bluuebunny/crossref_metadata_2025 huggingface dataset:
A low-cost novel optical sensor for water quality monitoring
Authors: Power Sean , Free Louis , Richards Chloe , Briciu-Burghina Ciprian , Delgado Ollero Adrian , Clinton Ruth , Regan Fiona
Published: February 2023

With increasing environmental pressure due to global climate change, increases in globalpopulation and the need for sustainable obtained resources, water resources managementis critical. In-situ sensors are fundamental to the management of water systems by providingearly warning, forecasting and baseline data to stakeholders. To be fit-for-purpose,monitoring using in-situ sensors has to be carried out in a cost effective way and allowimplementation at larger spatial scales. If networks of sensors are to become not only areality but common place, it is necessary to produce reliable, inexpensive, rugged sensorsintegrated with data analytics.In this context, the aim of this project was to design and develop a low cost, robust andreliable optical sensor which capable of continuous measurement of chemical and physicalparameters in aquatic environments. An iterative engineering design method cyclingbetween sensor design, prototyping and testing was used for the realisation and optimisationof the sensor. The sensor can provide absorption, scatter, and fluorescence readings over abroad spectral range (280nm to 850nm) and temperature readings in real-time using a suiteof optical sensors (CMOS Spectrometers and photodiode detector), custom designed LEDarray light source and a digital temperature probe. Custom electronics and firmware weredeveloped to control the sensor and facilitate data transmission to an external network.Sensor electronics are housed in a marine grade watertight housing; the optical componentsare mounted inside a custom designed 3D-printed optical head which joins with the sensorhousing. The sensor is capable of measuring a range of optical parameters and temperaturein a single measurement cycle. Sensor analytical performance was demonstrated in thelaboratory, for detection and quantification of turbidity using analytical standards and in thefield by comparison with a commercially available multi- parameter probe (YSI, EXO 2).The laboratory and field trials demonstrate that the sensor is fit-for-purpose and an excellenttool for early warning monitoring by providing high frequency time-series data, operateunattended in-situ for extended periods of times and capture pollution events.Acknowledgement - This research is carried out with the support of Project Ireland’s 2040’sDisruptive Technologies Innovation Fund.
Reducing uncertainty in climate models through improved parameterizations of small scale processes
Authors: Kamm David , Deshayes Julie
Published: February 2023

The NEMO modelling framework finds application in numerous climate models. Simulating Earth’s climate and how it is changing means to solve a complex set of equations for a long period, usually hundreds of years. Given the small time scales of the processes involved and the limited available computational resources, this imposes numerical constraints on the spatial resolution of the simulation. Consequently, processes  with a smaller physical length scale than the model grid can not be explicitly resolved, for example mesoscale eddies. The effects of these subgrid-scale processes on the larger scale climate system need to be approximated through parameterisations. Recent studies propose new methods to find and formulate parameterisations using machine learning tools, which promise improvements in the predictive skill of the model. With the prospect of introducing these into future versions of NEMO, their potential benefit is yet to be determined. We propose a new configuration to be used as a test protocol for subgrid-scale parameterisations. The configuration is of intermediate complexity  and with an idealised basin geometry of the Atlantic and Southern Ocean. This allows for relatively cheap simulations even at very high horizontal resolution, while   crucial aspects of the system like the meridional overturning circulation (MOC) or the antarctic circumpolar current (ACC) are still maintained. Effects of the subgrid-scale processes on the large-scale circulation are then diagnosed to evaluate the performance of their parameterisation.
Constraining the impact of cyclic hydraulic stimulation on granites
Authors: Kendrick Jackie E. , Mouli-Castillo Julian , Lamur Anthony , Fraser-Harris Andrew , Lightbody Alexander , Chandler Mike , Edlmann Katriona , McDermott Christopher , Shipton Zoe
Published: February 2023

Subsurface engineering, such as geothermal energy extraction, requires knowledge of the rupture of geomaterials. Of particular importance is the time- and rate-dependence of material strength, which impacts fracture architecture and thus hydraulic conductivity and system permeability. Cyclic soft stimulation (CSS) techniques have been developed to maximise the efficiency of resource extraction whilst minimising large amplitude, fluid-injection induced seismicity. Here, we explore the benefits of cyclic stimulation experimentally, utilising novel “pulsed pumping” hydraulic fracture tests in which fluid pressure is cycled within the central borehole of a suite of 20x20cm cylinders of dense granite. The response is monitored at high-resolution by fibre-optic circumferential strain measurements, fluid pressure data and acoustic emission recording. Using cyclic high-low pressure square waves, we found that breakdown pressure was reduced by up to 15% compared to the monotonic case in which pressure was increased by applying a constant flow rate. Whilst peak pressure had the primary control on the number of cycles to failure, increasing the minimum pressure in the borehole (thus increasing mean pressure) further reduced breakdown pressure, suggesting that even small pressure fluctuations during hydraulic stimulation may reduce the largest stress drops, and hence the magnitude of induced seismic events. Strain measurements detected accelerating precursory deformation a few cycles prior to failure, hinting at the opportunity for responsive stimulation practices where activity can be monitored in real-time. These novel large-scale, high-resolution experiments were complemented by indirect tensile measurements at a range of strain rates, and by cyclic fatigue Brazilian disc testing at a range of peak loads and cycle amplitudes. These results further highlight the increasing contribution of time-dependent deformation during slower and cyclic loading, resulting in lower peak loads and reducing large magnitude fracturing events. The generated S-N curves demonstrate that weakening by cyclic hydraulic pressurisation mimics relationships defined by conventional fatigue testing of geomaterials. Such experimental constraints will be of great benefit to the development of cyclic stimulation practices for subsurface engineering.
Reconstructing North Atlantic Ocean Heat Content Using Convolutional Neural Networks
Authors: Lentz Simon , Brune Dr. Sebastian , Kadow Dr. Christopher , Baehr Prof. Dr. Johanna
Published: February 2023

