bluuebunny/crossref_metadata_2025 huggingface dataset:
Brown carbon aerosols in the Asian outflow region: Seasonal variations and source-specific light absorption properties
Authors: Zhu Chunmao , Miyakawa Takuma , Taketani Fumikazu , Kunwar Bhagawati , Deshmukh Dhananjay , Kawamura Kimitaka , Kanaya Yugo
Published: March 2024

Light-absorbing organic aerosols (BrC) play a significant role in Earth's climate, but their sources and optical properties remain unclear. We investigated seasonal variations and source-specific contributions to BrC in the Asian outflow region, focusing on Fukue Island, a gateway from the continent to the North Pacific. We conducted parallel analyses of BrC light absorption and organic aerosol composition over a year. We found that BrC levels were markedly higher during winter-spring, coinciding with air masses carrying emissions from Asian sources. Water-soluble BrC absorption exhibited a strong correlation with a specific marker for fossil fuel combustion, suggesting its significant influence on BrC levels. Biomass burning emissions, as indicated by another marker, contributed to BrC levels, particularly in spring. The fraction of BrC soluble in methanol was significantly higher during summer, suggesting potential contributions from additional sources, such as local biogenic emissions, present in air masses from this period. This study sheds light on the seasonal dynamics and source-specific light absorption characteristics of BrC in the Asian outflow region. Our findings contribute to a better understanding of BrC sources and their impact on climate.
On the hydrostatic approximation in rotating stratified flow
Authors: Wirth Achim
Published: March 2024

Hydrostatic models were and still are the workhorses for realistic simulations of the ocean dynamics, especially for climate applications. The hydrostatic approximation is formally first order in $\gamma=H/L$, where $H$ is the vertical and $L$ the horizontal scale of the phenomenon considered. For stratified rotating flow the dynamics can be separated in balanced flow and wave motion. It is shown that for the linear balanced motion the hydrostatic approximation is exact and for wave motion it is second order, obtaining the leading prefactors. The validity of the hydrostatic approximation therefore also relies on the ratio of the amplitude of wave motion to balanced motion. This ratio adds considerably to the quality of the hydrostatic approximation for larger scale flows in the atmosphere and the ocean.  Imposing the divergenceless condition is a linear projection of the dynamical variables in the subspace of divergenceless vector fields, for both the Navier-Stokes and the hydrostatic formalism. Both projections are local in Fourier space.  Calculating the difference of the two projections, the expression of the error, scaling and prefactors, done by the hydrostatic approximation is obtained. Analyzing the eigen-space of the projector, it is shown that for rotating-buoyant vortical-flow the hydrostatic-approximation is of third order for buoyant forcing, second order for horizontal and first order for vertical dynamical forcing.  Using the Heisenberg-Gabor limit it is shown that for large scale ocean dynamics, the difference of the dynamics of the projection-evolution operator between the two formalisms is insignificant. It is shown that the hydrostatic approximation is appropriate for realistic ocean simulations with vertical viscosities larger than $\approx10^{-2}$m$^2$s$^{-1}$. A special emphasis is on unveiling the physical interpretation of the calculations.  
The geometry of the Main Himalayan Thrust in Central Nepal (85°E) derived from thermo-kinematic modeling of thermochronological data in the Gyirong region (southern China)
Authors: Hetzel Ralf , Hölzer Kyra , Wolff Reinhard , Xu Qiang , Dunkl István , Anczkiewicz Aneta , Li Zhenyu
Published: March 2024

The subsurface structure of the Himalaya is dominated by the seismically active Main Himalayan Thrust fault, which accommodates about half of the plate convergence between India and Asia. In Central Nepal, geological, geophysical, and geodetic studies indicate that the Main Himalayan Thrust has a flat-ramp-flat geometry, which causes cooling, erosion, and exhumation of rocks to be faster above the mid-crustal ramp than above the adjacent upper and lower flats (Brewer & Burbank 2006; Robert et al. 2011; Hubbard et al. 2016). However, in the northern High Himalaya the ramp geometry, in particular its width and depth are not well resolved. To place further constraints on the northward extent and depth of the ramp, we present low-temperature thermochronological data and U-Pb zircon ages from the Gyirong region, China. Our zircon U-Pb data show that the protolith of the High Himalayan orthogneisses is 478±4 Ma old, and was intruded by a large leucogranite at 19.5±0.7 Ma. The Pliocene-Quaternary cooling of the study area is constrained by apatite fission track ages and apatite and zircon (U-Th)/He ages between ~5 Ma and ~0.5 Ma, which show a marked trend of southward-younging ages (Wolff et al. 2022). Together with published cooling ages from Nepal farther south, the ages define a U-shaped pattern across the Main Himalayan Thrust, with ages increasing to the north and south from a minimum in the High Himalaya. A thermo-kinematic model, in which the geometry of the mid-crustal ramp was varied, explains the age data and suggests that the ramp has a dip of ~22°N. Compared to previous models, our new ages require that the ramp has a greater width (~55 km) and reaches a greater depth (~34 km) below the northern High Himalaya. Still farther north, the fault is presumably developed as a gently-dipping ductile shear zone, which forms the prominent reflector visible in seismic reflection data.ReferencesBrewer, I.D., Burbank, D.W. (2006). J. Geophys. Res. 111, B09409.Hubbard, J., Almeida, R., Foster, A., et al. (2016). Geology 44, 639-642.Robert, X., Van der Beek, P., Braun, J., et al. (2011). J. Geophys. Res. 116, B05202.Wolff, R., Hölzer, K., Hetzel, R., et al. (2022). Tectonophysics 834, 229378.
A comparative analysis of source apportionment of PM2.5 in Baengnyeong-do and Seoul metropolitan area using receptor model
Authors: Han Jinseok , Song Huejun , Kim Kyoungchan , Lee Chunsang , Choi Dayeong , Joo Hungsoo , Ahn Junyong , Seo Seokjun
Published: March 2024

