bluuebunny/crossref_metadata_2025 huggingface dataset:
Hyperspectral Analysis for Protected Agriculture land cover mapping: A Remote Sensing Approach
Authors: Parmeggiani Davide , Despini Francesca , Costanzini Sofia , Teggi Sergio , la Cecilia Daniele
Published: March 2024

Spaceborne and airborne remote sensing data serve as powerful tools for the analysis andmonitoring of both urban and agricultural territories, with diverse applications contingent upon spatialresolution. In recent years, remote sensing imagery has been utilized for the recognition of protectedagriculture landcovers, such as greenhouses and mulch. Various studies in the scientific literature havefocused on satellite sensors like Sentinel-2 and WorldView-3, mapping the presence of protectedagriculture surfaces and implementing specific indices for recognition.A recurrent limitation in these studies lies in the often insufficient spatial resolution of the sensors,particularly for identifying smaller-sized greenhouses. Additionally, spectral resolution is crucial. Whilesome laboratory studies analyse the spectral characteristics of plastic surfaces typical of protectedagriculture, they often neglect the issue of mixed pixels inherent in satellite or aerial detection.The aim of this study is to analyze images from the AVIRIS airborne sensor over the agricultural area ofSalerno in southern Italy. AVIRIS, a hyperspectral sensor with over 400 bands covering the visible (VIS) tothe shortwave infrared (SWIR) region, provided images with a spatial resolution of 1m and 3m. Wescrutinize these images to discern the spectral signatures of different types of greenhouses in the studyarea, subsequently comparing them with other land cover classes. For this, we employ supportive tools,including specific spectral indices and transformations such as Tasselled Cap and Principal ComponentsAnalysis (PCA). We implement the Region of Interest (ROI) separability technique to identify distinctivespectral features in the signatures of protected agriculture coverings that differentiate them from othersurfaces. Finally, the spectral signatures obtained from AVIRIS offer the opportunity to simulate spectralresponses of other satellite sensors with lower spatial and/or spectral resolutions, assessing the suitabilityof currently available data for recognizing this specific type of surface.
Monitoring tools and best practice guidelines for attribution and quantification of potential CO2 leakages into deep waters at ocean crust CCS sites
Authors: Esposito Mario , Bhattacharya Sayoni , Achterberg Eric
Published: March 2024

Carbon dioxide (CO2) removal (CDR) methodologies have been proposed as essential measures to limit global warming and mitigate climate change. Carbon mineralisation, a natural process that involves the reaction of CO2 in the fluid form with reactive geologic formations to produce water-insoluble carbonates, when accelerated can permanently remove and sequester CO2 from the atmosphere at a scale of gigatons per year. The estimated storage capacity along mid-ocean ridges exceeds anthropogenic CO2 emissions by orders of magnitude but to date no offshore carbon capture storage (CCS) at ocean crust is performed. For offshore CCS, there are challenges in implementing accurate monitoring strategies and in developing robust and cost-effective technologies to support these strategies. Here we compare the sensitivities to leakage attribution and quantification of different approaches considered suitable for monitoring offshore CCS sites. As a proof of concept, we limited our target environment to the water column, specifically ocean waters below 1000 m. The effectiveness of the considered monitoring techniques and technologies was compared in terms of spatial and temporal coverage, reliability, costs and technology readiness level. A theoretical monitoring system design was proposed in order to provide guidelines for prompt detection of CO2 leakages into deep ocean waters. Appropriate monitoring tools and solid protocols for the assessment of potential environmental impacts will enhance public confidence and support efficient management of CCS operations in deep water ocean crust.
The response of Martian magnetotail to interplanetary coronal mass ejection events: joint observations of Tianwen-1 and MAVEN
Authors: Wang Limin , Li Lei , Li Wenya , Xie Lianghai , Zhang Yiteng , Tang Binbin , Kong Linggao , Zhang Aibing , Qiao Fuhao , Ma Jijie
Published: March 2024

The Martian magnetotail serves as an important channel for the escape of planetary ions, with abundant dynamic processes. After Tianwen-1 successfully entered the scientific orbit around Mars, the sun is becoming increasingly active. With the orbital apoapsis ~10,760 km, Tianwen-1 completed its first magnetotail phase from March to July 2022, providing a good opportunity to investigate the response of the Martian far magnetotail to interplanetary coronal mass ejections (ICMEs). We made a preliminary analysis of the dynamic tail under an ICME impact on 16 May 2022, with Tianwen-1 monitoring magnetotail and Mars Atmosphere and Volatile EvolutioN (MAVEN) providing upstream measurements. Based on MAVEN observations, the arrival of the ICME was determined to be around 05:10 UT on 16 May 2022. Subsequently, a significant increase in the energy levels of H+ and O+ ions was seen when Tianwen-1 entered the magnetotail about one and a half hours later. Tianwen-1 continuously detected a subset of O+ ions with energies exceeding 1 keV. Accordingly, the escape rate of O+ became ~6.2 times greater during this ICME, and the highest O+ enhancement happened between 1 keV and 3 keV. The disturbance lasted 39 hours before returning to a quiet level. Furthermore, we conducted a statistical analysis on the escape rate of O+ in the far magnetotail (attitude higher than 2 Mars radius) during 11 ICME events from March to July 2022. The ion loss rates substantially increased during ICME events, especially for O+ with energy above several keV. This observation suggests the presence of effective acceleration processes in the Martian tail under ICME conditions.
Iron-associated organic carbon as a major carbon sink in permafrost-affected peatlands of Northeast China
Authors: Yang Liang , Jiang Ming , Yu Zicheng , Zou Yuanchun
Published: March 2024