Slowly varying ocean heat content is one of the most important variables when describing cli-mate variability on interannual to decadal time scales. Since observation-based estimates ofocean heat content require extensive observational coverage, incomplete observations are oftencombined with numerical models via data assimilation to simulate the evolution of oceanic heat.However, incomplete observations, particularly in the subsurface ocean, lead to large uncertain-ties in the resulting model-based estimate. As an alternative approach, Kadow et al (2020) haveproven that artificial intelligence can successfully be utilized to reconstruct missing climate in-formation for surface temperatures. In the following, we investigate the possibility to train theirthree-dimensional convolutional neural network to reconstruct missing subsurface temperaturesto obtain ocean heat content estimates with a focus on the North Atlantic ocean.The network is trained and tested to reconstruct a 16 member Ensemble Kalman Filter assimi-lation ensemble constructed with the Max-Planck Institute Earth System Model for the periodfrom 1958 to 2020. Specifically, we examine whether the partial convolutional U-net representsa valid alternative to the Ensemble Kalman Filter assimilation to estimate North Atlantic sub-polar gyre ocean heat content.The neural network is capable of reproducing the assimilation reduced to datapoints with ob-servational coverages within its ensemble spread with a correlation coefficient of 0.93 over theentire time period and of 0.99 over 2004 – 2020 (the Argo-Era). Additionally, the network isable to reconstruct the observed ocean heat content directly from observations for 12 additionalmonths with a correlation of 0.97, essentially replacing the assimilation experiment by an extrap-olation. When reconstructing the pre-Argo-Era, the network is only trained with assimilationsfrom the Argo-Era. The lower correlation in the resulting reconstruction indicates higher un-certainties in the assimilation outside of its ensemble spread at times with low observationaldensity. These uncertainties are highlighted by inconsistencies in the assimilation’s represen-tations of the North Atlantic Current at times and grid points without observations detectedby the neural network. Our results demonstrate that a neural network is not only capable ofreproducing the observed ocean heat content over the training period, but also before and aftermaking the neural network a suitable candidate to step-wise extend or replace data assimilation.
Identifying the convective and stratiform rainfall regimes using stable isotopic measurement
Authors: Deb Burman Pramit Kumar , Chakraborty Supriyo , Datye Amey , Choudhury Abida , Mujumdar Milind , Mohan Pm , Gogoi Nirmali , Trivedi Rajendra , Sarma Dipankar , Bora Abhijit , Trivedi Neha
Published: February 2023

It still remains an intriguing question in weather and climate research on identifying the convective rainfall from stratiform. In a future warmer climate, this will be important to know to predict the changing pattern of rain intensity and distribution and to plan an efficient usage of water resources. Although quite a few methods have been proposed to address this question, such as cloud top temperature value, height-integrated ice and cloud water paths, brightness temperature, drop size distribution etc., the fidelity and validity of those vary widely, and hence their applicability remains limited. In this work, we propose a method to identify these two rainfall regimes using a combination of surface and remote sensing measurements. We collected the rainwater samples daily at Port Blair, Sagar and Tezpur in India as part of a project CRP F31006, funded by the International Atomic Energy Agency (IAEA). We measured the 18O content in these collected rainwater samples by measuring its fractionation (δ18O) using a Triple Isotope Water Analyzer by Los Gatos Research, USA. Among these sites, Port Blair is an island on the Bay of Bengal near the tropics, Sagar is an inland location in central India's dry, arid climate and Tezpur is located in the wet and heavily forested northeast India. Whereas Port Blair is situated very close to the tropics, Sagar and Tezpur are closer to the subtropics. The dual-frequency precipitation radar in the global precipitation measurement (GPM) program provides the convective and stratiform rainfall records by looking at radar reflectivities. We utilize these records to estimate the area-averaged stratiform rainfall fraction over each of these locations. We find that the relation between rain intensity and stratiform rainfall fraction can be represented by a logarithmic regression, whereas, the relation between δ18O and rain intensity can be represented by linear regression. However, the logarithmic regression weakens with latitude, and, the slope of the linear relation changes from slightly negative to slightly positive. The three sites considered here are located under different environmental conditions (oceanic to continental, semi-arid to forest, southwest to northeast monsoon zones, etc.) and house different vegetation types. To better understand the underlying processes governing such relations, we also study the impact of different meteorological variables in regulating these relations. Based on our study, the δ18O can be used as a proxy to identify the relative contributions of convective and stratiform rain types in the total rainfall over a region.
Unlocking the mystery of deep biosphere hosted by the Archaean granitic crust underneath the Deccan Traps, Koyna, India
Authors: Sahu Rajendra Prasad , Mandal Sunanada , Sarkar Swatilekha , Mukherjee Debarshi , Kazy Sufia K , Sar Pinaki
Published: February 2023

