bluuebunny/crossref_metadata_2025 huggingface dataset:
A computational framework for personal multi-exposure assessment using space-time activity and socio-economic data
Authors: Schmitz Oliver , de Hoogh Kees , Probst-Hensch Nicole , Jeong Ayoung , Flückiger Benjamin , Vienneau Danielle , Hoek Gerard , Kyriakou Kalliopi , Vermeulen Roel C. H. , Karssenberg Derek
Published: March 2024

The construction of simulation models for personal exposure analysis requires the integration of field-based data representing spatially distributed values (e.g. air pollution, noise, temperatures), agent-based data (e.g. daily activities, residential and work locations) and socio-economic data (e.g. age, social economic status, mode of commute) to fully cover the space-time activity patterns of cohort participants. In addition, evaluating the associated uncertainty is necessary as potentially not all required input variables are known.We developed a modelling framework implemented in Python providing modules for 1) the specification of agents' activity diaries including the durations of activities and their spatial contexts, i.e. the location of a person during that activity, commute trips between residential and work location are thereby routed using OpenStreetMap data; 2) incorporating multiple environmental factors potentially on different temporal and spatial scales; 3) personal exposure assessment by calculating, for each time step and environmental factor, average exposure values within the spatial contexts. The modules can be combined in a Python script for exposure assessment of all agents in a cohort, including Monte Carlo simulations.We show results from a modelling study conducted for the province of Utrecht, the Netherlands. The study area covers about 500000 residential address locations covering urban and rural areas. We used cadastral and census data to define characteristic diurnal activity profiles describing different characteristics such as social economic status and commute type (e.g. car, bicycle, on foot). We calculated individual exposures to NO2, PM2.5 and noise in Monte Carlo mode and demonstrate the spatial variability of exposures per activity profile and the associated uncertainty. The personal exposures for commuter profiles show more contrast across addresses compared to the homemaker profiles.Our activity-based mobility simulation provides a representative description of space-time activities of individuals. The calculated personal exposures can be used for further epidemiological analysis to investigate the relationship between air pollution exposure and chronic diseases such as diabetes or cardiovascular disease.
Turbulence in thermally-driven slope winds
Authors: Zardi Dino
Published: March 2024

The atmospheric boundary layer (ABL) in mountainous regions is characterised by a variety of airflows, originating from complex landform forcing, which encompass a range of scales of motion, from synoptic scale flows to very local phenomena, such as the daily-periodic thermally-driven circulations developing over inclines and in the valleys under clear sky and in the absence of major synoptic forcing. These airflows, and turbulence generated therein, affect a variety of processes, including surface-atmosphere exchanges of momentum, energy and mass, and transport across a variety of scales. They may also contribute to the initiation of orographic convection. The talk focuses on the simplest of these flows, namely slope winds, outlines the state of our present understanding, from measurements as well as from numerical model simulations, and highlights still open questions concerning the structure of turbulence properties and their representation in terms of similarity. Ongoing efforts to investigate these flows within the current initiative TEAMx - Multi-scale transport and exchange processesin the atmosphere over mountains – programme and experiment (http://www.teamx-programme.org/)  are also presented.
Unravelling the carbon cycle at the tree and forest scale: a TREE4FLUX initiative in Central African Tropical Forests
Authors: Hicter Pauline , Hubau Wannes , Beeckman Hans
Published: March 2024

Tropical forests play an essential role in the carbon cycle. However, climate change threatens their ability to store carbon. Specifically, understanding the perturbation of climatic regimes on carbon uptake mechanisms is crucial. However, our knowledge concerning the spatial and temporal carbon distribution over trees and forests is limited, especially in the context of tropical forests of Central Africa. The TREE4FLUX project aims to fill these gaps for the first time in the forests of Congo Basin forests, by focusing research at different scales around the CongoFlux tower in the Yangambi Biosphere Reserve (DRC). On the forest ecosystem scale, carbon uptake can be monitored by measurements of CO2 exchanges between the atmosphere and the vegetation using the Eddy Covariance approach. Carbon assessments are also possible through tree-growth measurements within a network of permanent inventory plots. However, refining the carbon cycle at the tree scale requires a detailed study of the numerous inextricable metabolic processes that underlie tree growth, e.g. photosynthesis, wood formation, or respiration. Because they are largely controlled by various climatic drivers, climate-growth relationships over time remain hard to establish. The chronology of carbon uptake and attribution to the different mechanisms remain elusive preventing a grasp of the intra-annual variations of these periodic processes and their articulation over time. This is the case of xylogenesis or wood formation in which each phase is differently involved in the carbon cycle and sensitive to various climatic drivers. To understand the sensitivity of tree growth to climate, we need to untangle the cambium’s role in wood formation. For that purpose, monitoring cambial phenology helps characterize the distribution, allocation, and short- and long-term carbon storage in woody material. While tree growth uptakes carbon, respiration releases carbon into the atmosphere at various levels. Heterotrophic and autotrophic respirations have a decisive role in the carbon cycle at the forest scale but face significant misunderstandings in this regard. To upscale our understanding from individual tree to forest scale, we imperatively need respiration monitoring in both living and decayed trees. This requires unravelling the metabolic processes driving both autotrophic and heterotrophic respiration, i.e. the tree growth and decayed process, respectively. Characterization of carbon fluxes according to an integrative approach over climatic variations is required to understand how environmental changes affect ecosystem dynamics and their ability to provide ecosystem services
Predicting Global Ionosphere in Three Dimensions: Integrating Data Assimilation with Convolutional Neural Networks
Authors: Mengist Chalachew Kindie , Seo Kyong-Hwan
Published: March 2024

