bluuebunny/crossref_metadata_2025 huggingface dataset:
Connectivity as a driver of biodiversity and functioning in riverine landscapes: A dynamic, graph theoretic approach.
Authors: Funk Andrea , Baldan Damiano , Meulenbroek Paul , Pont Didier , Recinos Brizuela Sonia , Bondar-Kunze Elisabeth , Hein Thomas
Published: March 2024

Connectivity is a crucial property of the riverine landscape. Reduction of connectivity, i.e. habitat fragmentation and isolation effects, impacting ecological functions and biotic communities, is one of the most critical threats to river-floodplain systems. Using a graph theoretical approach for analyzing possible transport pathways in the system (directed, undirected, overland, seepage), we could show that essential ecological functions related to sediment composition and quality, hydrochemical conditions, and macrophyte coverage can be predicted and importance of waterbodies in the network and their main connectivity deficits can be identified. In a second step we are now integrating biotic communities in the predictive framework. Dependent on dispersal model and habitat preferences the different taxonomic groups show clear pattern i.e. drifting invertebrate organisms are highly driven on directed transport whereas fish as active swimmers are more dependent on connectivity in the waterbody network or organism with terrestrial or flying dispersal (amphibia or flying insects) are dependent on overland connectivity. Further they interact with the ecological functions in the system. Using a temporal dataset based on eDNA (environmental DNA) we can further show that ecosystem conditions and distributions of biotic communities are dependent on different transport/movement pathways changing with hydrological conditions (flood to low flow conditions). The dynamic graph theoretic approach can, therefore, be used as an essential tool for prioritizing water bodies for nature-based solutions.
(Science) Communication is Key - Analysing and Adapting Outreach Approaches and Internal Workflows to Support HELPING as a Major International Scientific Initiative.
Authors: Payne Tarryn , Orieschnig Christina
Published: March 2024

The IAHS’ scientific decades are unique as international scientific initiatives in the hydrological community, linking researchers from around the globe, from different professional backgrounds, and at different career stages. The current HELPING - Science for Solutions decade in particular aims to address the current water crises by synthesising hydrological knowledge, establishing links between local and global processes, applying cross-cutting methods, and providing science-based decision support. This approach aims to involve not just hydrologists worldwide, but also practitioners, decision-makers, and a broader general public. Given these objectives, (science) communication is paramount. It is also the main focus of a dedicated HELPING Working Group. This study aims to analyse which internal and external communication approaches can be harnessed to support the success of an international scientific initiative like HELPING, both through streamlining internal workflows between participants, and through providing a coherent, easily digestible message to an external public. It strives to assess communication lessons learned from previous IAHS scientific decades - Predictions in Ungauged Basins (PUB) and Panta Rhei - while also taking into account the changes in communication technology since the launch of the first decade in 2003. Subsequently, it casts the net wider and analyses successful communication and diffusion approaches used by similar scientific initiatives launched recently at different scales. On a meta level, the different approaches needed for internal and external HELPING communication are assessed. What strategies can be leveraged to raise awareness for the scientific decade within the hydrological community, especially among researchers in locations and at career stages where they would typically not be highly involved? What tools can be harnessed to communicate the aims and achievements of HELPING to a wider public and to encourage interactions and participation? And, ultimately, what workflows are needed to assess the progress and impact of the HELPING decade itself, and to track all the initiatives and research by individual hydrologists carried out under its banner? Keywords: science communication, IAHS, scientific decades
Large porewater-derived carbon outwelling across two mangrove-seascapes revealed by radium isotopes
Authors: Cabral Alex , M. S. Reithmaier Gloria , Y. Y. Yau Yvonne , C. Cotovicz Jr. Luiz , Barreira João , Viana Bárbara , Hayden Juliana , Bouillon Steven , Brandini Nilva , E. de Rezende Carlos , L. Fonseca Alessandra , R. Santos Isaac
Published: March 2024