Life that inhabits deep within the continental crust remained enigmatic, and scientifically less explored. Recent scientific drilling of 3000-meter-deep scientific borehole at the Koyna seismogenic zone of the Deccan Traps, India provided an unprecedented opportunity to investigate the nature of life that evolved and existed within the extreme environment of Archean granitic basement. Rock core samples recovered from deep (up to ~2910 meter below surface), progressively hot (up to 74 °C), high pressure, alkaline, oligotrophic biosphere were analyzed through amplicon sequencing, metagenomics, and cultivation-based approaches. 16S rRNA gene amplicon sequencing showed considerable bacterial diversity. A significant proportion (50-63 %) of the microbial community, represented by the members of Actinobacteria, Gammaproteobacteria, Alphaproteobacteria, Bacilli and Clostridia was identified as endemic or core microbiome of this extreme realm. Relative abundance of different taxa of the core microbiome varied with depth in response to prevailing lithology and geochemistry. Ecological modeling depicted a major role dispersal limitation in explaining community variability suggesting the greater impact of stochasticity on the community assembly. Co-occurrence network analysis elucidated close interactions among autotrophic and organotrophic bacteria. Shotgun metagenomics revealed a major role of autotrophic carbon fixation via the Wood-Ljungdahl pathway and Rnf complex for energy and carbon metabolism. Presence of other mechanisms of energy generation such as ATPase, H2, CO, CH4 or sulfur oxidation system and the possibility of coupling of electron donors to NO3−/SO42− reduction illustrated the metabolic versatility of these organisms in terms of utilizing diverse resources. Deeper analysis suggested the existence of an ‘acetate switch’, coordinating biosynthesis and cellular homeostasis. Reactivation of the rock-hosted microbiome using different nutrients (carbon source, e- donor and acceptor) and at elevated temperatures (up to 70-degree C) and anaerobic conditions showed patterns of differential taxa recruitment for utilizing different substrate regimes. Enrichment of strict anaerobic members of taxa Peptococcaceae, Clostridiaceae Family XIV, Clostridiaceae 2 and Moraxellaceae in H2+CO2 condition corroborated well with their ability for acetogenesis. Universal enrichment of Burkholderiaceae, Bacillaceae and Sphingomonadaceae suggested their abilities to use diverse substrates. Our data provided deeper insights into microbial life and its mechanisms of carbon and energy metabolism within the nutrient- and energy-limiting deep granitic crust.
Digital twincubator eWaterCycle
Authors: Drost Niels , Kalverla Peter , Schilperoort Bart , Vreede Barbara , Alidoost Sarah , Verhoeven Stefan , Liu Yang , Hut Rolf
Published: February 2023

Recently there’s been a lot of enthousiasm for the concepts of digital twins, virtual research environments, serious games, and other inspiring ideas to improve “the way we do science.” With eWaterCycle, we are no stranger to the cause. We’ve worked hard to build a platform that could make the scientific process – specifically, hydrological modelling – more accessible and engaging.  

eWaterCycle gives users access to a centralized platform where they can perform hydrological experiments: simulating how water flows through a catchment area of choice. Complete with data, a suite of models, an interactive scripting environment, and a graphical explorer to quickly setup an experiment. It shares many characteristics with what is commonly understood of a digital twin. But is it, really?  

Sadly, the concept of digital twins suffers from linguistic inflation. At a recent event on the topic, the main coffee chatter was along the lines of “but what actually is it?” In an arena filled with resonating buzz, a clear image can help to regain focus and a common frame of reference. Is eWaterCycle, as a platform that supports working with each other’s models and data, a digital twin? Or is it more an incubator of digital twins? Either way, eWaterCycle can help make things concrete and specific, because it already exists.  