Because PM2.5 causes high-risk diseases, various research and policies are being implemented in these days. Identification of emission sources is the fundamental research and it should be continued. In this study, we attempted to compare and analyze the emission source contribution and PM2.5 characteristics for Baengnyeong-do (background area) and Seoul (the capital area) in Korea. PMF receptor model and back-trajectory analysis were used using the PM2.5 and its components data collected by the Atmospheric Environment Research Institute during 2020 to 2021. In the results of PMF model, 9 pollution sources (secondary sulfate, secondary nitrate, vehicles, biomass burning, dust, industry, sea salt, coal combustion, oil combustion) were estimated in both Baengnyeong-do and Seoul. Secondary aerosol, vehicles and biomass burning showed the high contributions to PM2.5 pollutions at both receptor sites. In the backtrajectory analysis, we found the air mass for long-range transport from China to Seoul via Baengnyeong-do. As a result of PM2.5 characteristics, nitrate concentrations in Seoul were higher than those in Baengnyeong-do. Therefore, mitigation of nitrate pollution and the control of NOx emission sources including should be necessary in the metropolitan.Acknowledgments:This research was supported by Particulate Matter Management Specialized Graduate Program through the Korea Environmental Industry & Technology Institute (KEITI) funded by the Ministry of Environment (MOE)
Quantifying permafrost organic carbon remineralization after redeposition on the ocean floor, using  δ13C and F14C.
Authors: Ruben Manuel , Hefter Jens , Gentz Torben , Schubotz Florence , Wei Bingbing , Liu Bo , Fritz Michael , Irrgang Anna Maria , von Jackowski Anabel , Geibert Walter , Mollenhauer Gesine
Published: March 2024

Arctic permafrost is a critical global tipping element in a warming climate. Annually, the erosion of coastal permafrost discharges an estimated 5 to 14 Tg of organic carbon (OC) into the Arctic Ocean. Although this previously stored OC has the potential to be reintroduced into the atmosphere, thus accelerating human-induced climate change, little is known about the benthic remineralization processes of permafrost OC after erosion and redeposition on the ocean floor. Our research quantified fluxes of dissolved inorganic carbon (DIC) and analyzed its isotopic composition of nearshore sediments in the Canadian Beaufort Sea, specifically off Herschel Island. Our findings showed a DIC release of 0.217 mmo/m²/d, with an average signature of δ13C = -22.44 ± 72 ‰ and F14C = 0.548 ± 0.007. Utilizing a model that combines two carbon isotopes, we estimate that approximately 38 ± 10% of the released DIC is a result of subsurface degradation of redeposited permafrost OC, with an additional 15 ± 12% originating from redeposited active layer OC. Additionally, isotopic endmember analysis was utilized on bacterial membrane lipids from live sedimentary bacteria to determine the relative utilization of OC sources in bacterial communities within shallow subsurface sediment (
Modelling Agrivoltaics in a climate perspective for water-energy-food nexus analysis
Authors: Rapella Lia , Drobinski Philippe , Faranda Davide
Published: March 2024