In this study, we present a novel approach to improve ionosphere prediction by combining the Ionospheric Data Assimilation Four-Dimensional (IDA4D) algorithm with Convolutional Neural Networks (CNNs). The IDA4D algorithm constructs a three-dimensional global electron density by assimilating ground-based GPS slant total electron content (STEC), radio occultation STEC, and radio occultation NmF2 data into the IRI model. The IDA4D outputs are fed into CNNs to learn spatiotemporal patterns. Results are validated with ionosonde and CODE TEC data, demonstrating significant improvements and reducing the root-mean-square error (RMSE) of Nmf2 and vertical TEC by 34% and 51%, respectively, compared to the IRI model. Furthermore, the IDA4D technique successfully reconstructed storm time enhancement of the northern crest equatorial ionization anomaly during late evening hours, resulting from upward and northward plasma transport. The combination of IDA4D and CNNs predicts a three-dimensional electron density more accurately than the IRI model for up to two days.
Modeling Dynamic Systems for Sustainable Development 
Authors: Selin Noelle , Giang Amanda , Clark William
Published: March 2024

We summarize recent progress in dynamic modeling of nature-society systems to inform efforts towards sustainable development.  Drawing on lessons learned from a series of virtual workshops and a journal Special Feature, we identify and highlight examples of novel methods and advances, focusing on four stages of modeling practice -- defining purpose, selecting components, analyzing interactions, and assessing interventions. We highlight insights for researchers interested in assessing the implementation of system-wide sustainability strategies, with a focus on human well-being as an overarching objective, including methods that incorporate nature-society interactions into sectoral decision-support models, simulating cross-sector connections and differing contexts, and implementing computational and statistical approaches that evaluate decision scenarios under uncertainty. We additionally highlight techniques that can serve to foreground issues of power differentials among actors, including methods that can capture diverse societal actions and their agency, and incorporate different perspectives and normative visions. As a concrete example of the utility of a set of methods and advances from this survey of coupled nature-society systems modeling, we show how advances in computational techniques can be used to assess the degree to which national-scale climate policies in the United States can impact air pollution exposure to different racial/ethnic groups. 
Data assimilation in glacier mass balance modeling
Authors: Cao Wenxue , Schmidt Louise Steffensen , Aalstad Kristoffer , Westermann Sebastian , Schuler Thomas V
Published: March 2024

The accurate quantification of glacier mass balance is of vital importance for the evaluation of climate change impact and the management of hydrological resources. However, traditional modeling methodologies on a regional scale are frequently plagued by uncertainties in forcing data, model structure, and parameters. Data assimilation emerges as an effective technique to incorporate observations into modeling, thereby reducing the uncertainty of results. In this study, we evaluate the performance of different ensemble-based schemes, including the Ensemble Smoother (ES) and the Ensemble Smoother-Multiple Data Assimilation (ES-MDA), to incorporate albedo derived from MODIS satellite observations and in-situ mass-balance measurements vis stakes into the full energy balance model CryoGrid applied to Svalbard glaciers. Our primary aim is to enhance the accuracy of both the reconstruction and prediction of glacier mass balance in the Svalbard region through the synergistic use of observational data and model. In a range of experiments, we analyze the performance of different assimilation methods and different observation products. The implementation of ES-MDA has demonstrated marked improvements, while the variations in parameter dynamics have varied effects on the results. We compare the prior and posterior states to help disentangle which process or forcing has the most impact on the uncertainty of the model’s results.
Boron isotope signals of benthic foraminifera during the Santonian
Authors: Wolfgring Erik , Amaglio Giulia , Petrizzo Maria Rose
Published: March 2024

