bluuebunny/crossref_metadata_2025 huggingface dataset:
Exploring the attenuation of ice at elevated confining pressure
Authors: Goldsby David , Hager Travis , Maor Ron
Published: March 2024

The anelastic response of planetary materials to dynamic forcing and the associated dissipation of elastic strain energy influence a wide range of geophysical processes across seismic to tidal to isostatic rebound timescales, such as the attenuation of seismic waves in Earth’s mantle and tidal heating of icy satellites orbiting large host planets.  Most attenuation experiments on ice and other planetary materials are conducted at relatively low stresses, to mimic seismic attenuation and to prevent fracturing of experimental samples in the absence of confining pressure. Typically, these experiments explore a linear, amplitude-independent regime, primarily governed by mechanisms associated with grain boundaries. At sufficiently high stresses, nonlinear attenuation in planetary materials is predicted to occur above some critical strain amplitude from the motion of dislocations and becomes important, for example, in exoplanetary systems wherein highly eccentric orbits of planets with close proximity to host stars can yield enormous tidal stresses.
To study the onset of nonlinear dissipation behavior of ice and the associated amplitude dependence, we conducted cyclic deformation experiments in a high-pressure gas apparatus at elevated confining pressures. Cylindrical ice samples with a uniform initial grain size of ~0.3 mm were sealed in indium jackets, pressurized to 30 MPa and loaded to a median differential stress in the range 4 to 8 MPa.  The samples were then subjected to sinusoidal stress oscillations from 0.5 to 3 MPa about the median stress at frequencies of 10-3 to 5x10-3 Hz.  The resulting sample strain was measured with a strain gauge sandwiched between the outer surface of the ice cylinder and the 0.4 mm-thick indium jacket. The phase lag between the applied stress and the resulting strain response was used to calculate the inverse quality factor, Q-1. For experiments conducted at 260 K, we observe a marked dependence of Q-1 on stress amplitude at three different frequencies.  We also observed that Q-1 decreases, and Young’s modulus increases, with increasing forcing frequency.  Our experimental results will be evaluated in the context of existing theoretical models of nonlinear attenuation. 
Tailpipe on-road emissions of carbonaceous aerosols and gaseous pollutants from light-duty and heavy-duty vehicles for Indian on-road conditions
Authors: Khan Mohd Shahzar , Kumari Jyoti , Kumar Niraj , Kumar Rahul , Habib Gazala
Published: March 2024

Emissions from road transport sector plays vital role in urban air pollution, climate change and human health risk. Diesel exhaust emissions are the primary source of carbonaceous aerosols including particulate matter (PM) and black carbon. Emissions of particulate and gaseous pollutants from vehicles depend on technology, fuel type, road type and driving behaviour. In present study, the real-world tailpipe emission factors of pollutants fromgoods and heavy-duty vehicles are presented for the first time from on-road operation of vehicles in India. A versatile source sampling system (VS3) has been designed, evaluated, and was used for emission measurement to collect the fine aerosol (PM2.5) mass on filters from tailpipe exhaust and near real-time characterization of its optical properties as aerosol absorption and scattering coefficients. The sampler comprises of an inlet system, portable dilution system, PM sampler, real-time particle characterizing instruments and power supply. The dilution tunnel was evaluated for particle loss at different dilution ratios and was found insignificant particle losses inside the tunnel at 40 to 60 dilution ratio.
The on-road emission measurement of Tractors, LMV-goods and HDV-trucks were conducted in Nalanda districts, Bihar, India for determining emission factors of climate relevant pollutants PM2.5, elemental carbon (EC), organic carbon (OC), and water-soluble inorganic ions (WSIIs) and trace elements along with gaseous pollutants including CO, CO2 and NOx. The PM2.5 mass collected on Teflon filters were utilised for gravimetric and trace elements analysis, while tissue quartz filters were used for EC-OC analysis, and nylosorb filters for WSIIs. PM2.5 emission factors obtained in present study were in the range of 2.0 – 2.5 gkg-1, 0.8 – 1.5 gkg-1 and 0.8 – 2.6 gkg-1 for Tractors, LMV-goods and HDV-trucks respectively which were found to be significantly different from previous laboratory based chassis dynamometer studies. These new on-road emission factors would be helpful in emission inventories improvement which has implications in climate change assessment. The emission factors (gkg-1) of other climate relevant pollutants and total nationalemissions of pollutants from road transport sector will be discussed in this paper.
How groundwater abstraction and tree management affect water levels and peat properties in a groundwater fed peatland
Authors: Halliday Elaine , Verhoef Anne , Robinson Steve
Published: March 2024

