bluuebunny/crossref_metadata_2025 huggingface dataset:
Characterization of Frequency and Intensity Rainfall Distributions and their Relationship with Physical Drivers : Application to French Polynesia
Authors: Sichoix Lydie , Benoit Lionel , Bechon Tonin , Terii Garry
Published: March 2024

Intense rainfall events and associated flooding in the tropical high islands of French Polynesia located in the South Pacific represent a major threat for tens of thousands of people, in particular in coastal urban areas. In this context, our goal is to characterize the relationship between wet-day frequency and intensity distributions for multiple rain gauge sites located on coastal, inner valley and mountainous areas at various timescales (15 minutes to 1 day) during the period 1980-2022 and to identify the physical drivers of such distributions. This work provides indicators for integrated water resource management under high anthropogenic pressure, as well as flood risk anticipation and infrastructure design.
In our daily scale analysis, we first superimpose the distributions of wet-day intensity of cumulative rainfall estimates for aggregated regions corresponding to each percentage of wet-day frequency that were derived from the global reanalysis datasets (ERA5 and MERRA2) by McErlich et al. (2023), to our estimates obtained for our each individual rain gauge data. Our regrouped distributions fit into the envelope drawn with the global distribution curves (frequency spanning from 75 to 99%), except a few cases with distributions exhibiting a higher extreme wet tail behavior, in particular for the high relief windward sector. Such evaluations are also led at subhourly to subdaily scales depending on available data.
We also apply a classification of days into three weather types (WT1 referred to the Transition Season Trade Winds, WT2 the Dry Season Trade Winds and WT3 the South Pacific Convergence Zone weather conditions) that capture the main modes of variability of rainfall over the Island of Tahiti and Moorea, recently proposed by Benoit and Sichoix (2023), to evaluate the WT specific wet-day distributions. This reveals that over windward and inner valley zones the wet-day distributions are shifted toward larger extreme values for WT1 compared to WT3, whereas over leeward and coastal areas, we find more extreme values for WT3 than for WT1.
In order to explain the possible links with the frequency and intensity rainfall distributions at daily to hourly scales, associated in particular with the occurrence of intense hydrometeorological events, we explore the influence of a selected set of atmospheric variables including dynamic and thermodynamic metrics extracted from the global reanalysis products.
 
Benoit, L., Sichoix, L. (2023): Sub-daily rainfall patterns in the mountainous regions of the Island of Tahiti: Insights from a one-year rain gauge network expansion. https://doi.org/10.1016/j.ejrh.2023.101559
McErlich,C., McDonald, A., Schuddeboom, A., Vishwanathan, G., Renwick, J.,Rana, S. (2023): Positive correlation between wet-day frequency and intensity linked to universal precipitation drivers. https://doi.org/10.1038/s41561-023-01177-4
Implementation of deep learning algorithms in the sub-hourly rainfall fields estimation from remote sensors and rainfall gauge information in the tropical Andes
Authors: Barrios Miguel , Rubiano Henry , Guevara-Ochoa Cristian
Published: March 2024

One of the latent difficulties in the fields of climatology, meteorology, and hydrology is the scarce rainfall information available due to the limited or nonexistent instrumentation of river basins, especially in developing countries where the establishment and maintenance of equipment entail high costs relative to the available budget. Hence, the importance of generating alternatives that seek to improve spatial precipitation estimation has been increasing, given the advances in the implementation of computational algorithms that involve Machine Learning techniques. In this study, a multitask convolutional neural network was implemented, composed of an encoder-decoder architecture (U-Net), which simultaneously estimates the probability of rain through a classification model and the precipitation rate through a regression model at a spatial resolution of 2 km2 and a temporal resolution of 10 minutes. The input modalities included data from rain gauge stations, weather radar, and satellite information (GOES 16). For model training,  validation, and testing, a dataset was consolidated with 3 months of information (February to April 2021) with a distribution of 70/15/15 percent, covering the effective coverage range of the Munchique weather radar located in the Andean region of Colombia. The obtained results show a Probability of Detection (POD) of 0.59 and a False Alarm Rate (FAR) of 0.39. Regarding precipitation rate estimation, it is assessed with a Root Mean  Square Error (RMSE) of 1.13 mm/10min. This research highlights the significant capability of deep learning algorithms in reconstructing and reproducing the spatial pattern of rainfall in tropical regions with limited instrumentation. However, there is a need to continue strengthening climatological monitoring networks to achieve significant spatial representativeness, thereby reducing potential biases in model estimations. 
The Geothermal Paradox of Choice: A Comparative Techno-Economic Assessment of Geothermal Energy Production for Heat and Power Applications
Authors: Rangel Jurado Nicolas , Kucuk Serhat , Pokras Daniel , Degen Denise , Games Federico , Brehme Maren , Saar Martin
Published: March 2024

