automatically extracts 64 features from the raw waveform signal. The second autoencoder applies a sequence of fully connected layers to extract the same number of features from the spectrum of the signals. We assess the performance of each classifier and compare the results. To improve the predictive performance of the seismic system, we employ different post-processing, e.g. adaption of classification thresholds and ensembling the predictions from the three classifiers. The final model is tested with the continuous seismic data of the last winter season to potentially be used as an operational, near-real-time detection system.
The Weather On Demand weather forecast framework - Recent developments and outlook
Authors: Rognvaldsson Olafur , Stanislawska Karolina
Published: March 2024

Belgingur Ltd. has created a novel weather forecasting framework, called Weather On Demand – WOD, that is deployable in the cloud and on in-house hardware and which can be customised for any location world-wide at a very short notice.The WOD framework is a distributed system for:Running the WRF weather model for data-assimilation and forecasts by either triggering scheduled or on-demand jobs.
Gathering upstream weather forecasts and observations from a wide variety of sources.
Processing data for long to medium-term storage.
Making results available through APIs.
Making data files available to custom post-processors.
Much effort is put into starting processing as soon as the required data becomes available and in parallel when possible.Recent additions to the WOD system include the potential of:Optional use of the hybrid data assimilation techniques of the WRF Data Assimilation system [1, 2].
Set up a multi-domain dispersion forecast of volcanic ash and gases.
Use of the Verif [3] verification package to compare forecasts, both upstream and WOD, to observations.
Using different sources of initial data to that of the boundary forcing data.
On-going developments focuses on the use of in-situ UAV profiles and radar data as input to the WOD data assimilation system.We have further started experimenting with using global models, both conventional NWP models as well as novel ML models (cf. abstract no. EGU24-15614).References:[1] Xuguang Wang, Dale M. Barker, Chris Snyder, and Thomas M. Hamill, 2008: A hybrid ETKF–3DVAR data assimilation scheme for the WRF model. Part I: Observing system simulation experiment. Mon. Wea. Rev., 136, 5116–5131.[2] Xuguang Wang, Dale M. Barker, Chris Snyder, and Thomas M. Hamill, 2008: A Hybrid ETKF–3DVAR Data Assimilation Scheme for the WRF Model. Part II: Real Observation Experiments. Mon. Wea. Rev., 136, 5132–5147.[3] Nipen, T. N., R. B. Stull, C. Lussana, and I. A. Seierstad, 2023. Verif: A Weather-Prediction Verification Tool for Effective Product Development. Bulletin of the American Meteorological Society 104, 9; 10.1175/BAMS-D-22-0253.1.
On critical dependence of atmospheric circulation response to regional SST biases on background SST
Authors: Zhao Yuan-Bing , Zagar Nedjeljka , Lunkeit Frank
Published: March 2024

This study examines how the geographic location of sea surface temperature (SST) biases influences global atmospheric responses. Utilizing an intermediate-complexity atmospheric model, 106 century-long simulations with idealized SST perturbations—emulating biases in coupled climate models—were performed. The intensity of the global atmospheric response to SST biases is evaluated by quantifying changes in global wave energy and interannual variance. The findings underscore the response's dependency on local background SST. Notably, with an imposed SST bias of +1.5 K, a significant global response is triggered once background SST surpasses approximately 25°C. This geographic dependency is related to the critical SST threshold for intense convection. Consequently, these results highlight the need for heightened focus on tropical oceans, especially the Indo-West Pacific, where SST biases can significantly impact the accuracy of global climate simulations.
Monitoring the changes in glacial lakes in the Southern Alps, New Zealand from 2000-2023 using an Object-Based Image Analysis (OBIA) approach in Google Earth Engine (GEE)
Authors: Morgan Tomos , McNabb Robert , Dunlop Paul
Published: March 2024