Groundwater-fed peatlands are a rare and vital ecosystem providing rich biodiversity, carbon storage and regulation of the hydrological cycle. Management of these species and carbon stores are essential for maintaining a healthy ecosystem. In parallel to this, groundwater aquifers are a common source of relatively clean drinking water, but they are under pressure from population growth and climate change. Groundwater abstraction can lead to a reduction in groundwater levels within groundwater-fed peatlands, affecting their condition, for example by facilitating tree encroachment. Therefore, sustainable water supply needs to balance water demand against other unintentional environmental impacts on these ecosystems. Greywell Fen is located in Southern England, situated above a chalk aquifer that is used to provide drinking water to the area. The fen has been designated a site of special scientific interest (SSSI) in recognition of its important flora. However, the significant fen vegetation species have been declining in recent decades in favour of extensive tree growth throughout the site. New management of the area has included the reintroduction of grazing and large areas of tree clearance. Our research concerns the impacts of groundwater abstraction and woodland management on the health of the fen. Extensive water level monitoring connected to areas with variable tree expansion and clearance is being used to determine if tree management is having an effect on water levels within the fen. In addition, peat cores have been sampled in the different areas to determine if tree management and/or water level changes are impacting peat properties, as an indication of drying and decline in fen health. Peat properties studied include pH, water content, C:N, and organic matter decomposition. The latter was performed using FTIR spectroscopy. The monitoring data have been fed into the Soil-Water-Atmosphere-Plant (SWAP) model to improve our understanding of the hydrology of the system and how the groundwater abstraction affects fen water levels. The model has also been used to determine how management can be optimised to enable sustainable abstraction. The results of the in-depth monitoring and modelling are presented here.
Infiltration and Transport of PVC microplastic particles in saturated quartz sand columns
Authors: Tumwet Faith
Published: March 2024

Since microplastics are a perversive pollutant in all environmental compart-ments, posing a risk to the subsurface and entering the aquatic environment via erosion pathways, it is necessary to understand their transport behav-iours. The morphological descriptors used to characterize microplastic parti-cles are usually highly subjective. This research aims to understand and characterize microplastic movement based on their morphological descrip-tion. The infiltration and transport behaviour of 125 – 200 μm Polyvinyl chloride (PVC) plastic fragments in saturated quartz sand (1.6 – 2.0 mm) columns were investigated. Retention profiles at different ultrapure water flow rates (2.0 – 3.5 ml/min) were compared and analysed. At the beginning and end of each column test, the microplastic particles were carefully exam-ined, identified, and quantified by light microscopy. Each particle was char-acterized by dimensionless 3D morphological descriptors that can describe any particle shape. The results indicated that the transport distance of micro-plastic particles increased as their diameter decreased. PVC microplastic par-ticles, whose morphology was more 1-dimensional, were more susceptible to fragmentation within the column, promoting infiltration. Microplastic degra-dation into fragments appeared to significantly influence movement. This study offers preliminary insights into infiltration depths and morphology-dependent fragmentation of secondary microplastics in saturated coarse sand, highlighting the limitations of 2D projected images conventionally used to study the transport of microplastics.
Numerical Simulation Study on the Microphysical Processes of Typhoon Wipha Using a New Moisture Conditioning Method
Authors: Hu Zhaoxia , Yang Wenxia , Lei Hengchi
Published: March 2024

With the non-static mesoscale model ARPS (Advanced Regional Prediction System), by using the NCEP/ NCAR global final analysis data, and the global surface and sounding data under MICAPS (Meteorological Information Comprehensive Analysis and Process System) assimilated in 4 dimensions as the initial field and lateral boundary conditions, the initial moisture field is adjusted according to the surface accumulated rain of each 6 h, and the triple nested simulation is conducted to produce 3 dimensional high time -spatial (Wipha) are analysed. The results are significant for understanding the cloud structure and the precipitation forming mechanisms of the Typhoon storm. It is found that the ice phase microphysical processes are the key storm mechanisms in different periods. The melting graupel confirms that the rain is the most important mechanism. The intensity of the production of graupel varies in accordance with the Typhoon.
Key Words: Typhoon storm, microphysical processes, dynamic factor.
Using Acousmatic storytelling for flood risk engagement in unreached communities
Authors: Ockelford Annie , Schauerman Julia , MacRitchie Jennifer , Follett Elizabeth , Klaar Megan , Smith Mark , Wolstenholme Josh
Published: March 2024