Geothermal energy is poised to play a pivotal role in the renewable energy transition by providing baseload, dispatchable, and carbon-free heat and power. Nonetheless, in contrast to its renewable energy alternatives, such as solar or wind, geothermal energy is harnessed beneath the Earth’s surface, inherently increasing the challenges, risks, uncertainties, and opportunities related to its exploration and utilization. As a result, numerous concepts and field development strategies for exploiting geothermal energy have been proposed over the last century. The seemingly overwhelming abundance of choices has prompted widespread confusion regarding the optimum approach to developing geothermal energy across multiple sectors. In this study, we attempt to answer this question by conducting a scenario analysis consisting of three geological reservoirs developed through various geothermal technologies to generate heat and electricity. Stochastic analyses for each of the geological reservoirs considered is also performed in a second set of simulations to account for subsurface uncertainties. Using a combination of advanced numerical simulators, we evaluate and compare the techno-economic performance of water-based geothermal systems (i.e., Conventional Hydrothermal Systems, Enhanced Geothermal Systems (EGS) and Advanced Geothermal Systems (AGS)) against their conceptual counterparts that use CO2 as the subsurface working fluid (i.e., CO2 Plume Geothermal (CPG), CO2-EGS, and CO2-AGS). Our results show that water-based energy extraction and open-loop configurations distinctly favor higher production temperatures owing to the superior thermodynamic properties of water and the ability to accommodate larger reservoir volumes, respectively. However, these operating conditions also exhibit lower heat-to-electricity conversion efficiencies, thereby significantly impacting economic returns when electricity generation is intended. In contrast, the value of CO2-based energy extraction and closed-loop configurations, to some extent overlooked in direct-heat-use applications, is considerably highlighted when targeting electricity production. Our work underscores the critical interplay between a geothermal reservoir's thermal and hydraulic performances across various system types. A comprehensive analysis of the relationships exposed in this study can assist geothermal operators in selecting appropriate end-user applications, predicting long-term reservoir performance, and ultimately enhancing the economic success of geothermal projects.
Assessing the Impact of Climate Change-Induced Droughts in the Gimcheon Watershed: A GIS and Remote Sensing Approach for Informed Water Resource Management
Authors: Talpur Zenobia , Eun Lee Jeong , Ahmed Mansoor , Chung Il-Moon
Published: March 2024

This study employs advanced Geographical Information Systems (GIS) and Remote Sensing (RS) techniques to thoroughly analyze the impact of climate change-induced droughts in the Gimcheon watershed within the Nakdong River Basin, South Korea. Using sophisticated statistical models and up-to-date climate projections, our research uncovers a significant 20% reduction in average annual precipitation over the past decade, leading to a concerning 15% decrease in water availability within the watershed. By integrating hydrological modeling and GIS, we identify a troubling 25% increase in the frequency of drought-affected areas within the watershed. Our socio-economic analysis further highlights the seriousness of these trends, with an estimated 30% decline in agricultural productivity and a consequent 10% reduction in income for communities directly dependent on water-intensive farming practices. In response to these alarming findings, our study recommends an Integrated Water Resources Management (IWRM) strategy, utilizing GIS to pinpoint strategic locations for innovative water-use efficiency measures. Statistical analysis underscores a significant 20% gap in existing water management practices, emphasizing the urgent need for targeted interventions. Furthermore, the integration of GIS-driven early warning systems demonstrates an impressive 40% improvement in response times to impending drought events. This abstract, supported by robust statistical figures, emphasizes the urgency of adopting GIS-informed IWRM strategies to effectively address the profound impacts of climate change-induced droughts in the Gimcheon watershed, offering valuable insights for policymakers and water resource managers in the face of evolving climate challenges. Acknowledgment : Research for this paper was carried out under the KICT Research Program (Development of IWRM-Korea Technical Convergence Platform Based on Digital New Deal) funded by the Ministry of Science and ICT. 
Transfer learning for Antarctic bed topography super-resolution
Authors: Bente Kim , Marchant Roman , Ramos Fabio
Published: March 2024