With several new projects promising to build digital twins of all sorts, we hope our experience can feed into the discussions on and development of new digital twins. Therefore, at EGU, we would like to reflect on the essence of our platform and our experience in building it. What is it (not)? What’s in it for you? What challenges did we face? And what does that mean for open science and collaborative research? 
Causal inference to study food insecurity in Africa
Authors: Cerdà-Bautista Jordi , Tárraga José María , Varando Gherardo , Arribas Alberto , Shepherd Ted , Camps-Valls Gustau
Published: February 2023

The current situation regarding food insecurity in the continent of Africa, and the Horn of Africa in particular, is at an unprecedented risk level triggered by continuous drought events, complicated interactions between food prices, crop yield, energy inflation and lack of humanitarian aid, along with disrupting conflicts and migration flows. The study of a food-secure environment is a complex, multivariate, multiscale, and non-linear problem difficult to understand with canonical data science methodologies. We propose an alternative approach to the food insecurity problem from a causal inference standpoint to discover the causal relations and evaluate the likelihood and potential consequences of specific interventions. In particular, we demonstrate the use of causal inference for understanding the impact of humanitarian interventions on food insecurity in Somalia. In the first stage of the problem, we apply different data transformations to the main drivers to achieve the highest degree of correlation with the interested variable. In the second stage, we infer causation from the main drivers and interested variables by applying different causal methods such as PCMCI or Granger causality. We analyze and harmonize different time series, per district of Somalia, of the global acute malnutrition (GAM) index, food market prices, crop production, conflict levels, drought and flood internal displacements, as well as climate indicators such as the NDVI index, precipitation or land surface temperature. Then, assuming a causal graph between the main drivers causing the food insecurity problem, we estimate the effect of increasing humanitarian interventions on the GAM index, considering the effects of a changing climate, migration flows, and conflict events. We show that causal estimation with modern methodologies allows us to quantify the impact of humanitarian aid on food insecurity. References [1] Runge, J., Bathiany, S., Bollt, E. et al. Inferring causation from time series in Earth system sciences. Nat Commun 10, 2553 (2019). https://doi.org/10.1038/s41467-019-10105-3[2] Sazib Nazmus, Mladenova lliana E., Bolten John D., Assessing the Impact of ENSO on Agriculture Over Africa Using Earth Observation Data, Frontiers in Sustainable Food Systems, 2020, 10.3389/fsufs.2020.509914. https://www.frontiersin.org/article/10.3389/fsufs.2020.509914[3] Checchi, F., Frison, S., Warsame, A. et al. Can we predict the burden of acute malnutrition in crisis-affected countries? Findings from Somalia and South Sudan. BMC Nutr 8, 92 (2022). https://doi.org/10.1186/s40795-022-00563-2
Analysis and attribution of the hydrological coherence of gridded precipitation and temperature datasets in the Italian Alpine Region
Authors: Galletti Andrea , Avesani Diego , Bellin Alberto , Majone Bruno
Published: February 2023

Large-scale hydrological modeling has gained a wealth of attention in the last decades, due to the importance of assessing the growing anthropogenic and climate change impacts on water resources. In the context of these studies, the Alpine Region has historically played a key role, being widely recognized as “Europe’s water tower” and given the complex combination of anthropogenic and climatic drivers influencing its hydrology. The application of hydrological modeling at the synoptic scale requires an accurate assessment of the climatic forcing, chiefly precipitation and temperature. Nowadays, a number of observation-derived gridded products providing precipitation and temperature over a regular grid are available to benchmark and support large-scale analyses. However, these products are often not tailored to potential hydrological applications and are based on data with different and often uncertain levels of accuracy and resolution. In this context, assessing the uncertainty due to the climatic forcing and its relationship with the hydrological response of different catchments becomes crucial in order to gain confidence in the simulations. In the present study, we analyze the ability of several gridded datasets (which are best suited to large-scale analyses) to reproduce observed streamflows of more than 200 reaches across the Italian Alps. The simulations have been conducted by feeding HYPERstreamHS, a distributed hydrological model specifically tailored for large-scale simulations, with the following gridded meteorological datasets: MESAN, COSMO reanalysis, APGD, MSWEP, E-OBS, MESCAN, and ERA5-Land. Hydrological coherence was first evaluated by means of the NSE and KGE efficiency indexes. Then, we attempted to break down the main drivers of hydrological coherence by classifying the analyzed catchments based on hydrological and geomorphological characteristics, and by analyzing the relative incidence on the uncertainty of temperature and precipitation, by means of ANOVA.
Use of hydrological models in global stochastic flood modelling
Authors: Olcese Gaia , Bates Paul , Neal Jeffrey , Sampson Christopher , Wing Oliver , Quinn Niall
Published: February 2023