Mangroves have high CO2 sequestration capacity, storing large amounts of carbon on their biomass and sediments/soil. Mangrove carbon is also transported to the ocean, i.e. outwelling or lateral fluxes, where it can be stored for long time scales. Here, we used radium isotopes (224Ra and 223Ra) to resolve carbon and alkalinity outwelling to the ocean from two mangrove seascapes in Brazil. We sampled porewaters to define the source composition, mangrove creek waters to resolve tidal cycles, and performed transects away from the mangrove into continental shelf to trace mangrove carbon across the seascape. High-resolution observations of radium isotopes in the creek indicated that tidal pumping is the main driver of carbon exchange. Low pH (6.8 – 7.0) and high 224Ra activities (165 – 290 dpm 100L-1) were found during low tides, indicating mangrove porewater exchange. Radium mass balance models revealed porewater exchange at 20.0 ± 25.4 cm d-1 in the tropical mangrove and 3.0 ± 2.0 cm d-1 at the sub-tropical mangrove. Radium-derived transport rates of mangrove porewater to the continental shelf were higher in the mesotidal tropical (667 ± 313 m d-1) than the microtidal subtropical (371 ± 168 m d-1) seascape. Radium isotopes were positively correlated (p < 0.05) with dissolved inorganic (DIC), organic (DOC) and particulate organic (POC) carbon across the entire seascape. DIC as bicarbonate (HCO3-) was the main form of carbon on all scales in both mangrove seascapes, representing 57 – 82% of the total carbon pool. DOC and POC accounted for 5 – 12% and 1 – 7% of total carbon, respectively. Although mangrove waters emitted CO2 to the atmosphere (38.4 – 142.9 mmol m-2 d-1), both bays and continental shelves were a CO2 sink (-1.9 – -0.6 mmol m-2 d-1). Porewater-derived carbon outwelling exceeded carbon fluxes at the mangrove-bay and bay-shelf interfaces, indicating carbon transformations across the seascape continuum. Total carbon outwelled from mangroves were 3 – 4 times higher than soil carbon burial at both mangrove sites. Bicarbonate outwelling (31.0 – 71.6 mmol m-2 d-1) reaching the continental shelves increased mangrove soil carbon sequestration capacity by 234% in these mangrove systems. Hence, overlooking outwelling as a blue carbon sink mechanism would underestimate the role of mangroves in sequestering CO2 and mitigating climate change.
Surface-water nitrate exposure to world populations has expanded and intensified during 1970-2
Authors: Wang Junjie , Liu Xiaochen , Beusen Arthur , Middelburg Jack
Published: March 2024

Excessive nitrate in surface waters deteriorates water quality and threatens human health. Human activities have caused increased nitrate concentrations in global surface waters over the past 50 years. An assessment of the long-term trajectory of surface-water nitrate exposure to world populations and associated potential health risks is imperative but lacking. Here, we used global spatially explicit data on surface-water nitrate concentrations and population density simulated by the consistent integrated assessment model, in combination with thresholds for various health risks compiled from epidemiological studies, to quantify the long-term changes in surface-water nitrate exposure to world populations at multiple spatial scales. During 1970-2010, global populations potentially affected by acute health risks associated with surface-water nitrate exposure increased from 6 to 60 million persons per year, while populations at potential chronic health risks increased from 169 to 1361 million persons per year. Potential acute risks increasingly affected Asian countries. Populations potentially affected by chronic risks shifted from dominance by high-income countries (in Europe and North America) to middle-income countries (in Asia and Africa). To mitigate adverse health effects associated with surface-water nitrate exposure, anthropogenic nitrogen inputs to natural environments should be drastically reduced. Moreover, international and national standards of maximum nitrate contamination may need to be lowered. By leveraging biogeochemical, socio-economic, and epidemiological knowledge from different disciplines, this novel assessment evaluates the role of humans in driving environmental changes and environmental effects on humans in a consistent manner.
3D Digital Twins of the Ocean: towards an intuitive and realistic visualization of wave parameters
Authors: Llorach-Tó Gerard , Martínez Enoc , Del-Río Joaquín , Simarro Gonzalo , Pani Martino , Bucchi Andrea , Huang Ya , García-Ladona Emilio
Published: March 2024

Understanding and picturing the state of the sea surface according to wave parameters can be difficult for non-expert users. 3D digital twins of the ocean, i.e., realistic virtual copies of the sea state with live updates, can provide user-friendly visualizations. An animated visual representation offers users a more tangible reference to the actual sea state in the field than conventional swell and wind forecasts. Our work presents an interactive web-based open-source visualization of wave data in a 3D realistic environment. The wave data used is provided by a forecast model, CMEMS [1], and the in-situ observation platform OBSEA [2]. Both of these data products provide an open access API that can be accessed via the browser, following the FAIR principles. The challenge of this work is to translate the wave parameters of the data products into a real-time computer graphics simulation representing the real sea state. Different data products provide different parameters, for example, CMEMS forecast model computes wave significant height, wave period, and direction for ‘sea surface wave’, ‘wind wave’, ‘swell 1’, and ‘swell 2’, whereas OBSEA measures wave properties with an acoustic doppler wave array such as ‘Hm0’, ‘H1/10’, ‘H1/3’, and ‘directional spread’. We will discuss algorithms based on empirical observations to generate the virtual sea state from a selection of wave parameters. Subsequently both quantitative and qualitative metrics based on observations will be used to compare between the 3D digital twin and the real sea state. Preliminary results of the digital twin can be found at https://icatmar.github.io/CasablancaBuoy/ and https://cgi-dto.github.io/OBSEA/.  [1] Korres, G., Oikonomou, C., Denaxa, D., & Sotiropoulou, M. (2023). Mediterranean Sea Waves Analysis and Forecast (Copernicus Marine Service MED-Waves, MEDWAΜ4 system) (Version 1) [Data set]. Copernicus Marine Service (CMS). DOI: 10.25423/CMCC/MEDSEA_ANALYSISFORECAST_WAV_006_017_MEDWAM4[2] Del Rio, J. [et al.]. Obsea: a decadal balance for a cabled observatory deployment. "IEEE access", 13 Febrer 2020, vol. 8, p. 33163-33177.
A Long Short-Term Memory Neural Network for predicting Global Ionospheric Total Electron Content 24 hours ahead
Authors: Adolfs Marjolijn , Hoque Mohammed Mainul , Shprits Yuri Y.
Published: March 2024

