166 research outputs found
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Immersive VR Geovisualization for Landscape Restoration: From Meshes to Meaning
Full text linkLandscape restoration in semi-arid environments demands not only effective interventions but also communicative approaches that make complexity legible and foster ecological literacy. Immersive technologies such as Virtual Reality (VR) transform geospatial data into navigable environments, making processes tangible and transferable experiences. This study translates a remote restoration site in the Murcia region of Spain, characterized by limited accessibility and harsh conditions, into an immersive Virtual Reality geovisualization. A UAV structure-from-motion survey produced a high-resolution textured model that was geospatially situated with BlenderGIS (ESRI imagery over a 30 m digital elevation model) and then reconstructed in Twinmotion under a calibrated HDRI skydome. The scene is engineered for room-scale use with teleport-only locomotion and a uniform down-scale that enables hand-scale inspection of swales, ponds, ground cover, tree rows, and micro-topography. Three access points organize short narrated sequences that guide users from landscape overview to near-field readings, converting mesh into meaning. A pilot on Meta Quest 3 yielded encouraging signals: high presence and perceived realism, low discomfort, positive self-reported competence, and intent to re-engage. The pilot also surfaced technical priorities, including tighter blending between the VR layers, and bias-aware context. The contribution is a reproducible workflow that combines geospatial context and proxemic design to support spatial reasoning, knowledge transfer, and public communication. Strategically, the module offers a path toward a participatory, education-ready XR tool for regenerative practice, and a future platform for stakeholders, to support cognitive spatial reasoning and field-digital decision-making
Mapping Post-Rainfall Recovery in Arid Regions Using a Hierarchical U-Net
Full text linkThe United Arab Emirates (UAE) experienced an extreme rainfall event between April 15 and 17, 2024, and that resulted in severe flooding in its coastal regions. Dubai was among the most affected regions. This study applies a hierarchical deep learning model on PlanetScope imagery to detect flood inundation, quantify flood extent by land cover, and examine short-term recovery dynamics. While earlier work detailed the methodological development of a hierarchical U-Net model (Hong et al., in press), here we emphasize its application for monitoring resilience trajectories in an arid urban environment. Results show that approximately 22 km2 of land was flooded, with bare ground and built area most affected, while vegetation demonstrated greater resilience. Recovery dynamics reveal that vegetation and built area recovered rapidly within the first week, whereas bare ground recovered more slowly but continued to improve through the ten-day monitoring period. These findings highlight the importance of integrating fine-resolution satellite monitoring with deep learning approaches to better understand disaster recovery and inform urban resilience planning in desert cities
Implementing riverine biogeochemical inputs in ECCO-Darwin: a sensitivity analysis of terrestrial fluxes in a data-assimilative global ocean biogeochemistry model
Full text linkTerrestrial sources of carbon and nutrients drive biogeochemical cycles in coastal regions and in the global ocean. Quantifying their impact on the spatiotemporal variability of the ocean carbon cycle is pivotal to understanding the distinctive characteristics of ocean basins dominated by riverine inflow. ECCO-Darwin is a data-constrained, global-ocean biogeochemistry model that has heretofore lacked lateral inputs of carbon and nutrients. The objective of this study is to add this new capability to ECCO-Darwin and to carry out a suite of sensitivity experiments in order to quantify the impact of these lateral fluxes on coastal- and open-ocean biogeochemistry. In this work, we use an optimized version of the data-assimilative global-ocean biogeochemistry ECCO-Darwin model to perform a sensitivity analysis of the ocean to lateral inputs of carbon and nutrients. We generate riverine inputs by combining daily point-source freshwater discharge from JRA55-do with the Global NEWS 2 watershed model, accounting for lateral inputs from 5171 watersheds worldwide. The addition of riverine inputs drives a small CO2 outgassing (+0.02 Pg C yr−1) due to compensating processes at regional scales. In basins dominated by carbon runoff, such as the Tropical Atlantic and Arctic Oceans, the addition of riverine inputs increases CO2 outgassing (+13 % and +9 %, respectively). In contrast, runoff in nutrient-dominated Southeast Asia leads to increased CO2 uptake (+9 %). This new riverine biogeochemical input capability will enable future ECCO-Darwin solutions to better capture key processes that occur along coastal margins in global oceans.</p
Numerical simulation of a severe blowing snow event over the Prydz Bay Region