High-fidelity maps of Antarctica's subglacial bed topography constitute a critical input into a range of cryospheric models. For instance, ice flow models, which inform high-stakes sea level rise projections, rely on truthful and sufficiently detailed Digital Elevation Models (DEMs) of the ice-bedrock interface. Adversely, data collection of bed elevation profiles is extremely challenging, requiring airborne geophysical surveys of the vast landscape. Subsequent processing, as well as interpolation of the limited measurements onto a regular grid, introduce additional layers of uncertainty.While the prevalent continent-wide gridded data products for Antarctica's bed topography, like BedMachine or upcoming Bedmap3, which arise from laborious ongoing international collaborations, are limited to a spatial resolution of 500 meters, machine learning methods present new opportunities to go beyond existing resolutions. Super-resolution, a class of computer vision techniques, aims to increase the resolution of a given low-resolution image and thereby generate a high-resolution version of that image. With a grid of elevation values interpreted as a special case of a grid of pixel values (i.e. an image), super-resolution approaches can hence be applied to topography grids. While existing bed topography super-resolution approaches for Antarctica have been challenged by the lack of available gridded data at dense target resolutions, which are needed to train deep learning architectures, we propose a probabilistic approach based on Gaussian Processes (GPs), that generates more robust and uncertainty-aware high-resolution topographies without the need for gridded target resolution training data. In addition, our proposed method leverages abundant high-resolution ice surface data from satellites by transferring covariance patterns from the ice surface to the bed via a purpose-designed covariance function.We evaluate our multimodal Bayesian fusion model in a controlled topography reconstruction experiment over mountainous regions of East Antarctica, where we assess various models' skills to reconstruct original 500 m BedMachine topography, given the respective artificially degraded 1000 m, 1500 m, 2000 m, 2500 m, and 3000 m low-resolution input grids. As metrics we use root mean square error (RMSE), peak signal-to-noise ratio (PSNR), and the structural similarity index measure (SSIM) between reconstructions and withheld ground truth topographies. We compare our model against bilinear and bicubic interpolation baselines, DeepBedMap DEM and its multi-branch extension, MB_DeepBedMap DEM, as well as the Hybrid Attention Transformer (HAT), a pretrained state-of-the-art single image super-resolution model, for which we explore various fine-tuning strategies. Our results highlight the utility of our proposed uncertainty-aware and interpretable fusion model for the data-constrained endeavour of mapping Antarctica's subglacial bed topography at high resolutions.
Interaction between large strike-slip faults and reverse faulting shapes East Tibet since the Oligocene
Authors: Leloup Philippe Hervé
Published: March 2024

In seminal papers Paul Tapponnier revealed the existence of several hundred-km-long active strike-slip faults in and around the Tibetan plateau where, the India-Asia collision was expected to only produce reverse faults and folds (e.g., Tapponnier et Molnar, 1977). He latter further stressed out the role of such large strike-slip faults in the long-term building of the plateau, especially on its eastern side, (e.g., Tapponnier et al., 1982; 1990; 2001). the so-called extrusion tectonics model rises major discussions in particular on how SE Tibet evolved through time and in a more general way how the continental crust deforms. Completely different, sometime antagonistic, models have since been proposed for the geological and topographic evolution of SE Tibet, such as the channel flow hypothesis (e.g.; Royden et al., 1997; Clark and Royden, 2005). Central to this discussion are the Ailao Shan – Red River (ASRR) metamorphic belt and the eastern topographic margin of the Eastern Tibetan plateau.The ASRR has been interpreted as a major left-lateral faults allowing the ≥500 km lateral escape of Sundaland toward the SE during the Miocene (e.g.; Tapponnier et al. 1990, Leloup et al., 1995, 2001), linked with the opening of the South China Sea (Briais et al., 1993). On the other hand, other propose that the ASRR as a limited offset and / or is an exhumed piece of a lower crustal channel (e.g. Searle, 2006; Mazur et al., 2012; Chen et al., 2023).The eastern margin of the Tibetan plateau geological history is also disputed. It has been interpreted as a topographic step passively uplifted by eastward propagation of lower crustal channel flow during the Upper Miocene (e.g., Clark et al., 2005; Royden et al., 2008; Burchfiel et al., 2008). Other favour the existence of a ~800 km long thrust belt where thrusting take place since the Oligocene linked to crustal shortening (e.g.; Tapponnier et al., 2001; Liu-Zeng et al., 2008; Zhu et al., 2021; Pitard et al., 2021; Ge et al., 2023) in complex interaction with the Xianxuihe left-lateral fault since ~9 Ma (e.g.; Zhang et al., 2017).At the light of the thermochronological and geochronological data allowing to better constrain the timing of exhumation and deformation in the ASRR, the Xianshuihe fault and along the Eastern Tibetan margin we will discuss how the channel flow model appear flawed and how large strike-slip fault have interacted with reverse faults to shape eastern Tibet since the Oligocene.
Water in eclogitic garnet and clinopyroxene with oriented quartz and pargasite inclusions, W Norway
Authors: Spengler Dirk
Published: March 2024

This study analyses the H2O content in nominally anhydrous minerals (NAMs) in 10 eclogites from W Norway. Each sample has oriented lamellar to acicular inclusions in clinopyroxene, which are either quartz with pargasite or quartz/albite without pargasite [1]. Low-Al orthopyroxene and polycrystalline quartz inclusions in several of these samples provide evidence for UHP metamorphism in the stability fields of diamond and coesite. The H2O content is quantified using Fourier transform infrared spectroscopy (FTIR), unpolarised infrared radiation, spectra deconvolution, and the calibration of [2].Preliminary data was obtained from the analysis of the first 5 eclogites yield for garnet 22-379 and 16-31 µg g-1 structural H2O for samples with and without pargasite lamellae, respectively. The highest value occurs in a zoisite-bearing eclogite. If regarded separately, then the variation in the 4 zoisite-free eclogites shrinks to 16-32 µg g-1 H2O. Absorption bands characteristic for molecular H2O in garnet (centered at wavenumbers
Automated Mapping of Artificial Drainage in Peatlands Using Deep Learning and Very High-Resolution Aerial Imagery
Authors: Habib Wahaj , Connolly John
Published: March 2024

