bluuebunny/crossref_metadata_2025 huggingface dataset:
Assessment of composite sampling for determining the levels of fecal indicator and antibiotic resistant bacteria in irrigation water
Authors: Stocker Matthew , Smith Jaclyn , Pachepsky Yakov
Published: March 2024

The microbial quality of irrigation water is a major worldwide health concern. Assessments of irrigation water quality are conducted via the measurement of the fecal indicator bacteria Escherichia coli. More recently, the levels of antibiotic resistant bacteria in irrigation water have been highlighted as an emerging concern as they may be spread from surface waters to crops and soils. Composite sampling has been recommended in cases when larger sample sets cannot be collected across a waterbody. However there have been no reports which have compared the results of composite sampling with the spatial means or medians of fecal and antibiotic resistant bacteria within irrigation waters. The objectives of this work were to evaluate the representativeness of composite samples for estimating the levels of fecal and antibiotic bacteria in several irrigation ponds. In August and September of 2022, water samples were collected from dense sampling grids within five irrigation ponds in Maryland and Georgia, USA. Concentrations of generic, tetracycline, and cefotaxime-resistant E. coli and total coliforms were enumerated in all samples. Three composite samples were created for each pond: a composite of the interior, bank, and full sample sets. In general, we found the distributions of generic and antibiotic resistant bacteria concentrations did not significantly differ between bank and interior samples. Concentrations of antibiotic resistant bacteria ranged substantially across all the waterbodies. On average, the composite samples fell between the 60th and 70th percentile of the concentration distributions. In only a 9 and 14.5 % of cases (n = 90) did the composite sample value significantly differ (p < 0.05) from the mean or median, respectively, of the entire sample sets. Results of this work indicate that composite sampling may accurately be used to estimate the spatial mean or median of generic and antibiotic resistant bacteria concentrations in irrigation waters. These results will be used to improve the estimation of fecal contamination of irrigation waters as well as the presence of antibiotic resistant bacteria. 
Bridging History and Climate Science - ClimeApp: Data processing tool for the ModE-RA Global Climate Reanalysis
Authors: Warren Richard , Bartlome Niklaus , Wellinger Noémie
Published: March 2024

ClimeApp is a newly developed web-based processing tool for the state-of-the-art ModE-RA climate reanalysis. It presents temperature, precipitation and pressure reconstructions with global coverage and monthly resolution over the last 600 years. The app allows integration of historical information with climate data through composite, correlation and regression functions. The ModE project itself contains not one, but three experiments - ModE-RA, ModE-Sim and ModE-RAclim – all accessible through the app. These integrate a huge array of source material and allow the separation of the effects of external and internal forcing on the climate system in unprecedented ways. The app is designed to allow quick data processing for climatologists and easy use for non-climatologists. Specifically, it aims to help bring climate into history, where climatological data still has huge potential to advance historical research. This poster demonstrates the functions and applications of ClimeApp and the ModE-RA reanalysis. It also summarises opportunities for creating similar interfaces in other disciplines. ClimeApp is available at http://climeapp-modera.unibe.ch:3838/
Seeking consensus between Eulerian and Lagrangian moisture tracking methods for precipitation origin analysis in Atmospheric Rivers
Authors: Crespo-Otero Alfredo , Insua-Costa Damian , Míguez-Macho Gonzalo
Published: March 2024

Global warming is increasingly aggravating hydro-climate extremes, such as floods and droughts. In this context it is essential to understand the complex dynamics of the atmospheric branch of the water cycle, including the link between evaporation and precipitation. For this reason, many studies have investigated the origin of the moisture that feeds precipitation, which has led to a better understanding of atmospheric water transport. However, the lack of observations has prevented a direct validation of the different moisture tracking tools used for this purpose, and it is common to find large discrepancies between the results they provide.To fill this gap, we compare two different Lagrangian methodologies for moisture tracking based on the FLEXible PARTicle dispersion model (FLEXPART) against the Eulerian “Water Vapor Tracers” technique based on WRF (WRF-WVTs). Considering the results of WRF-WVTs as “ground truth”, we explore the discrepancies between the Eulerian and Lagrangian approaches for five precipitation events associated with ARs and, based on that, propose some physics-based adjustments to the Lagrangian tools. Our results show that Lagrangian methodologies using evaporation data instead of specific humidity data provide results much closer to those of WRF-WVTs. Specifically, they reduce large biases that underestimate remote sources (such as tropical ones), while overestimating local contributions. When we introduce our physical corrections, both methods improve remarkably, which means that these biases are strongly reduced and the results provided by the different techniques reach a consensus.
Enhancement of coastal winds and surface ocean currents with deep learning – The Copernicus Marine Service Evolution KAILANI project
Authors: Garcia-Leon Manuel , Aouf Lotfi , García-Valdecasas José María , Dalphinet Alice , Asensio Juan , Aznar Roland , Terrés José María , López Pérez Tania , G Sotillo Marcos
Published: March 2024