Full text linkAntarctic blowing snow is a critical process regulating the mass balance of the ice sheet. From 15–17 July 2022, a mid-latitude cyclone invaded the Prydz Bay region of East Antarctica. Automatic weather stations at Zhongshan Station recorded a maximum minute-averaged wind speed exceeding 30 m s−1, while lidar ceilometer data and manual observations indicated that blowing snow persisted for approximately 36 h, marking the most intense blowing snow event of that year. This study reproduced the process using the CRYOWRF model and found that the strong winds induced by the cyclone triggered blowing snow and generated complex nonlinear motions under the influence of local topography, in turn shaping the transport of blowing snow. Topographically forced strong winds also triggered heavy snowfall, which replenished the wind-eroded snow layer. After deposition, this snow was more easily entrained by winds, mixing with falling snow to form blizzards. These results highlight the complexity of blowing snow processes in Antarctic coastal zones, which encompass topographic forcing on atmospheric circulation as well as dynamic feedback between snowfall and blowing snow. Therefore, adopting high-resolution non-hydrostatic numerical models combined with multi-source observations to accurately capture the key physical details of this complex process is of irreplaceable significance for the precise assessment of the Antarctic regional surface mass balance.</p
Carbon emission reduction requires attention to the contribution of natural gas use: Combustion and leakage
Full text linkNatural gas will continue to replace coal in the process of global energy structure reform, but its leakage potential can delay the realization of global carbon neutrality. To quantify its impact, we established a carbon dioxide (CO2) and methane (CH4) flux detection platform on the 220 m platform of the Institute of Atmospheric Physics, Chinese Academy of Sciences, located in northwestern Beijing. The observation results indicated that the daily mean CO2 and CH4 fluxes were 12.21 ± 1.75 µmol m−2 s−1 and 95.54 ± 18.92 nmol m−2 s−1, respectively. The fluxes were significantly correlated with natural gas consumption, indicating that natural gas has become a common source of CH4 and CO2, the combustion of which releases CO2, while its leakage processes emit CH4. Vehicle-based identification demonstrated that CH4 can escape at the production, storage and use stages of natural gas. Based on natural gas consumption data, the upper limit of the calculated natural gas leakage rate in Beijing reached 1.12 % ± 0.22 %, indicating that the contribution of CH4 to climate change could reach 23 % of that of CO2 on a 20-year scale. Natural gas leakage was estimated to delay the time for China to achieve carbon neutrality by at least almost four years.</p
Bordering the academy: comment la frontière de Damoclès empêche de travailler sur la Palestine?
Full text linkAs the author carried out a research project on the question of borders in Palestine at the end of the 2010 decade, she offers a retrospective of her experience in the form of commented narrative reconstructions. Through this process, the author describes and analyzes the processes of silenciation and the obstacles encountered in conducting research on Palestine. The author first examines the physical entry to the study territory, to show how this moment involved a negotiation between ethics and lies. In addition to the classic considerations surrounding access to the field, this example will then reveal the institutional obstacles to the deployment of research that the author had to face, involving strong pressure from her peers and institutional disengagement. Finally, the author takes a look back to the formulation of the concept of «border of Damocles» to describe the experience of borders that cross the Palestinian space and the bodies that inhabit it. She proposes to apply this concept to academic space itself. In so doing, the author intends to raise questions about what is and what is not legitimate within the academic space, drawing a line between legitimate and illegitimate debate and reflecting on the power relations that act as structural brakes on the production of knowledge.</p
An operational global L-band soil moisture and vegetation optical depth dataset from optimized 40° SMOS brightness temperatures
Full text linkThe Soil Moisture and Ocean Salinity (SMOS) mission delivers the first multi-angular L-band observations for retrieving global soil moisture (SM) and vegetation optical depth (VOD), two critical variables for understanding terrestrial water and carbon cycles. However, the combined effects of non-identical fields of view and aliasing in multi-angular SMOS brightness temperature (TB) observations can introduce noise and biases when the TBs are averaged to a nominal incidence angle, as done in the SMOS L3 dataset, thereby degrading land parameter retrievals. To address this issue, an optimized SMOS TB dataset was initially produced at a fixed 40° incidence angle, consistent with the Soil Moisture Active Passive (SMAP) mission. We then developed the first SMOS mono-angular SM and VOD products designed to achieve performance comparable to SMAP and improved relative to conventional multi-angle SMOS retrievals. The 40° TB optimization was performed using the L-band Microwave Emission of the Biosphere (L-MEB) model, and the inversion relied on the SMAP-INRAE-BORDEAUX (SMAP-IB) algorithm, yielding a global 40° SMOS TB record and associated SM and VOD products for 2010–2024 at 25 km spatial resolution, collectively referred to as SMOS-IB. Results showed that the optimized 40° TB reached a performance level comparable to SMAP and improved relative to SMOS-L3, both in its sensitivity to in-situ SM from the International Soil Moisture Network (ISMN) and in the reduction of global pixel-scale noise. When multiple evaluation metrics are considered, the SMOS-IB SM and VOD data, benefiting from the use of the optimized TB as input and a newly optimized soil roughness (Hr) parameterization, showed improved performance compared with those derived from SMOS L3 40° TB or from the multi-angular SMOS products. The SMOS-IB TB, SM and VOD products can be used for L-band algorithm development and SMAP harmonization, global drought monitoring, and studies of vegetation water and biomass dynamics. SMOS-IB is publicly available at https://zenodo.org/records/17647385 (Xing et al., 2025)