Global flood risk is set to double by 2050. Adaptation to flood risk in a changing climate necessitates a radical reimagination of how we manage that risk. This reimagination has included a move away from using hard engineering solutions to one where interventions ‘work with nature’ & recognise rivers as complex, dynamic systems. Effective interventions using Nature Based Solutions (NBS) are increasingly recognised as capable of delivering multiple benefits including mitigation of drought & flooding, sediment capture, & habitat & biodiversity net gain. However, more importantly there is a recognition that NBS can also meet a wide range of community needs beyond biophysical goals, including support of community health & wellbeing & nature connectedness.
Traditionally, the design & monitoring of NBS schemes has been data-intensive & localised with a ‘top down’ approach delivered by ‘experts’, retaining a hard engineering mindset. There has been little capacity, & appetite, for embedded community co-design of schemes and where community stakeholders are involved, engagement tends to be one way. As such, implementation of NBS schemes often meets resistance since they are at odds with community perceived values & interests and have little regard for community ownership. For successful broad-scale implementation of NBS, an improved scientific knowledge base needs to be coupled with effective co-production of shared narratives, purposes & goals with communities. Further, these shared visions need to reach out to communities that are not traditionally engaged in this sphere using methodologies that cross traditional disciplinary boundaries.
Here we show the outputs from a project using Acousmatic storytelling which is a way of telling stories that uses recorded spoken word set within a composed soundscape that evokes memories and narratives of specific places and time periods. We worked with an intergenerational group of participants to develop an acousmatic story around the riverscapes of Liverpool, UK, detailing their experiences and memories of flooding as well as beginning to elicit what participants would want from the riverscapes in the future.  
The response of microseismicity to re-inflation at Askja caldera, Iceland: new insights from a dense nodal deployment
Authors: Winder Tom , Siggers Isabel , Rawlinson Nick , Brandsdóttir Bryndís , White Robert S.
Published: March 2024

Askja is an active volcano in Central Iceland that has experienced ~ 70 cm of uplift since August 2021, marking an abrupt end to decades of gradual deflation. We have operated a local seismic network around Askja since 2007, providing an exceptionally long time series of seismic data within which to search for patterns that relate to this sudden change in behaviour. Understanding what seismicity can tell us about the ongoing unrest at the volcano is crucial, because it is one of the few monitoring tools that is available year-round. Furthermore, joint interpretation of seismic and geodetic data is key to overcoming ambiguities in the interpretation of surface deformation measurements alone.
Between July-September 2023 we deployed a dense network of SmartSolo 3-component 5 Hz nodal seismometers within the caldera, to augment the long-running regional array. Their compact form factor allowed for deployment within areas previously inaccessible for deployment of broadband sensors, both due to access and local deployment conditions. Here we focus on investigating the improvement this provided to earthquake catalogue completeness and location accuracy, as well as discussing the data quality and lessons learned for future deployments.
Using the data from the nodal network results in an improvement of the magnitude of completeness by around 1 magnitude unit (to ML ~ -1.0), further elucidating known fault structures, and allowing new classes of events to be successfully detected and located. This includes a previously unknown family of long-period earthquakes, which occur at the approximate depth and location of the recent uplift signal, possibly providing direct evidence of magma ascent responsible for re-inflation of the caldera.
Solving Navier-Stokes Equations with Deep Learning Solvers for Ocean Phenomena Simulation
Authors: Gao He
Published: March 2024

The simulation of oceanic phenomena plays a pivotal role in understanding and predicting climate change, as well as in protecting marine ecosystems, sustainable utilization of marine resources, and effective disaster prevention and mitigation. These simulations are typically based on the Navier-Stokes equations; however, traditional numerical solutions are computationally expensive and present numerous challenges. With the advancement of deep learning technology, the application of Physics-Informed Neural Networks (PINNs) to solve the Navier-Stokes equations has become a burgeoning field, promising significant improvements in computational efficiency. However, existing methods are heavily dependent on extensive fluid model data and suffer from limited generalizability. To overcome these limitations, this paper proposes a novel, self-supervised, and highly generalizable numerical solution method for the Navier-Stokes equations. This method integrates convolutional neural networks under physical supervision, thereby allowing the equation to be solved and a variety of oceanic phenomena to be simulated without resorting to traditional fluid simulation data. Our experimental results indicate that this method can efficiently and effectively simulate oceanic phenomena under various environmental conditions, with its validity corroborated by alignment with observations from the SDGSAT-1 satellite. This study offers an efficient and innovative tool for the simulation and analysis of oceanic phenomena, thereby contributing significantly to climate research and marine resource management.
Evaluation of Various Machine Learning Algorithms for Bias Correction of Satellite-based Precipitation Estimates over Complex Topography
Authors: Yilmaz Koray K. , Sevinç Gökhan , Sağır Çağdaş , Karaman Orhan , Yilmaz M. Tugrul , Yucel Ismail
Published: March 2024

Reliable precipitation estimates are crucial for any hydrologic study. Representation of high spatio-temporal variability in precipitation using rain gauges is challenging over complex terrain. Geographical variability of Türkiye, such as orography, land–sea distribution and the high Anatolian peninsula strongly controls the climate and results in highly variable climate regimes. The objective of this study is the evaluation of tree-based machine learning algorithms (Random Forest & XGBoost) for bias correction of IMERGLate precipitation estimates over complex topography and climatic regimes. We utilized SHAP values to improve the transparency and the interpretability of machine learning models, thus to better understand the factors controlling the bias correction models. 301 quality-controlled rain gauges (244 for training and 57 for testing) were used, covering a 600 km wide North-South region from the Black Sea coast to the Mediterranean coast. The selected explanatory variables consist of daily IMERG precipitation estimates and probability of liquid precipitation, climate zones, aspect, elevation, distance to coast, effective terrain height, longitude and latitude. The results showed that both Random Forest and XGBoost algorithms significantly improved precipitation estimates. While the Random Forest Model provided better correlations, the XGBoost Model performed better in correcting the precipitation distribution. Both models show high performance in error correction and have similar Kling-Gupta performance. Analysis of SHAPLEY values showed that the IMERG product, effective terrain height, distance to coast and elevation are the most important variables in the precipitation bias correction process.
Enhancing Urban Canopy Parameterization for Non-building Resolving Resolution Simulations: Integrating the Double Canyon Effect Parameterization (DCEP) Scheme into PALM
Authors: Schubert Sebastian , Salim Mohamed
Published: March 2024