Coastal stakeholders are adding ocean forecasts in their daily operations. The Copernicus Marine Service - Monitoring and Forecasting Centres (CMS - MFCs) are evolving to meet these user new demands. For instance, the wave forecast service at the Iberian-Biscay-Ireland area (IBI-WAV) is being upgraded with increases in model resolution (from 1/20º to 1/40º) and the delivery of new variables (e.g. maximum wave height). 
Enhancing coastal forcings (winds and surface currents) reinforces these forthcoming upgrades. Higher model resolution benefits local-scale phenomena representation (i.e. wind wave growth or wave refraction due to currents), but errors in the forcings may degrade the expected performance. The Copernicus Marine Service Evolution project KAILANI (2022-2024) aims to improve the accuracy of these operational forcings by correcting them with Artificial Neural Networks (ANNs). These ANNs are fed with remote sensing data as target datasets, allowing to predict complex spatial patterns by using the same forcings as predictors.
Three pilot sites have been selected to develop this methodology: (i) the Galician area (NE-Atlantic, macrotidal), (ii) the Canary Islands (NE-Atlantic, mesotidal) and (iii) the Ebro Delta (NW Mediterranean, microtidal).
The ANN for coastal winds has been trained with Satellite Synthetic Aperture Radar (SAR; data from the ESA Sentinel-1 mission). Due to the nature of the data (i.e. good spatial coverage, but with revisit times up to several days), the ANN architecture consists of a Generative Adversarial Network (GAN) that uses Convolutional layers for addressing the spatial dimension. The ANN is able to downscale ECMWF-IFS wind fields from 1/10º to 1/40º resolution, adding spatial features learnt a priori from SAR data. The ANN-predicted winds present lower speed bias (up to 30% in specific areas) and RMSD (up to 10%) than original ECMWF-IFS winds.  

Surface currents are predicted with ANNs based on Autoencoders (AE), using hourly-averaged HF-Radar data as target dataset (retrieved from Copernicus Marine In-Situ TAC). Spatial and time dimension are addressed with Convolutional and Long Short-term Memory (LSTM) layers, respectively. The input data includes (i) IBI-PHY currents and (ii) atmospheric forcings from ECMWF-IFS. The AE improves error metrics respect IBI-PHY, showing speed and directional biases close to 2 cm/s and 7º (achieving bias decreases of 45% and 60% in some areas, respectively).
These ANN-driven wind and currents forcings will be used in pre-operational tests by the IBI-WAV service. If successful, this methodology could be transferred into operations of the IBI NRT wave forecast system, as well as to be extended in other CMS regional MFCs. 
An Ionospheric Forecasting Model Based on Transfer Learning Using High-Resolution Global Ionospheric Maps
Authors: Mao Shuyin , Gou Junyang , Soja Benedikt
Published: March 2024

High-precision ionospheric prediction is essential for real-time applications of the Global Navigation Satellite System (GNSS), especially for single-frequency receivers. Various machine learning (ML) algorithms have been utilized for ionospheric forecasting and shown great potential. However, previous studies have primarily relied on IGS global ionospheric maps (GIMs) as training data to develop models for global vertical total electron content (VTEC) forecasting. The forecasting accuracy is thereby limited by the input IGS GIMs due to their low spatio-temporal resolution.Our previous work proposed a neural network-based (NN-based) global ionospheric model. GIMs generated with this approach showcased enhanced accuracy compared with conventional IGS GIMs as we can finely resolve VTEC irregularities. In this study, we benefit from these ML-based GIMs by employing the transfer learning principle to improve the quality of GIM forecasts. The ML-based model for 1-day ahead global VTEC forecasting is first trained based on a series of IGS GIMs from 2004 to 2022. Then, it is fine-tuned using the recent NN-based GIMs from 2020 to 2022. In this context, the model can gain good generalizability from the large dataset of IGS GIMs while having comparable accuracy with NN-based GIMs. Different machine learning approaches, including convolution long short-term memory (ConvLSTM) network and transformer, are implemented and compared. To validate their performance, we perform hindcast studies to compare the 1-day ahead forecasts of our model with satellite altimetry VTEC and conducted single-frequency precise point positioning tests based on the forecast maps.
TROPOMAG - Influence of geomagnetic storms on the TROPOsphere dynamics: Can the Earth’s MAGnetic field be considered a proxy of climate changes? Some results
Authors: Santarelli Lucia , Bruno Valentina , Coco Igino , De Gregorio Sofia , De Michelis Paola , Giannattasio Fabio , Madonia Paolo , Pezzopane Michael , Pietrella Marco , Rossi Massimo , Tozzi Roberta
Published: March 2024

The TROPOMAG project investigates the possible effects of changes of the Earth’s magnetic field on the atmosphere and weather conditions with the aim to better quantify the natural sources of the atmospheric variability. This need raises to assess the observed climate trends more correctly, with a consequent better understanding of manmade effects on climate. Specifically, this work explores possible connections between atmospheric pressure anomalies and the occurrence of geomagnetic storms. To accomplish this task pressure data, recorded over some Italian volcanic areas, are analysed according to different methods and considering geomagnetic indexes. This work describes and discusses corresponding preliminary results.
 Changes in erosion and sediment dynamics in a retreating world: high resolution provenance analysis from detrital apatite
Authors: Jess Scott , Schoenbohm Lindsay , Enkelmann Eva
Published: March 2024