In this study a long short-term memory (LSTM) network architecture is utilized to make 24-hour ahead global ionospheric total electron content (TEC) predictions. The preceding 3-day historical TEC data, geographic longitude and latitude, universal time and day of year are used as model input parameters. We investigated the LSTM performance using proton density, solar wind forcing parameters and interplanetary magnetic field components as external model drivers. Other drivers such as ionospheric disturbance index SYM-H, solar radio flux index F10.7 and geomagnetic activity index Hp30 were included in the investigations as well. The above-mentioned investigated parameters were excluded in the final model development since they did not improve the model’s accuracy significantly. The model was trained using the rapid UQRG global ionosphere maps (GIMs) from the Universitat Politècnica de Catalunya (UPC) comprising a period of two solar cycles (1998-2020). The model’s performance was analyzed for a test dataset which was excluded from the training data and contained quiet and geomagnetic storm days together with a low and high solar activity period. In order to see the model’s performance for near real-time (RT) applications, the model was tested using the combined RT products of the international GNSS service (IGS), e.g. IRTG GIMs. The performance of the LSTM-based model was compared to another neural network (NN)-based method (feed forward NN) and the Neustrelitz TEC model (NTCM). The LSTM-based model was outperforming the two models for both cases, e.g. using the IRTG or UQRG maps as an input for the historical TEC data.
Results of high-resolution acoustic studies in Eastern Gulf of Finland
Authors: Budanov Leonid , Sergeev Alexander , Chekulaev Alexander , Neevin Igor , Zhamoida Vladimir , Ryabchuk Daria
Published: March 2024

Studies were carried out in three key areas in the eastern Gulf of Finland during joint cruises of the A.P. Karpinsky Russian Geological Research Institute and the Shirshov Institute of Oceanology of the Russian Academy of Sciences in 2023. Multibeam echosounding, sub-bottom profiling, and various sediment sampling techniques were performed, and a significant amount of new geological and geophysical data was collected. Analyses of seismic sections, supported by sediment sampling data and a digital elevation model (DEM) of the sea floor, allowed for the revelation of six acoustic unions (AU) which are divided by reflecting interfaces and have unique acoustic waveforms. The sediments of the AUs were developed in different stages of deglaciation and in the postglacial period of the Late Pleistocene - Holocene. A complex analysis of collected and archived data allowed for the construction of DEMs of buried paleo-relief surfaces. Both the sea floor and paleo-relief DEMs allowed for the mapping and discovery of geomorphological features of landforms specific to the study area, such as submerged end moraine, drumlins, eskers, De Geer, etc. The studies provide new data on the Gulf of Finland basin deglaciation and establish sedimentological processes, features, and the impact of exogenous processes on the geological environment.
Sensitivity analysis of agricultural and hydrological droughts to rainfall deficits across India
Authors: Bilal Syed Bakhtawar , Gupta Vivek
Published: March 2024

Drought is a natural phenomenon characterized by an extended period of insufficient rainfall for a particular area. These deficit in rainfall leads to shortage of water reserves across surface and sub-surface storages. Variations in these shortages arise from diverse factors such as regional climatic variations, geographical features, and land-use patterns. The primary objective of this study is to assess the sensitivity of agricultural and hydrological systems to rainfall deficits across different climatic zones. We aim to quantify the degree of responsiveness of agricultural and hydrological droughts to varying precipitation deficiencies using various statistical and modeling techniques. By examining the diverse responses in different regions, this research seeks to enhance our understanding of precipitation shortages on drought dynamics.
Attributing child undernutrition from agricultural shocks to climate change in Burkina Faso
Authors: Dimitrova Asya , Laudien Rahel , Dimitrova Anna , Undorf Sabine , Waid Jillian
Published: March 2024

Climate change significantly threatens food security, particularly in low-income countries heavily reliant on subsistence and rainfed agriculture. Most existing empirical literature has examined the impacts of climate variability and extremes on undernutrition and agricultural outputs independently. There is a lack of studies exploring the causal pathway from climate change to agricultural shocks and their consequent nutritional and health impacts.In this study, we investigate the extent to which the current and historical burden of child stunting in Burkina Faso can be attributed to climate change-induced agricultural deficits. First, we combine individual anthropometric data from five rounds of the Demographic Health Survey (DHS) and provincial-level crop yield data to assess the association between child stunting and exposure to agricultural deficits at birth. We define agricultural deficits as annual deviations in crop yields from their long-term average for three major food crops in the region: maize, millet, and sorghum. Second, we employ observationally-derived climate reanalysis data as well as counterfactual and factual climate data from ATTRICI (four pairs of datasets based on different reanalysis data), part of ISIMIP3a. These are analysed with a statistical crop yield modelling approach to estimate crop yields with and without climate change, respectively.The epidemiological analysis reveals a non-linear health risk function, with risk of child stunting increasing rapidly when crop yields at birth are lower than the period average (
Analysis of the spatio-temporal characteristics of retreat caused by rockfall on a cliff with interbedded soft and hard rock layers
Authors: Fei Li , Choanji Tiggi , Derron Marc-Henri , Jaboyedoff Michel , Sun Chunwei
Published: March 2024