Impact of Wake Impingement on the Fatigue Loads in the Main Bearings and Blades of Offshore Wind Turbines
Full text linkThis study investigates the impact of wake impingement on the fatigue loads in the components of offshore wind turbines. More specifically, the focus of this paper lies on the blades and the main bearings of a geared wind turbine. For this, OpenFAST models of a deployed 8.4 MW turbine are coupled in the mid-fidelity wind farm simulation tool FAST.Farm, to simulate load time series of turbines operating under wake interactions. A two-step analysis is carried out: first, a parametric study explores the influence of turbine spacing and wake overlap on fatigue loads; second, a case study applies the framework to a real offshore wind farm in the Belgian North Sea. Results show that wake interactions can reduce blade root loading at below-rated wind speeds but significantly increase fatigue damage above rated conditions, while main bearing lifetimes exhibit strong asymmetries depending on the side of wake impingement. The farm-level analysis highlights spatial variability in component degradation, showcasing how damage maps can be obtained that link turbine position, inflow direction, and operating conditions to expected lifetime reductions. These findings underline the importance of considering wake effects in design, lifetime assessment, and operation and maintenance planning of offshore wind farms
A Year-Long Eddy Covariance Dataset over an Alpine Steppe: A Landscape Perspective on Carbon and Energy Fluxes
Full text linkThe Tibetan Plateau (TP) is warming rapidly, with future projections suggesting continued warming that may amplify climate–carbon feedbacks. However, sparse in-situ observations and pronounced spatial heterogeneity in vegetation, soil moisture, and climate have limited our understanding of these ecosystem responses. Here, we present a continuous record of carbon and energy fluxes measured at a landscape scale (~30 ha / 0.3 km2) in an alpine steppe ecosystem on the TP from July 2018 to June 2019. Flux measurements were quality-filtered and gap-filled to produce a complete seasonal record using two complementary approaches, marginal distribution sampling (MDS) and random forest (RF). Eddy covariance measurements represent integrated fluxes over their footprint area, which are often much smaller than most model grids or remote sensing pixels, particularly in grassland ecosystems. Owing to the higher measurement height (19 m) at this site, the measurement footprint closely aligns with the spatial resolution of Moderate Resolution Imaging Spectroradiometer (MODIS) products, facilitating more consistent landscape-scale comparisons. The data include quality flags indicating observed or gap-filled values, uncertainty estimates, footprint diagnostics, and auxiliary meteorological variables. The data described in this manuscript provides a robust foundation for examining carbon–climate interactions in alpine environments, supporting ecosystem modeling and satellite product validation
Methane fluxes from Arctic & boreal North America: comparisons between process-based estimates and atmospheric observations
Full text linkMethane (CH4) flux estimates from high-latitude North American wetlands remain highly uncertain in magnitude, seasonality, and spatial distribution. In this study, we evaluate a decade (2007–2017) of CH4 flux estimates by comparing 16 process-based models with atmospheric CH4 observations collected from in situ towers. We compare the Global Carbon Project (GCP) process-based models with a model inter-comparison from a decade earlier called The Wetland and Wetland CH4 Intercomparison of Models Project (WETCHIMP). Our analysis reveals that the GCP models have a much smaller inter-model uncertainty and have an average magnitude that is a factor of 1.5 smaller across Canada and Alaska. However, current GCP models likely overestimate wetland fluxes by a factor of two or more across Canada and Alaska based on tower-based atmospheric CH4 observations. The differences in flux magnitudes among GCP models are more likely driven by uncertainties in the amount of soil carbon or spatial extent of inundation than in temperature relationships, such as Q10 factors. The GCP models do not agree on the timing and amplitude of the seasonal cycle, and we find that models with a seasonal peak in July and August show the best agreement with atmospheric observations. Models that exhibit the best fit to atmospheric observation also have a similar spatial distribution; these models concentrate fluxes near Canada's Hudson Bay Lowlands. Current, state-of-the-art process-based models are much more consistent with atmospheric observations than models from a decade ago, but our analysis shows that there are still numerous opportunities for improvement.</p