Renewable energies (REs) are increasingly important in addressing the challenge of climate change. Their development and widespread use can significantly reduce greenhouse gas emissions from fossil fuels and help mitigate the effects of climate change. To achieve a "net-zero" carbon economy, the transition to a RE system must occur alongside a profound transformation of the agri-food sector. Agrivoltaics (AVs) offers an opportunity to achieve both of these goals simultaneously. AVs provides clean energy and it is an important tool for realizing a sustainable and circular food economy in rural and farming communities. Additionally, by placing photovoltaic (PV) panels over crop fields, AVs can avoid the competition between solar energy and agriculture for land-use. This can also help to mitigate the impact of climate change on crop productivity, which is expected to be negatively affected by a warmer and drier future climate.In our study, we developed a large-scale sub-grid AVs model to explore the inter-links between climate, the AVs system, and crops. This model enables a comprehensive evaluation of the effectiveness and efficiency of an AVs configuration within the context of the climate-water-energy-food nexus. Our approach involves coupling a PV model with the soil-vegetation-atmosphere-transfer model ORCHIDEE (Organising Carbon and Hydrology In Dynamic Ecosystems) to construct the AVs module. The PV layer simulates the effects of PV panels, altering solar radiation and wind speed taken from atmospheric forcings. Subsequently, these altered variables, along with other key atmospheric variables like air temperature and precipitation required by ORCHIDEE, are used as inputs to the hydro-vegetation layer. Leveraging ORCHIDEE capability to quantify terrestrial water and energy balances at the land surface, this integration allows for a comprehensive simulation of crop ecosystem behavior within an AVs system. Net Primary Production (NPP), Water Use Efficiency (WUE), and PV power potential (PVpot) are finally computed as ultimate outputs of our model, representing key indicators for the water-energy-food nexus. Focusing on the Iberian Peninsula and the Netherlands, we apply our model to assess three AVs configurations (fix-tilted array, sun tracking, sun antitracking) across three specific years (2015, 2018, 2020) for two types of crops. Specifically, we compare the performance of different configurations among themselves and against the situation without AVs systems to analyze different behaviors depending on climate conditions, crop type, and location and to explore the potential benefits of the AVs systems.
Development of a high-resolution real-time air quality forecasting system over northern Thailand.
Authors: Chotamonsak Chakrit
Published: March 2024

Biomass burning events occur consistently during the dry season in northern Thailand and neighboring Southeast Asian countries on an annual basis. The primary sources of these emissions are the burning of agricultural residues and forest fires, which contribute significantly to the presence of air pollutants in the region. Throughout the dry season, which spans from February to April, satellite data from MODIS detects fire hotspots throughout the Indochinese peninsula. These pollutants resulting from biomass burning not only degrade local air quality but also have adverse effects on public health. Numerous sectors and institutions have been actively addressing this recurring issue for several years. Nevertheless, the problem persists across the region. A major obstacle in tackling this problem is the absence of a comprehensive measurement tool, which is prohibitively expensive. Moreover, most of the available measurement instruments are concentrated in urban areas, leaving rural regions, near the emission sources, lacking in observational tools. Hence, in this study, we employ the Weather Research and Forecasting model coupled with Chemistry (WRF-Chem) as a regional air quality model to predict high-resolution real-time air quality and related meteorological conditions in northern Thailand. To achieve this, we calculate biomass burning areas using NASA MODIS Near Real-Time (NRT) active fire hotspot data and estimate pollutant emission rates using the 3BEM model. These data are then used to establish chemical profiles and initial boundary conditions for the WRF-Chem air quality model. Ultimately, this experimental air quality forecasting model is anticipated to serve as an early warning system and play a pivotal role in research and practical applications aimed at addressing air quality impacts and adaptation strategies in northern Thailand.
Machine Learning Forecasting of Extreme Winds: A Study on the Mediterranean West Coast in the Valencian Community
Authors: Guerrero-Navarro Gustavo Hazel , Martínez-Amaya Javier , Nieves Veronica
Published: March 2024

The occurrence of extreme wind events poses a significant threat to human populations, putting lives at risk and causing substantial damage to vital infrastructures. Coastal regions, in particular, face heightened vulnerability due to the unpredictable nature of the land-sea interface, presenting a formidable   challenge for accurate modeling. Thus, there is an urgent need for robust and efficient predictive techniques to anticipate and manage the impact of these severe wind phenomena. In response to this imperative, our study explores the application of an innovative machine learning forecasting methodology tailored for extreme winds, specifically focusing on the Mediterranean west coast in the Valencian community. Our approach involves analysis of historical meteorological station data from the Spanish meteorological agency. This data, combined with an extensive set of reanalysis data spanning from 1961 to 2019, is utilized for the identification and classification of extreme wind events. Employing a train-test procedure, we implemented a Random Forest binary classification model, enabling successful forecasting of extreme wind episodes up to 48h in advance. Notably, the precision of our model exhibited a remarkable range between 73% and 92%, varying with the lead-time across the considered regions. This methodology not only enhances forecasting capabilities but also provides insights into the intricate dependencies of meteorological variables, thereby advancing our understanding of complex atmospheric processes. This pioneering study, driven by artificial intelligence, contributes to the ongoing exploration of the complex dynamics of winds in coastal regions. The insights gained from our research extend beyond the Mediterranean west coast and have the potential for broader applicability  in other coastal areas. The results underscore the pivotal role of adaptive strategies in mitigating the impact of extreme weather events, emphasizing the importance of proactive measures in the face of escalating climate-related challenges.
Enhanced cooling efficiency of urban trees on hotter summer days in 70 cities of China
Authors: Yang Limei , Ge Jun , Cao Yipeng , Liu Yu , Luo Xing , Wang Shiyao , Guo Weidong
Published: March 2024