Retreat of rock cliffs due to rockfall is a common geological phenomenon in various environments. Both the rockfall events and the subsequent retreat pose potential risks to the infrastructure located both above and below the cliff. Extensive research on rock wall retreat has been conducted in both high alpine and coastal environments, addressing single rock wall instability, as well as linear and catchment-scale rock cliff dynamics under climate change. However, investigations into rock wall retreat in sub-alpine regions have primarily relied on inventories at the linear or regional scale. More attention is still required to understand the retreat of individual rock cliffs in sub-alpine environments. In this study, we focus on a subalpine molasse (sandstone-marls) cliff located at La Cornalle, Vaud, Switzerland, as the case study.Using monthly Structure-from-Motion (SfM) photogrammetry surveys, meteorological data from a weather station and the Swiss Meteorological Office, and rock temperature obtained from thermal couples installed in the subsurface of the rock from December 2019 to September 2023, we established the rockfall inventory, calculated the retreat rate for the cliff, and analyzed the spatial and temporal features of the retreat with the support of the correlation between meteorological parameters and rockfall data. We found that 4051 rockfalls were documented during the nearly four-year survey, resulting in an average retreat rate of 19.6 mm/year. Specifically, for the marl layers, the retreat rate is about 21.1 mm/year, and for the sandstone layers, it is about 23.7 mm/year. Regarding the newly formed and flat vertical cliff face resulting from a rock collapse between December 17, 2021, and January 11, 2022, the retreat rates vary significantly between the marl and sandstone layers. The retreat rate for the marl layer from the new face is measured at 45.5 mm/year, while for the sandstone layer, it is 22.5 mm/year. This discrepancy is reasonable due to the inherent weakness of marl compared to sandstone. Additionally, no sandstone overhang existed when the face was newly created. As time progresses, the retreat of the marl layer weakens the support for the overlying sandstone, leading to subsequent rockfalls from the sandstone layer. Rainfall played a crucial role in the retreat evolution, while freezing-thaw cycles did not show a clear impact on the occurrence of rockfalls. However, snow melting could be a triggering factor for rockfalls detected during the winter. Interestingly, extreme hot weather during the last two summers did not immediately trigger many rockfalls. Our study introduces a comprehensive rockfall inventory from the sub-alpine Molasse cliff and investigates potential contributing factors to rockfall events. Additionally, we propose a model to elucidate the historical evolution of rock cliff retreat.
Molecules and microbes: monitoring peatland health below the surface
Authors: Bell Nicholle , Kitson Ezra , Trifiro Gianluca , York Richard
Published: March 2024

Peatlands are organic matter rich (with over 60% organic matter) ecosystems that act as ‘carbon sinks’, storing many times the carbon stored by Earth’s forests. Peatlands act as sponges storing excess water from rain events and releasing it slowly, a mechanism that not only mitigates floods but also filters drinking waters. However, peatlands can only conduct these vital services when healthy and functioning, with a near surface water table and anoxic acidic conditions below the surface. Unfortunately, 80% of UK peatlands have been assessed as damaged mainly via drainage for repurposing the land for other uses. Rewetting peatlands by installing dams is one of the most common methods to restore these damaged bogs. While there is a large amount of evidence that rewetting restores the water table, questions remain whether rewetting successfully restores peatlands to their full health. To answer this question, we need to know what is happening below the surface and examine the roles of key players in peat formation and carbon cycling, namely the microbes and the carbon-containing molecules. It is not clear which of these players is more important, or how they depend on each other. To address this question, we are using the latest technologies (DNA/RNA sequencing, NMR spectroscopy and FT-ICR mass spectrometry) to uncover who they are, how they interact and how they are impacted by drainage and rewetting. The task is not easy as peat is an uncharacterised complex mixture on a molecular and microbial level and the key players could be found in different phases (solid or liquid). In this presentation, I will provide a brief overview of what insights the technologies we are using provide for below the surface characterisation of UK peatlands.
Assessing the possibilities of Sentinel products for qualifying and quantifying soil water status of agricultural systems in southern France 
Authors: Doussan Claude , Kalenga Tshingomba Urcel , Baghdadi Nicolas , Flamain Fabrice , Chapelet Arnaud , Pouget Guillaume , Courault Dominique
Published: March 2024