Peatlands, which cover a significant proportion of the wetland ecosystems globally, play a vital role in maintaining biodiversity and regulating water and climate. However, these ecosystems are currently undergoing degradation as a result of human activities, particularly the draining of peatlands for agricultural purposes, peat extraction, and forestry. Irish raised bogs, which constitute over half of the EU's oceanic raised bogs, have been extensively drained for various land-use activities. Efforts are being made to conserve these ecosystems by implementing measures such as rewetting, restoration, and rehabilitation. However, this requires the identification and accurate mapping of artificial drainage ditches. This study uses a U-net-based convolutional neural network to develop a very high-resolution map of the artificial drainage network in Irish raised bogs, covering an area of 523,000 hectares. The map also quantifies drainage in different land-use categories, such as industrial and domestic peat extraction. The results of this study will aid in implementing conservation activities, such as drain blocking to promote rewetting and improve carbon and greenhouse gas emission accounting at the national scale.
The IGSA-STD-1 Standard: Measuring Mars analog missions quality
Authors: Groemer Gernot
Published: March 2024

Analog research has become a common tool to devise scientific workflows and conducting technology developments for future planetary missions. In the past decade, a number of new analog facilities have emerge with a wide range of professionalism. Globally, more than a dozen active habitats are active, with a dozen missions annually each, with typically six to eight crew members. These are complemented with astronaut training field trips like the ESA PANGEA or CAVES initiative, or the NASA NEEMO missions.The scientific quality and safety standards vary, as reflected in scientific productivity and safety track record. In recent initiative, a new industry standard is evolving measuring performance and risk management dubbed the “International Guidelines and Standards in Analogs” (IGSA), that strives to establish common sets of metrics to help devise a robust science program, management and safety standards as well as checklists to raise awareness for quality in analog research.Such a guideline shall also help decision makers, mission managers and funding agencies aware of strength and weaknesses of analog campaigns. The first standard was approved for released in 2023 (IGSA-STD-1) by an international group of experienced analog mission managers and habitat directors, to a) improve space analog mission operations and safety, b) Create a more cohesive process across the space analog industry, c) Develop trust and integrity with the public, companies, nonprofit organizations, academia, researchers, innovators and funding agencies and d) develop a common language to measure and evaluate performance.Applicable to habitat owners, individuals, crews, researchers, innovators and mission organizers wanting to participate in or conduct an analog mission, the IGSA-STD-2 has the objectives to a) protect mission organizers, directors, and analog astronauts by ensuring safety and quality control, b) allow interoperability of research and missions among habitats, c) Promote international collaboration, d) improve fidelity of research, e) improve synergy, eliminate duplication of effort, and optimize resources and other.
Dynamics in human-shaped upper forest margins in southern European mountains
Authors: Urbinati Carlo , Tonelli Enrico , Garbarino Matteo , Vitali Alessandro
Published: March 2024

High-elevation areas in mountains ranges of the Mediterranean basin have been shaped by a millenary history of pastoral activities that have significantly depressed the upper forestlines. During the 20th century the same ecotones experienced the progressive decline of such practices and in some cases the following natural establishment of new cohorts of trees facilitated by the absence of relevant geo-morphological constrains and the influence of climate warming. These recent dynamics featured local differences since forestline expansion is strongly related to dispersal characteristics of the current forestline species, production of viable seeds, seed dispersal kernels and seedlings competition with ground vegetation. Our studies on upper forest margins of the Apennines (Italy) conducted over the last 20 years showed that Fagus sylvatica is the most widespread species (94%) but largely stable, whereas Pinus nigra, favored by extended plantations at lower altitude, is expanding upwards in treeless grasslands. We investigated this process in some upper ecotones sites in the Central Apennines largely controlled by microsite topography and the new tree proximity to the seed source. Similar processes occurred to other pine species naturally occurring in the Dinaric Alps of Albania and Montenegro and in the Iberian mountain ranges (Pyrenees and Iberian System) either as a high-altitude tree densification process or as a renewed treeline upshift. The increase of these young tree cohorts within the treeline ecotones can induce changes in major biogeochemical cycles, soil properties and mountain eco-hydrological processes, leading to a significant long-term plant diversity decline in species-rich grasslands. Forest densification could improve slope protection against landslides and snow avalanches but also produce more fuel and, together with climate warming, increase the risk of mountain wildfires. Further analysis at different spatio-temporal scales are necessary including better estimation of demographic and dispersal parameters in similar low-density expanding tree populations.
Identification and characterization of hailstorms over France using DPR-GPM sensor
Authors: Rivero Ordaz Laura , Merino Andrés , Navarro Andrés , Tapiador Francisco Javier , Sánchez José Luis , García-Ortega Eduardo
Published: March 2024

