bluuebunny/crossref_metadata_2025 huggingface dataset:
Air Quality Monitoring for Sustainable Development in Cluj-Napoca
Authors: Camarasan Horia , Radovici Andrei , Stefanie Horatiu , Mereuta Alexandru , Ajtai Nicolae , Botezan Camelia
Published: February 2023

Apart from affecting citizens health and wellbeing across urban and rural areas, air pollution has significant economic implications which inhibit progress, especially for developing countries. The role of air quality monitoring to ensure population wellness is a key topic in the current pursuit for the sustainable development goals, promoting active response from decision-makers to address acute socio-environmental challenges and to promote a resilient and sustainable future. In this study, an integrated air quality monitoring system was proposed within the municipality of Cluj-Napoca, by adopting a novel methodology for sensor-placement throughout the city, composing an efficient network of stations with high spatial and temporal resolution. The purpose of this network is to assist the population and local authorities by providing a platform that offers real-time mapped data related to local air quality and potential health threats. This approach was based upon a thorough geospatial data analysis of land-use in the area, where possible correlations between air quality and anthropic activities that generate emissions were studied. Using these factors, an analysis of possible placement locations was carried out, in order to determine the most representative network shape that would reflect the air quality variations throughout different locations of the study area and will prove to be cost-efficient by using a limited number of sensors. A series of maps with various networks morphologies, were generated and compared. An innovative method of using a “natural pattern”-based approach for the network morphology, proved to be the most efficient in fulfilling all the proposed requirements of the network.
Sulfur dioxide (SO2) trends over the urban regions of China during 2007–2020 using MERRA-2 and CAMSRA
Authors: Shaheen Abdallah , Yousefi Robabeh , Wang Fang , Ge Quansheng , Wu Renguang
Published: February 2023

Sulfur dioxide (SO2) plays a key role in the formation of atmospheric sulfate that can adversely affect urban environment, human health, air quality, and the Earth’s climate system. In this talk, we present results of SO2 trends over the 4 urban regions (YRD, BTH, PRD, and SCB) of China during 2007-2020, using the SO2 massconcentrations of the Modern-Era Retrospective Analysis for Research and Applications, version 2 (MERRA-2), and the SO2 total column of Copernicus Atmosphere Monitoring Service Reanalysis (CAMSRA). The SO2 concentration decreased significantly during 2007-2020 over the 4 regions: YRD, BTH, PRD, and SCB, with the rate of -0.04 µg.m-3 per year, -0.05 µg.m-3 per year, -0.01 µg.m-3per year, and -0.03 µg.m-3 per year, receptively. Using CAMSRA data, total column of SO2 also experienced significant decreasing trends during 2007-2020 over YRD, PRD, and SCB, with the rate of -0.15 mg.m-2 per year, -0.05 mg.m-2 per year, and -0.06 mg.m-2 per year, receptively. The decreasing SO2 levels after 2007 were mainly attributed to Chinese air pollution control policies. This work contributes to a better understanding of the impact of Chinese policies on the SO2 level over the urban regions of China.
Long-term changes in precipitable water vapour over India derived from satellite and reanalyses data for the past four decades (1980–2020)
Authors: Sarkar Subhadip
Published: February 2023

India has tropical monsoon climate with significant regional variability in rainfall and temperature, where precipitation is closely connected to precipitable water vapour (PWV). Here, the satellite and reanalysis data are applied to study the spatial and temporal changes of PWV over India in 1980–2020. We have also analysed its potential drivers such as precipitation, surface temperature and evapotranspiration during the same period. The distribution of annual PWV depicts the highest values over the east coast (40–50 mm) and lowest in western Himalaya (< 10 mm). The seasonal distribution shows highest PWV during monsoon (June-July-August-September, about 40–65 mm). Similarly, the monthly cycle of PWV shows the lowest amount in January, which gradually increases with time until it peaks in July, and then decreases thereafter. Interannual variations in PWV show a peak in 1997–1998, which is related to the strong El-Nino Southern Oscillation (ENSO) event during that period. Among the sources, sinks and drivers, evapotranspiration (0.6-–0.9), precipitation (0.7–0.9) and surface temperature (0.5–0.6) are highly correlated with PWV throughout India. The PWV trends in India are found significantly positive (0.6–0.9), which can be attributed to recent increase in surface temperature and thus the rise in atmospheric moisture.  This is concern for regional climate change as PWV is directly connected to water vapour and thus, to temperature and climate.
Effects of electron particle physics in global planetary models
Authors: Lapenta Giovanni , Schriver Dave , Baeke Hanne , Echterling Nicole , Walker Ray , El Alaoui Mostafa , Travnicek Pavel
Published: February 2023