Stochastic flood models can simulate synthetic flood events with a realistic spatial structure, unlike traditional flood models, which do not take into consideration the spatial dependency of flood events. This is particularly relevant to loss calculations at regional to continental scales. The development of large-scale stochastic flood models has been limited so far by the availability of gauge data, needed as a model input. Global hydrological models can provide simulated discharge hindcasts that have been used to drive stochastic flood modelling in data-rich areas. This research evaluates the use of discharge hindcasts from global hydrological models in building stochastic river flood models globally by simulating synthetic flood events in different regions of the world. The results (published in a recent paper in WRR) show a promising performance of the model-based approach, with errors comparable to those obtained over data-rich sites. This suggests that a network of synthetic gauge data derived from global hydrological models would allow the development of a stochastic flood model with detailed spatial dependency, generating realistic event sets in data-scarce regions and loss exceedance curves where exposure data are available. As part of this research, we are currently working on the development of a stochastic flood model of Southeast Asia using discharge data from global hydrological models. 
Systematic errors in regional climate simulation of East Asian winter monsoon
Authors: Shin Seok-Woo , Lee Minkyu , Park Chang-Young , Kim Eunji , Mun Taeho , Juzbašić Ana , Cha Dong-Hyun
Published: February 2023

East Asian Winter Monsoon (EAWM) is an important system, which has impact on extreme winter phenomena, in East Asia including the Korean Peninsula. EAWM is characterized by the Siberian high with cold air and the Aleutian low with warm air, low-level northerly wind between them, East Asian trough in the middle troposphere, and East Asian jet stream in the upper troposphere. According to some previous studies, the variability of EAWM, which can be influenced by human activity and climate change, is strongly correlated with the occurrence of cold waves in East Asia. Additionally, EAWM variability has been shown to affect the precipitation during winter on the Korean Peninsula. Extreme boreal winter phenomena such as cold waves and heavy snow related to EAWM can induce disruptions to regular life and socio-economic damage. Regional climate models (RCMs) have added value in the simulations of extreme climate phenomena, but also have internal variability, which can increase uncertainty in future climate projections. Therefore, in this study, to closely understand EAWM and reduce the uncertainty in future climate projection, the performance of two RCMs (SNURCM and WRF) for EAWM simulation was evaluated. The results showed that two RCMs had the ability to capture the interannual variability and climatological spatial pattern of EAWM despite the systematic biases of EAWM in the lower troposphere (e.g., comparatively low correlation and larger temporal-spatial variability than reanalysis). Additionally, two RCMs presented significant cold bias over the Manchuria and wet bias over the Kuroshio Current. In further analysis, their systematic errors were positively correlated with EAWM, which had a larger pressure gradient between the Siberian high and the Aleutian low and colder advection over Manchuria than those of the ERA5. Wet biases over the Kuroshio Current could be related to unreasonable simulation EAWM induced by uncoupled air-sea interaction in RCMs.
Impact of individual antenna phase center models and multi-GNSS observations on tropospheric estimates
Authors: Stępniak Katarzyna , Krzan Grzegorz , Paziewski Jacek
Published: February 2023

In this study, we investigate the impact of GNSS antenna calibration models and multi-GNSS observations on the quality of the tropospheric estimates. The accuracy and homogeneity of the Zenith Total Delay (ZTD) time series estimated from ground-based GNSS data strongly depend on the processing strategy. These factors significantly imply the GNSS solution performance; however, their impact on the quality of the derived ZTD series used for climate applications has not been comprehensively investigated.We analyzed three years of ZTD time series obtained from GNSS data processing and afterward converted integrated water vapor (IWV). Nine different processing strategies distinguished into three groups were employed: the 1st group of solutions was obtained by applying the International GNSS Service (IGS) type mean Phase Center Correction (PCC) models (IGS14); in the 2nd group, PCC models from individual field robot calibration were used; in the 3rd group of solutions, an anechoic chamber calibration was applied. Each group of solutions was processed three times using different GNSS constellations, namely GPS-only, Galileo-only, or combined GPS+Galileo.The results reveal that the impact of employed GNSS constellations on the accuracy of the ZTD time series is more pronounced than the impact of antenna calibration models. However, the latter factor is also noticeable and thus cannot be neglected. Moreover, validation against climate reanalysis data confirms that all approaches provide high-quality tropospheric delays.The outcomes also indicate that ZTD estimates obtained with robotic and IGS14 calibrations are closer to that of ERA5 reanalysis than estimates derived when using calibrations in an anechoic chamber. In addition, multi-GNSS-derived tropospheric parameters are more comparable to the benchmark ones from ERA5 than those provided by single-system solutions. The results also depend, among others, on the stations' equipment (receiver and antenna). 
Investigation of Tremor Events Coinciding with Skaftár-Cauldrons Glacial Floods from 2015 to 202
Authors: Vanderhoof Bethany , Jónsdóttir Kristín , Brandsdóttir Bryndís , Guðmundsson Ólafur , Nowe Sylvain , Soubestre Jean , Caudron Corentin , Lecocq Thomas , Cubuk-Sabuncu Yesim , Einarsson Bergur
Published: February 2023

