bluuebunny/crossref_metadata_2025 huggingface dataset:
Comparison of Stochastic versus Deep Learning methods for simulation and prediction of hydroclimatic time series
Authors: Tepetidis Nikolaos , Iliopoulou Theano , Dimitriadis Panayiotis , Koutsoyiannis Demetris
Published: February 2023

Deep-learning methods are receiving great scientific attention and increasingly gaining popularity in a variety of water-resources tasks as well. Yet till now they are less employed for the simulation of hydroclimatic timeseries, the stochastic properties of which are usually challenging and dealt by the application of stochastic methods. The latter are well-established for the analysis and simulation of hydroclimatic processes and are particularly successful in capturing their long-term dependence behavior, so-called Hurst-Kolmogorov (HK) dynamics. In this work, we aim to assess the suitability of a state-of-the-art deep learning algorithm, called Transformer Neural Network (TNN) for hydroclimatic processes, as it is claimed to have a good performance in time series data. The Transformer Neural Networks is a novel architecture that aims to track relationships in sequential data while it is suggested that it can handle long-range dependence. We apply the TNN for the simulation and prediction of timeseries from various hydroclimatic processes (such as rainfall, runoff,  temperature) and evaluate its performance in relation to the application of the HK algorithms.
Development of a GIS-based methodology for quantitative risk analysis of fragmental rockfalls in transport infrastructures
Authors: Lantada Nieves , Ruiz-Carulla Roger , Matas Gerard , Janeras Marc , Núñez-Andrés Maria Amparo , Corominas Jordi
Published: February 2023

Rockfall events can cause important direct losses such as partial or total damage to transport infrastructures, traffic disruptions, and casualties, but also other indirect consequences that alter the normal running of society, its mobility, and economic activity. A quantitative risk analysis (QRA) for rockfall hazard can help to improve investment in mitigation measures of the infrastructure in terms of benefits, time, and cost savings.  The fragmentation process during rockfall propagation determines the number and size of the fragments and their trajectories, energies, and run-out. Therefore, fragmentation has a significant but contrasting effect on the reach probability at transportation corridors, which should be considered in QRA.This study, conducted in the research Georisk project (Ref. PID2019-103974RB-I00, funded by MCIN/AEI/10.13039/501100011033), presents the development of an objective and a GIS-based automatable methodology for quantitative risk analysis on linear structures due to fragmentary rockfalls. The annual frequency of events is established for each source point from the photointerpretation of deposits using historical orthophotographs. Some different source points maps are created from a digital elevation model, one for each maximum volume interval that could be detached from the cliff. This expected maximum volume considering the outcrop geometry is estimated automatically for each point based on the number of neighbor source points, using GIS tools. The impact energies are calculated using the RockGIS rockfall model, a 3D simulator able to reproduce the fragmentation phenomenon by using the Rockfall Fractal Fragmentation Model. The impact energies of each fragment are registered in raster maps. The exposure and vulnerability of each of the exposed elements are estimated through the corresponding developed fragility curves, which define the relationship between the impact energy and the estimated damage based on the particular characteristics of the different elements exposed.The resulting risk values are presented in a georeferenced way along the road and for different dynamic exposed elements such as vehicles and people, in economic terms or the number of fatalities, for some given scenarios. The methodology and GIS techniques proposed have been implemented in a 27 km road corridor of great social and political interest in Catalonia (Spain).
Stability of the winter-time Arctic Ocean boundary layer in CMIP6 climate models evaluated against Soviet drifting stations, SHEBA and MOSAiC observations
Authors: Duffey Alistair , Mallet Robbie , Steckling Julia , Hermant Antoine , Dutch Victoria , Day Jonathan , Pithan Felix
Published: February 2023

The atmospheric boundary layer in the Arctic winter is characterised by strong and long-lived low level stability which arises from long-wave radiative cooling of the surface during the polar night. This atmospheric temperature inversion is a necessary condition for the positive lapse rate feedback, which is a major contributor to Arctic Amplification. In this study, we assess the low-level stability of the winter-time Arctic boundary layer using ground-based and radiosonde observations collected during the MOSAiC (2019-2020) and SHEBA (1997-1998) expeditions, and from Soviet drifting stations (1955-1991). We compare these observations with the representation of Arctic boundary layer stability in models participating in the latest phase of the Coupled Model Intercomparison Project (CMIP6). The observations show a bimodal distribution of clear and cloudy states which has been reported previously. In the clear state, longwave radiative cooling from the surface leads to strong inversions and a stably stratified boundary layer. Whereas, in the cloudy state, inversions are weaker and not confined to the surface. Previous work has shown that many CMIP5-era climate models fail to realistically represent the cloudy state and often overestimate low-level stability. Here, we assess the extent to which the CMIP6 models also show such biases and examine the representation of surface net longwave radiation and turbulent heat fluxes as potential sources of the biases. Finally, we show that across CMIP6 models, low level stability over sea-ice is correlated with inter-model variation in Arctic amplification.
Multihazard Analysis: Istanbul Microzonation Projects
Authors: Kara Sema , Duran Kemal , Yılmaz Deniz , Yapar Evrens Rıza , Karasu Muhammed Emin , Ergün Konukcu Betül
Published: February 2023