Glacial retreat quickly and dramatically changes erosion dynamics across catchments. As ice retreats, newly exposed valley walls and sediment can become the target of hillslope and fluvial erosion that in turn can significantly increase sediment fluxes downstream. These increasing fluxes have important implications for hydropower generation and water quality, presenting risks to biodiversity, ecosystem stability, and human inhabitants. Determining where this new influx of sediment is derived from, and hence what parts of catchments are experiencing the greatest erosion, requires the ability to trace exactly where is sediment derived from in the catchment.Recent analytical advances in the dating of apatite have improved its utility as a provenance tool. The advent of LA-ICP-MS techniques now allow thermochronometric, geochronometric, and chemical data to be collected from each individual grains of a detrital sample. As such, we are able to trace sediment sources across a partially glaciated catchment based on lithology, and source-rock elevation. In this work, we collected samples across the Bugaboo Glacier catchment in western Canada, where ice has retreated >2 km in the last century. Detrital samples were collected from the outwash river and two moraine samples, coupled with a bedrock elevation profile. Bedrock samples encompass the catchment’s two principal lithologies, a Cretaceous granitic intrusion, and Neoproterozoic metasediments. Thermochronometric dates range from 41.4 Ma at the highest elevation to 23.9 Ma at the lowest, while geochronometric dates range 68.7–151.3 Ma in granites to 90.5–1952 Ma in metasediments. Supplementary chemical data also help to highlight key differences between the lithologies.Dates and chemistry from moraine samples show they are likely derived primarily from upstream granitic sources, while sample from the modern outwash river suggests a greater mixture of sources. Detrital mixture models and multi-dimensional scaling suggest moraine samples are composed of sediment derived from a wide range of elevations within the catchment, while the sediments of the modern outwash river appear to be derived entirely from erosion of these moraines, left exposed by retreating ice. This suggests the widely documented increase in sediment flux during glacial retreat is primarily driven by the erosion of newly exposed unconsolidated moraines in catchments. Moreover, this work helps to highlight how the analysis of detrital apatite can be harnessed to produce a highly accurate provenance tool in glacial catchments.
Analyzing the performance and interpretability of hybrid hydrological models
Authors: Acuna Eduardo , Loritz Ralf , Alvarez Manuel , Kratzert Frederik , Klotz Daniel , Gauch Martin , Bauerle Nicole , Ehret Uwe
Published: March 2024

Hydrological hybrid models have been proposed as an option to combine the enhanced performance of deep learning methods with the interpretability of process-based models. Among the various hybrid methods available, the dynamic parameterization of conceptual models using LSTM networks has shown high potential. In this contribution, we extend our previous related work (Acuna Espinoza et al., 2023) by asking the questions: How well can hybrid models predict untrained variables, and how well do they generalize? We address the first question by comparing the internal states of the model against external data, specifically against soil moisture data obtained from ERA5-Land for 60 basins in Great Britain. We show that the process-based layer can reproduce the soil moisture dynamics with a correlation of 0.83, which indicates a good ability of this type of model to predict untrained variables. Moreover, we compare this method against existing alternatives used to extract non-target variables from purely data-driven methods (Lees et al., 2022), and discuss the differences in philosophy, performance, and implementation. Then, we address the second question by evaluating the capacity of such models to predict extreme events. Following the procedure proposed by Frame et al (2022), we train the hybrid models in low-flow regimes and test them in high-flow situations to evaluate the generalization capacity of such models and compare them against results from purely data-driven methods. Both experiments are done using large-sample data from the CAMELS-US and CAMELS-GB dataset.With these new experiments, we contribute to answering the question of whether hybrid models give an actual advantage over purely data-driven techniques or not.ReferencesAcuna Espinoza, E., Loritz, R., Alvarez Chaves, M., Bäuerle, N., & Ehret, U.: To bucket or not to bucket? Analyzing the performance and interpretability of hybrid hydrological models with dynamic parameterization. EGUsphere, 1–22. https://doi.org/10.5194/egusphere-2023-1980, 2023.Frame, J. M. and Kratzert, F. and Klotz, D. and Gauch, M. and Shalev, G. and Gilon, O. and Qualls, L. M. and Gupta, H. V. and Nearing, G. S., :Deep learning rainfall--runoff predictions of extreme events, Hydrology and Earth System Sciences, 26 ,3377-3392, https://doi.org/10.5194/hess-26-3377-2022, 2022Lees, T., Reece, S., Kratzert, F., Klotz, D., Gauch, M., De Bruijn, J., Kumar Sahu, R., Greve, P., Slater, L., and Dadson, S. J.: Hydrological concept formation inside long short-term memory (LSTM) networks, Hydrology and Earth System Sciences, 26, 3079–3101, https://doi.org/10.5194/hess-26-3079-2022,  2022.
Retrieval of Cloud Properties from Thermal Infrared Radiometry Using Convolutional Neural Network
Authors: Wang Quan , Zhou Chen , Husi Letu , Zhu Yannian , Zhuge Xiaoyong , Liu Chao , Weng Fuzhong , Wang Minghuai
Published: March 2024