We compare global models of Mercury done with the hybrid (particle ions and fluid electrons) and full kinetic (particles are used for both electrons and ions) models. We use the implicit particle in cell method based on the ECsim algorithm [1]. We study how energy exchanges in the magnetosphere of the planet are changed by representing the electrons as particles. We observe a more powerful energy exchange due to the presence of stronger features, larger more localised electron currents and sharper interfaces. The electron and also the ion energisation  is more intense leading to an overall increase of the energy transfer from the solar wind to the planetary magnetosphere. The electron distribution is far from Maxwellian, showing effects that cannot be captured by hybrid models such as the the presence of crescents, flat-top and multi beam distributions. Full particle models also provide more accurate description of reconnection. Using the electron agyrotropy, the new Lorentz indicator [2] and a new machine learning method [3], we investigate how reconnection is linked with current sheets, studying where and when reconnection happens and distinguishing electron from ion- scale reconnection.   [1] Lapenta, G., Schriver, D., Walker, R. J., Berchem, J., Echterling, N. F., El Alaoui, M., & Travnicek, P. (2022). Do We Need to Consider Electrons' Kinetic Effects to Properly Model a Planetary Magnetosphere? The Case of Mercury. Journal of Geophysical Research: Space Physics, 127(4), e2021JA030241.[2] Lapenta, G. (2021). Detecting reconnection sites using the Lorentz Transformations for electromagnetic fields. The Astrophysical Journal, 911(2), 147.[3] Lapenta, G., Goldman, M., Newman, D. L., & Eriksson, S. (2022). Formation and Reconnection of Electron Scale Current Layers in the Turbulent Outflows of a Primary Reconnection Site. The Astrophysical Journal, 940(2), 187.
Foreseeing the propensity of rivers to extreme floods
Authors: Basso Stefano , Merz Ralf , Tarasova Larisa , Miniussi Arianna
Published: February 2023

Notwithstanding hundreds of years of efforts, flooding is still the most common natural disaster. A reliable assessment of the impending flood hazard is indeed an outstanding challenge with severe consequences. Mistaken estimates of the odds and magnitude of extreme floods especially result in huge economic losses due to widespread destruction of infrastructure and properties.We show here that we can infer the propensity of rivers to generate extreme floods by means of two hydroclimatic and geomorphic descriptors of watersheds, which embody the spatial organization of the stream network and the characteristic streamflow dynamics of the river basin. These features are main determinants of a sharp increase of the magnitude of the rarer floods and of the flood value for which this marked growth of magnitude occurs, which we term flood divide as it separates ordinary from extreme floods. Their relevance is suggested by a novel ecohydrological approach to flood hazard assessment and confirmed by observations from hundreds of watersheds in the USA and Germany.We first ascertained the capability of the method to distinguish between basins which do not and exhibit a flood divide, and its ability to dependably estimate its magnitude. We then applied a dimensional reduction tool to pinpoint key physioclimatic controls of the occurrence of flood divides, verifying our results against data. Finally, we utilized descriptors of these controls (namely the hydrograph recession exponent and streamflow variability) within binary logistic regression to predict the possible occurrence of flood divides and extreme floods in river basins. Repeated analyses for independent realizations of subsets of data indicate good prediction accuracy.The identified controls of the propensity of rivers to generate extreme floods are readily estimated from primary hydroclimatic variables. The tool thus allows for inferring cases where extreme events shall be expected from short records of ordinary events, providing valuable information to raise awareness of the peril of floods in river basins.This study summarizes results of the DFG-funded project "Propensity of rivers to extreme floods: climate-landscape controls and early detection - PREDICTED" (Deutsche Forschungsgemeinschaft - German Research Foundation, Project Number 421396820).
Personalizing sustainable agriculture with causal machine learning
Authors: Sitokonstantinou Vasileios , Giannarakis Georgios , Lorilla Roxanne Suzette , Kontoes Charalampos
Published: February 2023

To fight climate change and accommodate the increasing population, global crop production has to be strengthened. To achieve the sustainable intensification of agriculture, transforming it from carbon emitter to carbon sink is a priority, and understanding the environmental impact of agricultural management practices is a fundamental prerequisite to that. At the same time, the global agricultural landscape is deeply heterogeneous, with differences in climate, soil, and land use inducing variations in how agricultural systems respond to farmer actions. The personalization of sustainable agriculture with the provision of locally adapted management advice is thus a necessary condition for the efficient uplift of green metrics, and an integral development in imminent policies. In this work, we formulate personalized sustainable agriculture as a Conditional Average Treatment Effect estimation task and use Causal Machine Learning for tackling it. Leveraging climate data, land use information and employing Double Machine Learning, we estimate the heterogeneous effect of sustainable practices on Soil Organic Carbon content at the field-level in Lithuania. We thus provide a data-driven perspective for targeting sustainable practices and effectively expanding the global carbon sink. This is a work towards gaining causal understanding from observations, with hypotheses and constraints, showing great potential for the Digital Twin Earth (DTE) paradigm.
Systemic risk exposure of global maritime transport, trade and supply-chains networks to climate-related port disruptions
Authors: Verschuur Jasper , Koks Elco , Hall Jim
Published: February 2023

With ports handling around 80% of global trade in volume, disruptions to ports can have widespread economic consequences. Apart from direct physical damages, climate shocks to ports can have systemic impacts, or knock-on effects, to other ports, trade flows and supply-chains given the interconnected networks ports are embedded in. Climate change, making climatic shocks more likely and severe, is expected to increase the systemic risk in the future. As such, present-day and future systemic risks as a result of climate-related port disruptions need to be quantified to identify cross-border vulnerabilities, as well are preparing ports, firms and countries for such port-related shocks, which are often not quantified using best practice tools for risk assessment.