Istanbul is located one of the most seismically active regions of the earth. For this reason the city has suffered damage due to earthquakes in its historical process. Three of them, occurring in 1509, 1766, 1894 respectively,    seriously affected Istanbul and caused great loses around the city during the Ottoman period. 1509 Earthquake caused extensive damage to many mosques, buildings and some part of the city walls in Istanbul. 1509 Earthquake caused extensive damage to many mosques, buildings and some part of the city walls in Istanbul. Another destructive earthquake occured in the east part of the Sea of Marmara in 1766. Not only many houses and public buildings collapsed but also The Ayvad Dam located north of the Istanbul were damaged in İstanbul because of the 1766 Earthquake. Third major earthquakes took place in the Gulf of Izmit in 1894 and had adverse impact on Istanbul. On Augsut 17, 1999 The Kocaeli Earthquake with a magnitude 7.6 was the not only devastating but also deadly earthquake for Istanbul in recent years. Despite the approximately 110 km epicenter distance, 3,073 buildings suffered extensive damage, 11,339 buildings had moderate damage and 454 people died and 1880 people injured in Istanbul. Damages in Istanbul especially Avcılar and Büyükçekmece during Kocaeli Earthquake in 1999 raised and improved the awareness on disaster risk management since then several scientific and institutional studies has been conducted for the potential earthquake of Istanbul. Istanbul Metropolitan Municipality (IMM) carried out two major geo-scientific studies called “microzonation studies” covering more than 700 km2 of Istanbul’s urbanized areas between 2006 and 2009.     And then IMM has just started new microzonation project in order to complete remainder urbanization area of Istanbul consists of districts of Büyükçekmece, Beylikdüzü, Çatalca, Esenyurt, Küçükçekmece, Beşiktaş, Şişli, Sarıyer covering approximately 257 km2. Esenyurt is the most populated district of Istanbul and the other districts host many Istanbulites. This project supports substantial hazard knowledges after the evaluation of geological, geotechnical and geophysical measurements in order comprehend these districts risk against the potential Istanbul earthquake,.  In the end “Land Suitability Maps” are derived from the combination of inputs using multi-hazard approach. Microzonation results could be used in land development/use plans, hazard identification in urban transformation, determination of the routes and characteristics of various types of engineering structures for making city resilient.
In Memory of Bruno Wilhelm: Impact of warmer climate periods on flood hazard in the European Alps
Authors: Rapuc William , Wilhelm Bruno , Amann Benjamin , Anselmetti Flavio S. , Arnaud Fabien , Blanchet Juliette , Brauer Achim , Czymzik Markus , Giguet-Covex Charline , Gilli Adrian , Glur Lukas , Grosjean Martin , Irmler Ralf , Nicolle Marie , Sabatier Pierre , Swierczynski Tina , Wirth Stefanie B.
Published: February 2023

Flooding is a ubiquitous natural hazard, and climate change is likely to exacerbate the risks worldwide. Mountainous areas, such as the densely populated Alps, are of particular concern because topography and atmospheric conditions can lead to large, flash floods, and because they are experiencing a high rate of warming, which will likely result in more intense precipitation events.In his latest publication, Bruno Wilhelm and his colleagues have compiled 33 paleoflood records to test the impact that these climate trends might have on the frequency and magnitude of floods in the Alps. The paleoflood records are derived from lake sediments, the only archive that guarantees continuity of records over long periods of time. The dataset passed a selection procedure of hydrological, sedimentary and geochronological controls, resulting in a final selection of 27 records that continuously cover the last 150 to 10,000 years, documenting a total of 7,792 floods. We perform three analyses with this dataset:(i) analogous to modern projections that assess changes in climate variables between current and warmer future conditions, we determine changes in the occurrence of large (≥10 year) floods between cooler and warmer past subperiods.(ii) following trend analysis techniques using modern streamflow data, we analyze trends in the occurrence of large (≥10-year) floods during warmer or cooler periods.(iii) we replicate the first analysis by considering the occurrence of extreme floods (≥100 years) between colder and warmer past subperiods.From these analyses, we show that a warming of +0.5-1.2°C led to a 25-50% decrease in the frequency of large floods (return period ≥10 years). This downward trend is not observed in records spanning less than 200 years (i.e., the maximum period of the instrumental series), but it is persistent in those ranging from 200 to 9000 years. In contrast, extreme floods (return period > 100 years) may increase with a similar degree of warming in some small Alpine catchments. This may result from a local intensification of extreme precipitation with higher temperature. The latest results of Bruno and his colleagues show how long and continuous paleoflood records can be used to unravel the complex relationships between climate and flooding.
Fingerprinting dissolved organic compounds: A potential tool for identifying the surface infiltration environments of meteoric groundwaters
Authors: Stillings Mark , Lunn Rebecca , Shipton Zoe
Published: February 2023