Utilizing solar-independent thermal infrared (TIR) radiances, a convolutional neural network (CNN)-based framework (TIR-CNN) is developed to consistently retrieve cloud properties from passive satellite observations during both daytime and nighttime conditions. This framework enables the retrieval of diverse cloud properties, including cloud mask, cloud optical thickness (COT), cloud effective radius (CER), cloud top height (CTH), cloud base height (CBH), column cloud phase, and the identification of single/multi-layer clouds. The TIR-CNN framework primarily consists of two branches. In the first branch, the inputs include TIR radiances, viewing geometry, and altitude, producing outputs such as cloud mask, COT, CER and CTH. The network is trained using daytime Moderate Resolution Imaging Spectroradiometer (MODIS) products over a full year, and the results are validated and evaluated using passive and active products in an independent year. The evaluation results demonstrate that the retrieved cloud properties are well consistent with available MODIS daytime (cloud mask, COT, CER, and CTH) and nighttime (cloud mask and CTH) products. The retrieved COT and CTH also show robust agreements with active sensors during both daytime and nighttime, indicating that the algorithm performs stably across the diurnal cycle. The second branch of the TIR-CNN framework receives inputs including TIR radiances, altitude, landcover, lifting condensation level, and the retrieved cloud products from the first branch. It generates outputs such as CBH, cloud phase, and single/multi-layer cloud identifications. The comprehensive training, validation, and testing procedures are conducted using radar-lidar products from CloudSat/CALIPSO. The estimation of global CBH results in root-mean-square errors of 1.19 km and 1.91 km for single- and multi-layer clouds, respectively. The cloud classifier achieves total accuracies of 82% for single-layer clouds and 85% for multi-layer clouds. In addition, the model has remarkable accuracy in identifying cloud phase within each pixel's vertical column, particularly in distinguishing mixed-phase clouds with an ice cloud top.
Detecting diurnal cycle and lifetime of pyrocumulonimbus using GOES-16 infrared data with a machine learning model 
Authors: Liu Fei , da Silva Arlindo
Published: March 2024

Satellite infrared (IR) cloud imagery has proven valuable in the identification of Pyrocumulonimbus (pyroCb) clouds. The substantial brightness temperature difference observed between warm shortwave IR wavelengths (~4 μm) and window IR wavelengths (~11 μm) has served as a reliable marker for detecting daytime pyroCb. However, this indicator becomes ineffective during nocturnal hours when the enhanced brightness temperature at 4 μm is solely a daytime phenomenon, arising from PyroCb microphysics that increase solar reflectivity of clouds. We have developed a machine learning model designed to detect pyroCb events during nighttime using IR channels from the Advanced Baseline Imager (ABI) aboard GOES-16. The model leverages the distinctive characteristics of daytime IR channels as its training data. We applied the trained model to five intense pyroCb events in western North America during August 2017. Furthermore, we have employed an established cloud-tracking tool known as Tracking and Object-Based Analysis of Clouds (tobac) to analyze the evolution of the clouds plumes and infer their lifetimes. Our research aims to extend this case study on a global scale, with the objective of creating a comprehensive database for the lifetimes of pyroCb events. Such a database will enhance our understanding of pyroCb dynamics, which is helpful for investigating the radiative implications and the potential impact on stratospheric chemistry.
Locating subglacial cavities and investigating basal conditions on glaciers with ambient seismic noise: toward acquisition optimization.
Authors: Larose Eric , Bontemps Noelie , Guillemot Antoine , Baillet Laurent
Published: March 2024

Subglacial cavities may trap a considerable quantity of liquid water, causing devastating outburst floods in densely populated mountain areas. Both active and passive geophysical methods are employed for the glacier-bedrock interface and intra-glacial characterization, including Ground Penetrating Radar (GPR), refraction seismic, borehole measurements, and surface nuclear magnetic resonance (SNMR). Ambient seismic noise can be collected by light and dense arrays at a relatively moderate cost, and allows to access some mechanical properties of the glacier, including the detection and localization of ice cavities and, possibly, basal detachment, taking advantage of spectral anomalies in the horizontal-to-vertical-spectral ratio (HVSR) and in the Vertical-to-Horizontal spectral ratio (VHSR). Specifically, a peak in the VHSR indicates a low impedance volume beneath the surface [1,2]. As a simple picture, we can refer to the “bridge” vibrating mode, where the vertical displacement in the middle of the bridge largely dominates other components of the movement.  Antunes et al. [2] furthermore noticed that the VHSR gives information about seismic energy anomalies generated by fluids in reservoirs since the wavefield is polarized mainly in the vertical direction.In this work, we apply the HVSR and VHSR techniques to locate a subglacial water-filled cavity in the Tête Rousse glacier (Mont Blanc area, French Alps), using 15 days of data collected in may, 2022 [3]. The results also confirm the general basal conditions of the glacier suggested by other methods, locating temperate areas of the glacier where basal detachments are possible.We evaluate the optimal seismic noise record duration to obtain a reliable and stable mapping of the VHSR over the glacier to properly locate the main cavity (or secondary cavities). In our case, results suggest that 6 days of record are enough to detect and locate a cavity [1] Saenger, E-H. et al: A passive seismic survey over a gas field: Analysis of low-frequency anomalies, Geophysics, 74 (2), O29–O40 (2009).[2] Antunes V. et al: Insights into the dynamics of the Nirano Mud Volcano through seismic characterization of drumbeat signals and V/H analysis. Journal of Volcanology and Geothermal Research, 431 (2022).[3] A. Guillemot, N. Bontemps, E. Larose, D. Teodor, S. Faller, L. Baillet, S. Garambois, E. Thibert, O. Gagliardini, C. Vincent: Investigating Subglacial Water-filled Cavities by Spectral Analysis of Ambient Seismic Noise : Results on the Polythermal Tête-Rousse Glacier (Mont Blanc, France), Geophys. Res. Lett. accepted (2024). DOI:10.1029/2023GL105038
Modeling seasonal sediment dynamics and landscape evolution in a marly badland catchment, Draix-Bléone Critical Zone Observatory, SE France
Authors: Le Bouteiller Caroline , Ariagno Coline , van der Beek Peter , Klotz Sebastien , Tucker Gregory , Campforts Benjamin
Published: March 2024