Increasing the urban tree cover percent (TCP) is widely recognized as an efficient way to mitigate urban heat. However, in the context of global warming, the response of urban trees’ cooling efficiency to ambient temperature remains largely unknown due to the complicated influences of ambient temperature on the physiological state of urban trees. In this study, we quantify the response of urban trees’ cooling efficiency to ambient temperature in 17 summers from 2003–2019 in 70 economically developed cities of China. The results show that the cooling efficiency of urban trees is enhanced with increasing ambient temperature, with values ranging from 0.002 to 0.055  per 1 ℃ increase in ambient temperature across the selected cities. This suggests additional cooling benefits provided by urban trees on hotter days, especially in cities with lower TCP levels. In addition, under the same TCP level, the additional cooling benefits are larger in warmer and wetter cities, as these cities have a sufficient water supply for urban tree transpiration. Finally, this study further confirmed that the enhanced cooling efficiency of urban trees on hotter days can additionally mitigate 3.64% of population exposure to urban heat stress. These results are expected to provide guidance for urban planners to alleviate urban heat risk by utilizing urban trees in a warming world.
Application of Forward and Backward Atmospheric Dispersion Models using Measurements
Authors: Suh Kyung-Suk , Kim Sora , Park Kihyun , Min Byung-Il , Choi Yoomi , Kim Jiyoon , Kim Min-Chae , Kim Hyeonjeong , Kim Kyeong-Ok
Published: March 2024

A Lagrangian atmospheric dispersion model has been developed to predict the behavior of pollutants released into the air from the unexpected accident of the industrial or nuclear power plants. The random walk method in Lagrangian model is adopted in the dispersion model for the estimation of the atmospheric concentration distribution of the released pollutants. The basic advantages of that method are the simplicity, flexibility and the ability to produce relatively accurate results. In three-dimensional space, a particle transport due to the advection and the turbulent diffusion. And the movement of the particle is represented by the sum of the movements due to the advection and the turbulence. In the model, the atmospheric dispersion is evaluated by the motion of fictitious particles consisting of a deterministic part due to the mean wind and a stochastic part related to the turbulent flow. Forward and backward atmospheric models based on Lagrangian approach were applied to estimate unknown source regions and release rates of pollutants released into the air from unexpected accidents. Simulated results were compared with the measurements of a field tracer experiment performed at the Yeonggwang nuclear power plant in Korea in May 1996. The release point was first determined by using the backward dispersion model, and a unit release approach was used to estimate the release rates of the tracer at the release point. Calculated forward dispersion patterns are well presented the transport patterns by westerly wind. The time-varying concentrations were also simulated at the sampling points. Although, measurements and simulations for time-varying concentrations generally agreed, some discrepancy appeared due to the insufficient measurements of wind data during the experiment.
Robust land surface temperature record for north China over the past 21,000 years
Authors: Guo Jingjing , Ziegler Martin , Wanders Niko , Vreeken Mike , Yin Qiuzhen , Lu Hao , Fuchs Louise , Dong Jibao , Sun Youbin , Peterse Francien
Published: March 2024

Numerous proxy reconstructions have provided general insight into late Quaternary East Asian Monsoon variability. However, challenges persist in precisely assessing absolute temperature impacts on proxy variations. Here we employ two independent paleothermometers, based on bacterial membrane lipids and clumped isotopes of snail shells, in the same section of the western Chinese Loess Plateau to establish a robust land surface temperature record spanning the past ca. 21,000 years. Our independent temperature records consistently reveal similar land surface temperatures between the Last Glacial Maximum and late Holocene, and a gradual cooling Holocene which contrasts with the climate model predictions. We propose that changes in soil moisture availability over the deglaciation modulates the land surface temperature recorded by the proxies. A land surface energy partitioning model confirms this mechanism, suggesting that effects of soil moisture availability should be properly considered when comparing proxy records with climate model outputs.
Contrasting carbon storage with timber production in managed and unmanaged Oak forests in Austria based on simulations and observations
Authors: Neumann Mathias , Pichler Jeremia , Lexer Manfred J.
Published: March 2024