Current methods for tracing the surface infiltration of meteoric groundwaters rely on isotope geochemistry and dye tracers, which can be used to determine groundwater age and altitude at the point of infiltration. Temporal and spatial variability in atmospheric conditions, and water-rock interactions, can make the interpretation of isotopes uncertain. Low tracer recovery and long residence times often make dye tracers impractical. Here, we propose a new method of groundwater tracing based on fingerprinting of natural dissolved organics (derived from local flora and fauna). We validate our method at the Grimsel Test and Mont Terri underground rock laboratories in Switzerland, located within fractured crystalline rock (granite) and sedimentary systems, respectively. Based on a non-targeted approach using two-dimensional gas chromatography, we derive detailed organic fingerprints for groundwater, surface soils, and lakewater and river water samples from each location. These organic fingerprints are then compared to determine the near-surface infiltration environments feeding individual groundwater samples. Using principal component analysis, we show that individual groundwater samples can be identified as having derived from identifiable surface sources. Our research demonstrates that dissolved natural organic molecules, and their relative abundance, are sufficiently well-preserved in groundwater over timescales of several decades, that they can be used to discriminate the near-surface environment(s) through which meteoric groundwater has infiltrated. Organic fingerprinting could prove a powerful tool for an improved understanding of groundwater flow systems, particularly when used in combination with other complimentary tracing techniques.
A new instrument for contributing to the soil science - policy interface: the EJP SOIL National Hubs
Authors: Chenu Claire , Visser Saskia , O'Toole Adam , Keesstra Saskia , Besse Anna , Carlenius Line
Published: February 2023

Driving effectively soil research towards practice and towards policy solutions requires that the involved stakeholders, i.e researchers, practitioners and both national and regional policy makers interact and share information and views. However, these communities rather function in isolation. The European Joint Co-fund programme, EJP SOIL (Towards climate-smart and sustainable agricultural soil management) developed an innovative instrument to this end in each of its 24 participating countries.EJP SOIL National Hubs are committees of soil stakeholders (farmers, farmers advisors and farmers organisations, industry and agrobusiness, NGOs, local or national governance and policy implementing representatives, scientists), that were set either de-novo for the EJP SOIL or based on existing committees. Their mission is to (i) provide feedback to the EJP SOIL activities and outputs, (ii) voice national position and needs, (iii) contribute to and learn from the work done in research and iv) support in the dissemination of EJP SOIL outcomesThese entities appeared as precious assets for developing a science-policy-practice interface. As an example, the EJP SOIL National Hubs contributed to elaborating the roadmap of the programme and more recently were informed and provided feedback on the programme outputs on soil data harmonization and sharing and on soil monitoring in the perspective of the future European soil health law.We performed an analysis of their composition and functioning gives insights on how to effectively create and use these entities, which is forecasted by the EU Mission “A Soil Deal for Europe” for all land uses.
Isotope biogeochemistry of the carbon-iron-sulfur cycle in a temperate coastal peatland after flooding by brackish seawater
Authors: Saban Rhodelyn , Boettcher Michael Ernest , Jenner Anna Kathrina , Anthony Sara  Elizabeth , Juransinski Gerald , Ehlert von Ahn Catia , Roeser Patricia , Schmiedinger Iris
Published: February 2023

Soils from a coastal peatland (Drammendorf,  southern Baltic Sea) were investigated for the biogeochemical impact of flooding with brackish seawater. The peatland was rewetted in late 2019 through the partial removal of a dyke and brackish water with high sulfate concentration from a lagoon (Kubitzer Bodden) allowed to intrude into the peatland. Soil cores were retrieved about 2 and 3 years after the initial rewetting event. Pore waters were extracted from soil cores using rhizons and samples were analyzed besides physical parameters for major ions, nutrients, water stable isotopes, dissolved inorganic carbon (DIC) concentrations, and stable isotopes in C and S species. Solid phase samples were analyzed for contents of CNS and acid-extractable metal and nutrient species, and the stable isotope composition of acid-volatile sulfide (AVS), chromium-reducible sulfide (CRS, pyrite).Results from the post-event campaigns are compared with pre-flooding conditions. Poree water generally showed a trend towards freshening with depth as remains from the pre-flooding conditions. . Different sites are furthermore characterized by different amounts of diagenetically released dissolved inorganic carbon (DIC). A mixing evaluation of ẟ13C-DIC signatures together with major ion concentrations reveals potential DIC sources, like organic matter/methane oxidation, carbonate dissolution and mixing with seawater-derived DIC. DIC from the dissolution of minor soil carbonates may lead to a relative enrichment of 13C in DIC. Brackish water intrusion and cation exchange are reflected by the downward gradients in pore water compositions, with Na and Mg decreasing and Ca increasing with depth. Soil organic carbon is dominating with inorganic carbon being a minor fraction in most parts. Dissolved pore water sulfate and high total sulfur in the top soils with decrease downward trends further reflect the importance of post-event enhanced sulfur cycling leading to characteristic sulfur isotope signatures. AVS is depleted in the top soils with highest contents at about 5 cmbsf. Pyrite sulfur dominates and show different enrichment zones with the sediment columns. Contents in CRS contents covary with TOC, indicating that the benthic diagenetic system is controlled by organic matter availability, as in normal marine sediments.  The study demonstrates the role of electron acceptor availability for benthic carbon cycling, and the kinetics of biogeochemical interactions upon oxidation of reduced carbon, mineral authigenesis/dissolution, and ion exchange processes. The results of implications for coastal processes in the humid climate zone during times of increasing seawater level rise.  
Anomalous scaling of aeolian sand transport reveals coupling to bed rheology
Authors: Pähtz Thomas , Tholen Katharina , Kamath Sandesh , Parteli Eric , Kroy Klaus
Published: February 2023