Permafrost-affected peatlands are hotspots globally because of their large carbon storage and climate sensitivity. However, there have been limited studies on the abundance and controlling factors of iron-associated organic carbon (Fe-OC) in these important ecosystems. Here we conducted a large-scale comparison study of soils across major terrestrial ecosystems—including croplands, forest, grasslands, wetlands and peatlands—to understand differences in the distribution of Fe-OC abundance. Our results show that the Fe-OC abundance of peatlands (13.3±9.6 g kg-1) are higher than that in non-peat forming wetlands (6.4±4.5 g kg-1) and mineral soils, such as croplands (9.1±9.3 g kg-1), forest (10.5±11.1 g kg-1) and grasslands (2.0±2.2 g kg-1), implying the efficient binding capacity of iron minerals with OC in peatlands. Further, our field and laboratory investigations focus on Northeast China, the major peatland-dominant region in China with permafrost-affected and non-permafrost peatlands—including Sphagnum-dominated bogs and sedge-dominated fens, to clarify the processes and mechanisms of Fe-OC accumulation. We find that permafrost-affected peatlands contain near 5-fold higher Fe-OC than non-permafrost peatlands. Our parallel factor analysis of fluorescence excitation-emission matrix results shows that microbial-derived carbon accounts for 25.5-90.7% of Fe-OC in permafrost peatlands, with an average contribution of 56.0%. Moreover, we observed a positive correlation between the Fe-OC abundance and the proportion of OC derived from microbes. Iron minerals in permafrost peatlands tend to bind a greater proportion of labile carbon—whether derived from plants or microbes—than in non-permafrost peatlands, suggesting that the presence of permafrost offers an important mechanism for climate change mitigation. Furthermore, nutrients (such as nitrate, phosphate and C:N ratio) are major controlling factors for Fe-OC in non-permafrost peatlands (with a total effect of up to 96.9%), while reactive Fe (with an effect of up to 96.9%) and other factors (including pH, climate, FeRB and microbial-derived OC) positively influence Fe-OC in permafrost peatlands. These findings demonstrate that iron minerals act as a crucial ‘OC protectors’ that greatly boost the rusty carbon sink in cryogenic ecosystems. Future climate warming and permafrost thaw will not only reduce low-temperature protection of previously frozen carbon—some of them labile—but also diminish the mineral-association protection of a large quantity of carbon.
Examining the Impact of Modeling Resolution on Soil Moisture Simulation Using Multi-Faceted Remote Sensing Data
Authors: Moradi Shirin , Li Fang , Mengen David , Vereecken Harry , Montzka Carsten
Published: March 2024

Sustainable irrigation practices are crucial for efficient water management, particularly as over 70% of the Earth's freshwater is dedicated to agricultural production. This study delves into the importance of optimizing modeling resolution to achieve reliable soil moisture assessments.Here, we investigate the spatio-temporal soil moisture simulation at the root zone on the Rur catchment in western Germany (covering approximately 2300 km2). Employing high spatial resolutions of 500 and 250 meters, our investigation utilizes CosmoRea6 atmospheric data and World Soil Information (ISRIC) soil grid and texture data to comprehensively characterize soil properties. The coupled land surface-subsurface model (CLM-ParFlow) is applied, considering intricate hydrological processes within the soil-plant-atmosphere system. Validation is conducted through a multi-faceted approach, incorporating data from the Soil Moisture Active Passive (SMAP) satellite, Cosmic-ray Neutron Sensor (CRNS) stations, and Synthetic Aperture Radar (SAR)-Sentinel-1, with a specific focus on the soil moisture assimilation using high-resolution Sentinel-1 data.The study explores the belief that increasing the resolution of input data and employing data assimilation techniques with high-resolution remote sensing data enhance the reliability of simulated soil moisture, particularly in areas with diverse soil textures and land uses. The outcomes bear significant implications for optimized modeling resolution, considering computational costs and sustainable irrigation practices. This understanding of soil moisture dynamics empowers stakeholders in agriculture to optimize water usage, improve crop productivity, and minimize environmental impacts.
Modeling Slow slip events on the Hikurangi subduction zone using Independent Component Analysis
Authors: Klein Emilie , Michel Sylvain , Jolivet Romain
Published: March 2024