Oaks (Quercus sp.) are common tree species growing under subtropical to (hemi-)boreal climates and are expected to become more widespread due to climate change and related adaptation management decisions. Due to their high wood density, valuable timber, resistance towards drought and long-life expectations, oaks can be promising candidates for future tree species for maintaining in-situ carbon storage in European forests as well as provisioning long-lived wood products. Forest structure is important for forest growth and forest value and is the legacy of historic forest management (or its absence), site conditions and tree species presence. Mechanistic modelling tools, such as the hybrid patch model PICUS, allow exploring possible trajectories of forest development and quantifying the effects of climate, stand density and management.Here we report on a case study in the project “OptFor-EU” in Oak-Hornbeam forests in Austria, that are already subject to water-limitations. We tested state-of-the-art stand establishment methods against naturally regenerated stands and found that large-dimension timber can be produced within less than 100 years, with appropriate intensive management routines. We demonstrate, by comparing simulations with observations, that intensified management will likely lead to reduced carbon storage and increased carbon uptake, indicating trade-offs between timber production and carbon storage. We expect that adaptive forest management alternatives can help balance forest ecosystem services and support knowledge-based decision support.ReferencesF. Irauschek, W. Rammer, M.J. Lexer, Evaluating multifunctionality and adaptive capacity of mountain forest management alternatives under climate change in the Eastern Alps, Eur. J. For. Res. 136 (2017) 1051–1069. https://doi.org/10.1007/s10342-017-1051-6.M. Neumann, H. Hasenauer, Thinning Response and Potential Basal Area — A Case Study in a Mixed Sub‐Humid Low‐Elevation Oak‐Hornbeam Forest, Forests. 12 (2021). https://doi.org/10.3390/f12101354.
A framework for observing and modelling ice-ocean interactions building on a community workshop organised by the Joint Commission on Ice-Ocean Interactions
Authors: Nias Isabel , McCormack Felicity , Cook Sue , Adusumilli Susheel , An Lu , Goldberg Daniel , Hattermann Tore , Nakayama Yoshihiro , Seroussi Hélène , Slater Donald
Published: March 2024

Mass loss from the Antarctic and Greenland Ice Sheets could lead to a rise in global mean sea level of 0.25 m by 2100 and several metres by 2300 if greenhouse gas emissions remain unmitigated. Uncertainties in these estimates are strongly related to ocean-driven ice melt, which can lead to grounding line retreat, thinning and acceleration of the fast-flowing regions of both Antarctica and Greenland. The processes of ocean-driven ice melt on large spatial and temporal scales are imperfectly known, and measurements are sparse, impacting the accuracy of ice sheet and ocean model projection studies. The Joint Commission on Ice-Ocean Interactions (JCIOI) hosted the first community workshop in October 2022 with the aims to: (1) identify critical knowledge gaps surrounding processes that govern ocean-driven melt of ice sheets across a range of spatio-temporal scales; and (2) identify options to address the knowledge gaps through observing, parameterising, and modelling ice-ocean interactions, and their impacts on ice mass loss and ocean dynamics. Community discussions from the workshop highlighted the need for concurrent and sustained measurements of ice, ocean and atmosphere properties at the ice sheet-ocean interface, and making best use of existing observations to improve models, capture observed changes, better understand physical mechanisms and improve future projections. Building on the workshop outputs, we propose to develop a framework for ice-ocean observations that details the essential measurements that need to be collected, and the temporal and spatial scales on which to measure. This framework will require widespread community engagement on key scientific questions, agreement and coordination, including protocols for data collection, processing, and sharing.
From sensors to decisions: Data flows to enhance irrigation efficiency for smallholder orchards
Authors: Thomas Felix , Pérez Pérez Juan Gabriel , Bonet Pérez de León Luis , Martínez-Gimeno Amparo , Vanella Daniela , Consoli Simona , Ramírez Cuesta Juan Miguel , Elomari Hicham , Farag Abousrie , Werban Ulrike
Published: March 2024

In the Mediterranean area, agriculture is subject to numerous demands caused by the interplay of climate change, population growth, changing food production patterns and the increasing need for nature conservation measures, enforcing an efficient usage of resources and the creation of resilient production systems. To ensure a more sustainable water use, water policies have been adopted in the European Union as well as in Northern Africa countries, such as Morocco and Egypt as irrigation is the largest water user in the Mediterranean region. Small farmers make up to two thirds of the agricultural areas and are therefore an important part of areas agricultural community. Estimates see up 35% possible water savings could be achieved by more efficient irrigation systems. New technologies and practices are currently adopted mostly by large farms. The challenge is therefore to increase the usage of efficient irrigation techniques by small farmers. We present a concept of data handling in a data chain, from the collection in the field towards calculated irrigation recommendations that are provided via mobile application. The idea behind it is to provide an irrigation management tool that aims to overcome barriers in adapting new technologies for smallholders. It is designed to provide irrigation recommendations for orange and olive orchards based on a bottom-up approach. The derived irrigation recommendations are dependent on the available input data based on sensor systems: the FAO-56 approach based on climate data, or a soil water balance model relying on soil moisture data. As the calculation of irrigation recommendations is based on the collected climate and soil moisture data, we are focusing on the possibilities of automated data quality control and the methods and obstacles of the data handling when providing the recommendations. The final product is derived in form of an application for mobile devices that is intuitive and easy to use. The data handling is hereby done using the python programming language and RESTful application programming interfaces, and the transfers are executed periodically using dockerized applications. The main advantage of the proposed workflow is the possibility to integrate data from a variety of sensors and platforms and the access for smallholders can be done via mobile phones. This way, the currently measured data on the agricultural fields and up-to-date irrigation needs are easily accessible. The system is currently under validation. We present the whole framework, starting at measured values by sensors and ending in the irrigation recommendation for the farmers available in the App.
How changing environmental conditions affect soil water isotopic composition in pre-Alpine grassland soils
Authors: Orlowski Natalie , Vollmer Tanja , Schneider Katrin
Published: March 2024