This study presents the integration of the Double Canyon Effect Parameterization (DCEP) into the microscale urban climate model PALM, specifically tailored for non-building resolving resolution simulations (~100m), where individual buildings are not explicitly resolved. The DCEP scheme introduces a sophisticated scheme characterizing street canyons by considering building and street width, orientation, and building height distribution. This implementation allows for the efficient representation of buildings while maintaining computational feasibility through parallelized computations, making it suitable for large-scale simulations. The DCEP scheme explicitly accounts for the energetic exchanges between buildings and the atmosphere across multiple vertical layers, capturing the dynamics of urban surfaces as well as the storage of energy within surfaces. To derive the necessary Urban Canopy Parameters (UCPs) for DCEP, traditionally requiring high-resolution data, we employ an approach based on Local Climate Zones (LCZ). The LCZ classification, consisting of 17 classes, provides the characteristics of various urban areas, allowing for a detailed representation of heterogeneity in different zones. This approach streamlines the derivation of UCPs by assigning typical parameters to land-use categories, thereby overcoming the complexity associated with high-resolution data processing. The pre-processor tool "palm_csd" has been expanded to directly read LCZ data in different projections and resolutions, providing users with the flexibility to customize parameters assigned to each LCZ class within defined limits. Simulations were conducted over a large domain to show the impact of the PALM/DCEP coupled model. Comparative analyses were performed between simulations with the base PALM model and the enhanced PALM/DCEP coupled model. The results highlight the significant contributions of the DCEP scheme in enhancing urban and environmental modeling during non-building resolving resolution simulations. This research contributes valuable insights into the adaptability and effectiveness of the DCEP approach in simulating large urban areas where building-resolving resolution is not practical. The findings pave the way for advancements in urban climate modeling, with implications for urban planning and environmental management in diverse urban landscapes.
European maps of crop / livestock categories and N budget parameters (timeseries 2000 – 2018) based on disaggregated CAPRI model data
Authors: Koeble Renate , Leip Adrian , Kempen Markus , Bielza Maria , Catarino Rui , Paracchini Maria Luisa , See Linda , Van Der Velde Marijn
Published: March 2024

The agricultural sector holds the greatest reduction potential to limit adverse effects of reactive nitrogen in the environment. Assessing the negative impacts of excessive release of reactive nitrogen into the biosphere requires spatially explicit information to capture e.g. hot spots of nitrogen surplus, nitrogen use efficiency, the impact on sensitive ecosystems or on ground/drinking water quality.The agricultural economic model CAPRI is one of the main tools applied by the European Commission for the ex-ante analysis of the impact of agricultural policies and agro-environmental legislation at regional level (NUTS2) in Europe. CAPRI builds on long-term time series of regional, national and international agricultural statistics (e.g. crop and livestock production, fertilizer use), market and trade data. Inputs (e.g. inorganic fertilizer) in CAPRI are explicitly linked to production which delivers the basis for connecting environmental indicators (e.g. nitrogen surplus) directly to individual activities.To provide the link between the agro-economic model CAPRI and the impact assessment of nitrogen use in agriculture on the environment at higher spatial resolution, we developed a procedure to disaggregate CAPRI regional data and provide maps of nitrogen input/output, crop and livestock production for the time series 2000 – 2018 at the level of Farm Structure Units (FSU) for 26 EU member states and the UK. The FSU are built by the spatial intersection of a 10 x 10 km2 INSPIRE compliant grid, the CAPRI NUTS2 region borders and the soil mapping units of the Harmonized World Soil Data Base (FAO/IIASA/ISRIC/ISS-CAS/JRC, 2009), having a median area of 12 km2 (minimum 1 km2, maximum 100 km2).The disaggregation procedure for 36 crop types and 18 livestock categories from the regional level to the FSU is driven by information from the gridded Farm Structure Survey (FSS, 2010) crop and livestock data at 10 x 10 km2 resolution, CORINE (2018) non-agricultural land cover shares, altitude and slope constraints for individual crops / crop classes derived from LUCAS survey data (https://ec.europa.eu/eurostat/web/lucas).Nitrogen inputs from mineral fertilizer and manure are disaggregated from the regional level to the FSU following the crops’ requirements. N input from atmospheric deposition and N supply by biological fixation is taken into account at FSU level. N from mineralization of soil organic matter could not be taken into account due to lack of data. N losses from volatilization and surface run-off, N removal by harvest, N in crop residues complete the N flow data set in agricultural areas at FSU level.
Open Science and FAIR in Geoinformatics education: Reality check
Authors: Löwner Ralf , Löwe Peter
Published: March 2024