Glacial lakes are growing rapidly, driven by climatic change and glacial retreat. The growth of glacial lakes may increase the magnitude and frequency of glacial lake outburst floods (GLOFs), posing a hazard to downstream populated regions. Satellite remote sensing provides a way to improve monitoring efforts, though automatic methods are needed to accurately and rapidly monitor changes in these lakes. In this study, we develop and apply an Object-Based Image Analysis (OBIA) approach to 71 multispectral Landsat 5-9 Top-Of-Atmosphere (TOA) satellite imagery in Google Earth Engine (GEE) to monitor the changes of 14 lake-terminating glacial lakes across the Southern Alps of New Zealand outside of the winter season (June-September) between 2000-2023. The Southern Alps of New Zealand are experiencing increasing glacial mass loss and despite previous glacial lake monitoring it remains necessary to continue monitoring these glacial lakes to understand the magnitude of their contribution to past regional ice mass loss. Our results show that the collective area of these 14 glacial lakes increased by 69% between 2000-2023, from 12.84 ± 0.06 km2 to 21.71 ± 0.1 km2. We evaluate the accuracy of this method by comparing automatically generated classification to manually classified points, using a stratified random sampling approach. Preliminary results derived for the accuracy of Landsat 9 satellite imagery resulted in an overall accuracy of 89%, with a producer’s accuracy and user’s accuracy of 98% and 96% respectively, for water. These preliminary results suggest that the method has the potential to map glacial lakes accurately and rapidly and can be applied to other glaciated regions.
Modeling human displacement in the 2022 Pakistan floods: Current gaps and opportunities.
Authors: Lohrey Steffen , Kam Pui Man , Biess Bianca , Cache Tabea , Di Vincenzo Sabrina , Horton Radley M. , Thalheimer Lisa
Published: March 2024

The 2022 Pakistan floods have been unprecedented in their extent. They affected around 33 million people, caused about 15 billion USD in damages, and took the lives of more than 1,800 persons, dominantly in the southern parts of the country.Effective disaster response requires fast assessments of likely impacts from hazardous weather to inform decision-makers and guide relief efforts for early action. Displacement modeling is a key technique towards these goals. However, displacement modeling which accounts for socio-economic components and uncertainties is methodologically challenging, and quantitative evidence largely remains limited and fragmented. Much work is needed to resolve these.This study aims at providing a case study for disaster displacement modeling by using the open-source impact assessment platform CLIMADA to investigate the extent by which flood-related hazards can be used to quantify displacement numbers in a data-limited region. Here, we estimate displacement from the 2022 Pakistan floods in Sindh province as a case study. We combine data on flood depth, exposed population, and provide impact functions that relate vulnerability of people likely to be displaced. We further use published numbers of affected people as target data for our model. The centerpiece of our analysis is the choice of impact functions. We test different forms of impact functions as well as assumptions about critical flood depths to proxy the number of displaced people, first using ex-ante assumptions, and then a numerically optimized version.With ex-ante assumptions, our model predicts a range of 1.94 to 5.65 million of displaced people in Sindh province, as compared to a total number of 6.76 million as reported by government sources. When we apply numerically optimized impact functions, the results closely resemble those obtained using the ex-ante assumptions, indicating that the current methods underestimate the extent of displacement. Additionally, we have evaluated the relationship between local vulnerability and the level of urbanization, and our findings reveal a negative correlation.We use this model to explain different displacement estimates for the 2022 floods across Pakistan and thereby contribute a case study to the growing field of displacement models, and towards the development of more refined ones. It highlights opportunities as well as limitations, and is a quantitative contribution to an existing discussion on how much disaster-related displacement can be modelled, and in how far assumptions can be generalized. These insights also support a better understanding of displacement and migration from future climate risks.
Signal decomposition of multi-source displacement fields with component analysis methods, applied to InSAR time series of the Epe gas storage cavern field (Germany)
Authors: Seidel Alison , Even Markus , Westerhaus Malte , Kutterer Hansjörg
Published: March 2024