Alpine and pre-Alpine grasslands offer crucial ecosystem services like fodder production, biodiversity support, carbon sequestration, and water retention. However, changing environmental conditions like rising temperatures threaten these grassland soils, potentially disrupting their functionality. Understanding how climate change and farming practices impact soil functions and eco-hydrological processes is vital for developing effective strategies to sustainably manage these grasslands.For this study, we conducted soil water isotope and soil water balance measurements from 2018-2019 in the grassland lysimeters of the TERENO Pre-Alpine observatory along an elevation gradient. Several lysimeters were translocated from the higher-elevation site Graswang (860 m a.s.l., control site) to the lower lying site at Fendt (600 m a.s.l., climate-change site). This gradient represents a 2°C temperature rise along with a 400 mm precipitation decrease at the climate-change site. Our study aimed to explore how elevated temperature and reduced precipitation affect soil hydrological and soil water isotopic composition seasonally, annually and with regard to soil depth.We did not find significant differences in the isotopic composition at 0.1m soil depth among the different lysimeter groups. Differences in soil water isotopic composition between the lysimeter groups become more pronounced at deeper soil layers, which are typically less affected by daily temperature fluctuations.Despite higher temperatures at the climate-change site, soil water isotopes closely followed the Local Meteoric Water Line, indicating minimal evaporation. However, the line-conditioned excess (lc-excess) significantly differed between the control and the climate-change site across depths. In contrast, no differences were found between the Fendt and Graswang climate-change site’s isotopic values at any depth. This suggests a stronger influence of actual evapotranspiration at the climate-change site visible in the lc-excess values. Overall, this research enhances our understanding of climate change's impact on water cycling through pre-Alpine grassland soils at varying altitudes. This insight could help to manage these grasslands sustainably in the face of climate change.
Forest Canopy Transpiration: A Key Moderator of Hydroclimate Variability and Extreme Rainfall in the Maritime Continent
Authors: Lo Min-Hui , Lee Ting-Hui , Hsu Jason , Chiang Chun-Lien , Kuo Yan-Ning
Published: March 2024

This study investigates the interannual variability of evapotranspiration (ET) in the Maritime Continent (MC), focusing on the dynamics behind its minimal fluctuations despite significant changes in precipitation due to the El Niño-Southern Oscillation. We analyze ET components - canopy evaporation (CE), canopy transpiration (CT), and soil evaporation (SE) - and uncover a self-compensating mechanism between CE and CT. During El Niño, increased CT offset decreased CE and SE, maintaining ET's stability. Conversely, La Niña shows an inverse pattern. Additionally, the research examines the impacts of deforestation on extreme precipitation in MC. Deforestation disrupts the ET balance by removing CT's stabilizing effect, amplifying ET variability, and altering precipitation patterns. Our findings propose a new precipitation paradigm in MC under deforestation: "rich-get-richer, poor-get-poorer, and the middle-class-also-get-poorer," marked by increased variability in extreme precipitation events. The study highlights the critical role of MC's forest canopy transpiration in moderating ET variability and its significant influence on the hydroclimatological cycle, especially under deforestation. This intricate interplay between deforestation, ET, and precipitation emphasizes the need to consider both local land use and broader climatic changes in understanding and managing the region's water cycle and extreme climate events.
The shallow subsurface characterization in relation to geothermal resources: Harnessing Passive Seismic Signals for Subsurface Imaging
Authors: Rezaeifar Meysam , Bean Christopher J. , Kiyan Duygu ,   DIG-team
Published: March 2024

This research illustrates how seismic interferometry using body and surface waves can be applied to various passive noise sources such as trains and borehole drilling to image and characterize the shallow subsurface. The work has a particular focus on the characterisation of  geothermal resources.
The initial scenario showcases seismic interferometry applications on a dense profile of 180 seismic sensors deployed along a railway for a 20-day period within a relatively simple geological setting. The recorded train-induced vibrations are used to image shallow subsurface structures (up to approximately 2 km deep). The outcome clearly reveals different interfaces and reflective surfaces that align with geotechnical data obtained from nearby boreholes.
In the subsequent case, a configuration of 100 nodes was deployed along the railway for a two-week duration, situated in a geologically complex area characterized by fault structures believed to influence the flow of hot spring fluids. Here, seismic interferometry involving body and surface waves was conducted on the vibrations generated by passing trains to image shallow subsurface structures. The findings exhibit a strong correlation between variations in S-wave velocity, imaged interfaces, and lateral changes in near-surface lithology.
The final case presents the outcomes of seismic interferometry performed on a dense network of 60 nodes deployed for a short period (e.g. three hours) near a geothermal drilling site in the Dublin Basin, Ireland. A comparison between the interferometry results and seismic sonic log data in the borehole, clearly highlights the potential of this technique, known as Seismic While Drilling (SWD), both for imaging deeper subsurface (beyond the depth of the borehole) structures and for monitoring the drilling site.
Groundwater storage change in Victoria, Australia observed by GRACE and ESA CCI soil moisture products
Authors: Park Taejun , Seo Ki-Weon , Ryu Dongryeol
Published: March 2024