Here, we combine quantified estimates of downtime risk (i.e. annual expected days per year) associated with both operational disruptions and climate extremes at 1320 ports with a dataset of (i) ship movements between ports, (ii) maritime transport freight flows, and (iii) dependencies between ports and global supply-chains. This allows us to quantify the systemic exposure of maritime transport, trade and supply-chains to port disruptions on a global-scale. We show how ports, countries and supply-chains are susceptible to both domestic and cross-border risks, as well as identifying future changes in systemic risk exposure for 2050 under three climate change scenarios.

This information allows countries and firms to identify the critical ports they depend on and make informed decisions to improve their resilience against port-related shocks, for instance by enhancing port resilience, trade or transport diversification, or increasing inventories and stocks.
Photosensitization is in the air and impacts the multiphase on oxidation capacity
Authors: George Christian
Published: February 2023

This presentation will be an invitation to think differently about the possible oxidation pathways occurring in secondary organic aerosols.Despite the importance of aerosols in atmospheric chemistry, climate and air pollution, our ability to assess their impact on atmospheric physics and chemistry is still limited due to insufficient understanding of many processes associated with the sources of particles, their chemical composition and morphology, and evolution of their composition and properties during their atmospheric lifetime. Indeed, atmospheric aerosols can be viewed as a complex conglomerate of thousands of chemical compounds forming a system that evolves in the atmosphere by chemical and dynamical processing including chemical interaction with oxidants and sunlight.A significant body of literature on photo-induced charge or energy transfer in organic molecules from other fields of science (biochemistry and water waste treatment) exists. Such organic molecules are aromatics, substituted carbonyls and/or nitrogen containing compounds – all ubiquitous in tropospheric aerosols. Multiphase processes have also been shown to produce light absorbing compounds in the particle phase. The formation of such light absorbing species could induce new photochemical processes within the aerosol particles and/or at the gas/particle interface. Therefore, while bulk phase aquatic photochemistry has recognized several of these processes that accelerate degradation of dissolved organic matter, only little is known about such processes in/on atmospheric particles.This presentation will discuss photosensitization in the troposphere as having a significant role in SOA formation and ageing as studied by means of laser transient absorption spectroscopy, flow tube and simulation chamber experiments, all coupled to advanced analytical techniques. We will provide kinetic and mechanistic information on how photosensitization may introduce new chemical pathways, so far unconsidered, which can impact both the chemical composition of the atmosphere and might thus contribute to close the current SOA underestimation.
A Quantile Generalised Additive Approach for Compound Climate Extremes: Pan-Atlantic Extremes as a Case Study
Authors: Olivetti Leonardo , Messori Gabriele , Jin Shaobo
Published: February 2023

We present an application of quantile generalised additive models (QGAMs) to study the rela-tionship between spatially compounding climate extremes - namely extremes that occur (near-)simultaneously in geographically remote regions. We take as example wintertime cold spellsin North America and co-occurring wet or windy surface weather extremes in Western Europe,which we collectively term Pan-Atlantic compound extremes. QGAMS are largely novel in cli-mate science applications and present three key advantages over conventional statistical modelsof weather extremes:1. they do not require a direct identification and parametrisation of the extremes themselves,since they model all quantiles of the distributions of interest;2. they do not require any a priori knowledge of the functional relationship between the predic-tors and the dependent variable;3. they make use of all information available, and not only of a small number of extreme values.Here, we use QGAMs to both characterise the co-occurrence statistics and investigate possibledynamical drivers of the Pan-Atlantic compound extremes. We find that recent cold spells inNorth America are a useful predictor of upcoming near-surface extremes in Western Europe,and that QGAMs can predict those extremes more accurately than conventional peak-over-threshold models.
Similar but different? Climate risk archetypes can help the municipal adaption process 
Authors: Riach Nils , Glaser Rüdiger
Published: February 2023

Municipalities are often challenged in adapting to climate change impacts, with knowledge gaps regarding climate risks being an important constraint hindering the development and implementation of adaption strategies. Although at the municipal level, risks are highly context specific, recurring patterns of hazard, exposure and vulnerability can often be identified. While indicator-driven climate risk assessments are widely used, they tend to apply a singular explanatory model instead of addressing the heterogeneity of cases and thus neglect similarities and differences of risk typologies. Such typologies include, for example, winter tourism-dependent municipalities in mountainous areas with declining snow reliability or agricultural communities that are increasingly affected by heat and drought.