We investigated the δ11Bisotope profiles in Cretaceous benthic foraminifera, focusing on the epibenthic species Notoplanulina rakauroana and Nuttallinella coronula. This study aims at understanding the implications of changes in δ11B isotope signatures for pH, associated with paleoenvironmental factors during the beginning of the Late Cretaceous cooling in the Santonian in the southern high latitudes.We examine 10 levels through the Santonian of International Ocean Discovery Program (IODP) Site U1513 in the Mentelle Basin, some kilometres offshore Western Australia. Following a biostratigraphic framework relying on planktonic foraminifera and calcareous nannofossils, foraminiferal samples of Notoplanulina and Nuttallinella, representing significant elements of bottom water fauna through this period of significant cooling in surface and bottom waters, have been processed.The geochemical signals preserved in the tests of epibenthic foraminiferal species are supposedly less prone to representing vital effects as some stability in the paleohabitat in bottom waters is implicit (see Rae et al. 2011). The analysis of well-preserved foraminiferal tests by Inductively Coupled Plasma Mass Spectrometry (ICPMS) is imperative of a cleaning protocol that involves several steps of ultrasonic baths in clear water and alcohol (see Henehan et al. 2019).The results of benthic foraminiferal δ11B add information to our understanding of the relationbetween climate shifts and changes in the geochemical composition visible in foraminiferal tests. Results help to reconstruct prevailing pH of bottom waters during the documented reorganization of oceanic pathways in the southern high latitudes and offer a further perspective on the extent of paleoenvironmental change in the bottom waters.References: Rae, JWB, Foster, GL, Schmidt, DN, Elliott, T, 2011. Boron isotopes and B/Ca in benthic foraminifera: Proxies for the deep ocean carbonate system, Earth and Planetary Science Letters, 302, 3–4.Henehan, MJ, Ridgwell, A, Thomas, E, et al., 2019. Rapid ocean acidification and protracted Earth system recovery followed the end-Cretaceous Chicxulub impact. PNAS, 116, 45, 22500-22504.
Changes in land-atmosphere coupling may amplify increases in very rare temperature extremes
Authors: Maraun Douglas , Schiemann Reinhard , Osso Albert , Jury Martin
Published: March 2024

Extreme heat events are becoming more severe. Attribution studies have demonstrated the effect of anthropogenic climate change on recent devastating events, including the heat waves in Canada in 2021, Northern India in 2022 and the Western Mediterranean in 2023. Such impactful events are very rare with return periods of 100 years and more even in present climate. Their rareness is in stark contrast to the typically considered return periods ranging from less than a year to maybe 20 years. This choice might often be inevitable because of practical limitations, mainly the length of observational and climate model records. But generalising from such analyses to extreme events in general tacitly assumes that very rare events respond to climate change in a similar way as the analysed moderate extreme events. Several studies investigating land-atmosphere feedbacks and atmospheric circulation changes indicate, however, that this assumtion may not be justified.Here we use three single model initial condition large ensembles (SMILES) to assess differences between projected changes in moderate heat extremes (represented by 2-year return values of the hottest day in a year) and very rare extreme events (represented by corresponding 200-year return values). We analyse changes from 1990-2014 to 2075-2099 according to the SSP5-8.5 scenario.We find large regions where projected changes in very extreme events are markedly different - both stronger or weaker - to those in moderate extreme events. Model uncertainty about these differences is very high though: all considered SMILES suggest that such regions exist, but they do not agree on the locations.  The underlying mechanisms, however, are robust across models: in regions of increasing soil moisture temperature coupling strength, changes in very rare events can be almost twice as high as those in moderate extremes. Vice versa, in regions of decreasing coupling strength, changes may be much weaker. These changes can to a large extent be traced back to changes in precipitation patterns, highlighting the role of atmospheric circulation changes.  The corresponding patterns emerge already over shorter time horizons and are thus relevant for mid-century projections, low emission scenarios and event attribution studies. Robust inference about these differences is impossible based on individual model simulations, but requires the sample size of SMILES.  Not accounting for these changes could lead to a dramatic misrepresentation of future climate risks from heat events. Our findings therefore confirm the importance of studies specifically targeting very extreme events.
Resolving the wakes of offshore wind infrastructure in two layer flows
Authors: Lloyd Charlie
Published: March 2024

To enable a global net zero future the next decade will see exponential growth of offshore wind renewable energy. This scale of development necessitates expansion into deeper, seasonally stratified, waters, for the first time. This transition from shallow well-mixed regions to deeper waters marks a fundamental change in the marine environment, due to the importance of vertical density gradients on fluid dynamics. Seasonal stratification is a vital control on marine ecosystems; primary production, biogeochemical cycling, and water column structure are all intricately linked through vertical mixing processes across the pycnocline. Understanding turbulence introduced in the water column by tidal flows past offshore infrastructure, and the subsequent effect on vertical mixing, is therefore vital for predicting and managing renewable energy impacts on the marine environment. This work is focussed on understanding the fundamental fluid dynamics of offshore wind infrastructure wakes in stratified flows, using direct numerical simulations. The tidal flows past the structures are approximated by a uniform quiescent background flow with a two-layer density profile. The flows past two types of infrastructure are investigated: A uniform vertical cylinder approximating a monopile, and a truncated cylinder approximating a floating spar-buoy or semi-submersible structure. The truncated cylinder has its length equal to the pycnocline depth, such that it penetrates through the upper half of the pynocline. Through these simulations we identify the process through which turbulence generated in the wake of the structures leads to vertical mixing across the pycnocline. While both cylinders weaken the pynocline by a similar amount, the processes that lead to vertical mixing differ significantly. The truncated cylinder directly leads to mixing in the lee of the structure due to the vertical displacement of fluid beneath it. In contrast, the full cylinder requires a transition from horizontally sheared flow to 3D turbulence before vertical mixing occurs. In addition to direct mixing of the pycnocline through turbulence, internal gravity waves are also observed in the wake of both cylinders, identified through a spectral decomposition of instantaneous slice data. Future work will aim to understand the interaction between waves and turbulence in the cylinder wakes, the influence of shear in the background flow on mixing in the wakes, and will develop sub-grid-scale closures appropriate for modelling full-scale tidal flows past offshore wind infrastructure. 
Atrazine Removal in Constructed Wetlands: Efficacy of Monocultures versus Polycultures
Authors: Pilla Sai Kiran , Jain Mahak , Ghosal Partha Sarathi , Gupta Ashok Kumar
Published: March 2024