Water management poses a pervasive challenge in southern France, exacerbated by increasing summer droughts linked to global warming. Water use during spring and summer increases and gets more variable in term of quantity used for crops. Agricultural water use is highly influenced by the diversity in irrigation practices and technics (sprinkler irrigation, drip irrigation, flooding, etc.) ; and can lead to tensions among water users. It is thus essential to estimate field water use at basin scale, as well as crop water status, in order to further optimize water delivered for irrigation. Advances in remote sensing, particularly with Sentinel 1 (S1) and 2 (S2) data, facilitated the development of soil moisture products (SMP) with improved spatial and temporal resolution to characterize soil water in agricultural plots. These SMP products are accessible through the Theia French public platform and suitable for main crops, with NDVI below 0.75 or surfaces with moderate roughness. These specifications can be met for a variety of crop conditions in the south of France. Yet, the validity of the SMP products under various agricultural plot conditions, considering slope, orientation, roughness, and soil moisture, remains to be assessed over extended time periods. From another point of view, such SMP products do not presently apply to orchards plots, which are however, an essential but overlooked component of water use in irrigation and deserve further examination with S1 and S2 data. The objective of our study is twofold: (i) to test SMP products for field crops in different settings and among years, (ii) to preliminary test if S1 data, combined to S2 data, may be linked to soil moisture in orchard plots. Results reveal for (i) that differences can appear between SMP products and soil moisture in various monitored plots, primarily due to variability within farming systems. Beyond a specific slope and vegetation threshold, the correlation does not improve significantly. For (ii), in orchards plots, using a time smoothing of data, S1 VV-retrodiffusion data and NDVI from S2 seem to correlate with soil moisture measurements, with an RMSE < 0.05 cm3/cm3 and enable detection of irrigation events. This study shows that S1 and S2 data are valuable in estimating soil moisture of agricultural plots, giving however some limits in their use, and gives some hope in their further use for orchards water management.
Assessment of total water storage and other variables over the Indus, Ganga, and Brahmaputra River basins
Authors: Yadav Mohit , Dimri Ashok Priyadarshan , Mal Suraj , Maharana Pyarimohan
Published: March 2024

In the present study, assessment of the abating total water storage (TWS) in the three river basins viz Indus (IRB), Ganga (GRB), and Brahmaputra (BRB) and its associated changes in precipitation across the Indian Himalayan Region (IHR) are examined. Time lead and lag relationship among TWS and other contributory factors viz., precipitation, evaporation, runoff, snow water equivalent (SWE), soil moisture, groundwater, etc., are assessed. In the present study precipitation dataset and TWS available from Global Precipitation Measurement Mission (GPM) and Gravity Recovery and Climate Experiment (GRACE) is used respectively while other variables were extracted from ERA5. Mann-Kendall and Theil Sen estimator test is used for calculating trend of precipitation and TWS during different seasons (winter, pre-monsoon, monsoon, post-monsoon). Our study supports, there is a decreasing trend of TWS over the Indus Ganga Brahmaputra (IGB) basin, though all the basins are drying but slower during monsoon. IRB shows maximum decrease in TWS in postmonsoon whereas over GRB and BRB it is observed in premonsoon. In all seasons, heat flux distributions suggested drying, especially over the higher reaches of the IHR and certain areas of the IRB. The changes in temporal and spatial distribution of TWS over IRB indicate a rapid drop in monsoonal moisture flux. More evaporation and runoff during the monsoon season reduce the TWS process. Present work will be of utmost importance for the policy or planning for state-level governance for societal benefit.
Glacial meltwater plume extent from multispectral and SAR satellite data
Authors: Edwards Laura
Published: March 2024

Research suggests that at least 40 % of mass loss from the Greenland ice sheet is related to calving at marine terminating glaciers and that the rate of calving is linked to glacier meltwater plume processes. Understanding plume extent and temporal and spatial variability is, therefore, important for estimating potential SLR.This work presents satellite data observations of plume extents from multispectral sensors as well as the novel use of satellite synthetic aperture radar (SAR) sensors for this application. Ocean fronts, large-scale upwelling and estuarine plumes have been isolated in the past using SAR data but application of SAR to smaller-scale glacial meltwater plume extent has not so far been presented. With the advent of higher resolution SAR imagery in recent years there is an opportunity to apply the technique to study these glacial meltwater plumes as their presence modifies the wind, wave and current interactions on a water surface and so influence the backscatter signal presented in the SAR intensity image.Plume observations from multispectral and SAR data are presented for the glacier-marine lagoon complex of Breiðamerkurjökull glacier and Jökulsárlón proglacial lake, Iceland. This system is a good analogue for a Greenlandic fjord-ocean systems and is where we will also conduct future fieldwork on the 3D structure of meltwater plumes.
The importance of Seasonality for Seagrass as Coastal Protection
Authors: Mohr Veronika , Zhang Wenyan , Schrum Corinna , Dolch Tobias
Published: March 2024

Seagrass is regarded with great expectations when it comes to nature-based coastal protection measures. Seagrass meadows dampen waves, reduce currents, and stabilize sediments in the coastal environment. However, most modeling studies estimating the magnitude of the coastal protection effect by seagrass assume a constant seagrass cover throughout the year. In temperate climates such as Northern and Central Europe the seagrass cover has considerable annual and interannual variations. The seagrass cover is highest in late summer and autumn and lowest in winter and early spring. At the same time, the physical forcing of waves and currents is at its maximum in winter, indicating a discrepancy between the seasons with the highest benefits of seagrass to coastal protection and the seasons with the most threat to the stability of the coast. In this study, we use a 3D baroclinic circulation model (SCHISM) coupled with a sediment model and a model of seagrass growth dynamics for estimating the significance of seasonality for coastal protection. A case study of a tidal basin in the northern Wadden Sea indicates that disregarding the seasonality can lead to substantial overestimations of the effectivity of seagrass for coastal protection.
A Stochastic Deep Learning Approach for Projecting Storm Surges in the Context of Climate Change
Authors: Treu Simon , Mengel Matthias , Frieler Katja
Published: March 2024