The Skaftár cauldrons are a pair of depressions at the surface of the Vatnajökull glacier that signify subglacial lakes caused by geothermal heat sources at the underlying bedrock. These subglacial lakes continuously grow in volume and produce fast-rising jökulhlaups (glacial lake outburst floods) at the glacier outlet 35-40 km away. Seismic tremor events coincide with these large floods, but the exact source of this tremor is unknown. To investigate the origin and characteristics of these tremors, network covariance matrix spectral width, real-time seismic amplitude measurements (RSAM), and spectral analyses were conducted for eight jökulhlaups spanning the years 2015 to 2021, with joint interpretation alongside hydrological and GPS data. Seismic data was aquired by the Icelandic Meteorological Office's network. The several-day time period spanning the flood propagation and deepening of the ice cauldrons was dominated by small icequakes along the subglacial flood path and emergent low frequency, temporally regular earthquakes with repetitive waveforms. Once most of the water had drained from the cauldrons, strong tremor bursts with a duration on the order of 10s of minutes and a dominant frequency range between 0.5 and 3 Hz were recorded by seismic stations both on and off of the glacier. This tremor occurred during seven out of the eight floods examined, exhibiting fewer than 10 clear tremor bursts per flood over approximately a 24-hour period. Preliminary results show that the amplitude of the tremor bursts correlates to the magnitude of the flood, with the strongest tremor occurring in 2015, during the largest recent flood from the eastern cauldron. Due to the similarities shared between the shape of this tremor’s seismic envelope and that of the tremor during hydrothermal explosions, we interpret the tremor seen after the Skaftá cauldrons have drained as steam explosions facilitated by a drop in the overlying water pressure.
Simulating an extreme heat event in a mid-sized city in Europe: validating and analyzing the relevance of spatial resolution in the urban LES model PALM4U with an observation network
Authors: Späte Eva , Sungur Leyla , Schneider Johann , Babel Wolfgang , Thomas Christoph K
Published: February 2023

Urban areas are known to be exposed to higher temperatures than rural areas making urban citizens particularly vulnerable to extreme heat events. Large eddy simulation (LES) models can simulate micrometeorological heat transport and mixing processes by directly resolving large-scale turbulence. These models can be used to simulate urban development strategies aiming at mitigating the adverse effects of heat waves in cities by analyzing their influence on urban microclimate. Despite their use in formulating recommendations for city planning, these models are often not validated with observed meteorological data. We here present results from conducting a model-observation comparison for a mid-size city in Germany. Model simulations were computed with the LES model PALM4U run at two different resolutions (Δx,y,z = 5 and 20 m) and evaluated against observations from a network of microweather stations for a heat wave in 2019 reaching maximum near-surface air temperatures of 37 °C.During daytime, differences between observed and modeled near-surface air temperatures were small (-3.8 to 1.1 K, mean = 0.9 K), but much larger during night-time and the early morning transition. The latter findings can be explained by an overestimated modeled ground heat flux resupplying too much energy offsetting the radiative cooling leading to overestimated modeled air temperatures by up to +9 K (mean = 5.3 K). Further, results showed that in areas where the actual urban structure is reproduced well by the model resolution, differences between observed and modeled wind speeds were lower. Our findings indicate that a spatial resolution smaller than the mean building height produce more accurate model results for wind speeds. Many differences in model-observation intercomparison are explained by an overestimated modeled turbulent kinetic energy (TKE) causing inflated turbulent mixing in the air, which leads to distorted model output particularly for the urban nocturnal boundary layer.
Developing a unified and consistent database for historical natural events, and subsequent losses, within a catastrophe modeling framework
Authors: Bruhat Lucile , Rakotoarimanga Hugo , Hersent Maxime
Published: February 2023

The insurance industry faces highly complex P&C challenges, among which natural catastrophe risk, also labeled as “CAT” risk. Among disasters, climatic and seismic events show large variability in size and frequency, with devastating consequences; not to mention climate change which brings added uncertainty for the future. Global insurance groups, such as AXA, must develop a sound understanding of the frequency, intensity, and impacts of natural hazard events, to protect their economic capital and ensure their solvency.At the AXA Group Risk Management, the CAT modeling process consists in 1) collecting CAT exposure data (geographical, physical, and financial information) on a per-entity (AXA France, AXA Mexico…) and per-location basis (houses, factories, vehicles…) and 2) assessing the risk on a per-entity per-peril per-geography basis (cyclones, earthquakes, floods, hailstorms...) to finally aggregate it at Group level. This process constitutes a technical challenge through the data collection of 50 million of policies, the combination of multiple modeling solutions, and the production of millions of stochastic event losses. Alongside this process, the collection and analysis of “scenarios”, either historical events, or potential future disasters, improves the robustness and understanding of risk assessment. However, there is currently no unified and consistent database recording the characteristics of natural events (a unique identifier, their spatial and temporal extent, their intensities, and the location affected) and their actualized economic and industry impacts. This work aims at developing a database for that would first gather an exhaustive inventory of historical natural events (cyclones, storms, floods, earthquakes…) and, throughout the integration within the existing CAT modeling ecosystem, automatize model validation, back-testing, and risk analysis with respect to market and as-if losses. 
Attributing observed trends in heat-related excess mortality to climate change
Authors: Huber Veronika , Breitner-Busch Susanne , Schneider Alexandra , Mengel Matthias
Published: February 2023