The Green Revolution in India, from 1967-68 to 1977-78, led to a significant shift in the country's agricultural landscape, transforming it from an insufficient food production country to a global agricultural power. This led to an increase in the use of pesticides, such as atrazine, which can pollute water sources and endanger aquatic habitats. This research aims to find sustainable and practical techniques for atrazine remediation within aquatic habitats. Literature suggests that macrophyte richness enhances the functionality of constructed wetlands (CWs), but the predominant practice is monocultures. The functional diversity within macrophyte communities is crucial for optimal performance of CWs for contaminant remediation. CWs with diverse growth forms exhibit enhanced plant growth and superior nutrient removal capabilities. The study evaluates atrazine removal efficacy of polyculture and monoculture plantation, monitoring the efficiency of various individual macrophyte, such as Canna indica and Phragmites Australis for atrazine detoxification. The findings could guide the formulation of sustainable and efficacious atrazine remediation strategies, safeguarding water quality and the integrity of aquatic ecosystems.
The observed evolution of sub-daily to multi-day heavy precipitation in Switzerland
Authors: Bauer Victoria , Scherrer Simon
Published: March 2024

Heavy precipitation events and their changes due to climate change affect many aspects of daily life in the Alpine region. In this study, we revisit the long-term (1901-2022) evolution of daily and multi-day heavy precipitation intensity and frequency, discuss trends for sub-daily to multi-day events in the recent period 1981-2022, and investigate possible elevation dependencies in the complex topography of Switzerland. Station measurements from the dense operational network of MeteoSwiss from all parts of the country and elevation levels are analyzed. We find that daily maximum precipitation and the frequency of precipitation events exceeding the 99th daily percentile have increased since 1901, with a peak in the 1980s and some decline thereafter. For the more recent period 1981-2022, positive trends in summer heavy precipitation intensity are found for short (10 minutes to 3 hours) events, but no changes are found in the frequency of these events. For longer events (one to five days), however, decreases in intensity and frequency are found, especially for the winter half-year. We hypothesize that the opposing trends on long and short time scales are caused by the superposition of thermodynamics (i.e. the main forcing of anthropogenic climate change) and internal variability of atmospheric dynamics. We also observe a small negative elevation dependence of the long-term trends up to 2300 m. For the 1981-2022 trends, no strong elevation dependence is found for sub-daily events. For daily events we find small opposing negative summer and positive winter elevation dependencies. The reason for these trends remains unclear. Our results underline the need to better investigate the interplay between climate change, internal variability of large-scale dynamics and elevation for heavy precipitation in the complex Alpine terrain. Longer observational records with high spatial and temporal resolution will help to answer this open question.
Mapping landslide susceptibility through physically-based modeling
Authors: Mevoli Federica Angela , Borselli Lorenzo , Santangelo Michele , Ugenti Angelo , de Lucia Daniela , Fazio Nunzio Luciano , Rossi Mauro
Published: March 2024