Time series of interferometric SAR (InSAR) images offer the potential to detect and monitor surface displacements with high spatial and temporal resolution, even for small and slow deformation processes. Yet, due to the nature of InSAR, the interferometric signal can contain a multitude of contributions. Different displacement source mechanisms could superpose each other, signals that are residuals of atmospheric and topographic effects could not be completely removed during processing of the time series or non-coherent noise could exist. Therefore, the criteria for the selection of temporally stable pixels are often rather strict, leading to significant reduction of the spatial resolution density.However, to understand the underlying processes of a deformation field, it is important to extract the displacement signals from the data at the best resolution possible and differentiate signals from different source mechanisms. Furthermore, being able to describe the displacement field as superposition of several simple mechanisms is a possible answer to the general question how the information content from tens of thousands of points each coming with a time series over hundreds of acquisitions can be extracted and comprehended.We address these issues, by determining the dominant displacement signals of different sources in a subset of reliable pixels of InSAR time series datasets with data driven component analysis methods. Subsequently we use models of these signals to identify their displacement patterns in previously not regarded pixels. We utilize the statistical principal component analysis for removing uncorrelated signal contributions and compare different blind source separation methods, such as independent component analysis and independent vector analysis for differentiating between displacements of different origin.We apply our method to a dataset of multiple orbits of Sentinel-1 InSAR time series from 2015 to 2022 above the gas storage cavern field Epe in NRW, Germany.  Epe displays a complex surface displacement field, consisting of trends caused by cavern convergence, cyclic gas pressure dependent contributions, as well as ground water dependent seasonal displacements. With our approach, we can successfully distinguish the signals of the different source mechanisms and obtain a dense spatial sampling of these signals. Our results show good agreement with geodetic measurements from GNSS and levelling and show a strong correlation to cavern filling levels and groundwater levels, suggesting causal relations.
Reaching the Data Space – Standard data procedures and defining responsibilities for common data elements
Authors: Soeding Emanuel , Poersch Andrea , Razeghi Yousef , Kottmeier Dorothee , Malinovschii Stanislav
Published: March 2024

At the Helmholtz Association, we strive to establish a well-formed harmonized data space, connecting information across distributed data infrastructures. This requires standardizing the description of data sets with suitable metadata to achieve interoperability and machine actionability. One way to make connections between datasets and to avoid redundancy in metadata is the consistent use of Persistent Identifiers (PIDs). PIDs are an integral element of the FAIR principles (Wilkinson et al. 2016) and recommended to refer to data sets. But also to other meta data such as people, organizations, projects, laboratories, repositories, publications, vocabularies, samples, instruments, licenses, and methods should be commonly referenced by PIDs, but not for all of these agreed identifiers exist. Consistently integrating the existing PIDs into data infrastructures can create a high level of interoperability allowing to build connections between data sets from different repositories according to common meta information. In HMC we start this process by implementing PIDs for people (ORCID) and organizations (ROR) in data infrastructures.Harmonizing PID metadata, however, is only the first step in setting up a data space. Here we shed some light on which strategies we recommend for the implementation within the Helmholtz Association and make suggestions, which stakeholder groups should be included in order to hold them responsible for maintaining them to shape the Helmholtz Data Space. The conclusions from this process do not only affect the implementation of PID metadata, but may also be used for the harmonization of vocabularies, digital objects, interfaces, licenses, quality flags and others, in order to connect our global data systems, to redefine stakeholder responsibility and to ultimately reach the data space.
A high resolution gridded European Atmospheric Potential Oxygen (APO) inventory 2005 - 202
Authors: el Malki Marya , Visschedijk Antoon , Denier van der Gon Hugo
Published: March 2024

Accurately attributing CO2 emissions is key to mitigating greenhouse gas (GHG) emissions under the European Green Deal. However, this poses significant challenges. To this end, the CO2MVS Research on Supplementary Observations (CORSO) project will support establishing the new European anthropogenic CO2 emissions Monitoring and Verification Support capacity (CO2MVS), which is being implemented within the Copernicus Atmosphere Monitoring Service (CAMS). To better separate the contribution of fossil fuels and natural sources to atmospheric CO2 emissions, CORSO will be using supplementary observations focused on co-emitted species, as well as other auxiliary observations, such as gaseous tracers. One such tracer is Atmospheric Potential Oxygen (APO), that integrates both emitted CO2 and the corresponding O2 uptake through the derivation of an oxidative ratio (OR). Here we present  the development and further refinement of a regional APO inventory that spans the European domain from 2005 to 2020 at a resolution of 6 x 6 km. The inventory builds upon earlier work conducted under the CO2 Human Emissions (CHE) project for the year 2015, leveraging CO2 and co-emitted species emissions from the CAMS-REG-v4 dataset produced by TNO. It expands on the methodology used to develop the COFFEE global dataset (Steinbach et al., 2011) by introducing several improvements. Oxygen consumption from fossil fuel combustion is dominated by CO2 but we also incorporated the O2 uptake from the production of the co-emitted species NOx, CO and SOx. Furthermore, the oxidative ratios associated with sea shipping are refined using detailed fuel classification data from the Finnish Meteorological Institute (FMI). We will compare the results against global APO datasets, including COFFEE and GridFED, and drawing conclusions on consistency across the different inventories. Finally, we aim to collaborate closely with end-users of the inventory under the CORSO project, to assess the interpretability of results and ascertain the dataset’s relevance in supporting robust modelling practices. Through these refinements, we aim to contribute to the growing body of knowledge dedicated to more accurately quantify anthropogenic CO2 emissions, to help inform effective policies that support nations in achieving their objectives under the European Green Deal.
The dynamic deep subsurface of high-energy beaches 
Authors: Massmann Gudrun ,   DynaDeep Project Team
Published: March 2024