Severe weather events, particularly hailstorms with large hydrometeors, cause heavy losses worldwide. The south of France is one of the European regions most affected by these hydrometeors and is also one of the most studied because an extensive hailpad network of detection devices that has been in operation there for more than three decades. These direct observations are extremely useful because provide a very complete and reliable "ground truth". Space-based sensors are becoming increasingly important in monitoring hailstorms. Global Precipitation Measurement (GPM) is an international mission designed to advance precipitation measurements from multispectral sensors. The GPM core satellite carries a powerful and unprecedented Dual-Frequency Precipitation Radar (DPR) for studying 3D precipitation characteristics. Furthermore, it improves the accuracy of precipitation estimation and facilitates the analysis of the microphysical structure of clouds. The objective of the present work was to evaluate the DPR sensor capability in identifying hailstorms. Data from more than 1000 hailpads during eight field campaigns in southern France were used. We identified eight hailstorms over France where DPR data were coincident with ground-based observations from hailpad network during 2014–2021. In addition, variables provided by the DPR sensor indicative of hail presence were studied. The Ku band demonstrated greater capacity in identifying hailstorms. Storms with larger reflectivity values (≥50 dBZ, Ku band), both near the surface and throughout the vertical column, were those with a more clearly defined vertical structure and thus more powerful convective development. The intensity of these hailstorms was confirmed with the ground-based data. This work could contribute to enhancing the detection and prediction of hailstorms, thereby helping to mitigate the associated risks.
Selecting optimal displacement models using an improved stochastic model in InSAR arc-based time series analysis
Authors: Brouwer Wietske , Chang Ling , Hanssen Ramon
Published: March 2024

In InSAR time series analysis for displacement studies, the essential parameter estimation part is performed on arcs between a reference point and an evaluation point. Usually, both points are scatterers that satisfy certain optimality conditions. The set of parameters to be estimated in the functional model can be different for each arc. Especially in the built environment, individual points may behave rather differently. Conventional approaches to estimate average displacement velocities for the entire time series length are therefore often sub-optimal. However, deviation from a single uniform parameterization for all arcs implies that for each arc the optimal model needs to be selected.Chang and Hanssen (2016) proposed a method to select the optimal functional model, i.e. parameterization, for each arc using multiple hypothesis testing (MHT). The selection was based on rejecting the conventional null hypothesis of linear steady-state displacement, satisfying Newton’s first law, in favor of an alternative hypothesis that is chosen from a library of canonical models. This procedure required the a priori selection of a significance level (related to the impact of the erroneous rejection of the null hypothesis), the discriminatory power (related to the impact of erroneously sustaining the null hypothesis), and the stochastic model of the arc observations. For the latter, a conservative uniform approximation was chosen.Recently, Brouwer and Hanssen (2023) developed a methodology to approximate the stochastic model for each scatterer in an InSAR time series analysis, based on amplitude behavior. By combining both approaches, i.e., applying the MHT approach for functional model selection using a point- and epoch-specific stochastic model, we significantly reduce both Type-1 and Type-2 errors, leading to the improved identification of dynamic mechanisms in a complex environment. We report on the mathematical background, the level of improvement in practical case studies as well as the numerical consequences of the approach.References:W.S. Brouwer, Y. Wang, F.J. van Leijen, and R.F. Hanssen. ”On the stochastic model for InSAR single arc point scatterer time series.” In IGARSS 2023-2023 IEEE International Geoscience and Remote Sensing Symposium, pp. 7902-7905. IEEE, 2023.L. Chang and R.F. Hanssen, ”A Probabilistic Approach for InSAR Time-Series Postprocessing,” in IEEE Transactions on Geoscience and Remote Sensing, vol. 54, no. 1, pp. 421-430, Jan. 2016, doi: 10.1109/TGRS.2015.2459037.
Concrete as a soil amendment for carbon capture: learnings from year one of an enhanced weathering field trial in County Wexford, Ireland.
Authors: Magee Ruadhan , Bryson Maurice , Hickey Leo , Chondrogiannis Christos , O'Dea Katie , van Acken David , McDermott Frank
Published: March 2024

In this study, we investigate the use of milled returned concrete as an enhanced weathering soil amendment, on two arable fields in County Wexford, Ireland. The applied concrete consists of portlandite (Ca(OH)2) cement with limestone (CaCO3) aggregate. The high cation concentration and rapid weathering kinetics of both components indicate good potential for carbonic acid neutralisation and atmospheric CO2 removal as soil-water dissolved bicarbonate (HCO3-). In spring 2023, prior to crop planting (oats and barley), both trial fields were divided into two sections. Milled returned concrete was applied to a ‘treatment’ section while no concrete was applied to a ‘control’ section. All other farming practices (ploughing, tilling, sowing and fertilisation) were equivalent across control and treatment. Twelve suction-cup lysimeters were installed in each field (6 control and 6 treatment) to collect soil-water samples across the growing season and the concentrations of bicarbonate, major cations and anions were measured to assess carbon removal. Preliminary results indicate that where nitrate (NO3-) levels are low in concrete amended sites, bicarbonate concentrations are elevated above control. However, where soil-nitrate levels are high, weathering liberated cations are balanced by nitrate, and bicarbonate production is suppressed. Our findings highlight the importance of fertiliser management for optimising CO2 removal outcomes of enhanced weathering.
Due diligence for deforestation-free supply chainswith Sentinel-2 imagery from the Copernicus DataSpace Ecosystem 
Authors: Balzter Heiko , Acil Nezha , Reading Ivan , Bika Konstantina , Drakesmith Toby , McNeill Chris , Cheesbrough Sarah , Byrne Justin
Published: March 2024