Accelerating groundwater depletion, driven by climate change and growing groundwater extraction for irrigation, has increased the need for accurate monitoring of this indispensable resource. Traditional methods, such as in-situ water table observations and pumping tests, have proven valuable for continued monitoring of groundwater availability and aquifer characteristics but are limited in assessing groundwater variations at a larger basin scale. In contrast, the Gravity Recovery and Climate Experiment (GRACE) offers a method to estimate basin-scale groundwater changes, although its estimates encompass not only groundwater in the aquifer but also surface water (e.g., lakes, rivers) and soil moisture in the vadose zone. To delineate groundwater variations accurately from GRACE observations, additional data sources are necessary.In this study, we use the European Space Agency’s Climate Change Initiative for Soil Moisture (ESA CCI SM) in the surface layer (top 0-2cm), which is extrapolated to the profile moisture content for the entire root zone (0-120cm). Utilizing the estimated profile soil moisture, we derive groundwater variations in the southern Victoria region of Australia by subtracting the ESA CCI SM derived soil moisture component from GRACE observations. The estimated groundwater variations agree well with the groundwater mass changes estimated from in-situ observations. This study presents an approach that integrates GRACE observations with profile soil moisture estimates derived from the ESA CCI SM product to assess groundwater variations. The validation against in-situ data indicates that satellite observations of soil moisture and gravity changes can provide robust estimation of basin-scale variations in both profile soil moisture and groundwater.
Including Dynamics in a Network Based Stochastic Multihazard Model
Authors: Bebbington Mark , Dunant Alexandre , Harte David , Whitehead Melody , Mead Stuart
Published: March 2024

We outline a conceptual approach to forecast multihazard risk from a cascade of natural hazards events. Network models have been proposed for cascades of natural hazard events, for example storm, flooded river, breached stop banks, damaged infrastructure. These have generally not taken time into account, with the cascade of events effectively assumed to occur instantaneously. We extend the methodology to account for multiple temporal processes, often occurring on quite different time scales, and hence incoporating variable delays. Further, since state of the art physical models generally involve heavy computation, we advocate the use of computationally simple probability distributions to describe the dynamics and interaction of the hazard events in our proposed network model. All model components have estimable parameters, which permits application to speciﬁc situations. This enables a larger number of simulations of the model, ensuring greater accuracy of probabilistic model forecasts. The modelling approach takes into account the dynamic and evolving nature of the temporal processes. Thus, it may be possible to identify key elements of the system that are most vulnerable, develop strategies for mitigating risks, and examine restoration strategies.
Deep Learning Modeling of Human Activity Affected Wildfire Risk by Incorporating Structural Features: A Case Study in Eastern China
Authors: Fan Gaofeng , He Zhonghua
Published: March 2024

Wildfire risk prediction is a critical component of disaster prevention and mitigation, often closely associated with local human activities in most regions. Recent studies demonstrate that employing joint modeling techniques using diverse datasets alongside Convolutional Neural Networks-Long Short-Term Memory Networks (CNN-LSTM) produces favorable predictive results. This approach effectively tackles certain drawbacks of fire weather indices (FWI), notably the insufficient consideration of surface coverage and coarse resolution. However, previous research inadequately explored variations in the impact of influencing factors across different categories and spatial orientations, neglecting the internal structural features within the samples. This study focuses on the six eastern provinces of China, utilizing a multi-source dataset comprising satellite-monitored wildfire products from 2012 to 2022, along with terrestrial ecology, terrain, and simulated meteorological elements. By introducing channel and spatial attention mechanisms, high-resolution imagery, and visual transformer model, this research optimizes the CNN-LSTM wildfire prediction model. Results indicate a noteworthy enhancement, elevating accuracy, Kappa coefficient, and AUC of ROC curves from 91.15%, 80.87%, and 97.01% to 93.30%, 85.63%, and 98.15%, respectively. This refined model not only refines high-risk prevention areas highlighted by FWI but also enhances understanding of mountain trails in hilly terrains. Consequently, it reduces false alarms in regions such as non-harvesting agricultural fields, reinforcing predictive risk assessment concerning potential human activities within forested areas. Sensitivity analysis reveals that while the impact of internal sample structural features on wildfire risk prediction is lower than meteorological elements, it surpasses the influence of terrain and terrestrial ecology elements. Thus, this study has developed a methodology integrating multiple attention mechanisms and sample structural features, furnishing high-precision daily kilometer-level wildfire risk prediction products. This approach holds substantial promise for the precise prevention and control of regional wildfires.
Uncertainties in deforestation emission baseline methodologies and implications for carbon markets
Authors: Teo Hoong Chen , Tan Nicole Hui Li , Zheng Qiming , Lim Annabel Jia Yi , Sreekar Rachakonda , Chen Xiao , Zhou Yuchuan , Sarira Tasya Vadya , De Alban Jose Don T. , Tang Hao , Friess Daniel A. , Koh Lian Pin
Published: March 2024