Coastal aquifers are dynamic, shaped by natural and anthropogenic boundary conditions acting on very different time scales from seconds (waves) up to millennia (sea-level rise). In subterranean estuaries (STEs), inland aquifers connect with the sea. With terrestrial freshwater and circulating seawater, chemically different waters mix in STEs and are modified before they discharge into coastal waters. The cooperative interdisciplinary project DynaDeep studies the subsurface of high-energy beaches, which have so far hardly been investigated due to the difficulties associated with working under high-energy conditions. We propose that these systems are particularly dynamic environments, where frequent sediment relocation affects groundwater flow and transport up to depths of tens of meters below the ground surface. This may lead to strong spatiotemporal variability of geochemical conditions, presumably attracting a unique microbial community. The state-of-the-art concept of groundwater flow and transport in STEs with an upper saline plume overtopping a freshwater discharge tube is likely distorted under such conditions, with consequences for the biogeochemical functioning of these STEs. Within DynaDeep, a unique cross-shore research site was established on the northern beach of the barrier island Spiekeroog facing the North Sea. It consists of permanent infrastructure, such as a pole with measuring devices, multi-level groundwater wells and an electrode chain. This forms the base for autonomous measurements, regular repeated sampling and interdisciplinary field campaigns using, for example, direct push techniques supported by modelling and experimental work to understand and quantify the functioning of the biogeochemical reactor. Field results show that morpho- and hydrodynamics are clearly affected by waves, tides and stormfloods. Stormfloods are particularly relevant and can be traced into the subsurface. In the infiltration zone, the groundwater is rather young as shown by age dating and temperature tracing. Oxygen and nirtrate reach deep into the subsurface and a redox transition from oxic to anoxic conditions occurs at 12-15 m depth. In contrast, relatively old and anoxic water discharges near the low water line. Numerical modelling aids in process understanding and hypothesis development, and generic models show that moderate deviations in hydrogeological parameters severely change both salinity as well as hydrogeochemical patterns. The dynamic nature of high-energy STEs, depending on frequencies and amplitudes of change in environmental conditions, as well as the global relevance for high-energy STEs have yet to be explored.
Exploring the role of evaporation in atmospheric heat transport and seasonal low-latitude precipitation biases in CMIP
Authors: Geen Ruth , Laguë Marysa , Fajber Robert
Published: March 2024

Tropical and subtropical precipitation impact millions of people via agriculture and rainfall driven disasters. However, a wide spread remains in future regional projections of low-latitude precipitation, dominated by uncertain shifts in rainfall, and models continue to show a variety of biases in the location and intensity of rain.The Energy Flux Equator framework has emerged as a powerful tool in interpreting the location of low-latitude rainfall via atmospheric heat transport (AHT), which in turn can be understood through the top of atmosphere and surface energy fluxes. Recent work using a novel decomposition of the zonal-mean AHT suggests that its spatial structure is dominated by the meridional structure of the latent heat flux. Here, we apply this decomposition to investigate intermodel differences in AHT on the seasonal timescale.We find that throughout the year, intermodel differences in total AHT and the latitude of maximum zonal mean precipitation both correlate strongly with the heat transport contribution attributed to evaporation. Curiously, spatial regressions appear to suggest that evaporation over land provides a key contribution to this spread, despite the net surface heat flux over land being close to balanced. To interrogate the causality underlying this correlation with land evaporation, we make use of the 1pctCO2-bgc simulations, in which only the carbon cycle responds to increasing carbon dioxide, with one consequence being altered evapotranspiration.
Coupling of thermo-hydro-mechanical modeling with seismicity modeling in a faulted geothermal reservoir
Authors: Habibi Rahim , Ulrich Thomas , Gabriel Alice-Agnes , Wasserman Joachim , Gaucher Emmanuel
Published: March 2024