At COP26, the Glasgow Leaders Declaration committed the majority of the world’s nations toending deforestation by 2030. On 29 June 2023, the EU Regulation on deforestation-freeproducts (EU) 2023/1115 entered into force. The main driver of deforestation and forestdegradation is the expansion of agricultural land for the production of commodities like cattle,wood, cocoa, soy, palm oil, coffee, rubber and derived products. Any trader wishing to sell thesecommodities on the EU single market or export from within it, must prove that the products donot originate from recently deforested land or have contributed to forest degradation.Satellite imagery provides the means of addressing the implementation of the EU Regulationon deforestation-free supply chains, and of strengthening forest governance through theprovision of timely information to national forest services. We present the PyEO near-real-timeforest alert system from Sentinel-2, a current operational application to reduce illegal logging inKenya, and its potential to support im- and exporters in demonstrating deforestation-free supplychains developed by the ForestMind project.The software implementation used the Python for Earth Observation (PyEO) library toautomatically extract information on forest loss from Sentinel-2 satellite imagery. It queries theCopernicus Data Space Ecosystem for new imagery, downloads the automatically selectedSentinel-2 images, applies a previously trained random forest machine learning model to detectforest loss, and generates a multi-layer analyst report.For the forest law enforcement in Kenya, the latest forest alerts are sifted and prioritised bythe Kenya Forest Service’s Forest Information Centre in Nairobi, and delegated to forest rangersin the field for investigation. Forest rangers navigate to the field site inside the forest reserve,accompanied by a local community scout, and report back to head office with their observationsand whether any arrests for illegal logging were made. Since its introduction in Kwale County in2019, over 2000 forest alerts have been investigated. The dominant cause of the deforestation isillegal logging, followed by charcoal production.For the due diligence application, a Forest Analyst can then use the analyst-report andadditional software tools to create company reports suitable for communication to im- andexporters for monitoring the impact of their supply chains on deforestation and forestdegradation. 
Addressing Water Scarcity in Texas: Evaluating Stormwater Quality and Quantity for Managed Aquifer Recharge
Authors: Scanlon Bridget , John Malito John , Fakhreddine Sarah
Published: March 2024

The use of stormwater for enhancing aquifer recharge has emerged as an innovative method of addressing increasing water scarcity in Texas. However, there is limited information on the potential (1) volumes of stormwater available for capture and (2) impacts of enhanced stormwater on groundwater quality. Specifically, the relationships between stormwater contaminant loading and various controlling factors have not been well characterized in Texas, along with the feasibility of stormwater capture. In this study we used publicly available datasets of streamflows and water quality to identify and inform opportunities for enhanced stormwater recharge in Texas aquifers. Further, we evaluated the potential availability of stormwater for capture. To do this, we evaluated publicly available water availability models (to ensure compliance with water rights) and environmental flow recommendations to reduce downstream ecological impacts. Statistical analyses show that individual site characteristics such as land cover likely impact various stormwater quality parameters including nutrients, metals, and microbial contaminants. The exact composition of stormwater quality can vary depending on the monitoring station and combination of different event, watershed, and site characteristics. Case studies of water quality in various stormwater recharge structures show that interim storage and partial pre-treatment of excess stormwater before injection can prevent degradation of groundwater quality. Furthermore, unappropriated high magnitude flows are often co-located with depleted major aquifers in Texas, including the Texas Gulf Coast and Trinity aquifers, underscoring the potential for using flood water for managed aquifer recharge to support sustainable water resources. Accordingly, this study provides a foundation for using large publicly available datasets to better understand and inform opportunities for enhanced stormwater recharge in Texas.
Evaluating Sentinel-1 volume scattering based snow depth retrievals over NASA SnowEx sites
Authors: Hoppinen Zachary , Palomaki Ross , Tarricone Jack , Brencher George , Dunmire Devon , Gagliano Eric , Marziliano Adrian , Adebisi Naheem , Bonnell Randall , Marshall Hans-Peter
Published: March 2024

Synthetic aperture radar will be at the forefront of future advancements in globalremote sensing of snow depth and snow water equivalent. Recently, snow depthretrievals using an empirical volume scattering approach with C-band Sentinel-1 (S1)data have been demonstrated over the European Alps and Northern Hemisphere, withthe most accurate results obtained in regions with dry, deep (>1.5 m) snowpacks andlittle vegetation influence. However, these S1-based snow depth retrievals havepreviously been compared only to point-based measurements or modeled snow depthproducts. In this study we develop an open-source version of the S1 snow depthretrieval technique and compare the results to spatially-distributed lidar snow depthmeasurements. The highly accurate and fine resolution lidar datasets were collectedduring the NASA Snow SnowEx 2020 and 2021 field campaigns at six study sitesacross the western United States. These regions represent different snow environmentsand characteristics than the datasets used for comparison in previous investigations.We compare the S1 and lidar snow depths at a range of spatial resolutions and interpretthe results within the context of snowpack, vegetation, and terrain characteristics. At 90m resolution, comparisons between lidar and S1 snow depth retrievals show low tomoderate correlations (R = 0.38) and high RMSE (0.98 m) averaged across the studysites, with improved performance at 500 m resolution (R = 0.59, RMSE = 0.69 m). Thedistribution of S1 and lidar snow depths are more similar in regions of deeper snow,lower forest coverage, higher incidence angles, dry snow, and at coarser spatialresolutions. Our results highlight limitations of the current S1 snow depth algorithm andpresent opportunities to improve the technique for future snow depth retrievals acrossvaried snow environments.
A Local Data Assimilation Method (Local DA v1.0) and its Application in a Simulated Typhoon Case
Authors: Wang Shizhang , Qiao Xiaoshi
Published: March 2024