Projections of sea level rise are vital for assessing the impacts of climate change, especially in coastal regions. Present sea level rise projections are primarily focused on monthly water levels, but tend to underrepresent the critical role of storm surges. There are some studies that also provide projections of storm surges along global coastlines based on numerical models using meteorological forcing data from Global Climate Models (GCMs). However, those applications are limited by coarse meteorological inputs as well as the computational demands placed by running numerical models for an ensemble of different GCMs and climate change scenarios.We propose a stochastic deep learning model trained on model output from numerical surge models. It is designed to capture the spatial and temporal dependencies that are characteristic of storm surge time series. Our approach generates potential storm surge scenarios that are consistent with GCM outputs but are not directly determined by those meteorological inputs. A second advantage is that the trained machine learning model has lower computational demands than traditional numerical models which makes it possible to explore different GCMs and climate change scenarios.
Drifting groundwater temperatures in the Netherlands: opportunities for hydrogeological analysis
Authors: Bense Victor , Kurylyk Barret
Published: March 2024

Temperature-depth profiles in the central part of the Netherlands collected over the past 7 years in a large number of piezometers document a regional increase in groundwater temperatures to depths of upto ~100 meters. This rise is congruent to observed increases in air temperature, related to climatic change. For some locations the data collected recently can be compared to similar observations done in the 1970-80s. Our observations show that the magnitude and rate of increase in groundwater temperature strongly vary by location and across depth. In part these differences can be explained by contrasts in land-surface conditions, but our analysis demonstrates that varying groundwater flow conditions also play an important role in explaining the observed patterns. Moreover, we show that an analysis of the transience in the temperature-depth profile can yield quantitative estimate of groundwater flow rates and subsurface hydraulic properties when combined with observations of hydraulic head gradients. We conclude that the current rising trends in groundwater temperature should provide a significant opportunity for the hydrogeological community to quantitatively analyze groundwater flow systems worldwide.
Current limitations in chemical abundance measurements for M dwarfs and implications for exoplanet characterisation  
Authors: Shan Yutong , Hatalova Petra , Tabernero Hugo , Timmermann Anina , Mamonova Elena , Werner Stephanie
Published: March 2024

Planets are assembled from the same material as their parent stars. Therefore, stellar chemical abundance measurements provide important constraints on the formation, composition, and interior structure of exoplanets. M dwarfs are the most prolific hosts of low-mass, rocky, potentially habitable exoplanets. However, owing to the complexity of their atmospheres (which are marred by molecular absorption), elemental abundances of M dwarfs are notoriously difficult to measure. For key rock-forming elements, state-of-the-art abundance precisions attainable from high-resolution spectroscopy of M dwarfs can be up to an order of magnitude poorer than that for sun-like stars. This would mean a comprehensive characterisation of planets around M dwarfs may be more elusive. We explore examples of consequences for uncertainties in exoplanet modelling, such as condensation sequences, bulk compositions, and core-mantle fractions. We discuss what is required from the stellar science community to make progress.  
Better than Multi-Model Means: combining climate models using multivariate graph cuts for improved CMIP6 projections
Authors: Schmutz Lucas , Thao Soulivanh , Vrac Mathieu , Allard Denis , Mariethoz Grégoire
Published: March 2024

Various approaches have been proposed to combine individual climate models and extract a robust signal from an ensemble, such as the Multi-Model Mean or the weighted Multi-Model Mean. However, they often rely on weights that are applied to models globally, overlooking the fact that individual models often demonstrate strengths in specific regions. This suggests that a more localized approach could improve climate projections based on models ensembles.So far, the only approach that really exploits the regional strengths of different models over multi-decadal timescales is the graph cuts approach (Thao et al., 2022). It consists in selecting for each grid point the most appropriate model, while at the same time considering the overall spatial consistency of the resulting field. Although this method showed encouraging results, outperforming other combination approaches, it is limited to optimizing for one variable, resulting in an inconsistent model selection for each and thus a loss of the multivariate relationships observed in the models. For instance it is known that precipitation and temperature are physically linked. Therefore having an independent model combination for each variable can result in inconsistencies. Moreover, the method was only applicable to multi-decadal averages, not allowing for retrieving distributional properties of the combined models such as extreme events.Here we present a series of improvements of graph cuts enabling to combine distributions of daily-means while preserving multivariate relationships, thus better capturing the complete span of climate dynamics. Using the Hellinger distance to measure model performance, we are able to select, at each grid location, the model that best represents the joint distribution of the target variables while minimizing the apparition of unrealistic discontinuities in the resulting fields. The resulting projections display more realistic extremes and compound events representations. REFERENCES Thao, S., Garvik, M., Mariethoz, G., & Vrac, M. (2022). Combining global climate models using graph cuts. Climate Dynamics, February. https://doi.org/10.1007/s00382-022-06213-4
Key roles of surface wave in the development of new generation ocean-related models
Authors: Qiao Fangli , Zhao Biao , Bao Ying
Published: March 2024