Geothermal energy is seen as an effective factor in the global energy transition for the supply of heat and electricity. To produce large amount of geothermal energy, reservoirs with high temperature, usually deep, should be targeted. At depth, aquifers are not common and most of the geothermal fluid circulates within a fault network. Hence, such geothermal reservoirs can be exploited using production and injection wells drilled in faulted geological formations. The operation of such faulted geothermal system generates seismic events, which sometimes can be felt by humans. The events result from the complicated thermo-hydro-mechanical and chemical response of the system against operational and geo-reservoir parameters, e.g. production flow rate, fault permeability, stress field, etc. Consequently, numerical simulation of the operation of faulted geothermal systems can provide operators and the public with information on the likely occurrence of seismic events during and after the lifetime of the powerplant. Considering seismic event as an additional response of the system to the THMC response in the numerical model makes the simulation more complicated and involves more parameters.In this study, to solve the simulation complexity, we propose to couple two finite element numerical codes, one based on the MOOSE framework and SeisSol. The former is used to simulate the THM response of the reservoir (chemical effects are not taken into account) to site operations. The latter is applied to simulate the dynamic seismic response of the fault(s). For the coupling, a bash script is written to call and execute each code, manage the feedback of the corresponding results and loop over time. Thus, the coupled modeling starts with the MOOSE-based code to simulate the THM behavior in relation to the operation until fault failure. For the time being, the Mohr-Coulomb failure criterion is used. Once failure has occurred, the bash calls SeisSol to simulate the seismic event based on the MOOSE outputs, e.g. stress, and propagates the rupture through the fault system. The outputs of the SeisSol, e.g. stress, will be imported into the MOOSE simulator to continue the simulation after the seismic event. Therefore, the outputs of each code are considered as initial counterpart conditions for the next step in the loop. Looping will continue for the predefined duration of the field operation.
Development of a near real-time demonstrator based on a very high-resolution global ocean model
Authors: Bricaud Clement , Chanut Jerome , Bourdalle Badie Romain , Malicet Mary , Drillet Yann
Published: March 2024

In the framework of the Copernicus Marine Environment Monitoring Service, Mercator Ocean International operates a global high-resolution forecasting systems at the resolution of 1/12°. Increasing resolution appears necessary to improve the quality of service and to satisfy the users’ needs in the operational application (Le Traon, 2019). Resolving scales below 100 kilometers, and in particular sub mesoscale processes (1-50 km), appears to be essential to better represent the circulation in the open ocean (Chassignet, 2017), and, to improve the large-scale representations thanks to a more explicit energy transfers between finer and larger scales (Fox-Kemper Baylor, 2019). A deeper understanding of their various contributions (geostrophic flows, tidal motions, waves, inertial currents) and their role in the global ocean kinetic energy budget will improve the knowledge of these energy transfers between different scales.  In 2019, it has been decided to go towards higher resolution and develop a new global sub mesoscale-permitting model. Benefiting from the context of the European H2020 IMMERSE project, a new 1/36° global configuration (2 to 3 km resolution), based on the NEMO 4.2 OGCM, has been developed. Thanks to the resolution increase, this model can resolve the Rossby radius in almost all open oceans areas at global scale quite everywhere and to span a large part of the internal wave spectrum. In 2022, a hierarchy of multi-year simulations at 1/4°, 1/12° and 1/36° resolution and with/without explicit tide representation has been performed: for each resolution, after a 3-years spin up without tidal forcing, 2 twin 3-years runs have been realized: one without tidal forcing and one forced by the 5 tidal components K1, O1, S2, M2, N2. These models are driven at the surface by the 8km/1hour ECMWF IFS system. Atmospheric pressure forcing has been activated. In 2023, in the framework of the EDITO-Model Lab project, the development of a near real-time demonstrator has been started. The system is based on the new 1/36° global configuration and constrained by a spectral nudging to the CMEMS/MOI global 1/12° real-time system (for temperature , salinity , horizontal velocities and  sea ice concentration).We propose a first evaluation of the benefits due to the resolution increase and tidal forcing. Circulation, energy, tidal representation and mixing of the experiments are compared to each other’s. 
Food loss & waste of staple crop products: mapping environmental impacts within the Nexus paradigm
Authors: Semeria Francesco , Falchetta Giacomo , Vinca Adriano , Laio Francesco , Ridolfi Luca , Tuninetti Marta
Published: March 2024