The University of Applied Sciences in Neubrandenburg, Germany has developed an independent and unique profile with regard to applied research, development, and knowledge transfer. This especially applies to the Bachelor and Master programmes in Geomatics within the Landscape Sciences and Geomatics department.Geoinformatics involves using all information that has a spatial reference. Some of the major areas are the recording and representation of the Earth’s surface (cartography and land registry), the processing of aerial photography and satellite and radar images, as well as geographic information systems. Application programs, databases, the Internet and software development form the basis for the use of geoinformatics in the geosciences, such as geodesy, geography, geology, geophysics and ecology.Geoinformatics requires a high degree of interdisciplinary work and this means that graduates enjoy excellent and exciting career opportunities in various application areas such as environmental protection, environmental monitoring, landuse planning, tourism and leisure industry, agriculture, and forestry.The curricula for both the undergraduate and postgraduate courses are strongly oriented towards the use of Free and Open Source Software (FOSS), open data and open standards. Since 2022 the studies include facultative seminars on Open Science / FAIR and Open Research Software. These courses reflect the paradigms of Open and FAIR and introduce the students to the values and best practices developed by the global open geospatial communities. The course provides a holistic overview and introduction to the organization, infrastructures and stakeholders of the OSGeo Foundation as an umbrella organization of quality-tested Free and Open Source Software (FOSS) projects, giving an overview of the software projects themselves (including OSGeoLive, QGIS and libraries such as gdal and proj), as well as the networked organizational structures such as GeoForAll, the national and regional OSGeo chapters, and the FOSS4G conference series, and providing opportunities for student participation and career building.Taking this as a starting point, Neubrandenburg University of Applied Sciences is aiming to participate in the global GeoFoAll Labs community in the mid-term.
Temporal dynamics of terrestrial carbon dioxide removal
Authors: Nützel Tobias , Mathesius Sabine , Krause Jens , Egerer Sabine , Ó Beoláin Conor , Bampoh Daniel , Falk Stefanie , Gerten Dieter , Obermeier Wolfgang , Pongratz Julia
Published: March 2024

Virtually all future scenarios in the IPCC AR6 keeping climate change well below 2°C include carbon dioxide removal (CDR), often leading to large transformations of global land surface and land use. Re-/afforestation (AR) and bioenergy with carbon capture and storage (BECCS) are the two most prominent CDR measures in those scenarios. The temporal evolution of carbon uptake and storage is very different between bioenergy plants, which are annually harvested to (ideally) permanent storage, and forests, which sequester carbon for decades on site but can be affected by disturbances. Additionally, while AR dominates current CDR deployment as tree seedlings and saplings can be planted right away, BECCS requires further processing and storage infrastructure leading to longer establishment time scales. Thus, BECCS covers only a tiny fraction of existing and announced amounts of CDR. Hence, depending on whether CDR is intended to support rapid, deep reductions of net emissions in the near term (as in the Nationally Determined Contributions of parties to the Paris Agreement) or to counterbalance residual emissions or even reach net negative emissions in the longer term, either AR or BECCS could be more effective. This will also vary across world regions. We compare the temporal dynamics of carbon storage efficiency between AR and BECCS with three state-of-the-art terrestrial biosphere models (JSBACH, LPJmL, LPJ-GUESS). We use a global, highly stylized setup where a fixed share per pixel of current agricultural land is replaced by forests or bioenergy plants, respectively. We analyze the effectiveness of the two CDR methods over time and in different world regions depending on the temporal CDR target. Furthermore, we quantify how the temporal dynamics are affected by the chosen start year of CDR (2015, 2030, 2050), background climate and CO2 concentrations (SSP1-2.6, SSP3-7.0),  natural disturbances and assumptions on management and plant parametrizations in the underlying vegetation models. We specifically consider temporal dynamics on current agricultural areas adjacent to biodiversity hotspots, since these could also become relevant for achieving ecosystem restoration targets. There, CDR through restoration of naturally occurring forests or grasslands with support from local communities can bring synergies for multiple ecosystem services, while premature deployment of AR in non-forest areas or crop-based BECCS would likely decrease biodiversity.
Modeling the Interplay between Urban Environmental Characteristics and Cyclist Route Preferences
Authors: Pandya Pranav , Llaguno-Munitxa Maider , Edwards Martin , Lacroix Emilie , Manoli Gabriele
Published: March 2024