Carbon credits generated through jurisdictional-scale avoided deforestation projects require accurate estimates of deforestation emission baselines, but there are serious challenges to their robustness. We assessed the variability, accuracy, and uncertainty of baselining methods by applying sensitivity and variable importance analysis on a range of typically-used methods and parameters for 2,794 jurisdictions worldwide. The median jurisdiction’s deforestation emission baseline varied by 171% (90% range: 87%-440%) of its mean, with a median forecast error of 0.778 times (90% range: 0.548-3.56) the actual deforestation rate. Moreover, variable importance analysis emphasised the strong influence of the deforestation projection approach. For the median jurisdiction, 68.0% of possible methods (90% range: 61.1%-85.6%) exceeded 15% uncertainty. Tropical and polar biomes exhibited larger uncertainties in carbon estimations. The use of sensitivity analyses, multi-model, and multi-source ensemble approaches could reduce variabilities and biases. These findings provide a roadmap for improving baseline estimations to enhance carbon market integrity and trust.
Climate Risk Assessment Framework for Real Estate Investments
Authors: Endendijk Thijs , van Ederen Daan , Botzen Wouter , de Moel Hans , Aerts Jeroen
Published: March 2024

Climate change is forming an increasingly larger risk for the financial sector, although climate-related financial risks may be underestimated by financial institutions and markets. Financial institutions, such as banks, pension funds, and insurers are mainly exposed to physical climate risks through their investments in real estate. In the absence of any adaptation actions, physical climate risks for these real estate investments are expected to increase because of the higher frequency and intensity of natural disasters in a changing climate. In response to the increasing financial risks associated with climate change, regulatory bodies have been actively shaping new legislation over the past years (e.g. TCFD, CSRD, EU Green Taxonomy).One of the main channels through which the financial sector is affected by flood risk is through physical damage to real estate. After this physical damage, housing prices decrease, and houses located in flood-prone regions sell with a discount compared to similar houses in other areas. Additionally, the credit standing of households diminishes, making mortgages more likely to default, increasing mortgage credit risks for lenders. The 2008 global financial crisis has shown that real estate and its underlying values are a pivotal part of the modern financial system. For this reason, it is imperative to monitor and assess how flood risk affects real estate markets and investors through both direct and indirect channels.These impacts from flooding are currently not yet fully integrated within the risk assessment framework of institutional investors. Dynamic integrated models for insurance markets do exist in the literature, where standard catastrophe flood risk models are matched with insurance sector outcomes. There is currently no clear overview of how physical climate risks affect the balance sheets and profitability of (institutional) real estate investors. This study provides a structured integrated framework for evaluating both the direct and indirect flood-related risks associated with investments in both residential and commercial real estate. Although our bottom-up Dynamic Integrated Flood Real Estate Impacts (DIFREI) model can be applied to other international contexts, we use a real estate portfolio from one of the largest financial service providers in the Netherlands to illustrate the framework’s use and outputs. The DIFREI models can be used to draw lessons for applications on real estate investment portfolios.
Hydroacoustic monitoring of submarine lava flows: the eruption of Fani Maoré volcano offshore Mayotte, Indian Ocean
Authors: Lavayssière Aude , Bazin Sara , Raumer Pierre-Yves , Royer Jean-Yves
Published: March 2024

With the vast majority of our planet volcanism occurring in the Oceans, moored networks of hydrophones have become efficient tools to monitor, and therefore better understand, underwater volcanic eruptions. As these instruments can record sounds from thousands of kilometers away, they are particularly relevant to detect earthquakes and volcanic eruptions remotely, allowing better geohazards assessment. In 2018, a major seismic and volcanic crisis gave rise to the Fani Maoré underwater volcano at 3500 m below sea level, ~50 km east offshore Mayotte, in the North Mozambique channel. The MAHY hydroacoustic network has been deployed in October 2020 to monitor the eruption and investigate the oceanic soundscape in the area. It consists of four hydrophones moored in the SOFAR channel in the water column around the volcano. Since their deployment, the hydrophones have continuously recorded all low-frequency sounds (0-120 Hz), particularly hundreds of impulsive signals resulting from the interaction between hot lava and cold seawater. An automatic detection method of these specific signals on spectrograms has been developed to investigate in details the spatio-temporal evolution of the new lava flows. This technique could be used worldwide to remotely detect and monitor active submarine eruptions in the absence of local monitoring networks or regular near-bottom seafloor surveys.