Archetype analysis provides a theoretically grounded framework for handling typologies which investigates patterns of a phenomenon at an intermediate level of abstraction, thus allowing for context sensitive generalizations of results. We develop an archetype analysis approach in order to better capture the heterogeneity of local climate risk settings at the municipal scale in the German state of Baden-Württemberg. Using cluster analysis, we derive nine climate risk archetypes with characteristic building block configurations of hazard, exposure & vulnerability indicators. The proposed data-driven approach aims at identifying broad socio-economic structures that are material to the understanding of local climatic risk. It is not the intention of the approach to conclusively explain specific local risks or recommend concrete municipal adaptation measures. Rather, this approach aims to highlight that the structures subject to risk exhibit great heterogeneity, suggesting that although different adaptation needs exist, basic structural data can help explain similarities in climate risk settings. We argue that an approach using archetypes can effectively support the adaption process by improving the understanding of spatial similarities and differences of municipal risks, which can be helpful in identifying archetypical adaptation measures.
Very Long-Period Seismic Signals and Collapse Events at the Kilauea Summit Crater in 2018
Authors: Ye Lingling , Lay Thorne , Kanamori Hiroo , Brodsky Emily , Ohminato Takao , Ichihara Mie , Yamakawa Kazuya , Tsuruoka Hiroshi , Satake Kenji
Published: February 2023

The 2018 Kilauea eruption and East Rift Zone dike intrusion have been accompanied by more than 60 earthquakes with magnitude 4.7 – 5.3 near the summit. Corresponding long-period moment tensor solutions (constrained to have no isotropic component) have nearly vertical P-axes with moderate to large non-double-couple components (~20%-80%). The predominantly normal faulting is consistent with summit deflation as magma drains to the rift zone. Ground velocities recorded by local broadband seismic stations (<5 km) along the edge of the crater show distinct behavior between mid-May events and subsequent events. From May 16 to 26, twelve M5 events with large eruptive plumes below the SE edge of Halema'uma'u crater generated very long-period (VLP) seismic pulses with durations of about 20-40 s at all azimuths, suggesting distinct static outward displacements. This group can be represented by either isotropic or CLVD source. Almost all VLP pulses ended with sharp arrivals that are likely from small collapses. Similar VLP signals had been observed during the 2000 Miyake-jima eruption (Kikuchi et al., 2001; Kumagai et al., 2001). After May 28, M5 events were located below both north and south edges of the crater and generated broadband ground motions at the same stations; with large amplitude fracture-generated high-frequency signals (often clipped). Seismicity at the summit was low during the VLP events but increased to have 20-40 events per hour before later M5 events followed by several hours of reduced activity. After May 28, seismicity has been quasi-periodic, with intervals from ~2 to ~1 day. Comparison with solid earth tides and volume strain variations suggests that they are not direct driving factors, because the time interval between collapse events varied significantly. We consider a model with two stages, stage 1 (from May 16 to 26) with inflation transients that causes VLP events with large ash/gas explosion, and stage 2 (after May 28) dominated by collapse with extensive shallow fracturing and weaker gas explosions. The rapid expansion of the VLP events may be due to water flashing to steam, or partial collapse into the magma causing abrupt inflation. We investigate these VLP events comprehensively with local broadband seismic data, infrasound signals, GPS and tilt signals in this study, along with comparisons with published studies for the 2018 Kilauea sequences. 
Input features importance to hydrological indices simulated by Land Surface and Global Hydrological Models
Authors: Brêda João Paulo , Melsen Lieke , van der Ploeg Martine , Athanasiadis Ioannis , Siqueira Vinícius , Verhoef Anne , Zeng Yijian , van Dijk Albert
Published: February 2023

A solid understanding of the global water cycle and how land surface processes respond to both changes in climate and pressure due to water use is essential for society. Although Land Surface Models (LSM) and Global Hydrological Models (GHM) are able to simulate the spatiotemporal variability of the water balance relatively reliably, intercomparison studies have indicated considerable differences between the models. Each LSM and GHM present a unique set of equations, parameters and configurations that contribute to the spread of simulated hydrological responses to meteorological forcings. In order to improve our understanding of modeling uncertainties, we propose a variable importance assessment for 5 LSM/GHM (JULES, HTESSEL, PCR-GLOBWB, SURFEX and ORCHIDEE) from the EartH2Observe (E2O) project. The output of the models and the meteorological forcings were collected from the Water Resources Reanalysis Tier 2 of the E2O project, which consists of a global dataset with spatial resolution of 0.25ox0.25o. We used soil texture and land cover datasets that most resemble the inputs used by each LSM/GHM during the E2O project. The models’ outputs were used to estimate 6 hydrological indices for every land cell: Evaporation-Precipitation ratio; Runoff-Precipitation ratio; Surface Runoff-Total Runoff ratio; median Soil Moisture variation caused by a Rainfall event; median Surface Runoff caused by a Rainfall event; and Soil Moisture temporal autocorrelation. Then, we evaluate the input features (meteorological, land cover, and soil texture) importance to the hydrological indices of each model using machine learning. With the analysis we aim to  examine a) How much the models differ and why? b) To what extent are the output differences related to the input features or/and to the models formulation? and c) How significant is each feature to the respective hydrological index?
AI for urban climate: an EO-based approach for high-resolution mapping of human exposure to heatwaves
Authors: Oliveira Ana , Cunha Rita , Leal Vasco , Galamba Manuel , Buroni Giovanni , Eugénio Guilherme
Published: February 2023