As cities grapple with the multifaceted challenges posed by climate change, the Brussels Capital Region (BCR) stands at the forefront of fostering sustainable urban mobility, particularly through the development of cycling infrastructure aimed at bolstering public health and well-being. Policy initiatives implemented in BCR such as 'Good Move' and 'Ville 30' have acted as catalysts, prompting a paradigm shift towards specialized cycling lanes and facilities, thereby enhancing the safety and convenience of cycling as a viable transportation alternative. However, the growing recognition of urban heat stress and thermal discomfort as significant public health concerns, particularly for users of urban soft mobility means, highlights the pressing need for immediate and targeted interventions from urban stakeholders. While it is widely recognized that weather conditions, especially during very hot and cold days, influence cycling behavior, as do urban environmental features like the urban fabric and the presence of green infrastructure in a street, there remains a need to establish quantifiable metrics for assessing the impact of thermal comfort on cycling behavior. This study aims to address this gap, offering a nuanced examination of the cycling routes and cycling behavior of the BCR. We propose a multidisciplinary approach that integrates geospatial, psychological, and environmental sciences to examine the complex interplay between cycling path planning, urban design, micrometeorology, and thermal comfort. Data spanning from 2019 to 2022 has been sourced from multiple channels, including Brussel Mobility, Google Street View (GSV) with semantic image classification, Local Climate Zone (LCZ) maps, and meteorological stations. Geospatial data for Elsene and Etterbeek has been collected. The initial findings reveal that creating green pathways in urban areas can lessen heat stress and enhance comfort for cyclists. Moreover, cyclists are inclined to steer clear of extremely hot or cold weather conditions. Integrating urban microclimatological conditions into the framework of urban cycling design, this research aims to steer policy development towards creating urban soft mobility solutions that are more comfortable, climate-adaptive, and prioritize health considerations.
Climatic drivers of cork growth depend on site aridity
Authors: Colangelo Michele , Camarero J. Julio , Sanchez-Miranda Angela , Matías Luis
Published: March 2024

Cork is one of the main non-timber forest products in the world. Most of its production is concentrated in the Iberian Peninsula, a climate change hotspot. Climate warming may lead to increased aridification and reduce cork production in that region. However, we still lack assessments of climate-cork relationships across ample geographical and climatic gradients explicitly considering site aridity. We quantified cork growth by measuring cork ring width and related it to climate variables and a drought index using dendrochronology. Four cork oak (Quercus suber L.) forests located from north eastern Spain to south western Morocco and subjected to different aridity levels were sampled. Our results showed that warm conditions in spring to early summer, when cork is formed, reduced cork width, whereas high precipitation in winter and spring enhanced it. The response of cork to increased water availability in summer peaked in the most arid and continental site considering 14-month long droughts. A severe drought caused a disproportionate loss of cork production in this site, where for every five-fold decrease in the drought index, the cork-width index declined by a factor of thirteen. Therefore, site aridity determines the responses of cork growth to the soil water availability resulting from accumulated precipitation during winter and spring previous to cork growth and until summer. In general, this cumulative water balance, which is very dependent on temperature and evapotranspiration rate, is critical for cork production, especially in continental, dry sites. The precipitation during the hydrological year can be used as a proxy of cork production in similar sites. Assessments of climate-cork relationships in the western Mediterranean basin could be used as analogues to forecast the impacts of aridification on future cork production.
Characterising Edge States: Measures on chaotic non-attracting invariant sets
Authors: Roemer Raphael , Ashwin Peter
Published: March 2024

In this work, we explore how to extend the concept of physical measures from attractors to chaotic non-attracting invariant sets. Building on Sweet and Ott’s work from 2000, we make their ideas rigorous by defining a measure on non-attracting sets in terms of Lebesgue Measure and show how to sample it numerically. We discuss its relevance for simple climate models and the sampling techniques’ limitations in the context of more complex and higher dimensional (climate) models. Knowing the measure of a non-attracting set, for example of a saddle or of an edge state (also known as melancholia state), also provides information about its fractal dimension and geometric complexity which can be useful to better understanding tipping phenomena and uncertainty close to a basin boundary.
Elevated CO2 effects on Zn and Fe nutrition in vegetables: A meta-analysis
Authors: Wang Xiaolin , Zhang Shengmin , Li Haichao , Odlare Monica , Skvaril Jan
Published: March 2024

The atmospheric carbon dioxide (CO2) concentration has been progressively increasing since the onset of the Industrial Revolution and has already reached at around 420 μmol mol⁻¹ nowadays. It is well recognized that elevated CO2 concentration stimulates the yield for C3 crops, but it also simultaneously changes the essential nutrients. However, compared with the main crops, far less attention has been devoted to the effects of elevated CO2 concentration on vegetable growth and quality. Vegetables are highly recommended in daily diets due to their diverse range of beneficial compounds, such as vitamins, antioxidants, minerals, and dietary fiber.  In controlled greenhouse vegetable cultivation, elevated CO2 has been widely adopted as an agricultural practice for enhancing plant growth. Thus, understanding both vegetable growth and nutrient status is crucial to assess the potential impacts of elevated CO2 on future food security in both natural and controlled environments. However, much more attention has been paid to biomass enhancement, and elevated CO2 effects on nutrient quality are less recognized. Among the nutrients, Zinc (Zn) and Iron (Fe) are essential elements in humans. Previous studies have demonstrated a decreasing trend of Zn and Fe in main crops such as wheat and rice with increased CO2, while less is known about whether this alleviation effect on Zn and Fe can apply to vegetables. Therefore, a meta-analysis was conducted in this study to evaluate the influence of elevated CO2 concentration in the atmosphere on vegetable Fe and Zn status, and quantify the potential impact of future climate on nutrition security.   
Increased quasi-resonant amplification and persistent summer weather extremes in multimodel climate projections with high emissions and aerosol forcing
Authors: Oliveira Guimarães Sullyandro , E. Mann Michael , Rahmstorf Stefan , Petri Stefan , A. Steinman Byron , J. Brouillette Daniel , Christiansen Shannon , Li Xueke
Published: March 2024