Badlands are particularly sensitive components of the critical zone where weathering, erosion and transport processes can be observed on human time-scales. Within the Draix-Bléone Critical Zone Observatory (CZO), SE France, water and sediment (both bedload and suspended load) fluxes and climatic drivers have been recorded since more than 35 years, making it an ideal natural laboratory to develop a landscape-evolution model (LEM) for badland evolution calibrated with field data. Based on these records and existing knowledge on the sediment dynamics of these marly catchments, the aim of this study is to develop a LEM that is able to reproduce the observed intra-annual sediment-flux variability, in particular the transition from transport-limited to supply-limited conditions that occurs during summer. Our model predicts soil thickness and sediment export at monthly timescales, thereby providing potential links between “classical” LEM that run at long time scales and event-scale models, and simulating the physical processes driving the sediment dynamics in these catchments at their relevant timescale. We use the annual hysteresis cycle between rainfall and sediment export recorded in the Draix catchments as a quantitative indicator of the adequacy of the model. First, we model the supply-limited regime observed in the second half of the year in the badlands, illustrated by an clockwise loop in the annual hysteresis pattern, using depth-dependent hillslope regolith production and erosion laws. Next, we express the impact of rainfall intensity, identified as the main trigger of sediment motion both on hillslopes and in the drainage network, in order to reproduce the observed non-linear relation between sediment export and rainfall during the first part of the year (illustrated by an initial anti-clockwise loop in the hysteresis cycle). Parameter calibration is performed using average annual sediment export and soil depth in specific compartments of the catchment. The model successfully reproduce the hysteresis pattern but further work is needed on the calibration to obtain consistent magnitudes of sediment export. This new landscape evolution model appears to be a relevant tool to model observed annual morphology changes in badlands and to predict badland evolution in a context of climate change.  
Divergent patterns of rainfall regimes in dry and humid areas of China
Authors: Hu Ying
Published: March 2024

Despite rising rainfall constraints on global climate-resilient agriculture, there is no clear consensus on the quantification of the wet season, leading to contentious issues in rainfall regime evolution and subsequent impacts on phenology and vegetation productivity. Hence, we conducted a comprehensive assessment of rainfall regimes between 1982 and 2020 by using a modified anomalous accumulation method on a daily scale at the pixel level. We observed divergent patterns of “wet areas becoming drier, and dry areas becoming wetter” with rainfall amount and rainy days increasing in dry regions, and decreasing in humid regions. The length of the wet season was extended in the dry regions and shortened in the wet regions, and the trends were linearly related on dryness. Simultaneously, as dryness increased, so did the length, number, and cumulative number of dry days. Concurrent increases in rainy days and dry spells indicated a seasonal rainfall regime trend toward more frequent extreme conditions in drier areas, which was not entirely consistent with a global intensification pattern of “dry getting drier and wet getting wetter”, implying increased potential risks of both floods and droughts in dry areas. For climate risk prediction, water resource allocation, and agricultural management, we advocate for a finer and more precise dynamic assessment of the wetting-drying pattern.
Towards Evaluating the Financial Responsibility of Carbon Majors for Climate-Related Damages
Authors: Andrijevic Marina , Schleussner Carl-Friedrich , Kikstra Jarmo , Heede Richard , Rogelj Joeri , Schmidt Sylvia , Simpkin Holly
Published: March 2024

In light of the global energy crisis and escalating climate change impacts, the liability of major fossil fuel companies is receiving heightened scrutiny, particularly in the context of climate litigation. This study initially establishes the feasibility of attributing climate damages to these companies. Utilizing the social cost of carbon methodology, we evaluate the damages inflicted by the top 25 oil and gas emitters from 1985 to 2018, comparing these to their financial profits. Our central estimate suggests partial damages of approximately 20 trillion USD, with the companies’ financial gains surpassing this by 50%, totaling around 30 trillion USD. This indicates the potential of carbon majors to cover their attributed damages while maintaining significant profits. In our analysis, we also explore how varying approaches to assigning responsibility and handling uncertainties in climate damages can markedly influence these findings. Additionally, we explore the role of sovereign wealth funds in perpetuating fossil-fuel derived wealth and the ensuing liability questions.
How do precipitation trends propagate through the terrestrial water cycle?
Authors: Stephan Ruth , Kroll Josephin , Orth Rene
Published: March 2024