In this study, we model Slow Slip Events (SSEs) occurring on the Hikurangi subduction zone, New Zealand, during the 2009-2022 time period based on the horizontal and vertical position time series of 130 GNSS stations. To do so, we divide the time series into two periods, one before and one after the 2016 Kaikoura earthquake. In both cases, the time series are corrected from instrumental and co-seismic offsets, and from a linear trend. The signal decomposition technique variational Bayesian Independent Component Analysis is then applied to the time series in order to separate the surface deformation signal due to SSEs from other sources of deformation. The independent components related to SSEs are linearly inverted for slip on the fault before being recombined. The method allows to model the slip history due to SSEs but relative to a secular slip trend which is dependent on the time period used to initially detrend the GNSS position time series and on the number of potential SSE within this period. The secular trend as well as the SSEs detection threshold vary thus spatially. To minimize the impact of the heterogeneous detection threshold, we use an iterative process which combines a SSE detection procedure based on slip rate with an inter-SSE linear trend correction determined from the previous iteration of SSEs’ detection. This allows to extract a catalog of transient events as well as to estimate the inter-SSE loading rate which is used to evaluate, with a Bayesian approach, the coupling map of the Hikurangi subduction zone. We then characterize those transient events (e.g. magnitude and duration), taking care to separate short-term SSEs occurring in the shallow part of the subduction (<20 km depth) from the long-term ones occurring in the deeper portions of the fault (>20 km depth). Lastly, we discuss about the slip budget and the impact of the Kaikoura earthquake on the slip-dynamics of the fault.
Climatic effects on trade-offs between carbon and water cycle on the Yangtze River Basin, China
Authors: Quan Yanying , Hutjes Ronald W.A. , Biemans Hester , Chen Xinping , Chen Xuanjing
Published: March 2024

It is widely believed that forest plays a vital role in mitigating and adapting climate changes. By adopting afforestation and reforestation, the atmospheric carbon can be captured and stored (sequestered) within plants and soil. Thus, tree expansions are witnessed over the whole world. However, large-scale forestation often requires a high amount of water resources, which may bring up pressures on the local water cycle. In this case, trade-offs between carbon sequestration and ecohydrology remain under discussion yet.China has launched several forestation projects since the last century. These projects spread over the country in both water-sufficient and water-limited areas. However, the changing climate conditions may alter local ecosystems and influence the survival of newly grown vegetations and the potential of carbon sequestration. Therefore, understanding the influences of climate change on carbon sequestration and hydrological response is of importance in national forestation management.In this study, we selected a Dynamic Global Vegetation Model (DGVM), the Lund–Potsdam–Jena Managed Land (LPJmL4), to study climatic influences on the carbon cycle and water cycle in the Yangtze River Basin, China. The model was implemented on a regional scale with increased resolution from 30mins to 5mins. Meanwhile, considering the complex terrain of the Yangtze River Basin, we classified the basin with a climatic zonation scheme to furtherly analyse the influence of climate.Our initial findings indicated that climate effects dominated the variations in the carbon and water cycle. Meanwhile, the future focus of ecological restoration, including forestation and protection, might need to shift from subtropical regions like the Yunnan-Guizhou plateau to western temperate alpine regions. This work can serve as recommendations for guiding national ecological restoration management.
GlacierGan: Visualizing the Alps during the Last Ice Age
Authors: Finley Brandon , Jouvet Guillaume
Published: March 2024

In this work, we develop a deep-learning generative model to offer a new visualization for the Alps and its glaciation over the last 120’000 years as if a satellite had passed over and taken high resolution images from above. This visualization utilizes a recently coupled climate-glacier evolution model, which uses the latest paleo-climate and ice thickness field reconstructions (Jouvet et al., 2023). The ultimate goal of this project is to use it in the “IceAgeCam”, a joint SNSF (Swiss National Science Foundaton) project developed by ZHDK, UZH and UNIL that aims to better inform the public about the cause of climate change in a long-term climatic context.To obtain such a visualization, we use an image-2-image translation model called Pix2PixHD (Wang et al., 2018). Similar to how one can use an image-2-image translation model to map images of winter to summer, or zebras to horses, we will map relevant fields of multi-band climatic images into artificial satellite images. Each multi-band image is composed of physical predictors such as ice thickness, ice velocity, precipitation, surface temperature, etc. In the end, the model produces semantically meaningful results that allow one to visualize the last glacial cycle. Moreover, although the motivation is rooted in the aforementioned ''IceAgeCam'' and seeks to visualize the last 120'000 years, it is a well-generalizable model, and as such, can be applied to visualize future simulations as well as terrain outside the Alps, given that the user has access to the same predictors. Finally, we aim to include this into IGM (the Instructed Glacial Model), a community-led glacier modeling software, such that it is user-friendly and easily accessible. Overall, we hope to advance efforts in the domain of remote sensing in relation to the cryosphere by providing a new way to visualize scientific results and foster community outreach.
A Dynamic HPC Probabilistic Tsunami Forecast Workflow for Real-time Hazard Assessment
Authors: Cordrie Louise , Ejarque Jorge , Sánchez-Linares Carlos , Selva Jacopo , Macías Jorge , Gibbons Steven J. , Bernardi Fabrizio , Tonini Bernardi , Badia Rosa M , Scardigno Sonia , Lorito Stefano , Romano Fabrizio , Løvholt Finn , Volpe Manuela , D'Anca Alessandro , de la Asunción Marc , Magni Valentina
Published: March 2024