As climate change prospects point towards the pressing need for local-scale adaptation measures, heat exposure becomes one of the key aspects in determining the health of the urban environment. In addition, many western metropolises are characterized by an ageing population which may lead to an increased community-level sensitivity to heat extremes – that is the case in many European Functional Urban Areas (FUAs), including in the Greater Lisbon (hereinafter Lisbon) area. Lisbon has already a track record of being regularly exposure to severe heatwaves (HW), and regional climate change prospects point to its aggravation in coming decades (frequency, duration, and severity), as with other Southern European cities. Accordingly, there is a pressing need to pinpoint the urban locations where people are relatively more exposed to the excess heat, which can lead to dehydration, cerebrovascular accidents or thrombogenesis.

 

In this study, air temperature measurements from citizen owned meteorological stations is retrieved from open data platforms, quality controlled and co-located with Earth Observation (EO) data and products to downscale the official air temperature forecasts (from deterministic numerical weather predictions, NWP) from the native regional scale (2.5km) up to a metric spatial resolution (200m). As the NWP model resolves the regional physical processes, the Machine Learning (ML) high-resolution output is able to adjust its bias to the specificities of the urban location, by accurately predicting the local contribution of the urban heat island (UHI) effect, quantifying the heat anomaly at the neighborhood scale. The cooling effect of the urban green infrastructure is also detected, providing mensurable scenarios to support future urban greening initiatives. In addition, with these results, the identification of short-term critical areas during heatwave events becomes possible, supporting the local public health stakeholders in their decision-making – i.e., regarding where and when to act.
Exploring the seasonal divergence among SPI, SPEI and SNEPI
Authors: Singh Gauranshi Raj , Chandrika Thulaseedharan Dhanya , Chakravorty Aniket
Published: February 2023

The realistic assessment of drought is subjected to substantial uncertainty in the presence of a multitude of drought indicators owing to their mutually exclusive methodologies, variant data sources employed, and changing variable behavior. Though temperature-driven divergence analyses among drought indicators are not unknown, in this study the authors attempt to unravel the quantum of disagreements the newly developed Standardized Net Precipitation Distribution Index (SNEPI) possesses with its contemporary counterparts. The inherent aim of the authors is to highlight that (1) climate change impacts propagating to drought dynamics are not solely driven by increasing global temperatures, but also by changing precipitation characteristics, (2) identify regions where the operational use of SNEPI is maximum, and (3) identify various physical processes that govern the evolution of index divergences through an attribution analysis. We found a persistent negative disagreement or an enhanced occurrence of dry extremes in the annual divergence of SPEI with SPI and SNEPI, which questions the reliability on the traditional drought indices. However, the seasonal dispersion of this annual divergence revealed a strong spatiotemporal signal of monsoon droughts by SNEPI, which the traditional SPEI neglected. Further, the attribution analysis revealed that the radiative fluxes governed the evolution of SPEI-SPI divergence. Consequently, the divergence between SPEI and SNEPI is driven by the characteristics of wet spells, with the relationship strengthening in the monsoon season and tropical climate zones. The authors suggest an expansion in the operational value of SNEPI in the tropical regions where these discrepancies/disagreements are profound. 
Remote-sensing applications for Au-epithermal deposits mapping and exploration targeting in Panagyurishte ore district, Bulgaria
Authors: Bogdanov Kamen , Velev Stefan , Krumov Ivan
Published: February 2023

Remote sensing UAV based technology combined with modern alteration mapping tools (SWIR, XRD Raman and XRF) for mineral detection has experienced great advances in the Cu-Au epithermal exploration targeting. We demonstrate quick and cost-efficient approach for epithermal gold exploration mapping and evaluation on examples of high-sulphidation epithermal Cu-Au deposits and prospects in the Panagyurishte ore district, Bulgaria.The Panagyurishte ore district is part of the global Tethyan-Eurasian Cu-Au belt that developed during the Mesozoic as a copper-rich, andesite-dominated magmatic arc system characterized by obvious affiliation of porphyry-copper (PCD) and epithermal Cu-Au ore deposits with granodiorite and andesite dominated magmatic complexes. The target mapping has lacked high-resolution data to identify and prove the geometry of the alteration mineral assemblages and ore controlling fault structures. When distal sensing is combined with field mapping and proximal modern mineral detection methods such as SWIR (1300-2500nm) and Raman spectroscopy, XRD and ore petrography is more efficient tool for detection of hydrothermally altered minerals and zones by their diagnostic spectral signatures.Drone based photogrammetry approach was applied for hydrothermal alterations mapping and targeting for Cu-Au epithermal deposits exploration in the Panagyurishte ore district, Bulgaria. Mineral alterations maps for Pesovets, Petelovo and Krassen Au-epithermal deposits was assembled using orthophoto model and TIR- 3D mapping to utilize the time and cost efficiency of the subsequent geological exploration field work. For classification and verification of drone orthophoto mosaic geological mapping and rock sampling was carried out in addition to XRF and XRD mapping and stream sediments sampling. UAV- based mapping with selected light bands was used to recognize different hydrothermal alterations styles such as advanced argillic (AAA), argillic (AA) propylitic (Prop) and phyllic (Phy) alteration styles that are overprinting andesitic volcanic sequences in the central part of Panagyurishte ore district. Radial and concentric fault structures and regional strike-slip fault zones have also been proved by UAV-based mapping. Domains of proximal hypogene AAA and AA and more distal propylitic halo as possible host of HS gold mineralization were clearly outlined. XRF mapping of the Pesovets lithocap indicate increasing of As (20-50ppm) and Ti (570-2400ppm) concentrations when approaching AA and AAA alteration domains and could also provide effective vectoring tool for targeting of epithermal Cu-Au mineralization. The recent study demonstrates UAV-based mineral mapping approach that will help to improve the exploration targeting and decision making in and eestimation of the Cu-Au mineral potential in cost-efficient manner.The study is supported by the Horizon 2020 co-funded GOLDENEYE project, which has received funds through the Grant Agreement 869398.
Impact of Climate Change on Non-stationary IDF Curves for Urban Areas
Authors: Kishan Rastogi Naman , Wadhwa Abhinav , P. Mujumdar Pradeep
Published: February 2023