The time and spatial scales of surface waves are several seconds and hundreds of meters, which are much smaller than those of ocean circulation and climate, months and thousands of kilometers or even bigger. As a result, ocean surface wave models are separated from ocean circulation models and climate models. During the past 2 decades, we find that surface waves play a dominant role in the vertical mixing of the upper ocean, and heavily modulate the air-sea momentum and heat fluxes. (1) By including surface waves in ocean general circulation models (OGCMs), the ever-standing simulated shallow mixed layer and over-estimated sea surface temperature (SST) especially in summer faced by nearly all OGCMs are dramatically reduced, 80-90% of common errors can be removed from OGCMs; (2) Although the forecasting error of Tropical Cyclone (TC) track is reduced by about half during the past decades, the forecasting of TC intensity has no much progress. By including surface waves, the TC intensity error is reduced by about 40%; (3) SST is a crucial parameter in the climate system. All climate models have huge SST simulation bias, which has lasted for half a century. By including surface waves, the SST bias can be reduced by about 60%. All the above suggests that surface waves should be included in new-generation ocean-related models, including ocean, TC, and climate models, to improve their forecasting ability.
Using atmospheric measurements to evaluate recent bottom-up trends and seasonal patterns in U.K. and Swiss N2O emissions
Authors: Saboya Eric , Manning Alistair J. , Levy Peter , Henne Stephan , Stanley Kieran M. , Pitt Joseph , Young Dickon , Say Daniel , Grant Aoife , Arnold Tim , Rennick Chris , Tomlinson Sam J. , Carnell Edward J. , Artoli Yuri , Stavart Ann , Spain T. Gerard , O'Doherty Simon , Rigby Matthew , Ganesan Anita
Published: March 2024

Atmospheric trace gas measurements can be used to independently assess national greenhouse gas inventories through inverse modelling. Here, atmospheric nitrous oxide (N2O) measurements are used to derive monthly U.K. N2O emissions for 2013-2022 – using the InTEM and RHIME inverse methods – and Swiss N2O emissions for 2017-2022 – using the ELRIS inverse method. We find mean U.K. emissions of 90.5±23.0 and 111.7±32.1 Gg N2O yr-1 for 2013-2022 and corresponding trends of -0.68±0.48 and -2.10±0.72 Gg N2O yr-2, respectively, derived using InTEM and RHIME. The 2013-2022 mean U.K. N2O emissions as reported by the U.K. National Atmospheric Emissions Inventory were relatively constant at 74 Gg N2O yr-1 across this period, which is 14-33% smaller than the U.K. emissions derived from atmospheric data. Top-down Swiss emissions of 10.8±3.8 Gg N2O yr-1 derived using atmospheric measurements were very comparable to those reported in the Swiss National Inventory: 11.5 (8.3 to 14.9) Gg N2O yr-1 over 2017-2021. Pronounced seasonal N2O emissions cycles are inferred in the U.K. and Swiss data with similar seasonal magnitudes observed in both countries. In the U.K., the primary seasonal peak occurs in the spring with a second smaller peak occurring in the late summer for certain years. The springtime peak has a long seasonal decline that contrasts with the sharp rise and fall of N2O emissions estimated from the bottom-up U.K. Emissions Model (UKEM). Similarly, Swiss seasonal N2O emissions peak during the summer with a second smaller peak also occurring in the late summer/early autumn for certain years. Bayesian inference is used to minimize the U.K. seasonal cycle mismatch between the average top-down (atmospheric data-based) and UKEM bottom-up (process model and inventory-based) seasonal emissions at a sub-sector level. Increasing agricultural manure management and decreasing synthetic fertiliser N2O emissions reduces some of the discrepancy between the average U.K. top-down and bottom-up seasonal cycles. Other possibilities could also explain these discrepancies, such as missing emissions from NH3 deposition, but these require further investigation.
Analysis of surface temperature and precipitation trends and climate indices in Spanish mountain areas
Authors: Viloria Ramón , Tricio Verónica
Published: March 2024