Landslide susceptibility is the likelihood of a landslide occurring in a specific area based on the local terrain conditions. Susceptibility does not take into account the size, duration, or frequency of occurrence of landslides. Different approaches and methods have been proposed to determine the likelihood of occurrence of landslides: geomorphological mapping, analysis of landslide inventories, heuristic terrain zoning, statistically-based classifications and physically based numerical modelling (Aleotti and Chowdhury, 1999; Guzzetti et al., 1999). The last two approaches are preferred for assessing susceptibility in quantitative terms. Today, statistically based methods are preferred for small-scale landslide susceptibility zonations. Performing this task by using physically-based approaches is more challenging, as the performance of numerical analyses usually requires detailed geomechanical and hydrological data, whose collection demands significant time and costly efforts.However, this work is primarily motivated by the following question: Can landslide susceptibility maps at smaller scales than detail-scale truly not be attained through the application of physically-based approaches?The authors show their first attempt in answering the question through the combined application of Geographic Information Systems (GIS) and a 2.5D Limit Equilibrium Method (LEM) implemented using the SSAP software (Borselli, 2023). The results obtained in a study area in Southern Italy and the physically-based landslide susceptibility map derived at basin-scale are presented and discussed. This preliminary but yet reproducible analysis allows to drive future efforts in physically-based susceptibility zonation. ReferencesAleotti, P., & Chowdhury, R. (1999). Landslide hazard assessment: summary review and new perspectives. Bulletin of Engineering Geology and the environment, 58, 21-44. DOI: https://doi.org/10.1007/s100640050066Borselli L. (2023). "SSAP 5.2 - slope stability analysis program". Manuale di riferimento. Del codice ssap versione 5.2. Researchgate.   DOI: https://dx.doi.org/10.13140/RG.2.2.19931.03361Guzzetti, F., Carrara, A., Cardinali, M., & Reichenbach, P. (1999). Landslide hazard evaluation: a review of current techniques and their application in a multi-scale study, Central Italy. Geomorphology, 31(1-4), 181-216. DOI: https://doi.org/10.1016/S0169-555X(99)00078-
Mitigation of nitrogen loss in rice fields through soil desaturation prior to re-irrigation and the application of controlled-release nitrogen fertilizer: a meta-analysis
Authors: Gbedourorou Sabi Kidirou , Tovihoudji Pierre G. , Vanclooster Marnik , Akponikpe Irénikatché P. B.
Published: March 2024

Water pollution by nitrogen residues from agricultural intensification has become a recurring problem, particularly in wetlands used for rice production. As a solution to remediate this issue, sustainable water and nutrient management are being explored. These practices involve matching water and nutrient availability with plant needs in space and time while ensuring production objectives are met. In this study, we performed a meta-analysis to synthesize the current knowledge on the effect of water and nutrient management practices on nitrogen losses and uptake by plants in rice cropping systems. Using a random effects model, we summarized the effect sizes of 103 observations from 27 peer-reviewed studies. Tree water management practices were evaluated: “Continuous Flooding” (used as control), “Alternate Wet and Dry (AWD)” and “Controlled Irrigation (CI)”. The response ratio (RR) of nitrate leaching and total nitrogen loss was negative for CI (-0.53 and -0.34, respectively) and AWD (-0.13 and -0.36, respectively). Regardless of water management practices (AWD or CI), desaturating the soil before re-irrigation reduced nitrate and total nitrogen losses. When considering the source of nitrogen input, water management practices involving desaturation of the soil before re-irrigating were effective in reducing nitrogen losses in urea-only applications. However, in the case of controlled release urea (CRF) applications, water management treatments (AWD or CI) were not necessary to reduce nitrogen losses, especially those due to ammonia volatilization. This result also indicates the effectiveness of CRF treatment in retaining the essential nitrogen component required for plant growth and development. Nevertheless, when nitrogen rates exceed 200 kg N/ha, adopting water management practices such as CI and AWD becomes necessary to decrease nitrate leaching and total nitrogen loss in rice fields. Regarding the rice grain yield, water management practices that involve reducing the amount of water (AWD and CI) have shown no significant effect on yield (RR 0.017 and -0.0001). In conclusion, AWD and CI water management practices have been shown to reduce nitrogen losses without compromising rice grain yield. Additionally, the application of CRF reduces nitrogen losses that may occur in a continuous flooding system.Key-words: Water management; Nitrogen; Rice; Controlled release urea; Water pollution
Enabling seamless integration of Copernicus and in-situ data
Authors: Sotiropoulos Iason , Papanikolaou Athos , Sekkas Odysseas , Polydoros Anastasios , Tsetsos Vassileios , Pisa Claudio , Rizou Stamatia
Published: March 2024

BUILDSPACE aims to combine terrestrial data from buildings collected by IoT devices with aerial imaging from drones equipped with thermal cameras and location annotated data from satellite services (i.e., EGNSS and Copernicus) to deliver innovative services at building scale, enabling the generation of high fidelity multi-modal digital twins and at city scale providing decision support services for energy demand prediction, urban heat and urban flood analysis. A pivotal element and the foundational support of the BUILDSPACE ecosystem is the Core Platform and it plays a crucial role in facilitating seamless data exchange, secure and scalable data storage, and streamlined access to data from three Copernicus services, namely the Land, Atmosphere, and Climate Change.The platform's underlying technology is robust, incorporating two key components: OIDC for user authentication and group authorization over the data, and a REST API to handle various file operations. OIDC stands for OpenID Connect, a standard protocol that enables secure user authentication and allows for effective management of user groups and their access permissions. On the other hand, the platform employs a REST API for seamless handling of file-related tasks, including uploading, downloading, and sharing. This combination ensures efficient and secure data exchange within the system. Additionally, the use of an S3 compatible file system ensures secure and scalable file storage, while a separate metadata storage system enhances data organization and accessibility. Currently deployed on a Kubernetes cluster, this platform offers numerous advantages, including enhanced scalability, efficient resource management, and simplified deployment processes. The implementation of the Core Platform has led to a current focus on integrating APIs from Copernicus services into the Core Platform's API. This ongoing effort aims to enhance the platform's capabilities by seamlessly incorporating external data, enriching the overall functionality and utility of the project.
What you do not know, and what you should know about lysimeter experiments
Authors: Puetz Thomas , Gerke Horst H. , Brueggemann Nicolas , Vereecken Harry , Groh Jannis
Published: March 2024