The Urgent Tsunami Computing procedures discussed herein are designed to quantify potential hazards resulting from seismically-induced tsunamis following an earthquake, with a temporal scope ranging from minutes to a few hours. The presented workflow employs comprehensive simulations, encompassing the entire tsunami propagation process, while accounting for uncertainties associated with source parameters, tsunamigenesis and wave propagation dynamics. Within the EuroHPC eFlows4HPC project, we present a High-Performance Computing (HPC) workflow tailored for urgent tsunami computation in which the Probabilistic Tsunami Forecast (PTF) code has been restructured and adapted for seamless integration into a PyCOMPSs framework. This framework enables parallel execution of tasks and includes simulations from Tsunami-HySEA numerical model within a unified computational environment. Of particular significance is the workflow's capability to incorporate new datasets, such as focal mechanism data, seismic records, or real-time tsunami observations. This functionality facilitates an "on-the-fly" update of the PTF, ensuring that the forecasting model remains responsive to the latest information. The development of this workflow involves a systematic exploration of diverse scenarios, realistic simulations, and the assimilation of incoming data. The overarching goal is to rigorously diminish uncertainties, thereby producing updated probabilistic forecasts without compromising precision and enhancing risk mitigation efforts far from the seismic source. Improved risk management, achieved by informing decision-making in emergency situations, underscores the importance of this development. We will showcase the technical advancements undertaken to tailor the workflow for HPC environments, spanning from the developers' perspective to that of the end user. Additionally, we will highlight the scientific enhancements implemented to leverage the full potential of HPC capabilities, aiming to significantly reduce result delivery times while concurrently enhancing the accuracy and precision of our forecasts.
Optimization of the calculation of the photodissociation rates in the stratosphere in the BASCOE module of the IFS-COMPO
Authors: Minganti Daniele , Chabrillat Simon , Bingen Christine , Huijnen Vincent , Remy Samuel , Metzger Swen , Williams Jason , Flemming Johannes
Published: March 2024

The Integrated Forecasting System (IFS-COMPO) at ECMWF is the global model used by the Copernicus Atmosphere Monitoring Service (CAMS) to provide forecasts and analyses of atmospheric composition, including greenhouse gases, aerosols and reactive gases. Within CAMS, the default configuration of IFS-COMPO relies on the CB05 chemistry scheme for the troposphere and the Belgian Assimilation System for Chemical ObsErvations (BASCOE) module for the stratospheric chemistry.Currently, the photodissociation rates (Js) in IFS-COMPO can be computed with two methods: Joffline (computationally efficient but inaccurate) and Jonline (more accurate but computationally very expensive). In this work, we implement and evaluate an optimized method for the calculations of the Js in the stratosphere that is as accurate as Jonline and as computationally efficient as Joffline. This method (Jvint) computes the Js using Jonline but on a coarser stratospheric vertical grid (L23) compared to the native vertical grid and will be available in the future cycle 49R1.We compare three IFS-COMPO simulations using the Jvint, Jonline and Joffline methods with the BASCOE Reanalysis of Aura MLS version 3 (BRAM3) for ozone and with observations from the TROPOspheric Monitoring Instrument (TROPOMI) for nitrous dioxide (NO2). For ozone, the Jvint method significantly reduces the bias with respect to BRAM3 compared to the Joffline method in the upper stratosphere. In the mid-lower stratosphere, however, the Jvint configuration delivers slightly larger bias with respect to BRAM3 when compared to the Joffline method, especially over the Arctic. This bias is consistent with the results from the Jonline method and with the bias reduction in the upper stratosphere. For NO2, the Jvint method also reduces the bias with respect to TROPOMI stratospheric columns compared to the Joffline method at all latitudes.Concerning the computational cost, it increases in the Jvint method compared to the Joffline method by 5% in December, 16% in July and 12% in November, while the Jonline method is two to three times as computationally expensive compared to Joffline. In addition, the Jvint method improves the seasonal variations of the ozone and NO2 columns compared to the Joffline method, and the differences between the Jvint and Jonline methods are insignificant for most of the stratospheric regions.We demonstrate the capability of the Jvint method to deliver improved stratospheric columns for ozone and NO2 compared to the Joffline method and significantly reduce the computational cost compared to the Jonline method. The Jvint configuration also includes the time-dependence of the solar flux, ensuring a physically consistent representation of the stratospheric photochemistry.
Monofractality in the solar wind at electron scales
Authors: David Vincent , Galtier Sébastien , Meyrand Romain
Published: March 2024

