Technology has recently enabled operators to deploy second-resolution SCADA metrics at scale and at reasonable cost, offering finer-grained statistical descriptors for applications such as load and bending-moment estimation and operational-state classification. This is also an opportunity to revisit fatigue monitoring at a SCADA-aligned timescale that is short enough to limit the truncation of boundary-spanning cycles inherent to conventional window-based counting yet aggregated enough to remain deployable across large fleets. This paper benchmarks the families of methods that make this opportunity actionable against continuous rainflow as a baseline: conventional window-based counting, a modified low-frequency fatigue dynamics (LFFD) formulation that explicitly resolves intra-window and inter-window damage contributions, and two reduced-information extrema-sequence representations – start–peaks–valleys–end (SPVE) and maximum and minimum (max–min). The evaluation uses 6 months of 15 s windows of SCADA and strain measurements acquired at 50 Hz from two instrumented tower sections of an onshore wind turbine and examines the influence of window length, sampling rate, and fatigue-curve formulation on damage computation (Eurocode 3 vs. DNV).
Reducing the aggregation window from 10 min to 15 s alone does not improve fatigue estimation, as shorter windows increasingly truncate the low-frequency cycles that dominate accumulated damage. The proposed modified LFFD framework overcomes this limitation by preserving cycle continuity across window boundaries, recovering 99.5 % of the baseline damage while requiring only a fraction of the original data volume. Reduced-information representations further demonstrate that fatigue can be estimated with high accuracy using strongly compressed stress sequences, with start–peaks–valleys–end (SPVE) and maximum and minimum (max–min) retaining 98.8 % and 96.2 % of the baseline damage, respectively. These findings establish a practical pathway towards SCADA-aligned, data-efficient fatigue monitoring, enabling scalable lifetime assessment across large wind turbine fleets while preserving the fidelity of high-resolution structural measurements.
]]>This article proposes a novel methodology capable of producing accurate predictions of the four previously mentioned biomass components, that integrates multi-source remote sensing observations acquired by synthetic aperture radars (ALOS-2 and Sentinel- 1), multispectral sensors (Sentinel-2), and ancillary data (on topography and land cover). The methodology resorts to an extreme gradient boosting regressor trained with field inventory data for the AGBD and BGBD, and ALS data for the LBD and SBD. It was demonstrated the importance of including all the proposed datasets and that reliable predictions were achieved. Namely, %RMSEs of 63.29%, 62.90%, 41.07%, 28.34% were achieved for the AGBD, BGBD, LBD, and SBD, respectively.]]>
We identify three key findings. First, land use change impacts well-being both directly and indirectly via shifts in ecosystem service supply. Forests provide the broadest range of services that support essential components of well-being, including nutrition, income, housing, and spiritual values. Second, we find three generic mechanisms through which well-being is affected, often involving trade-offs within or between well-being components. For example, gains in income can coincide with losses in cultural identity or environmental quality. Third, we identify three substitution processes that can mitigate ecosystem service loss’ impact on well-being. However, these processes often fail to maintain relational and collective components of well-being and it might be that ecosystem services are often not substituted to the same level.
Our findings highlight the need for a multidimensional, context-sensitive understanding of well-being in land system science. Development and conservation interventions should move beyond narrow economic framings and support multifunctional landscapes that sustain both ecosystem services and the full spectrum of human well-being in tropical forest frontiers.]]>
Another part of this paper uses EnviFlux to explore the effect of model error and observation bias on inferred surface fluxes in two example scenarios and with two observation types, namely surface in situ (SIS) and TCA. The first is a highly idealised case with a flux pair (a localised source and a localised sink), where no a priori information concerning their positions is provided. It is found that model errors in the assimilation can severely affect the inferred flux positions and amounts, but observation biases do not affect the positions, but do affect the amounts. The second scenario is closer to a real-world example of methane flux estimates where an a priori is refined with observations. The effect of model error is less than for the first scenario, but is still evident, and the observation biases affect the flux amounts. In both cases, the SIS observations allow a more accurate estimate of surface flux characteristics than the TCA observations, even though the experiments are setup to allow a fair comparison of the two. The last part of the paper raises some research questions that EnviFlux could help address.
]]>We present a method to reconstruct the missing magnetic field component using data from these two remaining components together with the spacecraft spin, yielding full three-component magnetic field vectors at spin period cadence and with an effective bandwidth of
Existing data from more than 40 studies were compiled, evaluated, and integrated into the database. In addition, approximately 650 cylindrical plugs retrieved from drill cores and outcrop samples were systematically analyzed with respect to their mineralogical, geochemical, petrophysical, and rock mechanical properties to close remaining data gaps. Particular emphasis was placed on sampling a broad spectrum of lithologies to ensure coverage of the major crystalline units in Germany.