Several studies and observations suggest that global warming processes are more prevalent in mountain areas, showing higher rates of warming and more pronounced changes in precipitation than in average land data. Snow and ice are highly sensitive to variations in climate. The importance of mountain areas as water reservoirs for the surrounding land and valleys at lower altitudes justifies paying special attention to this type of behaviour and to the changes brought about by global warming. This interest is enhanced by the special environmental sensitivity of mountain ecosystems, and the difficult balance between these fragile ecosystems and their use as tourist resources or winter resorts.In this paper we analyse climate data collected at mountain weather stations in Spain in time series up to 80 years. Stations in mountain areas are not numerous and are sometimes very scattered; nevertheless, we have selected the available data on temperatures, precipitation and other meteorological variables at stations located in the various mountain ranges throughout the Iberian Peninsula. For the selected stations, trends in temperatures (mean, maximum and minimum) have been studied and a seasonal analysis has been carried out. In addition, the data were processed with RClimDex, statistical and climatic software package, to evaluate Climate Extreme Indices. Cooling and warming patterns have been detected, and changes in precipitation have been analysed, trying to address the distinctive characteristics of mountain areas in the studies conducted. Monthly and seasonal assessments have also been carried out to detect changes in behaviour patterns. In general, good agreement with previously published data has been obtained, although not many studies have been carried out systematically in Spain, except in the Pyrenees area.
Sub-daily Land Surface Temperature data for urban heat monitoring from spaceenhanced by machine learning
Authors: Mollière Christian , Kondmann Lukas , Gottfriedsen Julia , Langer Martin
Published: March 2024

Urban heat islands are becoming a major health factor for cities in the eye of a warming planet. Fueled by impervious surfaces and rising temperatures, extreme heat may lead to 235,000 emergency room visits and 56,000 hospital admissions annually in the US alone in 2023 [1]. Beyond its economic impact, urban heat therefore puts a strain on wellbeing and health across the globe with visible effects on mortality.
Urban planning aims to mitigate extreme heat in cities, a challenge intensified by urban densification and climate change. However, accurately predicting and managing urban heat is complex due to the difficulty in measuring local physical processes, particularly in dynamically changing environments. The scarcity of granular measurements of land surface temperature compounds this issue. While satellite imagery from thermal instruments offers some assistance, traditional data sources often lack the necessary temporal density of observations. Rapid diurnal temperature fluctuations necessitate near-real-time monitoring for effective decision-making and a comprehensive understanding of urban temperature dynamics.
New Space constellations with higher temporal cadence are starting to close this data gap with enormous potential for urban development as well as extreme heat event anticipation. For example, OroraTech’s Forest constellation allows frequent observation of urban areas. With 2 satellites operating in orbit and 9 more planned to launch in 2024, we aim to provide Land Surface Temperature (LST) every 12 hours globally. Once our full constellation is operational in 2027, the update frequency will again improve to sub-hourly.
The native spatial resolution of Forest data at 200m is, however, a challenge for urban applications. We are currently exploring enhancing our imagery with artificial intelligence approaches to 70m to get from city quarter to building block level. These super-resolution techniques are the result of recent advancement in AI and image processing with promising results on our thermal data. Yet, the usability of super-resolved data for urban policy is underexplored. We aim to present preliminary findings of the accuracy of our super-resolution method compared with higher resolution Ecostress data and investigate the applicability of the results for urban planning as well as extreme heat event analysis. With this, we aim to help cities to mitigate and adapt to the new public health challenges as a result of extreme heat.
[1]: Yale Program on Climate Change Communication, 2023 https://yaleclimateconnections.org/2023/07/extreme-heat-will-cost-the-u-s-1-billion-in-health-car e-costs-this-summer-alone/
One-way linking of 3D long-term geodynamic models and short-term earthquake dynamic rupture models
Authors: Jourdon Anthony , Hayek Nicolas , May Dave , Gabriel Alice-Agnes
Published: March 2024

Tectonic deformation plays a crucial role in shaping the Earth's surface, with strain localization resulting in the formation of shear zones in depth and faults on the surface. These structures accommodate a significant portion of the displacement between tectonic plates. While long-term deformation can be approximated as continuous visco-plastic processes, earthquakes involve cycles of stress loading and unloading that trigger rapid and catastrophic elasto-plastic deformation. Earthquake dynamic rupture models offer valuable insights into studying and comprehending earthquakes. However, these models heavily rely on initial conditions that are often challenging to obtain solely from observations. Particularly, a mechanically self-consistent prestress state loading a fault prior a seismic event and 3D fault geometry, especially in depth, are commonly poorly constrained. Nonetheless, the prestress state and the fault geometry significantly impact earthquakes initiate, propagate, and arrest and the associate radiation of seismic waves and ground shaking.To address the lack of information on stress and fault geometry, one promising approach is to use long-term geodynamic numerical simulations. In this study, we employ pTatin3D, a visco-plastic finite element software, to simulate the evolution of strike-slip deformation in 3D over geological time scales. To ensure a physically consistent long-term model, the fault geometry is not prescribed but solved for based on the lithospheric rheology and tectonic plate velocities. However, the geodynamics model describes faults as continuous volumetric fields of finite deformation and strain-rate, rendering them 3D objects, while earthquake dynamic rupture models typically represent faults as 2D interfaces.In this study, we outline a new and versatile method to link 3D geodynamic simulations to rupture dynamics earthquake and seismic wave propagation modelling. We first extract 3D volumetric shear zones from the geodynamic model and automatically convert them into surface representations. Next, we generate meshes including these as faults for dynamic rupture models. Finally, we showcase 3D dynamic rupture models utilizing the stress states and faults self-consistently as derived from the long-term geodynamic model as initial conditions.