For many studies in the fields of soil, hydrology, agriculture, ecology, meteorology, and environmental sciences and across disciplines, conventional field experiments are inadequate because the variables cannot be measured properly or controlled experimentally. In the soil-plant-atmosphere continuum, lysimeters can be used as an integrative experimental approach that enables precise measurements of water and matter fluxes in combination with field crops. The term lysimeter basically refers to two different types of experimental equipment. Porous suction cups, as well as containers/vessels filled with soil substrates or other materials, are termed lysimeters. Lysimeters are vessels of various sizes filled with ecosystem compartments, taking a holistic approach as each compartment interacts dynamically within the biosphere.Lysimeter experiments are carried out in a wide variety of designs. To optimize the scientific exploitation of lysimeter data, various prerequisites should be met. The complexity of lysimeter experiments will be explained in more detail, the advantages of lysimeters, but also the restrictions and limitations will be examined in more detail. We would like to suggest some hints, norms, and rules for conducting lysimeter experiments that can optimize and increase the benefit and profit of lysimeter experiments. Special attention is paid to the important technical details that can significantly influence the quality of lysimeter measurements. The latest technical developments are also briefly presented.
The importance of small glaciers for accurate projection of future runoff in High Mountain Asia
Authors: von der Esch Alexandra , Huss Matthias , Van Tiel Marit , Berg Justine , Patadiya Tarang , Horton Pascal , Vijay Saurabh , Farinotti Daniel
Published: March 2024

High Mountain Asia is characterized by a substantial glacier coverage with glaciers of varying sizes. These glaciers are crucial in the area's hydrological cycle since they feed large rivers such as the Indus, Ganges, and Brahmaputra rivers. However, ongoing climate change is having a significant negative impact on glacier mass and projections show strong further declines of glacier mass in the future. This is raising concerns about future water security. How big the impact of the evolution of small glaciers (< 2 km2) is towards changing water availability remains to be investigated.Most studies focus on the regional evolution of glaciers as a whole, which means that small-scale glaciers are often overlooked due to larger glaciers dominating the signal in area and volume changes, despite the fact that small glaciers make up about 30% of the glacierized area in High Mountain Asia. To address this issue, we applied the Global Glacier Evolution Model (GloGEM) to simulate all ca. 100’000 glaciers of High Mountain Asia (Regions 13-15 of the Randolph Glacier Inventory v6.0) under various climate scenarios in the period of 1980-2100. We compared the spatio-temporal variability of the timing of peak water, as well as glacier volume change, between small and large glaciers for a set of approximately 30 catchments in the headwater of Indus, Ganges and Brahmaputra rivers.We find that there is a larger difference between future scenarios for the timing of peak water for smaller glacier, with it ranging from 2030-2060 and then runoff declining rapidly. Meanwhile, peak water for larger glaciers is likely to occur between 2070-2080 according to an intermediate emission scenario, with glacier runoff decreasing gradually thereafter. As for the ice volume change, smaller glaciers are expected to reach volumes close to zero near the year 2080, while larger glaciers are expected to reach this point only after 2100. The quicker response of small glaciers compared to large glaciers emphasize the need for a particular focus on small glaciers to better understand their responses to climate change and make accurate projections about local and regional scale near future water availability.
Understanding and predicting the spread of Phlebotomine sand flies in Europe
Authors: Wang Danyang , Hof Anouschka , Matson Kevin , van Langevelde Frank
Published: March 2024

Climate change influences the transmission of vector-borne diseases by affecting the distribution and survival of disease vectors. Numerous diseases are transmitted by phlebotomine sand flies (SFs), including Leishmaniasis. Several major sand fly-borne diseases (SFBDs) are responsible for high global disease burdens and high socio-economic costs. In Europe, 22 known SF vector species are largely confined to the Mediterranean Basin, yet global warming is predicted to drive the spread of SFs to large areas of Europe in the 21th century, an effect likely to be exacerbated by anthropogenic variables. However, the constraints to the geographic distributions of SFs are not well understood. This study aims to increase the understanding of the drivers of the spatial distributions of SFs. To achieve this, we use species distribution modelling (SDM) to assess the role of climate, land-use and socio-economic drivers in shaping the geographic distributions of all endemic SF vectors in Europe. With this knowledge, we predict future hotspots of SFs in Europe. Our predictions are spatially explicit, scenario-based, and informative for surveillance efforts.
The Potential of Generative AI for the Urban Water Sector
Authors: Taormina Riccardo
Published: March 2024