Solar wind turbulence at electron scale is generally thought to be governed primarily by kinetic Alfvén waves (KAW), but the monofractal behavior observed is a puzzling problem that contradicts the standard properties of intermittency. We investigate this long-standing problem using direct numerical simulations (DNS) of three-dimensional electron reduced magnetohydrodynamics (ERMHD). Both weak and strong KAW turbulence regimes are studied in the balanced case. After recovering the expected theoretical predictions for the magnetic spectra, a higher-order multiscale statistical analysis is performed. This study reveals a striking difference between the two regimes, with the emergence of monofractality only in weak turbulence, whereas strong turbulence is multifractal. This result, combined with recent studies, shows the relevance of collisionless weak KAW turbulence to describe the solar wind at electron scale.
Clay Minerals Investigation along the Martian Crustal Dichotomy to Assess Aqueous Conditions on Early Mars
Authors: Brossier Jeremy , Altieri Francesca , De Sanctis Maria Cristina , Frigeri Alessandro , Ferrari Marco , De Angelis Simone , Bruschini Enrico , Apuzzo Andrea
Published: March 2024

On Mars, the crustal dichotomy marks the boundary between the old cratered highlands and the lowlands. Infrared datasets reveal several sites with extensive clay-bearing deposits therein, indicating widespread aqueous conditions on early Mars [1]. Here we examine hyperspectral data to better characterize the near-infrared signatures of clay deposits, and verify an eventual mixture with carbonates. We investigate outcrops located in key regions located along this boundary, and compare them with carefully selected terrestrial analogs to follow-up and complement recent studies [e.g., 2–5]. Such outcrops are valuable targets to seek traces of past or present life on the planet, as clay minerals are known to store organic compounds [6]. Knowing the exact positions of absorption band centers in the near-infrared spectra is crucial to ascertain possible species present in these outcrops, and search for changes in clay mineralogies. Variations observed between the targeted clay deposits suggest subtle changes in the iron and magnesium content, or even the oxidation state of iron [7]. Mineralogical similarities observed in most regions suggest that clay deposits (i.e., ferrosaponites, vermiculites) may share a common weathering history. Conversely, nontronites extensively detected near Mawrth Vallis and its surroundings indicate different formation and alteration conditions. Nonetheless, the region may have shared a common aqueous history with other deposits found elsewhere on the planet, and then diverged to form more leached or oxidized clays with Al,Fe-rich compositions [8]. Additionaly, the coprecipitation of clays and carbonates throughout the outcrops further strengthens the exobiological potential of the selected regions, where biosignatures might still be preserved.
This work is in context with the ExoMars rover mission, planned to deploy the “Rosalind Franklin” rover to explore Oxia Planum. ExoMars rover will investigate clay deposits in surface and subsurface to search for signs of life on the planet, as biosignatures might still be preserved therein [6].

This work is funded by the Italian Space Agency (ASI) [Grant ASI-INAF n. 2023–3–HH.0].
References – [1] Carter et al. (2013) JGR Planets, [2] Brossier et al. (2022) Icarus, [3] Brossier et al. (2023) PSJ, [4] Brossier et al. (under review) PSS, [5] Mandon et al. (2021) Astrobiology, [6] Vago et al. (2017) Astrobiology, [7] Michalski et al. (2015) EPSL, [8] Carter et al. (2015) LPSC Abstracts.

 
Effects of adiabatic focusing in 1D coronal shock acceleration
Authors: Annie John Lidiya , Nyberg Seve , Vuorinen Laura , Vainio Rami , Afanasiev Alexandr , Poedts Stefaan , Wijsen Nicolas
Published: March 2024

Solar energetic particles (SEPs) pose a significant radiation hazard to both technologies and human beings in space. SEPs undergo acceleration to higher energy levels through various eruptive phenomena, specifically, solar flares and coronal mass ejections (CMEs). Based on these sources, SEP events can be classified as impulsive and gradual events, respectively, although more recent findings showed that large SEP events contain both sources (solar flare reconnection and CME shock waves). Greater risk is attributed to CME shock-driven events due to their larger quantity of accelerated particles and longer duration. In such SEP events, solar particles are considered to be accelerated by the diffusive shock acceleration process in CME-driven coronal shocks. The role of inhomogeneous magnetic field-induced adiabatic focusing in the one dimensional diffusive shock acceleration (DSA) process is not effectively studied. Hence, in our numerical study, we aim to understand the effects of adiabatic focusing on coronal shock acceleration and to investigate whether a free escape boundary could yield similar results in the absence of focusing. This involves employing two models: one featuring a finite and homogeneous upstream region, eliminating focusing, and another one incorporating a weakening magnetic field, facilitating particle escape through the focusing effect. We present the results from a one-dimensional oblique shock model with a mean free path similar to  Bell’s theory (Bell, 1978) using Monte Carlo simulations. This research is funded by the European Union’s Horizon 2020 research and innovation programme under the Marie Skłodowska-Curie grant agreement No 955620.References Bell, A. R. 1978, MNRAS, 182, 147
Satellite-based tracking of slow-moving landslides: challenges and perspectives
Authors: Mueting Ariane , Bookhagen Bodo
Published: March 2024