High-intensity rainfall in a short duration has become the primary reason for the flooding of urban areas, and quantifying this may help to reduce the destruction caused by the floods. Continuous human interventions, change in land use land-cover and urbanization have significantly altered the climate patterns in many places of the world. Urban infrastructure, economic activity, and social well-being are greatly affected by the increase in rainfall intensity resulting in more runoff, drainage system overflow, and subsequent flooding disasters. Water infrastructure planners and designers have traditionally used Intensity-Duration-Frequency (IDF) curves as tools for urban flood assessment and management. However, IDF curves created based on the stationarity hypothesis are inaccurate and may underestimate the present or future results due to continuous changes in climatic conditions. This study investigates the non-stationary behavior of IDF curves due to climate change. It is assumed that the likelihood of quantile occurrence changes with time. An optimal solution is determined by comparing Generalized Extreme Value (GEV) parameters with a stationary GEV incorporating time, space, location, and shape as covariates. These covariates are associated with the most significant physical processes, such as urbanization, local temperature changes, and global warming, that make the time series non-stationary. In addition, for downscaling the climate change model data to station-level data, a modified K-Nearest Neighbour (KNN) approach is used, incorporating non-stationarity wherever appropriate. The method is applied to 100 Telemetric Rain Gauge (TRGs) stations that are spatially dispersed throughout the urban catchment of Bangalore city, India. According to the findings, the spatial plots for IDFs can capture the current patterns and translate them into predictions of future rainfall intensities. The return period can be shortened by more than one-tenth of its length in the estimations of future rainfall intensities. These analyses along with a comparison study with the existing and future IDFs will help raise awareness and provide potential warnings to the existing water infrastructure systems.
Modes of interaction and varying feedback between groundwater and climate depending on soil characteristics
Authors: Vogelbacher Anastasia , Madani Kaveh , Shokri Nima
Published: February 2023

Climate, climate variability, and climate change could influence groundwater. Shifts in precipitation patterns, recharge, or snowmelt are among the several climate-related variables with important impacts on groundwater. However, the climate-groundwater relationship is not one-way. Groundwater can also impact the climate itself via its influence on different processes and variables such as evaporation, soil moisture, and vegetation. Understanding the interactions and the feedback relationship between groundwater and climate is crucial for sustainable water resource management and resilient adaptation to climate change. Current understanding of how climate influences groundwater and the resulting feedback from groundwater and its impacts on climate is limited. This is of particular importance in the face of projected climatic changes. Here, we aim to develop a simple analytical framework to extend the projection capabilities required to characterize the climate-groundwater interactions depending on the soil characteristics serving as an intermediate domain between the groundwater and climate systems. Our proposed analytical framework can be used to identify potential regions with significant two-way (bidirectional) interactions between climate and groundwater using soil characteristics and soil water retention curves following the theoretical lines discussed in Shokri and Salvucci (2011) and Or and Lehmann (2019). Using this framework, we identify regions of expected hydraulic connections between groundwater and soil surface, depending on the competition between capillary forces and the limiting gravity and viscous forces, and the groundwater depth (GWD) in the city of Hamburg. We argue that in regions with bidirectional interactions, groundwater is potentially more vulnerable to climate change and variability. Moreover, our initial results suggest that regions with finer textured soils are more sensitive to changes in evaporation and air temperature in terms of hydraulic connections between groundwater and the soil surface, which can influence the groundwater-climate interactions. Our analysis provides the basis for further investigation of the feedback impacts of groundwater on several variables, such as soil moisture, ground cooling capacity, and vegetation patterns under different climate change scenarios. ReferencesOr, D., & Lehmann, P. (2019). Surface evaporative capacitance: How soil typeand rainfall characteristics affect global‐scale surface evaporation. Water Resources Research, 55, 519–539.https://doi.org/10.1029/2018WR024050.Shokri, N., Salvucci, G. (2011). Evaporation from porous media in the presence of a water table. Vadose Zone J., 10, 1309-1318.
Preservation of the water concentration in mantle xenoliths. The cases of Peylenc & Ray Pic volcanoes (FMC)
Authors: Ingrin Jannick , Thomaidis Konstantinos , Drouka Maria
Published: February 2023