Integrating the hybrid and multiscale analyses and the parallel computation is necessary for current data assimilation schemes. A local data assimilation method, Local DA, is designed to fulfill these needs. This algorithm follows the grid-independent framework of the local ensemble transform Kalman filter (LETKF) and is more flexible in hybrid analysis than the LETKF. Local DA employs an explicitly computed background error correlation matrix of model variables mapped to observed grid points/columns. This matrix allows Local DA to calculate static covariance with a preset correlation function. It also allows using the conjugate gradient (CG) method to solve the cost function and allows performing localization in model space, observation space, or both spaces (double-space localization). The Local DA performance is evaluated with a simulated multiscale observation network that includes sounding, wind profiler, precipitable water vapor, and radar observations. In the presence of a small-size time-lagged ensemble, Local DA can produce a small analysis error by combining multiscale hybrid covariance and double-space localization. The multiscale covariance is computed using error samples decomposed into several scales and independently assigning the localization radius for each scale. Multiscale covariance is conducive to error reduction, especially at a small scale. The results further indicate that applying the CG method for each local analysis does not result in a discontinuity issue. The wall clock time of Local DA implemented in parallel is halved as the number of cores doubles, indicating a reasonable parallel computational efficiency of Local DA.
Calibration of Convective-scale Hourly Precipitation Based on the Frequency-Matching Method
Authors: Qiao Xiaoshi , Wang Shizhang , Zeng Mingjian
Published: March 2024

Calibration of convective-scale hourly precipitation based on the frequency-matching method was carried on using CMPASS observation and CMA-MESO 3km forecast data. The character of hourly precipitation bias was studied.The effect of frequency-matching method (FMM) on the bias correction of CMA-MESO 3km hourly precipitation forecasts was analyzed. In the bias characteristic analysis, the differences in precipitation intensity in different regions of the country and the differences in precipitation in different months were considered. The whole country was divided into 7 sub-regions for monthly analysis. In the bias correction based on the frequency-matching method, the daily variations of precipitation bias and the impact of increasing and decreasing precipitation values on the corrected precipitation scores were analyzed. The results show that CMA-MESO 3km forecasts have a wet bias in light rainfall in the cold season, while a dry bias dominates in moderate to heavy rainfall. In the warm season, except for the Tibet region, the hourly precipitation forecast bias of CMA-MESO 3km shows significant daily variations, with more precipitation in the afternoon and less at night and in the morning, especially for heavy rainfall. Therefore, whether to consider the daily variations of precipitation bias in the use of FMM correction mainly reflects in the summer, especially at night and in the morning. Considering the daily variations of precipitation bias is beneficial to improving the forecast skills (TS scores) for nighttime and morning in the summer. Further analysis shows that the positive contribution of FMM correction to forecast scores mainly comes from the increase in frequency adjustment, especially for heavy rainfall. However, for light rainfall with wet bias, FMM often results in negative contribution. Therefore, FMM has a significant improvement effect on heavy rainfall in winter and nighttime rainfall in summer. The reason for this result is that the hit rate of CMA-MESO hourly precipitation forecast is low, and the false alarm rate is generally high, especially for heavy rainfall. In this case, the increased precipitation significantly increases the hit rate, while the false alarm rate increases to a lesser extent, thereby improving the precipitation scores.
Aerosol particle effects on the atmospheric budgets of H2O2 and HOx radicals: studies utilizing a newly-developed multiphase model
Authors: Song Huan , Lu Keding , Dong Huabin , Tan Zhaofeng , Chen Zhongmin , Zeng Limin , Zhang Yuanhang
Published: March 2024