Authors: Thomas Pähtz, Katharina Tholen, Sandesh Kamath, Eric Parteli, Klaus KroyTitle: Anomalous scaling of aeolian sand transport reveals coupling to bed rheologyPredicting transport rates of windblown sand is a central problem in aeolian research, with implications for climate, environmental, and planetary sciences. Though studied since the 1930s, the underlying many-body dynamics is still incompletely understood, as underscored by the recent empirical discovery of an unexpected third-root scaling in the particle-fluid density ratio [1]. Here, by means of grain-scale simulations and analytical modeling, we elucidate how a complex coupling between grain-bed collisions and granular creep within the sand bed yields a dilatancy-enhanced bed erodibility. Our minimal saltation model robustly predicts both the observed scaling and a new undersaturated steady transport state that we confirm by simulations for rarefied atmospheres [2].[1] Pähtz, Durán, Scaling laws for planetary sediment transport from DEM-RANS numerical simulations, https://arxiv.org/abs/2203.00562[2] Tholen, Pähtz, Kamath, Parteli, Kroy, Anomalous scaling of aeolian sand transport reveals coupling to bed rheology , Physical Review Letters, accepted.
The Shear-Relative Variation of Inflow Angle and Its Relationship to Tropical Cyclone Intensification
Authors: Ming Jie
Published: February 2023

Characterizing inflow structure is important to better represent tropical cyclone impacts in numerical models. While much research has considered the impact of storm translation on the distribution of inflow angle, comparatively less research has examined its distribution relative to the environmental wind shear. This study analyzes data from 3,655 dropsondes in 44 storms to investigate the radial and shear-relative distribution of surface inflow angle. Emphasis is placed on its relationship with intensity change. The results show that the radial variation in the inflow angle is small and not significantly dependent on the shear magnitude or intensity change rate. In contrast, the azimuthal distribution of the inflow angle shows a significant asymmetry, with the amplitude of the asymmetry increasing with shear magnitude. The maximum inflow angle is located in the downshear side. The degree of asymmetry is larger in the outer core than in the eyewall. Intensifying storms have a smaller degree of asymmetry than steady-state storms under moderate shear.
Sentinel-1 and Deep Learning for rapid landslide mapping
Authors: Meena Sansar Raj , Nava Lorenzo , Bhuyan Kushanav , Monserrat Oriol , Catani Filippo
Published: February 2023

Multiple landslide events happen frequently across the world. They have the potential to wreak significant harm to both human life and infrastructure. Although a substantial amount of research has been conducted to address the speedy mapping of landslides using optical Earth Observation (EO) data, significant gaps and uncertainties remain when engaging with cloud obscuration and 24-hour functioning. To solve the issue, we investigate the use of SAR data to automatically map landslides with the aid of advanced deep learning segmentation models. We use a Deep Learning (DL) design developed for pixel-based classification, the so-called Attention U-Net, to evaluate the landslide mapping capability of bi- and tri-temporal SAR amplitude data from the Sentinel-1 satellite. Four separate combinations are investigated, each of which consists of two different amplitude combinations per two satellite orbits. Furthermore, the effect of augmentations is assessed individually for each dataset. Through F1-score and other standard criteria, the models' predictions are compared to an accurate landslide inventory collected by hand mapping on pre- and post-event PlanetScope data. The enhanced ascending tri-temporal SAR composite produced the best results. Augmentations have a beneficial influence on the rising Sentinel-1 orbit, but they harm the descending route (in this case). Our findings show that integrating SAR data with other data sources can aid in the rapid mapping of landslides, especially during storms and deep cloud cover. However, further research and improvements are required, starting from novel sample and pre-processing strategies to mitigate the effect of the geometric distortions on model performance.
Speleothem environmental magnetism: some examples from Brazil
Authors: Trindade Ricardo , Jaqueto Plinio , Carmo Janine , Fu Roger , Hess Kimberly , Feinberg Joshua , Stríkis Nicolas , Cruz Jr Francisco
Published: February 2023