Over the last decade, a combination of economic uncertainty, supply shocks, and extreme climate events has led to a renewed prevalence of undernourishment, posing a serious threat to the realization of the Zero Hunger Sustainable Development Goal. Future scenarios are likely to be even more challenging to its accomplishment, based on projected trends of population growth and human-induced climate change impacts. There is urgent need for the development and implementation of sustainable transformation pathways to make agri-food systems worldwide more resilient and capable to sustain these pressures. These pathways should include a wide range of actions, targeting all stages of the value chain. Reducing food loss and waste (FLW), which currently accounts for approximately one-third of the food produced, is considered among those with the largest potential, with significant environmental co-benefits on the Water-Energy-Food-Ecosystem Nexus. The presence of complex and tele-coupled trade networks however, together with the lack of robust and granular datasets, make it difficult for researchers to run detailed analyses on this issue.In this work we estimate the FLW associated to the consumption of a wide range of staple crops globally, disaggregating between the single food commodities and the different stages of the value chain. Moreover, we investigate the associated impacts on the water, land, and energy resources. The methodology applied allows us to trace the environmental impacts from the countries of production and manufacturing, where resources have been used, to the countries of consumption (from farm to fork) and backwards (from fork to farm), offering a dual perspective on the complex system. Our preliminary results show that over 20% of the quantities cultivated are wasted through FLW, globally. Transnational flows of FLW – and of associated virtual resources – compose a vast multi-layered network involving most of the countries worldwide. Differentiated impacts are observed, depending on the countries’ role in the network: while large exporters bear substantial impacts of FLW occurring abroad on their resources, net-importing nations transfer large portions of the environmental effects of the FLW associated with their consumptions onto foreign stocks. The ability to discern between the single food commodities, without aggregating primary and derived products, increases the level of specificity from past research. This detailed data is valuable for informing public policies, providing a more fine-grained approach to prioritize efforts in reducing FLW and its associated impacts.
Multi-Source Earth Observation Data for Drought Monitoring in the Borena Region in Southern Ethiopia
Authors: Weldemariam Elias Cherenet , Mulualem Getachew Mehabie , Yohannes Tinebeb , Nieto Héctor , Andreu Ana , Burchard-Levine Vicente
Published: March 2024

Drought is a recurring phenomenon in the Borena region of Southern Ethiopia. The imbalance between potential evaporation and precipitation during the growing season often results in drought conditions, posing significant threats to the biodiversity, agriculture and human activities. The zone has endured severe drought risk due to consecutive years of no rainfall, significantly impacting ecosystem services, livestock and agro-pastoralist communities. To mitigate the effects of droughts and to provide quick decision-making with timely information for an effective response, it is crucial to regularly analyze the information about its severity and its extent in terms of spatial and temporal pattern. This study analyzes the spatial and temporal pattern of drought in the Borena region, using integrated indices such the Composite Drought Index (CDI) from 2000 to 2022. The CDI, which incorporates the Precipitation Drought Index (PDI), the Temperature Drought Index (TDI), and the Vegetation Drought Index (VDI), are used as input to examine spatial and temporal drought patterns, providing a comprehensive view of drought conditions over the given area. Additionally, the Mann–Kendall trend test and Sen’s slope were employed to understand the trends of these indices and determine their magnitude of change.The study identified the occurrence of extreme drought events in recent years during 2007, 2011, 2014, 2016, 2017, and 2021 in Borena Zone. The findings also showed a decreasing trend in rainfall, an increase in temperature, and a diminishing trend in vegetation condition during the study period. Specifically, the computed mean growing season of the Normalized Difference Vegetation Index (NDVI) values ranged between -0.02352 to 0.0312, with 57.67% of the Borena region showing a decreasing trend. Future work will incorporate actual evapotranspiration (ET) estimates based on thermal infrared (TIR) imagery within the CDI, as this has the potential to more rapidly detect water stress in vegetation compared to spectral indices such as NDVI. These findings can guide the development of climate policies, disaster risk reduction and strategies in Ethiopia, contributing to the mitigation of future drought impacts and the promotion of sustainable dryland natural resources practices, including supporting early drought warning detection systems for agro-pastoralist communities.