Hydrogen peroxide (H2O2), hydroxyl radicals (OH), hydroperoxyl (HO2), and superoxide (O2-) radicals interacting with aerosol particles significantly affect the atmospheric pollutant budgets. A multiphase chemical kinetic box model (PKU-MARK), including the multiphase processes of transition metal ions (TMI) and their organic complexes (TMI-OrC), was built to numerically drive H2O2 chemical behaviors in the aerosol particle liquid phase using observational data obtained from a field campaign in rural China. Instead of relying on fixed uptake coefficient values, a thorough simulation of multiphase H2O2 chemistry was performed. In the aerosol liquid phase, light-driven TMI-OrC reactions promote OH, HO2/O2-, and H2O­2 recycling and spontaneous regenerations. The in-situ generated aerosol H2O2 would offset gas-phase H2O2 molecular transfer into the aerosol bulk phase and promote the gas-phase level. When combined with the multiphase loss and in-situ aerosol generation involving the TMI-OrC mechanism, the HULIS-Mode significantly improves the consistency between modeled and measured gas-phase H2O2 levels. The aerosol liquid phase could be a pivotal potential source of aqueous H2O­2 and influence the multiphase budgets. Our work highlights the intricate and significant effects of aerosol TMI and TMI-OrC interactions on the multiphase partitioning of H2O2 when assessing atmospheric oxidant capacity.
Climate responses to a rapid phaseout of sulfur in shipping emissions: A large ensemble study
Authors: Watson-Parris Duncan , Samset Bjørn H. , Allen Robert , Bollasina Massimo , Ekman Annica , Iles Carley , Joshi Manoj , Lewinschal Anna , Lund Marianne T. , Merikanto Joonas , Nordling Kalle , Persad Geeta , Stjern Camilla W. , Westervelt Dan , Wilcox Laura J. , Williams Andrew
Published: March 2024

In 2020, motivated by improving air quality in major ports and shipping lanes, the International Maritime Organization imposed strict new regulations on the sulfur content of shipping fuel. This led to a rapid reduction in the number of observed ship tracks (linear tracks of clouds brightened by aerosol perturbations; Watson-Parris et al. 2022), and presumably a commensurate reduction in anthropogenic aerosol forcing. The magnitude of this forcing, and the resulting temperature change, are uncertain however. The recent confirmation that 2023 was the hottest year on record can only partly be explained by the onset of the El Niño phase of the El Niño-Southern Oscillation (ENSO). Such warming, in addition to the sizable warming in NH ocean basins- geographically collocated with shipping- raise the question of how much shipping emissions changes might have contributed to this signal, and any extreme weather events associated with it. In this study we aim to answer this question by utilizing a large ensemble of fully-coupled Community Earth System Model version 2 (CESM2) simulations with and without the shipping emissions changes. We leverage the CESM2 large ensemble and choose 20 simulations with varying ENSO conditions from which to branch off with shipping emissions reduced to 20% of their baseline value. These are integrated forward for another 20 years, while non-shipping emissions follow the SSP3-7.0 scenario, in order to robustly explore the transient climate response.In this talk we will highlight the forced climate response, focusing on temperature (T), precipitation (P), and atmospheric circulation, both globally and in key regions such as the North Atlantic. Given the change in ENSO phase during 2023, we will also describe how this climate response is modulated by different ENSO conditions, the Atlantic Multidecadal Variability and other modes of climate variability. The underlying relevant climate processes, including cloud dynamics, radiative imbalances at the top of the atmosphere, and daily variability will be summarized to link our single model study to observed changes.References:[1] Watson-Parris, D., Christensen, M., Laurenson, A., Clewley, D., Gryspeerdt, E., Stier, P. “Shipping regulations lead to large reduction in cloud perturbations”. PNAS 119 (41) e2206885119: https://doi.org/10.1073/pnas.2206885119 (2022)
The geological potential of in-situ CO2 mineral storage within onshore UK formations
Authors: Montgomery Angus W. , Holdsworth Chris , Martin-Roberts Emma , Watt Ian , Gilfillan Stuart
Published: March 2024

Carbon capture and storage (CCS) is essential for meeting the UK’s legally binding net-zero targets by 2050. In-situ mineralisaton of CO2 in mafic rock has been established as a rapid, secure, and affordable method of geological CO2 storage by the Carbfix projects in Iceland. In this study, we use geochemical, stratigraphic, and volumetric analyses to assess the suitability of UK onshore mafic and ultramafic formations for in-situ mineral storage of CO2. We find that the total Mg2+, Ca2+, Fetot. oxide content of some UK formations is comparable to the geological reservoirs utilised by Carbfix in Iceland. We determine the volumes of the studied formations using a combination of boreholes, digitised cross sections and GIS calculated surface areas. We find that there are significant volumes of reactive rock available for CO2 mineral storage in the UK.Using a method developed by Callow et al. (2018) we determine the reactive surface area within connected pore volumes in the most suitable formations for in-situ CO2 mineral storage. Our results indicate that onshore UK formations have the theoretical potential to store multiple gigatonnes (Gt) of CO2. This is equivalent to the storage of decades’ worth of annual UK industrial CO2 emissions. Our findings highlight that in the Antrim Lava Group alone, there is between 1.6 and 21.9 million km2 of reactive surface area available for CO2 mineral storage. This equates to a potential theoretical CO2 storage capacity of between 8 and 110 GtCO2. These results demonstrate that the theoretical CO2 mineral storage capacity of onshore mafic and ultramafic rocks in the UK far exceeds the CO2 storage requirement for the UK to achieve net-zero GHG emissions by 2050. Future research efforts should prioritise the investigation of connected porosity, reactive surface area, impact by alteration and mineralisation rates specific to the formations identified by this study.