Slow-moving landslides represent a significant hazard to local communities and infrastructure in mountainous regions worldwide. Given their challenging and often inaccessible terrain, satellite imagery holds great potential for monitoring landslides from space. In this study we use optical data from Sentinel-2 and PlanetScope satellites for tracing surface displacement across slow-moving landslides through image cross-correlation. Our work particularly focuses on the variables affecting measurement precision, including orthorectification errors and mismatches due to variable shading or seasonal snow cover. Erroneous measurements can be reduced when image pairs are carefully selected based on their view angles and sun positions. This practice, however, severely limits the number of potential image pairs, resulting in disconnected networks of displacement maps. This in turn poses problems when solving for a displacement time series using an inversion technique. Here, we evaluate the effect of network connectivity and measurement noise on inversion results using both synthetic and real-world data. Our findings support the extraction of accurate displacement estimates from remotely sensed data, advancing the detection potential of landslides and their dynamic behaviors. 
Global evaluation of HCHO summertime diurnal variability using Pandonia Global Network (PGN)
Authors: Zhao Tianlang , Mao Jingqiu , Zhao Xiaoyi , Lind Elena Spinei , Hanisco Thomas
Published: March 2024

Formaldehyde (HCHO) serves as an important proxy for emissions of volatile organic compounds (VOCs) and their subsequent photochemistry affecting air quality and climate. Understanding HCHO diurnal variability is essential to accurately represent emissions, chemistry, and planetary boundary layer (PBL) mixing in chemical transport models (CTMs). Here we compare HCHO diurnal variations from Pandora Global Network (PGN), GEOS-CF (0.25°x0.25°) and GEOS-Chem (2°x2.5°) CTMs at 55 sites, to characterize the HCHO diurnal patterns in urban and rural sites over North America (NA), Europe (EU) and East Asia (AS) in 2021-2022 summers. We find that HCHO total column (HCHOcol) from GEOS-CF model shows a comparable stronger diurnal variability (quantified by relative amplitude) with that from PGN measurements, which is lower in GEOS-Chem (10-200% bias in late afternoon). While models and PGN show comparable HCHOcol at rural sites (e.g., ChapelHillNC and DearbornMI), PGN shows significantly higher (a factor of 2 - 3) local noon HCHOcol in some urban areas (e.g., Busan and Bangkok), suggesting missing Volatile Organic Compounds (VOCs) emissions in the models. We further examine the relationship between HCHOcol and HCHO near-surface concentration (HCHOsurf). While both model and PGN show a linear relationship (p
Local head differences and topography-driven groundwater flow to robustly identify gaining and losing streams
Authors: Huang Xiaohua , Ebeling Pia , Liu Guodong , Fleckenstein Jan , Schmidt Christian
Published: March 2024

Interactions between groundwater (GW) and surface water (SW) play a pivotal role in influencing water quantity, quality, and associated biogeochemical and ecological processes in stream networks. Understanding the spatial pattern of gaining and losing rivers is crucial for managing water resources at catchment scale. Each method to identify losing and gaining rivers, from point to reach to catchment scale, has distinct advantages and limitations. These limitations can potentially be mitigated by combining different approaches.In this study, we combined local information from hydraulic head differences between GW and SW with the regional information derived from topography-driven groundwater flow to robustly identify and characterize the spatial pattern of gaining and losing rivers in two study areas located at Central Germany –the Bode catchment and Free State of Thuringia. Central Germany has faced a drought period in the last five years, which has impacted groundwater levels. To evaluate local head differences, we compared the measured averaged groundwater levels (GWLs) and estimated surface levels (SWLs). The GWL data were obtained from 49 and 826 groundwater monitoring wells within a 1500 m distance from rivers in the Bode catchment and Thuringia, respectively. We developed a method for estimating SWLs across river networks by correcting a coarse DEM (25 m) based on the river bed elevations and river water depths recorded at gauging stations and river network topology. Uncertainties of SWL were also estimated and considered in the classification of gaining and losing rivers. Topography-driven discharge (gaining rivers) and recharge (losing rivers) areas are derived from groundwater upward and downward flow directions according to a 3D spectral solution.The analysis of head differences reveals a widespread occurrence of losing rivers. However, when combining the losing and gaining classifications from topographical-driven groundwater flow with the classifications from head differences, the fraction of river segments having the same classification from both methods is relatively low (around 50% in both study areas). Many river segments showed contradictory classifications from the two methods, with a notable observation being that rivers have losing classifications from head differences but gaining classifications from topographic analyses. Specifically, 41% of river segments in Thuringia and 7 out of 9 (78%) in the Bode catchment fall into this category. This mismatch typically occurred in urban and mining areas, indicating anthropogenically lowered GWLs.By combining local and regional scale methods, our study contributes to a more robust representation of patterns of gaining and losing rivers. Our analysis reveals the prominence of losing rivers despite the topographical classification of a gaining river highlights the anthropogenic impacts on local groundwater levels.
Forecasting Groundwater Level in Florida using Advanced Machine Learning Approaches
Authors: Javadi Saman , Najafi Mohsen , Golmohammadi Golmar , Mohammadi Kourosh , Neshat Aminreza
Published: March 2024