High-amplitude quasi-stationary atmospheric Rossby waves with zonal wave numbers 6-8 associated with the phenomenon of quasi-resonant amplification (QRA) have been linked to persistent summer extreme weather events in the Northern Hemisphere. QRA is not well-resolved in current generation climate models, however, necessitating an alternative approach to assessing their behavior. Using a previously-developed fingerprint-based semi-empirical approach, we project future occurrence of QRA events based on a QRA index derived from the zonally averaged surface temperature field, comparing results from CMIP5 and CMIP6 (Coupled Model Intercomparison Project). There is a general agreement among models, with most simulations projecting substantial increase in QRA index. Larger increases are found among CMIP6-SSP5-8.5 (42 models, 46 realizations), with 85% of models displaying a positive trend, as compared with 60% of CMIP5-RCP8.5 (33 models, 75 realizations), with a reduced spread among SSP5-8.5 models. CMIP6-SSP3-7.0 (25 models, 28 realizations) simulations display qualitatively similar behavior to SSP5-8.5, indicating a substantial increase in QRA events under business-as-usual emissions scenarios, and the results hold regardless of the increase in climate sensitivity in CMIP6. Also, the aerosol forcing plays a substantial role in CMIP5 and CMIP6 models; a reduction in aerosol loading reduces Arctic amplification, and mitigates potential increases in QRA-related persistent extreme weather events. Our analysis suggests that anthropogenic warming will likely lead to an even more substantial increase in QRA events (and associated summer weather extremes) than indicated by past analyses.
Enhancing Regional NWP Model with GNSS Zenith Total Delay Assimilation: A WRF and WRFDA 3D-Var Approach in the Greater Region of Luxembourg
Authors: Rehman Haseeb Ur , Teferle Felix Norman , Hunegnaw Addissu , Schumann Guy , Zus Florian , Muraleedharan Thundathil Rohith
Published: March 2024

Compared to alluvial floods, flash or pluvial floods are difficult to predict because they result from intense and brief periods of extreme precipitation. Luxembourg has a history of being impacted by floods, with notable occurrences in January 2011, May 2016, December 2017, January 2018, February 2019, and February 2020. However, July 2021 stands out as the most severe flood year on record in the region. In this study we are aiming to develop, a high-resolution numerical weather prediction (NWP) model for effective local heavy rainfall prediction in a nowcasting scenario and provide real time for flood simulation. The modeling relies on the Weather Research and Forecasting (WRF) model, which incorporates local Global Navigation Satellite System (GNSS) data assimilation and local precipitation observations to simulate small-scale, high-intensity convective precipitation.As part of this, we will also test run the LISFlood flood model in an operational inundation forecast mode, meaning that the flood model will be run with the WRF precipitation forecasts as inputs.The WRF model was configured for the Greater Region, utilizing a horizontal grid resolution of 12 km and incorporating high-resolution static datasets. Meteorological data i.e. July 13 -14 2021, from the Global Forecast System (GFS) were employed in the model setup as initial boundary condition. Zenith Total Delay (ZTD) data collected from various GNSS stations (112) across Germany and Luxembourg were assimilated into the model. Additionally, observational datasets including Surface Synoptic Observations (SYNOP), Upper Air Data, Radiosonde measurements (TEMP), and Tropospheric Airborne Meteorological Data Reporting (TAMDAR) were assimilated. Following this integration, an sensitivity analysis of various meteorological parameters such as precipitation, surface temperature (T2), and relative humidity was performed. Keywords: NWP, WRF, Flash flood, LISFlood, Weather forecast, High-Resolution, GNSS, ZTD
A novel automated framework to design optimal networks of atmospheric greenhouse gas stations
Authors: Matajira-Rueda David , Maiwald Robert , Abdallah Charbel , Vardag Sanam , Butz Andre , Lauvaux Thomas
Published: March 2024