Climate Change involves changes in precipitation. The propagation of precipitation changes into the water cycle is complex and dependent on e.g. aridity and land cover which are themselves affected by climate change. As a result, estimating effects on water fluxes such as evaporation and runoff as well as water resources such as soil moisture is not straightforward. This study maps future changes in the seasonal cycle of precipitation across the globe, as projected by Earth system models from the Coupled Model Intercomparison Project Phase 6 (CMIP6). Additionally, we analyse the propagation of the detected seasonal precipitation surpluses and deficits into the water cycle, and determine the main underlying controls. For this purpose we determine and compare seasonal changes in precipitation, evapotranspiration, runoff and soil moisture across the next decades. While this yields a first indication of the propagation of increasing or decreasing precipitation, we furthermore calculate correlations between the considered variables in each decade as an independent measure of the relation between precipitation and the water cycle components. In this context we use partial correlations to better separate water cycle couplings from the impact of other meteorological forcings such as radiation. A particular focus of our analysis will be on uncertainties of (i) precipitation trends and (ii) their projected propagation into the water cycle across the CMIP6 model ensemble to distinguish robust patterns from areas with high uncertainties. Our analysis helps to understand changes in future water fluxes and resources and the underlying robustness, which can inform the development of Earth system models as well as water resources management.
Optimizing hydrological modeling on real urban catchment: impact of calibration data selection
Authors: Assaf Mohammed N. , Salis Nicolo , Creaco Enrico , Tamellini Lorenzo , Sauro Manenti , Todeschini Sara
Published: March 2024

Hydrological models are crucial in various engineering applications, including streamflow forecasting and flood risk estimation. Tools like the Stormwater Management Model (SWMM) are indispensable for efficacious water resource management. Calibrating these models is a necessary step to minimize parameter uncertainties and ensure accurate representation of a catchment area's hydrological response. However, the calibration process often faces challenges due to the need for extensive parameter adjustments. Sensitivity analysis (SA) is employed to mitigate these challenges by identifying and focusing on the most influential parameters, thereby streamlining the calibration process. In this work, the Morris method was applied to identify the sensitive parameters in the SWMM model, which were subsequently considered in the optimization process using Genetic Algorithms (GA). The results of the sensitivity analysis highly depend on the model output targets, such as total runoff volume and peak flow rate.The traditional approach of dividing data into calibration and evaluation subsets is a fundamental practice in model development. Nevertheless, the impact of data allocation on model evaluation performance has not received sufficient attention in the literature. This study investigates the influence of calibration data selection on model performance, utilizing high-resolution experimental rainfall-runoff data from the urban catchment of Cascina Scala in Pavia, Italy. Four criteria—rainfall depth, mean intensity, hydrograph's center of mass, and maximum rainfall depth over five minutes—were employed to select the calibration set. From a total of 24 events, the four criteria were employed to select 8 events from 16 for calibration, while the remaining 8 events were designated for validation. The findings underscore that the selection of the calibration dataset substantially influences the optimally calibrated parameters, subsequently altering model performance.
NeGIX and TEGIX: two new indices to characterize the topside ionosphere with Swarm
Authors: Cahuasquí Juan Andrés , Hoque Mohammed Mainul , Jakowski Norbert , Vasylyev Dmytro , Buchert Stephan , Nykiel Grzegorz , Kriegel Martin , David Paul , Tagargouste Youssef , Berdermann Jens
Published: March 2024

Since its launch in 2013, ESA’s Swarm satellite constellation has pushed the frontiers of space weather research and monitoring by means of its broad spectrum of high-quality experiments on-board. Particularly, Swarm observations are being used to globally characterize small- to mid-scale perturbations in the topside ionosphere that may cause severe amplitude and phase scintillations of trans-ionospheric radio signals. Ionospheric scintillation can cause radio signal outage, as well as disruption of modern technological systems used for telecommunication, navigation and remote sensing.While performing the Swarm DISC project “Monitoring of Ionospheric Gradients at Swarm (MIGRAS)”, the MIGRAS team has profited from the close orbits and synchronization of Swarm satellites Alpha (A) and Charlie (C) to develop two new products that focus on the monitoring of small- to mid-scale plasma density irregularities with horizontal spatial scales in the order of about 100 km - the electron density (Ne) Gradient Ionospheric indeX (NeGIX), and the Total Electron Content (TEC) Gradient Ionospheric indeX (TEGIX). NeGIX estimates spatial Ne gradients using Langmuir probe measurements, and TEGIX estimates spatial TEC gradients using GNSS Precise Orbit Determination (POD) data of Swarm.In this work, we provide a comprehensive analysis of the capability of these two novel Swarm data products to characterize the perturbation state of the ionosphere at different geographic locations and conditions of geomagnetic activity. Our analysis covers the whole period of available Swarm observations to quantitively describe expected signatures of ionospheric variability, e.g. gradients at sunrise and sunset time, or equatorial crests. The analysis concentrates also on events of perturbed geomagnetic conditions to compare the performance of NeGIX and TEGIX with existing ground-based indices (e.g. GIX) and Swarm products (e.g. IPIR). Moreover, these indices have been developed technically compatible with Swarm’s and DLR’s operational data services. Therefore, our analysis validates and discusses their applicability for space weather science and purposes.Acknowledgement: The work is funded by the MIGRAS (Monitoring of Ionospheric Gradients At Swarm) project under the Swarm DISC Subcontract Doc. no: SW‐CO‐DTU‐GS‐133, Rev: 1, 13 September 2022.
Spatial and temporal distribution of precipitation and its relationship with landslides within the Aburrá Valley, northern Colombian Andes.
Authors: Herrera Daissy , Aristizábal Edier
Published: March 2024