Speleothems are classically used to reconstruct the Holocene paleoenvironment in the continents through geochemical proxies. In recent years, these cave formations have been also scrutinised for the significance of their environmental magnetism signal. In Brazil, we have been studying a set of caves encompassing different latitudes, climate and vegetation cover. In all these caves, the magnetic mineralogy composition is shown to be fairly homogeneous across the speleothem record, usually comprising magnetite as the main magnetic mineral, but also showing some amount goethite and hematite. Comparison of magnetic data from the speleothem, the soils above the cave, the sediments inside the cave and the bedrock (epikarst), show that the magnetic mineralogy deposited during speleothem precipitation essentially comes from the soil covering the cave, and is similar to that of sediments deposited inside the cave. In spite of its homogeneous composition, there are significant variations in the magnetic mineral content (magnetic enrichment) across the speleothem stratigraphy. Three main hypothesis for the enrichment were put forward in the literature: (i) episodic flooding events, (ii) higher precipitation, (iii) changes in soil and vegetation coverage. Here we describe some case studies from speleothems collected in caves from Brazil, where oxygen and carbon isotopes are compared to the environmental magnetism parameters of speleothems at different sampling resolutions in order to investigate the speleothem magnetic enrichment hypotheses, and discuss the potential of these techniques for paleoenvironmental studies.
Phosphate pools and oxygen signature in the hyper-arid Atacama Desert
Authors: Sun Xiaolei , Amelung Wulf , Tamburini Federica , Klumpp Erwin , Morchen Ramona , Bol Roland
Published: February 2023

The Atacama Desert is a temperate desert restricted by the Pacific Ocean and the Andeans, which is an ideal place to study the biogeochemical phosphate-water dynamics in the conditions with extreme limited water and biomass. We hypothesized that phosphate pools and oxygen signature change along with the increasing distance to the coast and thus aridity. The surface soils (0-10 cm) were sampled along the transect with distance to coast in Paposo region (~25°S) which is located in the Coastal Cordillera nearby the Pacific Ocean from 2.3 to 22.9 km, including 9 altitude sites (600 m, 900 m, 880 m, 920 m, 1000 m, 1200 m, 1450 m, 1700 m, 2110 m). Each site involved 3 samples surrounding the plant with a distance of 0-10 cm and other 3 samples far from the plant with 1 m. The Ca-bound P (HCl-extracted P followed the Hedley sequential P fractionation) accumulated along the increasing distance to coast within 37.9 km and could be described by a mono-exponential regression mode. However, an initial declining trend was detected for phosphate 18O of HCl-Pi and it reached a steady-state condition beyond 10 km from the coastline, which was the maximum distance that advective fog could penetrate inland. Only the nearest site at 2.3 km (600 m.a.s.l) to coast showed an isotope value within the range of full isotopic equilibrium with biologically cycled phosphate. Furthermore, the effects of the present plant distribution on surface soil Hedley P stocks and phosphate 18O signatures were very limited. We concluded that both P stocks and phosphate 18O signatures followed primarily the aridity gradient but phosphate 18O signatures could work as a tracer for long-term climate conditions.
Sensitivity of the observed and modeled discrepancy in tropical Pacific Sea Surface temperatures to the time interval
Authors: Dhame Shreya , Olonscheck Dirk , Rugenstein Maria
Published: February 2023

The time-evolving pattern of ocean surface warming in the Pacific Ocean affects the radiation budget and estimates of global climate sensitivity. Over the historical period, models consistently show a different equatorial Pacific SST warming pattern than observed.Some studies attribute the large discrepancies between the observed and modeled SST trends in the Pacific Ocean, in recent decades, to systemic model biases and their response to greenhouse gas forcing or model biases in the spatial and temporal pattern of multi-decadal variability (e.g., Seager et al, 2019, 2022; Wills et al, 2022). Other studies find that the observed warming pattern can be explained by internal variability (e.g., Olonscheck et al, 2020; Watanabe et al, 2021). Here, we examine whether these analyses of regional temperature changes in the tropical Pacific Ocean are sensitive to the time interval selected to calculate the multi-decadal trends and whether the sensitivity to the time interval can explain the conflicting results of previous studies.Olonscheck, D., Rugenstein, M., & Marotzke, J. (2020). Broad consistency between observed and simulated trends in sea surface temperature patterns. Geophysical Research Letters, 47(10), e2019GL086773.Seager, R., Cane, M., Henderson, N., Lee, D. E., Abernathey, R., & Zhang, H. (2019). Strengthening tropical Pacific zonal sea surface temperature gradient consistent with rising greenhouse gases. Nature Climate Change, 9(7), 517-522.Seager, R., Henderson, N., & Cane, M. (2022). Persistent discrepancies between observed and modeled trends in the tropical Pacific Ocean. Journal of Climate, 1-41.Watanabe, M., Dufresne, J. L., Kosaka, Y., Mauritsen, T., & Tatebe, H. (2021). Enhanced warming constrained by past trends in equatorial Pacific sea surface temperature gradient. Nature Climate Change, 11(1), 33-37.Wills, R. C., Dong, Y., Proistosecu, C., Armour, K. C., & Battisti, D. S. (2022). Systematic Climate Model Biases in the Large‐Scale Patterns of Recent Sea‐Surface Temperature and Sea‐Level Pressure Change. Geophysical Research Letters, 49(17), e2022GL100011.
Twenty years of IDC Reviewed Event Bulletin (REB) statistics using data from a sparse IMS network to one reaching near completion
Authors: Wang Haijun , Rambolamanana Gerard , Graham Gerhard , Le Bras Ronan , Bittner Paulina , Mumladze Tea , Kasmi Ali , Mohamed Ali Sherif , Chegeni Ehsan Ehsan , Villarroel Marcela , Applbaum David , Makhonina Mariia , Joseph Gutierrez Jimenez Aaron
Published: February 2023