The urban water sector is increasingly turning to AI and deep learning to address the complex challenges posed by growing demographics, climate change, and urbanization. Despite the pressing need, this sector has been relatively slow in adopting these technologies compared to others, primarily due to its conservative nature. However, the recent advancements in generative AI have opened new frontiers for innovation, presenting a crucial opportunity for the urban water sector to accelerate its technological evolution. Expected regulations, particularly from institutions like the European Union, should not be viewed as a hindrance but as a catalyst for enhanced collaboration between academia, industry, and public stakeholders. Such collaboration is essential to finally push the development and adoption of reliable and safe AI systems, ensuring alignment with regulatory frameworks.In this work, we first provide an overview of the latest trends in generative AI, focusing on how Large Language Models and Large Multimodal Models can benefit the urban water sector. Particularly, Large Multimodal Models can offer an added layer of explainability to predictive models working on imagery or other sensor data, a highly desirable feature for applications related to critical infrastructure. By literally asking these models to explain their decision-making processes with respect to the processed data streams, we can partially demystify the 'black box' nature of AI systems.This potential is highlighted for a case study on sewer defect detection, utilizing a Large Multimodal Model that processes both text and imagery. The predictive results on the publicly available SewerML dataset are encouraging with respect to existing deep learning methods. More importantly, we show that explanations provided by the Large Multimodal Model can enlighten the decision-making process, making it more transparent. This added layer of explanation offers valuable insights and may set a new trajectory for developing trustworthy AI methodologies in critical water infrastructure management.
A framework for early-stage coastal and estuarine tidal and mean sea surface correction from the Surface Water Ocean Topography mission
Authors: Monahan Thomas , Tang Tianning , Roberts Stephen , Adcock Thomas
Published: March 2024

The NASA Surface Water Ocean Topography mission (SWOT), launched on 16 December 2022, will provide the highest spatial and temporal altimetric measurements of coastal oceans to date. The mission is ideally suited to studying mesoscale and submesoscale processes and is expected to enhance our understanding of coastal tides greatly. Although improved tidal analysis and prediction is, of course, useful for studying tides, arguably more important is the accurate removal of tidal variability from SWOT observations. This is a consequence of the fact that the tidal signal often dominates other sub-mesoscale processes which are of high interest to SWOT researchers. While SWOT presents unprecedented spatial resolution, the temporal sparsity renders the applications of conventional tidal analysis methods difficult in the early stages of the mission. Despite significant improvements in global and regional barotropic tidal models in the past few decades, the complexity of coastal and estuarine tides as well as the relatively limited in-situ measurements available for assimilation can lead to significant errors when used for tidal corrections. Further complications are introduced by the uncertainty in mean sea surface (MSS) estimates from gridded MSS Products. These errors can account for large percentages of the global Sea Level Anomaly error and grow significantly over rough bathymetry. As such, the accurate assessment of the uncertainty for the released data products and corrections derived from primary SWOT data are critical to the early success of SWOT science teams. We develop a fully Bayesian variant of tidal harmonic analysis to achieve tidal super-resolution and MSS correction for early-stage (
Passive Assessment of Geophysical Instruments Performance using Electrical Network Frequency Analysis
Authors: Koymans Mathijs , de Zeeuw-van Dalfsen Elske , Evers Läslo , Assink Jelle
Published: March 2024

The electrical network frequency (ENF) of the alternating current operated on the power grid is a well-known source of noise in digital recordings. The noise (i.e., signal) is widespread and appears not just in close proximity to high-voltage power lines, but also in instruments simply connected to the mains powers grid. This omnipresent, anthropogenic signal is generally perceived as a nuisance in the processing of geophysical data. Research has therefore been mainly focused on its elimination from data, while its benefits have gone largely unexplored. It is shown that mHz fluctuations in the nominal ENF (50 - 60Hz) induced by variations in power usage can be accurately extracted from geophysical data. This information represents a persistent time-calibration signal that is coherent between instruments over national scales. Cross-correlation of reliable reference ENF data published by electrical grid operators with estimated ENF data from geophysical recordings allows timing errors to be resolved at the 1s level. Furthermore, it is shown that a polarization analysis of particle motion at the ENF may assist in the detection of instrument orientation anomalies at the surface. Furthermore, it is explored whether this method can be applied to determine orientations of geophones inside seismic boreholes.
XDGGS: Xarray Extension for Discrete Global Grid Systems (DGGS)
Authors: Kmoch Alexander , Bovy Benoît , Magin Justus , Abernathey Ryan , Strobl Peter , Coca-Castro Alejandro , Fouilloux Anne , Loos Daniel , Odaka Tina
Published: March 2024