Mismanagement of groundwater resources leads to groundwater depletion and other environmental issues such as quality reduction and subsidence. Water table prediction is essential for optimal management of groundwater resources. Hence, artificial intelligence (AI) methods have been used widely to predict water tables in recent years. This paper adopted some new methods of machine learning, e.g., categorical boosting (CATBoost), extreme gradient boosting (XGBoost), and Convolutional neural network-Long Short-Term Memory (CNN-LSTM), to predict water table. The key input parameters were evaporation/transpiration, rainfall, temperature, and water table in the prior month. To better compare the models, simulations were executed in daily and monthly periods. DeLuca Preserve located in Florida was selected to test the proposed algorithms.  The results indicated that in general machine learning algorithms are appropriate approaches to predict water tables. CNN-LSTM algorithm with RMSE = 0.22 m and R2 = 0.96 showed better performance in predicting daily groundwater levels.  However, monthly water tables were predicted much better using CATBoost algorithm with RMSE = 0.11 m and R2 = 0.99.
Stronger Southern Ocean carbon uptake in high-resolution ocean biogeochemistry simulations
Authors: Patara Lavinia , Hauck Judith , Rieck Jan Klaus , Ödalen Malin , Oschlies Andreas , Gürses Özgür
Published: March 2024

It is increasingly recognized that the way Southern Ocean mesoscale eddies are represented in ocean models influences air-sea CO2 fluxes and their response to climate change. In this study, we assess the Southern Ocean carbon uptake since the 1960s in a hierarchy of global ocean biogeochemistry models (GOBMs) based on the NEMO-MOPS and FESOM-REcoM models. The horizontal resolutions of the GOBMs range from 1° and 0.5° resolutions (“eddy-parameterized”) to 0.25° and 0.1° resolutions (“eddy-rich”, where eddies are explicitly represented). We find that the “eddy-rich” models have steeper density surfaces across the ACC with respect to “eddy-parameterized” models, in better agreement with observations. A larger amount of deep waters low in anthropogenic carbon (Cant) is thereby transported to the surface, leading to a 10% higher Cant uptake and storage. Natural CO2 (Cnat), which integrated over the whole Southern Ocean is directed into the ocean, shows a somewhat higher ingassing in the “eddy-rich” models. As a result, the net CO2 uptake is about 14% higher in the “eddy-rich” with respect to the “eddy-parameterized” models. Trends over the 1958-2018 period reveal a gradual wind-driven reduction of Cnat uptake in all configurations, but this trend is about 40% weaker in the 0.1° model with respect to the lower resolution models. At the same time, the upward trend in the residual meridional overturning circulation (MOC) is weaker in the 0.1° model, supporting the hypothesis of a more pronounced “eddy-compensation” of the wind-driven Cnat trends. Our study suggests that GOBMs using standard eddy parameterizations may underestimate the net and anthropogenic CO2 uptake by about 10%, and emphasizes the importance of adequately simulating mesoscale eddies for better constraining the Southern Ocean carbon uptake in changing climate conditions.
Methodology for Spatially Distributed Rainfall Erosivity Calculations at the Conterminous United States to Support Soil Erosion Studies
Authors: Momm Henrique , Wells Robert , Seever Thomas , ElKadiri Racha , Bingner Ron
Published: March 2024

Research and action agencies in the US work collaboratively to develop and use soil erosion technology to support the development of field-specific conservation plans. These tools and accompanying databases are applied in all counties throughout the country covering a wide range of natural and anthropogenic physical conditions. Climate, particularly precipitation, constitutes one of the key drivers directly related to soil detachment and transport. Observations spanning over 30 years have demonstrated that estimated long-term average annual soil loss in agricultural fields is the result of the cumulative effect of many small and moderate-sized storms along with the impact of occasional severe ones. In the Revised Universal Soil Loss Equation version 2 (RUSLE2) technology, the effect of rainfall is represented by the rainfall runoff erosivity index R. This index is designed to serve as an estimation of the potential storm energy specific to each location. In this study, we propose and evaluate a methodology to generate continuous surfaces of monthly R for the continental US from discrete 15-min precipitation data. Over 2000 stations covering more than 50 years of 15-min precipitation data were used. Storm identification algorithms were implemented and evaluated through comparison with existing tools. Outlier events were identified and removed using a 50-year recurrent interval calculated for each station. Using 30-years of recorded data, a custom universal kriging algorithm was employed to generate a smooth continuous surface as a raster grid. This step included a boxcox transformation of the station data, directional variogram fitting, and the removing of external trends using elevation, long-term annual precipitation totals, and distance to the coast. Predicted surfaces were compared with existing RUSLE2 surfaces for the same time period with great level of agreement. The proposed methodology is intended to be comprehensive and reproductible such that it can serve as a template for future updates of erosivity maps for the entire continent at a county-scale. This methodology provides the means for future systematic updates to the RUSLE2 climate database to account for climatic changes and to support continued national efforts in reducing soil erosion and conserving natural resources. 
The seismic velocity structure and tectonic features of the Africa-Europe plate boundary region in the Atlantic: new high-quality geophysical data
Authors: Neres Marta , Prada Manel , Grevemeyer Ingo , Gomez de la Peña Laura , Merino Irene , Brito Pedro , Terrinha Pedro , Ranero César
Published: March 2024