As of 14 December 2020, almost 90% of the IMS facilities (including radionuclide laboratories) were built and certified, data is transmitted in either real-time or on request from IMS stations to IDC for processing and analyzing. IDC analysts review automatic bulletins generated continuously and release the Reviewed Event Bulletin (REB) on a daily basis since February 2000. We present the statistics of mostly natural seismicity waveform events processed and analyzed over the past 20 years, as the network grew in size and became established. Multiple parameters including magnitude for those events associated with detections from seismic, hydroacoustic and infrasonic stations are analyzed. Techniques and rules related to waveform data analysis and the need to correct the automatic bulletin are discussed. This discussion should be beneficial for analysts work and data processing system optimization.
Multi-sector assessment and design for large scale water-energy-food-environment systems in Africa
Authors: Harou Julien , Basheer Mohammed , Etichia Mikiyas , Gonzalez Cabrera Jose , Panteli Mathaios , Calzadilla Alvaro
Published: February 2023

Interdependencies between resource systems are projected to increasingly complicate efforts to develop individual water, energy, food and environment sectors and set their growth policies. Given the urgent need to mitigate climate change, investments in energy and water supply security are being co-driven by financial and international political incentives to decarbonize, and so the scrutiny of plans to modify interconnected resource systems will continue to increase. Additionally, given multiple government needs and priorities, economic, environment and social dimensions should be explicitly considered in the prioritisation of new interventions, whether they be new policies or new infrastructure. This talk considers two river basins in Africa, the Nile river basin and the Volta river basin. It describes efforts of the ‘Future Design and Assessment of water-energy-food-environment Mega-Systems’ (FutureDAMS) project to understand and recommend new interventions in these systems at country or multi-country scale. To this end four technical pillars were needed: credible and computationally efficient large-scale simulation of individual sectors, a multi-agent software integration framework that connects disciplinary models at run time, an efficient search technology to sift through large intervention spaces given multiple objectives and multiple uncertainties including climate change, and stakeholder facing tools and outputs. The talk reviews in detail two areas of model building and their application: linking river basin and economic analysis and linking river basins and regional power grids for joint intervention assessment. The talk will discuss which issues most complicated modelling, what solutions were found, and how future research might improve upon them to generate more useful understanding and decision-support for multi-sector human-natural systems.
Meta-learning for water level prediction
Authors: Slaimi Asma , Scriney Michael , Hegarty Susan , Regan Fiona , E. O’Connor Noel
Published: February 2023

In the Artificial intelligence (AI) sense, meta-learning is the ability of an artificially intelligent machine first to learn how to conduct different complex tasks, taking the principles it utilised to learn one task and applying them to other different tasks. Hence, the general concept of "learning how to learn". Machine learning provides capabilities to learn from past data and generates models for future prediction, which can be helpful for multiple catchment management tasks, such as water elevation monitoring and flood prediction.Our initial studies focused on predicting and evaluating the ML-based hydrologic time-series models based on their predictive performance. We used eight machine learning algorithms to predict river water levels, including Baseline, Linear, Dense, MultiDense, CNN, RNN, GRU and LSTM techniques. The eight models were employed for one hour ahead of river water level forecasting in 70 hydrometric stations in Ireland. The results show that the NN-based models generally performed well in predicting the water level, with some differences in each model's performance for different stations. These results suggest that a single machine learning model may be sufficient for forecasting river water levels in one location and perform poorly in another. Hence, there is no overall best model; and the selected model may significantly impact the desired results.This study's main goal was to investigate a meta-learning-based approach for water level prediction. The proposed Meta-learning approach comprises two phases; Learning and meta-learning. The meta-learning process uses the outcomes of the previous experiments to accomplish the Learning Training and Practising phases of the meta-learner. Later the outcome of the previous step will be the Databases to create the learner (learning about learning phase). Creating meta-learning models can help AI models to generalise learning methods and acquire new skills more quickly. We expect the meta-learning model to adjust well when generalising to previously unknown datasets and environments that have never been encountered during training.Keywords: Machine learning (ML), meta-learning,  water-level prediction,  hydrologic time-series forecasting.
The benefits of rural electrification to improve water access and irrigation in Sub-Saharan Africa, a water-energy-land assessment framework applied to Zambia.
Authors: Vinca Adriano , Falchetta Giacomo , Ireland Gregory , Tuninetti Marta , Awais Muhammad , Byers Edward , Semeria Francesco , Giordano Vittorio
Published: February 2023