The ability of xenoliths to preserve water lithospheric signatures remains an unsolved question for many years. We report water content in olivine and pyroxenes of peridotite xenoliths from Peylenc and Ray Pic volcanoes (French Massif central, FMC). On each site xenoliths were sampled from products of an explosive eruption (volcanic breccia and pyroclastic deposit) and an effusive eruption (frozen magma chamber and a lava flow).

In Peylenc, the xenoliths from the breccia have systematically more water than the xenoliths from the basalt quarry: ol 1-9, opx 60-95 and cpx 250-380 wt. ppm H2O versus ol <0.2, opx 20-55 and cpx 110-240 wt. ppm H2O.

In Ray Pic, water content in xenoliths from the lava flow is independent of its location in the lava: ol < 1, opx 190-270 and cpx 430-640 wt. ppm H2O. Results suggest that the cooling and solidification of the lava had no impact on water content.

The xenoliths from the pyroclastic deposit have systematically more water: ol 3-12, opx 330-460 and cpx 810-890 wt. ppm H2O. These values are either comparable with or lower than the values reported previously from the same locality1.

The study shows that xenoliths recovered from explosive eruptions have higher water content than the ones from effusive eruptions, but also that water content can be different from one explosive event to another.

Conclusion is that water content can rapidly be reset during magma degassing prior to eruption. Degassing controls water content of xenoliths.

Among the xenoliths studied, several have spectral signatures different from others. This different spectral signature has also been reported from other volcanoes2, 3. The coexisting of different spectral signatures, which have not been erased during degassing, are probably the only OH signatures fully preserved from depth.

1 Azevedo-Vannson S.,et al. Chemical Geology 575 120257 (2021)

2 Denis C.M.M. et al. Lithos 226 256-274 (2015)

3 Patkó L. et al. Chemical Geology 507 23-41 (2019)
Reconstructing a new terrestrial water storage deficit index to detect and quantify drought in the Yangtze River Basin
Authors: Wan Xuewen , Chao Nengfang , Hu Ying , Wang Jiangyuan , Liu Zheng , Zou Kaihui
Published: February 2023

With the intensification of global climate change, droughts have occurred frequently in the Yangtze River Basin (YRB), which has caused significant impacts on human production, life, and socio-economic development. To reduce the damage caused by drought in the YRB, the drought characteristics must be comprehensively detected and quantified. Here, the spatial and temporal variability of precipitation, runoff, soil moisture, terrestrial water storage, and groundwater in the YRB from the Gravity Recovery and Climate Experiment (GRACE), hydrological and in situ observations were comprehensively estimated by decomposing them into seasonal, subseasonal, trend, and interannual observations. The new weighted GRACE drought standardisation index (WGDSI) was reconstructed using the component contribution ratio and compared with the standardised soil moisture index (SSI), standardised precipitation index (SPI06), and standardisation runoff index (SRI). Additionally, the drought characteristics identified based on observations of the water storage deficit, severity, peak, duration, and recovery time were also quantified using the WGDSI over the YRB. The results indicated that changes in soil moisture, terrestrial water storage, and groundwater in the YRB increased from 2003 to 2019 and mainly based on seasonal and interannual signals. The correlation coefficients between the WGDSI and the SSI, SPI06, and SRI were 0.92, 0.62, and 0.79, respectively, which represented increases of 9%, 14%, and 21% compared to that with the unweighted GRACE drought standardisation index, respectively. The interannual variability of the hydrologic variables was more consistent with drought events in the YRB, which was beneficial for detecting drought. Two serious droughts occurred in the YRB from 2003 to 2019. In 2006, a continuous 7-month-long drought occurred, with a peak at -28.974 km3, severity of -174.767 km3∙month, average drought recovery rate of 0.83 km3/month, and recovery time of 30 months, while in 2011, a continuous 5-month-long drought occurred, with a peak at -18.384 km3, severity of -78.106 km3/month, average drought recovery rate of 0.40 km3/month and recovery time of 39 months. The above results indicate that the WGDSI can be used to monitor and quantify drought over the YRB. The index proposed in this study can be applied to generate new datasets and methods for detecting and quantifying global drought.
Two-Dimensional Modeling of Infiltration in Heterogeneous Soils: The Effects of Topsoil-Subsoil Interface and Wheel Track Compaction
Authors: Jeřábek Jakub , Zumr David
Published: February 2023