 Landslides are extensively distributed across the globe. About 17% of deaths due to natural hazards reported in the last decade are attributed to landslides. The spatial and temporal distribution of landslides are related to static and dynamic factors. The first group involves terrain aspects and land use, and the second group includes triggering factors, such as rainfall and earthquakes. The  61% of worldwide landslides recorded are triggered by rainfall. In Colombia, the percentage reaches 92%, becoming the main factor that triggers landslides. The Aburrá Valley is located in the central northern Andes, characterized by its complex topography and one of Colombia's most densely populated valleys, with 3.9 million inhabitants. In this study, the spatial and temporal relationship between landslides and rainfall in the Aburrá Valley is unraveled. Three kinds of rainfall information are used: pluviograph (145 stations), radar and satellite. Regarding the landslide database, 940 landslides were compiled between 1921 and 2023. The temporal analysis includes the understanding of different time scales: decadal, annual, daily, and hourly because several macroclimatic aspects affect the precipitation regime, such as El Niño-Southern Oscillation (ENSO), Pacific Decadal Oscillation (PDO) and Intertropical Convergence Zone (ICZ). This understanding leads to knowing on what scale there is clear evidence that regional precipitation changes can affect the occurrence of landslides. Regarding the spatial analysis, the radar and satellite information complements the data of punctual pluviographic stations.The results show that the ENSO affects the development of rainfall regimes on all time scales. When the PDO and ENSO match, the effects of EL Niño and La Niña phases are exacerbated, resulting in lower and higher landslides, respectively. In general, Aburrá Valley exhibits a bimodal precipitation phase, where the annual cycle peak of landslides matches with the peaks of rainfall annual cycle; the Enso affects the cycle mentioned, showing that, especially in dry periods, the effects of Enso increase the rainfall difference and landslides register. The daily analysis demonstrates a peak shift between the two variables evaluated, showing that the landslides will need antecedent rainy days to trigger them. There is no clear relationship at the hourly scale because of the reduced number of hourly landslide events registered. Concerning the spatial variation, a hot-spot of landslide is located in the valley's east hill, where the rainfall events with more duration are placed. Another finding is that satellite information is highly correlated with on-site measurements when the antecedent precipitation is evaluated for more than 15 days. 
The Role of Inertia-Gravity Waves in the Mesoscale Energy Transfers from Global Storm-Resolving Simulations
Authors: Morfa Avalos Yanmichel A. , Stephan Claudia C.
Published: March 2024

This study investigates the spectral energy budget of the atmosphere using storm-resolving simulations from two state-of-the-art global circulation models. We examine different hypotheses to explain the mesoscale κ-5/3 spectrum of horizontal kinetic energy (HKE). These hypotheses include the direct forcing due to inertia-gravity waves (IGWs), a downscale cascade mediated by weakly interacting IGWs, or interactions between waves and the mean flow. The resolved mesoscale energy fluxes within the upper troposphere and the lower stratosphere reveal different dynamics between the two layers. The lower stratosphere is mainly energized by direct forcing due to vertically propagating IGWs, with a negligible HKE cascade. The primary contribution to the mesoscale energy spectrum in the troposphere is from spectral transfers across scales, while the direct forcing due to IGWs is limited. However, the normal mode decomposition of the circulation into linear Rossby waves and IGWs suggests that their interactions dominate the downscale cascade at mesoscales. This result aligns with the hypotheses that explain the downscale cascade based on resonant triad interactions between vortical and gravity-wave modes. Furthermore, it is shown that wave-wave interactions do not contribute to the resolved energy transfers, challenging the hypothesis that the downscale cascade is due to weakly nonlinearly interacting IGWs.
Projected Changes in Energy and Hydrologic Budget over Asian Dust Source Regions
Authors: Lim Sujeong , Park Seon Ki , Cassardo Claudio
Published: March 2024

Asian dust storms (ADSs) are widely considered to have originated in the Taklimakan and Gobi Deserts, Inner Mongolia, and northeast China. Because dust emissions are dependent on surface conditions such as wind speed and soil moisture, accurate land surface conditions in dust source areas are essential. We use regional climate projections from the Regional Climate Model version 4 (RegCM4) along with emission scenarios (Representative Concentration Pathways; RCP) from the Intergovernmental Panel on Climate Change Special Report on Emissions Scenarios to look into how the energy and water budgets of ADS source regions change in response to different climate change scenarios. To quantify changes in energy and hydrologic components over the source regions of ADS, we use the University of Torino model of land Processes Interaction with Atmosphere (UTOPIA), a diagnostic one-dimensional model that represents the interactions between the atmosphere, land surface, vegetation, and soil layers. We will examine the energy and hydrologic climate projections based on two emission scenarios (e.g., RCP4.5 and RCP8.5) with respect to ADS predictions.
Quantifying normal fault growth histories from Gilbert Delta stratigraphy using downstream grain size trends: Examples from the Kerinitis and Akrata Delta, Gulf of Corinth. 
Authors: Rezwan Nahin , Whittaker Alexander C. , Hobley Daniel , Zhou Ziqiang
Published: March 2024