Sub-Saharan Africa has a large portion of the population with no access to electricity, piped drinking water, or sanitation services. The lack of these basic services also affects farmers that mostly rely on rainfed agriculture instead of irrigation. Given the expected population growth and potential changes in hydrology and crop yield response due to climate change, future development for the region needs to be carefully studied to achieve increased access to basic services and, potentially, synergetic economic benefits in agriculture.Within the LEAP-RE 4 AFRI project, we developed a framework that combines three high-resolution, single-sector simulation models (crop water requirements, electricity demand and rural electricity dispatchment) to a long-term water-energy-land integrated assessment model to explore different scenarios of future development for Zambia. This helps understand which regions would benefit of better water access and irrigation potential due to improved rural electrification.We compare two scenarios of moderate and universal electricity-water access with a current trend scenario, we compare the expected costs and benefits for the rural population, including the economic benefits achievable by improving irrigation standards and crop yields.Although Zambia is a relatively water-abundant region, we focus on it as a case study with a framework that can be transferred to any other country in Sub-Saharan Africa, where climate change impact might have a significant impact on water scarcity, electricity generation potential and crop yields.
Idealized modelling of microphytobenthos resuspension by wind and tides in estuaries
Authors: Diez-Minguito Manuel , Padilla Enrique M. , de Swart Huib E.
Published: February 2023

This study further explores the relationships between suspended sediment concentration (SSC) and phytoplankton concentration (PC) in tidally-energetic estuaries. A 1DV idealized model is implemented to account for vertical advection of phytoplankton and sediments, their resuspension from the bottom by both tidal and wind shear stresses, vertical mixing processes, and light-induced growth of phytoplankton. Phytoplankton are considered to be comprised by both free (pelagic) phytoplankton and microphytobenthos (MPB). The combined wind-tide shear stress is modelled following the work of Colosimo et al. (2020), which assumes that the time-varying pressure gradients are balanced by the bed tidal friction only. The (scaled) wind-tide shear stress results thus in a linear superposition of both.The model is applied to the Guadalquivir River Estuary (SW Iberian Peninsula), which is regarded as a highly turbid and reduced-growth environment. Its lower stretches are partially covered by biofilms of MPB. The contribution of these biofilms to PC and the net primary production of the estuary is thought to be larger than that of the free phytoplankton.Model output from a sensitivity analysis of SSC and PC to wind speed and tidal currents indicate that the contribution of usual winds to MPB resuspension in the deeper parts of the cross-section is below 10% of that of the tides, whose currents may exceed 1m/s. On the contrary, the role of wind is dominant in the tidal flats where it shallows and tidal currents are weaker than 0.1m/s. These results suggest than a significant fraction of the observed biomass in the Guadalquivir estuary, particularly that comprised of MPB, could thus be resuspended by wind action in the shoals where they grew. 
Seasonal Forecasting using Machine Learning Algorithms for the continental Europe
Authors: Ünal Alper , Ilıcak Mehmet , Asan Busra , Unal Gozde
Published: February 2023

The ability to accurately predict seasonal and sub-seasonal weather patterns is of great significance in climate modeling, as extreme weather events and climate change become more prevalent. Furthermore, current climate models require extensive computational resources to generate monthly forecasts. Recent advancements in machine learning, accessibility of the vast amount of data, and efficiency of the ML models motivate the use of machine learning based approaches for seasonal forecasting.In this study, we present a sub-seasonal weather forecast model using a UNet based deep learning architecture that enables the learning of long-range spatiotemporal information. Our model is trained with surface air temperature data obtained from 10 different state-of-the-art CMIP6 model output, and finetuned using ERA5 atmospheric reanalysis dataset to increase the generalization in forecasting with real weather modeling data. We focus on the monthly temperature forecast of continental Europe and compare our results with physics-based climate models and ML methods. We evaluate our models using Rooted Mean Square Error (RMSE). Moreover, we analyze the effect of incorporating ancillary data such as topography maps into our model. Our method outperforms linear regression techniques by a high margin. We have found that using the temperature information from the preceding 3 to 4 years of data can improve the performance.We design different experimental settings to arrange monthly historical temperature information given to the deep learning model. In addition to the use of historical data consequently, we investigate the performance of periodical arrangements.
A machine learning emulator for forest carbon stocks and fluxes
Authors: Natel de Moura Carolina , Belda David Martin , Antoni Peter , Arneth Almut
Published: February 2023

Forests are a significant carbon sink of the total carbon dioxide (CO2) emitted by humans. Climate change is expected to impact forest systems, and their role in the terrestrial carbon cycle in several ways – for example, the fertilization effect of increased atmospheric CO2, and the lengthening of the growing season in northern temperate and boreal areas may increase forest productivity, while more frequent extreme climate events such as storms and windthrows or drought spells, as well as wildfires might reduce disturbances return period, hence increasing forest land loss and reduction of the carbon stored in the vegetation and soils. In addition, forest management in response to an increased demand for wood products and fuel can affect the carbon storage in ecosystems and wood products. State-of-the-art Dynamic Global Vegetation Models (DGVMs) simulate the forest responses to environmental and human processes, however running these models globally for many climate and management scenarios becomes challenging due to computational restraints. Integration of process-based models and machine learning methods through emulation allows us to speed up computationally expensive simulations. In this work, we explore the use of machine learning to surrogate the LPJ-GUESS DGVM. This emulator is spatially-aware to represent forests across the globe in a flexible spatial resolution, and consider past climate and forest management practices to account for legacy effects. The training data for the emulator is derived from dedicated runs of the DGVM sampled across four dimensions relevant to forest carbon and yield: atmospheric CO2 concentration, air Temperature, Precipitation, and forest Management (CTPM). The emulator can capture relevant forest responses to climate and management in a lightweight form, and will support the development of the coupled socio-economic/ecologic model of the land system, namely LandSyMM (landsymm.earth). Other relevant scientific applications include the analysis of optimal forestry protocols under climate change, and the forest potential in climate change mitigation. 
The UK’s NCAS Data Project: establishing transparent observational data workflows from field to user
Authors: Parton Graham , Brooks Barbara , Stephens Ag , Garland Wendy
Published: February 2023