This research proposes an optimal design framework for a mesoscale atmospheric greenhouse gas network dedicated to inverse flux monitoring at urban, regional, or national scales.The framework’s design is based on data processing of atmospheric concentrations using multiple machine learning techniques such as image processing and pattern recognition, among others, all of them powered by optimization algorithms, giving the solution process explorative and exploitative features over the problem search space. Besides, the data processing uses graph representation as it considers a discrete search space, which in turn allows for speeding up the information access in each stage, especially during the inverse analysis procedure.All of the above is framed with a learning system whose purpose is automatizing the processing when combining diverse data sources by mixing the supervised and unsupervised learning types in pre- and post-processing, respectively. On the one hand, the problem is related to the design of a monitoring network of greenhouse gases, in which it is required to decide the locations of a specific number of towers according to their measurement influence region, hence minimizing the number of towers while guaranteeing the appropriate parameter estimation.On the other hand, the solution strategy conducts a data analysis, where observed and fitted data are treated as spatial-temporal images. During the batch processing, these images are filtered, contrasted, binarized, classified, and clustered, among other operations to maximize the data analysis.Performance tests were based on reference datasets from the Weather Research and Forecasting model (here hourly simulated concentrations at 3 kilometers resolution over eastern France) as well as other synthetically and randomly generated concentration fields, which allowed for comparison of the proposed algorithm processing.According to the parametric and non-parametric tests used to evaluate the scheme, our framework is competitively capable of designing optimal monitoring networks by using data processing and high-performance computing.
Opportunity mapping for nature-based solutions for flood hazard reduction and water quality improvements with the SCIMAP toolkit
Authors: Reaney Sim
Published: March 2024

Nature-based solutions have the capability to slow and store water during storm events, leading to the attenuation of flood peaks and the capturing of sediments and associated nutrients. These features can also provide important habitats for wildlife and pollinators. However, these features do take land that may be agriculturally productive and require maintenance, both factors mean that they have potentially significant ongoing costs. Therefore, it is important to ensure that they are effective and correctly located within the landscape. For all pressures, the location needs to be in a location where the problems are likely to originate. However, for flood waters, it is important to target locations that are likely to contribute to moving water out of the flood peak and into the receding limb of the hydrograph, rather than moving water from the rising limb into the peak. It is also important not to move water from the peak discharge of one community into the peak discharge of another. Therefore, careful analysis and planning are needed to ensure that the benefits from the nature-based solutions are maximised for all in the catchment. The SCIMAP Toolkit provide an approach to assess the potential source area of flood waters, sediments, nutrients and FIOs and to ensure that the multiple benefits of the nature-based solution are realised. The toolkit uses a reduced complexity approach to map the generational of rapid runoff, the mobilisation of material and connectivity to the receiving waters. The SCIMAP-Flood module then considers the travel times to the impact points within the catchment, such as a community or key infrastructure.  This analysis is undertaken in a time-integrated way such that the potential benefits of a nature-based solution are optimised over a range of storms rather than fitting to the unique dynamics of a past event. The analysis is undertaken at landscape extent with sub-field detail, normally at a ground resolution of 1m with the catchment extent covering 1000s of km2.  This presentation shows the application of the SCIMAP approach to the spatial targeting of flood mitigation features and how these locations can co-deliver water quality benefits.
MAPME – Versatile analysis tool for big geospatial data in the context of sustainable development
Authors: Görgen Darius , Schielein Johannes
Published: March 2024

Natural ecosystems, especially primary forests, are impacted by the rapid expansion of human land use and global climate change, putting the most bio-diverse areas of our planet under threat. Large amounts of Earth Observation and analysis-ready data sets are made available (almost) for free. Yet, the usage of such data in conservation finance and policy making does currently not live up to its full potential. It is a complex endeavor to access relevant portions of Big Geospatial Datasets efficiently due to the high number of different data providers, formats and interfaces. Even more important, we need to generate information in an open and reproducible way to take informed decisions to allocate funds responsibly and maximize public goods and benefitsMAps for Planning, Monitoring and Evaluation (MAPME) is an collaborative initiative based on OpenScience principles to leverage the potential of geospatial data for relevant actors in the development cooperation sector. The initiative is driven by Free and Open Software (FOSS) enthusiasts within German (KfW, GIZ) and French (AFD, IRD) development institutions. Together with our partner countries we are key decision makers in the allocation of the so-called Official Development Assistance (ODA). To bridge the “last-mile” gap between vast amounts of openly available geospatial data sets and productive monitoring applications, we have developed an OpenSource software used within our institutions.The software is written in R and relies on the Geospatial Data Abstraction Library (GDAL) bindings provided by the `sf` and `terra` packages. It allows efficient analysis of large data collections on deforestation and greenhouse gas emissions such as Global Forest Watch (GFW). Focusing on expandability, everyone can include new in-house or open data sets, and custom analysis routines. Thus, the functionality can be extended to other sectors beyond forest monitoring. It opens the way to deliver crucial information on the state of ecosystems around the globe in a timely and reproducible way, allowing our institutions to make better allocation decisions.We will present the MAPME Initiative and shed a light on our approach to developing applications based on FOSS. We will showcase first data solutions build by our partners on top of the framework, such as a geospatial impact evaluation of preventing deforestation and a dashboard for continuous monitoring of protected areas of the German development cooperation portfolio.