Accurate prediction of infiltration into the soil is essential for a variety of applications, including irrigation planning, flood prediction, and pollutants transport. In this study, we applied a two-dimensional model for simulating infiltration in heterogeneous soils, focusing on the effects of topsoil-subsoil interface morphology with the presence of wheel tracks. The model is based on the Richards equation and includes different soil hydraulic properties (SHP) for three soil materials: topsoil, subsoil and wheel track. To examine the effects of the topsoil-subsoil interface and wheel track compaction on infiltration, we conducted field experiments on a 16 m2 plot with simulated rain with constant precipitation intensity. We collected soil moisture and soil water pressure data at different depths and used these data to optimize the SHPs. The topography of the soil surface and the morphology of topsoil-subsoil interface were also recorded using photogrammetric methods. The results of the model simulations show that the topsoil-subsoil interface and wheel track compaction have significant effects on infiltration. The topsoil-subsoil interface acts as an infiltration barrier. The morphology of the interface causes a large heterogeneity in the water flow field and completely diminishes the effect of the slope on the water flow. The wheel track caused an infiltration excess overland flow while the topsoil outside the wheel track exhibited saturation excess overland flow. Subsoil in the wheel track remained unsaturated throughout the rainfall simulation period, affecting water redistribution after the rainfall ended. This study demonstrates that even the small-scale heterogeneity in the shallow part of the soil profile strongly influences the water flow field. The disturbed flow field can affect the distribution of water and nutrients in the root zone and potentially cause the activation of preferential pathways due to the spatial variability of saturation at the topsoil-subsoil interface. The work presented above was supported by an EU TuDi project no. 101000224 and by State Environmental Fund of the Czech Republic project no. 085320/2022.
Global biogeography of the glacier-fed stream microbiome
Authors: Ezzat Leila , Peter Hannes , Bourquin Massimo , Michoud Grégoire , Fodelianakis Stilianos , Kohler Tyler , Lamy Thomas , Busi Susheel , Daffonchio Daniele , Deluigi Nicola , De Staercke Vincent , Marasco Ramona , Pramateftaki Paraskevi , Schön Martina , Styllas Michail , Tolosano Matteo , Battin Tom
Published: February 2023

 Glacier-fed streams (GFSs) serve as headwaters to many of the world’s largest river networks. Although being characterized by extreme environmental conditions (i.e., low water temperatures, oligotrophy) GFSs host an underappreciated microbial biodiversity, especially within benthic biofilms which play pivotal roles in downstream biogeochemical cycles. Yet, we still lack a global overview of the GFS biofilm microbiome. In addition, little is known on how environmental conditions shape bacterial diversity, and how these relationships drive global distribution patterns. This is particularly important as mountain glaciers are currently vanishing at a rapid pace due to global warming. Here, we used 16S rRNA gene sequencing data from the Vanishing Glaciers project to conduct a first comprehensive analysis of the benthic microbiome from 148 GFSs across 11 mountain ranges. Our analyses revealed marked biogeographic patterns in the GFS microbiome, mainly driven by the replacement of phylogenetically closely related taxa. Strikingly, the GFS microbiome was characterized by pronounced level of endemism, with >58% of the Amplicon Sequence Variants (ASVs) being specific to one mountain range. Consistent with the marked dissimilarities across mountain ranges, we found a very small taxonomic core including only 200 ASVs, yet accounting for >25% of the total relative abundance of the ASVs. Finally, we found that spatial effects such as dispersal limitation, isolation and spatially autocorrelated environmental conditions overwhelmed the effect of the environment by itself on benthic biofilm beta diversity. Our findings shed light on the previously unresolved global diversity and biogeography of the GFS microbiome now at risk across the world’s major mountain ranges because of rapidly shrinking glaciers.
Combining land surface modelling and Earth observations: the key role of soil moisture data to improve estimates of agricultural water uses
Authors: Modanesi Sara , De Lannoy Gabriëlle J. M , Bechtold Michel , Brocca Luca , Dari Jacopo , Busschaert Louise , Natali Martina , Massari Christian
Published: February 2023

Soil moisture is an essential climate variable and the main driver for water exchanges between the land surface and the atmosphere. An accurate knowledge of the soil moisture conditions is also crucial to estimate the amount of water needed or used for agricultural purposes.As human demand for water is increasing along with extreme drought events, an optimal agricultural management is paramount to cope with a drier and warmer future, e.g. in Mediterranean regions. Thus, the knowledge of soil moisture is central for monitoring agricultural drought, optimizing agricultural water uses (i.e., irrigation) and improving the water cycle and land-atmosphere processes understanding. Nevertheless, the point-based nature and limited spatial coverage of in situ soil moisture observations in conjunction with the poor parameterization of human processes in earth system models (i.e., unmodelled or wrongly modelled irrigation), undermine the ability to accurately monitor and forecast drought events as well as the capacity to safely manage water resources.Remote sensing observations offer a unique opportunity to fill these gaps as they can directly observe the processes of the plant-soil continuum. Here we provide insights on the value of satellite-based soil moisture and soil moisture-related measurements (i.e, radar backscatter) for land surface models and for agricultural drought research. We will show the utility of both classical coarse-scale and new high resolution observations for a number of applications that span from irrigation estimation, crop yield analysis, improvement of water cycle processes to estimation of small scale soil moisture variability across agricultural and mountaineous European pilot sites.