Within the UK the National Centre for Atmospheric Science (NCAS) operates a suite of observational instruments for atmospheric dynamics, chemistry and composition studies. These are principally made available through two facilities: the Atmospheric Measurement and Observations Facility (AMOF) and the Facility for Airborne Atmospheric Measurements (FAAM). Between these two facilities instrumentation can be on either campaign or long-term deployed in diverse environments (from polar to maritime; surface to high altitude), on a range of platforms (aircraft, ships) or dedicated atmospheric observatories.The wide range of instruments, spanning an operational time period from the mid 1990s to present, has traditionally been orientated to specific communities, resulting in a plethora of different operational practices, data standards and workflows. The resulting data management and usage challenges have been further exacerbated over time by changes of staff, instruments and end-user communities and their requirements. This has been accompanied by the wider end-user community seeking greater access to and improved use of the data, with necessary associated improvements in data production to ensure transparency, quality, veracity and, thus, overall reproducibility. Additionally, these enhancemed workflows further ensure FAIR data outputs, widening long-term re-use of the data. Seeking to address these challenges in a more harmonious approach across the range of AMOF and FAAM facilities, NCAS established the NCAS Data Project in 2018 bringing together key players in the data workflows to break down barriers and common standards and procedures through improved dialogue. The resulting NCAS ‘Data Pyramid’ approach, brings together representatives from the data provider, data archive and end-user communities alongside supporting software engineers within a common framework that enables cross-working between all partners. This has lead to new data standards and workflows being established to ensure 3 key objectives: 1) capturing and flow of the necessary metadata to automate data flows and quality control as much as possible in a timely fashion ‘from field to end-user’; 2) enhanced transparency and traceability in data production via linked externally visible documentation, calibration and code repositories; and, 3) data products meeting end-user requirements in terms of their content and established quality control. Finally, data workflows are further enhanced thanks to scriptable conformance checking throughout the data production lifecycle, built on the controlled data product and metadata standards.Thus, through the established workflows of the NCAS Data Project, the necessary details are captured and conveyed by both internal file-level and catalogue-level metadata to ensure that all three corners of the triangle of reproducibility, quality information, and provenance are able to be achieved in combination.
Reconstructing solar irradiance over the period 1996-2022
Authors: Ermolli Ilaria , Chatzistergos Theodosios
Published: February 2023

Knowledge of solar irradiance variability is critical to Earth's climate models and understanding the solar influence on Earth's climate. Direct measurements of the Total Solar Irradiance (TSI) have only been available since 1978 from instruments onboard multiple missions. Different calibrations of the individual instruments make estimates of the possible long-term trend in the TSI still uncertain. Knowledge of the Solar Spectral Irradiance (SSI) is even more undetermined. Here we use the carefully reduced observations from the Rome Precision Solar Photometric Telescope (Rome/PSPT) and semi-empirical irradiance models to reconstruct solar irradiance variations over the period 1996-2022. Results are discussed with respect to direct measurements and existing composite reconstructions.
Seasonal dust aerosol optical depth patterns using MODIS Deepblue aerosol product and MERRA-2 aerosol reanalysis
Authors: Che Yahui , Yu Bofu , Parsons Katherine
Published: February 2023

Dust storm as one of severe natural disasters occur frequently in central Australia. Ground-based networks with visibility and PM observation instruments are more often used for dust storm monitoring and research. With the development of satellite remote sensing and general circulation models (GCM), dust storm research has been with larger spatial coverage, especially where ground-based stations cannot be installed. The Deepblue (DB) aerosol product from the Moderate Resolution Imaging Spectroradiometer (MODIS) and the Modern-Era Retrospective analysis for Research and Applications, Version 2 (MERRA-2) provide long-term aerosol records over 20 years from 2000 to the present. In this study, a dust AOD dataset was produced using MODIS DB and MERRA-2 aerosol reanalysis. The comparison of the dust AOD with AERONET data shows that 71% of collocated data points are within an EE of . The dust AOD dataset was then used to study seasonal dust distribution in Australia. Results show that dust storms occur more frequently in eastern Australia than the western part. In the northern part of eastern Australia, dust storm intensity reached the peak in the spring while in the southern part dust storm occur more frequently in the summer. Additionally, dust storms obtained from MODIS differs from those from traditional site observations, which reveals that our understanding on dust storm over regions without observations might be with large uncertainty.