Traditional geospatial representations of the globe on a 2-dimensional plane often introduce distortions in area, distance, and angles. Discrete Global Grid Systems (DGGS) mitigate these distortions and introduce a hierarchical structure of global grids. Defined by ISO standards, DGGSs serve as spatial reference systems facilitating data cube construction, enabling integration and aggregation of multi-resolution data sources. Various tessellation schemes such as hexagons and triangles cater to different needs - equal area, optimal neighborhoods, congruent parent-child relationships, ease of use, or vector field representation in modeling flows.The fusion of Discrete Global Grid Systems (DGGS) and Datacubes represents a promising synergy for integrated handling of planetary-scale data.The recent Pangeo community initiative at the ESA BiDS'23 conference has led to significant advancements in supporting Discrete Global Grid Systems (DGGS) within the widely used Xarray package. This collaboration resulted in the development of the Xarray extension XDGGS (https://github.com/xarray-contrib/xdggs). The aim of xdggs is to provide a unified, high-level, and user-friendly API that simplifies working with various DGGS types and their respective backend libraries, seamlessly integrating with Xarray and the Pangeo scientific computing ecosystem. Executable notebooks demonstrating the use of the xdggs package are also developed to showcase its capabilities.This development represents a significant step forward, though continuous efforts are necessary to broaden the accessibility of DGGS for scientific and operational applications, especially in handling gridded data such as global climate and ocean modeling, satellite imagery, raster data, and maps.Keywords: Discrete Global Grid Systems, Xarray Extension, Geospatial Data Integration, Earth Observation, Data Cube, Scientific Collaboration
From meters of subsidence to millimeters of slow slip: monitoring deformation and associated uncertainties from InSAR
Authors: Jolivet Romain , Dalaison Manon , Raimbault Bryan , Pinel-Puysségur Béatrice , Rouet-Leduc Bertrand , Dérand Paul
Published: March 2024

Over the past two decades, InSAR evolved from the occasional processing of single interferograms over arid terrains to monitoring continuous time series of SAR acquisitions at the continental scale. Challenges, including atmospheric phase screen mitigation, automatic careful SAR image co-registration, ionospheric phase screen corrections, or discontinuous acquisition planning, were met through various technical and methodological advances by many research groups globally. The resulting methodologies now allow us to image a vast range of processes, from sudden large earthquakes to continuous subsidence involving metric to millimetric displacements. In addition to the ability to process datasets over continental scales, we can now measure natural signals of a few millimeters over distances lower than a kilometer.In recent years, we proposed technical solutions to issues that were seriously impeding our ability to measure small, millimeter-scale displacements over natural terrains. First, I will discuss the early development of tropospheric corrections using numerical weather models and highlight some of the most recent tools and methods stemming from there. Second, I will illustrate our approach to tackle the issue of continuously incoming SAR acquisitions, which we addressed by developing a data assimilation-based method involving a Kalman filter. This tool allows the rapid update of pre-existing time series of deformation as new SAR images are available while carefully propagating forward some of the uncertainties associated with time series analysis. Third, I will show how we handle the automatic denoising of InSAR time series using a fully convolutional neural network, allowing us to detect sub-millimeter tectonic fault slip with no prior knowledge of the faults. Fourth, I will present some recent developments about the effect of fading signals and time-dependent coherence evolution over temperate regions, depending on land covers.All these developments allowed us to image surface deformation processes, including several continuously creeping faults globally, transient tectonic slow slip events, intriguing post-seismic deformation signals, and strong subsidence patterns.
Machine Learning Assisted Analysis on Space-Borne Point Clouds for Detecting Landslide Affected Areas
Authors: Tsai Fuan , Dippold Elisabeth , Lo Chi-Chuan , Liu Chien-Liang
Published: March 2024

Landslide is one of the most frequently occurred and destructive natural hazards in Taiwan and many other places around the world. Using satellite images to help identify landslide affected regions can be an effective and economic alternative comparing to conventional ground-based measures. Our previous study developed a deep learning model to analyze bi-temporal satellite images for detecting landslide affected areas in mountainous areas. The deep learning model can successfully detect spatial (planar) changes of landslides from multi-temporal satellite images. However, in a long-term monitoring of landslide affected areas, it is common to observe existing landslides occurring repeatedly. In addition to planar expansions of existing landslides and increase the extents of landslide scars, it is also common that existing landslides collapse further and produce deeper craters. Therefore, it is necessary to detect and identify the volumetric changes of landslides for a better inventory. To address this issue, this research developed a systematic machine learning framework to analyze multi-temporal three-dimensional point clouds generated from stereo pairs of high-resolution satellite images. However, the lack of appropriate and adequate training data posed a great challenge. This study first modified existing high-resolution point clouds benchmark datasets to be more consistent with the relatively low-density space-borne point clouds for preliminary training. In addition, an integration of historical LiDAR point clouds and archived satellite images were also used to generate local training datasets for transfer learning. Experimental results indicate that the developed machine learning algorithms can be used to effectively analyze space-borne point clouds for detecting volumetric changes of landslides. The results not only can produce more accurate three-dimensional landslide inventories; they are also critical factors for hazard mitigation and policy decision support.