Gilbert-type deltas, built into rift basins, record interactions between fault-driven uplift and subsidence, sediment supply, and drainage network evolution. Typically forming at the junction of progressive fault segments, the syn-rift stratigraphy of uplifted Gilbert deltas offers a preserved record of the dynamic behavior of sedimentary source-to-sink systems through geological time. Of the physical parameters preserved in delta stratigraphy, grain size distributions contain unique information about sedimentary source-to-sink system dynamics over time. Specifically, downstream fining trends in syn-rift deposits reflect interactions between sediment supply to the basin and accommodation space creation driven by active faulting and subsidence. This work aims to understand how grain size distribution can quantify the syn-linkage phase of normal fault segments in the Gulf of Corinth. We examined two Pleistocene geological examples of uplifted Gilbert deltas with distinct tectonic configurations: 1) The Kerinitis Delta, formed by the relatively simple interaction of two same-aged fault segments, the Pirgaki and Mermoussia (P-M) Faults, which experienced a single linkage event; and 2) the more complicated Akrata Delta, formed by multiple linkage events between fault segments of the East Heliki Fault (EHF) and Derveni Fault (DF). We collected gravel grain size distribution data across 62 localities. Additionally, we captured scaled grain-size photographs in inaccessible areas. By tracing stratigraphic units and measuring their thicknesses, we reconstructed hanging-wall subsidence and paleo-fault slip rates. Using a self-similarity-based grain size fining model, we are able to reconstruct sediment supply rates and paleo-catchment erosion rates during the evolution of the fault systems. Further, we reconstructed the catchment averaged erosion rate to be markedly lower than the reconstructed footwall uplift, implying the landscape's transient response to fault growth. Our analysis demonstrates that grain size trends serve as a powerful tool for quantifying the complete growth histories of faults underlying normal fault-driven Gilbert delta systems. We demonstrate the feasibility of converting high-resolution grain size fining patterns preserved in Gilbert delta stratigraphy into reconstructed records of fault slip rates, hanging wall subsidence rates, sediment flux changes, and other key forcing parameters over 105 year timescales. This significantly expands the quantitative toolbox available to translate syn-rift sedimentary architecture into rich chronologies unravelling structural deformation patterns, including fault interactions, segment linkage, and overall progression.
Sustainable development training and outcomes (poster only session)
Authors: Özdemir Burcu
Published: March 2024

Environmental education is a type of education that aims to increase individuals' environmental awareness, raise awareness on sustainability issues and use natural resources more effectively. Providing environmental education to students provides many advantages and this education has both short-term and long-term outcomes. Some of these outcomes can be listed as sustainable life skills, social responsibility, innovation and solution orientation. In the short term, effects such as students' awareness, application to daily life, social participation, and basic environmental knowledge can be seen. In the long term; Students are expected to see effects such as sustainable living habits, leadership, innovation, environmentally friendly professions, and ecosystem protection. Finally, regarding climate change education in schools, which is this year's theme, it can be used to raise awareness among students about climate change awareness, sustainable energy use, environmental footprint awareness, climate justice, and sustainable use of natural resources. These connections between environmental education and global warming enable individuals to act more consciously, responsibly and effectively against environmental problems. This can contribute to society's greater resilience against major environmental threats such as global warming and climate change. Sustainable development training will be provided within the scope of science course. The training consists of various sub-headings. The duration of the training is determined as 20 lesson hours. Since the science course has 4 lesson hours each week, the training will be completed in a total of 5 weeks. As for the training implementation calendar, the practices will start after the semester break. The educational contents will be created by the researcher by scanning different sources and considering the science curriculum. The age level and readiness levels of the students will also be considered. Teaching activities will be alternated individually or as a group according to the characteristics of the activities. During the creation of the groups, different applications designed by the researcher will be organized in order to enable students to interact with different people in the class. A student-centered approach will be adopted in the teaching process. The aim of the training to be given is to raise awareness about the economical use of water resources, household solid waste materials, household liquid waste materials, recycling and reuse. In addition, local and global environmental problems, endangered living creatures, endangered species, unconscious hunting and conservation of species are among the targets. Finally, students are expected to be informed about energy resources and energy consumption. In line with these goals, it is expected that the cooperative learning of the students will develop in a positive way.
Planning and Execution of Juno’s Radio Occultation Campaign
Authors: Buccino Dustin , Parisi Marzia , Oudrhiri Kamal , Ryan Park , Steffes Paul
Published: March 2024

The latter part of Juno’s extended mission from 2023-2025 provides numerous opportunities to conduct radio occultation experiments of Jupiter’s atmosphere and ionosphere. Juno’s radio science instrumentation, consisting of dual X-band (8.4 GHz) and Ka-band (32 GHz) radio links to NASA’s Deep Space Network (DSN), was designed for precise measurements of Jupiter’s gravity field at a range-rate precision of