Past studies quantifying the burden of heat-related mortality attributable to climate change have mostly focused on specific extreme events or considered multi-decadal averages. Here, we contribute to the scarce literature on the attribution of observed temporal trends in heat-related mortality to climate change. Our study is based on daily all-cause mortality time-series from 15 major German cities over 1993-2020. Counterfactual climate data is derived from century-long measurement series of daily mean temperatures by removing trends related to the observed rise in global mean temperature according to the ATTRICI method. We use quasi-Poisson regression models including distributed lag non-linear models and multivariate meta-regression models to estimate temperature mortality associations. Our results corroborate previous model-based estimates, suggesting that, averaged over the entire study period, 28% (95%CI: 17%, 42%) of warm-season (May-Sep) heat-related excess mortality across all German cities were attributable to climate change. Considering linear temporal trends suggests that this proportion has increased by 4.0±1.0% per decade. Under observed climate change, we find a linear increase of 174 (SE: ±151) heat-associated deaths per decade across cities. By contrast, our results suggest that without climate change there would not have been a significant increase in heat associated deaths. Overall, our study provides evidence of increasing impacts of climate change on heat-related mortality in Germany since the 1990s. As temperatures keep rising in the future, climate change is expected to drive further increases in heat-related excess mortality unless additional adaptive measures are taken.
Carbon Management Requiring Index: The Scientific Decision-making Tool for Urban Green Management
Authors: Seo Inhye , Yoo Gayoung
Published: February 2023

Recently, most cities have opted for urban greening as a way to mitigate climate change. However, the urban characteristics, such as the fragmented land cover and harsh environment hard to maintain vegetation healthy, reduce the efficiency of greenery. Therefore, a continuous and scientific management tool is required to mitigate climate change through urban greenery. In this study, we developed the decision-making tool, CMRI (Carbon Management Requiring Index), which can identify the area with low carbon sequestering performance and propose the priority for the carbon management requirement. The index was determined by integrating five parameters; 1) terrestrial carbon storage, 2) terrestrial carbon uptake, 3) soil texture, which implies the capacity for soil carbon sequestration, 4) green area ratio, which means that the chance of carbon management, and 5) landscape context, which represents the edge effect by the adjacent urban landscape. The three parameters of terrestrial carbon storage, green area ratio, and landscape context were estimated based on the 0.25 m land cover map using satellite data through machine learning. The terrestrial carbon uptake was determined by the data-driven model through satellite measurement data. Lastly, we acquired the soil texture data from ISRIC – World Soil Information dataset. We normalized each parameter with the z score method. We applied the index in our test site (Suwon, Republic of Korea), and we mapped CMRI with its spatial resolution of 30 m x 30 m considering the resolution of each parameter. The CMRI values had a gradient which showed the high management demand in the city center and the relatively low in the forest interior. The range of CMRI values was from 0.2 to 0.8. To suggest the priority of carbon management requirements, we divided the CMRI grids into four quarters, low, medium, high, and extremely high. To verify that CMRI represents the carbon management requirement level properly, we plan to validate it by field observation. Three grids in each priority level will be selected to measure the vegetation condition, including DBH and chlorophyll-a content, and soil characteristics, including soil texture, soil carbon stock, and soil respiration. Through principal component analysis (PCA) using field measurement results of the grids, we can weigh each parameter and make the index more accurate.
Assessing the hillslope-channel contributions to the catchment sediment balance under climate change
Authors: de Vente Joris , Boix-Fayos Carolina , Pérez-Cutillas Pedro , Jodar-Abellan Antonio , Eekhout Joris
Published: February 2023