The LISA and ATLANTIS geophysical cruises were conducted in 2021 onboard the Spanish R/V Sarmiento de Gamboa. We collected wide-angle seismic reflection and refraction (WAS) data (ATLANTIS) and coincident streamer data (FRAME) to constrain the seismic velocity structure on the region to the west of the Gorringe Bank (West Iberia Margin), where the nature and structure of the lithosphere are mostly unknown. The LISA profile runs across different geological domains: from the Ampère seamount at SE, the West Horseshoe Abyssal Plain, the region of the Josephine seamount (at the intersection of the Tore-Madeira Rise with the Gloria fault) and the undisputed North Atlantic oceanic domain to the NW.WAS data were acquired with 21 ocean bottom hydrophones (OBH) spaced at ~15 km, along a NNW-SSE oriented, ~400 km long profile, at 250 Hz sampling frequency. The seismic source was designed to provide high penetration and map the entire crust and the upper mantle structure and consisted of two sub-arrays of 16 airguns with total volume of 5200 c.i., towed at 15 m depth. Multichannel seismic reflection (MCS) streamer data were also acquired with a 6 km long streamer towed at 23 m and using the same seismic source.OBH sections were analyzed for picking of Ps, Pg, PmP and Pn phases, and show high variability along the profile. Joint inversion of refraction and reflection travel times of key boundaries observed in the WAS and MCS data were used to build a final P-wave velocity (Vp) model, following a layer stripping strategy, and Vp uncertainty was evaluated using Monte Carlo analysis.In this work we focus on the northern part of the profile, sampled by the 14 northernmost OBH, to present the velocity structure and a seismic image across the region of the contact of the plate boundary and the Tore-Madeira Rise, near the Josephine seamount. A vertically and laterally complex velocity distribution is observed. An apparently low velocity (
Enhanced monitoring of rubber plantations in complex tropical regions: Integrating all Landsat/Sentinel data for precise classification and stand age estimation
Authors: Chen Bangqian , Dong Jingwei , Kou Weili , Wu Zhixiang , Yang Chuan , Xie Guishui
Published: March 2024

The rubber tree (Hevea brasiliensis), extensively cultivated in tropical regions, is a primary source of natural rubber. With increasing global demand for natural rubber, precise monitoring of cultivation dynamics of rubber plantations is crucial for regional economic development and ecosystem assessment. However, the significant spatial variability in rubber plantation phenology, interference from non-rubber deciduous forests, and cloud-free optical imagery limitations create considerable uncertainty in applying existing monitoring methods across varying latitudes. This study leverages extensive field surveys and over 30 years of Landsat/Sentinel-2 data to analyze the land use changes, growth processes in rubber seedling stages, and phenological changes during mature stages of rubber plantations. We developed a novel, high-precision identification and stand age estimation algorithm for rubber plantations on the Google Earth Engine (GEE) cloud platform, integrating multi-temporal remote sensing data, machine learning, and advanced algorithm optimization. Applications of this algorithm in countries with complex terrain and climate, such as Vietnam, Laos, Cambodia, and Myanmar, demonstrate higher robustness and accuracy. Ground data validation shows that the overall classification accuracy exceeds 95%, with an average stand age estimation error of less than two years. Spatial statistical analysis at 30-meter resolution aligns closely with data from authoritative sources like the Association of Natural Rubber Producing Countries (ANRPC), underscoring our method's effectiveness and reliability. Beyond mapping distribution and stand age structures of rubber plantations, this research supports future natural rubber production forecasts, environmental impact assessments, and sustainable policy development. Moreover, this study paves the way for novel applications of remote sensing in the monitoring of agriculture and forestry in tropical areas, setting a foundation for future advancements and innovations in these domains.

 

Keywords: Rubber plantations, classification, Landsat, Sentinel-2, Google Earth Engine
Integrated global assessment of the natural forest carbon potential
Authors: Mo Lidong , Zohner Constantin , Crowther Thomas , Ma Haozhi
Published: March 2024

Forests are a substantial terrestrial carbon sink, but anthropogenic changes in land use and climate have considerably reduced the scale of this system. Remote-sensing estimates to quantify carbon losses from global forests are characterized by considerable uncertainty and we lack a comprehensive ground-sourced evaluation to benchmark these estimates. Here we combine several ground-sourced and satellite-derived approaches to evaluate the scale of the global forest carbon potential outside agricultural and urban lands. Despite regional variation, the predictions demonstrated remarkable consistency at a global scale, with only a 12% difference between the ground-sourced and satellite-derived estimates. At present, global forest carbon storage is markedly under the natural potential, with a total deficit of 226 Gt (model range = 151–363 Gt) in areas with low human footprint. Most (61%, 139 Gt C) of this potential is in areas with existing forests, in which ecosystem protection can allow forests to recover to maturity. The remaining 39% (87 Gt C) of potential lies in regions in which forests have been removed or fragmented. Although forests cannot be a substitute for emissions reductions, our results support the idea that the conservation, restoration and sustainable management of diverse forests offer valuable contributions to meeting global climate and biodiversity targets.