The resulting open-access repository (Weydt et al., 2026; https://doi.org/10.48328/tudatalib-2167) currently contains approximately 25,700 data points covering up to 34 parameters measured on 8,200 samples from more than 3,400 sampling locations across eight federal states in Germany. It represents the first standardized national-scale compilation of geochemical, petrophysical, and mechanical rock property data for the crystalline basement in Germany. The database provides a robust empirical foundation for early-stage exploration, reservoir characterization, and the validation of analytical measurement methods. Future efforts will focus on expanding the database towards regions with low data density and on high-temperature and high-pressure experiments to improve the understanding of parameter variations under in situ conditions.]]>
In this paper, we detail the OpenSat4Weather data preparation process and draw statistics of data availability. We then provide a descriptive analysis of the dataset, including rainfall characteristics, derived from rain gauges, statistical properties of the SML received signal levels, and a comparison between SML and radar data. In addition, we illustrate examples of disturbances and anomalous patterns observed in raw SML signals. Finally, we provide some practical guidelines for processing and analyzing SML data. Our ultimate goal is to promote open research initiatives aimed at accelerating the development of SML-based applications. Enhancing rainfall monitoring by opportunistic sensors has the potential to provide substantial benefits, particularly in regions where conventional networks are sparse or unavailable.
]]>Long-path Differential Optical Absorption Spectroscopy of vertical trace gas profiles during the 2022 Alaskan Layered Pollution and Chemical Analysis (ALPACA) experiment in Fairbanks, Alaska, quantitatively track the frequent establishment of PSLs with heights of 20–40 m, which sometimes persist for several days. PSL trace gas mixing ratios often plateau at levels of up to 35 ppb SO2, 60 ppb NO2, 2.5 ppb HONO, 7 ppb HCHO, and low ozone as a result of surface emissions, gas-phase chemistry, and ineffective mixing with the background atmosphere. Parameterizing vertical mixing with observed surface temperature gradients and wind speed in the PACT-1D chemistry and transport model leads to excellent agreement of modeled and measured trace gas profiles. The PACT-1D transport scheme determines the residence time of pollutants within the PSL to be 1–4 h, confirming the strong influence of atmospheric mixing on the composition of sustained PSL events. Altitude-dependent ozone and NOx chemistry highlight the strong coupling between mixing and chemistry, which must be considered to quantify pollutant concentrations accurately in shallow PSLs.
]]>This paper presents the third version of the ENEA-REG regional Earth system model, specifically designed to simulate past and present climate variability and to produce centennial-scale climate projections over the Euro-Mediterranean region. Compared with the previous model version (i.e. ENEA-REG2), ENEA-REG3 introduces an enhanced representation of river discharge and floodplain processes and a substantially revised coupler implementing a time-averaged explicit atmosphere–ocean coupling strategy that improves the temporal consistency of the exchanged fluxes.
Model performances are assessed using a hindcast simulation driven by the ERA5 and ORAS5 reanalyses, together with two historical downscaling simulations driven by the CMIP6 models EC-Earth3 and MPI-ESM1-2-HR. Compared to ENEA-REG2, the results indicate a substantial improvement in simulated river discharge and reduced biases in the ocean component, particularly in regions strongly influenced by freshwater inputs, such as the Adriatic Sea and several coastal areas of the eastern Mediterranean, where the excessive salinization simulated by ENEA-REG2 is markedly reduced. The results also show close agreement among the hindcast and historical simulations, suggesting that model performance is largely governed by the regional coupled system and its parameterizations rather than by the driving global climate models. This consistency strengthens confidence in the robustness of future climate projections.]]>
EMAC simulations indicate that bromine dry deposition from very short-lived substances (VSLS) and sea-salt aerosol (SSA) dehalogenation can establish snow bromide reservoirs sufficient to sustain typical Arctic springtime BrO abundances. Bromine species partitioning upon deposition suggests that a lack of HBr deposition may suppress heterogeneous Br2 production during fall. SMELs reproduces the observed vertical bromide enhancement and depletion patterns and demonstrates that finite snowpit sampling can introduce substantial biases. The strong agreement between modeled and observed normalized bromide profiles suggests that bromine deposition and post-depositional processing operate on regional scales around Ny-Ålesund. Our results identify atmospheric bromine deposition as an important seasonal bromide reservoir and highlight the role of bromine speciation and snowpack processes in controlling Arctic bromine activation.]]>