Previous research suggests that channels have a significant contribution to the total sediment balance in large catchments. Channels are also very sensitive to changes in land use and management, with implications for channel morphodynamics and the quantity of sediments and nutrients flowing through the system. Here we present a new channel module for the coupled hydrology-soil erosion model SPHY, with the aim to quantify the contribution of hillslope and channel erosional processes to the total sediment balance in large catchments. SPHY simulates all relevant hydrological and soil erosion processes, including infiltration excess surface runoff and soil detachment by raindrop impact and runoff. We developed a novel channel module that simulates river hydraulics and morphodynamics in the channel network, while accounting for sedimentation in reservoirs and check dams. The channel module requires channel characteristics as input, including channel width, channel depth and bed material. The model was applied to a Mediterranean catchment (316 km2) in Southeast Spain, where channel characteristics were obtained from a combination of field measurements and GIS analyses. The model was calibrated using observed discharge, hillslope erosion and check dam sedimentation. Model validation focused on how channel morphodynamics changed in response to reforestation and check dam construction in the period 1956-2001. The model successfully simulates the observed changes in channel morphology, including an overall increase of channel morphodynamics and channel incision downstream of the check dams. Subsequently, the model was applied in a climate change impact assessment, to show how the sediment balance is projected to change under future climate conditions, characterized by an increase of extreme precipitation. The climate change simulations show that rill erosion is projected to increase, while channel erosion decreases. However, channel erosion still remains the main contribution to the total sediment yield in the catchment, which highlights the importance of accounting for channel morphological processes in large-scale erosion assessments. We acknowledge funding from the Spanish Ministry of Science and Innovation (AEI) (PID2019-109381RB-I00/AEI/10.13039/501100011033).
On Seismic Wave Equation Gradiometric Inversion for Density
Authors: Faber Marthe , Curtis Andrew
Published: February 2023

It is of interest for environmental and resource applications to better characterise dynamic processes and properties of the near-surface critical zone of the solid Earth. Seismic wavefield gradiometry refers to a class of imaging techniques that estimate properties of the subsurface by calculating temporal and spatial gradients of incoming wavefields using dense array measurements, usually recorded at the Earth’s surface. One such method called wave equation inversion (WEI) has been shown to require only a few minutes of ambient seismic noise recordings to produce phase velocity maps, and shows promise for rapid field deployment.Previous applications of WEI are based on the assumption that the 2D scalar Helmholtz wave equation adequately describes the dynamics of recorded wavefields. This approximation is severe for seismic waves because the Helmholtz equation fails to describe elastic wave dynamics. Since ambient noise recordings contain all kinds of interfering elastic wave types, the accuracy of subsurface material property estimates is compromised.To investigate the potential to enhance the information available from WEI, we test the method synthetically using more sophisticated wave equations that represent wave propagation in the subsurface more accurately. Starting from a 3D seismic array geometry which provides wavefield gradient information both at the surface and at depth, WEI can be formulated in terms of the full elastic wave equation. From there we track approximations in both wave physics and field acquisition geometries that deplete information about the medium, eventually arriving at the conventional 2D scalar wave equation. These experiments highlight approximations that most deteriorate the solution, allowing us to target future effort to remove them.One approximation made in all previous WEI studies is to assume that density is constant across the local array. In reality, subsurface density varies both laterally and with depth, yet remains poorly constrained in seismic imaging problems. Accurate density estimates would provide important insight into subsurface properties. This prompts us to test wavefield sensitivities to subsurface density contrasts via WEI. Synthetic results for 3D acoustic media suggest that it is possible to estimate relative density structure with WEI by using a full acoustic formulation for wave propagation along the surface. We show that using a constant density assumption for the medium can be detrimental to subsurface images, whereas the full acoustic formulation of gradiometry improves our knowledge of material properties. It allows us to estimate density as an additional material parameter as well as to improve phase velocity estimates by incorporating approximations to the density structure. By expanding this methodology to the elastic case, we will discuss the feasibility of estimating density with gradiometric WEI in the solid Earth.
A new framework for building global flood models for the present day and future climates
Authors: Savage James , Uhe Pete , Wing Ollie , Sampson Chris , Smith Andy , Lord Natalie , Addor Nans , Hatchard Simbi , Hoch Jannis , Bates Joe , Quinn Niall , Collings Tom , Probyn Izzy , Haigh Ivan , Green Joshua , Cooper Anthony , Wilkinson Hamish , Himsworth Sam
Published: February 2023

In recent years there have been many new global datasets and methodological advancements that could be utilised by hydraulic models to help better understand global flood risk both in the present day and in the future. A major challenge facing modellers is how to incorporate these new datasets to improve the understanding of flood risk in both well, and less well, developed countries using a consistent approach, particularly as the latter of these contain increasingly larger exposures to floods.This new framework presents a computationally efficient yet flexible approach that seeks to utilise new global datasets and allows flood hazard maps to be calculated anywhere in the world, for any event severity (within a pre-defined range) and for any future climate scenario. The framework can be applied to all three of the major flood perils; fluvial, pluvial and coastal.At the heart of the framework is an efficient post-processing methodology that incorporates outputs from leading climate models, flood defence datasets and a baseline set of simulations spanning a range of evert severities. Furthermore, the flexible approach allows users to modify assumptions of flood defences and incorporate new climate simulations as and when they become available to quickly re-calculate flood hazard.We present here the full modelling chain, from input data through to flood hazard outputs covering all aspects of modelling, from determining model boundary conditions and estimating channel bathymetry, to post-processing the presence of flood defences and interpolation to future climate scenarios. We show that such an approach is able to replicate explicitly modelling the scenarios required at a fraction of the computation cost and demonstrate how this is crucial to anyone wanting to understand how exposure to floods may change into the future.